From 1dc9a324341abcb7b7e7405ee783029fde2ccd81 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 20 Sep 2024 13:56:46 -0400 Subject: [PATCH 1/7] Notebook edits Also some extra results. Note that the notebook points to *my results directory*. That reference (and a filename) might need to be reset to the results you want to show. --- ...owPortfolioFourParams_estimate_results.csv | 41357 ++++++++++++++++ ...owPortfolioSub(Stock)_estimate_results.csv | 19323 ++++++++ code/notebooks/Model_Comparisons.ipynb | 95 +- code/notebooks/median_share.pdf | Bin 17692 -> 17105 bytes code/notebooks/median_share.svg | 566 +- code/notebooks/median_wealth.pdf | Bin 18335 -> 18371 bytes code/notebooks/median_wealth.svg | 709 +- 7 files changed, 61340 insertions(+), 710 deletions(-) create mode 100644 code/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv create mode 100644 code/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv diff --git a/code/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv b/code/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv new file mode 100644 index 0000000..e9266fc --- /dev/null +++ b/code/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv @@ -0,0 +1,41357 @@ +CRRA,4.130262462019113 +BeqFac,4099.4172974459125 +BeqShift,1.5605762058493682 +DiscFac,0.976477264112623 +time_to_estimate,564.7696936130524 +params,"{'CRRA': 4.130262462019113, 'BeqFac': 4099.4172974459125, 'BeqShift': 1.5605762058493682, 'DiscFac': 0.976477264112623}" +criterion,0.03160677727045485 +start_criterion,0.04761358470737734 +start_params,"{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,4 +message,Maximum number of criterion evaluations reached. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 1.5997660080756595, 'BeqFac': 3798.32770700145, 'BeqShift': 70.0, 'DiscFac': 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116.31543600000441, 117.6343872002326, 118.81335589988157, 119.98585169995204, 121.1860096999444, 122.37323410017416, 123.57454850012437, 124.76921389997005, 126.4212179002352, 126.60760340001434, 126.79196709999815, 126.97290260018781, 127.15962800011039, 127.34788580005988, 127.53500150004402, 127.73213839996606, 127.93292480008677, 128.12795529980212, 128.32260590000078, 128.51027300022542, 129.75297969998792, 130.94742600014433, 132.1255693999119], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 87, 88, 89]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}, 'five_steps': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}}" +multistart_info,"{'start_parameters': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.357791964783, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 6.019017903047592, 'BeqFac': 4094.4713174272265, 'BeqShift': 8.678234369345601, 'DiscFac': 0.9603698649490326}], 'local_optima': [Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.438e-06* 0.001161 +relative_params_change 5.434e-05 0.00258 +absolute_criterion_change 3.438e-07* 0.0001161 +absolute_params_change 0.00019 0.004757 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.703e-08* 1.469e-05 +relative_params_change 3.819e-07* 0.001782 +absolute_criterion_change 2.006e-08* 7.959e-06* +absolute_params_change 3.648e-06* 0.03719 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.0002068 0.02744 +relative_params_change 0.01485 0.1715 +absolute_criterion_change 2.068e-05 0.002744 +absolute_params_change 0.05016 0.2731 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.00129 0.00488 +relative_params_change 0.001706 0.006966 +absolute_criterion_change 0.001137 0.004302 +absolute_params_change 0.02421 0.1485 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375, 'DiscFac': 0.9875}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875, 'DiscFac': 0.8562500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25, 'DiscFac': 0.875}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75, 'DiscFac': 0.7250000000000001}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125, 'DiscFac': 0.6125}, {'CRRA': 16.1609375, 'BeqFac': 156.25, 'BeqShift': 66.71875, 'DiscFac': 0.9781250000000001}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'BeqShift': 17.5, 'DiscFac': 0.65}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'BeqShift': 37.1875, 'DiscFac': 1.00625}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'BeqShift': 2.1875, 'DiscFac': 0.70625}, {'CRRA': 11.140624999999998, 'BeqFac': 312.5, 'BeqShift': 28.4375, 'DiscFac': 0.78125}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'BeqShift': 35.0, 'DiscFac': 0.8}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'BeqShift': 56.875, 'DiscFac': 0.5375}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'BeqShift': 52.5, 'DiscFac': 0.9500000000000001}, {'CRRA': 13.798437499999999, 'BeqFac': 3906.25, 'BeqShift': 22.96875, 'DiscFac': 0.603125}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'BeqShift': 41.5625, 'DiscFac': 0.51875}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'BeqShift': 67.8125, 'DiscFac': 0.59375}, {'CRRA': 9.073437499999999, 'BeqFac': 1406.25, 'BeqShift': 5.46875, 'DiscFac': 0.753125}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'BeqShift': 21.875, 'DiscFac': 0.8375}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'BeqShift': 59.0625, 'DiscFac': 0.66875}, {'CRRA': 11.4359375, 'BeqFac': 7656.25, 'BeqShift': 14.21875, 'DiscFac': 0.528125}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'BeqShift': 50.3125, 'DiscFac': 0.74375}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'BeqShift': 19.6875, 'DiscFac': 0.55625}, {'CRRA': 6.7109375, 'BeqFac': 5156.25, 'BeqShift': 31.71875, 'DiscFac': 0.6781250000000001}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'BeqShift': 6.5625, 'DiscFac': 0.8187500000000001}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'BeqShift': 10.9375, 'DiscFac': 0.6312500000000001}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'BeqShift': 65.625, 'DiscFac': 0.7625000000000001}, {'CRRA': 4.348437499999999, 'BeqFac': 8906.25, 'BeqShift': 57.96875, 'DiscFac': 0.9031250000000001}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'BeqShift': 26.25, 'DiscFac': 0.575}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'BeqShift': 24.0625, 'DiscFac': 0.96875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'BeqShift': 30.625, 'DiscFac': 1.0625}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'BeqShift': 32.8125, 'DiscFac': 0.89375}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'BeqShift': 39.375, 'DiscFac': 0.6875}, {'CRRA': 1.9859375, 'BeqFac': 2656.25, 'BeqShift': 49.21875, 'DiscFac': 0.828125}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'BeqShift': 15.3125, 'DiscFac': 1.0437500000000002}, {'CRRA': 18.5234375, 'BeqFac': 6406.25, 'BeqShift': 40.46875, 'DiscFac': 1.053125}, {'CRRA': 1.690625, 'BeqFac': 5312.5, 'BeqShift': 63.4375, 'DiscFac': 1.08125}], 'exploration_results': array([4.85268873e-02, 5.81756522e-01, 7.85434558e-01, 1.08501898e+00, + 1.09147168e+00, 1.15508374e+00, 1.35896335e+00, 1.47857950e+00, + 1.60399255e+00, 1.93344372e+00, 1.94383457e+00, 1.97559443e+00, + 2.00706025e+00, 2.09257083e+00, 2.11464344e+00, 2.13130763e+00, + 2.24401741e+00, 2.32809571e+00, 2.43093035e+00, 2.58864817e+00, + 2.64782870e+00, 2.75968951e+00, 2.76063416e+00, 2.87160809e+00, + 2.96402039e+00, 3.18404442e+00, 3.25640118e+00, 3.28020017e+00, + 3.37801319e+00, 3.50195454e+00, 3.52157483e+00, 3.84631802e+00, + 4.29481111e+00, 4.50920970e+00, 4.92630064e+00, 5.00339546e+00, + 5.43544424e+00, 7.28251427e+00, 1.28207813e+01, 1.15051611e+03])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=409.96889222786086, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=1.0675306002702438, linear_terms=array([0., 0., 0., 0.]), square_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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candidate_x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=409.96889222786086, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=65.05504224478882, linear_terms=array([-178.31283383, 31.69042646, 4.09387681, 166.47545036]), square_terms=array([[ 2.47350263e+02, -4.37095660e+01, -5.86374731e+00, + -2.27026816e+02], + [-4.37095660e+01, 7.78069637e+00, 9.85890588e-01, + 4.03643693e+01], + [-5.86374731e+00, 9.85890588e-01, 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=30, candidate_x=array([1.11319430e+01, 4.10923639e+03, 0.00000000e+00, 6.76395240e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-0.01107643105638871, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=6.405763941060326, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30]), model=ScalarModel(intercept=41.718081219167644, linear_terms=array([-33.92226807, -4.49642876, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.44984291327620873, linear_terms=array([ 0.00794743, -0.01077897, 0.00817368, -0.21319472]), square_terms=array([[ 3.97811820e-04, 4.37458980e-04, -3.66650849e-04, + 2.01797654e-02], + [ 4.37458980e-04, 1.33275431e-03, -8.59796475e-04, + 5.23861571e-02], + [-3.66650849e-04, -8.59796475e-04, 7.72136489e-04, + -3.31731740e-02], + [ 2.01797654e-02, 5.23861571e-02, -3.31731740e-02, + 2.13462780e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07453425]), shift=array([6.36660607e+00, 4.09967655e+03, 2.39442508e+00, 1.02546575e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 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51, 52, 53, 54, 55, 56, 57, 58, 59, 68, 69]), step_length=0.25874156763788225, relative_step_length=1.2925437528754322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 55, 59, 60, 61, 62, 64, 65, 67, 68, 70, 71, 73, 75, 76]), model=ScalarModel(intercept=0.32097874490276107, linear_terms=array([ 0.05330163, -0.04745503, -0.07450486, 0.04707809]), square_terms=array([[ 0.04450147, -0.0463324 , -0.06634309, -0.27074592], + [-0.0463324 , 0.0485459 , 0.06911336, 0.29211727], + [-0.06634309, 0.06911336, 0.09925331, 0.40416762], + [-0.27074592, 0.29211727, 0.40416762, 2.04202383]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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model=ScalarModel(intercept=0.34700850772977443, linear_terms=array([ 0.00130973, -0.00799522, 0.00251598, -0.00868794]), square_terms=array([[ 9.89738156e-04, 1.15998085e-03, 4.51374301e-06, + 4.04254702e-02], + [ 1.15998085e-03, 1.51831925e-03, -1.74229161e-05, + 4.80403131e-02], + [ 4.51374301e-06, -1.74229161e-05, 6.25426571e-05, + -1.14623773e-03], + [ 4.04254702e-02, 4.80403131e-02, -1.14623773e-03, + 1.68845419e+00]]), scale=0.0500450307895338, shift=array([6.13836451e+00, 4.09991995e+03, 2.17603000e+00, 1.02524414e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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107, 108]), old_indices_used=array([93, 96]), old_indices_discarded=array([], dtype=int32), step_length=0.025046126340450683, relative_step_length=1.0009435880170834, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.03544529e+00, 4.10004123e+03, 2.06983129e+00, 1.02525473e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, + 108, 109]), model=ScalarModel(intercept=0.32056677142311285, linear_terms=array([ 0.00446261, -0.00014495, 0.00423272, -0.00809436]), square_terms=array([[ 9.12861112e-05, 1.40030653e-05, -4.69278487e-05, + 1.10061593e-02], + [ 1.40030653e-05, 5.90060514e-06, -2.58411283e-05, + 3.19050202e-03], + [-4.69278487e-05, -2.58411283e-05, 1.82744813e-04, + -1.75467315e-02], + [ 1.10061593e-02, 3.19050202e-03, -1.75467315e-02, + 2.41272819e+00]]), scale=0.0500450307895338, 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candidate_x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), index=111, x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), fval=0.30088852241782776, rho=0.4287596569977097, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 108, 110]), old_indices_discarded=array([ 46, 55, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 89, 90, 91, 92, 94, 107, 109]), step_length=0.12926168890188985, relative_step_length=1.2914537853468868, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.8362141099479552, 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old_indices_used=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), old_indices_discarded=array([ 0, 46, 47, 49, 50, 51, 53, 54, 55, 57, 58, 59, 60, + 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, + 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, + 87, 88, 89, 90, 91, 92, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.2829838581648968, linear_terms=array([-0.00276684, 0.03094096, 0.0087251 , -0.03929999]), square_terms=array([[ 3.07948101e-03, -8.53430126e-03, -1.46741811e-03, + 1.20443717e-01], + [-8.53430126e-03, 2.48589549e-02, 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97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 112, 113, 116, 118, 120, 121, + 122]), step_length=0.25843212314368597, relative_step_length=1.2909979226036032, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), model=ScalarModel(intercept=4.348347562426054, linear_terms=array([ 0.31927054, 0.46205338, -0.36145321, -14.31327039]), square_terms=array([[ 1.65442176e-02, 1.84070457e-02, -1.41804582e-02, + -4.63767949e-01], + [ 1.84070457e-02, 2.54749193e-02, -2.05872063e-02, + -7.55309262e-01], + [-1.41804582e-02, -2.05872063e-02, 1.89816565e-02, + 6.10967512e-01], + [-4.63767949e-01, -7.55309262e-01, 6.10967512e-01, + 2.48273398e+01]]), 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106, 107, 108, 109, + 110, 112, 116, 117, 118, 120, 121, 122, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), model=ScalarModel(intercept=0.6450590722968451, linear_terms=array([ 0.0687357 , 0.08298437, -0.06097559, -2.78513076]), square_terms=array([[ 4.13605440e-03, 4.60176141e-03, -3.54511454e-03, + -1.40984410e-01], + [ 4.60176141e-03, 6.36872983e-03, -5.14680157e-03, + -2.29612312e-01], + [-3.54511454e-03, -5.14680157e-03, 4.74541412e-03, + 1.85732745e-01], + [-1.40984410e-01, -2.29612312e-01, 1.85732745e-01, + 9.17763751e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11568783]), 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=135, candidate_x=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), index=135, x=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), fval=0.19084543631566314, rho=0.5530791700510971, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([129, 131, 132, 133, 134]), old_indices_discarded=array([], dtype=int32), step_length=0.12919783936681034, relative_step_length=1.2908158645176637, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, + 134, 135]), model=ScalarModel(intercept=0.6377265413639779, 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9.66584319e-01]), fval=0.03582935923944727, rho=0.9828610598307853, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, + 164, 166]), old_indices_discarded=array([138, 140, 141, 144, 145, 146, 147, 148, 149, 150, 165]), step_length=0.1030327904271319, relative_step_length=1.029400809647216, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18135530e+00, 4.09927774e+03, 1.04428032e+00, 9.66584319e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([147, 150, 151, 152, 154, 155, 157, 158, 159, 161, 162, 163, 166, + 167, 168]), model=ScalarModel(intercept=0.03566136640805834, linear_terms=array([-0.00392304, -0.0030313 , -0.0040903 , -0.03373295]), square_terms=array([[0.03330116, 0.02028974, 0.01235723, 0.30148468], + [0.02028974, 0.01390622, 0.00850498, 0.2067144 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=170, candidate_x=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.69177836e-01]), index=170, x=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.69177836e-01]), fval=0.034353350340771874, rho=0.9539572104328949, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([147, 148, 150, 151, 152, 154, 155, 156, 157, 158, 160, 164, 165, + 166, 167]), old_indices_discarded=array([138, 141, 144, 145, 146, 149, 153, 159, 161, 162, 163, 168, 169]), step_length=0.10332617043208771, relative_step_length=1.0323319698476128, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.69177836e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([147, 150, 151, 152, 155, 156, 158, 160, 164, 165, 166, 167, 168, + 169, 170]), model=ScalarModel(intercept=0.03898301695071224, linear_terms=array([ 0.00198454, 0.00051353, 0.00218802, -0.12559782]), square_terms=array([[ 4.15194564e-03, 1.58974126e-04, 9.74651446e-04, + -2.78085165e-02], + [ 1.58974126e-04, 9.21555760e-05, 4.16254383e-04, + -1.17750030e-02], + [ 9.74651446e-04, 4.16254383e-04, 2.48459042e-03, + -5.40677130e-02], + [-2.78085165e-02, -1.17750030e-02, -5.40677130e-02, + 1.69234291e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.1400007 ]), shift=array([4.17459728e+00, 4.09930253e+03, 1.14432864e+00, 9.59999297e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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rho=0.0016410550627144686, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([147, 150, 151, 152, 155, 156, 158, 160, 164, 165, 166, 167, 168, + 169, 170]), old_indices_discarded=array([137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 153, + 154, 157, 159, 161, 162, 163]), step_length=0.21107983641988134, relative_step_length=1.0544495282038355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17571078e+00, 4.09945171e+03, 1.29350788e+00, 9.75853572e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 155, 158, 165, 166, 167, 168, 169, + 170, 171]), model=ScalarModel(intercept=0.03278405592667447, linear_terms=array([-0.00058539, -0.00037664, -0.0011525 , 0.01693014]), square_terms=array([[ 4.39506574e-03, 1.51749098e-03, 5.21452806e-04, + -4.51551090e-02], + [ 1.51749098e-03, 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relative_step_length=1.002913343419474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.73777078e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 158, 165, 166, 167, 168, 169, 170, + 171, 172]), model=ScalarModel(intercept=0.035448800508248314, linear_terms=array([ 0.01859568, 0.0072831 , -0.00094604, -0.08987951]), square_terms=array([[ 4.78915876e-02, 1.75180826e-02, -6.37182435e-05, + -2.22312021e-01], + [ 1.75180826e-02, 6.76259777e-03, -5.22983618e-05, + -8.70414549e-02], + [-6.37182435e-05, -5.22983618e-05, 7.13103746e-04, + -5.40324974e-04], + [-2.22312021e-01, -8.70414549e-02, -5.40324974e-04, + 1.12940090e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.13770108]), shift=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.62298918e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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relative_step_length=1.0023815838258203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, + 181]), model=ScalarModel(intercept=0.032799850622037344, linear_terms=array([-0.00120015, -0.00149617, 0.00075662, -0.03985981]), square_terms=array([[ 2.96888836e-03, 4.77998749e-04, -2.25595211e-04, + 1.47872526e-02], + [ 4.77998749e-04, 8.86980525e-04, -5.90740802e-04, + 2.76631448e-02], + [-2.25595211e-04, -5.90740802e-04, 6.06547002e-04, + -1.77861139e-02], + [ 1.47872526e-02, 2.76631448e-02, -1.77861139e-02, + 8.69630174e-01]]), scale=0.1000900615790676, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 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model_indices=array([179, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, + 194, 195, 196]), model=ScalarModel(intercept=0.03162939399086311, linear_terms=array([-2.05014239e-05, -4.40701544e-07, -7.03347564e-06, 4.85199295e-04]), square_terms=array([[ 3.49864962e-04, -2.65476555e-06, 1.06943866e-04, + -4.62038714e-03], + [-2.65476555e-06, 3.14059178e-08, -9.72077663e-07, + 4.40297209e-05], + [ 1.06943866e-04, -9.72077663e-07, 3.86168856e-05, + -1.57987714e-03], + [-4.62038714e-03, 4.40297209e-05, -1.57987714e-03, + 7.04100722e-02]]), scale=0.01251125769738345, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=93, candidate_x=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), index=93, x=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), fval=0.040204861958739575, rho=0.04694682324737547, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([64, 69, 73, 74, 75, 76, 77, 78, 79, 84, 87, 88, 89, 90, 92]), old_indices_discarded=array([ 0, 61, 67, 72, 80, 81, 82, 83, 85, 86, 91]), step_length=0.024864348162927093, relative_step_length=1.0221854638064007, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([73, 74, 90, 92, 93]), model=ScalarModel(intercept=0.04020486195873957, linear_terms=array([-0.00706289, -0.00175749, 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scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=107, candidate_x=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01]), index=107, x=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01]), fval=0.03860803268572889, rho=1.1129757353667895, accepted=True, new_indices=array([ 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106]), old_indices_used=array([90, 92, 93, 94]), old_indices_discarded=array([], dtype=int32), step_length=0.006407991386109553, relative_step_length=1.0537425882483644, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107]), model=ScalarModel(intercept=0.03867072004563482, linear_terms=array([ 5.06639540e-04, -2.54682104e-05, 2.87144909e-04, -5.38984530e-04]), square_terms=array([[ 3.43854367e-04, -6.18791687e-06, 6.33233046e-05, + -5.63275002e-03], + [-6.18791687e-06, 3.56620027e-07, -1.07897494e-06, + 8.77935577e-05], + [ 6.33233046e-05, -1.07897494e-06, 1.44913336e-05, + -1.12583308e-03], + [-5.63275002e-03, 8.77935577e-05, -1.12583308e-03, + 1.08889227e-01]]), scale=0.012162346777236276, shift=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107]), old_indices_discarded=array([73, 90, 92]), step_length=0.012406463432134685, relative_step_length=1.0200715091724988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17514035e+00, 3.98534813e+03, 2.18746409e+00, 9.91956376e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 108]), model=ScalarModel(intercept=0.038080717924361705, linear_terms=array([ 0.00084081, -0.00022709, 0.00065496, 0.00050709]), square_terms=array([[ 1.66043955e-03, 4.81512877e-04, 1.42586039e-04, + -2.52393274e-02], + [ 4.81512877e-04, 1.54741908e-04, 4.03920393e-05, + -8.17303899e-03], + [ 1.42586039e-04, 4.03920393e-05, 2.37821246e-05, + -2.23426548e-03], + [-2.52393274e-02, 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State(trustregion=Region(center=array([4.15700474e+00, 3.98535408e+03, 2.17086332e+00, 9.90922363e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 108, 109]), model=ScalarModel(intercept=0.03707324900316479, linear_terms=array([ 0.00134055, -0.00050342, 0.00101212, 0.00208648]), square_terms=array([[ 5.91854237e-03, 1.94274484e-03, 1.34141717e-03, + -9.61364422e-02], + [ 1.94274484e-03, 7.09489463e-04, 4.55910967e-04, + -3.55979605e-02], + [ 1.34141717e-03, 4.55910967e-04, 3.52856825e-04, + -2.30368734e-02], + [-9.61364422e-02, -3.55979605e-02, -2.30368734e-02, + 1.81990976e+00]]), scale=0.048649387108945105, shift=array([4.15700474e+00, 3.98535408e+03, 2.17086332e+00, 9.90922363e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=110, candidate_x=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01]), index=110, x=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01]), fval=0.035977133549354015, rho=0.6750767721715603, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 108, 109]), old_indices_discarded=array([ 0, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, + 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 89, 90, 91, 95, 96, 107]), step_length=0.05126390814342671, relative_step_length=1.0537421165990575, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 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fval=0.034972454852913455, rho=0.5924956878197696, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 104, 105, 106, 108, + 109, 110]), old_indices_discarded=array([ 0, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 95, 96, 103, 107]), step_length=0.09887174690248846, relative_step_length=1.0161664183052885, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09950363e+00, 3.98542742e+03, 2.05703239e+00, 9.86875382e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 60, 69, 73, 90, 92, 94, 95, 98, 103, 104, 106, 108, 109, + 110, 111]), model=ScalarModel(intercept=0.0505341458401392, linear_terms=array([ 0.04469009, -0.16416707, -0.08838394, -0.37577055]), square_terms=array([[ 0.06180679, 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x=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01]), fval=0.033122729602777526, rho=-0.46260457325856585, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([113, 116, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, + 136, 137]), old_indices_discarded=array([111, 114, 115, 117, 118, 120, 121, 122, 123, 124, 125]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138]), model=ScalarModel(intercept=0.033088400599093445, linear_terms=array([-2.07963025e-04, 4.86452866e-05, 1.41008433e-04, 9.08365106e-05]), square_terms=array([[ 1.90554771e-03, -5.25048184e-05, 4.99344670e-04, + -2.51666427e-02], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 119, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), model=ScalarModel(intercept=0.0331182013788081, linear_terms=array([-1.97974768e-03, -1.06547349e-03, -5.51519758e-05, 1.81116415e-02]), square_terms=array([[ 2.38133820e-02, 8.51946412e-03, 4.28414957e-03, + -2.23713059e-01], + [ 8.51946412e-03, 3.13204553e-03, 1.55382928e-03, + -8.27366123e-02], + [ 4.28414957e-03, 1.55382928e-03, 8.16135432e-04, + -4.12818595e-02], + [-2.23713059e-01, -8.27366123e-02, -4.12818595e-02, + 2.19746582e+00]]), scale=0.048649387108945105, shift=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 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scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=140, candidate_x=array([4.11871670e+00, 3.98548023e+03, 1.82440548e+00, 9.84089102e-01]), index=139, x=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), fval=0.033044655661740516, rho=-0.568130481594652, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([113, 119, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), old_indices_discarded=array([111, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140]), model=ScalarModel(intercept=0.032951013003172214, linear_terms=array([-1.03938213e-04, -3.52725894e-06, 1.07593804e-04, 1.86042680e-03]), square_terms=array([[ 1.75065726e-03, -3.94983661e-06, 4.88863565e-04, + -2.44687091e-02], + [-3.94983661e-06, 1.79124813e-08, -1.19360841e-06, + 6.43531025e-05], + [ 4.88863565e-04, -1.19360841e-06, 1.52931166e-04, + -7.43453721e-03], + [-2.44687091e-02, 6.43531025e-05, -7.43453721e-03, + 3.86356869e-01]]), scale=0.024324693554472553, shift=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140]), old_indices_discarded=array([], dtype=int32), step_length=0.024637350930881603, relative_step_length=1.0128534970320957, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11559640e+00, 3.98544181e+03, 1.82857121e+00, 9.82636343e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, + 140, 141]), model=ScalarModel(intercept=0.03290815908255805, linear_terms=array([-4.11171815e-04, -5.00685998e-04, -1.68988412e-05, 1.54742229e-02]), square_terms=array([[ 6.62320972e-03, 2.37316712e-03, 2.54362470e-03, + -1.24735278e-01], + [ 2.37316712e-03, 9.45809690e-04, 9.92127010e-04, + -5.01657639e-02], + [ 2.54362470e-03, 9.92127010e-04, 1.08295792e-03, + -5.27667377e-02], + [-1.24735278e-01, -5.01657639e-02, -5.27667377e-02, + 2.67077637e+00]]), scale=0.048649387108945105, shift=array([4.11559640e+00, 3.98544181e+03, 1.82857121e+00, 9.82636343e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([4.10380907e+00, 3.98547361e+03, 1.79311439e+00, 9.81700943e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, + 141, 142]), model=ScalarModel(intercept=0.0326807222536389, linear_terms=array([-0.00209921, -0.00013072, -0.00011186, 0.03140504]), square_terms=array([[ 2.65673117e-02, 1.24220947e-04, 7.48136948e-03, + -3.80223461e-01], + [ 1.24220947e-04, 1.73529823e-06, 4.33828043e-05, + -2.32018221e-03], + [ 7.48136948e-03, 4.33828043e-05, 2.37936036e-03, + -1.17532688e-01], + [-3.80223461e-01, -2.32018221e-03, -1.17532688e-01, + 6.23372854e+00]]), scale=0.09729877421789021, shift=array([4.10380907e+00, 3.98547361e+03, 1.79311439e+00, 9.81700943e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=143, candidate_x=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), index=143, x=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), fval=0.03220931715356145, rho=1.0262736108824155, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, + 141, 142]), old_indices_discarded=array([ 0, 59, 60, 61, 63, 64, 66, 67, 69, 70, 73, 74, 75, + 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, + 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, + 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, + 122, 123, 124, 125, 126, 127]), step_length=0.1013394261738542, relative_step_length=1.0415282925036176, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, + 142, 143]), model=ScalarModel(intercept=0.08296945152155982, linear_terms=array([ 7.19376948e-02, -2.23100515e-04, 2.36320257e-02, -1.08022108e+00]), square_terms=array([[ 5.84409748e-02, -8.08424984e-06, 1.70531284e-02, + -7.64859744e-01], + [-8.08424984e-06, 3.61028975e-06, -1.27774505e-05, + 2.03813182e-04], + [ 1.70531284e-02, -1.27774505e-05, 5.62834359e-03, + -2.45854953e-01], + [-7.64859744e-01, 2.03813182e-04, -2.45854953e-01, + 1.14832543e+01]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13240388]), shift=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.67596123e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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candidate_x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=0.642468592930664, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, + 142, 143]), old_indices_discarded=array([ 0, 48, 49, 52, 53, 54, 56, 59, 60, 61, 62, 63, 64, + 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, + 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, + 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 129]), step_length=0.20552612749559135, relative_step_length=1.0561599010247422, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.38919509687156084, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144]), model=ScalarModel(intercept=0.5158401817968741, linear_terms=array([ 0.48374106, -0.21959826, -0.03034574, -2.45076283]), square_terms=array([[ 2.73512553e-01, -1.10681409e-01, -1.36794804e-02, + -1.22742522e+00], + [-1.10681409e-01, 5.00854227e-02, 7.37119045e-03, + 5.56398366e-01], + [-1.36794804e-02, 7.37119045e-03, 2.82983352e-03, + 7.80570977e-02], + [-1.22742522e+00, 5.56398366e-01, 7.80570977e-02, + 6.20455154e+00]]), scale=array([0.29003793, 0.29003793, 0.29003793, 0.20600944]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 8.93990557e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 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candidate_x=array([4.14231731e+00, 3.98555744e+03, 1.48330502e+00, 9.83650146e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-19.37116300456786, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144]), old_indices_discarded=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, + 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, + 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, + 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, + 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125, 126, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, + 144, 145]), model=ScalarModel(intercept=0.034194805708801616, linear_terms=array([ 0.00897016, -0.00531212, 0.00118509, -0.10933048]), square_terms=array([[ 2.60943087e-02, -1.08818961e-02, 2.66055361e-03, + -2.23169348e-01], + [-1.08818961e-02, 6.33814787e-03, -1.14716135e-03, + 1.30275256e-01], + [ 2.66055361e-03, -1.14716135e-03, 6.17083790e-04, + -2.45327892e-02], + [-2.23169348e-01, 1.30275256e-01, -2.45327892e-02, + 2.68630749e+00]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13349996]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.66500041e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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candidate_x=array([4.14173469e+00, 3.98579549e+03, 1.44950026e+00, 9.65493838e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-6.7613204113764, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, + 144, 145]), old_indices_discarded=array([ 0, 48, 52, 53, 54, 56, 60, 61, 62, 63, 64, 65, 66, + 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, + 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, + 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, + 145, 146]), model=ScalarModel(intercept=0.03223491974347327, linear_terms=array([-0.00435523, 0.00045162, -0.00113672, 0.06395075]), square_terms=array([[ 2.76484614e-02, -2.97346672e-03, 6.76823088e-03, + -3.49132230e-01], + [-2.97346672e-03, 3.93096161e-04, -8.27242646e-04, + 4.39088173e-02], + [ 6.76823088e-03, -8.27242646e-04, 1.87325427e-03, + -9.27089668e-02], + [-3.49132230e-01, 4.39088173e-02, -9.27089668e-02, + 5.01368528e+00]]), scale=0.09729877421789021, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), 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9.75472888e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.9167851356132374, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, + 145, 146]), old_indices_discarded=array([ 59, 130, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 144, 145, 146, 147]), model=ScalarModel(intercept=0.031852337676144094, linear_terms=array([-0.01334969, -0.000875 , -0.00212241, 0.00556698]), square_terms=array([[ 0.14858061, 0.00959735, 0.02815489, -0.28697043], + [ 0.00959735, 0.00062439, 0.00182044, -0.01825667], + [ 0.02815489, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, + 158, 159, 160]), model=ScalarModel(intercept=0.032556756784188255, linear_terms=array([-1.38069018e-03, -8.05850091e-05, -2.11446865e-04, 2.23013286e-02]), square_terms=array([[ 1.35249748e-03, 6.51569707e-05, 1.87461961e-04, + -1.84531760e-02], + [ 6.51569707e-05, 3.62304471e-06, 9.61360198e-06, + -1.02222512e-03], + [ 1.87461961e-04, 9.61360198e-06, 3.78176312e-05, + -2.78482419e-03], + [-1.84531760e-02, -1.02222512e-03, -2.78482419e-03, + 2.89607050e-01]]), scale=0.024324693554472553, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 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3.98567482e+03, 1.55173969e+00, 9.75875695e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.3377986236674669, accepted=False, new_indices=array([149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), old_indices_used=array([144, 145, 147, 148]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, + 160, 161]), model=ScalarModel(intercept=0.032618873867381576, linear_terms=array([-0.00076276, -0.00023605, -0.00050889, 0.01153604]), square_terms=array([[ 3.73432785e-04, 9.98864964e-05, 2.43531496e-04, + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, + 161, 162]), model=ScalarModel(intercept=0.03252324635513784, linear_terms=array([-4.14065539e-04, -9.94093190e-05, -9.38607562e-05, 5.40904684e-03]), square_terms=array([[ 1.20853884e-04, 2.50219420e-05, 2.98370200e-05, + -1.40554138e-03], + [ 2.50219420e-05, 5.65327048e-06, 6.58419488e-06, + -3.17355619e-04], + [ 2.98370200e-05, 6.58419488e-06, 8.42877891e-06, + -3.71203286e-04], + [-1.40554138e-03, -3.17355619e-04, -3.71203286e-04, + 1.78905730e-02]]), scale=0.006081173388618138, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 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+ [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=163, candidate_x=array([4.12785099e+00, 3.98565167e+03, 1.54696514e+00, 9.75920327e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.42060834910709954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([144, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, + 161, 162]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, + 174, 175]), model=ScalarModel(intercept=0.03190626622045728, linear_terms=array([-5.00916898e-05, -1.60685558e-06, -3.41876508e-05, 8.86221374e-04]), square_terms=array([[ 2.04825629e-05, 2.61345798e-07, 7.17448985e-06, + -2.80536546e-04], + [ 2.61345798e-07, 2.02583758e-08, 6.64912984e-08, + -1.61121192e-06], + [ 7.17448985e-06, 6.64912984e-08, 3.00963453e-06, + -1.09754930e-04], + [-2.80536546e-04, -1.61121192e-06, -1.09754930e-04, + 4.34084080e-03]]), scale=0.003040586694309069, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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rho=0.7825513778471532, accepted=True, new_indices=array([164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), old_indices_used=array([144, 162, 163]), old_indices_discarded=array([], dtype=int32), step_length=0.00327686813422291, relative_step_length=1.077709160655106, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, + 175, 176]), model=ScalarModel(intercept=0.031782193359979234, linear_terms=array([ 1.65934087e-05, -1.10658052e-05, -5.86797451e-05, 1.70650043e-05]), square_terms=array([[ 8.16385660e-05, 4.98651617e-07, 2.63205082e-05, + -1.11853097e-03], + [ 4.98651617e-07, 7.66151913e-08, 9.65330348e-08, + 7.67793318e-07], + [ 2.63205082e-05, 9.65330348e-08, 1.07174130e-05, + -4.09379667e-04], + [-1.11853097e-03, 7.67793318e-07, -4.09379667e-04, + 1.73744472e-02]]), scale=0.006081173388618138, shift=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], 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State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, + 176, 177]), model=ScalarModel(intercept=0.03179776772837414, linear_terms=array([ 4.32340121e-06, -2.77572135e-06, -1.55094590e-05, 6.03814167e-06]), square_terms=array([[ 2.04941628e-05, 1.12208524e-07, 6.82404742e-06, + -2.79715394e-04], + [ 1.12208524e-07, 1.42062247e-08, 1.38323893e-08, + 1.90493930e-07], + [ 6.82404742e-06, 1.38323893e-08, 2.73772264e-06, + -1.05054792e-04], + [-2.79715394e-04, 1.90493930e-07, -1.05054792e-04, + 4.34256904e-03]]), scale=0.003040586694309069, shift=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=178, candidate_x=array([4.12767420e+00, 3.98565133e+03, 1.55853065e+00, 9.77392943e-01]), index=176, x=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), fval=0.03177037559889115, rho=-0.606887945704526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, + 176, 177]), old_indices_discarded=array([162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.0015202933471545345, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, + 177, 178]), model=ScalarModel(intercept=0.03179737436356608, 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State(trustregion=Region(center=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), radius=0.0015202933471545345, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 172, 175, 176, 177, 178, 179, 180, + 181, 182]), model=ScalarModel(intercept=0.03178608463955804, linear_terms=array([ 3.42972101e-07, -9.23719283e-06, -9.47276440e-06, -3.66984129e-05]), square_terms=array([[ 4.88007131e-06, 6.05773731e-08, 1.64321365e-06, + -6.82359635e-05], + [ 6.05773731e-08, 1.03064274e-08, 3.96209788e-08, + -1.24824750e-06], + [ 1.64321365e-06, 3.96209788e-08, 6.89993730e-07, + -2.63109295e-05], + [-6.82359635e-05, -1.24824750e-06, -2.63109295e-05, + 1.09136604e-03]]), scale=0.0015202933471545345, shift=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 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179, 180, 181, 182, 183]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), radius=0.00038007333678863363, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([176, 179, 181, 182, 183, 184]), model=ScalarModel(intercept=0.03175331182967604, linear_terms=array([ 1.44825796e-05, 5.68223789e-06, 3.12884155e-06, -6.48553187e-05]), square_terms=array([[ 3.04833182e-07, 1.74043761e-09, 9.75099415e-08, + -4.26387864e-06], + [ 1.74043761e-09, 1.18031150e-09, 3.50110142e-10, + -8.60576506e-09], + [ 9.75099415e-08, 3.50110142e-10, 3.72017683e-08, + -1.52020078e-06], + [-4.26387864e-06, -8.60576506e-09, -1.52020078e-06, + 6.66267060e-05]]), scale=0.00038007333678863363, shift=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 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195, 196, 197]), model=ScalarModel(intercept=0.031752510632581374, linear_terms=array([ 3.15210974e-06, 1.82709590e-06, -1.37613869e-06, 3.74360868e-06]), square_terms=array([[ 8.81053685e-08, 8.14124215e-09, 2.77699484e-08, + -1.17114181e-06], + [ 8.14124215e-09, 1.16476778e-09, 2.48694392e-09, + -1.14449179e-07], + [ 2.77699484e-08, 2.48694392e-09, 1.04145633e-08, + -3.98004220e-07], + [-1.17114181e-06, -1.14449179e-07, -3.98004220e-07, + 1.72800512e-05]]), scale=0.00019003666839431682, shift=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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accepted=False, new_indices=array([186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197]), old_indices_used=array([179, 182, 184, 185]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), radius=9.501833419715841e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([182, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), model=ScalarModel(intercept=0.03175159379851204, linear_terms=array([ 1.86824538e-06, 1.22995147e-06, -5.54257272e-07, 2.15176219e-06]), square_terms=array([[ 2.36239271e-08, 1.19473473e-09, 8.68439146e-09, + -3.01777243e-07], + [ 1.19473473e-09, 2.91874089e-10, 2.77300727e-10, + -2.04750465e-08], + [ 8.68439146e-09, 2.77300727e-10, 3.78517422e-09, + -1.14699063e-07], + 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0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=199, candidate_x=array([4.12709842e+00, 3.98565129e+03, 1.55655152e+00, 9.77235429e-01]), index=182, x=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), fval=0.031736549972374545, rho=-2.4975925112841417, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([182, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Maximum number of criterion evaluations reached.', 'tranquilo_history': History for least_squares 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=30, candidate_x=array([1.51648385e+01, 3.87683317e+03, 1.36457640e+01, 5.78808741e-01]), index=0, x=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), fval=1.043279826158364, rho=-0.0038980287602399517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=6.043477609386606, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=187.5605981153043, linear_terms=array([-130.64991462, -7.14678784, 1.4954385 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28, 29, 30]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=3.021738804693303, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.31987187423087415, linear_terms=array([ 0.08669126, -0.1414405 , -0.04511992, -0.93701812]), square_terms=array([[ 9.04354688e-02, -3.61449104e-02, -5.43056331e-04, + -3.12175071e-01], + [-3.61449104e-02, 1.45220794e-01, 6.28594817e-02, + 9.92180671e-01], + [-5.43056331e-04, 6.28594817e-02, 3.01737068e-02, + 4.17797683e-01], + [-3.12175071e-01, 9.92180671e-01, 4.17797683e-01, + 6.88478390e+00]]), scale=array([2.25187544, 2.25187544, 2.25187544, 0.3 ]), shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 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model=ScalarModel(intercept=0.36692545777675056, linear_terms=array([ 0.0504758 , -0.0372444 , -0.07437049, -1.26119166]), square_terms=array([[ 2.70404887e-02, -1.63616628e-03, -5.79863045e-03, + -1.65660526e-01], + [-1.63616628e-03, 5.45410262e-03, 1.26900356e-02, + 1.88720243e-01], + [-5.79863045e-03, 1.26900356e-02, 3.03357143e-02, + 4.56342497e-01], + [-1.65660526e-01, 1.88720243e-01, 4.56342497e-01, + 7.14920792e+00]]), scale=array([1.12593772, 1.12593772, 1.12593772, 0.3 ]), shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=0.7554347011733258, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 32, 33, 35, 37, 39, 40, 41, 44, 45]), model=ScalarModel(intercept=1.1652708843206574, linear_terms=array([-0.07983151, 0.15956484, 0.02243179, -1.84754634]), square_terms=array([[ 9.66381815e-03, -8.77228945e-03, -1.01216987e-03, + 7.11865361e-02], + [-8.77228945e-03, 1.45920997e-02, 1.45705218e-03, + -1.67346793e-01], + [-1.01216987e-03, 1.45705218e-03, 2.60612725e-04, + -1.44287039e-02], + [ 7.11865361e-02, -1.67346793e-01, -1.44287039e-02, + 2.15953524e+00]]), 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candidate_index=63, candidate_x=array([6.64993454e+00, 3.86733307e+03, 2.20199258e+01, 9.63254363e-01]), index=46, x=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), fval=0.610030672784944, rho=-5.047496342917129, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 51, 55, 57, 59, 60, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), radius=0.04721466882333286, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.5645793736320908, linear_terms=array([ 0.02415958, -0.02485336, 0.02479561, -0.34196441]), square_terms=array([[ 1.39237450e-03, -1.62359879e-03, 1.62536290e-03, + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00]), radius=0.09442933764666572, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.51062010022679, linear_terms=array([ 0.01710986, -0.01628909, 0.0164215 , -0.04435784]), square_terms=array([[ 1.44592132e-03, -1.90647266e-03, 1.93858106e-03, + -5.80270672e-02], + [-1.90647266e-03, 2.93939927e-03, -3.00816915e-03, + 9.37944524e-02], + [ 1.93858106e-03, -3.00816915e-03, 3.08082456e-03, + -9.62367159e-02], + [-5.80270672e-02, 9.37944524e-02, -9.62367159e-02, + 3.30228437e+00]]), scale=array([0.07037111, 0.07037111, 0.07037111, 0.07037111]), shift=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 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x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-0.7561149550917772, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 83, 87, 91, 93, 94, 97, 98, 99, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=0.000184432300091144, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 133]), model=ScalarModel(intercept=0.5418053134335727, linear_terms=array([ 1.59107911e-06, 1.91604113e-06, -8.39570529e-07, 4.03636914e-06]), 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2.20809063e+01, 1.03486530e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.22806525934078076, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([132, 137, 143, 146, 149, 151, 153, 154]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=2.881754688924125e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([146, 151, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, + 165, 166, 167]), model=ScalarModel(intercept=0.5417883802201522, linear_terms=array([-1.57307408e-07, 3.76367420e-07, -8.62401148e-08, 2.16807204e-07]), square_terms=array([[ 7.45581521e-13, -3.85892702e-13, 6.62025307e-14, + 4.90598744e-11], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1.4408773444620624e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, + 167, 168]), model=ScalarModel(intercept=0.5417884361277132, linear_terms=array([ 6.07312569e-08, -6.26140517e-08, -2.39455329e-08, -7.21746347e-08]), square_terms=array([[1.43221103e-13, 6.68409194e-15, 5.30999617e-15, 1.31473717e-11], + [6.68409194e-15, 1.21687971e-14, 5.19067626e-15, 1.39183805e-12], + [5.30999617e-15, 5.19067626e-15, 2.72373187e-15, 5.63318896e-13], + [1.31473717e-11, 1.39183805e-12, 5.63318896e-13, 1.81511860e-09]]), scale=1.4408773444620624e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 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160, 161, 162, 163, 164, 165, 166, 169, + 170, 171]), model=ScalarModel(intercept=0.5417883303665125, linear_terms=array([ 1.00975851e-07, -7.33946297e-08, -1.00245295e-07, -6.28597511e-08]), square_terms=array([[ 6.85916348e-14, -6.85250533e-15, -1.18418124e-14, + 5.40192714e-12], + [-6.85250533e-15, 3.20426702e-14, 2.77120093e-14, + 1.21474159e-12], + [-1.18418124e-14, 2.77120093e-14, 3.20099549e-14, + 1.56287813e-12], + [ 5.40192714e-12, 1.21474159e-12, 1.56287813e-12, + 8.74768291e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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rho=-0.7375647236811499, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, + 170, 171]), old_indices_discarded=array([167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 169, 170, + 171, 172]), model=ScalarModel(intercept=0.5417883710603923, linear_terms=array([ 1.43992102e-07, -3.92495599e-08, -9.39836000e-08, -9.01031376e-08]), square_terms=array([[ 8.53994530e-14, -1.24453204e-14, -2.50307988e-14, + 4.70275038e-12], + [-1.24453204e-14, 1.24566358e-14, 1.43974985e-14, + 6.71297877e-13], + [-2.50307988e-14, 1.43974985e-14, 2.77619876e-14, + 1.47049227e-12], 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, + 172, 173]), model=ScalarModel(intercept=0.5417883675324741, linear_terms=array([ 1.43818395e-07, -3.70058572e-08, -9.73353633e-08, -8.86837355e-08]), square_terms=array([[ 8.52602282e-14, -1.20995611e-14, -2.52176727e-14, + 4.70638556e-12], + [-1.20995611e-14, 1.43089742e-14, 1.24025960e-14, + 6.47585103e-13], + [-2.52176727e-14, 1.24025960e-14, 2.84449624e-14, + 1.50365410e-12], + [ 4.70638556e-12, 6.47585103e-13, 1.50365410e-12, + 8.75615148e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), 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1.03486742e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.7821082957639802, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, + 172, 173]), old_indices_discarded=array([158, 161, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 169, 170, 171, 172, + 173, 174]), model=ScalarModel(intercept=0.5417883646362189, linear_terms=array([ 1.39054002e-07, -3.67486274e-08, -9.76276358e-08, -9.03367442e-08]), square_terms=array([[ 8.31510649e-14, -1.19535743e-14, -2.39921551e-14, + 4.77544224e-12], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, + 174, 175]), model=ScalarModel(intercept=0.5417883618029782, linear_terms=array([ 1.06044142e-07, -4.72439863e-08, -1.20660778e-07, -9.21589515e-08]), square_terms=array([[ 6.99537303e-14, -8.73865731e-15, -1.76658488e-14, + 5.29092065e-12], + [-8.73865731e-15, 1.75516544e-14, 1.97422769e-14, + 8.07740656e-13], + [-1.76658488e-14, 1.97422769e-14, 4.40075585e-14, + 1.87057890e-12], + [ 5.29092065e-12, 8.07740656e-13, 1.87057890e-12, + 8.75720374e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=176, candidate_x=array([6.69792315e+00, 3.86727825e+03, 2.20809018e+01, 1.03486746e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.8834804979032714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, + 174, 175]), old_indices_discarded=array([158, 160, 161, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, 174, + 175, 176]), model=ScalarModel(intercept=0.5417882175351199, 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1.03486684e+00]), fval=0.5417879962626828, rho=0.38593680496603683, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([151, 157, 163, 164, 165, 169, 170, 171, 172, 173, 174, 175, 176, + 177, 178]), old_indices_discarded=array([156, 158, 159, 160, 161, 162, 166, 167, 168]), step_length=1.0046483357496498e-06, relative_step_length=1.0046483357496498, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 180 entries., 'history': {'params': [{'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 1.9420465373776397, 'BeqFac': 3579.5856142194925, 'BeqShift': 63.884357717706486, 'DiscFac': 0.6683149691599166}, {'CRRA': 1.1, 'BeqFac': 4156.0657257953635, 'BeqShift': 70.0, 'DiscFac': 0.9091286558648171}, {'CRRA': 19.746753699543984, 'BeqFac': 3660.8959750636873, 'BeqShift': 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State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.2988708045687646, linear_terms=array([ 0.05873433, -0.00758318, -0.003689 , 0.60294498]), square_terms=array([[ 2.09679873e-02, 1.70244323e-04, -6.43571996e-03, + 2.00584793e-01], + [ 1.70244323e-04, 5.37915084e-04, -1.12211972e-03, + 1.14935938e-02], + [-6.43571996e-03, -1.12211972e-03, 4.22275088e-03, + -8.44849663e-02], + [ 2.00584793e-01, 1.14935938e-02, -8.44849663e-02, + 2.28513600e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.13445693]), shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.65543066e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 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2.38204183e+00, 9.80265374e-01])), candidate_index=74, candidate_x=array([6.36660607e+00, 4.09967655e+03, 2.39442508e+00, 1.02552112e+00]), index=74, x=array([6.36660607e+00, 4.09967655e+03, 2.39442508e+00, 1.02552112e+00]), fval=0.4060535289976826, rho=1.3056858002306273, accepted=True, new_indices=array([62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]), old_indices_used=array([ 0, 61]), old_indices_discarded=array([], dtype=int32), step_length=0.05004503078942863, relative_step_length=0.9999999999978986, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.36660607e+00, 4.09967655e+03, 2.39442508e+00, 1.02552112e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.44984291327620873, linear_terms=array([ 0.00794743, -0.01077897, 0.00817368, -0.21319472]), 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candidate_x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), index=128, x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), fval=0.2860214168257209, rho=1.395194798708464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), old_indices_discarded=array([ 59, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 112, 113]), step_length=0.05013889760653118, relative_step_length=1.0018756471025494, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), model=ScalarModel(intercept=0.2809513915508127, linear_terms=array([0.00283903, 0.01786394, 0.01405315, 0.08551073]), square_terms=array([[ 2.87201002e-03, -8.38100805e-03, -4.93196038e-03, + 1.13296034e-01], + [-8.38100805e-03, 2.61008372e-02, 1.54731491e-02, + -3.32316666e-01], + [-4.93196038e-03, 1.54731491e-02, 9.29788666e-03, + -1.97733985e-01], + [ 1.13296034e-01, -3.32316666e-01, -1.97733985e-01, + 4.54041405e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), old_indices_discarded=array([ 46, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, + 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, + 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, + 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, + 127, 128]), model=ScalarModel(intercept=0.28601605918758766, linear_terms=array([ 0.00549346, -0.00187638, 0.00228974, -0.00413326]), square_terms=array([[ 9.45819260e-05, 2.05993647e-05, 2.22032641e-06, + 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118, 129]), step_length=0.0500760576419539, relative_step_length=1.0006199786858077, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 122, 123, 124, 125, 126, 127, 128, + 129, 130]), model=ScalarModel(intercept=0.27698506927654987, linear_terms=array([0.00886586, 0.00136488, 0.00678477, 0.02502439]), square_terms=array([[ 1.93358601e-04, 9.16030067e-06, 2.22153096e-05, + 9.68917980e-03], + [ 9.16030067e-06, 2.87649923e-05, 1.19928618e-04, + -1.05626890e-02], + [ 2.22153096e-05, 1.19928618e-04, 5.97417848e-04, + -4.71540074e-02], + [ 9.68917980e-03, -1.05626890e-02, -4.71540074e-02, + 5.10191499e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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State(trustregion=Region(center=array([5.75997591e+00, 4.09989866e+03, 1.83299422e+00, 1.02268458e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 117, 119, 123, 124, 125, 126, 127, 128, 129, + 130, 131]), model=ScalarModel(intercept=0.6958602300848257, linear_terms=array([ 0.00317513, 0.13661317, 0.00432578, -2.82344851]), square_terms=array([[ 8.92272971e-04, -9.23980154e-04, 1.92374401e-04, + 4.40514039e-02], + [-9.23980154e-04, 1.66500637e-02, -3.49295764e-04, + -3.72598035e-01], + [ 1.92374401e-04, -3.49295764e-04, 5.50846736e-04, + 6.72728739e-03], + [ 4.40514039e-02, -3.72598035e-01, 6.72728739e-03, + 9.12381886e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11324733]), shift=array([5.75997591e+00, 4.09989866e+03, 1.83299422e+00, 9.86752670e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=132, candidate_x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), index=132, x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), fval=0.22297505850584612, rho=0.8392169936307204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 117, 119, 123, 124, 125, 126, 127, 128, 129, + 130, 131]), old_indices_discarded=array([ 0, 46, 47, 49, 51, 53, 54, 55, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 112, 113, 116, 118, 120, 121, + 122]), step_length=0.25843212314368597, relative_step_length=1.2909979226036032, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 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2.38204183e+00, 9.80265374e-01])), candidate_index=133, candidate_x=array([5.31243818e+00, 4.09945113e+03, 1.98217346e+00, 1.00539435e+00]), index=132, x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), fval=0.22297505850584612, rho=-0.629024754891979, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, + 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, + 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, + 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, + 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 112, 116, 117, 118, 120, 121, 122, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), 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0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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candidate_x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), index=134, x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), fval=0.21950785606017936, rho=0.08682007098967585, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 46, 49, 54, 55, 59, 60, 61, 62, 65, 70, 75, 76, 77, + 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, + 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 112, 116, 117, 118, 120, 121, 122, + 124, 133]), step_length=0.23555149344412082, relative_step_length=1.176697714677913, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([129, 131, 132, 133, 134]), model=ScalarModel(intercept=0.21950785606017942, linear_terms=array([ 0.03127405, -0.02069695, 0.00761499, -0.24328735]), square_terms=array([[ 4.10019284e-03, -3.03558341e-03, 1.01299676e-03, + -8.66155089e-02], + [-3.03558341e-03, 2.31725603e-03, -7.31760870e-04, + 7.02087855e-02], + [ 1.01299676e-03, -7.31760870e-04, 3.83990035e-04, + -3.05692894e-02], + [-8.66155089e-02, 7.02087855e-02, -3.05692894e-02, + 3.73315598e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=140, candidate_x=array([4.04441463e+00, 4.09892900e+03, 7.09170793e-01, 9.87246327e-01]), index=139, x=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), fval=0.07568421749234616, rho=-97.55333914328999, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), old_indices_discarded=array([ 0, 35, 36, 37, 40, 42, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, + 118, 119, 120, 121, 122, 123, 124, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=141, candidate_x=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), index=141, x=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), fval=0.04733711349557875, rho=1.7912506175389145, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, + 139, 140]), old_indices_discarded=array([ 45, 46, 49, 54, 59, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, + 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, + 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 126, 127]), step_length=0.49338773191709673, relative_step_length=1.2323594474146116, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 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1.13237819e+00, 8.76265269e-01]), index=141, x=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), fval=0.04733711349557875, rho=-9.235171467382486, accepted=False, new_indices=array([142]), old_indices_used=array([ 42, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, + 141]), old_indices_discarded=array([ 0, 35, 36, 37, 44, 45, 46, 47, 48, 49, 50, 51, 52, + 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, + 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, + 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, + 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, + 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 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7.93779346e-01, 9.63841347e-01]), index=146, x=array([4.28402294e+00, 4.09922505e+03, 7.93779346e-01, 9.63841347e-01]), fval=0.043027026165379276, rho=0.8364890346486437, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([138, 139, 140, 141, 145]), old_indices_discarded=array([], dtype=int32), step_length=0.10044974878343069, relative_step_length=1.0035936355587007, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28402294e+00, 4.09922505e+03, 7.93779346e-01, 9.63841347e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144, 145, 146]), model=ScalarModel(intercept=0.051759709428797325, linear_terms=array([ 0.04242777, -0.01063285, -0.01550282, -0.40220491]), square_terms=array([[ 8.10319276e-02, -2.05701314e-02, -2.47633390e-02, + -8.14965374e-01], + [-2.05701314e-02, 5.48111505e-03, 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2.38204183e+00, 9.80265374e-01])), candidate_index=148, candidate_x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=0.46328473374724694, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([138, 139, 140, 141, 144, 145, 146, 147]), old_indices_discarded=array([], dtype=int32), step_length=0.10031413807620175, relative_step_length=1.002238748718894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144, 145, 146, 147, 148, 149]), model=ScalarModel(intercept=0.03951342557884055, linear_terms=array([ 0.00394695, 0.0005794 , -0.00109741, -0.02210072]), 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=151, candidate_x=array([4.17512138e+00, 4.09923356e+03, 9.60804470e-01, 9.70574510e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=-1.0172672979680422, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([138, 140, 141, 144, 145, 146, 147, 148, 149, 150]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([141, 145, 146, 147, 148, 149, 150, 151]), model=ScalarModel(intercept=0.03959982994266096, linear_terms=array([ 2.95767902e-03, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=177, candidate_x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), index=177, x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), fval=0.03175931998394472, rho=0.6690416360198942, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176]), old_indices_discarded=array([], dtype=int32), step_length=0.050499371349656076, relative_step_length=1.0090786348405505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177]), model=ScalarModel(intercept=0.03248246414257855, 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177, 178, 179]), model=ScalarModel(intercept=0.032130564354299555, linear_terms=array([-0.00156055, -0.00139965, 0.00036611, -0.025743 ]), square_terms=array([[ 4.05055456e-03, 1.48919579e-03, -6.07642697e-04, + 3.44846053e-02], + [ 1.48919579e-03, 1.66608590e-03, -7.81966055e-04, + 3.87222711e-02], + [-6.07642697e-04, -7.81966055e-04, 5.86968019e-04, + -1.75898376e-02], + [ 3.44846053e-02, 3.87222711e-02, -1.75898376e-02, + 9.06040243e-01]]), scale=0.1000900615790676, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.006255628848691725, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([181, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, + 196, 197]), model=ScalarModel(intercept=0.03163372180134707, linear_terms=array([-1.40416035e-05, -1.06245855e-05, -6.25328365e-06, 2.93425222e-04]), square_terms=array([[ 8.39288020e-05, 3.25915482e-06, 2.80895929e-05, + -1.12787517e-03], + [ 3.25915482e-06, 1.50804018e-07, 1.22778171e-06, + -5.07296461e-05], + [ 2.80895929e-05, 1.22778171e-06, 1.11271310e-05, + -4.26777604e-04], + [-1.12787517e-03, -5.07296461e-05, -4.26777604e-04, + 1.76074601e-02]]), scale=0.006255628848691725, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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model_indices=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), model=ScalarModel(intercept=0.03163462823062035, linear_terms=array([-9.39533430e-06, -5.39540217e-06, 2.16599626e-07, 1.49312835e-04]), square_terms=array([[ 2.34139611e-05, 1.09806616e-06, 5.53797697e-06, + -3.00949207e-04], + [ 1.09806616e-06, 5.92778360e-08, 2.79762167e-07, + -1.59957868e-05], + [ 5.53797697e-06, 2.79762167e-07, 1.56644529e-06, + -7.74361413e-05], + [-3.00949207e-04, -1.59957868e-05, -7.74361413e-05, + 4.40255574e-03]]), scale=0.0031278144243458623, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-2.567463867080543, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Maximum number of criterion evaluations reached.', 'tranquilo_history': History for least_squares function with 200 entries., 'history': {'params': [{'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 1.5997660080756595, 'BeqFac': 3798.32770700145, 'BeqShift': 70.0, 'DiscFac': 0.7024407930795087}, {'CRRA': 1.1, 'BeqFac': 4399.8970420338355, 'BeqShift': 69.76434738104037, 'DiscFac': 0.9316481833606611}, {'CRRA': 17.36460836924718, 'BeqFac': 3912.562988977825, 'BeqShift': 70.0, 'DiscFac': 0.5294729131673447}, {'CRRA': 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 47]), model=ScalarModel(intercept=0.5832383280438845, linear_terms=array([-0.04366208, -0.07974917, 0.0121024 , -0.66338862]), square_terms=array([[ 1.24405099e-02, 1.84037162e-02, -3.21723600e-03, + 1.98213656e-01], + [ 1.84037162e-02, 2.76097603e-02, -4.77741170e-03, + 2.90439616e-01], + [-3.21723600e-03, -4.77741170e-03, 8.37149132e-04, + -5.12297952e-02], + [ 1.98213656e-01, 2.90439616e-01, -5.12297952e-02, + 3.19738903e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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46, 47, 48, 49]), model=ScalarModel(intercept=0.5792867438043003, linear_terms=array([ 0.0620906 , 0.05580846, -0.01892313, 1.03442734]), square_terms=array([[ 4.84671874e-02, 5.47513283e-02, -1.60884447e-02, + 6.51384141e-01], + [ 5.47513283e-02, 6.23780615e-02, -1.82999844e-02, + 7.33136697e-01], + [-1.60884447e-02, -1.82999844e-02, 5.38147700e-03, + -2.16952904e-01], + [ 6.51384141e-01, 7.33136697e-01, -2.16952904e-01, + 9.00073831e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.25071503]), shift=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.50715032e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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rho=-0.8879908119721849, accepted=False, new_indices=array([49]), old_indices_used=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([29, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.03451296e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.5573166557730902, linear_terms=array([ 0.00667821, 0.00321645, -0.01330197, -0.63280993]), square_terms=array([[ 1.70163608e-04, -2.35024264e-05, 4.37192061e-05, + 6.94446216e-03], + [-2.35024264e-05, 1.13911009e-04, -3.53635828e-04, + -2.56390159e-02], + [ 4.37192061e-05, -3.53635828e-04, 1.12835620e-03, + 7.88536699e-02], + [ 6.94446216e-03, -2.56390159e-02, 7.88536699e-02, + 5.83233513e+00]]), scale=0.1999253572962513, shift=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.03451296e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + 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0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=51, candidate_x=array([1.64945740e+01, 4.11487264e+03, 2.89034468e+01, 7.23765992e-01]), index=51, x=array([1.64945740e+01, 4.11487264e+03, 2.89034468e+01, 7.23765992e-01]), fval=1.4897043906479308, rho=1.3253298909081184, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([], dtype=int32), step_length=0.20737450196904972, relative_step_length=1.0372596291612985, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.64945740e+01, 4.11487264e+03, 2.89034468e+01, 7.23765992e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.5712773779296673, linear_terms=array([ 0.00451862, -0.00766814, -0.00455592, 0.72567413]), square_terms=array([[ 2.46474336e-02, 1.86445689e-02, 1.15517772e-02, + -4.04530686e-01], + [ 1.86445689e-02, 1.43523254e-02, 8.89363271e-03, + -3.18012389e-01], + [ 1.15517772e-02, 8.89363271e-03, 5.51208229e-03, + -1.96417562e-01], + [-4.04530686e-01, -3.18012389e-01, -1.96417562e-01, + 7.75618481e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.26087238]), shift=array([1.64945740e+01, 4.11487264e+03, 2.89034468e+01, 7.60872379e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 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4.11457467e+03, 2.86054681e+01, 6.91390214e-01]), index=51, x=array([1.64945740e+01, 4.11487264e+03, 2.89034468e+01, 7.23765992e-01]), fval=1.4897043906479308, rho=-2.3945165636124486, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([36, 40, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.64945740e+01, 4.11487264e+03, 2.89034468e+01, 7.23765992e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=0.5954556348969029, linear_terms=array([ 0.04817372, 0.02615546, 0.03135887, -0.58636171]), square_terms=array([[ 5.82275630e-03, 3.53153586e-03, 4.89353593e-03, + -1.41492276e-01], + [ 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2.85279127e+01, 7.55457411e-01]), index=58, x=array([1.61338301e+01, 4.11492371e+03, 2.85465763e+01, 7.83158466e-01]), fval=1.2823500253026578, rho=-1.8695920228577874, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 37, 38, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([35, 42, 44, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.61338301e+01, 4.11492371e+03, 2.85465763e+01, 7.83158466e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([37, 38, 52, 53, 55, 56, 57, 58, 60]), model=ScalarModel(intercept=0.7130176708649442, linear_terms=array([ 0.18150897, -0.10693861, -0.13561257, -0.52397561]), square_terms=array([[ 0.09519715, -0.06113223, -0.07628176, -0.3769189 ], + [-0.06113223, 0.03939804, 0.04912459, 0.2452991 ], + [-0.07628176, 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9.60369865e-01])), candidate_index=74, candidate_x=array([1.61054017e+01, 4.11493959e+03, 2.84979739e+01, 8.82702225e-01]), index=74, x=array([1.61054017e+01, 4.11493959e+03, 2.84979739e+01, 8.82702225e-01]), fval=0.9775713703296768, rho=0.3927792887058814, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.012495369871457078, relative_step_length=1.0000028042819056, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.61054017e+01, 4.11493959e+03, 2.84979739e+01, 8.82702225e-01]), radius=0.024990669662031412, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([58, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.9865800164210952, linear_terms=array([-0.00066646, 0.00067361, 0.00088316, 0.01131446]), square_terms=array([[ 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old_indices_discarded=array([66, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([81, 82, 83, 84, 85, 86, 87, 88, 89]), model=ScalarModel(intercept=0.9730606049983257, linear_terms=array([ 4.91514813e-04, -2.73745659e-05, 8.04246166e-05, -1.02088743e-03]), square_terms=array([[ 9.70139608e-06, 2.32782735e-07, -1.25262833e-07, + 4.46639539e-04], + [ 2.32782735e-07, 9.98791914e-09, -4.25450829e-09, + 1.13008219e-05], + [-1.25262833e-07, -4.25450829e-09, 9.84307051e-09, + -7.04515004e-06], + [ 4.46639539e-04, 1.13008219e-05, -7.04515004e-06, + 2.11324714e-02]]), scale=0.012495334831015706, shift=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01])), 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102, + 104, 105]), old_indices_discarded=array([ 38, 55, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, + 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, + 83, 84, 85, 86, 87, 88, 99, 103]), step_length=0.04998143202530104, relative_step_length=1.0000018547169698, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.59191640e+01, 4.11502516e+03, 2.84625747e+01, 8.89512165e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 89, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 104, + 105, 106]), model=ScalarModel(intercept=0.9691950560652018, linear_terms=array([ 0.00283956, -0.00127135, 0.00147188, 0.00055916]), square_terms=array([[ 8.52870166e-04, 1.58642942e-04, -1.97086495e-05, + 3.42270741e-02], + [ 1.58642942e-04, 4.16119024e-05, 3.64811022e-07, + 6.31819464e-03], + [-1.97086495e-05, 3.64811022e-07, 6.90244812e-06, + -8.46572541e-04], + [ 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candidate_x=array([1.53845479e+01, 4.11482259e+03, 2.79903380e+01, 8.64861786e-01]), index=108, x=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), fval=0.9585677871382879, rho=-0.7052809316351738, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 92, 93, 95, 96, 97, 100, 101, 102, 104, 105, 106, + 107, 108]), old_indices_discarded=array([ 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, + 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, + 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, + 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, + 87, 88, 90, 91, 94, 98, 99, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108]), model=ScalarModel(intercept=0.9577031847372262, linear_terms=array([ 0.03178872, 0.0310778 , -0.00399409, 0.28392775]), square_terms=array([[ 1.95437081e-03, 4.77684675e-03, -7.59671438e-04, + -4.27399704e-02], + [ 4.77684675e-03, 1.38624719e-02, -2.20752863e-03, + -1.50579650e-01], + [-7.59671438e-04, -2.20752863e-03, 3.56515716e-04, + 2.41971764e-02], + [-4.27399704e-02, -1.50579650e-01, 2.41971764e-02, + 1.98161272e+00]]), scale=0.1999253572962513, shift=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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fval=0.9585677871382879, rho=-1.049281869869592, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108]), old_indices_discarded=array([ 37, 38, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 91, + 92, 94, 95, 97, 98, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 101, 102, 103, 104, 105, 106, 107, + 108, 110]), model=ScalarModel(intercept=0.9578113150011967, linear_terms=array([-0.00143314, -0.01180295, 0.00580005, 0.11249413]), square_terms=array([[ 2.42515718e-03, 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9.60369865e-01])), candidate_index=148, candidate_x=array([1.55154556e+01, 4.11505055e+03, 2.82784344e+01, 8.97881759e-01]), index=148, x=array([1.55154556e+01, 4.11505055e+03, 2.82784344e+01, 8.97881759e-01]), fval=0.9510829335205345, rho=1.309794548498942, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([129, 130, 131, 132, 135, 136, 138, 139, 140, 141, 142, 143, 144, + 145, 146]), old_indices_discarded=array([108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, + 122, 123, 124, 125, 126, 127, 128, 133, 134, 137, 147]), step_length=0.049982000650298855, relative_step_length=1.0000132314628813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.55154556e+01, 4.11505055e+03, 2.82784344e+01, 8.97881759e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 130, 131, 135, 136, 138, 139, 141, 143, 144, 145, 146, + 147, 148]), model=ScalarModel(intercept=0.9514697127444787, linear_terms=array([ 0.00347102, 0.00456633, -0.00497031, 0.03017323]), square_terms=array([[ 8.67970814e-04, -4.63342592e-04, 5.07629257e-04, + 3.23447862e-02], + [-4.63342592e-04, 2.76014123e-04, -3.02039517e-04, + -1.74676282e-02], + [ 5.07629257e-04, -3.02039517e-04, 3.30858301e-04, + 1.91948157e-02], + [ 3.23447862e-02, -1.74676282e-02, 1.91948157e-02, + 1.23621672e+00]]), scale=0.09996267864812565, shift=array([1.55154556e+01, 4.11505055e+03, 2.82784344e+01, 8.97881759e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 130, 131, 135, 136, 138, 139, 141, 143, 144, 145, 146, + 147, 148]), old_indices_discarded=array([105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, + 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 132, 133, 134, + 137, 140, 142]), step_length=0.105280468337507, relative_step_length=1.0531977510136585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54791737e+01, 4.11498376e+03, 2.83511980e+01, 8.94338923e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 137, 140, 141, 143, 144, 145, 146, 147, + 148, 149]), model=ScalarModel(intercept=0.9504811473000055, linear_terms=array([ 0.00337976, -0.00166767, 0.00065203, 0.00032394]), square_terms=array([[ 6.88080741e-05, 3.26320216e-05, -1.14612730e-05, + 4.04897732e-03], + [ 3.26320216e-05, 2.38843862e-05, -8.53007545e-06, + 2.37987944e-03], + [-1.14612730e-05, -8.53007545e-06, 3.08030290e-06, + -8.37502206e-04], + [ 4.04897732e-03, 2.37987944e-03, -8.37502206e-04, + 2.69451472e-01]]), scale=0.049981339324062825, shift=array([1.54791737e+01, 4.11498376e+03, 2.83511980e+01, 8.94338923e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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142]), step_length=0.049982181792418395, relative_step_length=1.0000168556578708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, + 149, 150]), model=ScalarModel(intercept=0.9481669388008567, linear_terms=array([ 0.00491681, -0.00191175, 0.00090832, -0.00794232]), square_terms=array([[ 4.53197713e-04, 9.69734758e-05, -4.03223571e-05, + 2.17424813e-02], + [ 9.69734758e-05, 2.70238997e-05, -1.14028932e-05, + 5.09292923e-03], + [-4.03223571e-05, -1.14028932e-05, 4.97292295e-06, + -2.10313696e-03], + [ 2.17424813e-02, 5.09292923e-03, -2.10313696e-03, + 1.09787792e+00]]), scale=0.09996267864812565, shift=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, + 150, 151]), model=ScalarModel(intercept=0.9447208950935511, linear_terms=array([ 0.00747774, -0.00115453, 0.00019389, -0.01790295]), square_terms=array([[ 2.32523138e-03, 1.54081854e-04, -2.97872562e-06, + 1.00174053e-01], + [ 1.54081854e-04, 1.28584982e-05, -4.90411666e-07, + 6.98051990e-03], + [-2.97872562e-06, -4.90411666e-07, 4.85734809e-07, + -6.45942014e-05], + [ 1.00174053e-01, 6.98051990e-03, -6.45942014e-05, + 4.46348961e+00]]), scale=0.1999253572962513, shift=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 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4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), index=152, x=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), fval=0.9352639178281498, rho=1.2203446548292984, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, + 150, 151]), old_indices_discarded=array([ 37, 38, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, + 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, + 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 132, 133, 134, + 137, 138, 139, 140, 142]), step_length=0.20002826060126488, relative_step_length=1.0005147086212836, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, + 151, 152]), model=ScalarModel(intercept=1.0726433496908907, linear_terms=array([-0.02343661, -0.00889048, -0.00275083, -1.36662877]), square_terms=array([[4.86288425e-03, 6.82588240e-04, 2.46055974e-04, 1.78667944e-01], + [6.82588240e-04, 1.16954213e-04, 3.75054128e-05, 2.65804533e-02], + [2.46055974e-04, 3.75054128e-05, 1.68608179e-05, 9.53055835e-03], + [1.78667944e-01, 2.65804533e-02, 9.53055835e-03, 6.81382039e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.24785877]), shift=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 8.52141232e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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candidate_x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), index=153, x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), fval=0.9223250761250302, rho=0.7752349469770148, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, + 151, 152]), old_indices_discarded=array([ 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 111, 112, 113, 114, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, + 130, 131, 132, 133, 134, 137, 138, 139, 140, 142]), step_length=0.5161364791949027, relative_step_length=1.2908229505627111, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, + 152, 153]), model=ScalarModel(intercept=1.8249779819419052, linear_terms=array([-1.76627203e-01, -2.47141400e-03, -2.38775460e-03, -4.85764149e+00]), square_terms=array([[ 2.34532115e-02, 1.92781711e-04, 1.57766385e-04, + 5.44396171e-01], + [ 1.92781711e-04, 6.31379450e-06, -3.02452352e-06, + 5.07453896e-03], + [ 1.57766385e-04, -3.02452352e-06, 9.75935474e-06, + 4.07694325e-03], + [ 5.44396171e-01, 5.07453896e-03, 4.07694325e-03, + 1.30477707e+01]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , 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scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=154, candidate_x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=1.1251274278087409, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, + 152, 153]), old_indices_discarded=array([ 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, + 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, + 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, + 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, + 139, 140, 147]), step_length=1.0323679882696024, relative_step_length=1.2909417822620541, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=1.5994028583700104, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), model=ScalarModel(intercept=1.1326265739323547, linear_terms=array([-0.09637569, -0.14379396, 0.09712327, -1.55727297]), square_terms=array([[ 0.04121845, 0.02313126, -0.01642407, 0.42608071], + [ 0.02313126, 0.01788942, -0.01256294, 0.26548283], + [-0.01642407, -0.01256294, 0.00885428, -0.18607463], + [ 0.42608071, 0.26548283, -0.18607463, 4.71648178]]), scale=array([1.19191507, 1.19191507, 1.19191507, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=155, candidate_x=array([1.30586935e+01, 4.11715430e+03, 2.80233711e+01, 8.97432554e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=-0.7704978841646354, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), old_indices_discarded=array([ 20, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, + 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, + 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, + 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, + 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, + 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, + 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, + 121, 122, 123, 124, 126, 128, 129, 130, 131, 132, 133, 134, 135, + 137, 138, 139, 140, 141, 142, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), model=ScalarModel(intercept=1.1326265739323542, linear_terms=array([-0.04818785, -0.07189698, 0.04856163, -1.55727297]), square_terms=array([[ 1.03046127e-02, 5.78281380e-03, -4.10601701e-03, + 2.13040355e-01], + [ 5.78281380e-03, 4.47235416e-03, -3.14073444e-03, + 1.32741416e-01], + [-4.10601701e-03, -3.14073444e-03, 2.21357021e-03, + -9.30373154e-02], + [ 2.13040355e-01, 1.32741416e-01, -9.30373154e-02, + 4.71648178e+00]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=156, candidate_x=array([1.36546511e+01, 4.11655835e+03, 2.86193287e+01, 8.98242801e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=-0.8333994975616199, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), old_indices_discarded=array([ 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, + 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, + 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, + 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, + 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, + 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, + 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, + 141, 142, 147, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 145, 146, 148, 149, 150, 151, 152, 153, + 154, 156]), model=ScalarModel(intercept=0.9323612178144098, linear_terms=array([-0.01259834, 0.01279683, -0.02998382, -0.59640575]), square_terms=array([[ 4.02828891e-03, -1.26463784e-03, 2.65461049e-03, + 1.14730233e-01], + [-1.26463784e-03, 4.66105839e-04, -9.83258053e-04, + -3.67550589e-02], + [ 2.65461049e-03, -9.83258053e-04, 2.08826577e-03, + 7.82898537e-02], + [ 1.14730233e-01, -3.67550589e-02, 7.82898537e-02, + 3.40732089e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.2369916 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.63008405e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, + 169]), model=ScalarModel(intercept=0.8621110105805507, linear_terms=array([ 0.05259407, 0.01444784, -0.04650949, 1.3449937 ]), square_terms=array([[ 1.06382832e-02, 1.77761267e-03, -1.34152433e-02, + 2.24240960e-01], + [ 1.77761267e-03, 3.91467956e-04, -2.20266751e-03, + 3.98071647e-02], + [-1.34152433e-02, -2.20266751e-03, 1.72375098e-02, + -2.79030554e-01], + [ 2.24240960e-01, 3.98071647e-02, -2.79030554e-01, + 4.86559567e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 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168, 169, + 170]), model=ScalarModel(intercept=0.8521861809194315, linear_terms=array([ 0.01363795, 0.00599446, -0.00829727, 0.87225482]), square_terms=array([[ 1.25459512e-04, 6.75203715e-05, -2.81566396e-05, + 4.82302182e-03], + [ 6.75203715e-05, 1.45256704e-04, 1.00392769e-04, + -6.60298780e-03], + [-2.81566396e-05, 1.00392769e-04, 3.70387367e-04, + -2.84734924e-02], + [ 4.82302182e-03, -6.60298780e-03, -2.84734924e-02, + 2.26757906e+00]]), scale=0.09996267864812565, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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new_indices=array([], dtype=int32), old_indices_used=array([154, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, + 170]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 158, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, + 181, 182, 183]), model=ScalarModel(intercept=0.8751612738683985, linear_terms=array([ 0.01069739, -0.0351117 , 0.00524044, 0.03150041]), square_terms=array([[ 2.80038988e-04, -1.92877967e-03, 4.46333870e-04, + -8.04311081e-03], + [-1.92877967e-03, 1.45965092e-02, -3.44462599e-03, + 6.72455897e-02], + [ 4.46333870e-04, -3.44462599e-03, 8.32232273e-04, + -1.63487093e-02], + [-8.04311081e-03, 6.72455897e-02, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, + 183, 184]), model=ScalarModel(intercept=0.8806140250059065, linear_terms=array([ 0.00348081, 0.00103192, 0.00045499, -0.00120431]), square_terms=array([[ 7.93493802e-06, 1.82672879e-07, 1.06786659e-07, + 2.40577351e-04], + [ 1.82672879e-07, 1.04969220e-05, 4.04967602e-06, + -1.03166451e-03], + [ 1.06786659e-07, 4.04967602e-06, 1.68666246e-06, + -3.96649049e-04], + [ 2.40577351e-04, -1.03166451e-03, -3.96649049e-04, + 1.12239205e-01]]), scale=0.024990669662031412, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 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2.92122061e+01, 9.24232050e-01]), index=185, x=array([1.42268091e+01, 4.11595541e+03, 2.92122061e+01, 9.24232050e-01]), fval=0.8889267485376284, rho=0.3128499910431618, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, + 183, 184]), old_indices_discarded=array([], dtype=int32), step_length=0.024992174326975865, relative_step_length=1.0000602090686166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42268091e+01, 4.11595541e+03, 2.92122061e+01, 9.24232050e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, + 184, 185]), model=ScalarModel(intercept=0.8804191957868968, linear_terms=array([ 0.00262124, -0.00038687, 0.00011566, -0.00743838]), square_terms=array([[ 1.53514229e-04, -1.70092698e-06, 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square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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step_length=0.049987606704656753, relative_step_length=1.0001253944107678, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.41773813e+01, 4.11596240e+03, 2.92102030e+01, 9.25929272e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 171, 172, 173, 174, 175, 178, 179, 180, 181, 182, 183, 184, + 185, 186]), model=ScalarModel(intercept=0.8785580942724209, linear_terms=array([ 1.43240649e-03, 6.25673925e-03, 2.30520842e-03, -6.05240410e-05]), square_terms=array([[ 1.42477966e-03, -6.93147300e-04, -2.32265295e-04, + 4.96992757e-02], + [-6.93147300e-04, 3.68402635e-04, 1.23036392e-04, + -2.42488611e-02], + [-2.32265295e-04, 1.23036392e-04, 4.52593068e-05, + -8.08632256e-03], + [ 4.96992757e-02, -2.42488611e-02, -8.08632256e-03, + 1.77434649e+00]]), scale=0.09996267864812565, shift=array([1.41773813e+01, 4.11596240e+03, 2.92102030e+01, 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[2.87187500e+00, 4.68750000e+03, 3.28125000e+01, 8.93750000e-01], + [2.28125000e+00, 9.37500000e+03, 3.93750000e+01, 6.87500000e-01], + [1.98593750e+00, 2.65625000e+03, 4.92187500e+01, 8.28125000e-01], + [1.70468750e+01, 2.18750000e+03, 1.53125000e+01, 1.04375000e+00], + [1.85234375e+01, 6.40625000e+03, 4.04687500e+01, 1.05312500e+00], + [1.69062500e+00, 5.31250000e+03, 6.34375000e+01, 1.08125000e+00]]), 'exploration_results': array([4.85268873e-02, 5.81756522e-01, 7.85434558e-01, 1.08501898e+00, + 1.09147168e+00, 1.15508374e+00, 1.35896335e+00, 1.47857950e+00, + 1.60399255e+00, 1.93344372e+00, 1.94383457e+00, 1.97559443e+00, + 2.00706025e+00, 2.09257083e+00, 2.11464344e+00, 2.13130763e+00, + 2.24401741e+00, 2.32809571e+00, 2.43093035e+00, 2.58864817e+00, + 2.64782870e+00, 2.75968951e+00, 2.76063416e+00, 2.87160809e+00, + 2.96402039e+00, 3.18404442e+00, 3.25640118e+00, 3.28020017e+00, + 3.37801319e+00, 3.50195454e+00, 3.52157483e+00, 3.84631802e+00, + 4.29481111e+00, 4.50920970e+00, 4.92630064e+00, 5.00339546e+00, + 5.43544424e+00, 7.28251427e+00, 1.28207813e+01, 1.15051611e+03])}}" diff --git a/code/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv b/code/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv new file mode 100644 index 0000000..196e1d5 --- /dev/null +++ b/code/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv @@ -0,0 +1,19323 @@ +CRRA,2.0 +BeqFac,1.0 +time_to_estimate,57.54076361656189 +params,"{'CRRA': 2.0, 'BeqFac': 1.0}" +criterion,0.8276471824376574 +start_criterion,0.8265504175460967 +start_params,"{'CRRA': 2.0, 'BeqFac': 1.0}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message, +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 2.116888877683535, 'BeqFac': 1.1622867532298418}, {'CRRA': 1.8776126026706215, 'BeqFac': 0.8418186958741936}, {'CRRA': 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28.02404379984364, 28.248444000259042, 28.4726595999673], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}" +convergence_report, +multistart_info,"{'start_parameters': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.069e-09** 7.731e-08* +relative_params_change 4.41e-06* 6.026e-05 +absolute_criterion_change 4.137e-07* 7.859e-06* +absolute_params_change 8.763e-06* 9.915e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 2.28125, 'BeqFac': 9375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}], 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, + 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, + 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, + 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, + 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}" +algorithm_output,"{'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=86.15955603265024, linear_terms=array([ 0.05089577, -0.19533528]), square_terms=array([[ 1.51497851e-05, -5.81440784e-05], + [-5.81440784e-05, 2.23153914e-04]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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square_terms=array([[0.22002817, 0.41060667], + [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=15, candidate_x=array([1.98543255, 0.95216916]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.192688611597553, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 3, 5, 6, 7, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.025, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], + [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=16, candidate_x=array([2.01033614, 1.0227644 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6632385320428, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.827647182437657, linear_terms=array([-45.37479674, 16.0066913 ]), square_terms=array([[ 6419.23080123, -2264.48759797], + [-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=17, candidate_x=array([2.004237 , 1.01176031]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7120258298457, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], + [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=18, candidate_x=array([1.99690325, 0.99457059]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6996277758512, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.003125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.8276471824376497, linear_terms=array([ 33.51528457, -14.29880894]), square_terms=array([[ 3502.17413253, -1494.15175942], + [-1494.15175942, 637.45816047]]), scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=19, candidate_x=array([1.9987484 , 0.99713645]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117679378963, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], + [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=20, candidate_x=array([2.00093264, 1.00125388]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7086956369039, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00078125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], + [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=21, candidate_x=array([2.00037411, 1.00068591]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117058020676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.000390625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.8276471824376602, linear_terms=array([ 10.88378502, -10.70264801]), square_terms=array([[ 369.32716463, -363.18052667], + [-363.18052667, 357.13618598]]), scale=0.000390625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], + [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], + [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=24, candidate_x=array([2.00008235, 1.00005249]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71151997705, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], + [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], + [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=26, candidate_x=array([1.99997671, 0.99999267]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711721703526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], + [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], + [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], + [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], + [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], + [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], + [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], + [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], + [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=37, candidate_x=array([2.00000097, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-19.954818815240166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=78.99436811656406, linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], + [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], + [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], + [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], + [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=41, candidate_x=array([2.00000087, 0.99999951]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.540882296216793, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), old_indices_discarded=array([29, 30, 31, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], + [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=45, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=46, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=47, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=48, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=51, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=53, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=58, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=61, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=62, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=76, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=81, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=82, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=83, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=109, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=110, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=111, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=112, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 113 entries., 'multistart_info': {'start_parameters': [array([2., 1.]), array([ 6.92609782, 183.76536854])], 'local_optima': [{'solution_x': array([2., 1.]), 'solution_criterion': 0.8276471824376574, 'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-499.88400427494435, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.1, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), model=ScalarModel(intercept=69.9966297435316, linear_terms=array([ 5.52347875, 10.30766762]), square_terms=array([[0.22002817, 0.41060667], + [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], 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6, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=61.19193548717778, linear_terms=array([3.65115817, 8.68434653]), square_terms=array([[0.11012808, 0.26194167], + [0.26194167, 0.62303309]]), scale=0.05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], + [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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[-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=17, candidate_x=array([2.004237 , 1.01176031]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7120258298457, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], + [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=18, candidate_x=array([1.99690325, 0.99457059]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6996277758512, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.003125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.8276471824376497, linear_terms=array([ 33.51528457, -14.29880894]), square_terms=array([[ 3502.17413253, -1494.15175942], + [-1494.15175942, 637.45816047]]), scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], + [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], + [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], + [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], + [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], + [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], + [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], + [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=28, candidate_x=array([2.00000611, 0.99999986]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711677104554, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=3.0517578125e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.8276471824376628, linear_terms=array([-3.97279126, 9.21962533]), square_terms=array([[ 49.20880826, -114.19849305], + [-114.19849305, 265.01954175]]), scale=3.0517578125e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], + [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], + [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=31, candidate_x=array([2.00000074, 1.00000067]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116856529549, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], + [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=32, candidate_x=array([1.99999924, 0.99999934]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.746700442186407, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32]), model=ScalarModel(intercept=43.05839744142132, linear_terms=array([-37.69007116, 44.3240894 ]), square_terms=array([[ 16.75517786, -19.70434065], + [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=33, candidate_x=array([2.00000009, 0.999999 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-2.9522733070987055, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=56.93444143408821, linear_terms=array([ -6.96601288, -17.26411453]), square_terms=array([[0.43120475, 1.06866988], + [1.06866988, 2.64852213]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], + [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=35, candidate_x=array([2.00000098, 0.99999981]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-7.891442250906298, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], + [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], + [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=37, candidate_x=array([2.00000097, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-19.954818815240166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=78.99436811656406, linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], + [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], + [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], + [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], + [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=41, candidate_x=array([2.00000087, 0.99999951]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.540882296216793, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), old_indices_discarded=array([29, 30, 31, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], + [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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[-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=56, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=57, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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53, + 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=61, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=62, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=76, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=81, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=82, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=83, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=84, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=85, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=86, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=87, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 113 entries., 'history': {'params': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 2.116888877683535, 'BeqFac': 1.1622867532298418}, {'CRRA': 1.8776126026706215, 'BeqFac': 0.8418186958741936}, {'CRRA': 2.1528425841636905, 'BeqFac': 0.8710071921921023}, {'CRRA': 1.9202699787802653, 'BeqFac': 1.18342061965957}, {'CRRA': 1.8016623674015406, 'BeqFac': 1.0257329262859587}, {'CRRA': 1.8134776737972333, 'BeqFac': 0.9278236754336386}, {'CRRA': 1.9669947321437662, 'BeqFac': 0.802742167978713}, {'CRRA': 2.197271497523536, 'BeqFac': 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 6, 8, 11, 13, 14, 15]), model=ScalarModel(intercept=112.52716726953476, linear_terms=array([-5.49527902, 1.83435019]), square_terms=array([[87.85384367, 61.32646973], + [61.32646973, 42.95209287]]), scale=array([ 9.45 , 65.14312703]), shift=array([ 10.55 , 119.682045])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 6, 9, 10, 14, 15, 16]), model=ScalarModel(intercept=111.07641141436483, linear_terms=array([-11.32539952, -1.19335547]), square_terms=array([[37.72655582, 33.29962208], + [33.29962208, 29.74641185]]), scale=array([ 9.45 , 92.41258601]), shift=array([10.55 , 92.41258601])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=118.5566856481718, linear_terms=array([16.30216759, -3.21271579]), square_terms=array([[23.7595385 , -7.01292519], + [-7.01292519, 2.0824903 ]]), scale=array([ 9.45 , 59.8410225]), shift=array([10.55 , 59.8410225])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([ 7.25637091, -2.42042422]), square_terms=array([[ 6.4002053 , -7.80908346], + [-7.80908346, 9.72016944]]), scale=array([ 9.45 , 65.14312703]), shift=array([10.55 , 65.14312703])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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shift=array([10.55 , 16.28578176])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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candidate_x=array([1.1 , 4.95638609]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.5089417781776332, accepted=False, new_indices=array([25]), old_indices_used=array([16, 17, 18, 20, 22, 23, 24]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=107.07780108961828, linear_terms=array([13.09888074, 0.39394958]), square_terms=array([[1.63256660e+01, 2.16792836e-02], + [2.16792836e-02, 7.25065498e-04]]), scale=array([4.15983125, 4.07144544]), shift=array([5.25983125, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=27, candidate_x=array([1.92771902, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=0.11582332012959147, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 20, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.650947401024822, relative_step_length=0.07084549240131369, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=104.55965217810557, linear_terms=array([8.8072047 , 0.73944899]), square_terms=array([[14.54343784, 0.6300655 ], + [ 0.6300655 , 0.02792488]]), scale=array([4.48530495, 4.07144544]), shift=array([5.58530495, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=29, candidate_x=array([3.06341409, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09805438131359735, accepted=False, new_indices=array([28]), old_indices_used=array([17, 18, 20, 22, 24, 25, 26, 27]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=4.594134213489858, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=101.7963759557694, linear_terms=array([0.88312694, 0.23121981]), square_terms=array([[4.43153858, 0.34430387], + [0.34430387, 0.02688032]]), scale=array([2.44958223, 2.03572272]), shift=array([3.54958223, 2.03572272])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=30, candidate_x=array([3.25174178, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09261502613162106, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=2.297067106744929, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=101.90622993713308, linear_terms=array([0.11164869, 0.06828134]), square_terms=array([[6.11612797e-05, 3.74045927e-05], + [3.74045927e-05, 2.28756423e-05]]), scale=array([1.43172087, 1.01786136]), shift=array([2.53172087, 1.01786136])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=31, candidate_x=array([1.1, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.5313962286424387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 26, 27, 28, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=1.1485335533724645, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=101.81701589262005, linear_terms=array([0.00903626, 0.02869081]), square_terms=array([[4.00983753e-07, 1.27315403e-06], + [1.27315403e-06, 4.04236127e-06]]), scale=array([0.92279019, 0.50893068]), shift=array([2.02279019, 0.50893068])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=32, candidate_x=array([1.1, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-4.229463057608299, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 26, 27, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=0.5742667766862323, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=25.447067538457258, linear_terms=array([ 2.26209655e-03, -5.08941351e+01]), square_terms=array([[ 1.00543625e-07, -2.26209655e-03], + [-2.26209655e-03, 5.08941351e+01]]), scale=array([0.50893068, 0.25446534]), shift=array([1.92771902, 0.25446534])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=33, candidate_x=array([1.92240301, 0.50893056]), index=33, x=array([1.92240301, 0.50893056]), fval=101.70281707849445, rho=0.0004965664827465271, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.5089583250388706, relative_step_length=0.8862750653551305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92240301, 0.50893056]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=101.70281707849445, linear_terms=array([ 0.00255143, -0.04817497]), square_terms=array([[ 3.20040297e-08, -6.04285701e-07], + [-6.04285701e-07, 1.14098510e-05]]), scale=0.28713338834311614, shift=array([1.92240301, 0.50893056])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=34, candidate_x=array([1.9072569, 0.7956642]), index=34, x=array([1.9072569, 0.7956642]), fval=101.67554062166661, rho=0.565470242720224, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.28713338834311625, relative_step_length=1.0000000000000004, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9072569, 0.7956642]), radius=0.5742667766862323, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=101.6703406833042, linear_terms=array([ 0.00091326, -0.08127158]), square_terms=array([[ 4.10166532e-09, -3.65011616e-07], + [-3.65011616e-07, 3.24827770e-05]]), scale=0.5742667766862323, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=35, candidate_x=array([1.9009229 , 1.36989604]), index=34, x=array([1.9072569, 0.7956642]), fval=101.67554062166661, rho=-0.19509771603556256, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), old_indices_discarded=array([30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9072569, 0.7956642]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=101.7002045598023, linear_terms=array([-0.01763113, -0.01363475]), square_terms=array([[1.52829874e-06, 1.18188538e-06], + [1.18188538e-06, 9.13992149e-07]]), scale=0.28713338834311614, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([-3.30543881e-05, -5.13835828e-02]), square_terms=array([[5.37232193e-12, 8.35136165e-09], + [8.35136165e-09, 1.29823272e-05]]), scale=0.5742667766862323, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=37, candidate_x=array([2.13477466, 1.5454763 ]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-0.8259599057600131, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 27, 29, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([22, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 27, 29, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=101.70477092673009, linear_terms=array([ 0.00172715, -0.01865006]), square_terms=array([[ 1.46652301e-08, -1.58357648e-07], + [-1.58357648e-07, 1.70997279e-06]]), scale=0.28713338834311614, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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scale=0.14356669417155807, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=39, candidate_x=array([2.26814041, 0.91880982]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-2.429463248081529, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39]), model=ScalarModel(intercept=101.6761802726613, linear_terms=array([0.0004533 , 0.00066536]), square_terms=array([[1.01045073e-09, 1.48316938e-09], + [1.48316938e-09, 2.17703977e-09]]), scale=0.07178334708577903, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=40, candidate_x=array([2.09406231, 0.91188484]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=1.0728799070913182, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577915, relative_step_length=1.0000000000000016, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=101.68098851485126, linear_terms=array([-0.0050226 , 0.00236335]), square_terms=array([[ 1.24047463e-07, -5.83696319e-08], + [-5.83696319e-08, 2.74654058e-08]]), scale=0.14356669417155807, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=41, candidate_x=array([2.22396921, 0.85076534]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=-2.526612521827417, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=101.67433941881659, linear_terms=array([0.00112428, 0.00054592]), square_terms=array([[6.21590003e-09, 3.01827897e-09], + [3.01827897e-09, 1.46559757e-09]]), scale=0.07178334708577903, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=42, candidate_x=array([2.02948871, 0.88053044]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=0.46666748151376614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577888, relative_step_length=0.9999999999999979, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=101.680416054201, linear_terms=array([-0.00475057, -0.00077785]), square_terms=array([[1.10974697e-07, 1.81707727e-08], + [1.81707727e-08, 2.97524560e-09]]), scale=0.14356669417155807, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=43, candidate_x=array([2.17116917, 0.90372619]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=-1.523359554750816, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=101.66905458361765, linear_terms=array([ 0.00365778, -0.00773732]), square_terms=array([[ 6.57986625e-08, -1.39184073e-07], + [-1.39184073e-07, 2.94416414e-07]]), scale=0.07178334708577903, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=44, candidate_x=array([1.99881359, 0.94542948]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=0.8000735183892913, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577906, relative_step_length=1.0000000000000004, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=101.66837110968486, linear_terms=array([0.00492682, 0.00116565]), square_terms=array([[1.19375993e-07, 2.82435251e-08], + [2.82435251e-08, 6.68222052e-09]]), scale=0.14356669417155807, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=45, candidate_x=array([1.85910309, 0.91237844]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=-2.1991263376100285, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([33, 35, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=101.66380899320433, linear_terms=array([ 0.00235297, -0.00665188]), square_terms=array([[ 2.72293463e-08, -7.69776254e-08], + [-7.69776254e-08, 2.17616492e-07]]), scale=0.07178334708577903, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=46, candidate_x=array([1.9748781 , 1.01310475]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=0.3720268111515636, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([38]), step_length=0.07178334708577894, relative_step_length=0.9999999999999987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=101.66834084528847, linear_terms=array([0.00105253, 0.00192198]), square_terms=array([[5.44817640e-09, 9.94871690e-09], + [9.94871690e-09, 1.81669903e-08]]), scale=0.14356669417155807, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=47, candidate_x=array([1.90592301, 0.88718179]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-5.4105642931805455, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), old_indices_discarded=array([33, 35, 37, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=101.65730356105429, linear_terms=array([ 0.00098995, -0.00629255]), square_terms=array([[ 4.82013494e-09, -3.06388241e-08], + [-3.06388241e-08, 1.94753374e-07]]), scale=0.07178334708577903, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=48, candidate_x=array([1.96372399, 1.0840162 ]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.9918406732109903, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([38, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.03589167354288952, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([36, 40, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=101.6617873815051, linear_terms=array([-0.00012068, -0.00134631]), square_terms=array([[7.16320381e-11, 7.99106267e-10], + [7.99106267e-10, 8.91459804e-09]]), scale=0.03589167354288952, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=49, candidate_x=array([1.97808243, 1.0488531 ]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-1.8650485155978065, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([36, 40, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49]), model=ScalarModel(intercept=101.65745624776355, linear_terms=array([0.00318564, 0.00116839]), square_terms=array([[4.99142631e-08, 1.83069319e-08], + [1.83069319e-08, 6.71438853e-09]]), scale=0.01794583677144476, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=50, candidate_x=array([1.9580296 , 1.00692568]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.4185964575065688, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 49, 50]), model=ScalarModel(intercept=101.65593428559325, linear_terms=array([-0.00102209, 0.00072439]), square_terms=array([[ 5.13823243e-09, -3.64162989e-09], + [-3.64162989e-09, 2.58093974e-09]]), scale=0.00897291838572238, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=51, candidate_x=array([1.98219895, 1.00791645]), index=51, x=array([1.98219895, 1.00791645]), fval=101.6550490329974, rho=0.7066458575122126, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722404, relative_step_length=1.0000000000000027, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98219895, 1.00791645]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51]), model=ScalarModel(intercept=101.65780983947639, linear_terms=array([0.00063274, 0.00063126]), square_terms=array([[1.96914913e-09, 1.96454291e-09], + [1.96454291e-09, 1.95994747e-09]]), scale=0.01794583677144476, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=52, candidate_x=array([1.96947888, 0.99525735]), index=51, x=array([1.98219895, 1.00791645]), fval=101.6550490329974, rho=-1.2760889741412444, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98219895, 1.00791645]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 49, 50, 51, 52]), model=ScalarModel(intercept=101.65521942995059, linear_terms=array([-0.00089145, 0.00057592]), square_terms=array([[ 3.90872913e-09, -2.52522797e-09], + [-2.52522797e-09, 1.63141934e-09]]), scale=0.00897291838572238, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=53, candidate_x=array([1.98973589, 1.00304738]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=0.8268176302277769, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.00897291838572237, relative_step_length=0.999999999999999, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=101.65686345501824, linear_terms=array([-9.30404245e-05, 5.65023341e-04]), square_terms=array([[ 4.25771576e-11, -2.58565972e-10], + [-2.58565972e-10, 1.57024014e-09]]), scale=0.01794583677144476, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=54, candidate_x=array([1.99265166, 0.98534 ]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=-0.993946834585867, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=101.65447794468584, linear_terms=array([-0.00081271, -0.0001358 ]), square_terms=array([[3.24870780e-09, 5.42864165e-10], + [5.42864165e-10, 9.07134529e-11]]), scale=0.00897291838572238, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=55, candidate_x=array([1.9985861 , 1.00452623]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=0.7239326168613196, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722428, relative_step_length=1.0000000000000053, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=101.65497283502123, linear_terms=array([-0.00108512, -0.00030579]), square_terms=array([[5.79156561e-09, 1.63209915e-09], + [1.63209915e-09, 4.59935676e-10]]), scale=0.01794583677144476, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=56, candidate_x=array([2.0158592 , 1.00939379]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-1.2143715762219611, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), old_indices_discarded=array([48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=101.65457751207414, linear_terms=array([-0.00046326, 0.00016734]), square_terms=array([[ 1.05559287e-09, -3.81311452e-10], + [-3.81311452e-10, 1.37741006e-10]]), scale=0.00897291838572238, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=57, candidate_x=array([2.0070253 , 1.00147777]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-0.49478560723833387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=101.65438327884436, linear_terms=array([-3.96566587e-05, 1.41729139e-05]), square_terms=array([[ 7.73528167e-12, -2.76451634e-12], + [-2.76451634e-12, 9.88011935e-13]]), scale=0.00448645919286119, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=58, candidate_x=array([2.00281085, 1.00301635]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=1.4696474008540954, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([51, 53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.004486459192861143, relative_step_length=0.9999999999999896, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=101.65419419312354, linear_terms=array([-0.00041313, 0.00011216]), square_terms=array([[ 8.39487554e-10, -2.27914839e-10], + [-2.27914839e-10, 6.18772410e-11]]), scale=0.00897291838572238, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=101.65422823536524, linear_terms=array([-5.03968696e-05, 6.48567467e-06]), square_terms=array([[ 1.24925668e-11, -1.60769358e-12], + [-1.60769358e-12, 2.06897326e-13]]), scale=0.00448645919286119, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=60, candidate_x=array([2.00726062, 1.0024437 ]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-6.962482671698848, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([51, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60]), model=ScalarModel(intercept=101.65364736975285, linear_terms=array([0.00018313, 0.0002147 ]), square_terms=array([[1.64949201e-10, 1.93390034e-10], + [1.93390034e-10, 2.26734687e-10]]), scale=0.002243229596430595, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=61, candidate_x=array([2.00135514, 1.0013096 ]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=0.793249003571856, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=0.0022432295964306204, relative_step_length=1.0000000000000113, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=101.65397564855002, linear_terms=array([ 0.00011335, -0.00016284]), square_terms=array([[ 6.31929509e-11, -9.07881463e-11], + [-9.07881463e-11, 1.30433654e-10]]), scale=0.00448645919286119, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=62, candidate_x=array([1.99879211, 1.00499188]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=-1.6387481559100683, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=101.65328749627801, linear_terms=array([0.00020435, 0.00031288]), square_terms=array([[2.05388937e-10, 3.14474469e-10], + [3.14474469e-10, 4.81497170e-10]]), scale=0.002243229596430595, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=63, candidate_x=array([2.0001285 , 0.99943145]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=0.3483172374666223, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.002243229596430552, relative_step_length=0.9999999999999809, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=101.65353276184649, linear_terms=array([6.33509679e-05, 2.13247340e-04]), square_terms=array([[1.97403131e-11, 6.64483812e-11], + [6.64483812e-11, 2.23673623e-10]]), scale=0.00448645919286119, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=64, candidate_x=array([1.99885087, 0.99513076]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-1.9869393097877708, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), old_indices_discarded=array([51, 54, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=101.65357133359919, linear_terms=array([-3.07662222e-05, 5.62449717e-05]), square_terms=array([[ 4.65581493e-12, -8.51148305e-12], + [-8.51148305e-12, 1.55601855e-11]]), scale=0.002243229596430595, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=65, candidate_x=array([2.00120502, 0.99746342]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-3.471200895305958, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 55, 57, 58, 60, 61, 62, 63, 64]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.0011216147982152974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 58, 61, 63, 64, 65]), model=ScalarModel(intercept=101.6534281159708, linear_terms=array([-3.92952930e-05, 6.94120144e-06]), square_terms=array([[ 7.59502203e-12, -1.34160032e-12], + [-1.34160032e-12, 2.36983043e-13]]), scale=0.0011216147982152974, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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-1.56138570e-05]), square_terms=array([[ 2.57872636e-11, -5.56080152e-12], + [-5.56080152e-12, 1.19913900e-12]]), scale=0.0005608073991076487, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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scale=0.00028040369955382435, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=68, candidate_x=array([2.00007012, 0.9991572 ]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-0.04231075182430718, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68]), model=ScalarModel(intercept=101.65315912374552, linear_terms=array([-3.56979582e-06, -1.26759543e-05]), square_terms=array([[6.26809943e-14, 2.22573351e-13], + [2.22573351e-13, 7.90333613e-13]]), scale=0.00014020184977691218, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=69, candidate_x=array([2.00016651, 0.99956641]), index=69, x=array([2.00016651, 0.99956641]), fval=101.65314728156433, rho=0.8992450217010943, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.0001402018497769173, relative_step_length=1.0000000000000366, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00016651, 0.99956641]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69]), model=ScalarModel(intercept=101.65316287886624, linear_terms=array([ 1.18463065e-05, -2.23664617e-05]), square_terms=array([[ 6.90263703e-13, -1.30325487e-12], + [-1.30325487e-12, 2.46061507e-12]]), scale=0.00028040369955382435, shift=array([2.00016651, 0.99956641])), vector_model=VectorModel(intercepts=array([2.65613912, 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candidate_index=70, candidate_x=array([2.00003526, 0.9998142 ]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=1.087904744350226, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538049, relative_step_length=0.9999999999999306, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=101.6531539010588, linear_terms=array([ 5.98849665e-05, -1.62873618e-05]), square_terms=array([[ 1.76394390e-11, -4.79753001e-12], + [-4.79753001e-12, 1.30482008e-12]]), scale=0.0005608073991076487, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 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candidate_x=array([1.99949411, 0.99996138]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=-0.5127930577353325, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=101.65314877678284, linear_terms=array([ 9.71223485e-06, -1.61205500e-05]), square_terms=array([[ 4.63967456e-13, -7.70101908e-13], + [-7.70101908e-13, 1.27822963e-12]]), scale=0.00028040369955382435, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=72, candidate_x=array([1.99989056, 1.00005438]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=0.35552372044377767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538801, relative_step_length=1.0000000000001987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=101.65315386439156, linear_terms=array([4.03977606e-05, 5.28856412e-06]), square_terms=array([[8.02719343e-12, 1.05085843e-12], + [1.05085843e-12, 1.37570304e-13]]), scale=0.0005608073991076487, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=73, candidate_x=array([1.9993345 , 0.99998159]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.334113586767923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=101.65311655752888, linear_terms=array([-1.71237318e-05, -2.37865075e-05]), square_terms=array([[1.44226858e-12, 2.00344952e-12], + [2.00344952e-12, 2.78298373e-12]]), scale=0.00028040369955382435, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=74, candidate_x=array([2.00005439, 1.00028195]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.5627670521816485, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=101.65313160034756, linear_terms=array([-5.77420288e-06, -6.18898976e-06]), square_terms=array([[1.63996024e-13, 1.75776594e-13], + [1.75776594e-13, 1.88403415e-13]]), scale=0.00014020184977691218, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=75, candidate_x=array([1.9999862, 1.0001569]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.41697664424331127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 69, 70, 71, 72, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75]), model=ScalarModel(intercept=101.65311148563875, linear_terms=array([5.50427867e-06, 9.82268200e-07]), square_terms=array([[1.49021920e-13, 2.65937649e-14], + [2.65937649e-14, 4.74580071e-15]]), scale=7.010092488845609e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76]), model=ScalarModel(intercept=101.65311305572646, linear_terms=array([-3.98365342e-06, 4.92341491e-06]), square_terms=array([[ 7.80571006e-14, -9.64711165e-14], + [-9.64711165e-14, 1.19229080e-13]]), scale=3.5050462444228044e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75, 76, 77]), model=ScalarModel(intercept=101.65311599269691, linear_terms=array([2.73513590e-06, 1.10171467e-06]), square_terms=array([[3.67965523e-14, 1.48216772e-14], + [1.48216772e-14, 5.97018206e-15]]), scale=7.010092488845609e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), 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State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78]), model=ScalarModel(intercept=101.6531107725162, linear_terms=array([-2.89439098e-06, 2.97676454e-06]), square_terms=array([[ 4.12063098e-14, -4.23790299e-14], + [-4.23790299e-14, 4.35851253e-14]]), scale=3.5050462444228044e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=101.65311560548442, linear_terms=array([2.27763281e-06, 1.31542912e-06]), square_terms=array([[2.55162430e-14, 1.47367077e-14], + [1.47367077e-14, 8.51107096e-15]]), scale=7.010092488845609e-05, shift=array([1.99993704, 1.000002 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=80, candidate_x=array([1.99987634, 0.99996694]), index=79, x=array([1.99993704, 1.000002 ]), fval=101.65310713409757, rho=-2.471821479499451, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75, 76, 77, 78, 79]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99993704, 1.000002 ]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=101.65310851811418, linear_terms=array([-2.55877294e-06, 2.22965678e-06]), square_terms=array([[ 3.22042240e-14, -2.80620313e-14], + [-2.80620313e-14, 2.44526185e-14]]), scale=3.5050462444228044e-05, shift=array([1.99993704, 1.000002 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([70, 72, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=101.65311312451377, linear_terms=array([-7.55959616e-07, -6.02717625e-07]), square_terms=array([[2.81090723e-15, 2.24110295e-15], + [2.24110295e-15, 1.78680477e-15]]), scale=7.010092488845609e-05, shift=array([1.99996347, 0.99997898])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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79, 80, 81, 82]), model=ScalarModel(intercept=101.65310380914096, linear_terms=array([-1.03266467e-05, 8.97242802e-06]), square_terms=array([[ 5.24527182e-13, -4.55741590e-13], + [-4.55741590e-13, 3.95976422e-13]]), scale=0.00014020184977691218, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=101.65311088628962, linear_terms=array([-3.79014379e-07, 1.04483358e-06]), square_terms=array([[ 7.06578965e-16, -1.94783489e-15], + [-1.94783489e-15, 5.36962031e-15]]), scale=7.010092488845609e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([1.46452562e-07, 8.40702212e-07]), square_terms=array([[1.05497769e-16, 6.05603664e-16], + [6.05603664e-16, 3.47643178e-15]]), scale=3.5050462444228044e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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-3.44961607e-06]), square_terms=array([[1.30781183e-14, 2.76673818e-14], + [2.76673818e-14, 5.85316635e-14]]), scale=7.010092488845609e-05, shift=array([2.00001226, 0.99998815])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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2.64648975e-15], + [2.64648975e-15, 1.74218917e-14]]), scale=3.5050462444228044e-05, shift=array([2.00001226, 0.99998815])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=1.7525231222114022e-05, shift=array([2.00001226, 0.99998815])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=88, candidate_x=array([2.0000252 , 0.99997633]), index=85, x=array([2.00001226, 0.99998815]), fval=101.65310254061681, rho=-17.199626709519745, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([79, 81, 82, 84, 85, 86, 87]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00001226, 0.99998815]), radius=8.762615611057011e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([82, 84, 85, 87, 88]), model=ScalarModel(intercept=101.65310249167362, linear_terms=array([1.36616311e-06, 9.02222634e-08]), square_terms=array([[9.18024928e-15, 6.06269386e-16], + [6.06269386e-16, 4.00384082e-17]]), scale=8.762615611057011e-06, shift=array([2.00001226, 0.99998815])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=89, candidate_x=array([2.00000352, 0.99998757]), index=89, x=array([2.00000352, 0.99998757]), fval=101.65310212694172, rho=0.30214250212998456, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([82, 84, 85, 87, 88]), old_indices_discarded=array([], dtype=int32), step_length=8.76261561092711e-06, relative_step_length=0.9999999999851755, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 90 entries., 'history': {'params': [{'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}, {'CRRA': 1.1, 'BeqFac': 168.99160456324418}, {'CRRA': 20.0, 'BeqFac': 181.63947671960705}, {'CRRA': 1.1, 'BeqFac': 196.1247826983854}, {'CRRA': 20.0, 'BeqFac': 198.68302674118215}, {'CRRA': 19.674724735180458, 'BeqFac': 167.47958678304056}, {'CRRA': 20.0, 'BeqFac': 167.52737770433455}, {'CRRA': 1.1, 'BeqFac': 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101.65311974675102, 101.65315157086414, 101.65311305572654, 101.65316741078925, 101.65312954989032, 101.6531165851576, 101.65311916923056, 101.65310998586972, 101.65311607703549, 101.65310713409755, 101.65311363548847, 101.65310505004693, 101.65310374790073, 101.65311504895925, 101.6531068745941, 101.6531025406168, 101.65310749657145, 101.65310371105045, 101.65310429279558, 101.65310212694172], 'runtime': [0.0, 0.5039537996053696, 0.6895224996842444, 0.8690349999815226, 1.0506610996089876, 1.2314923000521958, 1.4181299996562302, 1.6065336000174284, 1.7943613999523222, 1.9807438999414444, 2.1682199998758733, 2.3564733997918665, 2.536936099641025, 3.9799724998883903, 4.208065299782902, 4.435462199617177, 4.663008799776435, 4.890417299699038, 6.057654699776322, 6.294685699976981, 6.520818299613893, 6.750279899686575, 8.044913499616086, 8.273229599930346, 8.49854000005871, 8.736742899753153, 8.962798699736595, 9.189374499954283, 9.416601500008255, 9.64259839989245, 9.866047400049865, 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21.40902019990608, 21.63227880001068, 21.85612909961492, 22.079848199617118, 22.438155899755657, 22.660994099918753, 22.887149499729276, 23.114062799606472, 23.340816299896687], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]}}], 'exploration_sample': array([[2.0000000e+00, 1.0000000e+00], + [1.8818750e+01, 6.2500000e+02], + [1.2912500e+01, 1.2500000e+03], + [7.0062500e+00, 1.8750000e+03], + [1.7046875e+01, 2.1875000e+03], + [1.5275000e+01, 2.5000000e+03], + [4.6437500e+00, 3.1250000e+03], + [8.1875000e+00, 3.7500000e+03], + [1.1731250e+01, 4.3750000e+03], + [2.8718750e+00, 4.6875000e+03], + [1.0550000e+01, 5.0000000e+03], + [9.3687500e+00, 5.6250000e+03], + [3.4625000e+00, 6.2500000e+03], + [1.6456250e+01, 6.8750000e+03], + [7.5968750e+00, 7.1875000e+03], + [5.8250000e+00, 7.5000000e+03], + [1.4093750e+01, 8.1250000e+03], + [1.7637500e+01, 8.7500000e+03], + [2.2812500e+00, 9.3750000e+03], + [1.2321875e+01, 9.6875000e+03]]), 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, + 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, + 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, + 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, + 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}}" diff --git a/code/notebooks/Model_Comparisons.ipynb b/code/notebooks/Model_Comparisons.ipynb index 1f9535a..fe4c4a6 100644 --- a/code/notebooks/Model_Comparisons.ipynb +++ b/code/notebooks/Model_Comparisons.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 37, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,32 +17,34 @@ "from estimark.estimation import get_weighted_moments\n", "from estimark.parameters import age_mapping, init_calibration\n", "from estimark.scf import scf_data\n", - "from estimark.snp import snp_data_full" + "from estimark.snp import snp_data_full\n", + "results_dir = \"../../content/tables/TRP/\" # This is AJL's\n", + "results_dir = \"../estimark/content/tables/min/\" # This is MNW's" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "csv_file_path = \"../../content/tables/TRP/Portfolio_estimate_results.csv\"\n", + "csv_file_path = results_dir + \"Portfolio_estimate_results.csv\"\n", "res = pd.read_csv(csv_file_path, header=None)\n", "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.809736592657766, 1.0)" + "(9.25239894900598, 1.0)" ] }, - "execution_count": 39, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -55,27 +57,27 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "csv_file_path = \"../../content/tables/TRP/WarmGlowPortfolio_estimate_results.csv\"\n", + "csv_file_path = results_dir + \"WarmGlowPortfolioShiftAlt_estimate_results.csv\"\n", "res = pd.read_csv(csv_file_path, header=None)\n", "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.764682111110837, 1.0, 43.77305416364993, 26.255634058683412)" + "(4.177600674290874, 1.0, 2798.51512022846, 1.966935700692675)" ] }, - "execution_count": 41, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -96,27 +98,27 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "csv_file_path = \"../../content/tables/TRP/WealthPortfolio_estimate_results.csv\"\n", + "csv_file_path = results_dir + \"WealthPortfolio_estimate_results.csv\"\n", "res = pd.read_csv(csv_file_path, header=None)\n", "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(3.472387242558044, 1.0, 0.5306835102235502, 0.0)" + "(5.338780774481047, 1.0, 0.17065528804872485, 0.0)" ] }, - "execution_count": 43, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -130,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -139,22 +141,23 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "for agent in agents:\n", + " agent.update()\n", " agent.solve()\n", "\n", - " agent.track_vars = [\"aNrm\", \"cNrm\", \"t_age\", \"mNrm\", \"Share\"]\n", - " agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", + " agent.track_vars = [\"aNrm\", \"cNrm\", \"t_age\", \"bNrm\", \"Share\"]\n", + " #agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", " agent.T_sim = portfolio_agent.T_cycle + 1\n", " agent.initialize_sim()\n", " history = agent.simulate()\n", "\n", " raw_data = {\n", " \"Age\": agent.history[\"t_age\"].flatten() + 25 - 1,\n", - " \"nrmM\": agent.history[\"mNrm\"].flatten(),\n", + " \"nrmB\": agent.history[\"bNrm\"].flatten(),\n", " \"nrmC\": agent.history[\"cNrm\"].flatten(),\n", " \"Share\": agent.history[\"Share\"].flatten(),\n", " }\n", @@ -167,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -189,12 +192,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -207,24 +210,24 @@ "plt.figure()\n", "plt.plot(\n", " portfolio_agent.AgeMeans.Age,\n", - " portfolio_agent.AgeMeans.nrmM,\n", + " portfolio_agent.AgeMeans.nrmB,\n", " label=\"Life-Cycle Portfolio\",\n", - " alpha=0.5, # make line more faded\n", - " linewidth=1, # thinner line\n", + " #alpha=0.5, # make line more faded\n", + " #linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " warmglow_agent.AgeMeans.Age,\n", - " warmglow_agent.AgeMeans.nrmM,\n", + " warmglow_agent.AgeMeans.nrmB,\n", " label=\"Warm-Glow Portfolio\",\n", - " alpha=0.5, # make line more faded\n", - " linewidth=1, # thinner line\n", + " #alpha=0.5, # make line more faded\n", + " #linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " trp_agent.AgeMeans.Age,\n", - " trp_agent.AgeMeans.nrmM,\n", + " trp_agent.AgeMeans.nrmB,\n", " label=\"TRP Portfolio\",\n", - " alpha=1, # full color\n", - " linewidth=2, # thicker line\n", + " #alpha=1, # full color\n", + " #linewidth=2, # thicker line\n", ")\n", "plt.plot(\n", " moments_values[0],\n", @@ -237,6 +240,7 @@ "plt.ylabel(\"Wealth to Income Ratio\")\n", "plt.title(\"Wealth Medians for Portfolio Models\")\n", "plt.xlim(25, 95)\n", + "plt.ylim(0., 12.)\n", "plt.grid()\n", "plt.savefig(\"median_wealth.pdf\")\n", "plt.savefig(\"median_wealth.svg\")" @@ -244,12 +248,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -264,22 +268,22 @@ " portfolio_agent.AgeMeans.Age,\n", " portfolio_agent.AgeMeans.Share,\n", " label=\"Life-Cycle Portfolio\",\n", - " alpha=0.5, # make line more faded\n", - " linewidth=1, # thinner line\n", + " #alpha=0.5, # make line more faded\n", + " #linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " warmglow_agent.AgeMeans.Age,\n", " warmglow_agent.AgeMeans.Share,\n", " label=\"Warm-Glow Portfolio\",\n", - " alpha=0.5, # make line more faded\n", - " linewidth=1, # thinner line\n", + " #alpha=0.5, # make line more faded\n", + " #linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " trp_agent.AgeMeans.Age,\n", " trp_agent.AgeMeans.Share,\n", " label=\"TRP Portfolio\",\n", - " alpha=1, # full color\n", - " linewidth=2, # thicker line\n", + " #alpha=1, # full color\n", + " #linewidth=2, # thicker line\n", ")\n", "plt.plot(\n", " snp_data_full[\"age\"],\n", @@ -292,6 +296,7 @@ "plt.ylabel(\"Risky Portfolio Share\")\n", "plt.title(\"Portfolio Share Medians for Portfolio Models\")\n", "plt.xlim(70, 95)\n", + "plt.ylim(0.,1.)\n", "plt.grid()\n", "plt.savefig(\"median_share.pdf\")\n", "plt.savefig(\"median_share.svg\")" @@ -307,7 +312,7 @@ ], "metadata": { "kernelspec": { - "display_name": "estimatingmicrodsops", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -321,9 +326,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/code/notebooks/median_share.pdf b/code/notebooks/median_share.pdf index 24a55f4a42384cd20cda444bc02ce724a39effe8..9c7523d2dd743d494425ebb1f53557cc90b70102 100644 GIT binary patch delta 3636 zcmZWpc|4Ts7q>5?L_&n7$)L!*?>jqUCPvAXov|c@F2<5Xh*v@y5e6mPC{mGZKV-c^ z$`&C@w75vg7P6K58{N5Cmg1z+i5?n(kyM=W4C~+ zmRPv*WW{~$gY%lv;Z+!*)Y*R2wQEGkH8loc-wYyWy^cGb(ijCN=Q;<)j@z{$-B{<=mA+Yg|;ayJa=I$9K29T?ZqtF)yZmHS`ndxWBLCl&A8FBq=6-+O52& 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src}/stata/SelectVarsUsingSCF1998.do | 530 +++--- {code => src}/stata/SelectVarsUsingSCF2001.do | 508 +++--- {code => src}/stata/SelectVarsUsingSCF2004.do | 544 +++---- {code => src}/stata/SelectVarsUsingSCF2007.do | 542 +++---- {code => src}/stata/WIRatioPopulation.do | 1440 ++++++++--------- {code => src}/stata/doAll.do | 0 {code => src}/tests.py | 0 71 files changed, 2387 insertions(+), 2387 deletions(-) rename {code => src}/README.md (100%) rename {code => src}/data/Cagetti2003.csv (100%) rename {code => src}/data/S&P Target Date glidepath.xlsx (100%) rename {code => src}/data/SCFdata.csv (100%) rename {code => src}/do_all.py (100%) rename {code => src}/estimark/__init__.py (100%) rename {code => src}/estimark/agents.py (100%) rename {code => src}/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv (100%) rename {code => src}/estimark/content/tables/min/Portfolio_estimate_results.csv (100%) rename {code => 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a/code/run_all.py b/src/run_all.py similarity index 100% rename from code/run_all.py rename to src/run_all.py diff --git a/code/run_all_msm.py b/src/run_all_msm.py similarity index 100% rename from code/run_all_msm.py rename to src/run_all_msm.py diff --git a/code/setup.py b/src/setup.py similarity index 100% rename from code/setup.py rename to src/setup.py diff --git a/code/stata/AppendDataUsingSCF1992_2007.do b/src/stata/AppendDataUsingSCF1992_2007.do similarity index 97% rename from code/stata/AppendDataUsingSCF1992_2007.do rename to src/stata/AppendDataUsingSCF1992_2007.do index b0dfa32..8eec271 100644 --- a/code/stata/AppendDataUsingSCF1992_2007.do +++ b/src/stata/AppendDataUsingSCF1992_2007.do @@ -1,34 +1,34 @@ -* AppendDataUsingSCF1992_2007.do -* This file gives selected varaibles of the Population -clear - -cd $basePath/$logPath -cap log close -cap log using ./AppendDataUsingSCF1992_2007.log, replace -cd $basePath/$stataPath - -** Construct data -do "SelectVarsUsingSCF1992.do" -do "SelectVarsUsingSCF1995.do" -do "SelectVarsUsingSCF1998.do" -do "SelectVarsUsingSCF2001.do" -do "SelectVarsUsingSCF2004.do" -do "SelectVarsUsingSCF2007.do" - -cd ../../Data/Constructed -append using SCF2004_population -append using SCF2001_population -append using SCF1998_population -append using SCF1995_population -append using SCF1992_population -drop if AGE<26 -drop if AGE>65 - -** Save data -save SCF1992_2007_population, replace - -/* Note: In the waves between 1995 and 2004, levels of normal income are reported. I interpret the level of normal income as being permanent income. Levels of normal income are not reported in the 1992 wave. Instead, in this wave there is a variable which reports whether the level of income is normal or not. Regarding the 1992 wave, only observations which report that the level of income is normal are used, and the levels of income of remaining observations in the 1992 wave are interpreted as the levels of permanent income. */ - -cd $basePath/$stataPath /* When program ends, make sure working directory is the program's directory */ - -log close +* AppendDataUsingSCF1992_2007.do +* This file gives selected varaibles of the Population +clear + +cd $basePath/$logPath +cap log close +cap log using ./AppendDataUsingSCF1992_2007.log, replace +cd $basePath/$stataPath + +** Construct data +do "SelectVarsUsingSCF1992.do" +do "SelectVarsUsingSCF1995.do" +do "SelectVarsUsingSCF1998.do" +do "SelectVarsUsingSCF2001.do" +do "SelectVarsUsingSCF2004.do" +do "SelectVarsUsingSCF2007.do" + +cd ../../Data/Constructed +append using SCF2004_population +append using SCF2001_population +append using SCF1998_population +append using SCF1995_population +append using SCF1992_population +drop if AGE<26 +drop if AGE>65 + +** Save data +save SCF1992_2007_population, replace + +/* Note: In the waves between 1995 and 2004, levels of normal income are reported. I interpret the level of normal income as being permanent income. Levels of normal income are not reported in the 1992 wave. Instead, in this wave there is a variable which reports whether the level of income is normal or not. Regarding the 1992 wave, only observations which report that the level of income is normal are used, and the levels of income of remaining observations in the 1992 wave are interpreted as the levels of permanent income. */ + +cd $basePath/$stataPath /* When program ends, make sure working directory is the program's directory */ + +log close diff --git a/code/stata/ReadMe.txt b/src/stata/ReadMe.txt similarity index 97% rename from code/stata/ReadMe.txt rename to src/stata/ReadMe.txt index 9ef1f0e..a2d392d 100644 --- a/code/stata/ReadMe.txt +++ b/src/stata/ReadMe.txt @@ -1,68 +1,68 @@ - + In order for the files in this directory to work properly, the Federal -Reserve's SCF datasets +Reserve's SCF datasets that the programs use must be located in -appropriate directories that are accessible to the +appropriate directories that are accessible to the programs. For -example, the 1992 SCF needs to be located at - +example, the 1992 SCF needs to be located at + ../../../Downloads/SCF/1992 - - + + An all the required SCF datasets in the appropriate directory -structure can be downloaded from - +structure can be downloaded from + -ftp://llorracc.net/VaultPub/Data/SCF - +ftp://llorracc.net/VaultPub/Data/SCF + or the entire set of SCF's (warning: it is very large) from - - + + ftp://llorracc.net/VaultPub/Data/SCF.zip - - + + which has SCF files downloaded on 2011/08/06. -Alternatively, the latest versions of the individual +Alternatively, the latest versions of the individual files can be -obtained directly from the Fed's website: - +obtained directly from the Fed's website: + -http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html - +http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html + -but if you download them one-by-one you need to make sure that you put them in a directory structure +but if you download them one-by-one you need to make sure that you put them in a directory structure corresponding -to the structure at - -ftp://llorracc.net/VaultPub/Data/SCF - +to the structure at + +ftp://llorracc.net/VaultPub/Data/SCF -(you don't need to download the extra files like codebooks etc; all that is needed for the programs to + +(you don't need to download the extra files like codebooks etc; all that is needed for the programs to work is the Stata scf files, like scf92.dta for the 1992 SCF, which should be in a directory 1992/scf92.dta) - -*********************************************************************************************************** - -doAll.do file runs all the programs. - -In Particular: - -1) SelectVarsUsingSCFXXXX.do: Selects the variables from the SCF raw data and construct the Permanent income, - wealth and the weights of each household in the population for the year XXXX. -2) AppendDataUsingSCF1992_2007.do: Appends the outcomes of the SelectVarsUsingSCFXXXX. -3) WIRatioPopulation.do: Constructs the Wealth to after tax permanent income ratio of each households. And save the - output "SCFdata.txt" in the folder "./Code/Mathematica/StructuralEstimation" which - is used by the Mathematica programs to estimate the structural parameters. - + +*********************************************************************************************************** + +doAll.do file runs all the programs. + +In Particular: + +1) SelectVarsUsingSCFXXXX.do: Selects the variables from the SCF raw data and construct the Permanent income, + wealth and the weights of each household in the population for the year XXXX. +2) AppendDataUsingSCF1992_2007.do: Appends the outcomes of the SelectVarsUsingSCFXXXX. +3) WIRatioPopulation.do: Constructs the Wealth to after tax permanent income ratio of each households. And save the + output "SCFdata.txt" in the folder "./Code/Mathematica/StructuralEstimation" which + is used by the Mathematica programs to estimate the structural parameters. + diff --git a/code/stata/SelectVarsUsingSCF1992.do b/src/stata/SelectVarsUsingSCF1992.do similarity index 98% rename from code/stata/SelectVarsUsingSCF1992.do rename to src/stata/SelectVarsUsingSCF1992.do index c597abf..94ab72d 100644 --- a/code/stata/SelectVarsUsingSCF1992.do +++ b/src/stata/SelectVarsUsingSCF1992.do @@ -1,267 +1,267 @@ -* This file selects variables, using SCF92 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) - - -clear - -** Set memeory -set memory 32m - -global startDir "`c(pwd)'" -global scfFldr 1992 -global scfFile scf92 -global SuffixForConstructedFile "_population" - -cd $startDir - -cd ../../../Downloads/SCF/$scfFldr - -cap confirm file $scfFile.dta -if _rc~=0 { - display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." - exit -} - - -** Load data and pick up necessary vars from original data -use y1 x42000 x42001 x5729 x5751 x7650 /// - x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// - x3804 x3807 x3810 x3813 x3816 x3818 /// - x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// - x3930 /// - x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// - x3610 x3620 x3630 x3631 /// - x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// - x3902 x4006 x3942 x3947 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// - x8166 x8167 x8168 x2422 x2506 x2606 x2623 /// - x507 x604 x614 x623 x716 x513 x526 /// - x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x1715 x1815 x1915 x2016 x2012 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3229 x3224 x3226 x3227 x3221 x3222 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3329 x3324 x3326 x3327 x3321 x3322 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3335 x507 x513 x526 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3408 x3412 x3416 x3420 x3424 x3428 /// - x4022 x4026 x4030 /// - x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x1417 x1517 x1617 x1621 x2006 /// - x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x427 x413 x421 x430 x424 x7575 /// - x2218 x2318 x2418 x2424 x2519 x2619 x2625 x7824 x7847 x7870 x7924 x7947 x7970 x1044 x1215 x1219 /// - x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// - x4010 x3932 x4032 /// - x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// - using "scf92.dta" - - -** Generate variables -* ID -gen ID = y1 /* ID # */ -gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ -gen YEAR = 1992 /* Indicates data is from which wave */ - -* Weight -gen WGT = x42001 - -* Income -gen INCOME = x5729 /* Income before tax */ -scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ -scalar CPIADJ = CPIBASE/2116 /* Adjust with CPI (adjusted to 1992$ price) */ -scalar CPILAG = 2103/2051 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ -gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ -keep if D_NORMINC == 1 /* Keep if inc level is normal */ - -* Asset -gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// - +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// - +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) -gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) - gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// - +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// - +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// - +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// - +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// - +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3706)*(11<=x9131 & x9131<=13) /// - +max(0,x3711)*(11<=x9132 & x9132<=13) /// - +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) - gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// - +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// - +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// - +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// - +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// - +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3706)*(x9131<11|x9131>13) /// - +max(0,x3711)*(x9132<11|x9132>13) /// - +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 -gen BOND = NOTXBND + MORTBND + GOVTBND + OBND - gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) - gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// - +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// - +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// - +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// - +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// - +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) - gen PMOP = x4436 - replace PMOP = 0 if x4436<=0 - replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP - replace PMOP = x5036 - replace PMOP = 0 if x5036<=0 - replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 - replace THRIFT = THRIFT + PMOP -gen RETQLIQ = IRAKH + THRIFT -gen SAVBND = x3902 -gen CASHLI = max(0,x4006) - gen ROTHMA = 0 - gen SOTHMA = 0 - gen COTHMA = 0 - replace ROTHMA = x3942 if x3947==1 | x3947==3 - replace SOTHMA = x3942 if x3947==2 | x3947==7 - replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 -gen OTHMA = ROTHMA+SOTHMA+COTHMA -gen OTHFIN = x4018+x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74) /// - +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74) /// - +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74) -gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ - -gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168) /// - +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) -replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) -gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// - +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *max(0,x1706)*(x1705/10000) /// - +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *max(0,x1806)*(x1805/10000) /// - +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *max(0,x1906)*(x1905/10000) /// - +max(0,x2002) -gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// - *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// - +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// - *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// - +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// - *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// - +max(0,x2012)-x2016 -replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// - -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 -gen FLAG781 = (NNRESRE!=0) -gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5)+max(0,x3121)*(x3122==1) /// - +max(0,x2723*(x2710==71))+max(0,x2740*(x2727==71)) /// - +max(0,x2823*(x2810==71))+max(0,x2840*(x2827==71)) /// - +max(0,x2923*(x2910==71))+max(0,x2940*(x2927==71)) /// - +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// - +max(0,x3221)*(x3222==1)+max(0,x2723*(x2710==72))+max(0,x2740*(x2727==72)) /// - +max(0,x2823*(x2810==72))+max(0,x2840*(x2827==72)) /// - +max(0,x2923*(x2910==72))+max(0,x2940*(x2927==72)) /// - +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// - +max(0,x3321)*(x3322==1)+max(0,x2723*(x2710==73))+max(0,x2740*(x2727==73)) /// - +max(0,x2823*(x2810==73))+max(0,x2840*(x2827==73)) /// - +max(0,x2923*(x2910==73))+max(0,x2940*(x2927==73)) /// - +max(0,x3335)+(x507/10000)*(x513+x526) /// - +max(0,x2723*(x2710==74))+max(0,x2740*(x2727==74)) /// - +max(0,x2823*(x2810==74))+max(0,x2840*(x2827==74)) /// - +max(0,x2923*(x2910==74))+max(0,x2940*(x2927==74)) /// - +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420)+max(0,x3424)+max(0,x3428) -gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 -gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN -gen ASSET = FIN+NFIN /* Total asset */ - -* Debt -gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 - gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *x1715*(x1705/10000) - gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *x1815*(x1805/10000) - gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *x1915*(x1905/10000) -gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 - gen FLAG782 = (FLAG781!=1 & ORESRE>0) -replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// - +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 - gen FLAG67 = (ORESRE>0) -replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 -gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// - +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// - +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 -gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) -gen INSTALL = x2218+x2318+x2418+x2424+x2519+x2619+x2625+x7824 /// - +x7847+x7870+x7924+x7947+x7970+x1044+x1215+x1219 -replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// - +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// - if FLAG781==0 & FLAG782==0 -replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// - +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 -replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// - +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// - +x2940*(x2927!=67&x2927!=78) -gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// - +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// - +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) -gen CASHLIDB = max(0,x4010) -gen CALLDBT = max(0,x3932) -gen ODEBT = max(0,x4032) -gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ - -* Net worth -gen NETW = ASSET-DEBT -replace NETW = NETW*CPIADJ - -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME - - -* Demographic vars -gen AGE = x14 /* Age */ -gen MARITST = x8023 /* Marital status */ -keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ - -gen EDUC = 0 -replace EDUC = 1 if x5902!=1 /* No high school deg */ -replace EDUC = 2 if x5902==1 /* High school deg */ -replace EDUC = 3 if x5904==1 /* College deg */ -* keep if EDUC == 3 /* Keep college graduates only */ - -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) -replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) -drop if INCOME<=0 -replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) - - -** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE - -cd "$startDir" -cd ../../Data/Constructed -** Save data -save "./SCF$scfFldr$SuffixForConstructedFile", replace - -** End in the same directory you started from -cd "$startDir" +* This file selects variables, using SCF92 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 1992 +global scfFile scf92 +global SuffixForConstructedFile "_population" + +cd $startDir + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x3942 x3947 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x1715 x1815 x1915 x2016 x2012 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3229 x3224 x3226 x3227 x3221 x3222 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3329 x3324 x3326 x3327 x3321 x3322 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3335 x507 x513 x526 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x2424 x2519 x2619 x2625 x7824 x7847 x7870 x7924 x7947 x7970 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf92.dta" + + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 1992 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2116 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2103/2051 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ +keep if D_NORMINC == 1 /* Keep if inc level is normal */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ROTHMA = 0 + gen SOTHMA = 0 + gen COTHMA = 0 + replace ROTHMA = x3942 if x3947==1 | x3947==3 + replace SOTHMA = x3942 if x3947==2 | x3947==7 + replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 +gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHFIN = x4018+x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5)+max(0,x3121)*(x3122==1) /// + +max(0,x2723*(x2710==71))+max(0,x2740*(x2727==71)) /// + +max(0,x2823*(x2810==71))+max(0,x2840*(x2827==71)) /// + +max(0,x2923*(x2910==71))+max(0,x2940*(x2927==71)) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1)+max(0,x2723*(x2710==72))+max(0,x2740*(x2727==72)) /// + +max(0,x2823*(x2810==72))+max(0,x2840*(x2827==72)) /// + +max(0,x2923*(x2910==72))+max(0,x2940*(x2927==72)) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1)+max(0,x2723*(x2710==73))+max(0,x2740*(x2727==73)) /// + +max(0,x2823*(x2810==73))+max(0,x2840*(x2827==73)) /// + +max(0,x2923*(x2910==73))+max(0,x2940*(x2927==73)) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x2723*(x2710==74))+max(0,x2740*(x2727==74)) /// + +max(0,x2823*(x2810==74))+max(0,x2840*(x2827==74)) /// + +max(0,x2923*(x2910==74))+max(0,x2940*(x2927==74)) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420)+max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x2424+x2519+x2619+x2625+x7824 /// + +x7847+x7870+x7924+x7947+x7970+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +cd "$startDir" +cd ../../Data/Constructed +** Save data +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/code/stata/SelectVarsUsingSCF1995.do b/src/stata/SelectVarsUsingSCF1995.do similarity index 98% rename from code/stata/SelectVarsUsingSCF1995.do rename to src/stata/SelectVarsUsingSCF1995.do index 670e373..2331f69 100644 --- a/code/stata/SelectVarsUsingSCF1995.do +++ b/src/stata/SelectVarsUsingSCF1995.do @@ -1,264 +1,264 @@ -* This file selects variables, using SCF95 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) - -clear - -** Set memeory -set memory 32m - -global startDir "`c(pwd)'" -global scfFldr 1995 -global scfFile scf95 -global SuffixForConstructedFile "_population" - -cd ../../../Downloads/SCF/$scfFldr - -cap confirm file $scfFile.dta -if _rc~=0 { - display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." - exit -} - - - -** Load data and pick up necessary vars from original data -use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// - x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// - x3804 x3807 x3810 x3813 x3816 x3818 /// - x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// - x3930 /// - x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// - x3610 x3620 x3630 x3631 /// - x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// - x3902 x4006 x3942 x3947 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// - x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// - x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// - x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3229 x3224 x3226 x3227 x3221 x3222 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3329 x3324 x3326 x3327 x3321 x3322 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3335 x507 x513 x526 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3408 x3412 x3416 x3420 x3424 x3428 /// - x4022 x4026 x4030 /// - x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x1417 x1517 x1617 x1621 x2006 /// - x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x427 x413 x421 x430 x424 x7575 /// - x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// - x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// - x4010 x3932 x7194 x4032 /// - x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// - using "scf95.dta" - -** Generate variables -* ID -gen ID = y1 /* ID # */ -gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ -gen YEAR = 1995 /* Indicates data is from which wave */ - -* Weight -gen WGT = x42001 - -* Income -gen INCOME = x5729 /* Income before tax */ -replace INCOME = x7362 if x7650!=3 -scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ -scalar CPIADJ = CPIBASE/2265 /* Adjust with CPI (adjusted to 1992$ price) */ -scalar CPILAG = 2254/2201 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ - -* Asset -gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// - +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// - +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) -gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) - gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// - +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// - +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// - +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// - +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// - +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3706)*(11<=x9131 & x9131<=13) /// - +max(0,x3711)*(11<=x9132 & x9132<=13) /// - +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) - gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// - +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// - +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// - +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// - +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// - +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3706)*(x9131<11|x9131>13) /// - +max(0,x3711)*(x9132<11|x9132>13) /// - +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 -gen BOND = NOTXBND + MORTBND + GOVTBND + OBND - gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) - gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// - +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// - +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// - +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// - +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// - +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) - gen PMOP = x4436 - replace PMOP = 0 if x4436<=0 - replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP - replace PMOP = x5036 - replace PMOP = 0 if x5036<=0 - replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 - replace THRIFT = THRIFT + PMOP -gen RETQLIQ = IRAKH + THRIFT -gen SAVBND = x3902 -gen CASHLI = max(0,x4006) - gen ROTHMA = 0 - gen SOTHMA = 0 - gen COTHMA = 0 - replace ROTHMA = x3942 if x3947==1 | x3947==3 - replace SOTHMA = x3942 if x3947==2 | x3947==7 - replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 -gen OTHMA = ROTHMA+SOTHMA+COTHMA -gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// - +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// - +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) -gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ - -gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// - +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) -replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) - * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 -gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// - +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *max(0,x1706)*(x1705/10000) /// - +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *max(0,x1806)*(x1805/10000) /// - +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *max(0,x1906)*(x1905/10000) /// - +max(0,x2002) -gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// - *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// - +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// - *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// - +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// - *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// - +max(0,x2012)-x2016 -replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// - -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 -gen FLAG781 = (NNRESRE!=0) -gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5)+max(0,x3121)*(x3122==1|x3222==6) /// - +max(0,x2723*(x2710==71))+max(0,x2740*(x2727==71)) /// - +max(0,x2823*(x2810==71))+max(0,x2840*(x2827==71)) /// - +max(0,x2923*(x2910==71))+max(0,x2940*(x2927==71)) /// - +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// - +max(0,x3221)*(x3222==1|x3222==6)+max(0,x2723*(x2710==72))+max(0,x2740*(x2727==72)) /// - +max(0,x2823*(x2810==72))+max(0,x2840*(x2827==72)) /// - +max(0,x2923*(x2910==72))+max(0,x2940*(x2927==72)) /// - +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// - +max(0,x3321)*(x3322==1|x3222==6)+max(0,x2723*(x2710==73))+max(0,x2740*(x2727==73)) /// - +max(0,x2823*(x2810==73))+max(0,x2840*(x2827==73)) /// - +max(0,x2923*(x2910==73))+max(0,x2940*(x2927==73)) /// - +max(0,x3335)+(x507/10000)*(x513+x526) /// - +max(0,x2723*(x2710==74))+max(0,x2740*(x2727==74)) /// - +max(0,x2823*(x2810==74))+max(0,x2840*(x2827==74)) /// - +max(0,x2923*(x2910==74))+max(0,x2940*(x2927==74)) /// - +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420)+max(0,x3424)+max(0,x3428) -gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 -gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN -gen ASSET = FIN+NFIN /* Total asset */ - -* Debt -gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 - gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *x1715*(x1705/10000) - gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *x1815*(x1805/10000) - gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *x1915*(x1905/10000) -gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 - gen FLAG782 = (FLAG781!=1 & ORESRE>0) -replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// - +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 - gen FLAG67 = (ORESRE>0) -replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 -gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// - +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// - +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 -gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) -gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// - +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 -replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// - +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// - if FLAG781==0 & FLAG782==0 -replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// - +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 -replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// - +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// - +x2940*(x2927!=67&x2927!=78) -gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// - +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// - +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) -gen CASHLIDB = max(0,x4010) -gen CALLDBT = max(0,x3932)*(x7194==5) -gen ODEBT = max(0,x4032) -gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ - -* Net worth -gen NETW = ASSET-DEBT -replace NETW = NETW*CPIADJ - -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME - - -* Demographic vars -gen AGE = x14 /* Age */ -gen MARITST = x8023 /* Marital status */ -keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ - -gen EDUC = 0 -replace EDUC = 1 if /* RTORESP==1 & */ x5902!=1 /* No high school deg */ -replace EDUC = 2 if /* RTORESP==1 & */ x5902==1 /* High school deg */ -replace EDUC = 3 if /* RTORESP==1 & */ x5904==1 /* College deg */ -* keep if EDUC == 3 /* Keep college graduates only */ - -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) -replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) -drop if INCOME<=0 -replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) - - -** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE - -cd "$startDir" -cd ../../Data/Constructed -** Save data -save "./SCF$scfFldr$SuffixForConstructedFile", replace - -** End in the same directory you started from -cd "$startDir" +* This file selects variables, using SCF95 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 1995 +global scfFile scf95 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x3942 x3947 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3229 x3224 x3226 x3227 x3221 x3222 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3329 x3324 x3326 x3327 x3321 x3322 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3335 x507 x513 x526 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x7194 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf95.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 1995 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2265 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2254/2201 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ROTHMA = 0 + gen SOTHMA = 0 + gen COTHMA = 0 + replace ROTHMA = x3942 if x3947==1 | x3947==3 + replace SOTHMA = x3942 if x3947==2 | x3947==7 + replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 +gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// + +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// + +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5)+max(0,x3121)*(x3122==1|x3222==6) /// + +max(0,x2723*(x2710==71))+max(0,x2740*(x2727==71)) /// + +max(0,x2823*(x2810==71))+max(0,x2840*(x2827==71)) /// + +max(0,x2923*(x2910==71))+max(0,x2940*(x2927==71)) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6)+max(0,x2723*(x2710==72))+max(0,x2740*(x2727==72)) /// + +max(0,x2823*(x2810==72))+max(0,x2840*(x2827==72)) /// + +max(0,x2923*(x2910==72))+max(0,x2940*(x2927==72)) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3222==6)+max(0,x2723*(x2710==73))+max(0,x2740*(x2727==73)) /// + +max(0,x2823*(x2810==73))+max(0,x2840*(x2827==73)) /// + +max(0,x2923*(x2910==73))+max(0,x2940*(x2927==73)) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x2723*(x2710==74))+max(0,x2740*(x2727==74)) /// + +max(0,x2823*(x2810==74))+max(0,x2840*(x2827==74)) /// + +max(0,x2923*(x2910==74))+max(0,x2940*(x2927==74)) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420)+max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932)*(x7194==5) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if /* RTORESP==1 & */ x5902!=1 /* No high school deg */ +replace EDUC = 2 if /* RTORESP==1 & */ x5902==1 /* High school deg */ +replace EDUC = 3 if /* RTORESP==1 & */ x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +cd "$startDir" +cd ../../Data/Constructed +** Save data +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/code/stata/SelectVarsUsingSCF1998.do b/src/stata/SelectVarsUsingSCF1998.do similarity index 98% rename from code/stata/SelectVarsUsingSCF1998.do rename to src/stata/SelectVarsUsingSCF1998.do index ce55f7b..a226443 100644 --- a/code/stata/SelectVarsUsingSCF1998.do +++ b/src/stata/SelectVarsUsingSCF1998.do @@ -1,265 +1,265 @@ -* This file selects variables, using SCF98 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) - -clear - -** Set memeory -set memory 32m - -global startDir "`c(pwd)'" -global scfFldr 1998 -global scfFile scf98 -global SuffixForConstructedFile "_population" - -cd ../../../Downloads/SCF/$scfFldr - -cap confirm file $scfFile.dta -if _rc~=0 { - display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." - exit -} - - -** Load data and pick up necessary vars from original data -use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// - x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// - x3804 x3807 x3810 x3813 x3816 x3818 /// - x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// - x3930 /// - x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// - x3610 x3620 x3630 x3631 /// - x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// - x3902 x4006 x6820 x6826 x6835 x6841 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// - x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// - x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// - x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// - x4022 x4026 x4030 /// - x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x1417 x1517 x1617 x1621 x2006 /// - x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x427 x413 x421 x430 x424 x7575 /// - x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// - x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// - x4010 x3932 x4032 /// - x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// - using "scf98.dta" - -** Generate variables -* ID -gen ID = y1 /* ID # */ -gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ -gen YEAR = 1998 /* Indicates data is from which wave */ - -* Weight -gen WGT = x42001 - -* Income -gen INCOME = x5729 /* Income before tax */ -replace INCOME = x7362 if x7650!=3 -scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ -scalar CPIADJ = CPIBASE/2405 /* Adjust with CPI (adjusted to 1992$ price) */ -scalar CPILAG = 2397/2364 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ - -* Asset -gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// - +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// - +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) -gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) - gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// - +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// - +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// - +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// - +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// - +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3706)*(11<=x9131 & x9131<=13) /// - +max(0,x3711)*(11<=x9132 & x9132<=13) /// - +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) - gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// - +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// - +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// - +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// - +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// - +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3706)*(x9131<11|x9131>13) /// - +max(0,x3711)*(x9132<11|x9132>13) /// - +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 -gen BOND = NOTXBND + MORTBND + GOVTBND + OBND - gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) - gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// - +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// - +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// - +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// - +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// - +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) - gen PMOP = x4436 - replace PMOP = 0 if x4436<=0 - replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP - replace PMOP = x5036 - replace PMOP = 0 if x5036<=0 - replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 - replace THRIFT = THRIFT + PMOP -gen RETQLIQ = IRAKH + THRIFT -gen SAVBND = x3902 -gen CASHLI = max(0,x4006) - gen RANNUIT = 0 - gen SANNUIT = 0 - gen CANNUIT = 0 - gen RTRUST = 0 - gen STRUST = 0 - gen CTRUST = 0 - replace RANNUIT = x6820 if x6826== 1|x6826==3 - replace SANNUIT = x6820 if x6826== 2|x6826==7 - replace CANNUIT = x6820 if x6826== 5|x6826==6|x6826==8|x6826==9|x6826==-7 - replace RTRUST = x6835 if x6841==1|x6841==3 - replace STRUST = x6835 if x6841==2|x6841==7 - replace CTRUST = x6835 if x6841==5|x6841==6|x6841==8|x6841==9|x6841==-7 - gen ROTHMA = max(0,(RANNUIT + RTRUST)) - gen SOTHMA = max(0,(SANNUIT + STRUST)) - gen COTHMA = max(0,(CANNUIT + CTRUST)) -gen OTHMA = ROTHMA+SOTHMA+COTHMA -gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// - +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// - +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) -gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ - -gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// - +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) -replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-max(0,x507))/10000)*(x513+x526) - * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 -gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// - +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *max(0,x1706)*(x1705/10000) /// - +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *max(0,x1806)*(x1805/10000) /// - +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *max(0,x1906)*(x1905/10000) /// - +max(0,x2002) -gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// - *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// - +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// - *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// - +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// - *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// - +max(0,x2012)-x2016 -replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// - -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 -gen FLAG781 = (NNRESRE!=0) -gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// - +max(0,x3121)*(x3122==1|x3122==6) /// - +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// - +max(0,x3221)*(x3222==1|x3222==6) /// - +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// - +max(0,x3321)*(x3322==1|x3322==6) /// - +max(0,x3335)+(x507/10000)*(x513+x526) /// - +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// - +max(0,x3424)+max(0,x3428) -gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 -gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN -gen ASSET = FIN+NFIN /* Total asset */ - -* Debt -gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 - gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *x1715*(x1705/10000) - gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *x1815*(x1805/10000) - gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *x1915*(x1905/10000) -gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 - gen FLAG782 = (FLAG781!=1 & ORESRE>0) -replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// - +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 - gen FLAG67 = (ORESRE>0) -replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 -gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// - +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// - +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 -gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) -gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// - +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 -replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// - +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// - if FLAG781==0 & FLAG782==0 -replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// - +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 -replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// - +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// - +x2940*(x2927!=67&x2927!=78) -gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// - +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// - +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) -gen CASHLIDB = max(0,x4010) -gen CALLDBT = max(0,x3932) -gen ODEBT = max(0,x4032) -gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ - -* Net worth -gen NETW = ASSET-DEBT -replace NETW = NETW*CPIADJ - -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME - - -* Demographic vars -gen AGE = x14 /* Age */ -gen MARITST = x8023 /* Marital status */ -keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ - -gen EDUC = 0 -replace EDUC = 1 if x5902!=1 /* No high school deg */ -replace EDUC = 2 if x5902==1 /* High school deg */ -replace EDUC = 3 if x5904==1 /* College deg */ -* keep if EDUC == 3 /* Keep college graduates only */ - -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) -replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) -drop if INCOME<=0 -replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) - - -** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE - -** Save data -cd "$startDir" -cd ../../Data/Constructed -** Save data -save "./SCF$scfFldr$SuffixForConstructedFile", replace - -** End in the same directory you started from -cd "$startDir" +* This file selects variables, using SCF98 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 1998 +global scfFile scf98 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x6820 x6826 x6835 x6841 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf98.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 1998 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2405 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2397/2364 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen RANNUIT = 0 + gen SANNUIT = 0 + gen CANNUIT = 0 + gen RTRUST = 0 + gen STRUST = 0 + gen CTRUST = 0 + replace RANNUIT = x6820 if x6826== 1|x6826==3 + replace SANNUIT = x6820 if x6826== 2|x6826==7 + replace CANNUIT = x6820 if x6826== 5|x6826==6|x6826==8|x6826==9|x6826==-7 + replace RTRUST = x6835 if x6841==1|x6841==3 + replace STRUST = x6835 if x6841==2|x6841==7 + replace CTRUST = x6835 if x6841==5|x6841==6|x6841==8|x6841==9|x6841==-7 + gen ROTHMA = max(0,(RANNUIT + RTRUST)) + gen SOTHMA = max(0,(SANNUIT + STRUST)) + gen COTHMA = max(0,(CANNUIT + CTRUST)) +gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// + +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// + +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-max(0,x507))/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +** Save data +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/code/stata/SelectVarsUsingSCF2001.do b/src/stata/SelectVarsUsingSCF2001.do similarity index 98% rename from code/stata/SelectVarsUsingSCF2001.do rename to src/stata/SelectVarsUsingSCF2001.do index 3a71c0e..e4ec3c2 100644 --- a/code/stata/SelectVarsUsingSCF2001.do +++ b/src/stata/SelectVarsUsingSCF2001.do @@ -1,254 +1,254 @@ -* This file selects variables, using SCF2001 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) - -clear - -** Set memeory -set memory 32m - -global startDir "`c(pwd)'" -global scfFldr 2001 -global scfFile scf2001 -global SuffixForConstructedFile "_population" - -cd ../../../Downloads/SCF/$scfFldr - -cap confirm file $scfFile.dta -if _rc~=0 { - display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." - exit -} - -** Load data and pick up necessary vars from original data -use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// - x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// - x3804 x3807 x3810 x3813 x3816 x3818 /// - x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// - x3930 /// - x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// - x3610 x3620 x3630 x3631 /// - x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// - x3902 x4006 x6820 x6835 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// - x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// - x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// - x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// - x4022 x4026 x4030 /// - x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x1417 x1517 x1617 x1621 x2006 /// - x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x427 x413 x421 x430 x424 x7575 /// - x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// - x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// - x4010 x3932 x4032 /// - x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// - using "scf2001.dta" - -** Generate variables -* ID -gen ID = y1 /* ID # */ -gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ -gen YEAR = 2001 /* Indicates data is from which wave */ - -* Weight -gen WGT = x42001 - -* Income -gen INCOME = x5729 /* Income before tax */ -replace INCOME = x7362 if x7650!=3 -scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ -scalar CPIADJ = CPIBASE/2618 /* Adjust with CPI (adjusted to 1992$ price) */ -scalar CPILAG = 2600/2529 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ - -* Asset -gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// - +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// - +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) -gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) - gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// - +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// - +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// - +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// - +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// - +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// - +max(0,x3706)*(11<=x9131 & x9131<=13) /// - +max(0,x3711)*(11<=x9132 & x9132<=13) /// - +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) - gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// - +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// - +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// - +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// - +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// - +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// - +max(0,x3706)*(x9131<11|x9131>13) /// - +max(0,x3711)*(x9132<11|x9132>13) /// - +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 -gen BOND = NOTXBND + MORTBND + GOVTBND + OBND - gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) - gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// - +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// - +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// - +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// - +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// - +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) - gen PMOP = x4436 - replace PMOP = 0 if x4436<=0 - replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP - replace PMOP = x5036 - replace PMOP = 0 if x5036<=0 - replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 - replace THRIFT = THRIFT + PMOP -gen RETQLIQ = IRAKH + THRIFT -gen SAVBND = x3902 -gen CASHLI = max(0,x4006) - gen ANNUIT = max(0,x6820) - gen TRUSTS = max(0,x6835) -gen OTHMA = ANNUIT+TRUSTS -gen OTHFIN = x4018 /// - +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// - x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// - +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// - x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// - +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// - x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) -gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ - -gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// - +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) -replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) - * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 -gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// - +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *max(0,x1706)*(x1705/10000) /// - +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *max(0,x1806)*(x1805/10000) /// - +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *max(0,x1906)*(x1905/10000) /// - +max(0,x2002) -gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// - *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// - +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// - *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// - +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// - *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// - +max(0,x2012)-x2016 -replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// - -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 -gen FLAG781 = (NNRESRE!=0) -gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// - +max(0,x3121)*(x3122==1|x3122==6) /// - +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// - +max(0,x3221)*(x3222==1|x3222==6) /// - +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// - +max(0,x3321)*(x3322==1|x3322==6) /// - +max(0,x3335)+(x507/10000)*(x513+x526) /// - +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// - +max(0,x3424)+max(0,x3428) -gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 -gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN -gen ASSET = FIN+NFIN /* Total asset */ - -* Debt -gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 - gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *x1715*(x1705/10000) - gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *x1815*(x1805/10000) - gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *x1915*(x1905/10000) -gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 - gen FLAG782 = (FLAG781!=1 & ORESRE>0) -replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// - +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 - gen FLAG67 = (ORESRE>0) -replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 -gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// - +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// - +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 -gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) -gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// - +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 -replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// - +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// - if FLAG781==0 & FLAG782==0 -replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// - +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 -replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// - +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// - +x2940*(x2927!=67&x2927!=78) -gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// - +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// - +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) -gen CASHLIDB = max(0,x4010) -gen CALLDBT = max(0,x3932) -gen ODEBT = max(0,x4032) -gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ - -* Net worth -gen NETW = ASSET-DEBT -replace NETW = NETW*CPIADJ - -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME - - -* Demographic vars -gen AGE = x14 /* Age */ -gen MARITST = x8023 /* Marital status */ -keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ - -gen EDUC = 0 -replace EDUC = 1 if x5902!=1 /* No high school deg */ -replace EDUC = 2 if x5902==1 /* High school deg */ -replace EDUC = 3 if x5904==1 /* College deg */ -* keep if EDUC == 3 /* Keep college graduates only */ - -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) -replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) -drop if INCOME<=0 -replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) - - -** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE - -** Save data -cd "$startDir" -cd ../../Data/Constructed -save "./SCF$scfFldr$SuffixForConstructedFile", replace - -** End in the same directory you started from -cd "$startDir" +* This file selects variables, using SCF2001 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 2001 +global scfFile scf2001 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3804 x3807 x3810 x3813 x3816 x3818 /// + x3506 x3507 x9113 x3510 x3511 x9114 x3514 x3515 x9115 x3518 x3519 x9116 x3522 x3523 x9117 x3526 x3527 x9118 x3529 x3706 x9131 x3711 x9132 x3716 x9133 x3718 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x3915 x3910 x3906 x3908 x7634 x7633 /// + x3610 x3620 x3630 x3631 /// + x4216 x4316 x4416 x4816 x4916 x5016 x4226 x4326 x4426 x4826 x4926 x5026 x4227 x4327 x4427 x4827 x4927 x5027 x4231 x4331 x4431 x4831 x4931 x5031 x4234 x4334 x4434 x4834 x4934 x5034 x4436 x5036 /// + x3902 x4006 x6820 x6835 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x4229 x4230 x4329 x4330 x4429 x4430 x4829 x4830 x4929 x4930 x5029 x5030 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf2001.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 2001 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = x5729 /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2618 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2600/2529 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// + +max(0,x3816)+max(0,x3818) + gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(11<=x9116 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(11<=x9117 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(11<=x9118 & x9118<=13)) /// + +max(0,x3706)*(11<=x9131 & x9131<=13) /// + +max(0,x3711)*(11<=x9132 & x9132<=13) /// + +max(0,x3716)*(11<=x9133 & x9133<=13) /// + +max(0,x3718)*(11<=x9133 & x9133<=13) + gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// + +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// + +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// + +max(0,x3518)*(x3519==1)*(x9116<11|x9116>13) /// + +max(0,x3522)*(x3523==1)*(x9117<11|x9117>13) /// + +max(0,x3526)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3529)*(x3527==1)*(x9118<11|x9118>13) /// + +max(0,x3706)*(x9131<11|x9131>13) /// + +max(0,x3711)*(x9132<11|x9132>13) /// + +max(0,x3716)*(x9133<11|x9133>13) /// + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) + gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// + +max(0,x4326)*(x4316==1|x4316==2|x4327==1|x4331==1) /// + +max(0,x4426)*(x4416==1|x4416==2|x4427==1|x4431==1) /// + +max(0,x4826)*(x4816==1|x4816==2|x4827==1|x4831==1) /// + +max(0,x4926)*(x4916==1|x4916==2|x4927==1|x4931==1) /// + +max(0,x5026)*(x5016==1|x5016==2|x5027==1|x5031==1) + gen PMOP = x4436 + replace PMOP = 0 if x4436<=0 + replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x5036 + replace PMOP = 0 if x5036<=0 + replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH + THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ANNUIT = max(0,x6820) + gen TRUSTS = max(0,x6835) +gen OTHMA = ANNUIT+TRUSTS +gen OTHFIN = x4018 /// + +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// + x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// + x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x4229)*(x4230==5)+max(0,x4329)*(x4330==5) /// + +max(0,x4429)*(x4430==5)+max(0,x4829)*(x4830==5) /// + +max(0,x4929)*(x4930==5)+max(0,x5029)*(x5030==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/code/stata/SelectVarsUsingSCF2004.do b/src/stata/SelectVarsUsingSCF2004.do similarity index 98% rename from code/stata/SelectVarsUsingSCF2004.do rename to src/stata/SelectVarsUsingSCF2004.do index 43fc016..cf10063 100644 --- a/code/stata/SelectVarsUsingSCF2004.do +++ b/src/stata/SelectVarsUsingSCF2004.do @@ -1,272 +1,272 @@ -* This file selects variables, using SCF2004 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) - -clear - -** Set memeory -set memory 32m - -global startDir "`c(pwd)'" -global scfFldr 2004 -global scfFile scf2004 -global SuffixForConstructedFile "_population" - -cd ../../../Downloads/SCF/$scfFldr - -cap confirm file $scfFile.dta -if _rc~=0 { - display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." - exit -} - - -** Load data and pick up necessary vars from original data -use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// - x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// - x3730 x3732 x3732 x3736 x3738 x3738 x3742 x3744 x3744 x3748 x3750 x3750 x3754 x3756 x3756 x3760 x3762 x3762 x3765 /// - x3506 x3507 x9113 x9113 x3510 x3511 x9114 x9114 x3514 x3515 x9115 x9115 x3518 x3519 x9116 x9116 x3522 x3523 x9117 x9117 x3526 x3527 x9118 x9118 x3529 x3527 x9118 x9118 x3730 x3732 x9259 x9259 x3736 x3738 x9260 x9260 x3742 x3744 x9261 x9261 x3748 x3750 x9262 x9262 x3754 x3756 x9263 x9263 x3760 x3762 x9264 x9264 /// - x3930 /// - x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x7785 x7787 /// - x3915 x3910 x3906 x3908 x7634 x7633 /// - x6551 x6559 x6567 x6552 x6560 x6568 x6553 x6561 x6569 x6554 x6562 x6570 /// - x11032 x11000 x11001 x11025 x11031 x11132 x11100 x11101 x11125 x11131 x11232 x11200 x11201 x11225 x11231 x11332 x11300 x11301 x11325 x11331 x11432 x11400 x11401 x11425 x11431 x11532 x11500 x11501 x11525 x11531 /// - x11259 x11559 /// - x3902 x4006 x6577 x6587 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// - x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// - x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// - x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// - x4022 x4026 x4030 /// - x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x1417 x1517 x1617 x1621 x2006 /// - x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x427 x413 x421 x430 x424 x7575 /// - x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// - x11027 x11070 x11127 x11170 x11227 x11270 x11327 x11370 x11427 x11470 x11527 x11570 /// - x4010 x3932 x4032 /// - x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// - using "scf2004.dta" - -** Generate variables -* ID -gen ID = y1 /* ID # */ -gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ -gen YEAR = 2004 /* Indicates data is from which wave */ - -* Weight -gen WGT = x42001 - -* Income -gen INCOME = max(0,x5729)+x6558+x6566+x6574+max(0,x6464)+max(0,x6469) /// - +max(0,x6474)+max(0,x6479)+max(0,x6484)+max(0,x6489) /// - +max(0,x6965)+max(0,x6971)+max(0,x6977)+max(0,x6983) /// - +max(0,x6989)+max(0,x6995) /* Income before tax */ -replace INCOME = x7362 if x7650!=3 -scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ -scalar CPIADJ = CPIBASE/2788 /* Adjust with CPI (adjusted to 1992$ price) */ -scalar CPILAG = 2774/2701 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ - * gen INCOMEAT = x5751 /* After tax income */ -* gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ -* keep if D_NORMINC == 1 /* Keep if inc level is normal */ - -* Asset -gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// - +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// - +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) -gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// - +max(0,x3736*(x3738!=4 & x3738!=30)) /// - +max(0,x3742*(x3744!=4 & x3744!=30)) /// - +max(0,x3748*(x3750!=4 & x3750!=30)) /// - +max(0,x3754*(x3756!=4 & x3756!=30)) /// - +max(0,x3760*(x3762!=4 & x3762!=30))+max(0,x3765) -gen MMDA = max(0,x3506)*((x3507==1)*(x9113>=11 & x9113<=13)) /// - +max(0,x3510)*((x3511==1)*(x9114>=11 & x9114<=13)) /// - +max(0,x3514)*((x3515==1)*(x9115>=11 & x9115<=13)) /// - +max(0,x3518)*((x3519==1)*(x9116>=11 & x9116<=13)) /// - +max(0,x3522)*((x3523==1)*(x9117>=11 & x9117<=13)) /// - +max(0,x3526)*((x3527==1)*(x9118>=11 & x9118<=13)) /// - +max(0,x3529)*((x3527==1)*(x9118>=11 & x9118<=13)) /// - +max(0,x3730*(x3732==4|x3732==30)*(x9259>=11 & x9259<=13)) /// - +max(0,x3736*(x3738==4|x3738==30)*(x9260>=11 & x9260<=13)) /// - +max(0,x3742*(x3744==4|x3744==30)*(x9261>=11 & x9261<=13)) /// - +max(0,x3748*(x3750==4|x3750==30)*(x9262>=11 & x9262<=13)) /// - +max(0,x3754*(x3756==4|x3756==30)*(x9263>=11 & x9263<=13)) /// - +max(0,x3760*(x3762==4|x3762==30)*(x9264>=11 & x9264<=13)) -gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// - +max(0,x3510)*((x3511==1)*(x9114<11 | x9114>13)) /// - +max(0,x3514)*((x3515==1)*(x9115<11 | x9115>13)) /// - +max(0,x3518)*((x3519==1)*(x9116<11 | x9116>13)) /// - +max(0,x3522)*((x3523==1)*(x9117<11 | x9117>13)) /// - +max(0,x3526)*((x3527==1)*(x9118<11 | x9118>13)) /// - +max(0,x3529)*((x3527==1)*(x9118<11 | x9118>13)) /// - +max(0,x3730*(x3732==4|x3732==30)*(x9259<11 | x9259>13)) /// - +max(0,x3736*(x3738==4|x3738==30)*(x9260<11 | x9260>13)) /// - +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// - +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// - +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// - +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen OMUTF = (x7785==1)*max(0,x7787) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF + OMUTF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 -gen BOND = NOTXBND + MORTBND + GOVTBND + OBND - gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 - gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// - +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// - +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// - +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// - +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// - +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) - gen PMOP = x11259 - replace PMOP = 0 if x11259<=0 - replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 - replace THRIFT = THRIFT + PMOP - replace PMOP = x11559 - replace PMOP = 0 if x11559<=0 - replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 - replace THRIFT = THRIFT + PMOP -gen RETQLIQ = IRAKH+THRIFT -gen SAVBND = x3902 -gen CASHLI = max(0,x4006) - gen ANNUIT = max(0,x6577) - gen TRUSTS = max(0,x6587) -gen OTHMA = ANNUIT+TRUSTS -gen OTHFIN = x4018 /// - +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// - x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// - +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// - x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// - +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// - x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) -gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ - -gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// - +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) -replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) - * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 -gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// - +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *max(0,x1706)*(x1705/10000) /// - +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *max(0,x1806)*(x1805/10000) /// - +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *max(0,x1906)*(x1905/10000) /// - +max(0,x2002) -gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// - *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// - +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// - *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// - +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// - *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// - +max(0,x2012)-x2016 -replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// - -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 -gen FLAG781 = (NNRESRE!=0) -gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// - +max(0,x3121)*(x3122==1|x3122==6) /// - +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// - +max(0,x3221)*(x3222==1|x3222==6) /// - +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// - +max(0,x3321)*(x3322==1|x3322==6) /// - +max(0,x3335)+(x507/10000)*(x513+x526) /// - +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// - +max(0,x3424)+max(0,x3428) -gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 -gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN -gen ASSET = FIN+NFIN /* Total asset */ - -* Debt -gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 - gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *x1715*(x1705/10000) - gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *x1815*(x1805/10000) - gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *x1915*(x1905/10000) -gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 - gen FLAG782 = (FLAG781!=1 & ORESRE>0) -replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// - +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 - gen FLAG67 = (ORESRE>0) -replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 -gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// - +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// - +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 -gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) -gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// - +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 -replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// - +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// - if FLAG781==0 & FLAG782==0 -replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// - +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 -replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// - +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// - +x2940*(x2927!=67&x2927!=78) -gen PENDBT = max(0,x11027)*(x11070==5)+max(0,x11127)*(x11170==5) /// - +max(0,x11227)*(x11270==5)+max(0,x11327)*(x11370==5) /// - +max(0,x11427)*(x11470==5)+max(0,x11527)*(x11570==5) -gen CASHLIDB = max(0,x4010) -gen CALLDBT = max(0,x3932) -gen ODEBT = max(0,x4032) -gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ - -* Net worth -gen NETW = ASSET-DEBT -replace NETW = NETW*CPIADJ - -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME - - -* Demographic vars -gen AGE = x14 /* Age */ -gen MARITST = x8023 /* Marital status */ -keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ - -gen EDUC = 0 -replace EDUC = 1 if x5902!=1 /* No high school deg */ -replace EDUC = 2 if x5902==1 /* High school deg */ -replace EDUC = 3 if x5904==1 /* College deg */ -* keep if EDUC == 3 /* Keep college graduates only */ - -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) -replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) -drop if INCOME<=0 -replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) - - -** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE - -** Save data -cd "$startDir" -cd ../../Data/Constructed -save "./SCF$scfFldr$SuffixForConstructedFile", replace - -** End in the same directory you started from -cd "$startDir" +* This file selects variables, using SCF2004 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 2004 +global scfFile scf2004 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3730 x3732 x3732 x3736 x3738 x3738 x3742 x3744 x3744 x3748 x3750 x3750 x3754 x3756 x3756 x3760 x3762 x3762 x3765 /// + x3506 x3507 x9113 x9113 x3510 x3511 x9114 x9114 x3514 x3515 x9115 x9115 x3518 x3519 x9116 x9116 x3522 x3523 x9117 x9117 x3526 x3527 x9118 x9118 x3529 x3527 x9118 x9118 x3730 x3732 x9259 x9259 x3736 x3738 x9260 x9260 x3742 x3744 x9261 x9261 x3748 x3750 x9262 x9262 x3754 x3756 x9263 x9263 x3760 x3762 x9264 x9264 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x7785 x7787 /// + x3915 x3910 x3906 x3908 x7634 x7633 /// + x6551 x6559 x6567 x6552 x6560 x6568 x6553 x6561 x6569 x6554 x6562 x6570 /// + x11032 x11000 x11001 x11025 x11031 x11132 x11100 x11101 x11125 x11131 x11232 x11200 x11201 x11225 x11231 x11332 x11300 x11301 x11325 x11331 x11432 x11400 x11401 x11425 x11431 x11532 x11500 x11501 x11525 x11531 /// + x11259 x11559 /// + x3902 x4006 x6577 x6587 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x11027 x11070 x11127 x11170 x11227 x11270 x11327 x11370 x11427 x11470 x11527 x11570 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf2004.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 2004 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = max(0,x5729)+x6558+x6566+x6574+max(0,x6464)+max(0,x6469) /// + +max(0,x6474)+max(0,x6479)+max(0,x6484)+max(0,x6489) /// + +max(0,x6965)+max(0,x6971)+max(0,x6977)+max(0,x6983) /// + +max(0,x6989)+max(0,x6995) /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/2788 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 2774/2701 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + * gen INCOMEAT = x5751 /* After tax income */ +* gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ +* keep if D_NORMINC == 1 /* Keep if inc level is normal */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// + +max(0,x3736*(x3738!=4 & x3738!=30)) /// + +max(0,x3742*(x3744!=4 & x3744!=30)) /// + +max(0,x3748*(x3750!=4 & x3750!=30)) /// + +max(0,x3754*(x3756!=4 & x3756!=30)) /// + +max(0,x3760*(x3762!=4 & x3762!=30))+max(0,x3765) +gen MMDA = max(0,x3506)*((x3507==1)*(x9113>=11 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(x9114>=11 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(x9115>=11 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(x9116>=11 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(x9117>=11 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259>=11 & x9259<=13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260>=11 & x9260<=13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261>=11 & x9261<=13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262>=11 & x9262<=13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263>=11 & x9263<=13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264>=11 & x9264<=13)) +gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// + +max(0,x3510)*((x3511==1)*(x9114<11 | x9114>13)) /// + +max(0,x3514)*((x3515==1)*(x9115<11 | x9115>13)) /// + +max(0,x3518)*((x3519==1)*(x9116<11 | x9116>13)) /// + +max(0,x3522)*((x3523==1)*(x9117<11 | x9117>13)) /// + +max(0,x3526)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3529)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259<11 | x9259>13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260<11 | x9260>13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen OMUTF = (x7785==1)*max(0,x7787) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF + OMUTF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 + gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// + +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// + +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// + +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// + +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// + +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) + gen PMOP = x11259 + replace PMOP = 0 if x11259<=0 + replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x11559 + replace PMOP = 0 if x11559<=0 + replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH+THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ANNUIT = max(0,x6577) + gen TRUSTS = max(0,x6587) +gen OTHMA = ANNUIT+TRUSTS +gen OTHFIN = x4018 /// + +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// + x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// + x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x11027)*(x11070==5)+max(0,x11127)*(x11170==5) /// + +max(0,x11227)*(x11270==5)+max(0,x11327)*(x11370==5) /// + +max(0,x11427)*(x11470==5)+max(0,x11527)*(x11570==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/code/stata/SelectVarsUsingSCF2007.do b/src/stata/SelectVarsUsingSCF2007.do similarity index 98% rename from code/stata/SelectVarsUsingSCF2007.do rename to src/stata/SelectVarsUsingSCF2007.do index a4912e7..e8e61cc 100644 --- a/code/stata/SelectVarsUsingSCF2007.do +++ b/src/stata/SelectVarsUsingSCF2007.do @@ -1,271 +1,271 @@ -* This file selects variables, using SCF2007 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) - -clear - -** Set memeory -set memory 32m - -global startDir "`c(pwd)'" -global scfFldr 2007 -global scfFile scf2007 -global SuffixForConstructedFile "_population" - -cd ../../../Downloads/SCF/$scfFldr - -cap confirm file $scfFile.dta -if _rc~=0 { - display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." - exit -} - -** Load data and pick up necessary vars from original data -use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// - x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// - x3730 x3732 x3732 x3736 x3738 x3738 x3742 x3744 x3744 x3748 x3750 x3750 x3754 x3756 x3756 x3760 x3762 x3762 x3765 /// - x3506 x3507 x9113 x9113 x3510 x3511 x9114 x9114 x3514 x3515 x9115 x9115 x3518 x3519 x9116 x9116 x3522 x3523 x9117 x9117 x3526 x3527 x9118 x9118 x3529 x3527 x9118 x9118 x3730 x3732 x9259 x9259 x3736 x3738 x9260 x9260 x3742 x3744 x9261 x9261 x3748 x3750 x9262 x9262 x3754 x3756 x9263 x9263 x3760 x3762 x9264 x9264 /// - x3930 /// - x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x7785 x7787 /// - x3915 x3910 x3906 x3908 x7634 x7633 /// - x6551 x6559 x6567 x6552 x6560 x6568 x6553 x6561 x6569 x6554 x6562 x6570 /// - x11032 x11000 x11001 x11025 x11031 x11132 x11100 x11101 x11125 x11131 x11232 x11200 x11201 x11225 x11231 x11332 x11300 x11301 x11325 x11331 x11432 x11400 x11401 x11425 x11431 x11532 x11500 x11501 x11525 x11531 /// - x11259 x11559 /// - x3902 x4006 x6577 x6587 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// - x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// - x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// - x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// - x4022 x4026 x4030 /// - x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x1417 x1517 x1617 x1621 x2006 /// - x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// - x427 x413 x421 x430 x424 x7575 /// - x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// - x11027 x11070 x11127 x11170 x11227 x11270 x11327 x11370 x11427 x11470 x11527 x11570 /// - x4010 x3932 x4032 /// - x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// - using "scf2007.dta" - -** Generate variables -* ID -gen ID = y1 /* ID # */ -gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ -gen YEAR = 2007 /* Indicates data is from which wave */ - -* Weight -gen WGT = x42001 - -* Income -gen INCOME = max(0,x5729)+x6558+x6566+x6574+max(0,x6464)+max(0,x6469) /// - +max(0,x6474)+max(0,x6479)+max(0,x6484)+max(0,x6489) /// - +max(0,x6965)+max(0,x6971)+max(0,x6977)+max(0,x6983) /// - +max(0,x6989)+max(0,x6995) /* Income before tax */ -replace INCOME = x7362 if x7650!=3 -scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ -scalar CPIADJ = CPIBASE/3062 /* Adjust with CPI (adjusted to 1992$ price) */ -scalar CPILAG = 3045/2961 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ - * gen INCOMEAT = x5751 /* After tax income */ -* gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ -* keep if D_NORMINC == 1 /* Keep if inc level is normal */ - -* Asset -gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// - +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// - +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) -gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// - +max(0,x3736*(x3738!=4 & x3738!=30)) /// - +max(0,x3742*(x3744!=4 & x3744!=30)) /// - +max(0,x3748*(x3750!=4 & x3750!=30)) /// - +max(0,x3754*(x3756!=4 & x3756!=30)) /// - +max(0,x3760*(x3762!=4 & x3762!=30))+max(0,x3765) -gen MMDA = max(0,x3506)*((x3507==1)*(x9113>=11 & x9113<=13)) /// - +max(0,x3510)*((x3511==1)*(x9114>=11 & x9114<=13)) /// - +max(0,x3514)*((x3515==1)*(x9115>=11 & x9115<=13)) /// - +max(0,x3518)*((x3519==1)*(x9116>=11 & x9116<=13)) /// - +max(0,x3522)*((x3523==1)*(x9117>=11 & x9117<=13)) /// - +max(0,x3526)*((x3527==1)*(x9118>=11 & x9118<=13)) /// - +max(0,x3529)*((x3527==1)*(x9118>=11 & x9118<=13)) /// - +max(0,x3730*(x3732==4|x3732==30)*(x9259>=11 & x9259<=13)) /// - +max(0,x3736*(x3738==4|x3738==30)*(x9260>=11 & x9260<=13)) /// - +max(0,x3742*(x3744==4|x3744==30)*(x9261>=11 & x9261<=13)) /// - +max(0,x3748*(x3750==4|x3750==30)*(x9262>=11 & x9262<=13)) /// - +max(0,x3754*(x3756==4|x3756==30)*(x9263>=11 & x9263<=13)) /// - +max(0,x3760*(x3762==4|x3762==30)*(x9264>=11 & x9264<=13)) -gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// - +max(0,x3510)*((x3511==1)*(x9114<11 | x9114>13)) /// - +max(0,x3514)*((x3515==1)*(x9115<11 | x9115>13)) /// - +max(0,x3518)*((x3519==1)*(x9116<11 | x9116>13)) /// - +max(0,x3522)*((x3523==1)*(x9117<11 | x9117>13)) /// - +max(0,x3526)*((x3527==1)*(x9118<11 | x9118>13)) /// - +max(0,x3529)*((x3527==1)*(x9118<11 | x9118>13)) /// - +max(0,x3730*(x3732==4|x3732==30)*(x9259<11 | x9259>13)) /// - +max(0,x3736*(x3738==4|x3738==30)*(x9260<11 | x9260>13)) /// - +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// - +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// - +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// - +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen OMUTF = (x7785==1)*max(0,x7787) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF + OMUTF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 -gen BOND = NOTXBND + MORTBND + GOVTBND + OBND - gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 - gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// - +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// - +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// - +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// - +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// - +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) - gen PMOP = x11259 - replace PMOP = 0 if x11259<=0 - replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 - replace THRIFT = THRIFT + PMOP - replace PMOP = x11559 - replace PMOP = 0 if x11559<=0 - replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 - replace THRIFT = THRIFT + PMOP -gen RETQLIQ = IRAKH+THRIFT -gen SAVBND = x3902 -gen CASHLI = max(0,x4006) - gen ANNUIT = max(0,x6577) - gen TRUSTS = max(0,x6587) -gen OTHMA = ANNUIT+TRUSTS -gen OTHFIN = x4018 /// - +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// - x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// - +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// - x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// - +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// - x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) -gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ - -gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// - +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) -replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) - * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 -gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// - +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *max(0,x1706)*(x1705/10000) /// - +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *max(0,x1806)*(x1805/10000) /// - +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *max(0,x1906)*(x1905/10000) /// - +max(0,x2002) -gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// - *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// - +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// - *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// - +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// - *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// - +max(0,x2012)-x2016 -replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// - -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 -gen FLAG781 = (NNRESRE!=0) -gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// - +max(0,x3121)*(x3122==1|x3122==6) /// - +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// - +max(0,x3221)*(x3222==1|x3222==6) /// - +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// - +max(0,x3321)*(x3322==1|x3322==6) /// - +max(0,x3335)+(x507/10000)*(x513+x526) /// - +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// - +max(0,x3424)+max(0,x3428) -gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 -gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN -gen ASSET = FIN+NFIN /* Total asset */ - -* Debt -gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// - +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 - gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// - *x1715*(x1705/10000) - gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// - *x1815*(x1805/10000) - gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// - *x1915*(x1905/10000) -gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 - gen FLAG782 = (FLAG781!=1 & ORESRE>0) -replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// - +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 - gen FLAG67 = (ORESRE>0) -replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 -gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// - +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// - +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 -gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) -gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// - +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 -replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// - +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// - if FLAG781==0 & FLAG782==0 -replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// - +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 -replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// - +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// - +x2940*(x2927!=67&x2927!=78) -gen PENDBT = max(0,x11027)*(x11070==5)+max(0,x11127)*(x11170==5) /// - +max(0,x11227)*(x11270==5)+max(0,x11327)*(x11370==5) /// - +max(0,x11427)*(x11470==5)+max(0,x11527)*(x11570==5) -gen CASHLIDB = max(0,x4010) -gen CALLDBT = max(0,x3932) -gen ODEBT = max(0,x4032) -gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ - -* Net worth -gen NETW = ASSET-DEBT -replace NETW = NETW*CPIADJ - -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME - - -* Demographic vars -gen AGE = x14 /* Age */ -gen MARITST = x8023 /* Marital status */ -keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ - -gen EDUC = 0 -replace EDUC = 1 if x5902!=1 /* No high school deg */ -replace EDUC = 2 if x5902==1 /* High school deg */ -replace EDUC = 3 if x5904==1 /* College deg */ -* keep if EDUC == 3 /* Keep college graduates only */ - -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) -replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) -drop if INCOME<=0 -replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) - - -** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE - -** Save data -cd "$startDir" -cd ../../Data/Constructed -save "./SCF$scfFldr$SuffixForConstructedFile", replace - -** End in the same directory you started from -cd "$startDir" +* This file selects variables, using SCF2007 +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) + +clear + +** Set memeory +set memory 32m + +global startDir "`c(pwd)'" +global scfFldr 2007 +global scfFile scf2007 +global SuffixForConstructedFile "_population" + +cd ../../../Downloads/SCF/$scfFldr + +cap confirm file $scfFile.dta +if _rc~=0 { + display "File $scfFile is not in the Downloads/SCF/$scfFldr folder; please see ReadMe.txt for instructions." + exit +} + +** Load data and pick up necessary vars from original data +use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// + x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// + x3730 x3732 x3732 x3736 x3738 x3738 x3742 x3744 x3744 x3748 x3750 x3750 x3754 x3756 x3756 x3760 x3762 x3762 x3765 /// + x3506 x3507 x9113 x9113 x3510 x3511 x9114 x9114 x3514 x3515 x9115 x9115 x3518 x3519 x9116 x9116 x3522 x3523 x9117 x9117 x3526 x3527 x9118 x9118 x3529 x3527 x9118 x9118 x3730 x3732 x9259 x9259 x3736 x3738 x9260 x9260 x3742 x3744 x9261 x9261 x3748 x3750 x9262 x9262 x3754 x3756 x9263 x9263 x3760 x3762 x9264 x9264 /// + x3930 /// + x3721 x3821 x3822 x3823 x3824 x3825 x3826 x3827 x3828 x3829 x3830 x7785 x7787 /// + x3915 x3910 x3906 x3908 x7634 x7633 /// + x6551 x6559 x6567 x6552 x6560 x6568 x6553 x6561 x6569 x6554 x6562 x6570 /// + x11032 x11000 x11001 x11025 x11031 x11132 x11100 x11101 x11125 x11131 x11232 x11200 x11201 x11225 x11231 x11332 x11300 x11301 x11325 x11331 x11432 x11400 x11401 x11425 x11431 x11532 x11500 x11501 x11525 x11531 /// + x11259 x11559 /// + x3902 x4006 x6577 x6587 x4018 x4022 x4020 x4026 x4024 x4030 x4028 /// + x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// + x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// + x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x4022 x4026 x4030 /// + x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x1417 x1517 x1617 x1621 x2006 /// + x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// + x427 x413 x421 x430 x424 x7575 /// + x2218 x2318 x2418 x7169 x2424 x2519 x2619 x2625 x7183 x7824 x7847 x7870 x7924 x7947 x7970 x7179 x1044 x1215 x1219 /// + x11027 x11070 x11127 x11170 x11227 x11270 x11327 x11370 x11427 x11470 x11527 x11570 /// + x4010 x3932 x4032 /// + x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// + using "scf2007.dta" + +** Generate variables +* ID +gen ID = y1 /* ID # */ +gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ +gen YEAR = 2007 /* Indicates data is from which wave */ + +* Weight +gen WGT = x42001 + +* Income +gen INCOME = max(0,x5729)+x6558+x6566+x6574+max(0,x6464)+max(0,x6469) /// + +max(0,x6474)+max(0,x6479)+max(0,x6484)+max(0,x6489) /// + +max(0,x6965)+max(0,x6971)+max(0,x6977)+max(0,x6983) /// + +max(0,x6989)+max(0,x6995) /* Income before tax */ +replace INCOME = x7362 if x7650!=3 +scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ +scalar CPIADJ = CPIBASE/3062 /* Adjust with CPI (adjusted to 1992$ price) */ +scalar CPILAG = 3045/2961 /* Income is the previous year's income level, CPILAG adjust income to survey year */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ + * gen INCOMEAT = x5751 /* After tax income */ +* gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ +* keep if D_NORMINC == 1 /* Keep if inc level is normal */ + +* Asset +gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// + +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// + +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// + +max(0,x3529)*(x3527==5) +gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// + +max(0,x3736*(x3738!=4 & x3738!=30)) /// + +max(0,x3742*(x3744!=4 & x3744!=30)) /// + +max(0,x3748*(x3750!=4 & x3750!=30)) /// + +max(0,x3754*(x3756!=4 & x3756!=30)) /// + +max(0,x3760*(x3762!=4 & x3762!=30))+max(0,x3765) +gen MMDA = max(0,x3506)*((x3507==1)*(x9113>=11 & x9113<=13)) /// + +max(0,x3510)*((x3511==1)*(x9114>=11 & x9114<=13)) /// + +max(0,x3514)*((x3515==1)*(x9115>=11 & x9115<=13)) /// + +max(0,x3518)*((x3519==1)*(x9116>=11 & x9116<=13)) /// + +max(0,x3522)*((x3523==1)*(x9117>=11 & x9117<=13)) /// + +max(0,x3526)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3529)*((x3527==1)*(x9118>=11 & x9118<=13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259>=11 & x9259<=13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260>=11 & x9260<=13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261>=11 & x9261<=13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262>=11 & x9262<=13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263>=11 & x9263<=13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264>=11 & x9264<=13)) +gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// + +max(0,x3510)*((x3511==1)*(x9114<11 | x9114>13)) /// + +max(0,x3514)*((x3515==1)*(x9115<11 | x9115>13)) /// + +max(0,x3518)*((x3519==1)*(x9116<11 | x9116>13)) /// + +max(0,x3522)*((x3523==1)*(x9117<11 | x9117>13)) /// + +max(0,x3526)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3529)*((x3527==1)*(x9118<11 | x9118>13)) /// + +max(0,x3730*(x3732==4|x3732==30)*(x9259<11 | x9259>13)) /// + +max(0,x3736*(x3738==4|x3738==30)*(x9260<11 | x9260>13)) /// + +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// + +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// + +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// + +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen OMUTF = (x7785==1)*max(0,x7787) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF + OMUTF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 +gen BOND = NOTXBND + MORTBND + GOVTBND + OBND + gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 + gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// + +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// + +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// + +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// + +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// + +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) + gen PMOP = x11259 + replace PMOP = 0 if x11259<=0 + replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 + replace THRIFT = THRIFT + PMOP + replace PMOP = x11559 + replace PMOP = 0 if x11559<=0 + replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 + replace THRIFT = THRIFT + PMOP +gen RETQLIQ = IRAKH+THRIFT +gen SAVBND = x3902 +gen CASHLI = max(0,x4006) + gen ANNUIT = max(0,x6577) + gen TRUSTS = max(0,x6587) +gen OTHMA = ANNUIT+TRUSTS +gen OTHFIN = x4018 /// + +x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==65|x4020==66|x4020==71| /// + x4020==72|x4020==73|x4020==74|x4020==77|x4020==80|x4020==81|x4020==-7) /// + +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// + x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) +gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ + +gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// + +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) +replace x507 = 9000 if x507 > 9000 +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) + * replace HOUSES = (x7134/10000)*x716 if x701==-7 & x7133==1 +gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// + +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *max(0,x1706)*(x1705/10000) /// + +(x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *max(0,x1806)*(x1805/10000) /// + +(x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *max(0,x1906)*(x1905/10000) /// + +max(0,x2002) +gen NNRESRE = (x1703==1|x1703==2|x1703==3|x1703==4|x1703==5|x1703==6|x1703==7|x1703==10|x1703==11|x1703==13|x1703==15|x1703==24|x1703==45|x1703==46|x1703==47|x1703==48|x1703==51|x1703==-7) /// + *(max(0,x1706)*(x1705/10000)-x1715*(x1705/10000)) /// + +(x1803==1|x1803==2|x1803==3|x1803==4|x1803==5|x1803==6|x1803==7|x1803==10|x1803==11|x1803==13|x1803==15|x1803==24|x1803==45|x1803==46|x1803==47|x1803==48|x1803==51|x1803==-7) /// + *(max(0,x1806)*(x1805/10000)-x1815*(x1805/10000)) /// + +(x1903==1|x1903==2|x1903==3|x1903==4|x1903==5|x1903==6|x1903==7|x1903==10|x1903==11|x1903==13|x1903==15|x1903==24|x1903==45|x1903==46|x1903==47|x1903==48|x1903==51|x1903==-7) /// + *(max(0,x1906)*(x1905/10000)-x1915*(x1905/10000)) /// + +max(0,x2012)-x2016 +replace NNRESRE = NNRESRE-x2723*(x2710==78)-x2740*(x2727==78)-x2823*(x2810==78) /// + -x2840*(x2827==78)-x2923*(x2910==78)-x2940*(x2927==78) if NNRESRE!=0 +gen FLAG781 = (NNRESRE!=0) +gen BUS = max(0,x3129)+max(0,x3124)-max(0,x3126)*(x3127==5) /// + +max(0,x3121)*(x3122==1|x3122==6) /// + +max(0,x3229)+max(0,x3224)-max(0,x3226)*(x3227==5) /// + +max(0,x3221)*(x3222==1|x3222==6) /// + +max(0,x3329)+max(0,x3324)-max(0,x3326)*(x3327==5) /// + +max(0,x3321)*(x3322==1|x3322==6) /// + +max(0,x3335)+(x507/10000)*(x513+x526) /// + +max(0,x3408)+max(0,x3412)+max(0,x3416)+max(0,x3420) /// + +max(0,x3424)+max(0,x3428) +gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 +gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN +gen ASSET = FIN+NFIN /* Total asset */ + +* Debt +gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// + +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace MRTHEL = x805+x905+x1005+.5*(max(0,x1136)) if (x1108+x1119+x1130)<1 + gen MORT1 = (x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// + *x1715*(x1705/10000) + gen MORT2 = (x1803==12|x1803==14|x1803==21|x1803==22|x1803==25|x1803==40|x1803==41|x1803==42|x1803==43|x1803==44|x1803==49|x1803==50|x1803==52|x1803==999) /// + *x1815*(x1805/10000) + gen MORT3 = (x1903==12|x1903==14|x1903==21|x1903==22|x1903==25|x1903==40|x1903==41|x1903==42|x1903==43|x1903==44|x1903==49|x1903==50|x1903==52|x1903==999) /// + *x1915*(x1905/10000) +gen RESDBT = x1417+x1517+x1617+x1621+MORT1+MORT2+MORT3+x2006 + gen FLAG782 = (FLAG781!=1 & ORESRE>0) +replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2840*(x2827==78) /// + +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 + gen FLAG67 = (ORESRE>0) +replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 +gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// + +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// + +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) +gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// + +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 +replace INSTALL = INSTALL+x2723*(x2710==78)+x2740*(x2727==78) /// + +x2823*(x2810==78)+x2840*(x2827==78)+x2923*(x2910==78)+x2940*(x2927==78) /// + if FLAG781==0 & FLAG782==0 +replace INSTALL = INSTALL+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67) /// + +x2840*(x2827==67)+x2923*(x2910==67)+x2940*(x2927==67) if FLAG67==0 +replace INSTALL = INSTALL+x2723*(x2710!=67&x2710!=78)+x2740*(x2727!=67&x2727!=78) /// + +x2823*(x2810!=67&x2810!=78)+x2840*(x2827!=67&x2827!=78)+x2923*(x2910!=67&x2910!=78) /// + +x2940*(x2927!=67&x2927!=78) +gen PENDBT = max(0,x11027)*(x11070==5)+max(0,x11127)*(x11170==5) /// + +max(0,x11227)*(x11270==5)+max(0,x11327)*(x11370==5) /// + +max(0,x11427)*(x11470==5)+max(0,x11527)*(x11570==5) +gen CASHLIDB = max(0,x4010) +gen CALLDBT = max(0,x3932) +gen ODEBT = max(0,x4032) +gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ + +* Net worth +gen NETW = ASSET-DEBT +replace NETW = NETW*CPIADJ + +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME + + +* Demographic vars +gen AGE = x14 /* Age */ +gen MARITST = x8023 /* Marital status */ +keep if MARITST == 1 /* Keep if married following Cagetti(2003) */ + +gen EDUC = 0 +replace EDUC = 1 if x5902!=1 /* No high school deg */ +replace EDUC = 2 if x5902==1 /* High school deg */ +replace EDUC = 3 if x5904==1 /* College deg */ +* keep if EDUC == 3 /* Keep college graduates only */ + +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) +drop if INCOME<=0 +replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) + + +** Keep necessary vars +keep HHID YEAR WGT INCOME NETW WIRATIO AGE + +** Save data +cd "$startDir" +cd ../../Data/Constructed +save "./SCF$scfFldr$SuffixForConstructedFile", replace + +** End in the same directory you started from +cd "$startDir" diff --git a/code/stata/WIRatioPopulation.do b/src/stata/WIRatioPopulation.do similarity index 97% rename from code/stata/WIRatioPopulation.do rename to src/stata/WIRatioPopulation.do index 2882028..8c4fb3e 100644 --- a/code/stata/WIRatioPopulation.do +++ b/src/stata/WIRatioPopulation.do @@ -1,720 +1,720 @@ -/* This program gives the Summary statistics for Income, Net Worth and -Wealth/Income Ratio of the Married Households whose ages are between -31 and 55. -The program builts on the results obtained by doAll.do file, so run this -file after running the doAll.do file. -*/ - -cd $basePath/$stataPath - -cd ../../Data/Constructed - -*************************************************************************************************** -/* Specifies the list of percentiles of INCOME, NETW and WIRATIO. p50 represents 50th percentile: median. - Modify the list if you want to obtain results for different percentiles.*/ -global percentiles = "p50" - -scalar AgeRange1 = `"26-30"' -scalar AgeRange2 = `"31-35"' -scalar AgeRange3 = `"36-40"' -scalar AgeRange4 = `"41-45"' -scalar AgeRange5 = `"46-50"' -scalar AgeRange6 = `"51-55"' -scalar AgeRange7 = `"56-60"' - -*************************************************************************************************** -************************************* 1992 Survey Summary ****************************************** - -use $basePath/Data/Constructed/SCF1992_2007_population, clear -keep if YEAR == 1992 -xtset HHID -sort HHID -by HHID: gen OBS=_n -xtset HHID OBS - -egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ -egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ -replace AVGINC = AVGINC/AVGWGT - -egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ -replace AVGNETW = AVGNETW/AVGWGT - -gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ - -************************************** Age Range Selection ***************************** - -xtsum HHID -keep if OBS==1 -drop OBS - -keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ -gen AGEID = int((AGE-26)/5)+1 -xtset AGEID -sort AGEID HHID - -***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** - -/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained - using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. - (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) - which is based on after tax income.) - */ - - /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ - matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) -svmat RawMat -rename RawMat1 RAWIRATIO -rename RawMat2 RAWI - -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ -local N=_N - -qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ - replace RAWI = AVGINC[`i'] in 10 - ipolate RAWIRATIO RAWI, gen(TEMP) epolate - replace TXIRATIO = TEMP[10] in `i' - drop TEMP - } -replace RAWI =. in 10 -replace TXIRATIO = 1 if TXIRATIO < 1 - -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ - -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** - -qui: sum AGEID, d -local size=r(max) /* size is used as an index number in the following loops*/ - -gen AVGINCBYAGE = . -gen OBSBYAGE = . - -qui foreach p in $percentiles { - gen `p'INCBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d - replace AVGINCBYAGE = r(mean) if AGEID==`k' - replace `p'INCBYAGE = r(`p') if AGEID==`k' - replace OBSBYAGE = r(N) if AGEID==`k' - } - } - -gen AVGNETWBYAGE = . - -qui foreach p in $percentiles { - gen `p'NETWBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d - replace AVGNETWBYAGE = r(mean) if AGEID==`k' - replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - -gen AVGWIRATIOBYAGE = . - -qui foreach p in $percentiles { - gen `p'WIRATIOBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d - replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' - replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' - } - } - -gen AGERANGE= `""' -qui forvalues k=1/`size' { - replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ - } - -by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All1992", replace - -************************************* 1995 Survey Summary ****************************************** - -use $basePath/Data/Constructed/SCF1992_2007_population, clear -keep if YEAR == 1995 - -xtset HHID -sort HHID -by HHID: gen OBS=_n -xtset HHID OBS - -egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ -egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ -replace AVGINC = AVGINC/AVGWGT - -egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ -replace AVGNETW = AVGNETW/AVGWGT - -gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ - -************************************** Age Range Selection ***************************** - -xtsum HHID -keep if OBS==1 -drop OBS - -keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ -gen AGEID = int((AGE-26)/5)+1 -xtset AGEID -sort AGEID HHID - -***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** - -/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained - using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. - (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) - which is based on after tax income.) - */ - - /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ - matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) -svmat RawMat -rename RawMat1 RAWIRATIO -rename RawMat2 RAWI - -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ -local N=_N - -qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ - replace RAWI = AVGINC[`i'] in 10 - ipolate RAWIRATIO RAWI, gen(TEMP) epolate - replace TXIRATIO = TEMP[10] in `i' - drop TEMP - } -replace RAWI =. in 10 -replace TXIRATIO = 1 if TXIRATIO < 1 - -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ - -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** - -qui: sum AGEID, d -local size=r(max) /* size is used as an index number in the following loops*/ - -gen AVGINCBYAGE = . -gen OBSBYAGE = . - -qui foreach p in $percentiles { - gen `p'INCBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d - replace AVGINCBYAGE = r(mean) if AGEID==`k' - replace `p'INCBYAGE = r(`p') if AGEID==`k' - replace OBSBYAGE = r(N) if AGEID==`k' - } - } - -gen AVGNETWBYAGE = . - -qui foreach p in $percentiles { - gen `p'NETWBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d - replace AVGNETWBYAGE = r(mean) if AGEID==`k' - replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - -gen AVGWIRATIOBYAGE = . - -qui foreach p in $percentiles { - gen `p'WIRATIOBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d - replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' - replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' - } - } - -gen AGERANGE= `""' -qui forvalues k=1/`size' { - replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ - } - -by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All1995", replace - -************************************* 1998 Survey Summary ****************************************** - -use $basePath/Data/Constructed/SCF1992_2007_population, clear -keep if YEAR == 1998 - -xtset HHID -sort HHID -by HHID: gen OBS=_n -xtset HHID OBS - -egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ -egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ -replace AVGINC = AVGINC/AVGWGT - -egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ -replace AVGNETW = AVGNETW/AVGWGT - -gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ - -************************************** Age Range Selection ***************************** - -xtsum HHID -keep if OBS==1 -drop OBS - -keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ -gen AGEID = int((AGE-26)/5)+1 -xtset AGEID -sort AGEID HHID - -***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** - -/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained - using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. - (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) - which is based on after tax income.) - */ - - /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ - matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) -svmat RawMat -rename RawMat1 RAWIRATIO -rename RawMat2 RAWI - -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ -local N=_N - -qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ - replace RAWI = AVGINC[`i'] in 10 - ipolate RAWIRATIO RAWI, gen(TEMP) epolate - replace TXIRATIO = TEMP[10] in `i' - drop TEMP - } -replace RAWI =. in 10 -replace TXIRATIO = 1 if TXIRATIO < 1 - -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ - -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** - -qui: sum AGEID, d -local size=r(max) /* size is used as an index number in the following loops*/ - -gen AVGINCBYAGE = . -gen OBSBYAGE = . - -qui foreach p in $percentiles { - gen `p'INCBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d - replace AVGINCBYAGE = r(mean) if AGEID==`k' - replace `p'INCBYAGE = r(`p') if AGEID==`k' - replace OBSBYAGE = r(N) if AGEID==`k' - } - } - -gen AVGNETWBYAGE = . - -qui foreach p in $percentiles { - gen `p'NETWBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d - replace AVGNETWBYAGE = r(mean) if AGEID==`k' - replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - -gen AVGWIRATIOBYAGE = . - -qui foreach p in $percentiles { - gen `p'WIRATIOBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d - replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' - replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' - } - } - -gen AGERANGE= `""' -qui forvalues k=1/`size' { - replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ - } - -by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All1998", replace - -************************************* 2001 Survey Summary ****************************************** - -use $basePath/Data/Constructed/SCF1992_2007_population, clear -keep if YEAR == 2001 - -xtset HHID -sort HHID -by HHID: gen OBS=_n -xtset HHID OBS - -egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ -egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ -replace AVGINC = AVGINC/AVGWGT - -egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ -replace AVGNETW = AVGNETW/AVGWGT - -gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ - -************************************** Age Range Selection ***************************** - -xtsum HHID -keep if OBS==1 -drop OBS - -keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ -gen AGEID = int((AGE-26)/5)+1 -xtset AGEID -sort AGEID HHID - -***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** - -/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained - using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. - (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) - which is based on after tax income.) - */ - - /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ - matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) -svmat RawMat -rename RawMat1 RAWIRATIO -rename RawMat2 RAWI - -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ -local N=_N - -qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ - replace RAWI = AVGINC[`i'] in 10 - ipolate RAWIRATIO RAWI, gen(TEMP) epolate - replace TXIRATIO = TEMP[10] in `i' - drop TEMP - } -replace RAWI =. in 10 -replace TXIRATIO = 1 if TXIRATIO < 1 - -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ - -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** - -qui: sum AGEID, d -local size=r(max) /* size is used as an index number in the following loops*/ - -gen AVGINCBYAGE = . -gen OBSBYAGE = . - -qui foreach p in $percentiles { - gen `p'INCBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d - replace AVGINCBYAGE = r(mean) if AGEID==`k' - replace `p'INCBYAGE = r(`p') if AGEID==`k' - replace OBSBYAGE = r(N) if AGEID==`k' - } - } - -gen AVGNETWBYAGE = . - -qui foreach p in $percentiles { - gen `p'NETWBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d - replace AVGNETWBYAGE = r(mean) if AGEID==`k' - replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - -gen AVGWIRATIOBYAGE = . - -qui foreach p in $percentiles { - gen `p'WIRATIOBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d - replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' - replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' - } - } - -gen AGERANGE= `""' -qui forvalues k=1/`size' { - replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ - } - -by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All2001", replace - -************************************* 2004 Survey Summary ****************************************** - -use $basePath/Data/Constructed/SCF1992_2007_population, clear -keep if YEAR == 2004 - -xtset HHID -sort HHID -by HHID: gen OBS=_n -xtset HHID OBS - -egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ -egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ -replace AVGINC = AVGINC/AVGWGT - -egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ -replace AVGNETW = AVGNETW/AVGWGT - -gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ - -************************************** Age Range Selection ***************************** - -xtsum HHID -keep if OBS==1 -drop OBS - -keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ -gen AGEID = int((AGE-26)/5)+1 -xtset AGEID -sort AGEID HHID - -***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** - -/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained - using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. - (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) - which is based on after tax income.) - */ - - /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ - matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) -svmat RawMat -rename RawMat1 RAWIRATIO -rename RawMat2 RAWI - -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ -local N=_N - -qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ - replace RAWI = AVGINC[`i'] in 10 - ipolate RAWIRATIO RAWI, gen(TEMP) epolate - replace TXIRATIO = TEMP[10] in `i' - drop TEMP - } -replace RAWI =. in 10 -replace TXIRATIO = 1 if TXIRATIO < 1 - -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ - -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** - -qui: sum AGEID, d -local size=r(max) /* size is used as an index number in the following loops*/ - -gen AVGINCBYAGE = . -gen OBSBYAGE = . - -qui foreach p in $percentiles { - gen `p'INCBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d - replace AVGINCBYAGE = r(mean) if AGEID==`k' - replace `p'INCBYAGE = r(`p') if AGEID==`k' - replace OBSBYAGE = r(N) if AGEID==`k' - } - } - -gen AVGNETWBYAGE = . - -qui foreach p in $percentiles { - gen `p'NETWBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d - replace AVGNETWBYAGE = r(mean) if AGEID==`k' - replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - -gen AVGWIRATIOBYAGE = . - -qui foreach p in $percentiles { - gen `p'WIRATIOBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d - replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' - replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' - } - } - -gen AGERANGE= `""' -qui forvalues k=1/`size' { - replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ - } - -by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All2004", replace - -************************************* 2007 Survey Summary ****************************************** - -use $basePath/Data/Constructed/SCF1992_2007_population, clear -keep if YEAR == 2007 - -xtset HHID -sort HHID -by HHID: gen OBS=_n -xtset HHID OBS - -egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ -egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ -replace AVGINC = AVGINC/AVGWGT - -egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ -replace AVGNETW = AVGNETW/AVGWGT - -gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ - -************************************** Age Range Selection ***************************** - -xtsum HHID -keep if OBS==1 -drop OBS - -keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ -gen AGEID = int((AGE-26)/5)+1 -xtset AGEID -sort AGEID HHID - -***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** - -/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained - using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. - (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) - which is based on after tax income.) - */ - - /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ - matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) -svmat RawMat -rename RawMat1 RAWIRATIO -rename RawMat2 RAWI - -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ -local N=_N - -qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ - replace RAWI = AVGINC[`i'] in 10 - ipolate RAWIRATIO RAWI, gen(TEMP) epolate - replace TXIRATIO = TEMP[10] in `i' - drop TEMP - } -replace RAWI =. in 10 -replace TXIRATIO = 1 if TXIRATIO < 1 - -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ - -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** - -qui: sum AGEID, d -local size=r(max) /* size is used as an index number in the following loops*/ - -gen AVGINCBYAGE = . -gen OBSBYAGE = . - -qui foreach p in $percentiles { - gen `p'INCBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d - replace AVGINCBYAGE = r(mean) if AGEID==`k' - replace `p'INCBYAGE = r(`p') if AGEID==`k' - replace OBSBYAGE = r(N) if AGEID==`k' - } - } - -gen AVGNETWBYAGE = . - -qui foreach p in $percentiles { - gen `p'NETWBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d - replace AVGNETWBYAGE = r(mean) if AGEID==`k' - replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - -gen AVGWIRATIOBYAGE = . - -qui foreach p in $percentiles { - gen `p'WIRATIOBYAGE = . - qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d - replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' - replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' - } - } - -gen AGERANGE= `""' -qui forvalues k=1/`size' { - replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ - } - -by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All2007", replace - -************************************ 2001-2007 Population WIRATIO , AGEID and WEIGHT ******************************* -cd $basePath/Data/Constructed/ - -use All2007, clear -append using All2004 -append using All2001 -append using All1998 -append using All1995 -append using All1992 - -keep HHID YEAR AGEID AGERANGE AVGWIRATIO AVGWGT -sort AGEID AVGWIRATIO - -bysort AGEID: gen N=_N -egen SUMAVGWGT = sum(AVGWGT), by(AGEID) -gen WGTPOP = (AVGWGT/SUMAVGWGT)*N -gen WIRATIOPOP = AVGWIRATIO - -order WIRATIOPOP AGEID WGTPOP -keep WIRATIOPOP AGEID WGTPOP - -save "./WIRATIO_Population", replace -save"./SCFdata",replace - -cd $basePath -outfile using "./Code/Mathematica/StructuralEstimation/SCFdata.txt", replace - -************************************************************************************************** - -cd $basePath/$stataPath /* When program ends, make sure working directory is the program's directory */ +/* This program gives the Summary statistics for Income, Net Worth and +Wealth/Income Ratio of the Married Households whose ages are between +31 and 55. +The program builts on the results obtained by doAll.do file, so run this +file after running the doAll.do file. +*/ + +cd $basePath/$stataPath + +cd ../../Data/Constructed + +*************************************************************************************************** +/* Specifies the list of percentiles of INCOME, NETW and WIRATIO. p50 represents 50th percentile: median. + Modify the list if you want to obtain results for different percentiles.*/ +global percentiles = "p50" + +scalar AgeRange1 = `"26-30"' +scalar AgeRange2 = `"31-35"' +scalar AgeRange3 = `"36-40"' +scalar AgeRange4 = `"41-45"' +scalar AgeRange5 = `"46-50"' +scalar AgeRange6 = `"51-55"' +scalar AgeRange7 = `"56-60"' + +*************************************************************************************************** +************************************* 1992 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 1992 +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All1992", replace + +************************************* 1995 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 1995 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All1995", replace + +************************************* 1998 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 1998 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All1998", replace + +************************************* 2001 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 2001 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All2001", replace + +************************************* 2004 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 2004 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All2004", replace + +************************************* 2007 Survey Summary ****************************************** + +use $basePath/Data/Constructed/SCF1992_2007_population, clear +keep if YEAR == 2007 + +xtset HHID +sort HHID +by HHID: gen OBS=_n +xtset HHID OBS + +egen AVGWGT = sum(WGT), by(HHID) /* Generates one Weight for each Household */ +egen AVGINC = sum(INCOME*WGT), by(HHID) /* This line and the following line Generate the Average Income for each Household */ +replace AVGINC = AVGINC/AVGWGT + +egen AVGNETW = sum(NETW*WGT), by(HHID) /* This line and the following line Generate the Average Net Worth for each Household */ +replace AVGNETW = AVGNETW/AVGWGT + +gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income Ratio for each Household */ + +************************************** Age Range Selection ***************************** + +xtsum HHID +keep if OBS==1 +drop OBS + +keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ +gen AGEID = int((AGE-26)/5)+1 +xtset AGEID +sort AGEID HHID + +***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** + +/* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. + (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) + which is based on after tax income.) + */ + + /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ + matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) +svmat RawMat +rename RawMat1 RAWIRATIO +rename RawMat2 RAWI + +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +local N=_N + +qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ + replace RAWI = AVGINC[`i'] in 10 + ipolate RAWIRATIO RAWI, gen(TEMP) epolate + replace TXIRATIO = TEMP[10] in `i' + drop TEMP + } +replace RAWI =. in 10 +replace TXIRATIO = 1 if TXIRATIO < 1 + +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** + +qui: sum AGEID, d +local size=r(max) /* size is used as an index number in the following loops*/ + +gen AVGINCBYAGE = . +gen OBSBYAGE = . + +qui foreach p in $percentiles { + gen `p'INCBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + replace AVGINCBYAGE = r(mean) if AGEID==`k' + replace `p'INCBYAGE = r(`p') if AGEID==`k' + replace OBSBYAGE = r(N) if AGEID==`k' + } + } + +gen AVGNETWBYAGE = . + +qui foreach p in $percentiles { + gen `p'NETWBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + replace AVGNETWBYAGE = r(mean) if AGEID==`k' + replace `p'NETWBYAGE = r(`p') if AGEID==`k' + } + } + +gen AVGWIRATIOBYAGE = . + +qui foreach p in $percentiles { + gen `p'WIRATIOBYAGE = . + qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' + replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' + } + } + +gen AGERANGE= `""' +qui forvalues k=1/`size' { + replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ + } + +by AGEID: gen OBS=_n +save "$basePath/Data/Constructed/All2007", replace + +************************************ 2001-2007 Population WIRATIO , AGEID and WEIGHT ******************************* +cd $basePath/Data/Constructed/ + +use All2007, clear +append using All2004 +append using All2001 +append using All1998 +append using All1995 +append using All1992 + +keep HHID YEAR AGEID AGERANGE AVGWIRATIO AVGWGT +sort AGEID AVGWIRATIO + +bysort AGEID: gen N=_N +egen SUMAVGWGT = sum(AVGWGT), by(AGEID) +gen WGTPOP = (AVGWGT/SUMAVGWGT)*N +gen WIRATIOPOP = AVGWIRATIO + +order WIRATIOPOP AGEID WGTPOP +keep WIRATIOPOP AGEID WGTPOP + +save "./WIRATIO_Population", replace +save"./SCFdata",replace + +cd $basePath +outfile using "./Code/Mathematica/StructuralEstimation/SCFdata.txt", replace + +************************************************************************************************** + +cd $basePath/$stataPath /* When program ends, make sure working directory is the program's directory */ diff --git a/code/stata/doAll.do b/src/stata/doAll.do similarity index 100% rename from code/stata/doAll.do rename to src/stata/doAll.do diff --git a/code/tests.py b/src/tests.py similarity index 100% rename from code/tests.py rename to src/tests.py From 78b5bb5ee8b7c6d98d87c865f3cfb588dcd07e5e Mon Sep 17 00:00:00 2001 From: alanlujan91 Date: Fri, 20 Sep 2024 15:24:57 -0400 Subject: [PATCH 3/7] improved packaging --- .copier-answers.yml | 13 + .git_archival.txt | 3 + .gitattributes | 1 + .github/CONTRIBUTING.md | 89 + .github/dependabot.yml | 11 + .github/release.yml | 5 + .github/workflows/cd.yml | 60 + .github/workflows/ci.yml | 71 + .gitignore | 29 +- .pre-commit-config.yaml | 89 + .readthedocs.yaml | 17 + EstimatingMicroDSOPs.Rproj | 13 - LICENSE | 19 + README.md | 53 +- .../tables/TRP/Portfolio_estimate_results.csv | 282 ++-- .../TRP/WealthPortfolio_estimate_results.csv | 1451 +++++++---------- docs/conf.py | 64 + docs/index.md | 17 + environment.yml | 2 +- noxfile.py | 107 ++ pyproject.toml | 157 ++ reproduce.sh | 2 +- src/estimark/__init__.py | 11 + src/estimark/_version.pyi | 4 + src/estimark/py.typed | 0 src/notebooks/Model_Comparisons.ipynb | 83 +- src/notebooks/Portfolio.ipynb | 50 +- src/notebooks/WarmGlowPortfolio.ipynb | 16 +- src/notebooks/WealthPortfolio.ipynb | 16 +- src/notebooks/median_share.pdf | Bin 17105 -> 17055 bytes src/notebooks/median_share.svg | 274 ++-- src/notebooks/median_wealth.pdf | Bin 18371 -> 18241 bytes src/notebooks/median_wealth.svg | 555 ++++--- src/setup.py | 12 - tests/test_package.py | 9 + 35 files changed, 1978 insertions(+), 1607 deletions(-) create mode 100644 .copier-answers.yml create mode 100644 .git_archival.txt create mode 100644 .gitattributes create mode 100644 .github/CONTRIBUTING.md create mode 100644 .github/dependabot.yml create mode 100644 .github/release.yml create mode 100644 .github/workflows/cd.yml create mode 100644 .github/workflows/ci.yml create mode 100644 .pre-commit-config.yaml create mode 100644 .readthedocs.yaml delete mode 100644 EstimatingMicroDSOPs.Rproj create mode 100644 LICENSE create mode 100644 docs/conf.py create mode 100644 docs/index.md create mode 100644 noxfile.py create mode 100644 pyproject.toml create mode 100644 src/estimark/_version.pyi create mode 100644 src/estimark/py.typed delete mode 100644 src/setup.py create mode 100644 tests/test_package.py diff --git a/.copier-answers.yml b/.copier-answers.yml new file mode 100644 index 0000000..0589b90 --- /dev/null +++ b/.copier-answers.yml @@ -0,0 +1,13 @@ +# Changes here will be overwritten by Copier; NEVER EDIT MANUALLY +_commit: 2024.08.19 +_src_path: gh:scientific-python/cookie +backend: hatch +email: alanlujan91@gmail.com +full_name: Alan Lujan +license: MIT +org: econ-ark +project_name: estimark +project_short_description: Estimating Microeconomic Dynamic Stochastic Optimization + Problems +url: https://github.com/econ-ark/EstimatingMicroDSOPs +vcs: true diff --git a/.git_archival.txt b/.git_archival.txt new file mode 100644 index 0000000..7c51009 --- /dev/null +++ b/.git_archival.txt @@ -0,0 +1,3 @@ +node: $Format:%H$ +node-date: $Format:%cI$ +describe-name: $Format:%(describe:tags=true,match=*[0-9]*)$ diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..00a7b00 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +.git_archival.txt export-subst diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 0000000..0c1bfb4 --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,89 @@ +See the [Scientific Python Developer Guide][spc-dev-intro] for a detailed +description of best practices for developing scientific packages. + +[spc-dev-intro]: https://learn.scientific-python.org/development/ + +# Quick development + +The fastest way to start with development is to use nox. If you don't have nox, +you can use `pipx run nox` to run it without installing, or `pipx install nox`. +If you don't have pipx (pip for applications), then you can install with +`pip install pipx` (the only case were installing an application with regular +pip is reasonable). If you use macOS, then pipx and nox are both in brew, use +`brew install pipx nox`. + +To use, run `nox`. This will lint and test using every installed version of +Python on your system, skipping ones that are not installed. You can also run +specific jobs: + +```console +$ nox -s lint # Lint only +$ nox -s tests # Python tests +$ nox -s docs -- --serve # Build and serve the docs +$ nox -s build # Make an SDist and wheel +``` + +Nox handles everything for you, including setting up an temporary virtual +environment for each run. + +# Setting up a development environment manually + +You can set up a development environment by running: + +```bash +python3 -m venv .venv +source ./.venv/bin/activate +pip install -v -e .[dev] +``` + +If you have the +[Python Launcher for Unix](https://github.com/brettcannon/python-launcher), you +can instead do: + +```bash +py -m venv .venv +py -m install -v -e .[dev] +``` + +# Pre-commit + +You should prepare pre-commit, which will help you by checking that commits pass +required checks: + +```bash +pip install pre-commit # or brew install pre-commit on macOS +pre-commit install # Will install a pre-commit hook into the git repo +``` + +You can also/alternatively run `pre-commit run` (changes only) or +`pre-commit run --all-files` to check even without installing the hook. + +# Testing + +Use pytest to run the unit checks: + +```bash +pytest +``` + +# Coverage + +Use pytest-cov to generate coverage reports: + +```bash +pytest --cov=estimark +``` + +# Building docs + +You can build the docs using: + +```bash +nox -s docs +``` + +You can see a preview with: + +```bash +nox -s docs -- --serve +``` diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..6c4b369 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,11 @@ +version: 2 +updates: + # Maintain dependencies for GitHub Actions + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + groups: + actions: + patterns: + - "*" diff --git a/.github/release.yml b/.github/release.yml new file mode 100644 index 0000000..9d1e098 --- /dev/null +++ b/.github/release.yml @@ -0,0 +1,5 @@ +changelog: + exclude: + authors: + - dependabot + - pre-commit-ci diff --git a/.github/workflows/cd.yml b/.github/workflows/cd.yml new file mode 100644 index 0000000..efc7d06 --- /dev/null +++ b/.github/workflows/cd.yml @@ -0,0 +1,60 @@ +name: CD + +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + release: + types: + - published + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + # Many color libraries just need this to be set to any value, but at least + # one distinguishes color depth, where "3" -> "256-bit color". + FORCE_COLOR: 3 + +jobs: + dist: + name: Distribution build + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: hynek/build-and-inspect-python-package@v2 + + publish: + needs: [dist] + name: Publish to PyPI + environment: pypi + permissions: + id-token: write + attestations: write + contents: read + runs-on: ubuntu-latest + if: github.event_name == 'release' && github.event.action == 'published' + + steps: + - uses: actions/download-artifact@v4 + with: + name: Packages + path: dist + + - name: Generate artifact attestation for sdist and wheel + uses: actions/attest-build-provenance@v1.4.1 + with: + subject-path: "dist/*" + + - uses: pypa/gh-action-pypi-publish@release/v1 + with: + # Remember to tell (test-)pypi about this repo before publishing + # Remove this line to publish to PyPI + repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..98cc7c2 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,71 @@ +name: CI + +on: + workflow_dispatch: + pull_request: + push: + branches: + - main + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + # Many color libraries just need this to be set to any value, but at least + # one distinguishes color depth, where "3" -> "256-bit color". + FORCE_COLOR: 3 + +jobs: + pre-commit: + name: Format + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + - uses: pre-commit/action@v3.0.1 + with: + extra_args: --hook-stage manual --all-files + - name: Run PyLint + run: pipx run nox -s pylint -- --output-format=github + + checks: + name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }} + runs-on: ${{ matrix.runs-on }} + needs: [pre-commit] + strategy: + fail-fast: false + matrix: + python-version: ["3.8", "3.13"] + runs-on: [ubuntu-latest, windows-latest, macos-14] + + include: + - python-version: "pypy-3.10" + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + allow-prereleases: true + + - name: Install package + run: python -m pip install .[test] + + - name: Test package + run: >- + python -m pytest -ra --cov --cov-report=xml --cov-report=term + --durations=20 + + - name: Upload coverage report + uses: codecov/codecov-action@v4.5.0 + with: + token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.gitignore b/.gitignore index 1f1e676..7578f0d 100644 --- a/.gitignore +++ b/.gitignore @@ -137,18 +137,25 @@ dmypy.json # Cython debug symbols cython_debug/ -.vscode/* +# setuptools_scm +src/*/_version.py -# Local History for Visual Studio Code -.history/ -# Built Visual Studio Code Extensions -*.vsix -.idea/* +# ruff +.ruff_cache/ -.ipynb_checkpoints +# OS specific stuff +.DS_Store +.DS_Store? +._* +.Spotlight-V100 +.Trashes +ehthumbs.db +Thumbs.db + +# Common editor files +*~ +*.swp + +# MyST build outputs _build -.Rproj.user -*.Rproj -*.Rhistory -*.dta diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..90f6435 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,89 @@ +ci: + autoupdate_commit_msg: "chore: update pre-commit hooks" + autofix_commit_msg: "style: pre-commit fixes" + +exclude: ^.cruft.json|.copier-answers.yml$ + +repos: + - repo: https://github.com/adamchainz/blacken-docs + rev: "1.18.0" + hooks: + - id: blacken-docs + additional_dependencies: [black==24.*] + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: "v4.6.0" + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-merge-conflict + - id: check-symlinks + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + - id: name-tests-test + args: ["--pytest-test-first"] + - id: requirements-txt-fixer + - id: trailing-whitespace + + - repo: https://github.com/pre-commit/pygrep-hooks + rev: "v1.10.0" + hooks: + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal + + - repo: https://github.com/rbubley/mirrors-prettier + rev: "v3.3.3" + hooks: + - id: prettier + types_or: [yaml, markdown, html, css, scss, javascript, json] + args: [--prose-wrap=always] + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.6.1" + hooks: + - id: ruff + args: ["--fix", "--show-fixes"] + - id: ruff-format + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: "v1.11.1" + hooks: + - id: mypy + files: src|tests + args: [] + additional_dependencies: + - pytest + + - repo: https://github.com/codespell-project/codespell + rev: "v2.3.0" + hooks: + - id: codespell + + - repo: https://github.com/shellcheck-py/shellcheck-py + rev: "v0.10.0.1" + hooks: + - id: shellcheck + + - repo: local + hooks: + - id: disallow-caps + name: Disallow improper capitalization + language: pygrep + entry: PyBind|Numpy|Cmake|CCache|Github|PyTest + exclude: .pre-commit-config.yaml + + - repo: https://github.com/abravalheri/validate-pyproject + rev: "v0.19" + hooks: + - id: validate-pyproject + additional_dependencies: ["validate-pyproject-schema-store[all]"] + + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: "0.29.1" + hooks: + - id: check-dependabot + - id: check-github-workflows + - id: check-readthedocs diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..67c194c --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,17 @@ +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +version: 2 + +build: + os: ubuntu-22.04 + tools: + python: "3.12" + commands: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + - uv venv + - uv pip install .[docs] + - .venv/bin/python -m sphinx -T -b html -d docs/_build/doctrees -D + language=en docs $READTHEDOCS_OUTPUT/html diff --git a/EstimatingMicroDSOPs.Rproj b/EstimatingMicroDSOPs.Rproj deleted file mode 100644 index 8e3c2eb..0000000 --- a/EstimatingMicroDSOPs.Rproj +++ /dev/null @@ -1,13 +0,0 @@ -Version: 1.0 - -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: Sweave -LaTeX: pdfLaTeX diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..a438ea6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,19 @@ +Copyright 2024 Alan Lujan + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 10a265e..ff45258 100644 --- a/README.md +++ b/README.md @@ -1,38 +1,27 @@ -# EstimatingMicroDSOPs +# estimark -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/EstimatingMicroDSOPs/HEAD) +[![Actions Status][actions-badge]][actions-link] +[![Documentation Status][rtd-badge]][rtd-link] -To reproduces all the results in the repository first clone this repository locally: +[![PyPI version][pypi-version]][pypi-link] +[![Conda-Forge][conda-badge]][conda-link] +[![PyPI platforms][pypi-platforms]][pypi-link] -``` -# Clone this repository -$ git clone https://github.com/econ-ark/EstimatingMicroDSOPs +[![GitHub Discussion][github-discussions-badge]][github-discussions-link] -# Change working directory to EstimatingMicroDSOPs -$ cd EstimatingMicroDSOPs -``` + -Then you can either use a local virtual env(conda) or [nbreproduce](https://github.com/econ-ark/nbreproduce) to -reproduce to the results. - -#### A local conda environment and execute the do_all.py file. - -``` -$ conda env create -f environment.yml -$ conda activate estimatingmicrodsops -# execute the script, select the appropriate option and use it to reproduce the data and figures. -$ python do_all.py -``` - -#### [nbreproduce](https://github.com/econ-ark/nbreproduce) (requires Docker to be installed on the machine). - -``` -# Install nbreproduce -$ pip install nbreproduce - -# Reproduce all results using nbreproduce -$ nbreproduce -``` - -## References + +[actions-badge]: https://github.com/econ-ark/EstimatingMicroDSOPs/workflows/CI/badge.svg +[actions-link]: https://github.com/econ-ark/EstimatingMicroDSOPs/actions +[conda-badge]: https://img.shields.io/conda/vn/conda-forge/estimark +[conda-link]: https://github.com/conda-forge/estimark-feedstock +[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github +[github-discussions-link]: https://github.com/econ-ark/EstimatingMicroDSOPs/discussions +[pypi-link]: https://pypi.org/project/estimark/ +[pypi-platforms]: https://img.shields.io/pypi/pyversions/estimark +[pypi-version]: https://img.shields.io/pypi/v/estimark +[rtd-badge]: https://readthedocs.org/projects/estimark/badge/?version=latest +[rtd-link]: https://estimark.readthedocs.io/en/latest/?badge=latest + diff --git a/content/tables/TRP/Portfolio_estimate_results.csv b/content/tables/TRP/Portfolio_estimate_results.csv index dbb6cf4..4372856 100644 --- a/content/tables/TRP/Portfolio_estimate_results.csv +++ b/content/tables/TRP/Portfolio_estimate_results.csv @@ -1,9 +1,9 @@ -CRRA,9.252342476844415 -time_to_estimate,59.81896948814392 -params,{'CRRA': 9.252342476844415} -criterion,0.6423583236273489 -start_criterion,0.6339645453827032 -start_params,{'CRRA': 9.252398949005967} +CRRA,9.252286005027539 +time_to_estimate,60.753241539001465 +params,{'CRRA': 9.252286005027539} +criterion,0.6423582605057705 +start_criterion,0.6339648081630582 +start_params,{'CRRA': 9.252342476844415} algorithm,multistart_tranquilo_ls direction,minimize n_free,1 @@ -12,26 +12,26 @@ success, n_criterion_evaluations, n_derivative_evaluations, n_iterations, -history,"{'params': [{'CRRA': 9.252398949005968}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 9.250471173613674}, {'CRRA': 8.78977900155567}, {'CRRA': 9.23120907149584}, {'CRRA': 9.238513428928496}, {'CRRA': 9.191477678194621}, {'CRRA': 9.310226442437255}, {'CRRA': 9.25067493918715}, {'CRRA': 9.250726122006544}, {'CRRA': 9.249064681404075}, {'CRRA': 9.253920740493475}, {'CRRA': 9.256013167345424}, {'CRRA': 9.254206058175695}, {'CRRA': 9.251495394421104}, {'CRRA': 9.251947171713537}, {'CRRA': 9.252624837652185}, {'CRRA': 9.252511893329077}, {'CRRA': 9.252342476844415}], 'criterion': [0.6423583869781233, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6423691326265972, 0.6492941434834837, 0.6423764030742694, 0.6423752558032019, 0.6425217924878079, 0.6425146733526689, 0.6423685181986346, 0.6423683643642798, 0.6423801867774521, 0.6423693080646328, 0.6423866017619942, 0.6423727772903676, 0.6423636962874574, 0.6423598024158105, 0.6423594854558661, 0.6423587872768556, 0.6423583236273489], 'runtime': [0.0, 3.3926007600093726, 3.6385346090246458, 3.8182367410045117, 4.073105274001136, 4.283541666023666, 4.494341033016099, 4.736494988028426, 4.96336116202292, 5.179848953004694, 5.533184596017236, 5.69610069601913, 5.942016065004282, 21.336237280018395, 22.686877734027803, 24.069558680028422, 25.560004253027728, 26.859713567013387, 28.15134494501399, 29.419637827028055, 30.714213685016148, 32.14109974101302, 33.454856365016894, 34.775876878004055, 36.0627207980142, 37.38446952102822, 38.785011164000025, 40.11220667202724, 41.4234611930151, 42.74076091501047, 44.06118227602565], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" +history,"{'params': [{'CRRA': 9.252342476844415}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 9.250458955049714}, {'CRRA': 8.789725353002193}, {'CRRA': 9.231195795349796}, {'CRRA': 9.23850082029189}, {'CRRA': 9.191470898591822}, {'CRRA': 9.310169617324693}, {'CRRA': 9.250657961285336}, {'CRRA': 9.250710524700677}, {'CRRA': 9.248963361277593}, {'CRRA': 9.253968442160518}, {'CRRA': 9.255956673124432}, {'CRRA': 9.254149574984424}, {'CRRA': 9.25143892777441}, {'CRRA': 9.251890702309414}, {'CRRA': 9.252568364111916}, {'CRRA': 9.252455420478165}, {'CRRA': 9.252286005027539}], 'criterion': [0.6423583236273489, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.642369169571296, 0.6492959495923358, 0.6423762404892832, 0.6423752797264956, 0.6425219143922716, 0.642514400144132, 0.6423685692715722, 0.6423684112220268, 0.6423805942108727, 0.6423699960365742, 0.6423863674367805, 0.6423725213049074, 0.6423641176751165, 0.6423603114677403, 0.6423590691348976, 0.642358466533662, 0.6423582605057704], 'runtime': [0.0, 3.3893006040002547, 3.7743795520000276, 3.9942729670001427, 4.308895564000068, 4.500098180999885, 4.797666575000221, 5.097781724000015, 5.30452481400016, 5.586473449000096, 5.8145816500000365, 6.019138186999953, 6.258510488999946, 21.4172058070003, 22.745588674999908, 24.07027555800005, 25.37667203000001, 26.848738280999896, 28.141902802999994, 29.463674151000305, 30.76145256100017, 32.056981691000146, 33.34122019300003, 34.749019994000264, 36.131662315000085, 37.520465677000175, 38.87502066600018, 40.19993009200016, 41.659364199000265, 43.079727706000085, 44.41924671800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" convergence_report, -multistart_info,"{'start_parameters': [{'CRRA': 9.252398949005968}], 'local_optima': [Minimize with 1 free parameters terminated. +multistart_info,"{'start_parameters': [{'CRRA': 9.252342476844415}], 'local_optima': [Minimize with 1 free parameters terminated. The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 9.862e-08* 9.862e-08* +relative_criterion_change 9.827e-08* 9.827e-08* relative_params_change 6.104e-06* 6.104e-06* -absolute_criterion_change 6.335e-08* 6.335e-08* +absolute_criterion_change 6.312e-08* 6.312e-08* absolute_params_change 5.647e-05 5.647e-05 -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252398949005967}, {'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.64235839, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252342476844415}, {'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.64235832, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895]), radius=0.9252398949005969, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583869781233, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252398949005969, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], +algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583236273489, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -79,10 +79,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=0, candidate_x=array([9.25239895]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.25239895]), radius=0.9252398949005969, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.6366130167243159, linear_terms=array([0.00015204]), square_terms=array([[0.07297192]]), scale=0.9252398949005969, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=0, candidate_x=array([9.25234248]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.6366126588198241, linear_terms=array([0.00014855]), square_terms=array([[0.07297151]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -130,10 +130,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=14, candidate_x=array([9.25047117]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-67.84262787184853, accepted=False, new_indices=array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.46261994745029844, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.6391740801217988, linear_terms=array([0.00084041]), square_terms=array([[0.01834804]]), scale=0.46261994745029844, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=14, candidate_x=array([9.25045896]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-71.73087780998843, accepted=False, new_indices=array([ 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.46261712384222076, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.6391738833063486, linear_terms=array([0.0008387]), square_terms=array([[0.01834796]]), scale=0.46261712384222076, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -181,10 +181,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=16, candidate_x=array([9.23120907]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-0.9360366242557278, accepted=False, new_indices=array([15]), old_indices_used=array([ 0, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.23130997372514922, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.640036605619869, linear_terms=array([0.00027282]), square_terms=array([[0.0045447]]), scale=0.23130997372514922, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=16, candidate_x=array([9.2311958]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.9346804797475832, accepted=False, new_indices=array([15]), old_indices_used=array([ 0, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.23130856192111038, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.6400364245764906, linear_terms=array([0.00027196]), square_terms=array([[0.00454468]]), scale=0.23130856192111038, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -232,10 +232,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=17, candidate_x=array([9.23851343]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-2.0600316699171524, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.11565498686257461, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6424111679010795, linear_terms=array([0.00061553]), square_terms=array([[0.00116854]]), scale=0.11565498686257461, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=17, candidate_x=array([9.23850082]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-2.083816827569783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.11565428096055519, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6424109502570059, linear_terms=array([0.00061503]), square_terms=array([[0.00116854]]), scale=0.11565428096055519, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -283,10 +283,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=18, candidate_x=array([9.19147768]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-1.0079617617718157, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.057827493431287305, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19]), model=ScalarModel(intercept=0.6423642263710555, linear_terms=array([8.47856679e-06]), square_terms=array([[0.00028439]]), scale=0.057827493431287305, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=18, candidate_x=array([9.1914709]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-1.0107374685291592, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.057827140480277595, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19]), model=ScalarModel(intercept=0.6423641764261762, linear_terms=array([8.28433083e-06]), square_terms=array([[0.00028439]]), scale=0.057827140480277595, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -334,10 +334,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=20, candidate_x=array([9.25067494]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-80.16115218067975, accepted=False, new_indices=array([19]), old_indices_used=array([ 0, 14, 16, 17]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.028913746715643653, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.6423649815517477, linear_terms=array([4.11261778e-06]), square_terms=array([[7.10839727e-05]]), scale=0.028913746715643653, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=20, candidate_x=array([9.25065796]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-84.9118930196347, accepted=False, new_indices=array([19]), old_indices_used=array([ 0, 14, 16, 17]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.028913570240138797, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.642364947951701, linear_terms=array([4.01212888e-06]), square_terms=array([[7.10835612e-05]]), scale=0.028913570240138797, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -385,10 +385,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=21, candidate_x=array([9.25072612]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-83.86519702867479, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.014456873357821826, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 20, 21]), model=ScalarModel(intercept=0.6423665985864432, linear_terms=array([4.15685709e-06]), square_terms=array([[1.80234953e-05]]), scale=0.014456873357821826, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=21, candidate_x=array([9.25071052]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-89.09165927837773, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.014456785120069399, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 20, 21]), model=ScalarModel(intercept=0.6423666277820237, linear_terms=array([4.21338439e-06]), square_terms=array([[1.80260164e-05]]), scale=0.014456785120069399, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -436,10 +436,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=22, candidate_x=array([9.24906468]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-45.476949115574584, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 20, 21]), old_indices_discarded=array([18, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.007228436678910913, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 17, 20, 21, 22]), model=ScalarModel(intercept=0.6423679396996458, linear_terms=array([-9.52115187e-07]), square_terms=array([[4.52250154e-06]]), scale=0.007228436678910913, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=22, candidate_x=array([9.24896336]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-45.22714683487297, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 20, 21]), old_indices_discarded=array([18, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.007228392560034699, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 17, 20, 21, 22]), model=ScalarModel(intercept=0.6423680016960622, linear_terms=array([-1.01758484e-06]), square_terms=array([[4.52377587e-06]]), scale=0.007228392560034699, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -487,10 +487,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=23, candidate_x=array([9.25392074]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-108.96714719747298, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 17, 20, 21, 22]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.0036142183394554566, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423659966814586, linear_terms=array([-8.6015844e-06]), square_terms=array([[1.06092272e-06]]), scale=0.0036142183394554566, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=23, candidate_x=array([9.25396844]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-101.98829389658573, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 17, 20, 21, 22]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0036141962800173497, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173907, linear_terms=array([-8.15794512e-06]), square_terms=array([[1.06328797e-06]]), scale=0.0036141962800173497, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -538,10 +538,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=24, candidate_x=array([9.25601317]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-3.495769267406339, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.0018071091697277283, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423659966814583, linear_terms=array([-4.3007922e-06]), square_terms=array([[2.65230679e-07]]), scale=0.0018071091697277283, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=24, candidate_x=array([9.25595667]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.6772491566976204, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0018070981400086748, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173909, linear_terms=array([-4.07897256e-06]), square_terms=array([[2.65821992e-07]]), scale=0.0018070981400086748, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -589,10 +589,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=25, candidate_x=array([9.25420606]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-3.4524236211503827, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.0009035545848638641, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 23, 25]), model=ScalarModel(intercept=0.6423677993194055, linear_terms=array([4.10125993e-07]), square_terms=array([[6.47204357e-08]]), scale=0.0009035545848638641, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=25, candidate_x=array([9.25414957]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.597936150162183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0009035490700043374, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 23, 25]), model=ScalarModel(intercept=0.6423678826985401, linear_terms=array([4.59211257e-07]), square_terms=array([[6.46936017e-08]]), scale=0.0009035490700043374, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -640,10 +640,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=26, candidate_x=array([9.25149539]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-14.054500660204207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 23, 25]), old_indices_discarded=array([22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.00045177729243193207, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=0.6423669214956031, linear_terms=array([4.45886181e-07]), square_terms=array([[1.61297046e-08]]), scale=0.00045177729243193207, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=26, candidate_x=array([9.25143893]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-13.573507187210918, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 23, 25]), old_indices_discarded=array([22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0004517745350021687, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=0.6423670437711565, linear_terms=array([4.63065568e-07]), square_terms=array([[1.6122968e-08]]), scale=0.0004517745350021687, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -691,10 +691,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=27, candidate_x=array([9.25194717]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-3.232911680156261, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 23, 25, 26]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.00022588864621596604, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.6423579766776862, linear_terms=array([-1.31913132e-06]), square_terms=array([[4.08571524e-09]]), scale=0.00022588864621596604, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=27, candidate_x=array([9.2518907]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-4.368840765358844, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 23, 25, 26]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00022588726750108435, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.6423580233416285, linear_terms=array([-1.44028832e-06]), square_terms=array([[4.10130588e-09]]), scale=0.00022588726750108435, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -742,10 +742,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=28, candidate_x=array([9.25262484]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-0.8340197126941936, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.00011294432310798302, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.6423591585016506, linear_terms=array([-9.46880328e-08]), square_terms=array([[9.96719279e-10]]), scale=0.00011294432310798302, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=28, candidate_x=array([9.25256836]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5183479506363351, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00011294363375054218, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.6423590665246325, linear_terms=array([-2.47320622e-07]), square_terms=array([[1.00136317e-09]]), scale=0.00011294363375054218, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -793,10 +793,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=29, candidate_x=array([9.25251189]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-4.249921661170417, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=5.647216155399151e-05, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.6423583374948495, linear_terms=array([2.74128743e-07]), square_terms=array([[2.45374978e-10]]), scale=5.647216155399151e-05, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=29, candidate_x=array([9.25245542]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5789901455335319, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=5.647181687527109e-05, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.6423582471741802, linear_terms=array([1.85886653e-07]), square_terms=array([[2.45317725e-10]]), scale=5.647181687527109e-05, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -844,10 +844,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=30, candidate_x=array([9.25234248]), index=30, x=array([9.25234248]), fval=0.6423583236273489, rho=0.2312020963811631, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=5.647216155324486e-05, relative_step_length=0.9999999999867785, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 31 entries., 'multistart_info': {'start_parameters': [array([9.25239895])], 'local_optima': [{'solution_x': array([9.25234248]), 'solution_criterion': 0.6423583236273489, 'states': [State(trustregion=Region(center=array([9.25239895]), radius=0.9252398949005969, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583869781233, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252398949005969, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=30, candidate_x=array([9.25228601]), index=30, x=array([9.25228601]), fval=0.6423582605057705, rho=0.33979447206310537, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=5.647181687606917e-05, relative_step_length=1.0000000000141325, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 31 entries., 'multistart_info': {'start_parameters': [array([9.25234248])], 'local_optima': [{'solution_x': array([9.25228601]), 'solution_criterion': 0.6423582605057705, 'states': [State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583236273489, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -895,10 +895,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=0, candidate_x=array([9.25239895]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.25239895]), radius=0.9252398949005969, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.6366130167243159, linear_terms=array([0.00015204]), square_terms=array([[0.07297192]]), scale=0.9252398949005969, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=0, candidate_x=array([9.25234248]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.6366126588198241, linear_terms=array([0.00014855]), square_terms=array([[0.07297151]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -946,10 +946,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=14, candidate_x=array([9.25047117]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-67.84262787184853, accepted=False, new_indices=array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.46261994745029844, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.6391740801217988, linear_terms=array([0.00084041]), square_terms=array([[0.01834804]]), scale=0.46261994745029844, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=14, candidate_x=array([9.25045896]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-71.73087780998843, accepted=False, new_indices=array([ 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.46261712384222076, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.6391738833063486, linear_terms=array([0.0008387]), square_terms=array([[0.01834796]]), scale=0.46261712384222076, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -997,10 +997,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=16, candidate_x=array([9.23120907]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-0.9360366242557278, accepted=False, new_indices=array([15]), old_indices_used=array([ 0, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.23130997372514922, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.640036605619869, linear_terms=array([0.00027282]), square_terms=array([[0.0045447]]), scale=0.23130997372514922, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=16, candidate_x=array([9.2311958]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.9346804797475832, accepted=False, new_indices=array([15]), old_indices_used=array([ 0, 11, 12, 13, 14]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.23130856192111038, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.6400364245764906, linear_terms=array([0.00027196]), square_terms=array([[0.00454468]]), scale=0.23130856192111038, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1048,10 +1048,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=17, candidate_x=array([9.23851343]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-2.0600316699171524, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.11565498686257461, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6424111679010795, linear_terms=array([0.00061553]), square_terms=array([[0.00116854]]), scale=0.11565498686257461, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=17, candidate_x=array([9.23850082]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-2.083816827569783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.11565428096055519, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6424109502570059, linear_terms=array([0.00061503]), square_terms=array([[0.00116854]]), scale=0.11565428096055519, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1099,10 +1099,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=18, candidate_x=array([9.19147768]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-1.0079617617718157, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.057827493431287305, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19]), model=ScalarModel(intercept=0.6423642263710555, linear_terms=array([8.47856679e-06]), square_terms=array([[0.00028439]]), scale=0.057827493431287305, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=18, candidate_x=array([9.1914709]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-1.0107374685291592, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.057827140480277595, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19]), model=ScalarModel(intercept=0.6423641764261762, linear_terms=array([8.28433083e-06]), square_terms=array([[0.00028439]]), scale=0.057827140480277595, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1150,10 +1150,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=20, candidate_x=array([9.25067494]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-80.16115218067975, accepted=False, new_indices=array([19]), old_indices_used=array([ 0, 14, 16, 17]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.028913746715643653, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.6423649815517477, linear_terms=array([4.11261778e-06]), square_terms=array([[7.10839727e-05]]), scale=0.028913746715643653, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=20, candidate_x=array([9.25065796]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-84.9118930196347, accepted=False, new_indices=array([19]), old_indices_used=array([ 0, 14, 16, 17]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.028913570240138797, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.642364947951701, linear_terms=array([4.01212888e-06]), square_terms=array([[7.10835612e-05]]), scale=0.028913570240138797, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1201,10 +1201,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=21, candidate_x=array([9.25072612]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-83.86519702867479, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.014456873357821826, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 20, 21]), model=ScalarModel(intercept=0.6423665985864432, linear_terms=array([4.15685709e-06]), square_terms=array([[1.80234953e-05]]), scale=0.014456873357821826, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=21, candidate_x=array([9.25071052]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-89.09165927837773, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.014456785120069399, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 20, 21]), model=ScalarModel(intercept=0.6423666277820237, linear_terms=array([4.21338439e-06]), square_terms=array([[1.80260164e-05]]), scale=0.014456785120069399, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1252,10 +1252,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=22, candidate_x=array([9.24906468]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-45.476949115574584, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 20, 21]), old_indices_discarded=array([18, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.007228436678910913, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 17, 20, 21, 22]), model=ScalarModel(intercept=0.6423679396996458, linear_terms=array([-9.52115187e-07]), square_terms=array([[4.52250154e-06]]), scale=0.007228436678910913, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=22, candidate_x=array([9.24896336]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-45.22714683487297, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 20, 21]), old_indices_discarded=array([18, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.007228392560034699, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 17, 20, 21, 22]), model=ScalarModel(intercept=0.6423680016960622, linear_terms=array([-1.01758484e-06]), square_terms=array([[4.52377587e-06]]), scale=0.007228392560034699, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1303,10 +1303,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=23, candidate_x=array([9.25392074]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-108.96714719747298, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 17, 20, 21, 22]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.0036142183394554566, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423659966814586, linear_terms=array([-8.6015844e-06]), square_terms=array([[1.06092272e-06]]), scale=0.0036142183394554566, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=23, candidate_x=array([9.25396844]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-101.98829389658573, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 17, 20, 21, 22]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0036141962800173497, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173907, linear_terms=array([-8.15794512e-06]), square_terms=array([[1.06328797e-06]]), scale=0.0036141962800173497, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1354,10 +1354,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=24, candidate_x=array([9.25601317]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-3.495769267406339, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.0018071091697277283, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423659966814583, linear_terms=array([-4.3007922e-06]), square_terms=array([[2.65230679e-07]]), scale=0.0018071091697277283, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=24, candidate_x=array([9.25595667]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.6772491566976204, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0018070981400086748, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6423664047173909, linear_terms=array([-4.07897256e-06]), square_terms=array([[2.65821992e-07]]), scale=0.0018070981400086748, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1405,10 +1405,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=25, candidate_x=array([9.25420606]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-3.4524236211503827, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.0009035545848638641, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 23, 25]), model=ScalarModel(intercept=0.6423677993194055, linear_terms=array([4.10125993e-07]), square_terms=array([[6.47204357e-08]]), scale=0.0009035545848638641, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=25, candidate_x=array([9.25414957]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-3.597936150162183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 22, 23]), old_indices_discarded=array([24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0009035490700043374, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 14, 20, 21, 23, 25]), model=ScalarModel(intercept=0.6423678826985401, linear_terms=array([4.59211257e-07]), square_terms=array([[6.46936017e-08]]), scale=0.0009035490700043374, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1456,10 +1456,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=26, candidate_x=array([9.25149539]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-14.054500660204207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 23, 25]), old_indices_discarded=array([22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.00045177729243193207, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=0.6423669214956031, linear_terms=array([4.45886181e-07]), square_terms=array([[1.61297046e-08]]), scale=0.00045177729243193207, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=26, candidate_x=array([9.25143893]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-13.573507187210918, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 20, 21, 23, 25]), old_indices_discarded=array([22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.0004517745350021687, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 20, 21, 23, 25, 26]), model=ScalarModel(intercept=0.6423670437711565, linear_terms=array([4.63065568e-07]), square_terms=array([[1.6122968e-08]]), scale=0.0004517745350021687, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1507,10 +1507,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=27, candidate_x=array([9.25194717]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-3.232911680156261, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 23, 25, 26]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.00022588864621596604, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.6423579766776862, linear_terms=array([-1.31913132e-06]), square_terms=array([[4.08571524e-09]]), scale=0.00022588864621596604, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=27, candidate_x=array([9.2518907]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-4.368840765358844, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21, 23, 25, 26]), old_indices_discarded=array([14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00022588726750108435, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.6423580233416285, linear_terms=array([-1.44028832e-06]), square_terms=array([[4.10130588e-09]]), scale=0.00022588726750108435, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1558,10 +1558,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=28, candidate_x=array([9.25262484]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-0.8340197126941936, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=0.00011294432310798302, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.6423591585016506, linear_terms=array([-9.46880328e-08]), square_terms=array([[9.96719279e-10]]), scale=0.00011294432310798302, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=28, candidate_x=array([9.25256836]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5183479506363351, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=0.00011294363375054218, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.6423590665246325, linear_terms=array([-2.47320622e-07]), square_terms=array([[1.00136317e-09]]), scale=0.00011294363375054218, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1609,10 +1609,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=29, candidate_x=array([9.25251189]), index=0, x=array([9.25239895]), fval=0.6423583869781233, rho=-4.249921661170417, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25239895]), radius=5.647216155399151e-05, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.6423583374948495, linear_terms=array([2.74128743e-07]), square_terms=array([[2.45374978e-10]]), scale=5.647216155399151e-05, shift=array([9.25239895])), vector_model=VectorModel(intercepts=array([ 0.04871317, 0.12404612, 0.14884861, 0.1938165 , 0.21740855, - 0.23242116, 0.2333601 , 0.06702318, -0.0801855 , -0.06712552, - -0.40905258, -0.41755015, -0.12516662, -0.098803 , -0.08942985, - -0.09321084, -0.09959793]), linear_terms=array([[0.], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=29, candidate_x=array([9.25245542]), index=0, x=array([9.25234248]), fval=0.6423583236273489, rho=-0.5789901455335319, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25234248]), radius=5.647181687527109e-05, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.6423582471741802, linear_terms=array([1.85886653e-07]), square_terms=array([[2.45317725e-10]]), scale=5.647181687527109e-05, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, + 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, + -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , + -0.09320898, -0.09959608]), linear_terms=array([[0.], [0.], [0.], [0.], @@ -1660,7 +1660,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [[0.]], - [[0.]]]), scale=0.9252398949005969, shift=array([9.25239895])), candidate_index=30, candidate_x=array([9.25234248]), index=30, x=array([9.25234248]), fval=0.6423583236273489, rho=0.2312020963811631, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=5.647216155324486e-05, relative_step_length=0.9999999999867785, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 9.252398949005968}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 10.177638843906566}, {'CRRA': 8.32715905410537}, {'CRRA': 9.250471173613674}, {'CRRA': 8.78977900155567}, {'CRRA': 9.23120907149584}, {'CRRA': 9.238513428928496}, {'CRRA': 9.191477678194621}, {'CRRA': 9.310226442437255}, {'CRRA': 9.25067493918715}, {'CRRA': 9.250726122006544}, {'CRRA': 9.249064681404075}, {'CRRA': 9.253920740493475}, {'CRRA': 9.256013167345424}, {'CRRA': 9.254206058175695}, {'CRRA': 9.251495394421104}, {'CRRA': 9.251947171713537}, {'CRRA': 9.252624837652185}, {'CRRA': 9.252511893329077}, {'CRRA': 9.252342476844415}], 'criterion': [0.6423583869781233, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6724707147424193, 0.6726932800669494, 0.6423691326265972, 0.6492941434834837, 0.6423764030742694, 0.6423752558032019, 0.6425217924878079, 0.6425146733526689, 0.6423685181986346, 0.6423683643642798, 0.6423801867774521, 0.6423693080646328, 0.6423866017619942, 0.6423727772903676, 0.6423636962874574, 0.6423598024158105, 0.6423594854558661, 0.6423587872768556, 0.6423583236273489], 'runtime': [0.0, 3.3926007600093726, 3.6385346090246458, 3.8182367410045117, 4.073105274001136, 4.283541666023666, 4.494341033016099, 4.736494988028426, 4.96336116202292, 5.179848953004694, 5.533184596017236, 5.69610069601913, 5.942016065004282, 21.336237280018395, 22.686877734027803, 24.069558680028422, 25.560004253027728, 26.859713567013387, 28.15134494501399, 29.419637827028055, 30.714213685016148, 32.14109974101302, 33.454856365016894, 34.775876878004055, 36.0627207980142, 37.38446952102822, 38.785011164000025, 40.11220667202724, 41.4234611930151, 42.74076091501047, 44.06118227602565], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 9.25239895], + [[0.]]]), scale=0.9252342476844415, shift=array([9.25234248])), candidate_index=30, candidate_x=array([9.25228601]), index=30, x=array([9.25228601]), fval=0.6423582605057705, rho=0.33979447206310537, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=5.647181687606917e-05, relative_step_length=1.0000000000141325, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 9.252342476844415}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 9.250458955049714}, {'CRRA': 8.789725353002193}, {'CRRA': 9.231195795349796}, {'CRRA': 9.23850082029189}, {'CRRA': 9.191470898591822}, {'CRRA': 9.310169617324693}, {'CRRA': 9.250657961285336}, {'CRRA': 9.250710524700677}, {'CRRA': 9.248963361277593}, {'CRRA': 9.253968442160518}, {'CRRA': 9.255956673124432}, {'CRRA': 9.254149574984424}, {'CRRA': 9.25143892777441}, {'CRRA': 9.251890702309414}, {'CRRA': 9.252568364111916}, {'CRRA': 9.252455420478165}, {'CRRA': 9.252286005027539}], 'criterion': [0.6423583236273489, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.642369169571296, 0.6492959495923358, 0.6423762404892832, 0.6423752797264956, 0.6425219143922716, 0.642514400144132, 0.6423685692715722, 0.6423684112220268, 0.6423805942108727, 0.6423699960365742, 0.6423863674367805, 0.6423725213049074, 0.6423641176751165, 0.6423603114677403, 0.6423590691348976, 0.642358466533662, 0.6423582605057704], 'runtime': [0.0, 3.3893006040002547, 3.7743795520000276, 3.9942729670001427, 4.308895564000068, 4.500098180999885, 4.797666575000221, 5.097781724000015, 5.30452481400016, 5.586473449000096, 5.8145816500000365, 6.019138186999953, 6.258510488999946, 21.4172058070003, 22.745588674999908, 24.07027555800005, 25.37667203000001, 26.848738280999896, 28.141902802999994, 29.463674151000305, 30.76145256100017, 32.056981691000146, 33.34122019300003, 34.749019994000264, 36.131662315000085, 37.520465677000175, 38.87502066600018, 40.19993009200016, 41.659364199000265, 43.079727706000085, 44.41924671800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 9.25234248], [ 8.1875 ], [10.55 ], [12.9125 ], @@ -1669,5 +1669,5 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25239895] [15.275 ], [ 4.64375 ], [17.6375 ], - [ 3.4625 ]]), 'exploration_results': array([0.64235839, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, + [ 3.4625 ]]), 'exploration_results': array([0.64235832, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" diff --git a/content/tables/TRP/WealthPortfolio_estimate_results.csv b/content/tables/TRP/WealthPortfolio_estimate_results.csv index e97119b..c995cfc 100644 --- a/content/tables/TRP/WealthPortfolio_estimate_results.csv +++ b/content/tables/TRP/WealthPortfolio_estimate_results.csv @@ -1,10 +1,10 @@ -CRRA,5.35399091577092 -WealthShare,0.1710302407154898 -time_to_estimate,208.24305033683777 -params,"{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}" -criterion,0.24222754644165564 -start_criterion,0.2389045493044743 -start_params,"{'CRRA': 5.354006322483765, 'WealthShare': 0.17100463259932858}" +CRRA,5.335577372664163 +WealthShare,0.1706005756625005 +time_to_estimate,202.92073488235474 +params,"{'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}" +criterion,0.2421983863534466 +start_criterion,0.23890510137815316 +start_params,"{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}" algorithm,multistart_tranquilo_ls direction,minimize n_free,2 @@ -13,19 +13,19 @@ success, n_criterion_evaluations, n_derivative_evaluations, n_iterations, -history,"{'params': [{'CRRA': 5.837906205607757, 'WealthShare': 0.17768435978844974}, {'CRRA': 5.521440822118637, 'WealthShare': 0.01}, {'CRRA': 6.355277172375491, 'WealthShare': 0.01}, {'CRRA': 5.320535238840023, 'WealthShare': 0.01}, {'CRRA': 6.330844268900851, 'WealthShare': 0.6950553265561836}, {'CRRA': 6.355277172375491, 'WealthShare': 0.34431980448135446}, {'CRRA': 6.226403636138772, 'WealthShare': 0.01}, {'CRRA': 5.320535238840023, 'WealthShare': 0.01}, {'CRRA': 5.875481040447152, 'WealthShare': 0.6950553265561836}, {'CRRA': 6.343115925559374, 'WealthShare': 0.6950553265561836}, {'CRRA': 5.42192271850144, 'WealthShare': 0.6950553265561836}, {'CRRA': 5.320535238840023, 'WealthShare': 0.3927845660013606}, {'CRRA': 5.320535238840023, 'WealthShare': 0.6950553265561836}, {'CRRA': 6.260289192255453, 'WealthShare': 0.1414416437258815}, {'CRRA': 5.586422994097967, 'WealthShare': 0.12009017405786418}, {'CRRA': 5.696471798014226, 'WealthShare': 0.13230174451397797}, {'CRRA': 5.910749514856811, 'WealthShare': 0.15649323241599017}, {'CRRA': 5.801270203852185, 'WealthShare': 0.16823018219702343}, {'CRRA': 5.874111015856876, 'WealthShare': 0.15737844781264468}, {'CRRA': 5.764781908128255, 'WealthShare': 0.16842199875974878}, {'CRRA': 5.83762293702503, 'WealthShare': 0.15849023997730602}, {'CRRA': 5.728295327191924, 'WealthShare': 0.1687167695944529}, {'CRRA': 5.655320864957503, 'WealthShare': 0.16922976706263002}, {'CRRA': 5.801158262739447, 'WealthShare': 0.159625327541431}, {'CRRA': 5.582345042721493, 'WealthShare': 0.16957706599752984}, {'CRRA': 5.728061206699304, 'WealthShare': 0.1612420849769127}, {'CRRA': 5.655213987125576, 'WealthShare': 0.16219328110045608}, {'CRRA': 5.54577405830501, 'WealthShare': 0.1641467052653886}, {'CRRA': 5.564089269324569, 'WealthShare': 0.16867455696897235}, {'CRRA': 5.527610331306062, 'WealthShare': 0.1695245283248613}, {'CRRA': 5.454569298934131, 'WealthShare': 0.1657673350712589}, {'CRRA': 5.491057478350771, 'WealthShare': 0.16462446060590527}, {'CRRA': 5.509363764950676, 'WealthShare': 0.16935796524808727}, {'CRRA': 5.472878982838546, 'WealthShare': 0.1697534682609821}, {'CRRA': 5.39990991296233, 'WealthShare': 0.1705867470386037}, {'CRRA': 5.2539840063420415, 'WealthShare': 0.166169845381478}, {'CRRA': 5.326948348474455, 'WealthShare': 0.17199264976389753}, {'CRRA': 5.363429877974597, 'WealthShare': 0.17140117312121952}, {'CRRA': 5.381674772858575, 'WealthShare': 0.17165503946704128}, {'CRRA': 5.354324915656451, 'WealthShare': 0.16988722969298148}, {'CRRA': 5.358866777462407, 'WealthShare': 0.17113518644845302}, {'CRRA': 5.349755525044417, 'WealthShare': 0.17069814042911424}, {'CRRA': 5.354305613394689, 'WealthShare': 0.17110730029989854}, {'CRRA': 5.345135297716727, 'WealthShare': 0.17071002327939397}, {'CRRA': 5.349745787974868, 'WealthShare': 0.17125844781552682}, {'CRRA': 5.352024899000856, 'WealthShare': 0.17108068833293297}, {'CRRA': 5.355446817343039, 'WealthShare': 0.1712165807131758}, {'CRRA': 5.353737844217815, 'WealthShare': 0.17136997487615657}, {'CRRA': 5.354591141806533, 'WealthShare': 0.17114188868178082}, {'CRRA': 5.354307242363997, 'WealthShare': 0.17096478260213566}, {'CRRA': 5.354234900928805, 'WealthShare': 0.17109845000305954}, {'CRRA': 5.354273892151148, 'WealthShare': 0.17096136013364222}, {'CRRA': 5.3541640557109185, 'WealthShare': 0.17110619605176702}, {'CRRA': 5.35426853425294, 'WealthShare': 0.1711185203084774}, {'CRRA': 5.354235173394749, 'WealthShare': 0.1710806362107799}, {'CRRA': 5.354200577562492, 'WealthShare': 0.17107210010953106}, {'CRRA': 5.354130693185321, 'WealthShare': 0.17105814085154764}, {'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}], 'criterion': [0.2500179178839741, 1.1792461131204435, 0.9197765436682048, 1.2595171604963433, 33.16445541254984, 1.4878207969751798, 0.9532709482124387, 1.2595171604963433, 36.93789575006835, 33.071347222276614, 41.45067088403316, 2.9856980365173467, 42.60257108837822, 0.27371069489945155, 0.3353446406768353, 0.29385520517736274, 0.25245645388982746, 0.24610730524369168, 0.2512862430006085, 0.24542189740001585, 0.25015888395593905, 0.24507292693629096, 0.2440744520975429, 0.2488794160642582, 0.24318410996735987, 0.24711505738750136, 0.24589227104352215, 0.24442274744445994, 0.24306721295336706, 0.24288435328669916, 0.2433661384295173, 0.24398380010235204, 0.2428345891015183, 0.24267293680244378, 0.24231674775792228, 0.2439014107925056, 0.24236160354034061, 0.2423113173928428, 0.24240633196487957, 0.24234281133746066, 0.2422660131172327, 0.24228370883608596, 0.2422367332032626, 0.24227422877909757, 0.2422985499732716, 0.2422412748554663, 0.2422567765689739, 0.24229594886956057, 0.2422455073255868, 0.24223700359046088, 0.2422350327190185, 0.24223767262404658, 0.24223687844797048, 0.24223948283887395, 0.24223166413037345, 0.2422301948200769, 0.24222785386695275, 0.24222754644165562], 'runtime': [0.0, 1.3006106629909482, 1.3461875649809372, 1.392138064984465, 1.4359841749828774, 1.5009825789893512, 1.550953859987203, 1.593609842006117, 1.638624159997562, 1.686485532001825, 1.7453936449892353, 1.7891343509836588, 1.8489449310000055, 3.2682942989922594, 4.4358367919921875, 5.712266588991042, 6.875062373001128, 8.065197699994314, 9.26269722500001, 10.43409699498443, 11.618591404985636, 12.797749530989677, 13.943540325999493, 15.100036732997978, 16.27896274998784, 17.441876513999887, 18.707559181988472, 19.848269279988017, 21.01465619399096, 22.18544425399159, 23.350925145001383, 24.513529393996578, 25.67127582000103, 26.822254801983945, 27.962893974996405, 29.11491555799148, 30.30321866099257, 31.654834265005775, 32.9144726920058, 34.122135265992256, 35.37244299199665, 36.586844901001314, 37.83399780999753, 39.01701513200533, 40.18066080898279, 41.38312898899312, 42.55371054899297, 43.73981825000374, 45.013344446982956, 46.170059423981, 47.351473324000835, 48.54954139099573, 49.72759446298005, 50.919856733991764, 52.213698393985396, 53.48934546398232, 54.798059503984405, 56.106529726006556], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]}" -convergence_report,"{'one_step': {'relative_criterion_change': 4.409062648304273e-07, 'relative_params_change': 6.0459488881039185e-06, 'absolute_criterion_change': 1.0679964274062925e-07, 'absolute_params_change': 2.8559467214664423e-05}, 'five_steps': {'relative_criterion_change': 4.409062648304273e-07, 'relative_params_change': 6.0459488881039185e-06, 'absolute_criterion_change': 1.0679964274062925e-07, 'absolute_params_change': 2.8559467214664423e-05}}" -multistart_info,"{'start_parameters': [{'CRRA': 5.354006322483765, 'WealthShare': 0.17100463259932858}, {'CRRA': 5.837906205607757, 'WealthShare': 0.17768435978844974}], 'local_optima': [Minimize with 2 free parameters terminated. +history,"{'params': [{'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}, {'CRRA': 5.521836423832173, 'WealthShare': 0.01}, {'CRRA': 6.355319463479058, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 6.288942410133527, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.34434498212577297}, {'CRRA': 6.227220953739785, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 5.871508721893411, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.6700100023631337}, {'CRRA': 5.428342638379923, 'WealthShare': 0.6950727963110019}, {'CRRA': 5.320570644267783, 'WealthShare': 0.39643867888963663}, {'CRRA': 5.320570644267783, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.129876895883085, 'WealthShare': 0.14106398655854144}, {'CRRA': 5.5792578490706015, 'WealthShare': 0.1204690698151589}, {'CRRA': 5.696390372221354, 'WealthShare': 0.1330305333078522}, {'CRRA': 5.764661943417508, 'WealthShare': 0.16014227844012982}, {'CRRA': 5.618872311285, 'WealthShare': 0.1672901244784106}, {'CRRA': 5.360185106482181, 'WealthShare': 0.15621623896813125}, {'CRRA': 5.4728571815195926, 'WealthShare': 0.16421014778193593}, {'CRRA': 5.545825951300451, 'WealthShare': 0.16306458747685207}, {'CRRA': 5.582398435175709, 'WealthShare': 0.1685319632864777}, {'CRRA': 5.509355844762908, 'WealthShare': 0.16418872811097143}, {'CRRA': 5.545831963262839, 'WealthShare': 0.1638629137313917}, {'CRRA': 5.564154615154598, 'WealthShare': 0.16862293277010182}, {'CRRA': 5.527673129514142, 'WealthShare': 0.16928670293386877}, {'CRRA': 5.454701789923809, 'WealthShare': 0.16996692667547664}, {'CRRA': 5.308764908353464, 'WealthShare': 0.17181847436097414}, {'CRRA': 5.0970292962882615, 'WealthShare': 0.1601742594497508}, {'CRRA': 5.454606416226443, 'WealthShare': 0.16345692042672777}, {'CRRA': 5.381637011093981, 'WealthShare': 0.1642816207639356}, {'CRRA': 5.272807055506822, 'WealthShare': 0.16562610692660415}, {'CRRA': 5.326977544603738, 'WealthShare': 0.17075638690650644}, {'CRRA': 5.290536727275727, 'WealthShare': 0.1654939898644262}, {'CRRA': 5.34522111280142, 'WealthShare': 0.17069763426574053}, {'CRRA': 5.336100860393708, 'WealthShare': 0.17086100179753094}, {'CRRA': 5.354343627621392, 'WealthShare': 0.1706887162046996}, {'CRRA': 5.345222085332012, 'WealthShare': 0.17075761374684867}, {'CRRA': 5.331561427746527, 'WealthShare': 0.1728165644941382}, {'CRRA': 5.333816121531683, 'WealthShare': 0.17032107739522934}, {'CRRA': 5.337237317831098, 'WealthShare': 0.17035022974547837}, {'CRRA': 5.335530348688637, 'WealthShare': 0.17082137941624043}, {'CRRA': 5.336665303591373, 'WealthShare': 0.17028845981607194}, {'CRRA': 5.334960873512694, 'WealthShare': 0.17089262349762813}, {'CRRA': 5.335767080396652, 'WealthShare': 0.170983623728545}, {'CRRA': 5.3355388649474635, 'WealthShare': 0.1706791061179343}, {'CRRA': 5.335333663634032, 'WealthShare': 0.17047473426234255}, {'CRRA': 5.335554589018792, 'WealthShare': 0.17053744817902458}, {'CRRA': 5.335343081312356, 'WealthShare': 0.17034634254745087}, {'CRRA': 5.33559928332507, 'WealthShare': 0.17040210920806037}, {'CRRA': 5.335580340581636, 'WealthShare': 0.1706038967420634}, {'CRRA': 5.335454088688302, 'WealthShare': 0.17053748476047093}, {'CRRA': 5.335633144546121, 'WealthShare': 0.17065220536865475}, {'CRRA': 5.335596501563392, 'WealthShare': 0.170572140462395}, {'CRRA': 5.335595445166626, 'WealthShare': 0.1705940558344041}, {'CRRA': 5.335583910614445, 'WealthShare': 0.17061205807039284}, {'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}], 'criterion': [0.2500249942408325, 1.1791007356386276, 0.9197651867022865, 1.2595025990222253, 33.4909498055013, 1.4882139243569137, 0.9530633832431904, 1.2595025990222253, 36.978934548688066, 26.44344185974536, 41.385698106382975, 3.0981667931180743, 42.608449629101216, 0.2728700599071999, 0.3342173205988753, 0.29198968043547885, 0.2481648865140715, 0.24373445213195505, 0.25114503099349234, 0.24406213167656582, 0.24489531582637777, 0.24315780951732555, 0.24426227832547462, 0.24453862473009094, 0.24306497226632034, 0.24285876672075696, 0.24259437399648875, 0.24243354455545987, 0.25079678704331854, 0.24441635199641626, 0.24414569958048554, 0.2439788766246561, 0.24225547458407035, 0.24394629290622924, 0.24227514910123488, 0.24223921155653808, 0.24227947343936865, 0.24227295597179582, 0.24250574148919085, 0.24223949661361607, 0.24225288167948408, 0.2422306308030337, 0.2422548267052162, 0.24224813492764274, 0.24225797040581512, 0.242202396887387, 0.24220875273143883, 0.2422004189428712, 0.24224389187159454, 0.24223135088965633, 0.2421984742592525, 0.24219915623837365, 0.24220019073252083, 0.24219911551794257, 0.2421985513731777, 0.24219868532057515, 0.24219838635344662], 'runtime': [0.0, 1.296167903999958, 1.3449491520000265, 1.3858779940001114, 1.4279043609999462, 1.4686698719997366, 1.514770218999729, 1.5592568910001319, 1.6084964980000223, 1.6590743809997548, 1.7047839209999438, 1.761857082000006, 1.8163866790000611, 3.181013745999735, 4.325823927999863, 5.600623542999983, 6.755316591999872, 7.917994205000014, 9.077396046000104, 10.294875745999889, 11.503319416000068, 12.720127907000006, 14.032752316999904, 15.207649181999841, 16.429454532999898, 17.670099559999926, 18.8472972149998, 20.135531741999785, 21.277609068999936, 22.423084480999933, 23.553762927999742, 24.684132019000117, 25.817971458000102, 26.998730913000145, 28.1869263640001, 29.34812033800017, 30.50678410599994, 31.65761656199993, 32.799299537000024, 33.94402660300011, 35.26958909099994, 36.47545633799973, 37.66156821799996, 38.83101894899983, 39.99145682000017, 41.17240058000016, 42.34739114900003, 43.51756579399989, 44.678886351000074, 45.83479911199993, 46.989458055999876, 48.12758910999992, 49.42098368999996, 50.618441345000065, 51.84377754599973, 52.98982088799994, 54.21043696800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}, 'five_steps': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}}" +multistart_info,"{'start_parameters': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}], 'local_optima': [Minimize with 2 free parameters terminated. The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 3.06e-07* 7.996e-06* -relative_params_change 7.965e-05 0.0001471 -absolute_criterion_change 7.413e-08* 1.937e-06* -absolute_params_change 1.634e-05 5.063e-05 +relative_criterion_change 5.606e-08* 5.606e-08* +relative_params_change 0.0001132 0.0001132 +absolute_criterion_change 1.358e-08* 1.358e-08* +absolute_params_change 3.272e-05 3.272e-05 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -34,21 +34,21 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 1.269e-06* 3.793e-05 -relative_params_change 0.0001652 0.0004544 -absolute_criterion_change 3.074e-07* 9.187e-06* -absolute_params_change 0.0001425 0.000324 +relative_criterion_change 3.629e-07* 0.0001686 +relative_params_change 1.947e-05 0.00153 +absolute_criterion_change 8.791e-08* 4.083e-05 +absolute_params_change 4.454e-06* 0.0005847 -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.354006322483765, 'WealthShare': 0.17100463259932858}, {'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([2.42229590e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([2.42227546e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.5837906205607757, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.2500179178839741, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.2500249942408325, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -113,11 +113,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=0, candidate_x=array([5.83790621, 0.17768436]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.5837906205607757, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=5.453595147945511, linear_terms=array([-0.61048798, 16.31048865]), square_terms=array([[ 0.04668694, -0.92878469], - [-0.92878469, 25.23647844]]), scale=array([0.51737097, 0.34252766]), shift=array([5.83790621, 0.35252766])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=0, candidate_x=array([5.83794505, 0.17769839]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=5.323973668078372, linear_terms=array([-0.75583641, 15.97016397]), square_terms=array([[ 0.06851782, -1.1616573 ], + [-1.1616573 , 24.80625807]]), scale=array([0.51737441, 0.3425364 ]), shift=array([5.83794505, 0.3525364 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -182,11 +182,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=13, candidate_x=array([6.26028919, 0.14144164]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.09994429546320836, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.29189531028038784, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), model=ScalarModel(intercept=1.029847416702383, linear_terms=array([-0.15206957, 3.84463924]), square_terms=array([[ 0.02831952, -0.37138794], - [-0.37138794, 8.69674828]]), scale=array([0.25868548, 0.21318492]), shift=array([5.83790621, 0.22318492])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=13, candidate_x=array([6.1298769 , 0.14106399]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=-0.10249831672421726, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), model=ScalarModel(intercept=1.020144031837767, linear_terms=array([-0.15036877, 3.82287309]), square_terms=array([[ 0.02811468, -0.37120958], + [-0.37120958, 8.70439816]]), scale=array([0.2586872, 0.2131928]), shift=array([5.83794505, 0.2231928 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -251,11 +251,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=14, candidate_x=array([5.58642299, 0.12009017]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.36587111256709887, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), old_indices_discarded=array([ 4, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), model=ScalarModel(intercept=0.3307652689377624, linear_terms=array([-0.05352468, 1.12834325]), square_terms=array([[ 0.01880846, -0.24791449], - [-0.24791449, 4.39579594]]), scale=0.14594765514019392, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=14, candidate_x=array([5.57925785, 0.12046907]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=-0.3686077630799093, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), old_indices_discarded=array([ 4, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), model=ScalarModel(intercept=0.32337401785450964, linear_terms=array([-0.05509149, 1.09845952]), square_terms=array([[ 0.02085975, -0.26486803], + [-0.26486803, 4.42259018]]), scale=0.14594862634683553, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -320,11 +320,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=15, candidate_x=array([5.6964718 , 0.13230174]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.287744747266337, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), old_indices_discarded=array([ 2, 4, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15]), model=ScalarModel(intercept=0.2547685338865855, linear_terms=array([-0.01144467, 0.12662975]), square_terms=array([[0.00075227, 0.00196842], - [0.00196842, 0.43154335]]), scale=0.07297382757009696, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=15, candidate_x=array([5.69639037, 0.13303053]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=-0.290539326522971, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), old_indices_discarded=array([ 2, 4, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 13, 14, 15]), model=ScalarModel(intercept=0.26967457001726336, linear_terms=array([0.00305809, 0.10758911]), square_terms=array([[2.03063183e-04, 7.58408569e-03], + [7.58408569e-03, 4.14525606e-01]]), scale=0.07297431317341777, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -389,11 +389,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=16, candidate_x=array([5.91074951, 0.15649323]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.08075482115611035, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.250017917883974, linear_terms=array([0.00170653, 0.02764723]), square_terms=array([[5.79166409e-05, 1.55153703e-03], - [1.55153703e-03, 9.94464848e-02]]), scale=0.03648691378504848, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=16, candidate_x=array([5.76466194, 0.16014228]), index=16, x=array([5.76466194, 0.16014228]), fval=0.24816488651407154, rho=0.12380898705859822, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.07535669323632875, relative_step_length=1.0326468309095207, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.76466194, 0.16014228]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 13, 14, 15, 16]), model=ScalarModel(intercept=0.24980518122836323, linear_terms=array([ 0.00949845, -0.02386839]), square_terms=array([[0.00257834, 0.05299243], + [0.05299243, 1.55867884]]), scale=0.14594862634683553, shift=array([5.76466194, 0.16014228])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -458,11 +458,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=17, candidate_x=array([5.8012702 , 0.16823018]), index=17, x=array([5.8012702 , 0.16823018]), fval=0.24610730524369165, rho=0.7658862060878359, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.03783620090025583, relative_step_length=1.0369800285975477, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.8012702 , 0.16823018]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15, 16, 17]), model=ScalarModel(intercept=0.253495790709351, linear_terms=array([-0.0041094 , 0.05810197]), square_terms=array([[1.86338211e-04, 5.35098646e-03], - [5.35098646e-03, 4.21903878e-01]]), scale=0.07297382757009696, shift=array([5.8012702 , 0.16823018])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=17, candidate_x=array([5.61887231, 0.16729012]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=0.43892226114088795, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 6, 7, 13, 14, 15, 16]), old_indices_discarded=array([ 2, 5, 8, 10, 11, 12]), step_length=0.14596475101996004, relative_step_length=1.0001104818423312, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), model=ScalarModel(intercept=0.3762029190210623, linear_terms=array([0.00847171, 1.1355556 ]), square_terms=array([[ 0.0351543 , -0.12066294], + [-0.12066294, 4.22970628]]), scale=array([0.2586872 , 0.20798866]), shift=array([5.61887231, 0.21798866])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -527,11 +527,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=18, candidate_x=array([5.87411102, 0.15737845]), index=17, x=array([5.8012702 , 0.16823018]), fval=0.24610730524369165, rho=-0.5899514667309471, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.8012702 , 0.16823018]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2454286818777391, linear_terms=array([0.00117876, 0.00100895]), square_terms=array([[5.55180026e-05, 1.53917542e-03], - [1.53917542e-03, 9.97372256e-02]]), scale=0.03648691378504848, shift=array([5.8012702 , 0.16823018])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=18, candidate_x=array([5.36018511, 0.15621624]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.28174894853494853, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=0.24992252725033823, linear_terms=array([0.00316437, 0.06723442]), square_terms=array([[9.24265460e-04, 3.06288719e-02], + [3.06288719e-02, 1.73234651e+00]]), scale=0.14594862634683553, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -596,11 +596,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=19, candidate_x=array([5.76478191, 0.168422 ]), index=19, x=array([5.76478191, 0.168422 ]), fval=0.2454218974000159, rho=0.5947364727296277, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.03648879990395198, relative_step_length=1.0000516930238224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.76478191, 0.168422 ]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.253681629181515, linear_terms=array([-0.00232564, 0.05166204]), square_terms=array([[1.50344993e-04, 5.87973754e-03], - [5.87973754e-03, 4.19729698e-01]]), scale=0.07297382757009696, shift=array([5.76478191, 0.168422 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=19, candidate_x=array([5.47285718, 0.16421015]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.10605520364386403, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 6, 8, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.24923278591953518, linear_terms=array([0.00140877, 0.03201995]), square_terms=array([[2.08936369e-04, 7.01345842e-03], + [7.01345842e-03, 4.30945644e-01]]), scale=0.07297431317341777, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -665,11 +665,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=20, candidate_x=array([5.83762294, 0.15849024]), index=19, x=array([5.76478191, 0.168422 ]), fval=0.2454218974000159, rho=-0.7653738840742371, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.76478191, 0.168422 ]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.24478338352931536, linear_terms=array([0.00104386, 0.00067952]), square_terms=array([[5.17736477e-05, 1.49720466e-03], - [1.49720466e-03, 1.00208068e-01]]), scale=0.03648691378504848, shift=array([5.76478191, 0.168422 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=20, candidate_x=array([5.54582595, 0.16306459]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.5716837122875763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 16, 17, 19, 20]), model=ScalarModel(intercept=0.24447966591289924, linear_terms=array([ 0.00057646, -0.00219486]), square_terms=array([[4.59383981e-05, 1.38128890e-03], + [1.38128890e-03, 1.04480430e-01]]), scale=0.03648715658670888, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -734,11 +734,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=21, candidate_x=array([5.72829533, 0.16871677]), index=21, x=array([5.72829533, 0.16871677]), fval=0.24507292693629099, rho=0.3416935494926942, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.03648777162651111, relative_step_length=1.0000235109351172, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.72829533, 0.16871677]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.24391675099982854, linear_terms=array([0.00218857, 0.00331565]), square_terms=array([[2.17190744e-04, 6.18094476e-03], - [6.18094476e-03, 4.05581192e-01]]), scale=0.07297382757009696, shift=array([5.72829533, 0.16871677])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=21, candidate_x=array([5.58239844, 0.16853196]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=0.938417610541786, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.03649501064612971, relative_step_length=1.0002152554530293, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.2493403145907468, linear_terms=array([0.00206841, 0.03132845]), square_terms=array([[2.20612437e-04, 6.20169647e-03], + [6.20169647e-03, 4.20600928e-01]]), scale=0.07297431317341777, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -803,11 +803,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=22, candidate_x=array([5.65532086, 0.16922977]), index=22, x=array([5.65532086, 0.16922977]), fval=0.24407445209754292, rho=0.4777137071010113, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([1]), step_length=0.07297626535254732, relative_step_length=1.0000334062571683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.65532086, 0.16922977]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2530356753079221, linear_terms=array([-0.00506925, 0.09004345]), square_terms=array([[5.16559769e-04, 2.11789911e-02], - [2.11789911e-02, 1.68392550e+00]]), scale=0.14594765514019392, shift=array([5.65532086, 0.16922977])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=22, candidate_x=array([5.50935584, 0.16418873]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.40755145676280513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 0, 3, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=0.24973373889649686, linear_terms=array([0.00287452, 0.0154878 ]), square_terms=array([[0.00011611, 0.00170606], + [0.00170606, 0.10513658]]), scale=0.03648715658670888, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -872,11 +872,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=23, candidate_x=array([5.80115826, 0.15962533]), index=22, x=array([5.65532086, 0.16922977]), fval=0.24407445209754292, rho=-0.5666553713621563, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15, 17, 19, 20, 21, 22]), old_indices_discarded=array([ 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.65532086, 0.16922977]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 14, 15, 17, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.24311624432173012, linear_terms=array([0.00154361, 0.00363361]), square_terms=array([[1.87587564e-04, 5.59513446e-03], - [5.59513446e-03, 4.10801212e-01]]), scale=0.07297382757009696, shift=array([5.65532086, 0.16922977])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=23, candidate_x=array([5.54583196, 0.16386291]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.3707124284955786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 20, 21, 22, 23]), model=ScalarModel(intercept=0.24348377086944023, linear_terms=array([5.04148881e-05, 1.91725030e-04]), square_terms=array([[9.67169504e-06, 3.22879765e-04], + [3.22879765e-04, 2.62610807e-02]]), scale=0.01824357829335444, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -941,11 +941,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=24, candidate_x=array([5.58234504, 0.16957707]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=0.6121148476317633, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 17, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 1, 16, 18]), step_length=0.0729766486457952, relative_step_length=1.0000386587327563, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=0.2489283617541748, linear_terms=array([0.00068245, 0.07288776]), square_terms=array([[5.95004131e-04, 2.31002626e-02], - [2.31002626e-02, 1.68009048e+00]]), scale=0.14594765514019392, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=24, candidate_x=array([5.56415462, 0.16862293]), index=24, x=array([5.56415462, 0.16862293]), fval=0.24306497226632034, rho=2.022284735880162, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.018244046821077237, relative_step_length=1.0000256817886963, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56415462, 0.16862293]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.24352266720979443, linear_terms=array([ 0.00058615, -0.00048178]), square_terms=array([[4.77510213e-05, 1.43280356e-03], + [1.43280356e-03, 1.04666941e-01]]), scale=0.03648715658670888, shift=array([5.56415462, 0.16862293])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1010,11 +1010,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=25, candidate_x=array([5.72806121, 0.16124208]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=-2.230953042384717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 21, 22, 23, 24]), old_indices_discarded=array([ 0, 3, 6, 7, 8, 10, 11, 12, 13, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 21, 22, 24, 25]), model=ScalarModel(intercept=0.24930004394171532, linear_terms=array([-2.38904522e-06, 3.69499149e-02]), square_terms=array([[1.38670904e-04, 5.63202076e-03], - [5.63202076e-03, 4.20322174e-01]]), scale=0.07297382757009696, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=25, candidate_x=array([5.52767313, 0.1692867 ]), index=25, x=array([5.52767313, 0.1692867 ]), fval=0.24285876672075696, rho=0.35571310395335953, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([1]), step_length=0.03648752369461524, relative_step_length=1.0000100612911693, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52767313, 0.1692867 ]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.24314216726921375, linear_terms=array([0.00031071, 0.00112818]), square_terms=array([[1.58692645e-04, 5.04893216e-03], + [5.04893216e-03, 4.20396152e-01]]), scale=0.07297431317341777, shift=array([5.52767313, 0.1692867 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1079,11 +1079,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=26, candidate_x=array([5.65521399, 0.16219328]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=-1.2961620458518521, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 21, 22, 24, 25]), old_indices_discarded=array([ 0, 3, 7, 11, 16, 18, 20, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=0.24709268914155363, linear_terms=array([0.00119193, 0.01731959]), square_terms=array([[5.69678824e-05, 1.57856574e-03], - [1.57856574e-03, 1.04842584e-01]]), scale=0.03648691378504848, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=26, candidate_x=array([5.45470179, 0.16996693]), index=26, x=array([5.45470179, 0.16996693]), fval=0.24259437399648873, rho=1.0590927394756011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 7, 11, 15, 16, 18]), step_length=0.07297450997400595, relative_step_length=1.0000026968474196, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.45470179, 0.16996693]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=0.2426437155645201, linear_terms=array([ 0.00066012, -0.0002101 ]), square_terms=array([[6.76799313e-04, 2.11994953e-02], + [2.11994953e-02, 1.68722879e+00]]), scale=0.14594862634683553, shift=array([5.45470179, 0.16996693])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1148,11 +1148,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=27, candidate_x=array([5.54577406, 0.16414671]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=-0.5277296110416976, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 21, 22, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.01824345689252424, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 22, 24, 26, 27]), model=ScalarModel(intercept=0.2435860331988614, linear_terms=array([0.00019367, 0.00162664]), square_terms=array([[1.03223473e-05, 3.21675100e-04], - [3.21675100e-04, 2.62070841e-02]]), scale=0.01824345689252424, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=27, candidate_x=array([5.30876491, 0.17181847]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=0.35150966109351695, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 3, 7, 8, 10, 11, 12, 13, 15, 16, 17, 23]), step_length=0.14594862668524397, relative_step_length=1.000000002318682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), model=ScalarModel(intercept=0.37246777346864934, linear_terms=array([0.00441854, 1.18206703]), square_terms=array([[ 0.02195466, -0.04742476], + [-0.04742476, 4.2726438 ]]), scale=array([0.2586872 , 0.21025284]), shift=array([5.30876491, 0.22025284])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1217,11 +1217,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=28, candidate_x=array([5.56408927, 0.16867456]), index=28, x=array([5.56408927, 0.16867456]), fval=0.24306721295336703, rho=0.5286886005963327, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 22, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.018278068411800873, relative_step_length=1.0018972018012011, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56408927, 0.16867456]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 21, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.242964314677221, linear_terms=array([ 0.00054927, -0.00108451]), square_terms=array([[4.50168218e-05, 1.34975492e-03], - [1.34975492e-03, 1.03947965e-01]]), scale=0.03648691378504848, shift=array([5.56408927, 0.16867456])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=28, candidate_x=array([5.0970293 , 0.16017426]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.7113501343654184, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 22, 23, 24, + 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), model=ScalarModel(intercept=0.2464194023851543, linear_terms=array([-0.00084176, 0.08200684]), square_terms=array([[6.47218524e-04, 2.11981078e-02], + [2.11981078e-02, 1.79973195e+00]]), scale=0.14594862634683553, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1286,11 +1287,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=29, candidate_x=array([5.52761033, 0.16952453]), index=29, x=array([5.52761033, 0.16952453]), fval=0.24288435328669916, rho=0.32939034392246147, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 21, 22, 24, 25, 26, 27, 28]), old_indices_discarded=array([1]), step_length=0.036488838982131434, relative_step_length=1.0000527640428647, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52761033, 0.16952453]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 22, 24, 26, 27, 28, 29]), model=ScalarModel(intercept=0.2420019335469184, linear_terms=array([0.00573273, 0.02861724]), square_terms=array([[0.00050338, 0.00674964], - [0.00674964, 0.41972427]]), scale=0.07297382757009696, shift=array([5.52761033, 0.16952453])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=29, candidate_x=array([5.45460642, 0.16345692]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.5703889919895696, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 14, 15, 16, 17, 20, 21, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24664926765654258, linear_terms=array([-0.00024156, 0.04118899]), square_terms=array([[1.70654218e-04, 5.36001424e-03], + [5.36001424e-03, 4.50004550e-01]]), scale=0.07297431317341777, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1355,11 +1356,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=30, candidate_x=array([5.4545693 , 0.16576734]), index=29, x=array([5.52761033, 0.16952453]), fval=0.24288435328669916, rho=-0.07956672210493755, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 22, 24, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 3, 7, 11, 17, 18, 19, 20, 21, 23, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52761033, 0.16952453]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 22, 24, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24488473081300505, linear_terms=array([0.0009977 , 0.01563392]), square_terms=array([[5.57194237e-05, 1.40119061e-03], - [1.40119061e-03, 1.05208668e-01]]), scale=0.03648691378504848, shift=array([5.52761033, 0.16952453])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=30, candidate_x=array([5.38163701, 0.16428162]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.668055965403151, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), old_indices_discarded=array([ 1, 11, 14, 17, 20, 21, 23, 24, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 26, 27, 29, 30]), model=ScalarModel(intercept=0.24521815600368138, linear_terms=array([0.00026426, 0.02038709]), square_terms=array([[4.74795190e-05, 1.30395809e-03], + [1.30395809e-03, 1.12532052e-01]]), scale=0.03648715658670888, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1424,11 +1425,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=31, candidate_x=array([5.49105748, 0.16462446]), index=29, x=array([5.52761033, 0.16952453]), fval=0.24288435328669916, rho=-0.5685237934133627, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 22, 24, 26, 27, 28, 29, 30]), old_indices_discarded=array([15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52761033, 0.16952453]), radius=0.01824345689252424, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 24, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24302431962819412, linear_terms=array([7.55308733e-05, 5.46770179e-04]), square_terms=array([[9.74635032e-06, 3.06763575e-04], - [3.06763575e-04, 2.62189415e-02]]), scale=0.01824345689252424, shift=array([5.52761033, 0.16952453])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=31, candidate_x=array([5.27280706, 0.16562611]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.8314331419387987, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 26, 27, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31]), model=ScalarModel(intercept=0.2425172780472681, linear_terms=array([1.04804479e-05, 1.37888168e-03]), square_terms=array([[1.12857867e-05, 2.91582077e-04], + [2.91582077e-04, 2.87671916e-02]]), scale=0.01824357829335444, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1493,11 +1494,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=32, candidate_x=array([5.50936376, 0.16935797]), index=32, x=array([5.50936376, 0.16935797]), fval=0.24283458910151834, rho=0.693413890433385, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 24, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.018247326571858506, relative_step_length=1.000212113272011, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.50936376, 0.16935797]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 22, 24, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.2429324718675529, linear_terms=array([1.80345936e-04, 8.51487840e-05]), square_terms=array([[3.88321243e-05, 1.22404574e-03], - [1.22404574e-03, 1.04906924e-01]]), scale=0.03648691378504848, shift=array([5.50936376, 0.16935797])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=32, candidate_x=array([5.32697754, 0.17075639]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=5.498532498656186, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.018243578293354667, relative_step_length=1.0000000000000124, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 7, 18, 19, 26, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=0.24442267125914174, linear_terms=array([0.00031052, 0.01755316]), square_terms=array([[4.95899758e-05, 1.31876158e-03], + [1.31876158e-03, 1.12502932e-01]]), scale=0.03648715658670888, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1562,11 +1563,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=33, candidate_x=array([5.47287898, 0.16975347]), index=33, x=array([5.47287898, 0.16975347]), fval=0.24267293680244376, rho=0.9673817824711848, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 22, 24, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 1, 26]), step_length=0.0364869257187115, relative_step_length=1.000000327066934, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47287898, 0.16975347]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 24, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.2427761119591001, linear_terms=array([ 2.39189726e-04, -1.29669188e-05]), square_terms=array([[1.58583544e-04, 4.77994302e-03], - [4.77994302e-03, 4.19573360e-01]]), scale=0.07297382757009696, shift=array([5.47287898, 0.16975347])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=33, candidate_x=array([5.29053673, 0.16549399]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=-1.1605097139907963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 18, 19, 26, 27, 29, 30, 31, 32]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31, 32, 33]), model=ScalarModel(intercept=0.24240138874383832, linear_terms=array([-1.34706482e-05, -2.00977445e-04]), square_terms=array([[1.12310877e-05, 2.93698037e-04], + [2.93698037e-04, 2.87879022e-02]]), scale=0.01824357829335444, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1631,11 +1632,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=34, candidate_x=array([5.39990991, 0.17058675]), index=34, x=array([5.39990991, 0.17058675]), fval=0.24231674775792228, rho=1.9020634332530058, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 24, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([ 1, 3, 7, 11, 15, 17, 19, 21, 22, 23, 25, 26]), step_length=0.07297382758305467, relative_step_length=1.0000000001775664, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.39990991, 0.17058675]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 7, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.2430089349062019, linear_terms=array([0.00175618, 0.07611165]), square_terms=array([[7.68632058e-04, 2.17025796e-02], - [2.17025796e-02, 1.79761440e+00]]), scale=0.14594765514019392, shift=array([5.39990991, 0.17058675])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=34, candidate_x=array([5.34522111, 0.17069763]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=-2.4579564090648676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.00912178914667722, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 32, 33, 34]), model=ScalarModel(intercept=0.24231372257835046, linear_terms=array([-3.67885812e-05, -1.56295915e-04]), square_terms=array([[2.74057907e-06, 7.34739042e-05], + [7.34739042e-05, 7.18730958e-03]]), scale=0.00912178914667722, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1700,12 +1701,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=35, candidate_x=array([5.25398401, 0.16616985]), index=34, x=array([5.39990991, 0.17058675]), fval=0.24231674775792228, rho=-0.72186797844346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 1, 3, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, - 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.39990991, 0.17058675]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([27, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.24231384996316413, linear_terms=array([ 0.00018402, -0.00403364]), square_terms=array([[1.69468241e-04, 4.40244170e-03], - [4.40244170e-03, 4.37739735e-01]]), scale=0.07297382757009696, shift=array([5.39990991, 0.17058675])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=35, candidate_x=array([5.33610086, 0.170861 ]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=0.45299609567353283, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 32, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.009123915567284637, relative_step_length=1.000233114422316, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2423307750427185, linear_terms=array([-1.78909546e-05, -1.98777282e-05]), square_terms=array([[1.11719188e-05, 2.91489798e-04], + [2.91489798e-04, 2.87505621e-02]]), scale=0.01824357829335444, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1770,11 +1770,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=36, candidate_x=array([5.32694835, 0.17199265]), index=34, x=array([5.39990991, 0.17058675]), fval=0.24231674775792228, rho=-0.24843012199644884, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 1, 3, 7, 11, 14, 15, 21, 22, 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.39990991, 0.17058675]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([27, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.24238669883858652, linear_terms=array([ 8.94506136e-05, -1.39054452e-03]), square_terms=array([[4.22380429e-05, 1.09259613e-03], - [1.09259613e-03, 1.11165757e-01]]), scale=0.03648691378504848, shift=array([5.39990991, 0.17058675])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=36, candidate_x=array([5.35434363, 0.17068872]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-2.963143065159759, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.00912178914667722, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 32, 34, 35, 36]), model=ScalarModel(intercept=0.24228466564231269, linear_terms=array([-2.24742123e-05, 1.60446770e-05]), square_terms=array([[2.72376463e-06, 6.51825427e-05], + [6.51825427e-05, 7.14572234e-03]]), scale=0.00912178914667722, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1839,11 +1839,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=37, candidate_x=array([5.36342988, 0.17140117]), index=37, x=array([5.36342988, 0.17140117]), fval=0.24231131739284278, rho=0.05653604311640793, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([27, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([ 3, 7, 28]), step_length=0.03648912499019746, relative_step_length=1.0000606026906524, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36342988, 0.17140117]), radius=0.01824345689252424, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37]), model=ScalarModel(intercept=0.2423113173928431, linear_terms=array([-2.76319807e-05, -7.53809665e-04]), square_terms=array([[1.12978733e-05, 3.07815866e-04], - [3.07815866e-04, 3.20364135e-02]]), scale=0.01824345689252424, shift=array([5.36342988, 0.17140117])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=37, candidate_x=array([5.34522209, 0.17075761]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-1.5642167142763028, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 32, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.00456089457333861, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 34, 35, 36, 37]), model=ScalarModel(intercept=0.24224211188369624, linear_terms=array([ 2.56124148e-06, -7.33810091e-04]), square_terms=array([[6.58450434e-07, 1.62540985e-05], + [1.62540985e-05, 1.74025458e-03]]), scale=0.00456089457333861, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1908,11 +1908,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=38, candidate_x=array([5.38167477, 0.17165504]), index=37, x=array([5.36342988, 0.17140117]), fval=0.24231131739284278, rho=-3.7871858271077357, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36342988, 0.17140117]), radius=0.00912172844626212, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37, 38]), model=ScalarModel(intercept=0.24232506941100967, linear_terms=array([1.70862499e-05, 1.21892565e-03]), square_terms=array([[2.52449325e-06, 5.92746750e-05], - [5.92746750e-05, 6.98298434e-03]]), scale=0.00912172844626212, shift=array([5.36342988, 0.17140117])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=38, candidate_x=array([5.33156143, 0.17281656]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-1.626890346177331, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.002280447286669305, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.2422610199987972, linear_terms=array([3.24076423e-06, 1.09212079e-04]), square_terms=array([[1.64689944e-07, 3.97469068e-06], + [3.97469068e-06, 4.42322743e-04]]), scale=0.002280447286669305, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -1977,11 +1977,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=39, candidate_x=array([5.35432492, 0.16988723]), index=37, x=array([5.36342988, 0.17140117]), fval=0.24231131739284278, rho=-0.28093054493142594, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36342988, 0.17140117]), radius=0.00456086422313106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 38, 39]), model=ScalarModel(intercept=0.2423113173928428, linear_terms=array([1.93621445e-05, 1.20738405e-04]), square_terms=array([[6.05669989e-07, 1.43497719e-05], - [1.43497719e-05, 1.80621102e-03]]), scale=0.00456086422313106, shift=array([5.36342988, 0.17140117])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=39, candidate_x=array([5.33381612, 0.17032108]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-0.01817829767899888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.0011402236433346526, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39]), model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([-5.63600427e-06, 5.10967489e-05]), square_terms=array([[4.70616627e-08, 8.13103968e-07], + [8.13103968e-07, 1.09902069e-04]]), scale=0.0011402236433346526, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2046,11 +2046,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=40, candidate_x=array([5.35886678, 0.17113519]), index=40, x=array([5.35886678, 0.17113519]), fval=0.2422660131172327, rho=2.040639609470749, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.004570846223013415, relative_step_length=1.0021886202688801, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35886678, 0.17113519]), radius=0.00912172844626212, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24230697792475, linear_terms=array([4.91531731e-06, 4.01574216e-04]), square_terms=array([[2.56045838e-06, 6.11904777e-05], - [6.11904777e-05, 7.09374545e-03]]), scale=0.00912172844626212, shift=array([5.35886678, 0.17113519])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=40, candidate_x=array([5.33723732, 0.17035023]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-0.7671475430136175, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.0005701118216673263, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 39, 40]), model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([2.67123864e-06, 2.32015646e-06]), square_terms=array([[1.01503457e-08, 2.55683066e-07], + [2.55683066e-07, 2.70609068e-05]]), scale=0.0005701118216673263, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2115,11 +2115,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=41, candidate_x=array([5.34975553, 0.17069814]), index=40, x=array([5.35886678, 0.17113519]), fval=0.2422660131172327, rho=-1.4993631427071992, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35886678, 0.17113519]), radius=0.00456086422313106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 39, 40, 41]), model=ScalarModel(intercept=0.24228491419551926, linear_terms=array([8.49239494e-06, 2.39836883e-05]), square_terms=array([[6.14732854e-07, 1.27371737e-05], - [1.27371737e-05, 1.83146540e-03]]), scale=0.00456086422313106, shift=array([5.35886678, 0.17113519])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=41, candidate_x=array([5.33553035, 0.17082138]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=3.1246566740586057, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0005718859490511061, relative_step_length=1.0031118936958565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.0011402236433346526, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39, 40, 41]), model=ScalarModel(intercept=0.24224726707979996, linear_terms=array([-2.89843844e-07, 5.08377823e-05]), square_terms=array([[4.27453955e-08, 9.21556966e-07], + [9.21556966e-07, 1.10052928e-04]]), scale=0.0011402236433346526, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2184,11 +2184,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=42, candidate_x=array([5.35430561, 0.1711073 ]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=3.562002347325252, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.004561249312406207, relative_step_length=1.0000844334004055, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00912172844626212, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.24229278798868248, linear_terms=array([5.19719019e-06, 3.68333221e-04]), square_terms=array([[2.55059029e-06, 6.04556474e-05], - [6.04556474e-05, 7.06166757e-03]]), scale=0.00912172844626212, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=42, candidate_x=array([5.3366653 , 0.17028846]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-1.9455689355183137, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.0005701118216673263, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 39, 40, 41, 42]), model=ScalarModel(intercept=0.24223360243650394, linear_terms=array([ 2.91278438e-06, -3.50722253e-06]), square_terms=array([[1.00813101e-08, 2.51773556e-07], + [2.51773556e-07, 2.71406595e-05]]), scale=0.0005701118216673263, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2253,11 +2253,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=43, candidate_x=array([5.3451353 , 0.17071002]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-3.526203505209563, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00456086422313106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.24226761562351934, linear_terms=array([ 9.11408781e-06, -4.65800448e-05]), square_terms=array([[6.22234181e-07, 1.39495570e-05], - [1.39495570e-05, 1.81742371e-03]]), scale=0.00456086422313106, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=43, candidate_x=array([5.33496087, 0.17089262]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-5.535264368026378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43]), model=ScalarModel(intercept=0.24224023352414065, linear_terms=array([-1.87742787e-06, -5.21106061e-06]), square_terms=array([[3.04573962e-09, 8.14917983e-08], + [8.14917983e-08, 6.83492010e-06]]), scale=0.00028505591083366315, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2322,11 +2322,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=44, candidate_x=array([5.34974579, 0.17125845]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-6.3021226718174725, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00228043211156553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.24227590126096604, linear_terms=array([1.99857765e-06, 8.90347166e-06]), square_terms=array([[1.57449078e-07, 3.62430439e-06], - [3.62430439e-06, 4.50545254e-04]]), scale=0.00228043211156553, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=44, candidate_x=array([5.33576708, 0.17098362]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-8.092327093834392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44]), model=ScalarModel(intercept=0.24223295230251193, linear_terms=array([-5.31736790e-07, 2.27975409e-05]), square_terms=array([[7.19415359e-10, 1.86503337e-08], + [1.86503337e-08, 1.61403964e-06]]), scale=0.00014252795541683157, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2391,11 +2391,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=45, candidate_x=array([5.3520249 , 0.17108069]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-2.3278538400000417, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.001140216055782765, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([39, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.2422604755293883, linear_terms=array([-2.61201951e-06, -1.20401397e-05]), square_terms=array([[4.05327539e-08, 9.79447033e-07], - [9.79447033e-07, 1.12921382e-04]]), scale=0.001140216055782765, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=45, candidate_x=array([5.33553886, 0.17067911]), index=45, x=array([5.33553886, 0.17067911]), fval=0.24220239688738698, rho=1.2842031311521878, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.00014252795541681761, relative_step_length=0.9999999999999021, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553886, 0.17067911]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.24222997040159935, linear_terms=array([5.08790955e-06, 9.87341516e-06]), square_terms=array([[2.55137299e-09, 5.67163305e-08], + [5.67163305e-08, 6.70555493e-06]]), scale=0.00028505591083366315, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2460,11 +2460,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=46, candidate_x=array([5.35544682, 0.17121658]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-6.392707920855079, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([39, 40, 41, 42, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.0005701080278913825, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([39, 42, 45, 46]), model=ScalarModel(intercept=0.24224718857995056, linear_terms=array([ 2.51070195e-06, -1.40348420e-05]), square_terms=array([[9.89078864e-09, 2.54954799e-07], - [2.54954799e-07, 2.83831506e-05]]), scale=0.0005701080278913825, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=46, candidate_x=array([5.33533366, 0.17047473]), index=45, x=array([5.33553886, 0.17067911]), fval=0.24220239688738698, rho=-0.7071442609659108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553886, 0.17067911]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.24221820849154518, linear_terms=array([-8.49124020e-07, 1.52697526e-05]), square_terms=array([[7.31788970e-10, 1.94307162e-08], + [1.94307162e-08, 1.63646545e-06]]), scale=0.00014252795541683157, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2529,11 +2529,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=47, candidate_x=array([5.35373784, 0.17136997]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-9.761421022287955, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([39, 42, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00028505401394569125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([39, 42, 46, 47]), model=ScalarModel(intercept=0.2422674286871516, linear_terms=array([-4.83904426e-06, -1.53169699e-06]), square_terms=array([[3.15265250e-09, 9.17365476e-08], - [9.17365476e-08, 7.04918151e-06]]), scale=0.00028505401394569125, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=47, candidate_x=array([5.33555459, 0.17053745]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=0.1367487913232024, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.00014252795541680403, relative_step_length=0.9999999999998068, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.24221648568928017, linear_terms=array([7.61123169e-06, 1.35871487e-05]), square_terms=array([[2.37686475e-09, 4.53613218e-08], + [4.53613218e-08, 6.64587957e-06]]), scale=0.00028505591083366315, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2598,11 +2598,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=48, candidate_x=array([5.35459114, 0.17114189]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-1.76601101681869, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([39, 42, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00014252700697284563, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 47, 48]), model=ScalarModel(intercept=0.24223673320326256, linear_terms=array([5.02372003e-07, 3.17577167e-05]), square_terms=array([[7.11686537e-10, 2.09000216e-08], - [2.09000216e-08, 1.66981772e-06]]), scale=0.00014252700697284563, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=48, candidate_x=array([5.33534308, 0.17034634]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=-3.283522683589635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.2422186103167294, linear_terms=array([-1.17163675e-06, 6.22141261e-06]), square_terms=array([[7.43556893e-10, 1.99248388e-08], + [1.99248388e-08, 1.65585538e-06]]), scale=0.00014252795541683157, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2667,11 +2667,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=49, candidate_x=array([5.35430724, 0.17096478]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-0.0087460529528702, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=7.126350348642281e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 48, 49]), model=ScalarModel(intercept=0.24223673320326244, linear_terms=array([2.13529941e-06, 3.25430275e-07]), square_terms=array([[1.72071804e-10, 4.10474480e-09], - [4.10474480e-09, 4.36348657e-07]]), scale=7.126350348642281e-05, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=49, candidate_x=array([5.33559928, 0.17040211]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=-5.589036142419969, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=7.126397770841579e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([41, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.2422175625561015, linear_terms=array([-1.08529643e-06, -2.15586658e-06]), square_terms=array([[2.13416381e-10, 6.09539586e-09], + [6.09539586e-09, 4.20627375e-07]]), scale=7.126397770841579e-05, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2736,11 +2736,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=50, candidate_x=array([5.3542349 , 0.17109845]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=0.788996851874173, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=7.12641605962148e-05, relative_step_length=1.0000092208460127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=0.00014252700697284563, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 47, 48, 49, 50]), model=ScalarModel(intercept=0.24224492204264927, linear_terms=array([-3.76156364e-06, 1.64186210e-05]), square_terms=array([[9.45521287e-10, 2.62809647e-08], - [2.62809647e-08, 1.70525908e-06]]), scale=0.00014252700697284563, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=50, candidate_x=array([5.33558034, 0.1706039 ]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=0.8769913650774368, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([41, 45, 46, 47, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=7.126397770841627e-05, relative_step_length=1.0000000000000069, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 44, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.24222108854190627, linear_terms=array([2.91505429e-06, 2.31914584e-06]), square_terms=array([[6.76506500e-10, 1.48024983e-08], + [1.48024983e-08, 1.66662830e-06]]), scale=0.00014252795541683157, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2805,11 +2805,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=51, candidate_x=array([5.35427389, 0.17096136]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=-0.16458879109056918, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 47, 48, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=7.126350348642281e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51]), model=ScalarModel(intercept=0.2422353089967809, linear_terms=array([ 1.17750480e-06, -1.71985531e-07]), square_terms=array([[1.67731546e-10, 4.23329742e-09], - [4.23329742e-09, 4.36229157e-07]]), scale=7.126350348642281e-05, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=51, candidate_x=array([5.33545409, 0.17053748]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.1962253015731078, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 44, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=7.126397770841579e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([41, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.2422098774430771, linear_terms=array([-1.71847573e-06, -1.85797440e-06]), square_terms=array([[2.17594755e-10, 6.01691056e-09], + [6.01691056e-09, 4.20747740e-07]]), scale=7.126397770841579e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2874,11 +2874,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=52, candidate_x=array([5.35416406, 0.1711062 ]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=-1.5548413629872948, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=3.563175174321141e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52]), model=ScalarModel(intercept=0.24223624887821155, linear_terms=array([-5.72724933e-08, -9.62016662e-08]), square_terms=array([[4.08764832e-11, 1.01998995e-09], - [1.01998995e-09, 1.09056071e-07]]), scale=3.563175174321141e-05, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=52, candidate_x=array([5.33563314, 0.17065221]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.7054787268641671, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([41, 45, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=3.563198885420789e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([45, 47, 50, 51, 52]), model=ScalarModel(intercept=0.24219982346462499, linear_terms=array([-2.48089008e-07, 6.03400460e-07]), square_terms=array([[3.67208696e-11, 7.72936282e-10], + [7.72936282e-10, 1.04547223e-07]]), scale=3.563198885420789e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -2943,11 +2943,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=53, candidate_x=array([5.35426853, 0.17111852]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=-49.234038408931376, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=1.7815875871605703e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 50, 52, 53]), model=ScalarModel(intercept=0.24223497560711219, linear_terms=array([-4.74432307e-08, 4.02785705e-06]), square_terms=array([[1.01749646e-11, 2.52377502e-10], - [2.52377502e-10, 2.76008124e-08]]), scale=1.7815875871605703e-05, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=53, candidate_x=array([5.3355965 , 0.17057214]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-1.0528351576259545, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=1.7815994427103947e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([45, 47, 50, 52, 53]), model=ScalarModel(intercept=0.24219994234056103, linear_terms=array([-3.77226186e-07, 2.60098977e-07]), square_terms=array([[9.65464377e-12, 1.64344963e-10], + [1.64344963e-10, 2.60967257e-08]]), scale=1.7815994427103947e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3012,11 +3012,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=54, candidate_x=array([5.35423517, 0.17108064]), index=54, x=array([5.35423517, 0.17108064]), fval=0.24223166413037348, rho=0.8391433969707881, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 50, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=1.7815875871611863e-05, relative_step_length=1.0000000000003457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35423517, 0.17108064]), radius=3.563175174321141e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.24223597836991295, linear_terms=array([4.47141236e-07, 1.37410695e-07]), square_terms=array([[4.06123488e-11, 1.05080752e-09], - [1.05080752e-09, 1.09096371e-07]]), scale=3.563175174321141e-05, shift=array([5.35423517, 0.17108064])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=54, candidate_x=array([5.33559545, 0.17059406]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.16779295077987869, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 50, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=8.907997213551973e-06, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 53, 54]), model=ScalarModel(intercept=0.2421984742592526, linear_terms=array([-9.96134608e-08, -2.19237776e-07]), square_terms=array([[3.52007307e-12, 5.01984429e-11], + [5.01984429e-11, 6.41022815e-09]]), scale=8.907997213551973e-06, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3081,11 +3081,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=55, candidate_x=array([5.35420058, 0.1710721 ]), index=55, x=array([5.35420058, 0.1710721 ]), fval=0.24223019482007688, rho=3.168897351422072, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=3.563336405853496e-05, relative_step_length=1.0000452493982102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35420058, 0.1710721 ]), radius=7.126350348642281e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.24223407516118386, linear_terms=array([2.08971335e-06, 5.06945130e-07]), square_terms=array([[1.70894503e-10, 4.32417031e-09], - [4.32417031e-09, 4.36442958e-07]]), scale=7.126350348642281e-05, shift=array([5.35420058, 0.1710721 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=55, candidate_x=array([5.33558391, 0.17061206]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.8865365759330065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([50, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=4.453998606775987e-06, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 54, 55]), model=ScalarModel(intercept=0.24219847425925245, linear_terms=array([7.44562175e-08, 8.11094797e-08]), square_terms=array([[7.34577640e-13, 2.02182033e-11], + [2.02182033e-11, 1.62644037e-09]]), scale=4.453998606775987e-06, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3150,11 +3150,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=56, candidate_x=array([5.35413069, 0.17105814]), index=56, x=array([5.35413069, 0.17105814]), fval=0.24222785386695275, rho=1.0942674916087185, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52, 53, 54, 55]), old_indices_discarded=array([], dtype=int64), step_length=7.126490760503242e-05, relative_step_length=1.0000197031936533, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35413069, 0.17105814]), radius=0.00014252700697284563, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.24222977403320833, linear_terms=array([6.45945040e-06, 1.64764211e-06]), square_terms=array([[7.69583087e-10, 1.76987495e-08], - [1.76987495e-08, 1.74604758e-06]]), scale=0.00014252700697284563, shift=array([5.35413069, 0.17105814])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=56, candidate_x=array([5.33557737, 0.17060058]), index=56, x=array([5.33557737, 0.17060058]), fval=0.2421983863534466, rho=0.8018411091024058, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([50, 54, 55]), old_indices_discarded=array([], dtype=int64), step_length=4.453998606601968e-06, relative_step_length=0.9999999999609298, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 57 entries., 'multistart_info': {'start_parameters': [array([5.35399092, 0.17103024]), array([5.83794505, 0.17769839])], 'local_optima': [{'solution_x': array([5.3540173 , 0.17104959]), 'solution_criterion': 0.24222753286188017, 'states': [State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.535399091577092, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.24222754644165562, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3219,11 +3219,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=57, candidate_x=array([5.35399092, 0.17103024]), index=57, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=0.046437840364656036, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([47, 48]), step_length=0.0001425347086457425, relative_step_length=1.0000540365861912, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 58 entries., 'multistart_info': {'start_parameters': [array([5.35400632, 0.17100463]), array([5.83790621, 0.17768436])], 'local_optima': [{'solution_x': array([5.35396236, 0.17102975]), 'solution_criterion': 0.24222765324129839, 'states': [State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.5354006322483765, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.24222959010961218, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=0, candidate_x=array([5.35399092, 0.17103024]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.535399091577092, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=3.4201048201667734, linear_terms=array([-0.28177681, 11.21026201]), square_terms=array([[ 1.80970210e-02, -4.44098984e-01], + [-4.44098984e-01, 1.94933118e+01]]), scale=array([0.47448509, 0.31775767]), shift=array([5.35399092, 0.32775767])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3288,11 +3288,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=0, candidate_x=array([5.35400632, 0.17100463]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.5354006322483765, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=3.4205094337836357, linear_terms=array([-0.2805156 , 11.21176097]), square_terms=array([[ 1.79834702e-02, -4.41911648e-01], - [-4.41911648e-01, 1.94960999e+01]]), scale=array([0.47448646, 0.31774554]), shift=array([5.35400632, 0.32774554])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=13, candidate_x=array([5.82847601, 0.15225999]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.15200421358915187, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.267699545788546, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 11, 13]), model=ScalarModel(intercept=0.3453241266842719, linear_terms=array([0.02479079, 0.95756552]), square_terms=array([[0.00728976, 0.05998005], + [0.05998005, 3.79816812]]), scale=array([0.23724255, 0.19913639]), shift=array([5.35399092, 0.20913639])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3357,11 +3357,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=13, candidate_x=array([5.82849278, 0.15221958]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.15263749851610584, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.26770031612418826, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 11, 13]), model=ScalarModel(intercept=0.34521638850922853, linear_terms=array([0.02481357, 0.95677547]), square_terms=array([[0.00728307, 0.06008121], - [0.06008121, 3.79651515]]), scale=array([0.23724323, 0.19912393]), shift=array([5.35400632, 0.20912393])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=14, candidate_x=array([5.11674837, 0.16207636]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.46143358417508423, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 11, 13]), old_indices_discarded=array([ 4, 8, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.133849772894273, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 5, 6, 7, 11, 13, 14]), model=ScalarModel(intercept=0.2361989522624684, linear_terms=array([0.00800855, 0.11334879]), square_terms=array([[0.00196917, 0.02968529], + [0.02968529, 1.62675202]]), scale=0.133849772894273, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3426,11 +3426,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=14, candidate_x=array([5.11676309, 0.16209309]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.46139147245990136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 11, 13]), old_indices_discarded=array([ 4, 8, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.13385015806209413, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 5, 6, 7, 11, 13, 14]), model=ScalarModel(intercept=0.2362023136012516, linear_terms=array([0.00800373, 0.11292454]), square_terms=array([[0.00196812, 0.02971222], - [0.02971222, 1.6263466 ]]), scale=0.13385015806209413, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=15, candidate_x=array([5.2200408 , 0.16416724]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.3292785496260621, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 5, 6, 7, 11, 13, 14]), old_indices_discarded=array([ 3, 4, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0669248864471365, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15]), model=ScalarModel(intercept=0.24255126823870177, linear_terms=array([-0.00173985, 0.04550415]), square_terms=array([[1.04037898e-04, 4.03282410e-03], + [4.03282410e-03, 4.06667977e-01]]), scale=0.0669248864471365, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3495,11 +3495,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=15, candidate_x=array([5.22005581, 0.16417695]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.3296965309559252, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 5, 6, 7, 11, 13, 14]), old_indices_discarded=array([ 3, 4, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.06692507903104707, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15]), model=ScalarModel(intercept=0.2425514564515167, linear_terms=array([-0.00173243, 0.04533645]), square_terms=array([[1.04215022e-04, 4.03453276e-03], - [4.03453276e-03, 4.06638215e-01]]), scale=0.06692507903104707, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=16, candidate_x=array([5.42083761, 0.16292119]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.5254278897156385, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.03346244322356825, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.24222754644165548, linear_terms=array([2.40076968e-05, 3.04590501e-05]), square_terms=array([[3.81907287e-05, 9.69902721e-04], + [9.69902721e-04, 9.61595844e-02]]), scale=0.03346244322356825, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3564,11 +3564,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=16, candidate_x=array([5.42085343, 0.16292196]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.527969662572183, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.03346253951552353, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.2422295901096121, linear_terms=array([ 2.26580939e-05, -4.08992370e-05]), square_terms=array([[3.81901974e-05, 9.69943906e-04], - [9.69943906e-04, 9.61508064e-02]]), scale=0.03346253951552353, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=17, candidate_x=array([5.32053009, 0.17135876]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-12.331804806172741, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.016731221611784124, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.24222754644165576, linear_terms=array([-5.69494381e-05, -5.53184385e-04]), square_terms=array([[9.07291274e-06, 2.18209656e-04], + [2.18209656e-04, 2.36720867e-02]]), scale=0.016731221611784124, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3633,11 +3633,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=17, candidate_x=array([5.32054565, 0.17135825]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-12.961189488478485, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.016731269757761767, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.2422295901096123, linear_terms=array([-5.65606521e-05, -5.81926201e-04]), square_terms=array([[9.07975120e-06, 2.17792130e-04], - [2.17792130e-04, 2.36612418e-02]]), scale=0.016731269757761767, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=18, candidate_x=array([5.3707248 , 0.17126653]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-2.7558354422098033, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.008365610805892062, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.2422275464416555, linear_terms=array([1.38880687e-05, 4.03818545e-03]), square_terms=array([[2.16087810e-06, 4.68859000e-05], + [4.68859000e-05, 5.46996031e-03]]), scale=0.008365610805892062, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3702,11 +3702,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=18, candidate_x=array([5.3707404 , 0.17126161]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-2.7201415121532704, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.008365634878880883, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.2422295901096123, linear_terms=array([1.34598825e-05, 3.65869991e-03]), square_terms=array([[2.16122199e-06, 4.72270582e-05], - [4.72270582e-05, 5.52216987e-03]]), scale=0.008365634878880883, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=19, candidate_x=array([5.36002884, 0.16483287]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-1.151992301822513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.004182805402946031, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.24222754644165576, linear_terms=array([ 3.59888783e-05, -3.78793239e-05]), square_terms=array([[5.09713542e-07, 1.18397453e-05], + [1.18397453e-05, 1.50374245e-03]]), scale=0.004182805402946031, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3771,11 +3771,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=19, candidate_x=array([5.36046292, 0.16542818]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-1.2036288742643446, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.004182817439440442, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.24222959010961212, linear_terms=array([ 3.60728759e-05, -8.14075763e-05]), square_terms=array([[5.09140331e-07, 1.18923756e-05], - [1.18923756e-05, 1.50154474e-03]]), scale=0.004182817439440442, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=20, candidate_x=array([5.34980904, 0.17116531]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-1.286789678419487, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0020914027014730155, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.24222754644165584, linear_terms=array([-2.51820360e-05, -6.10052937e-05]), square_terms=array([[1.51629548e-07, 3.66995116e-06], + [3.66995116e-06, 3.77296137e-04]]), scale=0.0020914027014730155, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3840,11 +3840,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=20, candidate_x=array([5.34982539, 0.17125839]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-1.7584614706899044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.002091408719720221, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.2422295901096121, linear_terms=array([-3.94682286e-05, -1.07284576e-04]), square_terms=array([[1.63719723e-07, 3.86879923e-06], - [3.86879923e-06, 3.77411497e-04]]), scale=0.002091408719720221, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=21, candidate_x=array([5.35608532, 0.17132867]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-1.568071751203749, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0010457013507365078, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.24222754644165548, linear_terms=array([-5.59040445e-06, 1.86243050e-04]), square_terms=array([[3.53347737e-08, 8.32607267e-07], + [8.32607267e-07, 9.15341276e-05]]), scale=0.0010457013507365078, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3909,11 +3909,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=21, candidate_x=array([5.35610317, 0.17152493]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-1.5793099561442583, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.0010457043598601104, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.242229590109612, linear_terms=array([-6.14047801e-06, 1.70331260e-04]), square_terms=array([[3.55438753e-08, 8.12487591e-07], - [8.12487591e-07, 9.07168416e-05]]), scale=0.0010457043598601104, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=22, candidate_x=array([5.35427497, 0.17002386]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.6233361747230197, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.0005228506753682539, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.24222754644165556, linear_terms=array([ 1.31043474e-05, -1.84619410e-05]), square_terms=array([[7.50259381e-09, 1.55081065e-07], + [1.55081065e-07, 2.36939961e-05]]), scale=0.0005228506753682539, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -3978,11 +3978,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=22, candidate_x=array([5.35431584, 0.17000579]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.7125655885495248, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.0005228521799300552, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.24222959010961226, linear_terms=array([ 2.21750742e-05, -1.65754846e-05]), square_terms=array([[8.04044878e-09, 8.13963755e-08], - [8.13963755e-08, 2.36740749e-05]]), scale=0.0005228521799300552, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=23, candidate_x=array([5.35353155, 0.1712816 ]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-3.5213900190638943, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.00026142533768412694, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.24222754644165576, linear_terms=array([-4.74482435e-05, -2.44726668e-05]), square_terms=array([[1.41323716e-08, 1.52966509e-07], + [1.52966509e-07, 5.98698669e-06]]), scale=0.00026142533768412694, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4047,11 +4047,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=23, candidate_x=array([5.35350687, 0.17119025]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-1.4644750163969473, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.0002614260899650276, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.2422295901096124, linear_terms=array([-2.62742633e-05, -1.98653516e-05]), square_terms=array([[8.22172600e-09, 8.86003727e-08], - [8.86003727e-08, 5.96022144e-06]]), scale=0.0002614260899650276, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=24, candidate_x=array([5.35423677, 0.1711431 ]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.37132867808536096, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=0.00013071266884206347, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.24222754644165614, linear_terms=array([-2.07495743e-06, 2.73291039e-05]), square_terms=array([[6.59873106e-10, 1.88545139e-08], + [1.88545139e-08, 1.43548055e-06]]), scale=0.00013071266884206347, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4116,11 +4116,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=24, candidate_x=array([5.35423597, 0.1711491 ]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.6200026560932351, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=0.0001307130449825138, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.24222959010961245, linear_terms=array([-1.79500884e-06, 2.05867756e-05]), square_terms=array([[5.79123464e-10, 1.50701874e-08], - [1.50701874e-08, 1.46264317e-06]]), scale=0.0001307130449825138, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=25, candidate_x=array([5.35400433, 0.17090022]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.7059482012607562, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=6.535633442103174e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.24222754644165567, linear_terms=array([ 8.98967423e-06, -8.17920862e-06]), square_terms=array([[3.56744076e-10, 2.72970406e-09], + [2.72970406e-09, 3.66603832e-07]]), scale=6.535633442103174e-05, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4185,11 +4185,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=25, candidate_x=array([5.35402239, 0.17087491]), index=0, x=array([5.35400632, 0.17100463]), fval=0.24222959010961218, rho=-0.9579828303290001, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35400632, 0.17100463]), radius=6.53565224912569e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.24222959010961237, linear_terms=array([ 1.05788421e-05, -7.94918103e-06]), square_terms=array([[5.36753228e-10, 3.62921811e-09], - [3.62921811e-09, 3.65110140e-07]]), scale=6.53565224912569e-05, shift=array([5.35400632, 0.17100463])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=26, candidate_x=array([5.35394182, 0.17107358]), index=0, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=-0.3128693343239847, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35399092, 0.17103024]), radius=3.267816721051587e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.24222754644165556, linear_terms=array([-7.13779397e-06, -5.28962750e-06]), square_terms=array([[3.64809042e-10, 1.76605938e-09], + [1.76605938e-09, 9.18716003e-08]]), scale=3.267816721051587e-05, shift=array([5.35399092, 0.17103024])), vector_model=VectorModel(intercepts=array([ 0.039855 , 0.08727496, 0.08464579, 0.10728423, 0.11930841, + 0.1319508 , 0.14808462, 0.15979372, 0.07460467, 0.12637411, + -0.21040967, -0.24904034, -0.05785571, -0.0376285 , -0.03109732, + -0.03263715, -0.0247748 ]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4254,11 +4254,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=26, candidate_x=array([5.35395333, 0.17104337]), index=26, x=array([5.35395333, 0.17104337]), fval=0.2422277273737417, rho=0.14083638536414766, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=6.563987106445188e-05, relative_step_length=1.0043354291567896, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35395333, 0.17104337]), radius=0.0001307130449825138, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 23, 24, 25, 26]), model=ScalarModel(intercept=0.24224352491633633, linear_terms=array([-3.08093940e-06, 5.60854271e-06]), square_terms=array([[6.31003624e-10, 1.52848775e-08], - [1.52848775e-08, 1.46005813e-06]]), scale=0.0001307130449825138, shift=array([5.35395333, 0.17104337])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.535399091577092, shift=array([5.35399092, 0.17103024])), candidate_index=27, candidate_x=array([5.3540173 , 0.17104959]), index=27, x=array([5.3540173 , 0.17104959]), fval=0.24222753286188017, rho=0.0015295632727075413, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=3.2719461113430184e-05, relative_step_length=1.0012636541899151, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 28 entries., 'history': {'params': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 5.1093119034531025, 'WealthShare': 0.01}, {'CRRA': 5.828476006589486, 'WealthShare': 0.15198745519212556}, {'CRRA': 4.879505824952354, 'WealthShare': 0.33185149063946}, {'CRRA': 5.828476006589486, 'WealthShare': 0.4997551746631117}, {'CRRA': 5.649517952308574, 'WealthShare': 0.01}, {'CRRA': 5.828476006589486, 'WealthShare': 0.010842937001314876}, {'CRRA': 4.879505824952354, 'WealthShare': 0.28861369993918135}, {'CRRA': 5.828476006589486, 'WealthShare': 0.6447330294382924}, {'CRRA': 5.694910146460927, 'WealthShare': 0.6455153315340556}, {'CRRA': 4.890302007181595, 'WealthShare': 0.6455153315340556}, {'CRRA': 4.879890995481528, 'WealthShare': 0.01}, {'CRRA': 4.929924157788898, 'WealthShare': 0.6455153315340556}, {'CRRA': 5.828476006589486, 'WealthShare': 0.15225999422840883}, {'CRRA': 5.116748370361637, 'WealthShare': 0.16207635543589285}, {'CRRA': 5.220040802323193, 'WealthShare': 0.16416724396618038}, {'CRRA': 5.420837614218516, 'WealthShare': 0.1629211859056509}, {'CRRA': 5.320530085228392, 'WealthShare': 0.17135876148314994}, {'CRRA': 5.370724795910354, 'WealthShare': 0.17126652634633668}, {'CRRA': 5.360028842271749, 'WealthShare': 0.1648328683516738}, {'CRRA': 5.3498090449241085, 'WealthShare': 0.1711653098868402}, {'CRRA': 5.356085319371155, 'WealthShare': 0.1713286726894636}, {'CRRA': 5.354274968633282, 'WealthShare': 0.17002385843905796}, {'CRRA': 5.3535315455809025, 'WealthShare': 0.17128159597424597}, {'CRRA': 5.354236772470478, 'WealthShare': 0.17114309869726121}, {'CRRA': 5.354004328918206, 'WealthShare': 0.1709002180664024}, {'CRRA': 5.353941824468242, 'WealthShare': 0.1710735823210765}, {'CRRA': 5.354017300200224, 'WealthShare': 0.17104959078075785}], 'criterion': [0.24222754644165562, 1.3527895501225145, 0.25585219407190074, 1.7031686075554116, 7.114963255276454, 1.1323588986508293, 1.0629740860263677, 0.9739545965561691, 24.151208938585803, 25.14913495293009, 31.421723888560248, 1.4661445151338668, 31.05022360234911, 0.25555761258405385, 0.24846046601007443, 0.24524990438076233, 0.24469829766742782, 0.24234471653621914, 0.24237853905195023, 0.2439613583848765, 0.2422745753945035, 0.24227377574409864, 0.2423139315422859, 0.24228992204868513, 0.24224780659238057, 0.24224638784441083, 0.2422313312843987, 0.2422275328618802], 'runtime': [0.0, 1.3785228689998803, 1.4328838589999577, 1.470578452999689, 1.5146917719998783, 1.5656984319998628, 1.6097452690000864, 1.65279088699981, 1.7004059599998982, 1.7587006379999366, 1.8216978010000275, 1.8793760539997493, 1.9314585709998937, 104.02275508299999, 105.23868029999994, 106.46841068100002, 107.68304327499982, 109.34099135399993, 110.5392876169999, 111.69529685399993, 113.02670071900002, 114.160099747, 115.31350987099995, 116.4846465669998, 117.68432192499995, 118.91863271700004, 120.25697914400007, 121.4351695360001], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {'solution_x': array([5.33557737, 0.17060058]), 'solution_criterion': 0.2421983863534466, 'states': [State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.2500249942408325, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4323,11 +4323,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=27, candidate_x=array([5.35402897, 0.17093677]), index=26, x=array([5.35395333, 0.17104337]), fval=0.2422277273737417, rho=-2.4450769350997383, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35395333, 0.17104337]), radius=6.53565224912569e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 24, 25, 26, 27]), model=ScalarModel(intercept=0.24222502087756123, linear_terms=array([ 7.45158661e-06, -5.05511895e-06]), square_terms=array([[2.70437387e-10, 3.09657346e-09], - [3.09657346e-09, 3.66169028e-07]]), scale=6.53565224912569e-05, shift=array([5.35395333, 0.17104337])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=0, candidate_x=array([5.83794505, 0.17769839]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=5.323973668078372, linear_terms=array([-0.75583641, 15.97016397]), square_terms=array([[ 0.06851782, -1.1616573 ], + [-1.1616573 , 24.80625807]]), scale=array([0.51737441, 0.3425364 ]), shift=array([5.83794505, 0.3525364 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4392,11 +4392,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=28, candidate_x=array([5.35389811, 0.17107935]), index=26, x=array([5.35395333, 0.17104337]), fval=0.2422277273737417, rho=-0.5341074942650346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 24, 25, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35395333, 0.17104337]), radius=3.267826124562845e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 26, 27, 28]), model=ScalarModel(intercept=0.2422294912910428, linear_terms=array([-6.90237896e-06, -8.63730234e-06]), square_terms=array([[3.99937459e-10, 9.87661306e-10], - [9.87661306e-10, 9.14882246e-08]]), scale=3.267826124562845e-05, shift=array([5.35395333, 0.17104337])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=13, candidate_x=array([6.1298769 , 0.14106399]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=-0.10249831672421726, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), model=ScalarModel(intercept=1.020144031837767, linear_terms=array([-0.15036877, 3.82287309]), square_terms=array([[ 0.02811468, -0.37120958], + [-0.37120958, 8.70439816]]), scale=array([0.2586872, 0.2131928]), shift=array([5.83794505, 0.2231928 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4461,11 +4461,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=29, candidate_x=array([5.3539736 , 0.17106901]), index=26, x=array([5.35395333, 0.17104337]), fval=0.2422277273737417, rho=-0.24050336499745487, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35395333, 0.17104337]), radius=1.6339130622814225e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 26, 28, 29]), model=ScalarModel(intercept=0.24222996390810195, linear_terms=array([-2.12423430e-07, 3.41299828e-07]), square_terms=array([[8.81693417e-12, 1.70394692e-10], - [1.70394692e-10, 2.28517123e-08]]), scale=1.6339130622814225e-05, shift=array([5.35395333, 0.17104337])), vector_model=VectorModel(intercepts=array([ 0.03985211, 0.08726933, 0.08463892, 0.10727527, 0.11929469, - 0.13193558, 0.14806457, 0.15973009, 0.07453542, 0.12630368, - -0.21049255, -0.24911785, -0.05784038, -0.03761571, -0.03108242, - -0.03262226, -0.02475777]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=14, candidate_x=array([5.57925785, 0.12046907]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=-0.3686077630799093, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), old_indices_discarded=array([ 4, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), model=ScalarModel(intercept=0.32337401785450964, linear_terms=array([-0.05509149, 1.09845952]), square_terms=array([[ 0.02085975, -0.26486803], + [-0.26486803, 4.42259018]]), scale=0.14594862634683553, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4530,11 +4530,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5354006322483765, shift=array([5.35400632, 0.17100463])), candidate_index=30, candidate_x=array([5.35396236, 0.17102975]), index=30, x=array([5.35396236, 0.17102975]), fval=0.24222765324129839, rho=0.1881625207794738, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 26, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=1.6339130622582518e-05, relative_step_length=0.9999999999858189, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 31 entries., 'history': {'params': [{'CRRA': 5.354006322483765, 'WealthShare': 0.17100463259932858}, {'CRRA': 5.109526968790541, 'WealthShare': 0.01}, {'CRRA': 5.828492778686707, 'WealthShare': 0.15204504729462334}, {'CRRA': 4.879519866280823, 'WealthShare': 0.3315815110617215}, {'CRRA': 5.828492778686707, 'WealthShare': 0.49970354850069737}, {'CRRA': 5.650144671106818, 'WealthShare': 0.01}, {'CRRA': 5.828492778686707, 'WealthShare': 0.010830254980025156}, {'CRRA': 4.879519866280823, 'WealthShare': 0.2883553842161002}, {'CRRA': 5.828492778686707, 'WealthShare': 0.6450105503098645}, {'CRRA': 5.694966409211402, 'WealthShare': 0.64549108880227}, {'CRRA': 4.889974668525427, 'WealthShare': 0.64549108880227}, {'CRRA': 4.8798282733752, 'WealthShare': 0.01}, {'CRRA': 4.9304750591958575, 'WealthShare': 0.64549108880227}, {'CRRA': 5.828492778686707, 'WealthShare': 0.1522195849634129}, {'CRRA': 5.116763094382295, 'WealthShare': 0.162093093389721}, {'CRRA': 5.220055806675767, 'WealthShare': 0.16417695361641313}, {'CRRA': 5.420853433224794, 'WealthShare': 0.1629219620717402}, {'CRRA': 5.320545651419049, 'WealthShare': 0.17135824640963598}, {'CRRA': 5.370740404051837, 'WealthShare': 0.17126161290924083}, {'CRRA': 5.360462921972393, 'WealthShare': 0.1654281801257648}, {'CRRA': 5.34982538641343, 'WealthShare': 0.17125838633404758}, {'CRRA': 5.356103173754721, 'WealthShare': 0.17152493029175916}, {'CRRA': 5.3543158433102125, 'WealthShare': 0.17000578603141864}, {'CRRA': 5.35350687499139, 'WealthShare': 0.171190245201562}, {'CRRA': 5.354235972636878, 'WealthShare': 0.17114910483656004}, {'CRRA': 5.354022385759902, 'WealthShare': 0.17087491031408678}, {'CRRA': 5.353953334391117, 'WealthShare': 0.1710433734654895}, {'CRRA': 5.354028970281308, 'WealthShare': 0.17093676618504597}, {'CRRA': 5.353898112243326, 'WealthShare': 0.17107935423371562}, {'CRRA': 5.353973599154986, 'WealthShare': 0.17106900953584717}, {'CRRA': 5.353962360456047, 'WealthShare': 0.17102975372461904}], 'criterion': [0.24222959010961218, 1.3526915601449605, 0.2557979077425905, 1.6976202707447787, 7.111771579168412, 1.1321339712554153, 1.0630922980278235, 0.9704855162793572, 24.20832805041196, 25.143500249276755, 31.418132454405693, 1.466178734043127, 31.038987308177695, 0.25560122414249775, 0.2484456388278747, 0.2452469160005554, 0.24469779683963266, 0.24234464265008338, 0.24237873573754437, 0.2437043378605563, 0.2422976408174732, 0.24231413788529724, 0.24231749216591117, 0.2422670790738343, 0.24225011378979655, 0.2422486854656542, 0.2422277273737417, 0.2422421007520363, 0.24223254767968563, 0.24223037955292165, 0.2422276532412984], 'runtime': [0.0, 1.2662754060002044, 1.309790532977786, 1.3521457499882672, 1.39415722200647, 1.4378280069795437, 1.4971206579939462, 1.5400017309875693, 1.5876122879853938, 1.6510058109997772, 1.696333269996103, 1.749355880980147, 1.8118952319782693, 104.28601489798166, 105.44708380999509, 106.8947216969973, 108.07735092300572, 109.80713732200093, 111.00220471099601, 112.19977979498799, 113.38472365800408, 114.5276050149987, 115.70550290797837, 116.90770325798076, 118.22135159099707, 119.39549850899493, 120.57577711797785, 121.75113257797784, 122.92057242800365, 124.09315577999223, 125.26397062599426], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}}, {'solution_x': array([5.35399092, 0.17103024]), 'solution_criterion': 0.24222754644165564, 'states': [State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.5837906205607757, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.2500179178839741, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=15, candidate_x=array([5.69639037, 0.13303053]), index=0, x=array([5.83794505, 0.17769839]), fval=0.25002499424083247, rho=-0.290539326522971, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), old_indices_discarded=array([ 2, 4, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 13, 14, 15]), model=ScalarModel(intercept=0.26967457001726336, linear_terms=array([0.00305809, 0.10758911]), square_terms=array([[2.03063183e-04, 7.58408569e-03], + [7.58408569e-03, 4.14525606e-01]]), scale=0.07297431317341777, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4599,11 +4599,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=0, candidate_x=array([5.83790621, 0.17768436]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.5837906205607757, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=5.453595147945511, linear_terms=array([-0.61048798, 16.31048865]), square_terms=array([[ 0.04668694, -0.92878469], - [-0.92878469, 25.23647844]]), scale=array([0.51737097, 0.34252766]), shift=array([5.83790621, 0.35252766])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=16, candidate_x=array([5.76466194, 0.16014228]), index=16, x=array([5.76466194, 0.16014228]), fval=0.24816488651407154, rho=0.12380898705859822, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.07535669323632875, relative_step_length=1.0326468309095207, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.76466194, 0.16014228]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 13, 14, 15, 16]), model=ScalarModel(intercept=0.24980518122836323, linear_terms=array([ 0.00949845, -0.02386839]), square_terms=array([[0.00257834, 0.05299243], + [0.05299243, 1.55867884]]), scale=0.14594862634683553, shift=array([5.76466194, 0.16014228])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4668,11 +4668,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=13, candidate_x=array([6.26028919, 0.14144164]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.09994429546320836, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.29189531028038784, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), model=ScalarModel(intercept=1.029847416702383, linear_terms=array([-0.15206957, 3.84463924]), square_terms=array([[ 0.02831952, -0.37138794], - [-0.37138794, 8.69674828]]), scale=array([0.25868548, 0.21318492]), shift=array([5.83790621, 0.22318492])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=17, candidate_x=array([5.61887231, 0.16729012]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=0.43892226114088795, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 6, 7, 13, 14, 15, 16]), old_indices_discarded=array([ 2, 5, 8, 10, 11, 12]), step_length=0.14596475101996004, relative_step_length=1.0001104818423312, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), model=ScalarModel(intercept=0.3762029190210623, linear_terms=array([0.00847171, 1.1355556 ]), square_terms=array([[ 0.0351543 , -0.12066294], + [-0.12066294, 4.22970628]]), scale=array([0.2586872 , 0.20798866]), shift=array([5.61887231, 0.21798866])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4737,11 +4737,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=14, candidate_x=array([5.58642299, 0.12009017]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.36587111256709887, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 13]), old_indices_discarded=array([ 4, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), model=ScalarModel(intercept=0.3307652689377624, linear_terms=array([-0.05352468, 1.12834325]), square_terms=array([[ 0.01880846, -0.24791449], - [-0.24791449, 4.39579594]]), scale=0.14594765514019392, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=18, candidate_x=array([5.36018511, 0.15621624]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.28174894853494853, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 11, 14, 15, 16, 17]), old_indices_discarded=array([ 2, 4, 5, 6, 8, 9, 10, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=0.24992252725033823, linear_terms=array([0.00316437, 0.06723442]), square_terms=array([[9.24265460e-04, 3.06288719e-02], + [3.06288719e-02, 1.73234651e+00]]), scale=0.14594862634683553, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4806,11 +4806,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=15, candidate_x=array([5.6964718 , 0.13230174]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.287744747266337, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 5, 6, 7, 8, 13, 14]), old_indices_discarded=array([ 2, 4, 9, 10, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15]), model=ScalarModel(intercept=0.2547685338865855, linear_terms=array([-0.01144467, 0.12662975]), square_terms=array([[0.00075227, 0.00196842], - [0.00196842, 0.43154335]]), scale=0.07297382757009696, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=19, candidate_x=array([5.47285718, 0.16421015]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.10605520364386403, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 7, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 6, 8, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.24923278591953518, linear_terms=array([0.00140877, 0.03201995]), square_terms=array([[2.08936369e-04, 7.01345842e-03], + [7.01345842e-03, 4.30945644e-01]]), scale=0.07297431317341777, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4875,11 +4875,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=16, candidate_x=array([5.91074951, 0.15649323]), index=0, x=array([5.83790621, 0.17768436]), fval=0.25001791788397415, rho=-0.08075482115611035, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83790621, 0.17768436]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.250017917883974, linear_terms=array([0.00170653, 0.02764723]), square_terms=array([[5.79166409e-05, 1.55153703e-03], - [1.55153703e-03, 9.94464848e-02]]), scale=0.03648691378504848, shift=array([5.83790621, 0.17768436])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=20, candidate_x=array([5.54582595, 0.16306459]), index=17, x=array([5.61887231, 0.16729012]), fval=0.24373445213195502, rho=-0.5716837122875763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 7, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61887231, 0.16729012]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 16, 17, 19, 20]), model=ScalarModel(intercept=0.24447966591289924, linear_terms=array([ 0.00057646, -0.00219486]), square_terms=array([[4.59383981e-05, 1.38128890e-03], + [1.38128890e-03, 1.04480430e-01]]), scale=0.03648715658670888, shift=array([5.61887231, 0.16729012])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -4944,11 +4944,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=17, candidate_x=array([5.8012702 , 0.16823018]), index=17, x=array([5.8012702 , 0.16823018]), fval=0.24610730524369165, rho=0.7658862060878359, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.03783620090025583, relative_step_length=1.0369800285975477, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.8012702 , 0.16823018]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15, 16, 17]), model=ScalarModel(intercept=0.253495790709351, linear_terms=array([-0.0041094 , 0.05810197]), square_terms=array([[1.86338211e-04, 5.35098646e-03], - [5.35098646e-03, 4.21903878e-01]]), scale=0.07297382757009696, shift=array([5.8012702 , 0.16823018])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=21, candidate_x=array([5.58239844, 0.16853196]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=0.938417610541786, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 16, 17, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.03649501064612971, relative_step_length=1.0002152554530293, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.2493403145907468, linear_terms=array([0.00206841, 0.03132845]), square_terms=array([[2.20612437e-04, 6.20169647e-03], + [6.20169647e-03, 4.20600928e-01]]), scale=0.07297431317341777, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5013,11 +5013,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=18, candidate_x=array([5.87411102, 0.15737845]), index=17, x=array([5.8012702 , 0.16823018]), fval=0.24610730524369165, rho=-0.5899514667309471, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.8012702 , 0.16823018]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2454286818777391, linear_terms=array([0.00117876, 0.00100895]), square_terms=array([[5.55180026e-05, 1.53917542e-03], - [1.53917542e-03, 9.97372256e-02]]), scale=0.03648691378504848, shift=array([5.8012702 , 0.16823018])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=22, candidate_x=array([5.50935584, 0.16418873]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.40755145676280513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([ 0, 3, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=0.24973373889649686, linear_terms=array([0.00287452, 0.0154878 ]), square_terms=array([[0.00011611, 0.00170606], + [0.00170606, 0.10513658]]), scale=0.03648715658670888, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5082,11 +5082,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=19, candidate_x=array([5.76478191, 0.168422 ]), index=19, x=array([5.76478191, 0.168422 ]), fval=0.2454218974000159, rho=0.5947364727296277, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.03648879990395198, relative_step_length=1.0000516930238224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.76478191, 0.168422 ]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.253681629181515, linear_terms=array([-0.00232564, 0.05166204]), square_terms=array([[1.50344993e-04, 5.87973754e-03], - [5.87973754e-03, 4.19729698e-01]]), scale=0.07297382757009696, shift=array([5.76478191, 0.168422 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=23, candidate_x=array([5.54583196, 0.16386291]), index=21, x=array([5.58239844, 0.16853196]), fval=0.24315780951732555, rho=-0.3707124284955786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58239844, 0.16853196]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 20, 21, 22, 23]), model=ScalarModel(intercept=0.24348377086944023, linear_terms=array([5.04148881e-05, 1.91725030e-04]), square_terms=array([[9.67169504e-06, 3.22879765e-04], + [3.22879765e-04, 2.62610807e-02]]), scale=0.01824357829335444, shift=array([5.58239844, 0.16853196])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5151,11 +5151,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=20, candidate_x=array([5.83762294, 0.15849024]), index=19, x=array([5.76478191, 0.168422 ]), fval=0.2454218974000159, rho=-0.7653738840742371, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.76478191, 0.168422 ]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.24478338352931536, linear_terms=array([0.00104386, 0.00067952]), square_terms=array([[5.17736477e-05, 1.49720466e-03], - [1.49720466e-03, 1.00208068e-01]]), scale=0.03648691378504848, shift=array([5.76478191, 0.168422 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=24, candidate_x=array([5.56415462, 0.16862293]), index=24, x=array([5.56415462, 0.16862293]), fval=0.24306497226632034, rho=2.022284735880162, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.018244046821077237, relative_step_length=1.0000256817886963, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56415462, 0.16862293]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.24352266720979443, linear_terms=array([ 0.00058615, -0.00048178]), square_terms=array([[4.77510213e-05, 1.43280356e-03], + [1.43280356e-03, 1.04666941e-01]]), scale=0.03648715658670888, shift=array([5.56415462, 0.16862293])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5220,11 +5220,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=21, candidate_x=array([5.72829533, 0.16871677]), index=21, x=array([5.72829533, 0.16871677]), fval=0.24507292693629099, rho=0.3416935494926942, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.03648777162651111, relative_step_length=1.0000235109351172, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.72829533, 0.16871677]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.24391675099982854, linear_terms=array([0.00218857, 0.00331565]), square_terms=array([[2.17190744e-04, 6.18094476e-03], - [6.18094476e-03, 4.05581192e-01]]), scale=0.07297382757009696, shift=array([5.72829533, 0.16871677])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=25, candidate_x=array([5.52767313, 0.1692867 ]), index=25, x=array([5.52767313, 0.1692867 ]), fval=0.24285876672075696, rho=0.35571310395335953, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([1]), step_length=0.03648752369461524, relative_step_length=1.0000100612911693, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52767313, 0.1692867 ]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.24314216726921375, linear_terms=array([0.00031071, 0.00112818]), square_terms=array([[1.58692645e-04, 5.04893216e-03], + [5.04893216e-03, 4.20396152e-01]]), scale=0.07297431317341777, shift=array([5.52767313, 0.1692867 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5289,11 +5289,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=22, candidate_x=array([5.65532086, 0.16922977]), index=22, x=array([5.65532086, 0.16922977]), fval=0.24407445209754292, rho=0.4777137071010113, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([1]), step_length=0.07297626535254732, relative_step_length=1.0000334062571683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.65532086, 0.16922977]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 14, 15, 17, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2530356753079221, linear_terms=array([-0.00506925, 0.09004345]), square_terms=array([[5.16559769e-04, 2.11789911e-02], - [2.11789911e-02, 1.68392550e+00]]), scale=0.14594765514019392, shift=array([5.65532086, 0.16922977])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=26, candidate_x=array([5.45470179, 0.16996693]), index=26, x=array([5.45470179, 0.16996693]), fval=0.24259437399648873, rho=1.0590927394756011, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 7, 11, 15, 16, 18]), step_length=0.07297450997400595, relative_step_length=1.0000026968474196, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.45470179, 0.16996693]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=0.2426437155645201, linear_terms=array([ 0.00066012, -0.0002101 ]), square_terms=array([[6.76799313e-04, 2.11994953e-02], + [2.11994953e-02, 1.68722879e+00]]), scale=0.14594862634683553, shift=array([5.45470179, 0.16996693])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5358,11 +5358,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=23, candidate_x=array([5.80115826, 0.15962533]), index=22, x=array([5.65532086, 0.16922977]), fval=0.24407445209754292, rho=-0.5666553713621563, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 14, 15, 17, 19, 20, 21, 22]), old_indices_discarded=array([ 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.65532086, 0.16922977]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 14, 15, 17, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.24311624432173012, linear_terms=array([0.00154361, 0.00363361]), square_terms=array([[1.87587564e-04, 5.59513446e-03], - [5.59513446e-03, 4.10801212e-01]]), scale=0.07297382757009696, shift=array([5.65532086, 0.16922977])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=27, candidate_x=array([5.30876491, 0.17181847]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=0.35150966109351695, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 18, 19, 20, 21, 22, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 3, 7, 8, 10, 11, 12, 13, 15, 16, 17, 23]), step_length=0.14594862668524397, relative_step_length=1.000000002318682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.29189725269367106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), model=ScalarModel(intercept=0.37246777346864934, linear_terms=array([0.00441854, 1.18206703]), square_terms=array([[ 0.02195466, -0.04742476], + [-0.04742476, 4.2726438 ]]), scale=array([0.2586872 , 0.21025284]), shift=array([5.30876491, 0.22025284])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5427,11 +5427,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=24, candidate_x=array([5.58234504, 0.16957707]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=0.6121148476317633, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 17, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 1, 16, 18]), step_length=0.0729766486457952, relative_step_length=1.0000386587327563, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=0.2489283617541748, linear_terms=array([0.00068245, 0.07288776]), square_terms=array([[5.95004131e-04, 2.31002626e-02], - [2.31002626e-02, 1.68009048e+00]]), scale=0.14594765514019392, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=28, candidate_x=array([5.0970293 , 0.16017426]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.7113501343654184, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 3, 11, 14, 18, 19, 20, 21, 27]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 22, 23, 24, + 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.14594862634683553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), model=ScalarModel(intercept=0.2464194023851543, linear_terms=array([-0.00084176, 0.08200684]), square_terms=array([[6.47218524e-04, 2.11981078e-02], + [2.11981078e-02, 1.79973195e+00]]), scale=0.14594862634683553, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5496,11 +5497,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=25, candidate_x=array([5.72806121, 0.16124208]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=-2.230953042384717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 21, 22, 23, 24]), old_indices_discarded=array([ 0, 3, 6, 7, 8, 10, 11, 12, 13, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 17, 19, 21, 22, 24, 25]), model=ScalarModel(intercept=0.24930004394171532, linear_terms=array([-2.38904522e-06, 3.69499149e-02]), square_terms=array([[1.38670904e-04, 5.63202076e-03], - [5.63202076e-03, 4.20322174e-01]]), scale=0.07297382757009696, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=29, candidate_x=array([5.45460642, 0.16345692]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.5703889919895696, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 22, 25, 26, 27, 28]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 14, 15, 16, 17, 20, 21, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.07297431317341777, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24664926765654258, linear_terms=array([-0.00024156, 0.04118899]), square_terms=array([[1.70654218e-04, 5.36001424e-03], + [5.36001424e-03, 4.50004550e-01]]), scale=0.07297431317341777, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5565,11 +5566,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=26, candidate_x=array([5.65521399, 0.16219328]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=-1.2961620458518521, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 17, 19, 21, 22, 24, 25]), old_indices_discarded=array([ 0, 3, 7, 11, 16, 18, 20, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 21, 22, 24, 25, 26]), model=ScalarModel(intercept=0.24709268914155363, linear_terms=array([0.00119193, 0.01731959]), square_terms=array([[5.69678824e-05, 1.57856574e-03], - [1.57856574e-03, 1.04842584e-01]]), scale=0.03648691378504848, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=30, candidate_x=array([5.38163701, 0.16428162]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.668055965403151, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 22, 26, 27, 28, 29]), old_indices_discarded=array([ 1, 11, 14, 17, 20, 21, 23, 24, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 3, 7, 18, 19, 26, 27, 29, 30]), model=ScalarModel(intercept=0.24521815600368138, linear_terms=array([0.00026426, 0.02038709]), square_terms=array([[4.74795190e-05, 1.30395809e-03], + [1.30395809e-03, 1.12532052e-01]]), scale=0.03648715658670888, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5634,11 +5635,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=27, candidate_x=array([5.54577406, 0.16414671]), index=24, x=array([5.58234504, 0.16957707]), fval=0.24318410996735987, rho=-0.5277296110416976, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 21, 22, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.58234504, 0.16957707]), radius=0.01824345689252424, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 22, 24, 26, 27]), model=ScalarModel(intercept=0.2435860331988614, linear_terms=array([0.00019367, 0.00162664]), square_terms=array([[1.03223473e-05, 3.21675100e-04], - [3.21675100e-04, 2.62070841e-02]]), scale=0.01824345689252424, shift=array([5.58234504, 0.16957707])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=31, candidate_x=array([5.27280706, 0.16562611]), index=27, x=array([5.30876491, 0.17181847]), fval=0.24243354455545987, rho=-0.8314331419387987, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 3, 7, 18, 19, 26, 27, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30876491, 0.17181847]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31]), model=ScalarModel(intercept=0.2425172780472681, linear_terms=array([1.04804479e-05, 1.37888168e-03]), square_terms=array([[1.12857867e-05, 2.91582077e-04], + [2.91582077e-04, 2.87671916e-02]]), scale=0.01824357829335444, shift=array([5.30876491, 0.17181847])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5703,11 +5704,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=28, candidate_x=array([5.56408927, 0.16867456]), index=28, x=array([5.56408927, 0.16867456]), fval=0.24306721295336703, rho=0.5286886005963327, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 22, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.018278068411800873, relative_step_length=1.0018972018012011, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56408927, 0.16867456]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 21, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.242964314677221, linear_terms=array([ 0.00054927, -0.00108451]), square_terms=array([[4.50168218e-05, 1.34975492e-03], - [1.34975492e-03, 1.03947965e-01]]), scale=0.03648691378504848, shift=array([5.56408927, 0.16867456])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=32, candidate_x=array([5.32697754, 0.17075639]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=5.498532498656186, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.018243578293354667, relative_step_length=1.0000000000000124, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.03648715658670888, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 7, 18, 19, 26, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=0.24442267125914174, linear_terms=array([0.00031052, 0.01755316]), square_terms=array([[4.95899758e-05, 1.31876158e-03], + [1.31876158e-03, 1.12502932e-01]]), scale=0.03648715658670888, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5772,11 +5773,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=29, candidate_x=array([5.52761033, 0.16952453]), index=29, x=array([5.52761033, 0.16952453]), fval=0.24288435328669916, rho=0.32939034392246147, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 21, 22, 24, 25, 26, 27, 28]), old_indices_discarded=array([1]), step_length=0.036488838982131434, relative_step_length=1.0000527640428647, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52761033, 0.16952453]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 15, 22, 24, 26, 27, 28, 29]), model=ScalarModel(intercept=0.2420019335469184, linear_terms=array([0.00573273, 0.02861724]), square_terms=array([[0.00050338, 0.00674964], - [0.00674964, 0.41972427]]), scale=0.07297382757009696, shift=array([5.52761033, 0.16952453])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=33, candidate_x=array([5.29053673, 0.16549399]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=-1.1605097139907963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 18, 19, 26, 27, 29, 30, 31, 32]), old_indices_discarded=array([3]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31, 32, 33]), model=ScalarModel(intercept=0.24240138874383832, linear_terms=array([-1.34706482e-05, -2.00977445e-04]), square_terms=array([[1.12310877e-05, 2.93698037e-04], + [2.93698037e-04, 2.87879022e-02]]), scale=0.01824357829335444, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5841,11 +5842,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=30, candidate_x=array([5.4545693 , 0.16576734]), index=29, x=array([5.52761033, 0.16952453]), fval=0.24288435328669916, rho=-0.07956672210493755, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 15, 22, 24, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 3, 7, 11, 17, 18, 19, 20, 21, 23, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52761033, 0.16952453]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 14, 22, 24, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24488473081300505, linear_terms=array([0.0009977 , 0.01563392]), square_terms=array([[5.57194237e-05, 1.40119061e-03], - [1.40119061e-03, 1.05208668e-01]]), scale=0.03648691378504848, shift=array([5.52761033, 0.16952453])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=34, candidate_x=array([5.34522111, 0.17069763]), index=32, x=array([5.32697754, 0.17075639]), fval=0.24225547458407035, rho=-2.4579564090648676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32697754, 0.17075639]), radius=0.00912178914667722, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 32, 33, 34]), model=ScalarModel(intercept=0.24231372257835046, linear_terms=array([-3.67885812e-05, -1.56295915e-04]), square_terms=array([[2.74057907e-06, 7.34739042e-05], + [7.34739042e-05, 7.18730958e-03]]), scale=0.00912178914667722, shift=array([5.32697754, 0.17075639])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5910,11 +5911,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=31, candidate_x=array([5.49105748, 0.16462446]), index=29, x=array([5.52761033, 0.16952453]), fval=0.24288435328669916, rho=-0.5685237934133627, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 1, 14, 22, 24, 26, 27, 28, 29, 30]), old_indices_discarded=array([15]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.52761033, 0.16952453]), radius=0.01824345689252424, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 24, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24302431962819412, linear_terms=array([7.55308733e-05, 5.46770179e-04]), square_terms=array([[9.74635032e-06, 3.06763575e-04], - [3.06763575e-04, 2.62189415e-02]]), scale=0.01824345689252424, shift=array([5.52761033, 0.16952453])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=35, candidate_x=array([5.33610086, 0.170861 ]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=0.45299609567353283, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 32, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.009123915567284637, relative_step_length=1.000233114422316, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.01824357829335444, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2423307750427185, linear_terms=array([-1.78909546e-05, -1.98777282e-05]), square_terms=array([[1.11719188e-05, 2.91489798e-04], + [2.91489798e-04, 2.87505621e-02]]), scale=0.01824357829335444, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -5979,11 +5980,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=32, candidate_x=array([5.50936376, 0.16935797]), index=32, x=array([5.50936376, 0.16935797]), fval=0.24283458910151834, rho=0.693413890433385, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 24, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.018247326571858506, relative_step_length=1.000212113272011, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.50936376, 0.16935797]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 22, 24, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.2429324718675529, linear_terms=array([1.80345936e-04, 8.51487840e-05]), square_terms=array([[3.88321243e-05, 1.22404574e-03], - [1.22404574e-03, 1.04906924e-01]]), scale=0.03648691378504848, shift=array([5.50936376, 0.16935797])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=36, candidate_x=array([5.35434363, 0.17068872]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-2.963143065159759, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.00912178914667722, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([18, 27, 32, 34, 35, 36]), model=ScalarModel(intercept=0.24228466564231269, linear_terms=array([-2.24742123e-05, 1.60446770e-05]), square_terms=array([[2.72376463e-06, 6.51825427e-05], + [6.51825427e-05, 7.14572234e-03]]), scale=0.00912178914667722, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6048,11 +6049,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=33, candidate_x=array([5.47287898, 0.16975347]), index=33, x=array([5.47287898, 0.16975347]), fval=0.24267293680244376, rho=0.9673817824711848, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 22, 24, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 1, 26]), step_length=0.0364869257187115, relative_step_length=1.000000327066934, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47287898, 0.16975347]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 24, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.2427761119591001, linear_terms=array([ 2.39189726e-04, -1.29669188e-05]), square_terms=array([[1.58583544e-04, 4.77994302e-03], - [4.77994302e-03, 4.19573360e-01]]), scale=0.07297382757009696, shift=array([5.47287898, 0.16975347])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=37, candidate_x=array([5.34522209, 0.17075761]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-1.5642167142763028, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([18, 27, 32, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.00456089457333861, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 34, 35, 36, 37]), model=ScalarModel(intercept=0.24224211188369624, linear_terms=array([ 2.56124148e-06, -7.33810091e-04]), square_terms=array([[6.58450434e-07, 1.62540985e-05], + [1.62540985e-05, 1.74025458e-03]]), scale=0.00456089457333861, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6117,11 +6118,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=34, candidate_x=array([5.39990991, 0.17058675]), index=34, x=array([5.39990991, 0.17058675]), fval=0.24231674775792228, rho=1.9020634332530058, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 24, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([ 1, 3, 7, 11, 15, 17, 19, 21, 22, 23, 25, 26]), step_length=0.07297382758305467, relative_step_length=1.0000000001775664, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.39990991, 0.17058675]), radius=0.14594765514019392, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 7, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.2430089349062019, linear_terms=array([0.00175618, 0.07611165]), square_terms=array([[7.68632058e-04, 2.17025796e-02], - [2.17025796e-02, 1.79761440e+00]]), scale=0.14594765514019392, shift=array([5.39990991, 0.17058675])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=38, candidate_x=array([5.33156143, 0.17281656]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-1.626890346177331, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.002280447286669305, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.2422610199987972, linear_terms=array([3.24076423e-06, 1.09212079e-04]), square_terms=array([[1.64689944e-07, 3.97469068e-06], + [3.97469068e-06, 4.42322743e-04]]), scale=0.002280447286669305, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6186,12 +6187,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=35, candidate_x=array([5.25398401, 0.16616985]), index=34, x=array([5.39990991, 0.17058675]), fval=0.24231674775792228, rho=-0.72186797844346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 7, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 1, 3, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, - 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.39990991, 0.17058675]), radius=0.07297382757009696, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([27, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.24231384996316413, linear_terms=array([ 0.00018402, -0.00403364]), square_terms=array([[1.69468241e-04, 4.40244170e-03], - [4.40244170e-03, 4.37739735e-01]]), scale=0.07297382757009696, shift=array([5.39990991, 0.17058675])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=39, candidate_x=array([5.33381612, 0.17032108]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-0.01817829767899888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 34, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.0011402236433346526, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39]), model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([-5.63600427e-06, 5.10967489e-05]), square_terms=array([[4.70616627e-08, 8.13103968e-07], + [8.13103968e-07, 1.09902069e-04]]), scale=0.0011402236433346526, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6256,11 +6256,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=36, candidate_x=array([5.32694835, 0.17199265]), index=34, x=array([5.39990991, 0.17058675]), fval=0.24231674775792228, rho=-0.24843012199644884, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([27, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 1, 3, 7, 11, 14, 15, 21, 22, 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.39990991, 0.17058675]), radius=0.03648691378504848, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([27, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.24238669883858652, linear_terms=array([ 8.94506136e-05, -1.39054452e-03]), square_terms=array([[4.22380429e-05, 1.09259613e-03], - [1.09259613e-03, 1.11165757e-01]]), scale=0.03648691378504848, shift=array([5.39990991, 0.17058675])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=40, candidate_x=array([5.33723732, 0.17035023]), index=35, x=array([5.33610086, 0.170861 ]), fval=0.2422392115565381, rho=-0.7671475430136175, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33610086, 0.170861 ]), radius=0.0005701118216673263, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 39, 40]), model=ScalarModel(intercept=0.2422392115565383, linear_terms=array([2.67123864e-06, 2.32015646e-06]), square_terms=array([[1.01503457e-08, 2.55683066e-07], + [2.55683066e-07, 2.70609068e-05]]), scale=0.0005701118216673263, shift=array([5.33610086, 0.170861 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6325,11 +6325,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=37, candidate_x=array([5.36342988, 0.17140117]), index=37, x=array([5.36342988, 0.17140117]), fval=0.24231131739284278, rho=0.05653604311640793, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([27, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([ 3, 7, 28]), step_length=0.03648912499019746, relative_step_length=1.0000606026906524, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36342988, 0.17140117]), radius=0.01824345689252424, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37]), model=ScalarModel(intercept=0.2423113173928431, linear_terms=array([-2.76319807e-05, -7.53809665e-04]), square_terms=array([[1.12978733e-05, 3.07815866e-04], - [3.07815866e-04, 3.20364135e-02]]), scale=0.01824345689252424, shift=array([5.36342988, 0.17140117])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=41, candidate_x=array([5.33553035, 0.17082138]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=3.1246566740586057, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0005718859490511061, relative_step_length=1.0031118936958565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.0011402236433346526, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 38, 39, 40, 41]), model=ScalarModel(intercept=0.24224726707979996, linear_terms=array([-2.89843844e-07, 5.08377823e-05]), square_terms=array([[4.27453955e-08, 9.21556966e-07], + [9.21556966e-07, 1.10052928e-04]]), scale=0.0011402236433346526, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6394,11 +6394,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=38, candidate_x=array([5.38167477, 0.17165504]), index=37, x=array([5.36342988, 0.17140117]), fval=0.24231131739284278, rho=-3.7871858271077357, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36342988, 0.17140117]), radius=0.00912172844626212, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37, 38]), model=ScalarModel(intercept=0.24232506941100967, linear_terms=array([1.70862499e-05, 1.21892565e-03]), square_terms=array([[2.52449325e-06, 5.92746750e-05], - [5.92746750e-05, 6.98298434e-03]]), scale=0.00912172844626212, shift=array([5.36342988, 0.17140117])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=42, candidate_x=array([5.3366653 , 0.17028846]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-1.9455689355183137, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.0005701118216673263, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 39, 40, 41, 42]), model=ScalarModel(intercept=0.24223360243650394, linear_terms=array([ 2.91278438e-06, -3.50722253e-06]), square_terms=array([[1.00813101e-08, 2.51773556e-07], + [2.51773556e-07, 2.71406595e-05]]), scale=0.0005701118216673263, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6463,11 +6463,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=39, candidate_x=array([5.35432492, 0.16988723]), index=37, x=array([5.36342988, 0.17140117]), fval=0.24231131739284278, rho=-0.28093054493142594, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36342988, 0.17140117]), radius=0.00456086422313106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 38, 39]), model=ScalarModel(intercept=0.2423113173928428, linear_terms=array([1.93621445e-05, 1.20738405e-04]), square_terms=array([[6.05669989e-07, 1.43497719e-05], - [1.43497719e-05, 1.80621102e-03]]), scale=0.00456086422313106, shift=array([5.36342988, 0.17140117])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=43, candidate_x=array([5.33496087, 0.17089262]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-5.535264368026378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43]), model=ScalarModel(intercept=0.24224023352414065, linear_terms=array([-1.87742787e-06, -5.21106061e-06]), square_terms=array([[3.04573962e-09, 8.14917983e-08], + [8.14917983e-08, 6.83492010e-06]]), scale=0.00028505591083366315, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6532,11 +6532,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=40, candidate_x=array([5.35886678, 0.17113519]), index=40, x=array([5.35886678, 0.17113519]), fval=0.2422660131172327, rho=2.040639609470749, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([37, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.004570846223013415, relative_step_length=1.0021886202688801, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35886678, 0.17113519]), radius=0.00912172844626212, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24230697792475, linear_terms=array([4.91531731e-06, 4.01574216e-04]), square_terms=array([[2.56045838e-06, 6.11904777e-05], - [6.11904777e-05, 7.09374545e-03]]), scale=0.00912172844626212, shift=array([5.35886678, 0.17113519])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=44, candidate_x=array([5.33576708, 0.17098362]), index=41, x=array([5.33553035, 0.17082138]), fval=0.2422306308030337, rho=-8.092327093834392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553035, 0.17082138]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44]), model=ScalarModel(intercept=0.24223295230251193, linear_terms=array([-5.31736790e-07, 2.27975409e-05]), square_terms=array([[7.19415359e-10, 1.86503337e-08], + [1.86503337e-08, 1.61403964e-06]]), scale=0.00014252795541683157, shift=array([5.33553035, 0.17082138])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6601,11 +6601,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=41, candidate_x=array([5.34975553, 0.17069814]), index=40, x=array([5.35886678, 0.17113519]), fval=0.2422660131172327, rho=-1.4993631427071992, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35886678, 0.17113519]), radius=0.00456086422313106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 39, 40, 41]), model=ScalarModel(intercept=0.24228491419551926, linear_terms=array([8.49239494e-06, 2.39836883e-05]), square_terms=array([[6.14732854e-07, 1.27371737e-05], - [1.27371737e-05, 1.83146540e-03]]), scale=0.00456086422313106, shift=array([5.35886678, 0.17113519])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=45, candidate_x=array([5.33553886, 0.17067911]), index=45, x=array([5.33553886, 0.17067911]), fval=0.24220239688738698, rho=1.2842031311521878, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.00014252795541681761, relative_step_length=0.9999999999999021, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553886, 0.17067911]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.24222997040159935, linear_terms=array([5.08790955e-06, 9.87341516e-06]), square_terms=array([[2.55137299e-09, 5.67163305e-08], + [5.67163305e-08, 6.70555493e-06]]), scale=0.00028505591083366315, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6670,11 +6670,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=42, candidate_x=array([5.35430561, 0.1711073 ]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=3.562002347325252, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.004561249312406207, relative_step_length=1.0000844334004055, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00912172844626212, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([34, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.24229278798868248, linear_terms=array([5.19719019e-06, 3.68333221e-04]), square_terms=array([[2.55059029e-06, 6.04556474e-05], - [6.04556474e-05, 7.06166757e-03]]), scale=0.00912172844626212, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=46, candidate_x=array([5.33533366, 0.17047473]), index=45, x=array([5.33553886, 0.17067911]), fval=0.24220239688738698, rho=-0.7071442609659108, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33553886, 0.17067911]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.24221820849154518, linear_terms=array([-8.49124020e-07, 1.52697526e-05]), square_terms=array([[7.31788970e-10, 1.94307162e-08], + [1.94307162e-08, 1.63646545e-06]]), scale=0.00014252795541683157, shift=array([5.33553886, 0.17067911])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6739,11 +6739,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=43, candidate_x=array([5.3451353 , 0.17071002]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-3.526203505209563, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([34, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00456086422313106, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.24226761562351934, linear_terms=array([ 9.11408781e-06, -4.65800448e-05]), square_terms=array([[6.22234181e-07, 1.39495570e-05], - [1.39495570e-05, 1.81742371e-03]]), scale=0.00456086422313106, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=47, candidate_x=array([5.33555459, 0.17053745]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=0.1367487913232024, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.00014252795541680403, relative_step_length=0.9999999999998068, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=0.00028505591083366315, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.24221648568928017, linear_terms=array([7.61123169e-06, 1.35871487e-05]), square_terms=array([[2.37686475e-09, 4.53613218e-08], + [4.53613218e-08, 6.64587957e-06]]), scale=0.00028505591083366315, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6808,11 +6808,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=44, candidate_x=array([5.34974579, 0.17125845]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-6.3021226718174725, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00228043211156553, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([37, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.24227590126096604, linear_terms=array([1.99857765e-06, 8.90347166e-06]), square_terms=array([[1.57449078e-07, 3.62430439e-06], - [3.62430439e-06, 4.50545254e-04]]), scale=0.00228043211156553, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=48, candidate_x=array([5.33534308, 0.17034634]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=-3.283522683589635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.2422186103167294, linear_terms=array([-1.17163675e-06, 6.22141261e-06]), square_terms=array([[7.43556893e-10, 1.99248388e-08], + [1.99248388e-08, 1.65585538e-06]]), scale=0.00014252795541683157, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6877,11 +6877,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=45, candidate_x=array([5.3520249 , 0.17108069]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-2.3278538400000417, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([37, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.001140216055782765, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([39, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.2422604755293883, linear_terms=array([-2.61201951e-06, -1.20401397e-05]), square_terms=array([[4.05327539e-08, 9.79447033e-07], - [9.79447033e-07, 1.12921382e-04]]), scale=0.001140216055782765, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=49, candidate_x=array([5.33559928, 0.17040211]), index=47, x=array([5.33555459, 0.17053745]), fval=0.24220041894287114, rho=-5.589036142419969, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33555459, 0.17053745]), radius=7.126397770841579e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([41, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.2422175625561015, linear_terms=array([-1.08529643e-06, -2.15586658e-06]), square_terms=array([[2.13416381e-10, 6.09539586e-09], + [6.09539586e-09, 4.20627375e-07]]), scale=7.126397770841579e-05, shift=array([5.33555459, 0.17053745])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -6946,11 +6946,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=46, candidate_x=array([5.35544682, 0.17121658]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-6.392707920855079, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([39, 40, 41, 42, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.0005701080278913825, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([39, 42, 45, 46]), model=ScalarModel(intercept=0.24224718857995056, linear_terms=array([ 2.51070195e-06, -1.40348420e-05]), square_terms=array([[9.89078864e-09, 2.54954799e-07], - [2.54954799e-07, 2.83831506e-05]]), scale=0.0005701080278913825, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=50, candidate_x=array([5.33558034, 0.1706039 ]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=0.8769913650774368, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([41, 45, 46, 47, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=7.126397770841627e-05, relative_step_length=1.0000000000000069, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=0.00014252795541683157, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([35, 41, 44, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.24222108854190627, linear_terms=array([2.91505429e-06, 2.31914584e-06]), square_terms=array([[6.76506500e-10, 1.48024983e-08], + [1.48024983e-08, 1.66662830e-06]]), scale=0.00014252795541683157, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -7015,11 +7015,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=47, candidate_x=array([5.35373784, 0.17136997]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-9.761421022287955, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([39, 42, 45, 46]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00028505401394569125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([39, 42, 46, 47]), model=ScalarModel(intercept=0.2422674286871516, linear_terms=array([-4.83904426e-06, -1.53169699e-06]), square_terms=array([[3.15265250e-09, 9.17365476e-08], - [9.17365476e-08, 7.04918151e-06]]), scale=0.00028505401394569125, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=51, candidate_x=array([5.33545409, 0.17053748]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.1962253015731078, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 41, 44, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=7.126397770841579e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([41, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.2422098774430771, linear_terms=array([-1.71847573e-06, -1.85797440e-06]), square_terms=array([[2.17594755e-10, 6.01691056e-09], + [6.01691056e-09, 4.20747740e-07]]), scale=7.126397770841579e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -7084,11 +7084,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=48, candidate_x=array([5.35459114, 0.17114189]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-1.76601101681869, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([39, 42, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=0.00014252700697284563, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 47, 48]), model=ScalarModel(intercept=0.24223673320326256, linear_terms=array([5.02372003e-07, 3.17577167e-05]), square_terms=array([[7.11686537e-10, 2.09000216e-08], - [2.09000216e-08, 1.66981772e-06]]), scale=0.00014252700697284563, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=52, candidate_x=array([5.33563314, 0.17065221]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.7054787268641671, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([41, 45, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=3.563198885420789e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([45, 47, 50, 51, 52]), model=ScalarModel(intercept=0.24219982346462499, linear_terms=array([-2.48089008e-07, 6.03400460e-07]), square_terms=array([[3.67208696e-11, 7.72936282e-10], + [7.72936282e-10, 1.04547223e-07]]), scale=3.563198885420789e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -7153,11 +7153,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=49, candidate_x=array([5.35430724, 0.17096478]), index=42, x=array([5.35430561, 0.1711073 ]), fval=0.2422367332032626, rho=-0.0087460529528702, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35430561, 0.1711073 ]), radius=7.126350348642281e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 48, 49]), model=ScalarModel(intercept=0.24223673320326244, linear_terms=array([2.13529941e-06, 3.25430275e-07]), square_terms=array([[1.72071804e-10, 4.10474480e-09], - [4.10474480e-09, 4.36348657e-07]]), scale=7.126350348642281e-05, shift=array([5.35430561, 0.1711073 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=53, candidate_x=array([5.3355965 , 0.17057214]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-1.0528351576259545, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=1.7815994427103947e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([45, 47, 50, 52, 53]), model=ScalarModel(intercept=0.24219994234056103, linear_terms=array([-3.77226186e-07, 2.60098977e-07]), square_terms=array([[9.65464377e-12, 1.64344963e-10], + [1.64344963e-10, 2.60967257e-08]]), scale=1.7815994427103947e-05, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -7222,11 +7222,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=50, candidate_x=array([5.3542349 , 0.17109845]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=0.788996851874173, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=7.12641605962148e-05, relative_step_length=1.0000092208460127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=0.00014252700697284563, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 47, 48, 49, 50]), model=ScalarModel(intercept=0.24224492204264927, linear_terms=array([-3.76156364e-06, 1.64186210e-05]), square_terms=array([[9.45521287e-10, 2.62809647e-08], - [2.62809647e-08, 1.70525908e-06]]), scale=0.00014252700697284563, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=54, candidate_x=array([5.33559545, 0.17059406]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.16779295077987869, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 47, 50, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=8.907997213551973e-06, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 53, 54]), model=ScalarModel(intercept=0.2421984742592526, linear_terms=array([-9.96134608e-08, -2.19237776e-07]), square_terms=array([[3.52007307e-12, 5.01984429e-11], + [5.01984429e-11, 6.41022815e-09]]), scale=8.907997213551973e-06, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -7291,11 +7291,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=51, candidate_x=array([5.35427389, 0.17096136]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=-0.16458879109056918, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 47, 48, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=7.126350348642281e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51]), model=ScalarModel(intercept=0.2422353089967809, linear_terms=array([ 1.17750480e-06, -1.71985531e-07]), square_terms=array([[1.67731546e-10, 4.23329742e-09], - [4.23329742e-09, 4.36229157e-07]]), scale=7.126350348642281e-05, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], + [0., 0.]]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), candidate_index=55, candidate_x=array([5.33558391, 0.17061206]), index=50, x=array([5.33558034, 0.1706039 ]), fval=0.2421984742592525, rho=-0.8865365759330065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([50, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33558034, 0.1706039 ]), radius=4.453998606775987e-06, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 54, 55]), model=ScalarModel(intercept=0.24219847425925245, linear_terms=array([7.44562175e-08, 8.11094797e-08]), square_terms=array([[7.34577640e-13, 2.02182033e-11], + [2.02182033e-11, 1.62644037e-09]]), scale=4.453998606775987e-06, shift=array([5.33558034, 0.1706039 ])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, + 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, + -0.17875787, -0.21072965, -0.08571349, -0.06417308, -0.05746845, + -0.05903034, -0.05317732]), linear_terms=array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], @@ -7360,352 +7360,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [0., 0.]], [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=52, candidate_x=array([5.35416406, 0.1711062 ]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=-1.5548413629872948, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=3.563175174321141e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52]), model=ScalarModel(intercept=0.24223624887821155, linear_terms=array([-5.72724933e-08, -9.62016662e-08]), square_terms=array([[4.08764832e-11, 1.01998995e-09], - [1.01998995e-09, 1.09056071e-07]]), scale=3.563175174321141e-05, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=53, candidate_x=array([5.35426853, 0.17111852]), index=50, x=array([5.3542349 , 0.17109845]), fval=0.24223503271901853, rho=-49.234038408931376, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3542349 , 0.17109845]), radius=1.7815875871605703e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 50, 52, 53]), model=ScalarModel(intercept=0.24223497560711219, linear_terms=array([-4.74432307e-08, 4.02785705e-06]), square_terms=array([[1.01749646e-11, 2.52377502e-10], - [2.52377502e-10, 2.76008124e-08]]), scale=1.7815875871605703e-05, shift=array([5.3542349 , 0.17109845])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=54, candidate_x=array([5.35423517, 0.17108064]), index=54, x=array([5.35423517, 0.17108064]), fval=0.24223166413037348, rho=0.8391433969707881, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 50, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=1.7815875871611863e-05, relative_step_length=1.0000000000003457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35423517, 0.17108064]), radius=3.563175174321141e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.24223597836991295, linear_terms=array([4.47141236e-07, 1.37410695e-07]), square_terms=array([[4.06123488e-11, 1.05080752e-09], - [1.05080752e-09, 1.09096371e-07]]), scale=3.563175174321141e-05, shift=array([5.35423517, 0.17108064])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=55, candidate_x=array([5.35420058, 0.1710721 ]), index=55, x=array([5.35420058, 0.1710721 ]), fval=0.24223019482007688, rho=3.168897351422072, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=3.563336405853496e-05, relative_step_length=1.0000452493982102, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35420058, 0.1710721 ]), radius=7.126350348642281e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.24223407516118386, linear_terms=array([2.08971335e-06, 5.06945130e-07]), square_terms=array([[1.70894503e-10, 4.32417031e-09], - [4.32417031e-09, 4.36442958e-07]]), scale=7.126350348642281e-05, shift=array([5.35420058, 0.1710721 ])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=56, candidate_x=array([5.35413069, 0.17105814]), index=56, x=array([5.35413069, 0.17105814]), fval=0.24222785386695275, rho=1.0942674916087185, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52, 53, 54, 55]), old_indices_discarded=array([], dtype=int64), step_length=7.126490760503242e-05, relative_step_length=1.0000197031936533, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35413069, 0.17105814]), radius=0.00014252700697284563, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([42, 49, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.24222977403320833, linear_terms=array([6.45945040e-06, 1.64764211e-06]), square_terms=array([[7.69583087e-10, 1.76987495e-08], - [1.76987495e-08, 1.74604758e-06]]), scale=0.00014252700697284563, shift=array([5.35413069, 0.17105814])), vector_model=VectorModel(intercepts=array([ 0.04097031, 0.08957803, 0.08816081, 0.11232093, 0.12575895, - 0.13980376, 0.15781889, 0.17546538, 0.09348822, 0.15053254, - -0.17880431, -0.21077511, -0.08570458, -0.06416478, -0.05745986, - -0.05902133, -0.05316704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.5837906205607757, shift=array([5.83790621, 0.17768436])), candidate_index=57, candidate_x=array([5.35399092, 0.17103024]), index=57, x=array([5.35399092, 0.17103024]), fval=0.24222754644165564, rho=0.046437840364656036, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([42, 49, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([47, 48]), 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41.17240058000016, 42.34739114900003, 43.51756579399989, 44.678886351000074, 45.83479911199993, 46.989458055999876, 48.12758910999992, 49.42098368999996, 50.618441345000065, 51.84377754599973, 52.98982088799994, 54.21043696800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 5.35399092, 0.17103024], [ 7.00625 , 0.19375 ], [12.9125 , 0.1325 ], [ 4.64375 , 0.31625 ], @@ -7724,7 +7379,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83790621, [17.6375 , 0.8675 ], [ 5.825 , 0.745 ], [12.321875 , 0.959375 ], - [ 2.28125 , 0.92875 ]]), 'exploration_results': array([2.42229590e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, + [ 2.28125 , 0.92875 ]]), 'exploration_results': array([2.42227546e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..ba2680a --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +import importlib.metadata +from typing import Any + +project = "estimark" +copyright = "2024, Alan Lujan" +author = "Alan Lujan" +version = release = importlib.metadata.version("estimark") + +extensions = [ + "myst_parser", + "sphinx.ext.autodoc", + "sphinx.ext.intersphinx", + "sphinx.ext.mathjax", + "sphinx.ext.napoleon", + "sphinx_autodoc_typehints", + "sphinx_copybutton", +] + +source_suffix = [".rst", ".md"] +exclude_patterns = [ + "_build", + "**.ipynb_checkpoints", + "Thumbs.db", + ".DS_Store", + ".env", + ".venv", +] + +html_theme = "furo" + +html_theme_options: dict[str, Any] = { + "footer_icons": [ + { + "name": "GitHub", + "url": "https://github.com/econ-ark/EstimatingMicroDSOPs", + "html": """ + + + + """, + "class": "", + }, + ], + "source_repository": "https://github.com/econ-ark/EstimatingMicroDSOPs", + "source_branch": "main", + "source_directory": "docs/", +} + +myst_enable_extensions = [ + "colon_fence", +] + +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), +} + +nitpick_ignore = [ + ("py:class", "_io.StringIO"), + ("py:class", "_io.BytesIO"), +] + +always_document_param_types = True diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..06be69e --- /dev/null +++ b/docs/index.md @@ -0,0 +1,17 @@ +# estimark + +```{toctree} +:maxdepth: 2 +:hidden: + +``` + +```{include} ../README.md +:start-after: +``` + +## Indices and tables + +- {ref}`genindex` +- {ref}`modindex` +- {ref}`search` diff --git a/environment.yml b/environment.yml index 93d7053..2fb6bdb 100644 --- a/environment.yml +++ b/environment.yml @@ -18,4 +18,4 @@ dependencies: - DFO-LS - tranquilo - black[jupyter] - - -e ./code/ + - -e . diff --git a/noxfile.py b/noxfile.py new file mode 100644 index 0000000..8c927ef --- /dev/null +++ b/noxfile.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import argparse +import shutil +from pathlib import Path + +import nox + +DIR = Path(__file__).parent.resolve() + +nox.needs_version = ">=2024.3.2" +nox.options.sessions = ["lint", "pylint", "tests"] +nox.options.default_venv_backend = "uv|virtualenv" + + +@nox.session +def lint(session: nox.Session) -> None: + """ + Run the linter. + """ + session.install("pre-commit") + session.run( + "pre-commit", "run", "--all-files", "--show-diff-on-failure", *session.posargs + ) + + +@nox.session +def pylint(session: nox.Session) -> None: + """ + Run PyLint. + """ + # This needs to be installed into the package environment, and is slower + # than a pre-commit check + session.install(".", "pylint>=3.2") + session.run("pylint", "estimark", *session.posargs) + + +@nox.session +def tests(session: nox.Session) -> None: + """ + Run the unit and regular tests. + """ + session.install(".[test]") + session.run("pytest", *session.posargs) + + +@nox.session(reuse_venv=True) +def docs(session: nox.Session) -> None: + """ + Build the docs. Pass --non-interactive to avoid serving. First positional argument is the target directory. + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "-b", dest="builder", default="html", help="Build target (default: html)" + ) + parser.add_argument("output", nargs="?", help="Output directory") + args, posargs = parser.parse_known_args(session.posargs) + serve = args.builder == "html" and session.interactive + + session.install("-e.[docs]", "sphinx-autobuild") + + shared_args = ( + "-n", # nitpicky mode + "-T", # full tracebacks + f"-b={args.builder}", + "docs", + args.output or f"docs/_build/{args.builder}", + *posargs, + ) + + if serve: + session.run("sphinx-autobuild", "--open-browser", *shared_args) + else: + session.run("sphinx-build", "--keep-going", *shared_args) + + +@nox.session +def build_api_docs(session: nox.Session) -> None: + """ + Build (regenerate) API docs. + """ + + session.install("sphinx") + session.run( + "sphinx-apidoc", + "-o", + "docs/api/", + "--module-first", + "--no-toc", + "--force", + "src/estimark", + ) + + +@nox.session +def build(session: nox.Session) -> None: + """ + Build an SDist and wheel. + """ + + build_path = DIR.joinpath("build") + if build_path.exists(): + shutil.rmtree(build_path) + + session.install("build") + session.run("python", "-m", "build") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..2cce21e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,157 @@ +[build-system] +requires = ["hatchling", "hatch-vcs"] +build-backend = "hatchling.build" + + +[project] +name = "estimark" +authors = [ + { name = "Alan Lujan", email = "alanlujan91@gmail.com" }, +] +description = "Estimating Microeconomic Dynamic Stochastic Optimization Problems" +readme = "README.md" +license.file = "LICENSE" +requires-python = ">=3.8" +classifiers = [ + "Development Status :: 1 - Planning", + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", + "Typing :: Typed", +] +dynamic = ["version"] +dependencies = [] + +[project.optional-dependencies] +test = [ + "pytest >=6", + "pytest-cov >=3", +] +dev = [ + "pytest >=6", + "pytest-cov >=3", +] +docs = [ + "sphinx>=7.0", + "myst_parser>=0.13", + "sphinx_copybutton", + "sphinx_autodoc_typehints", + "furo>=2023.08.17", +] + +[project.urls] +Homepage = "https://github.com/econ-ark/EstimatingMicroDSOPs" +"Bug Tracker" = "https://github.com/econ-ark/EstimatingMicroDSOPs/issues" +Discussions = "https://github.com/econ-ark/EstimatingMicroDSOPs/discussions" +Changelog = "https://github.com/econ-ark/EstimatingMicroDSOPs/releases" + + +[tool.hatch] +version.source = "vcs" +build.hooks.vcs.version-file = "src/estimark/_version.py" + +[tool.hatch.envs.default] +features = ["test"] +scripts.test = "pytest {args}" + + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"] +xfail_strict = true +filterwarnings = [ + "error", +] +log_cli_level = "INFO" +testpaths = [ + "tests", +] + + +[tool.coverage] +run.source = ["estimark"] +report.exclude_also = [ + '\.\.\.', + 'if typing.TYPE_CHECKING:', +] + +[tool.mypy] +files = ["src", "tests"] +python_version = "3.8" +warn_unused_configs = true +strict = true +enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] +warn_unreachable = true +disallow_untyped_defs = false +disallow_incomplete_defs = false + +[[tool.mypy.overrides]] +module = "estimark.*" +disallow_untyped_defs = true +disallow_incomplete_defs = true + + +[tool.ruff] + +[tool.ruff.lint] +extend-select = [ + "B", # flake8-bugbear + "I", # isort + "ARG", # flake8-unused-arguments + "C4", # flake8-comprehensions + "EM", # flake8-errmsg + "ICN", # flake8-import-conventions + "G", # flake8-logging-format + "PGH", # pygrep-hooks + "PIE", # flake8-pie + "PL", # pylint + "PT", # flake8-pytest-style + "PTH", # flake8-use-pathlib + "RET", # flake8-return + "RUF", # Ruff-specific + "SIM", # flake8-simplify + "T20", # flake8-print + "UP", # pyupgrade + "YTT", # flake8-2020 + "EXE", # flake8-executable + "NPY", # NumPy specific rules + "PD", # pandas-vet +] +ignore = [ + "PLR09", # Too many <...> + "PLR2004", # Magic value used in comparison + "ISC001", # Conflicts with formatter +] +isort.required-imports = ["from __future__ import annotations"] +# Uncomment if using a _compat.typing backport +# typing-modules = ["estimark._compat.typing"] + +[tool.ruff.lint.per-file-ignores] +"tests/**" = ["T20"] +"noxfile.py" = ["T20"] + + +[tool.pylint] +py-version = "3.8" +ignore-paths = [".*/_version.py"] +reports.output-format = "colorized" +similarities.ignore-imports = "yes" +messages_control.disable = [ + "design", + "fixme", + "line-too-long", + "missing-module-docstring", + "missing-function-docstring", + "wrong-import-position", +] diff --git a/reproduce.sh b/reproduce.sh index 141bd11..6a8f990 100644 --- a/reproduce.sh +++ b/reproduce.sh @@ -19,4 +19,4 @@ fi conda activate estimatingmicrodsops # Execute script to reproduce figures -ipython code/run_all.py +ipython src/run_all.py diff --git a/src/estimark/__init__.py b/src/estimark/__init__.py index e69de29..44f3336 100644 --- a/src/estimark/__init__.py +++ b/src/estimark/__init__.py @@ -0,0 +1,11 @@ +""" +Copyright (c) 2024 Alan Lujan. All rights reserved. + +estimark: Estimating Microeconomic Dynamic Stochastic Optimization Problems +""" + +from __future__ import annotations + +from ._version import version as __version__ + +__all__ = ["__version__"] diff --git a/src/estimark/_version.pyi b/src/estimark/_version.pyi new file mode 100644 index 0000000..91744f9 --- /dev/null +++ b/src/estimark/_version.pyi @@ -0,0 +1,4 @@ +from __future__ import annotations + +version: str +version_tuple: tuple[int, int, int] | tuple[int, int, int, str, str] diff --git a/src/estimark/py.typed b/src/estimark/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/notebooks/Model_Comparisons.ipynb b/src/notebooks/Model_Comparisons.ipynb index fe4c4a6..3fb2521 100644 --- a/src/notebooks/Model_Comparisons.ipynb +++ b/src/notebooks/Model_Comparisons.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -18,13 +18,14 @@ "from estimark.parameters import age_mapping, init_calibration\n", "from estimark.scf import scf_data\n", "from estimark.snp import snp_data_full\n", - "results_dir = \"../../content/tables/TRP/\" # This is AJL's\n", - "results_dir = \"../estimark/content/tables/min/\" # This is MNW's" + "\n", + "results_dir = \"../../content/tables/TRP/\" # This is AEL's\n", + "# results_dir = \"../estimark/content/tables/min/\" # This is MNW's" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -35,16 +36,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9.25239894900598, 1.0)" + "(9.252286005027539, 1.0)" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -57,27 +58,27 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "csv_file_path = results_dir + \"WarmGlowPortfolioShiftAlt_estimate_results.csv\"\n", + "csv_file_path = results_dir + \"WarmGlowPortfolio_estimate_results.csv\"\n", "res = pd.read_csv(csv_file_path, header=None)\n", "res = res.set_index(res.columns[0])[res.columns[1]].to_dict()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4.177600674290874, 1.0, 2798.51512022846, 1.966935700692675)" + "(9.206778216146489, 1.0, 26.1368726540768, 50.64405071849033)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -98,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -109,16 +110,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(5.338780774481047, 1.0, 0.17065528804872485, 0.0)" + "(5.335577372664163, 1.0, 0.1706005756625005, 0.0)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -132,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -150,13 +151,13 @@ " agent.solve()\n", "\n", " agent.track_vars = [\"aNrm\", \"cNrm\", \"t_age\", \"bNrm\", \"Share\"]\n", - " #agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", - " agent.T_sim = portfolio_agent.T_cycle + 1\n", + " # agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", + " agent.T_sim = portfolio_agent.T_cycle\n", " agent.initialize_sim()\n", " history = agent.simulate()\n", "\n", " raw_data = {\n", - " \"Age\": agent.history[\"t_age\"].flatten() + 25 - 1,\n", + " \"Age\": agent.history[\"t_age\"].flatten() + 25,\n", " \"nrmB\": agent.history[\"bNrm\"].flatten(),\n", " \"nrmC\": agent.history[\"cNrm\"].flatten(),\n", " \"Share\": agent.history[\"Share\"].flatten(),\n", @@ -170,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -192,12 +193,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -212,22 +213,22 @@ " portfolio_agent.AgeMeans.Age,\n", " portfolio_agent.AgeMeans.nrmB,\n", " label=\"Life-Cycle Portfolio\",\n", - " #alpha=0.5, # make line more faded\n", - " #linewidth=1, # thinner line\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " warmglow_agent.AgeMeans.Age,\n", " warmglow_agent.AgeMeans.nrmB,\n", " label=\"Warm-Glow Portfolio\",\n", - " #alpha=0.5, # make line more faded\n", - " #linewidth=1, # thinner line\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " trp_agent.AgeMeans.Age,\n", " trp_agent.AgeMeans.nrmB,\n", " label=\"TRP Portfolio\",\n", - " #alpha=1, # full color\n", - " #linewidth=2, # thicker line\n", + " # alpha=1, # full color\n", + " # linewidth=2, # thicker line\n", ")\n", "plt.plot(\n", " moments_values[0],\n", @@ -240,7 +241,7 @@ "plt.ylabel(\"Wealth to Income Ratio\")\n", "plt.title(\"Wealth Medians for Portfolio Models\")\n", "plt.xlim(25, 95)\n", - "plt.ylim(0., 12.)\n", + "plt.ylim(0.0, 12.0)\n", "plt.grid()\n", "plt.savefig(\"median_wealth.pdf\")\n", "plt.savefig(\"median_wealth.svg\")" @@ -248,12 +249,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -268,22 +269,22 @@ " portfolio_agent.AgeMeans.Age,\n", " portfolio_agent.AgeMeans.Share,\n", " label=\"Life-Cycle Portfolio\",\n", - " #alpha=0.5, # make line more faded\n", - " #linewidth=1, # thinner line\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " warmglow_agent.AgeMeans.Age,\n", " warmglow_agent.AgeMeans.Share,\n", " label=\"Warm-Glow Portfolio\",\n", - " #alpha=0.5, # make line more faded\n", - " #linewidth=1, # thinner line\n", + " # alpha=0.5, # make line more faded\n", + " # linewidth=1, # thinner line\n", ")\n", "plt.plot(\n", " trp_agent.AgeMeans.Age,\n", " trp_agent.AgeMeans.Share,\n", " label=\"TRP Portfolio\",\n", - " #alpha=1, # full color\n", - " #linewidth=2, # thicker line\n", + " # alpha=1, # full color\n", + " # linewidth=2, # thicker line\n", ")\n", "plt.plot(\n", " snp_data_full[\"age\"],\n", @@ -296,7 +297,7 @@ "plt.ylabel(\"Risky Portfolio Share\")\n", "plt.title(\"Portfolio Share Medians for Portfolio Models\")\n", "plt.xlim(70, 95)\n", - "plt.ylim(0.,1.)\n", + "plt.ylim(0.0, 1.0)\n", "plt.grid()\n", "plt.savefig(\"median_share.pdf\")\n", "plt.savefig(\"median_share.svg\")" @@ -326,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/src/notebooks/Portfolio.ipynb b/src/notebooks/Portfolio.ipynb index 7d19e62..ea0d86a 100644 --- a/src/notebooks/Portfolio.ipynb +++ b/src/notebooks/Portfolio.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -28,16 +28,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(8.809736592657766, 1.0)" + "(9.252342476844415, 1.0)" ] }, - "execution_count": 3, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -60,12 +60,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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qvgOdTp/n0wkEuQhRIhAIBFeQWGyT2dlfZX3jzwHQ6600NPwwjQ3/BqPRkefTCQRHI0SJQCAQXCEkKcLi0h+wuPgZZDkCQFXlP6K19WewWmvyfDrBZWZvb+/c7+ORancf//jH0el0vOc97zmj4wgEAoHgYVAUmfWN/8Urr76L+fnfQJYjeNw3eeKJP6e391NCkAgeilgsxp07d/jDP/xDfu/3fu/c7++hKyVvvPEGn/nMZ7h+/fpZnkcgEAgEp2TfN8j09Efx+4cAsFpraWv991RUfJtYmic4NbIsMz8/z927dxkfHyeZTF7YfT+UKAkGg3z3d383v//7v88v//Ivn/WZBAKBQHACIpEVZmY/wdbWXwBgMDhoavwR6ut/EIPBmufTCS4b29vbDA0NMTQ0RCAQ0C4vLS2lv7+fpqYmPv7xj5/rGR5KlLz73e/m277t23jXu971QFESi8WIxWLa536//2HuUiAQCAQpkskgC4u/x/Lyf0KW44COmup/SkvLe7FYyvN9PMElIhwOMzIywt27d1lbW9Mut1qt9PX1cePGDWpra9HpdBfy/H1qUfLZz36W27dv88Ybb5zo9i+++CIf+chHTn0wgUAgEOSiKBJr6/+TublPEY/vAFBc9Azt7R/E5erJ8+kElwVJkpienubu3btMTU0hyzIAOp2O9vZ2+vv76ezsxGi8+FmYU93j8vIyP/mTP8nf/M3fYLWerDT4wgsv8N73vlf73O/3U19ff7pTCgQCwWOO1/sy0zMfIxgcB8Bma6S97QXKyt4lfCOCB6IoCuvr6wwNDTE8PEw4HNauq6qqor+/n2vXruF0OvN4StApiqKc9Mb/63/9L77jO74Dg8GgXSZJEjqdDr1eTywWy7nuKPx+Px6PB5/Ph9vtfviTCwQCwWNAODzP9MzH2dn5WwCMRjfNTT9OXd33oNeb83w6QaETCAS4d+8ed+/eZXt7W7vc4XBw/fp1+vv7qaqqOtH3uojn71NVSr7+67+e4eHhnMt+8Ad/kK6uLt7//vc/UJAIBAKB4GQkEj7mF36LlZX/iqIk0ekM1Nb+S5qbfgKzuSTfxxMUMIlEgomJCYaGhpidnSVdezAYDHR1ddHf309ra2tBPmefSpS4XC76+vpyLnM4HJSWlh66XCAQCASnR5YTrK79KXNzv0kyuQ9AaenbaW97AYejLb+HExQsiqKwtLTE0NAQo6OjOQMm9fX19Pf309vbi81my+MpH4xIdBUIBIICQFEUdne/zPTMi4TDswA4HO20t32A0tK35fl0gkJlb29PG+PNTlz1eDz09/fT399PaWlpHk94Oh5ZlHz5y18+g2MIBALB40swOMn0zIt4vS8BYDKV0NL8Hmpq/jl6vXjtKMglGo0yNjbG0NAQi4uL2uVms5menh76+/tpbGxEr798CxfF/3aBQCDIE/H4LnPzv8Hq6mcBGZ3ORH3999PU+G5MJjEIIMggyzJzc3MMDQ0dSlltaWmhv7+f7u5uzObLbX4WokQgEAguGFmOsbz8x8wv/A6SFASgvPybaGt9P3Z7Y55PJygktra2GBoa4t69ezkpq2VlZfT393P9+nU8Hk8eT3i2CFEiEAgEF4SiKGxvf4GZmY8TiS4B4HL20t7+QYqLn87z6QSFQigUYmRkhKGhoUMpq9euXaO/v19LWb1qCFEiEAgEF4A/MML09MfY338NALO5nNaWn6G6+jvQ6QpvNFNwsSSTSaanpxkaGspJWdXr9VrKakdHR15SVi+Sq/3TCQQCQZ6JxTaZnf011jf+HFDQ6y00NPwwjQ3/FqPRke/jCfKIoiisra1pKauRSES7rrq6mv7+fvr6+vKesnqRCFEiEAgE54AkRVha+gMWl/4jkqRGeldW/kPaWt+H1VqT59MJ8onf7+fevXsMDQ3lpKw6nU4tZbWysjKPJ8wfQpQIBALBGaIoCpub/x8zs79CLLYOgNt9k472D+Lx3Mzz6QT5Ih6Paymrc3NzWsqq0WjUUlZbWloKMmX1IhGiRCAQCM4In+82U9Mfxe+/C4DVUkNr27+nsuLbr6QpUXB/FEVhcXFRS1mNx+PadfX19dy4cYPe3t4TL7h9HBCiRCAQCB6RSGSV2dlfYXPr8wAYDHaaGn+E+vp/hcEgnnAeN9Ipq3fv3mV/f1+7vKioSEtZLSkR+4uOQogSgUAgeEiSySCLi7/H0vJ/RpZjgI7q6n9Ca8t7sVgq8n08wQUSi8UYGxvj7t27h1JWe3t76e/vp6Gh4VKmrF4kQpQIBALBKVEUifX1P2d27teIx1WjYlHR03S0fxCXqzfPpxNcFLIss7S0xN27dxkdHSWRSGjXtbS0cOPGDbq6ui59yupFIkSJQCAQnIK9vVeZmv4oweAYADZbA+1tL1BW9g3CN/KYsLe3x927dxkaGsppz5SUlHDjxg36+/uvVMrqRSJEiUAgEJyAcHiBmZmPs73zNwAYjS6am36currvRa8Xr4SvOvdrz/T19XHjxg3q6+uFMH1EhCgRCASC+5BI+FlY+G2WV/4LipJApzNQU/NdtDT/JGazMCteZUR75uIRokQgEAiOQJaTrK79KfPzv0kisQdAacnbaGv/AE5He55PJzhPRHsmfwhRIhAIBAfY2f0y09MvEg7PAOBwtNPe9gKlpV+X55MJzovj2jMWi4Xe3l7RnrkghCgRCASCFMHgFNMzH8PrfQkAk6mYlub3UFPzL9DrxcPlVUO0ZwoP8VcmEAgee+JxL3Pzv8na2p+iKBI6nYn6uu+jqenHMJnc+T6e4IwR7ZnCRYgSgUDw2CLLcVZW/ivzC79FMhkAoLz8G2lrfT92e1N+Dyc4U0R75nIgRIlAIHjsUBSF3d0vMzX9y0QiCwA4nT10tH+Q4uJn8ns4wZkh2jOXDyFKBALBY0UoNMP09EfZ9X4FAJOplLbWn6G6+jvR6R7vDa1XBdGeubwIUSIQCB4LEgkf8wu/xcrKf0VRkqpvpP4HaG56N0ajK9/HEzwioj1zNRCiRCAQXGkURWJ17b8zN/cpLW+krOxdtLf9LHZ7c55PJ3gURHvm6iFEiUAguLJ4915hevqXCQYngHTeyAcpLX1rnk8meBREe+bqIkSJQCC4ckQiy0zPfJzt7b8CwGj00NL8k9TW/kv0elOeTyd4GER7Jr9IwTihO1vnfj9ClAgEgitDMhlicfH3WFr+A2Q5Duipq/1uWlp+EpOpON/HE5wS0Z7JH4qskFgPEZ3wEp3wEl8JEIiGzv1+hSgRCASXHkWR2dj438zOfpJYfBOA4uJn6Wj/eZzOzjyfTnBaRHsmP8ixJLHpfSITXqKTe8iBeM71pmr7uZ9BiBKBQHCp8fnuMjX9S/j9dwGwWRtob3+BsrJvEKX8S4Roz+SHxE5Eq4bE5n0gKdp1OrMeS1sxtq4SrJ3FhHQxeP/5nkeIEoFAcCmJxTaZmf0kGxufA8BgcNDU+KPU1/8gBoMlz6cTnATRnrl4lKRMbN6nCpHJPZI7kZzrjaVWrF0lWLtKsDR70Bn1mSv9sXM/nxAlAoHgUiFJMZaX/zMLi7+LJIUBqK76TlpbfwaLpSLPpxOcBNGeuVgkf5zopJfIhJfY9D5KXMpcqddhaXZj7SrF2lWMqfz8WzT3Q4gSgUBwKVAUhe2dLzA9/SLR6DIAbvdNOjs+hNt9Pc+nEzwI0Z65OBRZIb4S0KohidVgzvV6pwlrp1oNsbYXobcWjhQonJMIBALBMQSCE0xP/RJ7+68CYDFX0tb2fior/6F4EitgRHvm4pAjSaLTe5oQkUOJnOtNdU7VG9JVgqnGiU5fmH83QpQIBIKCJR73Mjf/G6yu/ikgo9dbaGj4YRob/i1GoyPfxxMcg2jPnD+KopDcChOd2CMy4SW+6AM5c73OYsDaUaxWRDqLMbguh/ATokQgEBQcspxgdfW/MTf/mySTfgAqKr6Vttb3Y7PV5fl0gqMQ7ZnzR0nIROf2tWkZaS/XeGost2VMqk1udAb9Md+pcBGiRCAQFBS7u19havqjhMMzADid3XS0/zzFxU/n+WSCg4j2zPmT3I+lWjJeYjP7KImscohRh6WlCFtnMdauEoyltvwd9IwQokQgEBQE4fA809MfY2f3iwCYTCW0tryXmpp/hk5nyPPpBNmI9sz5ocgK8SU/0QnVH5LYyE1RNbjNmWpIWxF689X62xCiRCAQ5JVkMsD8wm+zvPzHKEoCnc5Ifd3309T0Y5hM7nwfT5BCtGfODzmcIDqlekNiU3vI4WTmSh2YG9xYu1R/iKnacaV/x0KUCASCvKAoEuvr/y8zs79KIrELQGnp22lv+yAOR0ueTyeA+7dnWltbtfaMySSWHJ4GRVFIbIQze2WW/JAJUkVnM2LtUJNULR3FGByPz+9XiBKBQHDh7O+/ydT0LxIIjAJgt7fQ3vYBysrekeeTCUC0Z84DOS4Rm9knOuklOrGH5Ms1qZqq7FpbxlzvRme4utWQ+yFEiUAguDCi0TWmZz7O1tb/BcBodNHc9BPU1X0Per0wQ+YT0Z45e5LeKNGJVJLq3D4ks/bKmPRYWotSQqQYY5E1fwctIIQoEQgE544kRVhc/I8sLn0GWY4BOmpr/gUtLT+F2Vya7+M9toj2zNmiSDKxBX+qGuIluZW7V8ZQbNGqIdYWDzrT1TKpngVClAgEgnNDURQ2tz7PzMzHicU2ACgqepqO9p/D5erJ8+keX0R75uyQgnGik6kk1ek9lGj2XhkwN3pSSarFGCvsotL0AIQoEQgE54LfP8zU9C/h8w0CYLXW0tb2AhXl3ywemPOAaM+cDYqskFgLqm2ZyT0SK4Eck6reYcTaUYK1uwRrezF6m3iaPQ3ityUQCM6UWGyb2blfY339fwIKer2NpsZ/R0PDD2MwiL75RSLaM2eDHEsSm94nkt4rE4jnXG+qcWRMqnWugt0rcxkQokQgEJwJshxjefmPmV/4HSRJ3UpaVfmPaW17H1ZLVZ5P93hxXHumtLSUGzducP36ddGeeQCJnYg2shub94GUZVI167G0FWttGYPbkseTXi2EKBEIBI+Eoijs7H6R6emPEomobQG36zodHT+Px3Mrz6d7fIjH44yNjXHnzp1D7Zm+vj5u3LhBXV2daM8cg2ZSHU+ZVHdyTarGUmsmSbXZg854+fbKXAaEKBEIBA9NMDTN9PRH8XpfAsBsLqet9X1UVX0HOp140D5vFEVhdXWVO3fuMDw8TDyeaSuI9syDkUJqkmp0fJfo1AGTqkGHpdmjbtntKsZUbs/fQQuESDBw7vchRIlAIDg1icQ+c/O/yerqf0NRJHQ6Mw0N/4qmxh/BaHTm+3hXnmAwyL1797hz5w7b29va5cXFxdy8eVNMzxyDoigktyNEx3eJjHuJL/oPmFRNmZHdjiL0lsf7KTLs97E6Psry+DAro8Msz82c+30+3r9xgUBwKmQ5ydraZ5md+3WSyX0Aysu/kfa2F7DZGvJ7uCuOJEnMzs5y+/ZtpqamkGV1W6zRaKSnp4dbt27R0NCAXi8qVNkoSZnYvE+dlhn3InmjOdebqhzqpEy3MKmGffusjI+wPDbCytgwO8uLuTdQlKO/8AwRokQgEJwIr/drTE3/MqHQFAAORwcd7T9HScnzeT7Z1WZ3d5c7d+4wNDREIJApn9fW1nLz5k36+vqwWsVUUzZSKKGZVKNTeyixA22Z1iJs3WpFxFj8+P7uQvt7qggZHWZlfITdlaVDtymta6Cu5xr1PX24axv4uc81neuZhCgRCAT3JRJZYnrmRba3vwCA0VhEa8tPUVPzL9DrxUPIeZA2rd6+fZulpcwThd1u5/r169y8eZPKyso8nrCwUBSF5GZYHdkdP7zgTu9U2zK27hIsbcXoLY9nkmrQu8vyuFoFWRkbwbu2cug2ZQ1N1HX3Ud/TR113H3ZPkXad3+8/9zOe6hHl05/+NJ/+9KdZWFgAoLe3lw996EN8y7d8y3mcTSAQ5JFkMsjC4qdZWvrPKEocnc5Abe330NL8E5hMRfk+3pVDURRWVla4c+cOIyMjmmlVp9PR1tbGzZs36ejowGgUQhBSbZm5dFtmF2nvwIK7arUtY+suxVTrfCzbMoHdHVbGhjUhsre+lnsDnY7yhibqevqo77lGbVcvdnd+vUin+t9dV1fHxz/+cdrb21EUhT/+4z/mH/2jf8SdO3fo7e09rzMKBIILRFFkNjY+x8zsJ4nHVRNlSfFbaO/4OZyO9jyf7uoRDAYZGhrizp077OzsaJeXlJRoplW3253HExYOUjBOdCI1LTO9jxLPassYdVhbi7B2l6ptmaLHLzvEv7PFyljGE7K/uZ57A52OisaWjAjp7sXmdOXnsMegU5RHc66UlJTwyU9+kh/6oR860e39fj8ejwefzyf+0ASCAsPnu83U1C/hD9wDwGZrpL39g5SVvlPkW5whkiQxMzPDnTt3ckyrJpOJnp4ebt68SWNj42P/O1cUhcRGmOjErtqWWT4Q6e4yYesqxdpdgqWtCL358WrL+LY2szwhw/i2NnOu1+n0VDS3aJ6Q2s5erM6Hn467iOfvh64DSpLEn/3ZnxEKhXj22WePvV0sFiMWy5TVLqInJRAITkc0tsHszCfZ2PxfABgMTpqb3k19/fej1z9+rzjPi52dHc20GgwGtcvr6uq4efMmvb29j71pVUnIxOb2iaRCzKT9A22ZWqfmDzHVPD5tGUVRVBEyNszymGpM9W9v5dxGp9dT2dyaEiHXqO3qwWJ35OnED8epRcnw8DDPPvss0WgUp9PJ5z73OXp6jt/2+eKLL/KRj3zkkQ4pEAjOB0mKsrT0Byws/h6yHAF01FT/U1pafxqLuSzfx7sSpBfh3b59m+XlZe1yu91Of38/N2/epKKiIo8nzD9SIK6N7MZm9lDicuZKox5rW5HqD+kqweB5PESyoijsb66n2jGqMTWwu51zG51eT1Vre0aEdHZjtl3ukLdTt2/i8ThLS0v4fD7+5//8n/zBH/wBf//3f3+sMDmqUlJfXy/aNwJBHlEUhe3tLzA981Gi0VUAPJ4BOtp/Hrf7Wp5Pd/lRFIXl5WXu3LnD6Ohojmm1vb1dM60aDI9XuyGNoigk1kNEx71EJrwklnOTQvVus7pXprsES+vj0ZZRFIW99bVMJWRsmOCeN+c2eoOBqtYOzRNS09mN2Wq7sDNeRPvmkT0l73rXu2htbeUzn/nMiW4vPCUCQX4JhWaZmvpFvHtfBcBiqaKt7WeprPj2x97D8KgEAgEtaTXbtFpaWsrNmze5fv36Y/u4pyQkorM+1aQ64UXyHdi0W+dMCZFSTDWOK/9/UVEUvGsrKRGiGlND+3s5t9EbjFS3d1Dfc4267mvUdHRhymN7r6A9JWlkWc6phAgEgsIkmQwyv/BbLC//EYqSRK8309Dwb2hq/HcYDBf3auuqIUkS09PTmmk1/TrPZDLR29vLzZs3aWhouPJPskch+eNEUibV2Mw+SiLTltGZ9FjairClpmUMbnMeT3r+KIrC7spSph0zPkLYt59zG4PJRHV7J3XdajumuqMTk/nxaFelOZUoeeGFF/iWb/kWGhoaCAQC/Mmf/Alf/vKX+eu//uvzOp9AIHhEFEVhc/P/MD3zceJx1RhXVvYuOto/KKLhH4Ht7W3NtBoKhbTL6+rquHXrFr29vVgsj9cTiqIoJNZC2m6ZxGow53qDx6yN7FpbPehMV7cto8gyOytLWcbUUSJ+X85tjCYz1R1dalhZ7zWq2zoxmq+2OHsQpxIlW1tbfN/3fR/r6+t4PB6uX7/OX//1X/MN3/AN53U+gUDwCAQC40xNfYR93xuAOuLb0f7zlJW9I88nu5zEYjFGR0e5c+dOjmnV4XBoptXy8vI8nvDikeMSsZl91ag64UX2H2jL1Ls0f4ip+uq2ZRRZZntpIdOOmRglGsidNjWaLdR0dKntmJ4+qto6MYoNzjmcSpT8p//0n87rHAKB4AxJJHzMzf06K6v/DZDR6200N72bhoZ/JUZ8T4miKCwtLWmm1UQiAaim1Y6ODm7evEl7e/tjZVqVfDEt0j06sw/JrLaMWY+lvVgVIl0lGFxX85W/LEtsL2ZEyOr4CNFQbmXIaLFQ29mT8oT0UdXWjsEoRMj9EHnFAsEVQlFk1tb/jNnZXyWRUJ37FRXfSnvbC1itNXk+3eUiEAhoSau7u7va5WnTan9/Py5XYaVhnheKrJBYDaaEyC6JtVDO9YYiizaya2kpQme6epuKZUlia2FOi21fHR8lFs79PZisNmq7erTdMZUt7RjEWoBTIX5bAsEVwecfYmryw1oaq8PRTkf7hygpeS7PJ7s8SJLE1NQUd+7cYXp6Ose02tfXx82bN6mvr7+yLYhs5LhEbHqfyPgu0UkvciCRuVIH5nqXtlvGWGm/cr8TWZLYnJ/RjKmrE2PEI+Gc25htNmq7ejVPSGVzG/orWjGLhRMsT3gffMNHRIgSgeCSE4/vMjv7q6yt/w9ATWNtaXkPdbXfg14vSsUn4TjTan19Pbdu3aKnp+exMK0m96NE022Z2X1IZhIjdGYD1o4irF2lWLuKMTivVltGSibZnJtRY9tTIiQRjeTcxmJ3qJWQVFhZRVPLlRQhkiTjXQ2xOe9jc97Pxryf/c0wkXjowV/8iAhRIhBcUmQ5yeranzA39+skk6qhrrrq/6G19d9jsTxeZsuHIRqNaqbVlZXMCneHw8GNGze4efMmZWVXO9VWkRXiKwFVhEx4SawfaMsUW9SR3e4SLM0edMar05aRkgk2Zme06Zi1yXESsWjObSwOR6oVo3pCypua0euvlghRFIXgXozNeb8qQhb8bC8GSGaNb6dxl55/RooQJQLBJWRv73Wmpj5MMDQJgMvZS0fnL1DkGcjzyQqbtGn19u3bjI2NaaZVvV6vmVbb2tqutGlVjknEpvfU3TKTXuTggbZMgzvVlinBWHF12jJSMsH6zBQro6onZG1ynGQ8N2PL6nRpfpC6nmuUNTReORESjybZXgywueBnY04VIeEDQXYAZpuRyiYXlc0eKpvdVDa5SShR/u2vnu/5hCgRCC4Rsdgm0zMfZ3Pz/wBgNBbR2vrT1Nb8c3S6q/XgeZb4/X7NtOr1ZvriZWVlmmnV+QjbUwud5F5Ui3SPze6DlNWWsRiwdhSr2SFdJRgcV6PlJ0sSm3MzLI0Mqe2YyTGSB4I+bS43dT19alhZ7zXK6hrQ6a9ONUiWFfbWQ2wu+LVKiHctxMEcd51eR2mtg8pmD1XNbiqb3RRV2A8tO0z4cytJ54EQJQLBJUCW4ywv/xHzC7+NJIUAHbW130Vry3sxmYrzfbyCJJlMaqbVmZkZzbRqNps102pdXd2VqQRko8gK8eV0W2aXxEauQdNQYsXWndot03Q12jKyLLG9MM/S6D2WR++xOjFKPJLrCbG5PdR391HXq3pCSmvrr5QICfvjbM772JhXRcjWop9EVDp0O2exJVX9UKsg5Y0uTAWyX0iIEoGgwNndfYmp6V8kHJ4DwO2+SWfnh3G7+vJ8ssJka2tLM62Gw5kn44aGBm7evHllTatyNEl0ek8VIpN7yKEDbZlGd0qIlGIst116MZYOK1seHWZ57B4r4yPEQrmeGKvDqZpSe6/T0HuN0vrGS/9zp0kmJLaXgpoPZHPOT8B7uJJhtBiobHTliBBHUeH+/xeiRCAoUCKRFaZnPsr29hcAMJlKaW97P1VV34FOd3Ve3Z0F0WiUkZER7ty5w+rqqna50+nkxo0b3Lhx40qaVpPeqDqyO+ElNufLbctY1baMrbsUS0fxpW/LKIqCd3VZq4Qsj40cSkw12+zUdfdS33ud+t7rVDQ2X4lKiKIo+LYi2jTM5oKfneUgsnywDwMl1Q4qm9QWTGWzh5JqO3rD5fkdCFEiEBQYkhRlcen3WVz8NLIcQ6czUFf3fbQ0/yRG4+MR1nUSFEVhcXFRS1pNJpPA1TatKrJCfMmv+kPGvSS3ctsyxlKrulumuwRLkxvdJXoyOoiiKOytr6kCZPQey2PDhxbYmSxWart7qe+5RkPvdSqaW6/EiG40lEhVP1JVkAU/sVDy0O1sbrMmQKqa3VQ0ujHbLvfT+uU+vUBwhVAUhZ2dv2Nq+peJRtW9KsVFz9DR8SGczs48n65w8Pv93L17lzt37rC3l1n1XlZWxq1bt7h+/fqVMq3K0STRqXRbxoscznpy0oO50aP5Q0zl9vwd9AzwbW2wNJIRIUHvbs71RpOZms5utR3Td/1KJKZKSZnd1WDKiOpnY96Hbyty6HYGo57yhlQbJjUN4yq1Xpl2VJrL/a8pEFwRwuF5pqZ/id3dvwfAYqmive0DVFR865V70HkYHmRavXXrFrW1tVfmd5XcjxId8xIZ21XbMnJ2W8aItbNYFSIdxejtl7ct49/ZTlVCVF+If3sr53qD0Uh1Rxf1Pddp6L1OVfvlXmCnKAoBb1QTIJvzfraXAkjJw5kgngpbqgKi+kBKa50YroAh+UEIUSIQ5JFkMsTC4u+ytPSfUZQ4Op2JhoYfpqnxRzAaHfk+Xt7Z2tri9u3b3Lt3L8e02tjYqJlWzVdg1buiKCTWQ0THdomMHd4tYyy3Ye1Ss0PMjZe3LRPc82baMaPD7G+u51yvNxioau2goU/1hFR3dGEyF64p80HEo0m2Uu2XjTn1fcR/OBPEYjdq1Y/KFg+VTW6sl9wD9LAIUSIQ5AFFUdja+r9Mz7xILLYBQGnJ2+jo+BB2e3OeT5dfEokEY2NjDA4OsrS0pF3ucrk002ppaWkeT3g2KJJMbN6vCRFpPytDIxViZustvdRtmbDfp1ZBRu+xNHqPvbWVnOt1Oj2VrW1qO6bnGjVdPZittjyd9tGQZQXvWigzDTPvx7seggNeVL1eR1m9M8eM6qm4/NNQZ4UQJQLBBRMMTjI59RH2918DwGqtp6P95ygr+/rH+oFpc3OT27dvMzQ0RDSqjjbqdDo6Ozu5desWra2tl960KsdS/pAxNchMiWT5Q4x6rO1F2HpUIXIZd8tEggE1tj0lRHaWF3NvoNNR0dhCfZ/ajqnt6sViv5yCK+TLimaf97O1GCARO5wJ4iqxZnwgzR7K650YCyQTpBARokQguCCSyQBz87/Jysp/QVEk9HoLTY0/QkPDv8ZgOP+dEoVIPB7XqiLLy8va5UVFRdy6dYubN2/icl3uiSMpECcytkt0bJfozH7O2K7ebsTaXYqtpwRLezH6S/ZkFQuH1AV2o/dYGh1me3Geg3GhZQ1N1PeqWSF13X3YnJfv3zMRl9heCmR5QXwE92KHbmeyGqhozJqGaXLj8Fze9lM+EKJEIDhnFEVmfePPmZn5FRIJdZqgvPybaG/7ADZbXZ5Plx82NzcZHBxkaGiIWCr6W6/X09nZycDAAC0tLegvab6EoigktyOaEIkvBXKuN5RasXWXYuspTflDLk91LB6NsDoxpvlCNudmUZRck2ZJbb0WVlbXcw2725On0z4ciqywvxXOCJAFPzsrQZQDmSA6HZTUOLOqIG6Kqxzo9Zfn37MQEaJEIDhH/P5hJqc+gt9/BwC7vYWO9g9RWvrWPJ/s4onH44yOjjI4OJizlbe4uJhbt25x48aNS1sVSeeHRMa8RMd2Se7kjnSa6pzYelQhYqy8PEvuErEoa5MTLI+pnpDN2WlkKbdFUVRVrYWV1fdcw1lckqfTPhyRYDxHgGwt+ImFD2eC2D1mbRKmskmNZjdbr/ZTqByPE5ueJjY+TnR8gu3h4XO/z6v9GxUI8kQiscfs7K+xuvZZQMFgcNDc9GPU1/8Aev3l8wo8ChsbGwwODnLv3r2cqkhXVxcDAwM0NzdfyqqIkpCITu+rFZFxb26su0GHpbUIW08Jtu5SDJekhJ9MJFifnlDbMSP32JiZRErmPkG7yyup71XDyup7r+MqvTxJuVJSZmc5yOaCL5UJ4se/fTgTxGjSU97oSplRVSHiLLZcGjH5MEg+H9GJSaLjY8TGJ4iOjxObm4Osf/+odNgzc9YIUSIQnCGKIrG69t+Znf01ksl9AKoq/xFtbe/HYqnM7+EukFgsplVFsmPfi4uLGRgY4MaNG5cy4EwKJYhOeImM7hKb3kNJZFoXOqsBa2eJOjHTUYz+EryKlpIJNmamU2Fl91ibnCCZyB1ZdZaUpiohqhDxVFTl6bSnQ1EUArtRNtLR7PNqNPtRmSDFVfbcaPZaB4ZLOnb9IBRFIbm+TnRigujYONGJcWJj4yTW1o68vcHjwdLdjbW7G2djA3zXd53r+Qr/r0YguCTs+waZmvwIgeAoAE5nFx3tv0Bx8VN5PtnFsb6+rlVF4nH1yU2v19Pd3c3AwABNTU2XriqS3FX9IZGxXeIL/pwRT4PHjDXVlrE0F/62XVmS2JybyWzSnRwjGcs1bNo9RSlPiCpEiqpqLkWFIBGT2FpQE1E35lQzaiSQOHQ7q8NEZYtbEyEVjVc3E0RJJonNzWntl+jEBLHxcSSf78jbm2prsXR3YU2JEGt3N8aqKu3f3+/3H/l1Z4kQJQLBIxKLbTMz+wk2Nj4HgNHopqXlp6it+Zfo9Vf/TywWizEyMsLg4CBrWa+2SkpKtKqIw3F5guAURSGxEtSESHIzd7+MqcqBtVcVIqYaR0E/YcuyxPbCvBbbvjI+QjyS266wutzU9/TR0NtPfe91SmrrCvpngtSCuu2I2oKZ87Ex52N3NXTIjKo36Cird2nTMJXNbtxlVzMTRA6FiE5Oqe2XiQmi4xPEpqZQ4ofD2jAasbS2Yu3qwtrTjaWrG2tXJwZP/k3JV/8RUyA4J2Q5wcrKf2Vu/jeRpCAANdX/jNbWn8Zsvjx99odlbW2NwcFBhoeHtaqIwWDIqYpclgd/JSkTm/NpEzNSduqmHixNHq0iYiwp3PFtRZbZWV7URnRXxoeJhXLTYS0OB3Xd12hIjemW1TcW/Cbdk1ZBnMUWqlo8VLWoPpDyehcGU2H/bA9Dcns7t/0yPkF8cfHQODaA3m5X2y+aAOnC0t6OvkCTkIUoEQgeAq/3Zaamf5FQaBoAt+s6HZ0fxuPuz/PJzpdoNKpVRdbXMxHhpaWlDAwM0N/ff2mqInIkSXRS3S8TndxDyQq+0pn1WDuKVSHSVVKw+2UURcG7upISIUOsjI0QCeSW2M02G3XdfdT3qCKkvKkZvb5w81AURcG/E2Fj7gFVEKOOigYXlS0eqppVIeIsvhyG4pOiyDLxxUW18jE2rgqRiXGk7Z0jb2+sqFDbL13p9ksXpvr6ghed2QhRIhCcgmh0jemZF9na+gsATKYS2lrfR3X1P0Gnuzx/+KdBUZScqkgiob5CNRgM9PT0MDAwQGNj46WoiiT3Y0TH1bZMbDZ30Z3eaVLTVHtKsbYWoSvAV9iKorC/scby6DBLo/dYGRsmtL+XcxujxUJtZ4/mC6lsaUNfwEm4p6mCVDZ7qGpxU9XiuXJVEDkWIzY1nap8pDwgk5Mo4fDhG+t0mJubc9sv3V0Yr8D6BSFKBIITIMsJlpb+E/MLv40sRwA9dXXfTUvzT2Ey5b8Pex5Eo1GGh4cZHBxkY2NDu7ysrEyritgLPCJcURQSG+HMorvVYM71xnKbJkTM9S50BRh85dvaZGl0SItuD3p3c643mszUdKqbdOt7r1PV1o7BWLiVnYergrhxFhdu2+y0SPv7atVjfEIbwY3NzcERI7c6iwVLZ6cmQKxdXVg6OtAX+N/ewyJEiUDwAPb332Ri8ue0Vo3H8wSdHR/G5erO88nOHkVRWF1dZXBwkJGRkZyqSG9vLwMDAzQ0NBR0VUSRFGILvsyiu70jFt31lGDtKS3IRXeB3R1tgd3y6DD+7c2c6/UGI9XtnVpqanV7F8YC9Qc87lUQRVFIrK4Rm0hVPsZVD0hybf3I2xuKirIqH2r1w9zUhM74+DxVPz4/qUBwShKJPWZmfoW19f8BqK2a9rYXqKr6joJ+Un4YIpGIVhXZ3Mw8CZaXlzMwMMD169cLuioix6TUortdopNe5PAxi+66SjC4CusJPOzbZ2lkSBvT3d/IfcLSGwxUtrarI7o916np7MJkKbyqwWmqIOX1Ls2QelWqIEoiQWxuTg0d0wTIBPIxY7Smujqs3d05I7jGysor99hyWoQoEQgOoCgKGxufY3rmRRIJL6BO1bS1vR+TqSi/hztDFEVhZWVFq4okU8mNRqNRq4rU19cX7IOkFIgTGd8lOuYlOrMHyQOL7rpK1PyQjsJadBePRlgdH2Vx+C5Lw3fZXlrIuV6n01PR3KqGlfX1U9vVg9lqy89h70MiJrG1mBYgj1cVRAqGiE2m2i+p8LHY9DRK4vDPj9GIpa1Nq3xYu7uxdHZicLsv/uCXACFKBIIsQqFZJiZ/nv391wBwODro6vwlioqeyPPJzo5IJMK9e/cYHBxka2tLu7yiokKrithshfckCJDYCmcW3S0HcoPMSqyp/TIlmBs9BbPoTpYkNmanUiJkiLWpCWQpN7q9vLGZhj7VE1Lb1YvVUVhpt49rFURdrridO/0yPkZicenI2+udTtXzkTWCa25tLdjx20JEiBKBAJCkKAuLn2Zx8TMoSgK93kpz80/QUP+v0OsL0zR4GhRFYXl5mcHBQUZHR3OqIn19fQwMDFBXV3ihWYqsEF8OaEIkuX3EorvuUmy9hbPoTh3TXWZx+C6Lw3dZGRs+FFjmKiun8dpNGq/109DXj91TlJ/DHsNJqyCOIotWAbnsVRBFkogvLuWEj0XHx5F2d4+8vbGyUhUgPd3qCG5PN6ba2ks1fntalCNyUM4aIUoEjz27uy8xOfUhIhH11U9p6dvp7PgwNlt9nk/26ITDYa0qsr29rV1eWVnJwMAA165dK7iqiJKQiM7sEx3zEhnfRQ4eWHTX4tEmZowFsugu4N1haXiIpeG7LI4MEdrz5lxvdThp6Oun4doNGq71U1RZXRACCnKrIJtzPjbm/eysBI+vgjR7qEwJEVcBB8ndDzkaJTY9nRM+Fp2cRIkcXs6HXn9g/FZtwRhLLtc25NOgKAoboQ1m9meY882p7/fnmFyfPPf7FqJE8NgSi20zPf3LbG59HgCLuZKOjg9RXv5NBfOE8TAoisLS0pJWFZFSY4Ymk0mritTW1hbUz5hedBcd2yU6dWDRncWQ8oeUYO0sKYhFd7FwiOXRYc0X4l1bybneaDJT09VD47UbNF67UVCBZQ9bBSmrd2I0FcbPcBqSe3s57ZfYxDixufmjx2+tViydHTnhY5aODvQFJtzPClmRM+Jjf04TIbP7s4STh/NRpKTYEiwQnDmKIrG6+qfMzv0qyWQA0FNf9320tLwHo9GV7+M9NOFwmKGhIQYHB9nZySQ+VlVVaVURq7VwXtkm96NERtS2TGzBB1nLWw3urEV3LflfdJdMJFifnlArIcN32ZiZRlGyDqzTUdXSRkNKhNR0dBfEmK5aBYmyMee7fxXEoKO84XJXQdTx29XU9EtmAV1y/Zjx2+LirOmXnsz4bQEHzT0ssiKzHlpndn+W2f1ZTYTM+maJJI+oDgFGnZFGdyOtRa3aW4WuglvcOtezClEieKwIBMaYmPw5/P4hAFyua3R1/hJu97U8n+zhUBSFxcVFBgcHGRsby6mKXLt2jYGBAWpqCmfLa3InQnhkh8jIDomV3CAzU5VdEyKmWmdez6zIMttLC1olZGV8lGQ8d5tucXUtDX39NF67QV3vNWzO/Ava01ZBKlPx7OUNl6sKokgS8fl5dex2dIzo2BjR8XHkQODI25saGg63XyoqCubv4qyQFZnV4Oqhqsecb+548aE30uRuUoWHJyNAGtwNmA746cSWYIHgjEgmQ8zN/wbLy38EyBgMTlpbf5q62u9Gp7s8D8ZpQqGQVhXZzTLiVVdXa1URi6Uw/BaJzRCRkV1ViKxnLYfTgbnRja23DFtPCcbS/JbIfVsbLKZ8IUsjQ4d2yNg9RZoIabjWj7usIk8nVXlcqiBKPE5sdlYVHmkBcpz/w2TC0t6W237p7MTgyr9gPEtkRWY1sMqsbzan9TLvmycqRY/8GpPeRJOnKUd4tHpaqXfXHxIf+USIEsGVZ3v7C0xOfYRYTI1Kr6j4Vjrafw6LpTLPJzsd6Qma119/nfHxca0qYjabc6oi+UZRFBLrISLDakUkZ2JGD5aWImx9Zdh6S/MaZBb2+9QdMsN3WRy5i29zI+d6k8WaygpRRUhZfX73+yTiqXTUOR+b8+r7I6sgHrO6Jbfl8lVB5GiU2OSkKjxSIuS4/A+d3a5WP7q7sfb0qFWQ1lZ0BdA2OyskWWI1uKq2XXyzWvvlQeKj2dOcKz6KWql31WPUF/5TfuGfUCB4SCKRVaamf5Gdnb8FwGqtp7Pzw5SVvj2/Bzsl8XickZERXn/99ZwdNDU1NQwMDNDX15f3qoiipEZ3UxURyZv1gGnQYW1ThYi1pxSDIz+vyhKxKKsTYyyNDLE4fJethbmcVe86vZ7q9i51TPfaDarbOvK2Q+Y0VZCyeleOIdVZbLkUbQkpGFS9H2Njqgl1bOzY/S96tztLfPRg7e3B3Nh4ZfwfkiyxElzJ8XqkxUdMih35NWa9mWZPMy1FLbQVtWkipM5VdynEx3Fc3pMLBMcgywmWV/6IubnfRJYj6HRGGhr+Nc1N78ZguDwueq/Xy5tvvsnt27eJRtUneaPRyLVr13jyySfzXhVRZIX4op/IyA6RkV0kX9aDp1GPtaMY27UybF0l6G0X/1AjyxKbczMsDasiZG1yDCmZG1pWVt+omVPrunsx2/ITpZ+IS2wv+nPCya5SFSS5t5clQNQKSHxx8cjbGkpKsPb25ggQU4FNiz0sSTnJSmBFq3ykRci8b564HD/yaywGi1r5SLVb0iKk1ll7qcXHcVy9n0jwWOPz3WZi8ucJBicAKPI8SWfnL+J0duT5ZCdDlmVmZ2d5/fXXmZ6e1i4vKiriySef5ObNm3ndQaNICrH5fbUiMrqDnPXEqTPr1dHdvjJ1dNdysU+WiqKwt76qJacuj90jFgrl3MZZWkZj3w0ar/VT39ePs/jisyauehUksbWVER+pt+MW0Bmrq7XWi7VbFSBXwYCalJMsB5a1dktahCz4Fu4rPlo8LTl+j9aiVmqdtRgKZJz8IhCiRHAlSCR8zM5+ktW1zwIKRmMR7W0/S3X1d6LTFX7CYiQS4e7du7zxxht4vZngrba2Np566ina2trQ5ykpUknKRGf3iQzvEB3bzVl2p7Ma1ETVvjKsHUXoLvhVe2h/LzWmO8TiyF2Cuzs511vsDup7r6fMqTcorr74SSRJktlZDrIx62N9Zp/1WR9h/+EnJnuqClKV2hNT3ugq6CpIegNudGxUm36Jjo0hbe8ceXtTY4MqPNIVkJ7LH0CWkBPHio+EfMQeHMBqsNLsaaatqC2n9VLjrHmsxMdxCFEiuNQoisLm5v9havqjJBLqFEp11XfS1vazmM2F/4C3sbHBG2+8wb1790ikzHwWi4WbN2/y5JNPUlpampdzKQl1625kZJfI+C5KNNPn19uNWHtKsV8rw9JadKEZIvFImOWxEXWr7vBddpZzWwAGo5Harh7NnFrZ0nbhoWXxSJKNeR/rMz7WZ31szvtIxuWc21y2Kogiy8QXFomOZ1dAxpF9vsM31usxtzRniY8erN3dl3oCJiEnWPIvHTKcLvgXSMrJI7/GZrRplY8WT4smQmqdtegvwQulfCFEieDSEg7PMzn5C3j3vgaA3d5KV+cvUVz8dJ5Pdn8kSWJ8fJzXX3+dpaXMYq+Kigqeeuoprl+/jjkPEwRyTCI66SUyskN0wouS9USqd5nU0d2+MizNF7fsTkomWZ+Z1KohGzOTyNlGSJ2OiqYWrRJS29mNyXKx464BbzRTBZnzsbsS5OCKEIvdSFWLh+o2D9WtHioa3RgLaHNxNkoySWx2Lqf9EhsfRw4fTvjURnBT4sPW04Ols/PSJqAmpASL/sUc4TG7P8uif5Gkcrz4yPZ6pNsv1Y7qqyE+FAXCXvDOwuK9c787IUoElw5ZjrGw+BkWFz+NLMfR6y00Nb2bxoYfRq8vjGyOowgEAgwODvLmm28SDKrBYXq9nu7ubp566ikaGhou/JWyHE0SGfeqrZmpPUhmhIjBY8HWV4rtWhnmBjc6/fmfTVEUdpYXteTUlfFREtHcPIqiymoarql5IfW917G5Lm4FvCwreNdCWhtmfXafoPfwdIS7zEpVq4fq1iKqWz2UVDsu5Pd3WuRYjNjUdEaAjI8Tm5xEiR3+mXRWK9bOTqy9PeoW3J4eLO3tl3IDbkJKsOBfOCQ+lvxLx4oPu9F+qOrRVtRGlaPqaoiPWAB2Z2F3Brxz6vvdGfWy6H7qNmIhn0CQg9f7MpNTHyIcngegpOStdHZ8BLu9Mc8nO5r0Hpo33niDsbExZFl90nc6nQwMDDAwMIDbfXFPqpDaMzOmju5GZ/ZByjzQGEqt2PrKsPeVYaq7mFRV/86WNiGzNDJE2Lefc73N5daW2TVe68dTUXXuZ0qTiEtszftZn1VFyMasj3g0d2RVp4OyehfVrR6q21QR4igqPHEsh0JEJye18dvo2BixmRlIHn4S1jsc6ghub6YFY25uRme8XE8ZcSmuio+U6Egvl1vyLyEpR+9xcZgcOZWPtAipclQVbHvtxCSisDefERvp995ZCG7e/2vddWCvB/76XI94uf6HCR5b4vEdpqdfZGPzfwFgNpfT0f5zVFR8W0E+UMTjcYaHh3n99dfZ3Mz8sTc0NPDkk0/S3d2N8QIf4KVAnMioOrobm9vP2TNjrLCrFZG+MkzVjnP/fUaDQZZH72kiZG99Ned6o8VCXXcfjSkhUt7QdGHr4MP+uCpAUn6QnaUA8oGpGJPFQGWzWxMglc1uzAWwJDAbye/PER/RsTHi8/Mc6isBhqIibfQ2LUBM9fUX9js/C2JSjAXfwiHPx3Jg+Vjx4TQ5DwmP1qJWKu2VBfmYcmKkJOwvZsSGJkBmwbcM3Kfa4SiHklYobYPS1tRbGxQ3g9kOfj/8iOdcj19Yf0kCwQEURWZt7X8wM/srJJM+QEdd7ffQ0vJeTKaLrTCcBK/XyxtvvMGdO3dyskWuX7/Ok08+SXV19YWdJbkfS2WI7BBf9Oc8FpmqHWqq6rUyTBXnO2IsJZOsT00wPzTI0vBdNudmc5bZ6fR6qto6VF9IXz/V7V0YTecfWqYoCvub4ZQAUYWIb/twdLnDY1YFSJvajimtdaA3FM4TdnJ3NzeCfWyMxMrKkbc1VlTk5H9Yu7sxVldfmifhhJxg0bfIzP4M0/vTmbZLYAlZkY/8GpfJdUh8tBS1XG7xIcsQWDtc7didgb0FOMZ8C4DFnREbBwWI9XwFx0kQokRQsASDk0xM/hw+320AnM4eurp+GY+7P88ny0WWZWZmZnjjjTdyskWKi4u1bBHbBRn/krsRIiO7hEd2SCznLicz1buwpyoi571nxre1ycLQbRaGBlkaGSJ+YE9JSW29Zk6t7+nDYnec63kApITM1lJA84NszPqIhg6MbeqgtMZBVcoLUt3qwVVqLYgnL0VRSG5sZARIagQ3uXl02d1UV5czfmvt7sZYXn7Bp344shNOZ/Zm1Pf7M/eddnGZXDmx6umsjwr7Jc09URQI7WRVO7IqHt45OGbBHgBGG5S05FY70gLEUab2HAsUIUoEBYckhZmf/y2Wlv8zipLEYHDQ0vJT1NV+L/oCSjCMRCLcuXOHN954g729Pe3yi84WSWyFtT0zRy6861OnZozn6HNIxGOsjI2wcHeQhaHbeNdyX6nb3B6art+k8fpNGq714yopO7ezpImGEupUTMqQurUQQErmvpo2mPRUNrk1P0hVixuLPf/LyRRFIbG8fKgCImX9P9PQ6TA3N2di2Ht7sHZ1YSgquvBznxZFUdgIbWhVj5n9Gab3ppnzzR0br+4wOWgratPaLemPy2xll1N8RH1ZYiNbgMxB7IiR6zR6IxQ3ZQmOLAHiqoFL1H7LpnAe4QUCYGfni0xOfZhoVPUZlJd/Ex3tP4/VenFtjwexvr6uZYskUyZBq9XKzZs3eeKJJ849W0RbeJeKd09uZY1qagvvSrH1lp3bwjtFUfCurmjVkJWxEZKJTCCYTq+npqObpv5bNN8YoKKp5Vw9CumU1LQhdX3Gx9566NDtbC5TajRXrYSUN7gwXGDOylEokkR8fj5XgIyPI6cmtHIwGLC0teW2YDo70TvOv9L0KCiKwm5091DlY2Z/hlDi8L8TZBJO24vbNQHSXtR+OQ2niUjWRMsBARLavs8X6sBTD6UtqTZLlgApagBD/gX0WSNEiaAgiEbXmZr+Rba3vwCA1VJDZ+dHKCt7Z55PppJMJrVskeXlZe3yyspKnnrqKa5du3au2SKKopBYCWoekeTuxS+8i4VDLI0MsXD3NvNDgwR2ch9MXaXlNN24RXP/AA3X+s+1JSNLMjsrwYwfZNZH2Hc4JbWo0p6qgqh+EE+FLa9PaEo8TmxmJmcHTHRyEiV6eOOrzmzG0tmZ04KxdHSgz/PyxQfhi/lyqh4z+zPM7s+yFzuiygMYdUaaPE1axaOtWH1f56y7XAmnUgL2FlMjtQcMpv6jPT4azsrD1Y60wdR0sbk7+eZUouTFF1/kz//8z5mYmMBms/Hcc8/xiU98gs7OzvM6n+CKoygKq2t/yszMx5GkEDqdgYb6H6K5+ccxGPK34yWN3+9ncHCQwcHBnGyRnp4ennzyyXPNFlFkhfiSX23NjO4i7V/swjtFltlamGNh6DbzdwdZmxpHkbNyTEwm6rr7aL4xQFP/ACW1def2u9BSUlNVkM0FP8lY7lSF3qCjvMGlVUGqWz3YzqlSdBKUeJzo9DTR4RGioyOqAJmehsTh+HGd3a62X7JaMJaWFnQXYPh9WMKJsCY+tLe9GbYiW0feXoeOBndDTtWjraiNRncjpsvyil+WVYFxpMF0EY6Z9AFUE+nBakdpq/qxtfBM+2l84QTLe2GWvGEmlx8wNnwGnOqR7O///u9597vfzZNPPkkymeQDH/gA3/iN38jY2BiOAi8fCgqPaHSN8YkP4PW+BIDHfZOuro/idOZX5KazRV5//XXGx8dzskWeeOIJBgYGcJ1TZLaiKMSXA0SGtgnf20EOZLVELmDhXdjvY3HoNvNDt1m8d+dQZkhxTR3N/bdo6r9FXU/fuaWnBveimgBZn90v+JRULQV1ZITIyDDRkVFiExMoRwgQvdudG8He04O5sQGdoTCrAulx2+n9aWb21KrH9P40q8HVY7+m2lGtVT3ai9ppLWql2dOMzXgJkl4VBYJbB6odqfd785A8XNXSMNlTguNgu6UN7CUFaTCNJ2XW9iMseVXhsewNayJkaTeMP5oxFsuxI1J9z5hTiZK/+qu/yvn8j/7oj6ioqGBwcJC3ve1tZ3owwdVFURQ2Nv6cqelfIpkMoNdbaG19H/V135/X5XnxeJx79+7x+uuvs7WVebXX0NDAU089RXd3N4ZzeuJIbIYI390mPLSN5M086GUW3pVi7Sg+84V3siSxNj3Bwl3VG7I5P5uTZWGy2mi81k9TSoicR3BZOiV1Y3aftRl1KibgPfzAXygpqeoemIWUABlRKyHj40e2YPQeD7beXqx9fVh7e7H29mKqvfilgCchKSdZCizleD6m96bvm/VRZivLqXq0FavL5Zxm5wWf/iGI7Klm0qPaLfHA8V+nN0FJ89HtFld1wQkPRVHYCcZZ3lMFx9JuSnzshVn2Rlj3RZAfENRa5rTQUGKjwurmM+d83keq+fpSy5hK7rPpMRaLEcuKLPb7/Y9yl4JLTiy2zcTkB9nZ+TsA3O4b9HR/EoejJW9n2t3d1bJF0v9XTSaTli1SVXU+CaJJb5Tw0DaRoS0SG5lXIDqzXl1411+Otb34zBfe+Xe2UgbV2ywNDxEL5xoNy5ta1GrIjQFqOrowGM+2tJ6IS2wt+LWAso05H/FI7phnoaSkalMwIyNERkaJjowQHR1FDh02Z+odDlV49PVh6+vFeu0aprrza2k9LLIisxpcVasevlnN9zHvmz92s63b7D7k+WgraqPYWnzBpz8l8dDxBtPw7n2+UAdF9YerHaUt4GkAQ2HZMSNxKSM6tIpHRPs8krhPWwmwmvQ0lNhpKLFTV2zXPm4otVNXbMNuVn9ev9/PZ37ofH+Wh/7NyrLMe97zHp5//nn6+vqOvd2LL77IRz7ykYe9G8EVYnPz80xM/gLJ5D46nYmW5vfQ0PDDeRnzTWeLvP7668zMzGiXl5SU8OSTT3Ljxo1zyRaRAnEi99SKSHwp69WYQYe1oxj7jQqs3SXoz7ANkYzHWRkf0YTI7spSzvVWl5um6ze1aoij6GyfaML+OBuzPtZm99mY9bG9eHRKalWLW80HafNQ2XTxKanpHBCt+jEyQmR09MhNuDqrVW299PVi6+vD2ncNc1NjQaWgKorCVnjrkOdj1jdL5JiMC5vRlhEfWSKk3FZecOJKIxlXA8OOyvMIrN3/a51VGbGRLUCKmwrKYCrLChv+qCYytPd7attlO3D0+HQanQ6q3VbqU2Lj4Psyp7lg/n11inJE7vAJ+JEf+RH+8i//kq9+9avU1dUde7ujKiX19fX4fL4L3/khyA/xuJfJqV9ga+svADUErbfnV/PiHUkkEgwNDfHKK6+wu5t5pdTe3s5TTz1Fa2vrmWeLyJEkkZEdwkPbxGb3M8mqOrC0FmHvL8fWW4r+jPIxFEVhb32NhaFBFu4Osjw2QjKe+RvU6fRUt3dqkzIVLa3oz3DKIeCNsja1x+r0PmvT+/i2CjMlNbm9rQqQkVHNByLtHn71rDOZsHR1YbvWh7W3D2tfH5bWloLaA+ONelWvR6rqkX4LHNOGMOvNNHuatapHe1E7bcVthbvZVlHAvwa707AznRId06oA2V+CY9JcAbAVH652lLap4WKW8/GGPQz+aIKl3TAre5lqx5I3woo3zMpehLh0n58RcFmMNJRmxEZacDSU2KkpsmIxPvrfuN/vx+PxnOvz90P9Vf3Yj/0Yn//85/nKV75yX0ECYLFYsBT4CJvg/Nje/hvGJz5IIrGLTmegqfFHaWr6UfT6i52KCIfDvPHGG7z22muEUyvYrVYrt27d4oknnrhvC/JhkOMS0Qkv4bvbRCe9OUvvzA0ubP3l2K+Xn1mOSDwSZmnkHgtDg8zfvY1/O9cl7ywppal/gKb+WzReu4HVeTY9f0VRCOxGWZ3aZ216j9WpfQK7B7wVBZCSmtzbIzoySnQ04wM5MgnVaMTS3p6qfvRh7evF2t6OrkA24QbigdyJlz01bt0b9R55e4POQKO78VDrpd5Vj7GAggg1ov5MtWMnJTp2UyIkcR+TpclxdHppaatqMC0AEpJqKF32Hm0q3Q8f3TpLY9TrqC22ZURHVpulvsSGx2YqmGrHo3Cq/5WKovDjP/7jfO5zn+PLX/4yzc3N53UuwSUnkfAzNf2LbGx8DgCHo52e7l/B7b5+oefwer288sor3LlzRws683g8PPvss9y8efNMBbMiyUSn94nc3SIy5kWJZ/q4xko79huqEDmLiHdFUdhenGf+7iALQ4OsTY4jS5n7MxiN1Hb3qeFl/bcorW88kwcsRVHwbUdYm95nbWqf1ek9gt7c0rFOr6Oi0UVNexE17aoQuciUVCkQIDo6mvGBDA+TWD1iUkSvx9LaolY/rvVh6+vD0tmJ3pr/sn0kGVE32h4IGtsIbRz7NXXOupxpl7aiNpo9zZgNhSGoNNIL43amM9WOnZT4uN+mWp1BbauUtWdaLWXtqgBxVeXdYKooCt5QXGurHDSVru0/2FBa6jBntVZsOS2WKrcV4wVWEyVFYTOWYDWWYCUaZyUaZ3bnfj6cs+FUouTd7343f/Inf8L//t//G5fLxcaG+gfi8XgubLeHoPDZ3f0K4xMvEIttADoaG/41zc3vwWC4uIrZysoKL7/8MuPj46Q7lFVVVTz//PP09PSc2RSNIivEF3yqYXV4BzmcMWwaSqzY+8ux95djqnr0kfmw38fi8F0WU96Q0H5uGFVRVTVN/QM03xigvucapjN4ck0vrVub3k9VQ/YJ7eeKEL1eR0WTm5qOImrbi6hq9VyYH0QOh4mOjxMZVtsv0ZER4gsLR97W3NSkVT9s165h7erKexJqQkqw4F/ICRqb2Z9hJbCCcsw210p7ZU7lo72onWZPM3ZT/nN9NNJ7W7R2S6rasTOtjtXeb2GcoyIlONqgtD0jPoqb8p5gGk1IWnvlUMXDGyYUv7+h1GLU57RV6lKVj4ZStfLhsFxc9SoqyazG4qxEE6xG4yxH46zE4qxGVRGyFouTPPBfUA4dkTJ8xpzKU3LcK60//MM/5Ad+4AdO9D0uoiclyA/JZJDpmRdZW/ssADZbIz09n6TIM3Ah9y/LMtPT07z88sssLi5ql7e1tfHcc8/R3Nx8ZtWCxGqQ8N1tIve2kfyZLBG904T9ejm2G+WY612PdH+yJLE+M6VFuW/MTueO61qs1PddpznVlimqevQoftWPElZbMalqSNifm5SqN+qobHJT21FMTXsRVS0eTOeQmXIQORYjNjGRMaKOjhCbnVMDrQ5gqqvLTMGkxnEN55QtcxIkWWI5sKxlfKRbL4v+RZLK0U/QxZZi2oszVY/0x25zAT1uxsOp6ZbpVLVjJvPx/fa2GG2ZVktZe0Z8lLaCrejCjn8QWVbYCsRyzaTeTLVj039/QylAldua5euwZSZZSuyUOS3oL2CMXVEUfEkpVeFIsBKLa9WOlWiC1Vic7fh9hGEKow6qLWZqLSbqrGbKEzE+fKOzcDwlD+mJFTwG7O29ytj4+4lG1Tjlurrvp631fRgM519BSyaT3Lt3j5dffpmdnR1ATV29du0azz33HJWVlWdyP4mtcGqEd5vkTsa8qbMasfWVYr9RjqWl6JGyMwLeHVWE3L3N4vAdYgdGT8sbmmi6oYqQms4ejI+Y+KnICt71kOYJWZveJxLI7W0bjHqqWtxqO6ajmKrm8w8p09JQR0a1PJDY9DQkDz+QGisrtfaLakTtxVicn1FVRVHYiewwtTfF1N6UVv2434I5p8l5aNS2raiNUtv57lA6MekUU83jkeX38C3f5wuzx2rbU+KjVf3YXZu3hXGBaEKrcuSaSlOG0uT9DaVOizFV7bAdMpXWFtmwnnGW0FFIisJWPKEKjmiu4FCrHXGCDzDGAtgNeuosZmqtJuqtZupSb2kRUmUxYch6YeX3+/nwOf5cIHbfCB4RSYowO/urLK/8EQBWay3d3Z+gpPjZc7/vSCTCG2+8weuvv65FwFssFp544gmefvrpM1Hyyf2omq56dztnA6/OpMfaXYK9vwJr58NniSiyzPrMFLODrzF3+w12lhZyrrc6nDRmjes6Sx7tiUqRFXZWg6ylWjFr0/tEQ7kixGjSU9Xqoaa9iNqOIiqa3BjP8YE2Ow1VNaKm0lDjh3fZGEpKUgLkmmpC7e3FVFFxbme7H5FkhLn9OU2ApN/2Y/tH3t5qsNJa1JoJG0uJkEp7ZWEYFCP7WYIjy+vhnb1/iqm16LDPIz3dYrr4tn5Skln3RXNaKxlTaQRv6PD/q2wMeh01RdajcztK7BTZz99Qmm6tpFspy9G41mpZicZZjyVInKBIUGoyUmdVBUadJS061M9rrWaKjYbC+L+XhRAlgofG57vN6Nj7iEQWAKip+ee0t72A0Xi+ZfK9vT1effVVbt++TSIV4+12u3nmmWe4desW1kf0UkjBOJHhHcJ3t4kvZoX96dNZIuVYu0sfOuY9EY2yOHxXEyI5Ue46HdWtHTTduEVT/wBVbe2PNK4rywo7ywHNE7I+s08snFttMJr1VLd6qOkoprZdFSHntTlXTUNdVMVH2gcyPo4SOTw2nJOGmqqEGKsufkOsrMisBddyhMf03jRLgSXkI0ZR9To9Te4m2ovbaS9q197XOGvyv2AunemheT2yKh/hneO/Tm9SRcZRXg976YWaTBVFwRdJsJiTTJqpdqztR5Ee4CgttpuOHJ1tKLFT7TlfQ+n9Wiurqc9P0lox6KDaYsoSG7mio8Zixn7BY/ZngRAlglMjyzHm5n6TxaXfB2Qs5kq6uj9GWenbz/V+19bWePnllxkdHdVaiZWVlTz33HP09fU9knlVjiaJjO6qWSIze5B+rtGBpdmDrb8cW1/ZQ2/gDXp3mR18nbnbr7M0PEQykXm1ZrbZab4xQOvAUzTdGMDmevgKjyzJbC8FWU21YtZnDqelmiwGqts8miekvNGF4RwevNTNxiu5cewFnoYaiAeY3pvWhMfU3hTT+9OEEofPDFBiLaG9uJ2O4g7trcXTgtWYxwkeRVGnWNIVj2yvx4OWxrmqD1Q8Ui2XosYLTTGVZIV1X4Sl3TCL3swelkVviMXdMIHo/Z+0zQY9dVl+jvpie85Ui8t6foZZWVHYPIPWik2v1wRGvdVMrSUjOOqsZirNJox5WLNw3ghRIjgV/sAIY2PvIxSaAqCq6h/T0f4hTCbPudyfoijMzMzw8ssvMz8/r13e0tLCc889R2tr60M/cSkJicjEnjrCO+kl22puqnNi76/Afr0Mg+f0U0Ppkd3ZN19jdvB1Nuemc653l1fS+sRTtA48TV1370NHuUuSzPZigNWplAiZ9ZGI5j7pmK0GqlPjubUdxZTXO88lqCy5vU3k3j0i94bVVszICNKJ0lD7MDc1XWgaalJOsuRfYmp/iilvRoCshY5OADXpTbQWtWrCIy1EymxlF3bmQ8SCamvlkNfjAbtbTI5UtSPb65EymV5gmFg4nmTZG2FxN8SSN6xVPtJej4R0/2pHuctCY7rFcqDaUeE6P0NpVJJZyxqTfZTWiublSFU7arNERyG2Vi4CIUoEJ0KWEywsfpqFhd9BUZKYTKV0d/0y5eXfeC73l0wmGR4e5pVXXtGW4+n1evr6+nj22Weprn64SRNFkonN7KuG1dFdlFhWlkiFTRUi/eUYy07fC08mEqyM3mNm8HXmBl8nsLuduVKno7qtg9aBp2kdeOqhc0OkpMzWgl8zpq7P+UnGckWIxW6kuk31g9R2FFNa5zzzB2g5FiM6Nkb03j0iQ0NE7g6RWDv8hF4IaajeqFdtu3gz7ZfZ/Vni8tHegipHVU7lo6O4gwZ3AyZ9HsZRZUlNLD0YJrYzc/8IdZ1erW4c5fW4oKVx6UVwS94s0ZESHosniEY3GXRahaMxlVTaWOrQhIftHMzW2a2V1VhCHZONZrwdj9paSRtIa62Xs7VyEQhRInggweAkY+PvIxAYBaC8/Jvp6vxFzOaznw6IRqO8+eabvPbaawQC6qs9s9nMwMAAzzzzDB7P6SsyiqIQX/SrI7zD28ihrCyRIosa895fjqnacWqhEPb7mL/zJrODr7EwdIdENOONMJotNF6/SesTT9Fy88mH2imTTEiaCFmd2mdzzkcykVv6tTpMWlBZbWcRpTXOM92em15KFxlKCZB794iOj0PiQAKlToelrQ1r/3Vs165feBpqXIoz55s7JEB2o0cHPtmMtpzWS9r/4bGcT9XvvoS9We2WrMqHdw6k+xgz7aWpaseBykdxExjPPxcoIcms7kWyWiyhjNfjBLkdbqtRFRpp0ZHK7FC9HTYMZy2ms1orWjZH1pjsykO0VjIm0ozgqLoCrRVJihCLbRCNrhOLbRCLbbCzu/jgL3xEhCgRHIuiSCwu/QFzc7+BosQxGj10dnyYysp/cOZlRZ/Px6uvvsrg4CDx1NSFy+Xi6aefZmBg4KHC+ZK+GOHBTUKDm0hZ0ed6pwnbtTLsNyrULJFTPHgoioJ3bYW5wdeZHXyNtckJlCyzo6O4hNZbT9H6xNPU913HZD7dE0MyLrEx71fbMVP7bM77kQ6MKNpcaRFSTG1HESXVjjMVIVIgQHR4WBUgKSEi7e0dup2htBRbf3/q7TrWvj4MZxRffz8URWEzvJkxnnpV38e8bx7pCL+EDh0N7gZNeKRFSK2r9mL3vCSianDYwTCx3WmIHP79ahgsmUyPg16PC4hQD0QTOa0V9WNVfDwopTS9CK6h1E5jSZb4SH3uOeOk34OtlYyJVBUhaydsrZSYDMd6OWotZkpMl7u1IklhotENYrF1orF1YlFVdERj6ykhskEyuX/o60KhBwu2R0WIEsGRhMPzjI69D7//DgBlpe+kq+ujWCxnO365sbHByy+/zMjICHIqBKuiokIzrxpPWeZXkjKR8V3Cb24SndrTlt/pLAZsfWXY+8uxtBahM5z8AUWWJFYnx5h98zXmbr/O3npu2by8qYXWAdUfUtnceipvRCImsTHnS03H7LG54Ec+EKNod5u1tNSajmKKq+xn9oCoSBKxmRkid4eI3BsiMjREfHYuJ6QN1DaMtadHrYL092Prv4GptubcH5jDiTDT+7nG06m9qWMXzbnN7kOtl9ai1otLO02bTLcnYWcqszhuZ1rN9Ljf4jh33YGKR+q9pw7OcWonHRi2uBtiMVXhWEwbTHdD7D1gJ0v22vuGEofWakmvvT+LRXBpgklJq24sp8TGcpYAOWlrpcp8IJfDasrydVzu1koyGcypcERjqvjIviyZ9D/4GwEGgx2LpRqrpQqLpYp4ogj44LmeX4gSQQ6KIrO88sfMzv4qshzFYHDS0f7zVFd/59k9ESoKc3NzfO1rX2Nubk67vKmpieeff562trZT31diI0TojQ3Cd7dy2jPmZg+OJyux9ZWhP0UPOhYOsTB0m9k3X2P+zptEs+KV9QYjDX3XaRl4itaBp3CXnVyoybLC1qKflXEvy+N7bMz5kA8Y+hxFFi0jpLajGE+F7cx+98nt7ZwKSGRkBCV8eNGZqb4e2/WUALnRj6WrC/05tmFkRWY5sJwjPKb2plgOHB3OZdQZafI0HRIgFfaKi3kFKyVTVY8p9W079X5n+v5JphZ3rr9D29/SAubzi7xX49EjWoVjcTclPlIiJPaAwLBSh/lAi8WhVTwqXJYz+537EslDYiP7/V7y/u0gAJtelzsia8k1kF7W1oqiKEhSMEtsHFXhWEeSThYFbzA4VMFhrcZiqdKER/pzi6UaozE3ldrv9yNEieDCiESWGRt/P/v7rwFQUvw83d0fx2qtOZPvL0kSIyMjvPzyy2ymNrTqdDp6e3t57rnnqKk53f3I0SThoW1Cb2yQWMkSDW4zjoFKHAOVpzKs+rY2mU21ZVbGRpCljLixuty03HyC1oGnaLx+C4v9ZK+80wvsVib2WB73sjq5dygnxFlsUcdzO1RfiKf8bESIHIsRHR3TKiCRoSGSa+uHbqd3OLBev6YKkOtqK8ZYen5por6YLyfvY2pvipn9GSLJw1klAOW28kNTLy2eFkwXsQclFsyIjZ3JjADxzoF8TAVBp4fi5ozwSLdbytrBUX4uJlNFUdgPJ470dix5w2z4oweLXzkY9Dpqi2yZKodmLnWc2Qitoih4ExIrsTjLkSzBkf48Fsf/AHEE4DEaUlWO3GpHWoBcxtaKoigkk/4ssbGeqnBsEItmqh2SdPRo+kGMRldKWFRhtVRjsWaqHemPzztP6mERokSAoiisrX2W6ZkXkaQQBoOdttafpbb2X57JH3csFmNwcJBXX301pbTBZDJx69YtnnnmGYpPEQmuyAqxeR/hNzeJjOygpE2fBh227hLsT1RhbS8+UXtGkWU2Zqc1IXIwTbW4pk5tyzzxNDUdXScOMYsGE6xMqiJkedxLYDc3DdNiN1LbWUx9dwl1XcVnIkIyZtShVCvmHtGJicNmVL0eS1ub5gOx9fdjbmlBd0YLCrNJyAkWfAuHEk+3wltH3t5isNBW1JZT+WgvbqfYes6R8Vqux1Sq7ZIWINPgP2K7cBqTXRUaZR1Q1ql+XN6pVj3OwWQqyQpr+5FD3o70xw/K7nCYDTSUOmhMCY76LG9HdZEV0yO2LBRFYTueZCUaZ+moFkssTvgEJtISk0ETG+n3DVnCw32G7aCLQBUc+ykPR0Z0ZFor6pskHa5YHoXR6FEFhrUqq7VSjcWaqXYYjefv7TovhCh5zEkk/IyNv4+dnb8FoMjzJN3dn8Bub3zk7x2Px3n99df52te+RiSV2OlwOHj66ad54oknsJ+w2gBZptU3N5G8mSd5Y6UdxxNV2G+WY3A+uL2QiEVZHB5iblDND8lOU9Xp9NR29dA68BQtA09TUlN7orNJCZn12X2Wx/dYmfCytRQge8Gr3qCjqsWjipDuYioa3Y88oisFAmomyNAQ0aF7RO7dO9qMWlaWqoCoAkQ1o559m2AnssOkdzKnAjLrmyV5zDbYWmdtTuWjo7iDBlfD+SaeSkk1zXRnKiM60iLkfi0XR4UqPMo7UgKkXRUh57C/JRxPaiIj29uxfMLsjkq35UhvR2OJnRKH+ZHEb3qV/VF+jnRWR/QBSaoAFWbjIdGRqXiYcJyDQD4vFEUhkfBmBEY03UpZz6pwbCDL94npz8JkKs6qcFTlVjgs1VitVRgMBbQN+hwQouQxJhAYZ3jkR4lEltDpzLS1/gz19T+ATvdoDwqJRILBwUFeeuklQqn0ztLSUp5//nmuXbuG6YRL5O5nWrX3l+N4sgpTnfOBD7TBPS9zt19n9s3XjkhTtdHUP0DrE0/TfMI0VUVR2F0NsTzuZWXcy9r0/qEx3ZIaB/VdqgipaS/CbH34PzUlmcwyo95LmVFnD90ubUa13VAnYqzX+8/cjCorMiuBFSa8E0x4Jxj3jjPhnWAncnREudPkPJR42lbUhtN8jq/kYkHVXKr5PCYzoWL3bbk0ZSoeZR1q1aO07UwnXLKzO7T2Skp4LO6G2QneP7sjnVSaDg1LVz4aStXU0kfJ7kjKCmux4/0cJ5lc0aHmcxwtOkzUWsxYL4mJVFFkEgnvAcPoRlZrZT0lOO6/SyeNyVSSaqVkiY4DFY6LWGBa6AhR8piyvv7/MjH588hyDKu1lmt9v43bff2RvqckSdy5c4evfOUrWpumqKiIt7/97Vy7du3EMfBnYVrdXV1m+tWvMTv4GhuzB9NUK2gdeJqWgaeo7+k7UZpqcC+mtWNWJryHNuna3Wbqu0uo7y6mrrsEx0OkwKZJbG3lhJJFRkePN6NmjeSetRk1ISWY9c1mBMjuOJN7k0dGruvQ0ehupLOkM0eAVDuqz6e/rygQ3Mr1eaS9H/6V47/OZFeFRnlnquqReittPbOWS1KSWd2PsLB72Nux5A0TfkB2h8dmOtLb0VBqp8ptfejsjrisjssuR+IsZ/k40u/XYwkeUIjBoIMai/mQp6M+9VZtMWHO0/bf06AoMvH47hEjsdmiYxNFOangKMWqtVNSRlFrVeZjSxUGw/nnxlwFhCh5zJDlGFNTv8Tq2p8CUFryNnp7P4XJ9PB9e1mWuXfvHn//93/PXqqF4Ha7edvb3sbNmzdPJEbkaJLw3W1Cbz68aXVvY43Jl19i8pWXDvlDqtJpqk88TdkJ0lTj0SSrU/upKRkvexu5osBoMVDbXqT5QkpqTh+8BiBHo0THxjUjauTe8WZUW/91rOmJmP5+jCVn9wo+lAgx6Z1k3DvOpHeSCe8EM/szJI6oLJj1ZtqL2+kq6aK7pFsTIucydpvTcpnKnXa5b8ulPNfnka5+uOvOpOUST8qs7KnVjYWU8JjfCbG4G2JlL0LyPm0MnQ5qPLaM4NCmWtSJlofN7oikNsvmio2E9vlGLMGDmitmnS5njf3B95dhckUVHDtZhtH1LMNoOghsE0V58Pgw6DCby45op1RnTapUoNdfXcEhyzKhUAi/38/aEanNZ40QJY8RkcgqwyPvJhAYBnQ0N/8EzU0/hu4hA6RkWWZsbIwvf/nL7Oyo5XuHw8Fb3/pWBgYGHtimyTathod3IHl606pva4PJV77K5MsvsbWQaWnoDQYar92g7annaLn1JM7i+z+By5LM1mJAq4ZszvmRs55YdDoob3RT360aVKtaPA+1STexvk548DaRO3dUP8jEBCQPPDjq9Vja21UfSKoVY25pObPdMDuRnUPVjyX/EsoRT1kus4uuki5NgHSVdNHkaTr7yPV0y0XzeUxlcj4e2HLJ8nmkPz6Dlos6RhtmYScjPBZ2Qyzshljdu39omMWo1yocjaVZxtISO3XFdswP8X8nlJSyKhyJQ5WOk2R0WPW6Q9WNbNFRYTaiL+DJFdXDsZtqqawTzRId6ctOIzgs5gqtnZI9DpupcFSg119MInE+kCSJQCCA3+/X3o76PJ0hFYvdv714FghR8piwu/sVRkZ/imRyH6OxiL7eT1Fa+nUP9b0URWFycpIvfelL2mivzWbj+eef56mnnsL8gBZC0hcj/GYqafUhTKv+nS2mXvkqk6+8lNOa0en1NPT10/ncW2l78llszuNH3hRFwbcV0UTI6uQe8QOL7NzlNq0lU9tRjPWUG4IVWSY+N0f4zUHCg4NEBgeP3A+jmVFThtSzMqOe1v9Raa/MFSClXdQ4ztCTorVcsnwe6arH/VouRluuzyMtQEpawPRo23ijCSmr2hFiYTesvt8Js+aL3HeM1m420FjqoKnUnvu+zE6ly3pqM7MvkTxSbKTfexMPzuhwGPTH+jnqrWbKTMaCHZfNTKms547FHvj8ZC0VPRZLRdZIbNWhtorZXI4+H/uMLoh4PH5IYBwUHcHgyTJNdDodTqeTkjOszh57X4pygszdM8Tv9+PxePD5fLjdD7+iXXAyFEVmfuF3mJ//TUDB5erjWt/vYLPVPcT3UpidneWLX/yiVsazWCw8++yzPPPMM1itxz9BpE2roTc2iU2f3rQa8O4w/erXmHjlJdanJrTLdTo99b19dD77Ntqeeha7+/i9JZFAXM0LmVCFSNCbq/otDiN1nSVaNcR9yqV8SjxOdGyM8OBtVYTcvo20v597I4MBa3c3tls3sd+4obZhah79if+0/o8mT5MmQNJvJdYzesCRkrC/eMSI7RREH9RyyfJ5pKddHrHlEo4nVeGxkyU6UpWPdd/9pyKcFiONpXaayrLFh/px+SlCw84zoyP7fVGBbpZVBUcgy8OR+z5tJj3plIrZXJ5lGlUDwDJ5HFdbcCiKQiwWOyQ2DoqO9MTjgzAYDLhcLtxu95FvLpcLp9OJwWC4kOdvUSm5wiQS+4yOvZfd3b8HoKbmX9DR/qGHMlwtLCzwxS9+kaWlJUDNGXn66ad57rnn7jvaq5lW72whhw+YVp+oxHbteNNqaH+Pqde+xuTLL7E6OZaJPtfpqOvqpfPZt9L+9HPHLrpLJiTWZ3xaNWRnOfdVgd6oo7rVk6qGlFBW7zrVq1s5FCJ89y6RwUG1JTM0hBLNfVDVWa3Y+vuxDwxgf2IAW38/esejVUEe1v+Rfjsz/0cimiU8JjMCxDt7/BK59PbabJ9H2vvxCC2X9H4Wrc2yk2m3bD1gG63LaqS5zEFjqYPmrGpHY6mD0hOO0SqKwn4qAn0pouZ0LEVVwfE4ZHQkk8Esw+jRbZWTBn9lplRyhUamvVJ5ZVsqiqIQDoePFRxp0ZHeD/YgTCbTsWIjLTjsdjv6lOhPSDI7wRib/hjL/iiby1E2/ets+mNs+qOsbXnP88cHhCi5svgDIwwPv5todAW93kJn5y9SU/1PTv19VlZW+OIXv6jFwRsMBp588kne8pa34Dxm+Zoclwjf2XqopNWw38f0ay8z+cpLrIyN5Cy7q+nopvO5t9Lx9PM4Sw4njqZHdZdGd1mZ8LI240M6MKpbWuvUKiHV7UWYTjFCmdzd1dow4TcHVT+IlFtSNxQVYRsYwH7rFvYnBrD29KA74Qj0UTyK/6OzpJNmT/Oj+z+SsZTXYwK2xjPv9+aP3+VitKm7W9I+j3TVo6T1oVsuvkhCa7GoVY90ZHqIneD9H6SL7aacFosqQuw0lToosptOJDxCSUkVGinBsRRJfxxjKRIncALRcRkzOjLbYtcOmUfTgiOZPHoX0UGMxqLMlIomNrLbK9VXdkpFlmWCweADBYckPbhNB2C1Wu8rONxuNxaLWs2TZQVvOM6mP8qWP8asL8rGcoBN/w5b/iibgSgbvhi7odh9W5Zy7GQBb4+CECVXkLW1/8Hk1C8gy3Fs1gauXfsdXK6eU32P9fV1vvSlLzE1NQWAXq/n1q1bvO1tbzu2bCf5YgRfWSP42gZKJFUVOYFpNRIMMP3ay0y9+lWWRoZQ5MyDe1VbB53PvpWOZ96Cu6z88H0mZFan9li4t8P88M6hloyjyKKO6Xap1RC7+2SvsNIJqeE3BwnfHiTy5iDxhYVDtzPV1GB7YgD7wBPYB249tCH1tP6PCnuFZjw9M/9HMga7M7nCY3tSjVQ/YvsuANYiqOhOVT6yjKae+lO3XNJR6Tmm0qyWy4MWw5U5zTSWZsRG+n1T6cm20cZkWU0jjWQJD63yETuRp6PcbKQhVd2ot5ppsFkKOqNDlmOZbbFp82iWjyMaXT9yW+xRGAzOQ22UzG6Vqx38lUwmj/RvZL8Fg0FO6pZwOp0PbKmYzWYURSEQS7Lpi7LpjzHvj7K5EmXLv82Gb5nNgCpCtgLRBwbvpTHqdVS4LFS6zLQ6YvQYQjTKESqkGLp9P9ce5Rd1kvs/5+8vuEAkKcrk1IdZX/8zQN3s29Pzq5hMx/ssDrK9vc2XvvQlxsbGANXg1N/fz9d93dcdGwcfXw0S/Ooq4aFt0iMJhlIrzmeqsd+sONK0Gg0FmXnjVaZeeYnF4bvIWa8OKlva6HjmLXQ++1Y8FZWHvjYSjLM4vMvCvR2WxrwkYpmvNZr01HWpWSH13SUn3qirSBKxqakcU2pyezv3RjqdOhUzcEsTIabq6gd+74M8lP+juIuu0jPyfyTjaoslR3xMqJMux4oPD5R3Q0WX+r68UxUjzspT7XJRFIXdUFwzk2qVj5QA8T8gKr3cZaE5LTiyqh0NpXbcD9jPkg4HO1zpUN+fZGS2yGhQBYctJTpSwiPdYimk7bKynCAW28yKNc/2b6jvE4mTleMz22Kz9qhYcz8v1F0qj0rav3E/0RE+IkfoKPR6PS6X676Cw+l0YjQaicQltgKq2NjwR5n0R9lcibDp32fDH1UrHP4YkROIZVD/TEsdqthodibpsARoU8JUS1GK4jFsURljDIgakUI2JL8LWSkCMo81gdjJWnCPghAlV4RIZInh4R8jEBwF9LS2/BSNjf/uxOO+Xq+XL3/5ywwPD2tqvq+vj7e//e2UlZUdur0iK0QnvQRfWiU2lzEvmpvcuN5ai7W7FN0Bf0YsHGZ28DUmX3mJxaHbSFmjsOWNzWpF5Nm3UFyVu5hPURT2NsIs3Nth4d4OG3O+nBKj3WOm6XoZzdfKqOsqxniClowcixG9dy9jSr1zB/mgE91kwtbXp3pBbt3CfusWBs/JBR6o+19m9mYY2R1hdGeUsd2xi/N/SAlVaGyPw9ZE5r13Fo6Jf8fihvKujPhIv3dVnVh8KIrCdiCmiY3sqZbFnTCB2P2FR5XbSmOpXfN5pFsujaV2HJbjH7Lk1O4VVXDEcnwdS9E4a7E4yQeoDrtBn6lyWM002HKrHoXi6ZDlJPH4lubjOGwe3SAe34YHyizQ6y0H2ikZwWG11hy5LfYqoCgKkUjk2FHY9NtJx2CNRmNOJeMoweFwOJAU2A6oHo1Nf4zpVHVj07/HVmCdDV+UTX/0gQI9G7fVSJXHSoNTodUSpF0foF6JUBKP4YwlMEcVdFE9UtSCtOlAWi9BoYRswZFMvR1GQjYEiJtDeK27Jz7TwyJEyRVgZ+dLjI69l2TSj8lUQl/vb1BS8vyJvnZ/f5+vfOUr3LlzRxMjXV1dvOMd76Cy8nCVQo5LhG9vEfzqKsmdlLtbD7Zr5bjeUou5PvfVUjwaYW7wdSZf+Srzd99EyloQV1rXoAmR0tr6nK+TJJn1GZ8mRHzbuU7ysnqnKkSul1Fe7zokgA4i+f2Eb9/WTKnR4WGUA8vq9A4Htps3sT8xgH1gAOu1a+jvM1F06HejyCz6FxnZGVHfdkeY9E4Skw4/qJ2p/0NKqC2WdMVje0IVH7szx2d8mF0pwdGVar+kPnbXnEh8pOPS53dCzO8Emc+qeizuhu6bWpoOD2vU/B2ZqZaGkuOj0tUJlmTGSBqJ5VQ6VqIP3r1i1uk082iDLZPVoYoPC6UFsGFWDf/aPmIcNvM+Ht9GOa6qlYVOZ84N/TrCPGoyFef9Zz5rsgO/7ic6kgczgo7BYrEcap8c9m9Y8YYTqm8jVeEY8UXZWo2y6V9j0z/Hpj/Kbih+X99GNlaTniq3lTqXnjZbiBaTj0YlQnkigicWxxKTMER0yFEj0o4daasISSkBMi+eJOC4ORxFFyNpChKxhPBbIngtITZNAVZNXhZ0mywnVojEg9hieuxRAybf+Q/rClFyiVEUibn5/8DCwm8D4Hb3c63vt7Faax7wlRAIBHjppZcYHBzUjFVtbW284x3voLb28CI6yR8n+MoaodfWtSkandWA46lqnM/VYCzKmNMSsSjzd95k8pWvMnf7DZLxzJNycXUtnc+9lc5n30pZfe7Sv1g4wdKol/l7OyyN7hLLmtbRG3XUdRbTfL2MxmtluEruLxYSGxs5ptTY9DQHHwkM5WVqGyZlSrV0dp54W66iKGyENhjZVQXI6M4oo7ujBBOH5/5dJhe9Zb30lfXRW9pLV0kXtc7a0z8RSEnVXHqw7bIzfR/x4VRbLdlVj4oudZncScydsSTzOyHmdkLMb6sCJP3x/Soeeh3UFtsOeTuaytTwMKvp6N9zMCkxFoxkTbHEclotwQeYSfVAjdVEg9WSVeHIiJBKsymv4WBnGf6l0xmxWCozm2K16kbaPFqN2VTy0OGIhYokSScyjMryg43HAHa7/b6Cw+VyEVMMqcqGKjbG/VE2V6NsjvvZ9G+z5Y+yFYjdN8k3G5NBR4XLSrXLQIc9TLM1QIPOT3UiQnE8hi2axBhVUCIGJL8FyetGUkqRyUQ5KMD9BqglQ4iYOUjQEmbfEmbbHGDdtM+Sfpt5aQV/cg9iCexxA/aoAde+AU/IgDNixBo30JU00I0LyLzQjCYS/NmJfsKHR4iSS0o87mV07L14vS8BUFv7PXS0f+CBccehUIivfe1rvP7669qrhKamJt75znfS0NBw+H7WsvwiKaOUocSK6/ka7E9Uok+V02VZYnHoDmMvfYnZN18jEcv8uXgqq+h8VhUi5Y3NOU/Gvu0wC/d2mb+3zfq0LydF1eYy0dhXStP1Muq7S45daqcoCvGFBcKvv0HktipCEquHV86bGxtzTKmmhoYTC4O96J5W/RjdGWVkZ4Td6OFSpsVgobukWxUgZb30lfbR4G5Af5onBlkC7/zhtsvu9PGjtiZHxueRXf3w1D1QfCQkmWVvOFX1CDG7na5+hNj0H1+61umgrthGc5mTprTwKFPfH5daGpVklqNxlv2hHBPpcqracRIzaaXZqIoOW26bpd5qpsZixpTHGPTMaOyaNq0STX38cOFfhzM40qOxZnPZIy/PLDQOCg6fz3ek4DiJYTQd+HWUSTT9scnqYDecZCMlONb9Me76o2yuxdj0bbEZWGLTHyWaOJnA0emgzGmh2mWk1R6j1RagweinVg5SGotij8UxR2SI6JAjJiS/A0kpRVJKUcj40+4nNhSSJMxBwuYwPkuYXXOQDfM+q/pd5pU1dqRtYlIQUwzsUSMun4GioAFXxIAtZqA6YaRWMQCHJxiP/qGs6PROdDonBkzAF072dQ+JECWXEL//njruG1tDr7fS1fVRqqv+8X2/JpFI8LWvfY2XX35Zm3Gvq6vjne98J83NuUJBkRWiU3sEv7pKbGZfu9zcmPKL9GT8IoHdHUa+9DcMf+kLBHYyxlB3eYUmRCqaW7XvL8sKG7P7LAzvMH9vl731XONUcbWD5utlNF0vo7LZfWxuSHJ3l9ArrxJ6+WVCr7xCcv3Avhi9Xg0pyzKlGo/wxhxFKBFibHeM0Z1RhneGGd0dZTV4WOQYdAbai9vpLVWrINfKrtFa1IpRf8I/K1lS97psjavCY3tSFR87U3BEywdQF8qVd2baLZr4uP+0i6IobAVizG2HmNsJpqoe6tuSN3zfV3ilDjPNZeoYbUu5M/VebbUcrHgkUmbS1wMhrc2SXfXYPEEUejqrQxUclhxfR53VjC1PZlLNx5EWHNqI7BrRmPp5Muk/wXfSpcK/DptFM+FfFehP+v/okpAWHEcJjfRlJ51QSRtGs8WGx+PRLrM5XEQwsR2Ms+WPsuGLshCIsbkaZcsfYcO/x6Y/SuAUvo0iu4lKp4UWZ5xWW4Ams58anY+KeAR3NIolmkQXBjliQNqwpcRGCRLNpJ9q46m3Y39H+ihRc4iAJcyeJciWyceaYY9F/Tpr8iZhKYAUj2KL6XFEDRTvGnCHjdijBuwJA9clIzocwEmykAzo9E5ICQ6dPv3mQKdTL9frHdgtOuwOPQ6PGewK/I/fOPHv7GEQia6XCEVRWF37U6amfglFiWOzNXL92qdxOjvv+3Wzs7N8/vOf15blVVVV8c53vpP29vZcMZKQCKX9IttZfpG+MpxvqcXSoP57ybLE/J1B7v3dXzF/+00tS8TqcNL9tnfQ/fzbqWrr0L53PJpkedzLwtAOCyO7RIOZVoNer6O6vUgTIp7yo/NL5EiE8JuDmgiJTUzkXK8zmbDduIH9ySew3RrAduPGiaLa41KcSe9kThtmzjd3ZA5Ik7uJvrK+nDaM1XgCz4miqAmnm2O51Y+daUge85rIaFOzPQ62XTwN9xUf/mhCExxzKdExt61WPe7n87Ca9DSXOWkpd9CSEiDNZQ5aypw547TpkLCFSJzFSIzFSJyFqPp+KRo70aZZR8pMqrVVsqoe9VYzrjyYSdXEUV+W2EhVNrIERyy2BTz4FbPR6NYMolZrDVZLjfq5Vum4evtUjtqhclB8nFZweDyeI6ocbpJGK8Gkga1gXJtM2cpqraR9GyfFbjZQ5bLQ6JJotwVVsaHfp1IOUByLYo0kMIRllIgOKWpGkks0wSFzMuO7gkLcFCJsDrNvDrJj9rNu3GdZv8UKawQUP5FkEENcwhFRKxtqK8WAJW7ElDSg4xR/Fzp7jsDQxEeWALGYjDgcehxuE44SB85KN64KJ44iCw6PBUeRBZvTlOPXE4muAg1JijAx+fNsbHwOgPKyb6Cn55P3HcMLBoN84Qtf4N69ewC4XC6+6Zu+iZ6eHi3BD0AKZPlFQim/iMWA46kq1S9SrD7x+ne2GfnSFxj+0t8Q3M3kZ9R193H967+J9qefx5jaexPwRjWT6srUHnLW2IPFbqSht5Tm62U09JZgOSJDQpEkomNjhL6mipDI7duHjKmW7m4czz6L47nnsA/cQm+7fyy8JEvM+eZU8bGrtmAm9yZJHjGJUuWooq+0T/OC9JT24Daf4I8wHlLFx+YwbI7Cxoj6Pn5MuJTRqmZ7pLM+0uKjqBH0Rz8IxZISy94wc2nxkSVCdoLHt1sMeh31xbaU4HDSXO6gtcxBc7kjZ1dLUlZYjanVjf+z72NxPc5CRA0IW4jGHhiHbkktfcsemc1usxTnIQpdkmIpD8dBwZFpr8jyg2O5dTpTprJhrdYER0aEVF+50diDguOoSsdpBMdRUykejwej1UEEM/6kQRMbC/4oGxtRNqdjbPnX2QosIJ3St1HvUui0h2gy+6kz+qnUeSmJhXFE45hCSZQwSAEj0p4n5dsoRVK6UbCSBB60HUbSJYmYQwQsQXbNAbaM+6wYdlhijX2dj7AUIJ4IY4lBUdBI0VamlVKSMFKmGABL6u1BGLWKxuEKh/q5yWjFbjfgcBlxFNtwVbpwVLhxFqlCw1Fkwe42P9RC0YtAiJJLQDi8wPDIuwkGJwA9ba0/Q0PDvzn2gV1RFO7cucPf/M3faPsPnnrqKd75znfm7KdJbIQIvLRK+O5Wxi9SbMH5fC2OJyrRW43IksTMm68x/Hd/xfydwUxVxOWm9+u+nmvv/EZKa+tRZIWtpQAL91ZYGN45FOnuKbfR1K+O7Va1eTAcKMErikJiaYnQK6+oQuS115D9uaVwY001jueeU4XIM89gLD2+J6ooCivBlcwkzM4I495xIsnDTzxFliLN/5GuhJTZHtDqURTYX4LNlOjYGFY/9s5z5BimwayGix2ceCluOlJ8yLLCxn4kJTiCWtVjfifEsjd83w215S5Lqsqhtlmay9SWS0NJxucRTEosRtVqx+d8ARY3drSqx0r0waOzVWYTTTZVZDTZLDRm5XWUX/CmWXVaZTdT0TggNqLRNRKJk40ymkylKZFRnVXhqNHGY1UfR2E+mD8MacHxoJbKSTgoONKVDqfLhWK0EcbMXlzPVkAVHKO+GJtLUdZ9ETb9PoIPGBXX7ifl26h1GWi3h2ixBqg3+ajW7VGa9OOKRrCEExCSkcJ6pIBT821ISg0yPYCBkyRuxA1RgpYge+Yg2yYf64ZdlnTr7Or3CCoBwlIAJRbDHTZQsmXAHTZgjxkxxw20yUZ06OFE7RRdVnUjIzDI+tygt2OzmnC4jDiLrTjLnTgr3DhLrFplw+4xH+u9uyxc7tM/Bmxv/y1j4z9DMhnAZCrlWt9/oLj4mfvcfpvPf/7zLC4uAmqr5tu//dupq1Nd24qiEJvaI/DVVWLT+9rXmRtcON9ai62nDJ1Bh397i+H/8wVGvvgFgnuZgKX6nmtcf9c30/bUcxiMRjYX/Hzls1PM3tki7MuUTHU6qGr1aGO7RZWHQ8ySe3uEX035Ql5+5ZA5Ve9y4XjmaezPPovzuecwNTYeK8S2w9s5RtTR3VH2Y/uHbmcz2jQPSFqIPHASJh5SfR8bqepHWojEjvEPOCuhshcq+6DqmvpxWQcYDleE9sNx5nb8WS2XIHPbaoT6/cx1DrNB83ekPR4tZU6ayuy4rCZkRWErnmQh1WK5HQywtLPLQiTGQiTObuL+TwAWvY4Gq5nGlOBosllotKmf119wSNgh82iW6DiNeVSvt6XaKdUZsaGJj3TE+aNtHS4kjkoZPaqlchLSguNgS8VqdxLXWwkpZvbiOjYDMZZ8qodjYzPKpj/IVmD3xNUNl8VIldtMhzNKmzVAo3mfWv0+ZYoXTzyILRxDH5KQQiBt2pAozRIcjSg4CQMPijKTkYmYQ/jMQXZNfjaMXpb1m2zrvfj0PkJSgFgihDUiUxJQWymOiAFr3Ehl0kiVolf3OJ2ofWPOrW5oFQ6HdrnNYsXhNOLwWHCWqa0UR4ktU93wWLA4CnfD81kiREmBoigSs3O/zuLipwHweG7R1/dbWC1VR94+kUjw1a9+lZdeeglZljGZTLzjHe/g6aefxmAwoCRkwne2CHx1leRW6k9Wl+UXaXQjJZPM3H6V4b/9K+aHbmsjtDaXm963v4tr7/wmSmpq8W2Huf3XK0y9tpGTH2KyGGjoLaHpehmNfaXYDiS5ytEokdu3NRESHR/PHdM1mbDfuIHjObUlY+3tRWc8/F9UkiWm9qa4s3VHe9sMbx66nUlvorO4U2vB9JX20expxnBMWwRFAd9yquWSetsYUTNAjqp+6E1qtaOyF6r6VBFS2QfO3Dj8WFJiYTvM/M5OarIl8+a9T+/bqNfRUGrXPB6aybTMQbnLQkxWWI6qrZXFaJw3IwEWpnY1f8eDMjtKTAYarRaaUmKj0WbWPq+yXMzo7NmZR9VpFbW1kmmppAWH1VqD0Vh0ZR7UswXHUVWO0wgOg8FwpHdDb7UTw0JQMbEb07HljzHmi7KxH2VzKcqGbx9/9Og1CAfR69QKXqMLOmwBWq1+6o0+qnReSqRdnNEQplAcgjLSvgHJW6wKDcqQlEaSyi2iWO47lZImoU8QMAfZM/vZNO6xpt9m07CL17BPUPETToaQoyGKA3qK9oy4wmorxZY00qQYAR0PbKfoAPSgc2S8G0dUOCxmGw6HBYfbjKPUrvo2Su3YU5UNZ5EFm8uEvoCSgPONMLoWILIcZ3T0vWxt/yUA9XU/QFvbzx67int+fp7Pf/7z7O6qJer29na+9Vu/leLiYuRwguDLawRfWUcOqZ4MncWA48mUX6TEim9rg+Ev/g0jX/4bQllVkYa+fq6/65tpfeIZEjGFmTe3mHp9g425zJOE0ayn5WY5HU9WUddZjMGU+eNSZJno2DihV14m/MorhN8cRDmw3dLS0aG2Y55/DvvAwJEbdCPJCMPbw9zeus2drTsMbQ8dimTXoaO1qDVnEqa9uB2z4RhDYTysVj8Oej9ivqNv76hICY9eqMyqfhgz3z8cTzK7FWJ6K8DMVpDprSCzW0EWveH7vlKscltTbZZM1aO5zEldkRW/rLCYMpKmqx4LqbTS9QfEoht0UGcx05hqsTQcqHicdzrpxZlHa1Lm0auxqj4tOO7XUgmFThb3fVBweDwebA4nstFGVGfBL5vYjcBGyiC6kZpUOc2uFLvZQI3LRLszSrstQKPZR61+n3K8FCV3sYVCGIJJpJCCFLchKWWpt1L1PSXAyf7twsYw++YAOyYf64YdNgw77Bj28ev8hCU/sUQQWyBBcTDVSokaMScMGGQTaorNCdFZcvwaB02iBqMdh92Ow23CWWLDWe7GWe7QPBtqO8WM8Zg8nsvKRTx/C1FSYEhSlOGRd7O7+2V0OjM93Z+gquofHnnbUCjEF77wBYaGhgB1idO3fMu30NPTA5JC8JV1/H+3hJIaezMUWXA+X4PjySoUI8wOvsbw3/01C/fuaBULu6coVRX5RlwllSwM7zL52gZLI7tahohOB/XdJXQ8XUVzf1lODzO+sqJVQsKvvoq0v59zZmNlpeoLeS7lCyk/vGRvN7LL3a27mggZ3x0neSBMymly0l/Rz83ym9ysuElfWd/RceyKAr6VTNUjXQHZneX46kdnVvslXf2o0G6yH44zsxXUhEf649X9442SLouRlgpnVtVDfV9XbGcfWZtmWYjENRGyGIk9cPOs06DPCA2rRRMgjTYzteec2aHuVtkgGl1NvR3O5ngU86glq7VyVcyjyWTyWKGRfjut4Ei3VFwuFyabk6TBShgz/qSRrQiahyMdX/6gxYZp0rtSmtwynbYQrVYf9cZ9KnV7lMm7uOL///beO06us773f59z5kyvW2a2q6yq1SVbQpbBTXLBGBtTbC4Bg2kh5gZwEgi/vMCQ5OILBJIb4oBvgm0cLmAgxsbG2JZlS25yU7esrtWutpfpvZzn98fMjnZXu6tdo7JaPe/X67xmd+acM88zz845n/3WAfREBmI5jIRKQfhGERw+mEDWiEAQ1xMEzVF6TEG61D769TADWoi4ESWZj0EijjcO7riGI110pZgKJhDaJPovaSiqA0YGiA5xqdjtdpwuK45y3IZrWEaKw2vBbD33lX/PBVKUXGDk8wl27/4cofCrqKqVpUt+TGXle07aTwjBrl27ePrpp8uBrBdffDHr16/HYrGQemuAyFMtFAaKxk5TwI776iZsi6qI9HWz57mneWvzsyQj4fI5ZyxdwdKrr2X2ytV0tyQ4+Fo3R7b3kk2fSCOtbnIxf00Ncy724/AUTZuFcJjEa6+XU3VzbW3Dxqo6HNjXrClbQ8wja6IIQVusje0928uumGPRYyfN2W/3s8q/ihWBFaz0r2SOd87JbphssphqWxYfpfiP9FjWj+qSy2VRKfZjcdn6Mdi/ZajwKFpAxs9wqXKaaa52MsfvZK7fyRy/i/oqG3ENWtO5cnDpoMWjPZM9ZQptrUVnRim+Y9DVMrMUWHomy6IXCumSuBgUHe2k052kSr9nMj1MxMpRDB4d3cIxnYJHh2apDAqNSCQyTHRMVHAM7aNSbNLmArOdnGYpxm/kTPSlBD2xLD2RdLn4V+YUmVGDWEwqdW6dec4088rWjVDRulEYwJEOYYqnKcQKo1s3RBUGozfoHImBQcQcY0CP0K0N0GsK0m8KEVIiJESUTCaKHkviiSu4S64Uc86EauiIyfxdKLZh1oyihaPkVlFcWC12HE47Tp8VR5UDp99V/HmcFFjJcKQouYDI5aLs2nUHkegONM3BsqX/ic+3+qT9+vv7eeKJJzh27BgAfr+fG2+8kcbGRrLHY4T/cJTssaJ7RXXqeK6ZiWV5BUe2vc7uTU/Rtmdn+Vx2j5fFV25gyVXXUsg7Ofh6Nwdf7yEeOnHTdVVYmbc6wLzVNVTUFV0ruZ4eYs8+S2zjsyTfeAOGdPjFZMK2bFk5Vde2ZDGKfsI0mzNyHAgeKIuQ7b3bCaZP7lQ6xzuHlf6VZRFS66gdfvNN9EPHdujedcL9EjwCYpSLsmoqZr6U3S8l64crgGEIOsKpIZaPWPnn8Rpi1XmsNPudzPW7mON3MqPajtlloZ8CR1NZWpIZjqTStCSzdGfH/890MKh00MIx1NXSeAaLheXzsZLIaB9h7egglWqfUMaKopixWmuxWRtKsRz15Qqkg+6W6RA8KoQgkUgMExsjRcdEK42OFBxWhxNRcqckDDPBvEZvUpTTYnuiafrjE6+7UeEwM9NlsMAeZ7YlSqMpTK0aosII4sn3YUmGIZrFSFCsuUHVkGDRqlJa7MTqb+SVPCFzjD5TiB6tn349TL8WJqKESeRjkIxijqfxxFWcKQ1rVsNU0EGYEJOyboxMgXWV4zh0kx2H04nDa8dZaT8pI2Wqp8CeT0hRcoGQzQbZufOTxOJ7MZncLF/+IB73smH75PN5Xn75ZV544QUKhQImk4krrriCtWvXImI5ok8dI7mzWFFV0VWc767HWKCz58VneGvzs6SiJWuBojBz6QqWXn0d/uZlHN0+wIHXuxloPxEUZ7aZmLPKz/w1NdQ2e1BUhWxbG7GNzxLbuJHUzp3DxmZubi67ZOyXrB5WtCyejbO7bzc7+nawo2cHu/t3n5SWq6s6S6qWsMK/gpWBlSyrXobHMuSimI5A547i1rG9+Bg5PvqHaa8aHnRaU7R+5BSd1oFkSXCUhEdfnCO9iTFbf6sKNFXYmVMSHs3VDpweCwWnia58gSOpDC3JDEdTxTTa8b5IFbrGTJulnD47GNcx8wz1Yyn2WAkOFxpl4VHcJhJAqmmOUtBofXmzDfl5ulg50un0MIEx8udoNFruETUew9NiPZhsDgomKxksxISZgaxGb8KgO5Ypio5IesKt582aSo3LxEJXijnWGDPNEeq1MNWE8BX6cWT60BMRRDRPvmzdGAwWPWHlEBOq9glZNceAHqFXG6BPD9FvKrpTokTJZKNosRiWeAZ3QsNesm5oho6BVgoEnQCjxm64SimwTmw2J06vE2elHVfAjbPCjsNrnlYpsOcTUpRcAGQyPezYeTuJxCF0vYIVyx/C5Vo4bJ/W1lYef/xx+vuLke7Nzc3ccMMNeB1uYpvbib3YASWzrX2FH+ViO689/d+8/cJz5boiDl8Fi6/YwPx1VzHQrnLgtW7aD4TKYRWqpjBzSRXz1gSYsbgSzaSSOXSI2MaNxDY+e1IFVduKFbg2bMC1YT3mxhMdfnuTvcVYkJ6iK+ZA6ADGCOuF2+xmhX9FWYRcVHkRFq0U6Z5NFFNvO7ZDZ0mADBwe/cOrnAt1y4fEfiwhbankaH+SQ70xjgxxvRwbSIwZuKdrCrOrii6XZr+DCp8Nk1MnZdM4ns1xNJXhaDJDWzozbv0Ol6Yyy25hts3C7BGPXv30XjiFMMhke4sCIzUoPNpL4qMoQiYSz2EyeUsiow6rraEkNurK4mM6ZKzkcrlRhcbQn7PZiVkinE5nsZy5241mKQqOYsComWBWoysh6I5m6Iqk6Y9nxq0nMxSPTafZbTDPHqPZEqOhVHejSvTjzvVjS/WixOMYcTGkomhJbDBUcEzMKpXS0vSbwiVXSph+PURQDZMoRDFSMdR4DGs8iytlwprR0As6itAxUCYhOBwjYjZcZeFh1u04XG5cFS6cVU6cfmfZlTKYBitdKVMPKUqmOalUBzt2/hmpVBsWSw0rlj+Ew9Fcfj2ZTLJx40Z27NgBgMPh4LrrrmPRwkUkt/UQ3diKUSrZbp7lQV/n481XHuOtzRsxSv/VzVi6giVXX49unc3hN/tp2dVPfkj9i9o5HuatrmHOKj8Wu4n0nj1FIfLMRrKlWicAaBr21ZcUhcjV69EDfgxh0BJpKYuQ7b3bR+0RU++sPyFC/CuZ7Z1dbFCXzxZjPgbFR8eOYkzIaC4YbxPUrYT6lVC3gmTVYg6GVQ71nHC3HO6L0xZMjtkW3G7WaK4uWjxqK+3YPBaEw0TYrHAsneVoKkNravxUWpuqMHOI2Jhlt9Bc+r1KP311BIYGkaZKsRzpYZaOLoQ4dcCi2VyN1dqA1Vo3zMIxKD5MJudpGe+5YmQcx2iiI5k8VdWKIjabrVTsy43J5jiRoVLQCWZNdKUUumNF68ZE3SkmVaHOpTPfmWSuPcYMvWjd8BPEVxjAme3DnOiFWHJI7EYl+aExHCXRARMrTR/XksPERr8pTFiJkM5FURJRtHgcayKPI2XCktMwGWYEJowJ/+mO5U4pBorabE5cXg/OSicuvwtnpX2Y2HB4LeiW6ZWVcqEgRck0JplsYfuOj5PJdGG1NrJyxX9hsxUtDkII9uzZw1NPPVW+oK5cuZINGzagHE8T/sNR8j3F502VVszvqWbn3qfZvemPFEqdf2csXcHCyz7AQLeTw2/2kIqduIF5A3bmr6lh3uoALp+Z5LZtxJ7ZSOzZZ8l3d5f3U8xmHOvW4dqwAeeVV2Dy+ehOdPNK5yu81PESr3e/TmRECq2qqMzzzSvHg6yoXkHAESg2n+vbP8QFs70YCzJa11tnTVl8iLoVdDkWsDdsZn9XlH3dUfZ1xTg2kBhTfHhsOnP8Tpqq7Li9VkwunbRNo1cTHEsVxUdinKwWXVGYaTMza4TomG2znLb6HcUg0o4R24n4jomkyiqKVuoYO9S6ccLaYbHUoWkTKV09NTEMg2QyOaorZfC5iZY313Udj8eDw+nCZDsRwxEzdAZyGj0plc5Ynu5IasLZKRaTyky3wkJnnLnWKE16hHo1SJUxgCffhz3dixbvxYjni43Zhlo3hlk6KphoyaiQKUqfKVQWHANamKiIINIxiMfQ4nFsSQNbxoQ5r6OgY6AiJvwnax4mMhhi5TDpDpxON84KT1FsVDvLQsPpK1k3XOYxm2hKzn+kKJmmxOMH2LHzE2Sz/djtzaxY8VC5KFowGOSJJ57g6NGjAFRXV/O+972POksV4SdbyBwsNtVTbCZs6/zs7tjMrmefJJ8tBqfWL1hM7fxraT9gI9xz4j9Em0tn7iUB5q+pobLGQuq114oWkU3PUQieCDRV7XacV1yOa8MGHO9+D4bNzI7eHbzU8RIvdb7EodChYXOxalaWVi8tW0GWVi/FabJDqGW4C6ZrF+RG+Y/V5oO6FVC3kmxgOYdMc9kTtbOvK8q+7hj7u6JjBpxWOc00+51U+mzYPWYKdp24TeW4kedYOks4P7avXgUareZhFo/ZNgvNdgv1FjOmP/HCWihkSKfbSaWPk0odL7pWUh3l2I6JBJGqqhmLpa4kNEbEddgazutOskII0un0mPEb7ySOw+EqtqI3dBtZ1UKs5FLpTmt0RHN0RTMT7gpr0zWaPQYXOWI0W6LM1MPUKEGqxADubB+2dA9qrAuRUoZYNaqHiI4q8qIaAy8TSYk1MAiaIsOsG0EtTCoXQSRjqIkYajyBLa2UgkUtCEwUFCbhThlaxtw1xMrhwGpz4fR4cVd7cFa7cFbaylYNZzkN9vz8W5OcPqQomYZEo3vYsfOT5PNhnM4FrFj+M8zmKgqFAq+88gpbtmwhn8+jaRqXX34571p6CYnn2km80V2M/9AUrBdXcTDxJm8+8yi5TDHtt6Z5Pv7ma2jbZyMdL154TWaV2curmbemhvomK8lXXia2cSPxzZsxhlR71DwenFddVRQi6y6lOzdQFCEdL/Fa12sk8yfEhILCkqolrKtfx6V1l7Ko8iL0WM8QF8x26Nw5ehEysxNqlyPqVhD2LWKfOocdUS9vl8RHS39iVB+8SVWYWe0gUFV0ueScJvqtCm2iQF92/JtMnUVnVklsDH1sspmxjNNt91QIYZDJ9BQtG6k2Uql2UuniYzp1nEz25AqzIykGkQ4NIK0ruVoGg0grz9sg0lwuN27Q6GTiOFwuV8nC4UDodrKKtVhlNKvRk1Zpjxl0RzMkxumCPOx8Fo15rhwLnUXB0WgKU6sMUFHox53tw5rqRon1ILJqWWyMKjwmaOHIU2BAD9NvChWzU0xhwoTJZ6KIRFFwaIkk1oyCOaejCQsFRZ2EO0UdpTlb0dJhMjmwO924fBW4Ap5iCXOfZZjgsLvNsqKoZEJIUTLNCIffZOeuT1MoxHG7l7F82f3oupdUKsVvfvObsnVk1qxZvO+696K/nSL2fDuidLE1L/ByTNvHa5t+SzZVFArVM5qpmnE1xw+6yWeK5n53lZUVG5poXugg++qLRSHy4kuI9Ikizabqalwb1uPasAFtxRK2D+zmpc6iEGmJtAwbd4W1gsvqL2Nd3TourVyCt3c/tL95Qogk+k6erGaB2qXka1bQ7VjAHpp5I1bB290J9nfHCI9hIvc5dOqqHDi9Vgy3TsSm0qYZhI2xXRlVuukk0THbXsx0+VN6tORyUVLpNtKpdlKpNlLp4mM63U4q1XHKfiua5sBma8RqbSg9Ds9cMZk852UQqWEY5fTYkVs4HJ50HMeghUOULBzxUh2O7pRKRwI6o5lx+wANpcKmssCVYYE9xixLhEYtVCpl3o8r24sl2YMS7cQoWIZYNaqHWDeqKFBdiuE4dZXRAgUGTBH69XDRraKHiBfCFFIRlGTRpaImU1izKnrBCpjIK8ok3Cn6KK6UUglzqxOn24uruhJ3oJQGWxYbVhy+C7fIl+TMIEXJNCIYfJlduz+PYaTwelezbOl/YDI5GRgY4Be/+AUDAwPous4N772BuaKW6NOtFCJFl4ypzk6X9zivbP4V6UTRwlFR14Sv4Sq6jlZilLJKKuocLL+sEn/PdhKbNpJ49VXIn7Ak6A0NuK65BteG9fTN8vFyVzE25I3uN0gXTggWTdFYVr2sKESqlrMgNoDa+jIcewm6dp4ciKqaEP6LSFUvpc26gN1GM6/EqtnbneRof2LUEuuaolBTacPjs6K6zcTsKp1miGmMWp1RBWbYzMx3WJlntzLPYWWO3cps+zsvl24YmWIcR6roYjnhaik+niplVlE0rJZ6rLai6LBZG4vio/SzrvvOyxvCYLZKOBweVXhEIpEJuVV0XcfpcqOXLBy5QcGRN9GT1miPQ2c0R/YUVWsHCdhVFrqSzLfHmGUO06CFCTCAL9+PM9uDOdmDEutGGJay+2SY8BiSGjuRLBUD40QMhx6izxQimY9gpCKIZBQlEUdNJDHnNEyGFYFGblLuFNtJrhRUJybNia1k3XDXVBatGyNiN+we80mdtiWSM40UJdOEvv5NvPXWFzGMLBUV72bpkh+jaTaOHj3Kr3/9a9LpNG63mw9dfiOWrXFypZohqttMsGaAF7b+v3KdEY+/Dm/tlfQcD5TTeQOz3CxZqOB48WFiTz0FuRNWCMvcObg2bEC/6j3s9kR4qfNlXup4ieOx4XU+/DY/lzVcxjr/St6VU3C3v1kUIZ07QQy/AQnfLCJVK2mxzGdnfhZbYgF2d2fHbC7ntJmoqLChu80kHSZ6LZCwacXmLCPQFJhls5SFx3xH8XG2zTLpImLFlvZ9Q0RHydVSsngUK5KO/+ev65VFwWFrxGZtwGZrKlk+mrBYas67mA4hxLDg0UHrxtDfJ1J1VFEUHE4nFpsTLA5yWrFTbDBnojet0Z6AjlieiRQYVRSod8AiV4K51iizStVF/QzgzffjyPRgTnSjJHoxhHWYdSNfTok98ZzANqHPIqRFT9TfMAVJ5iIY6SgkIxCPQTKJOauhCQuGMhnBoZTSYV0jMlOcmM0OHG4vrupqPDW+EdaN4qPFfmF0g5Wcf0hRMg3o6XmCvW//FULkqa6+hsWL/gVVtfDmm2/y5JNPYhgG9YFarrVdjLq/aPJWzCrx+gRbdvySWKjoGnFWBnBXv4eB7vpynEHDAi8LK3vRn3yIdCltGMB60UW4rruW8JoFvGI6xksdL7GtZxtZ44RoMKkmVvpXsi5wMZfhYG7vYZTW0UVIzjOTTu8qtrGIxyOzebHXQn4U64eigM9rxeqxkHWaGLAqpBwmsKgnWT9MCsy2WZnnsDCvZP2Y7yhaPiYT65HPx8rxHEU3y/FhwaWGMXZJeCi2s7fZhoqNIRYPawMm08QKTU0VBpu5jWblGHwunz91sKdJ17E7XGhWBwWTjVQpNbY3o9GZVDkWM8hMIGZUVWCm0+AiZ5y5tmLAaJ0awi8GM1R60BPdKKkghrCUslJKFg6GCI/SJphYCnNUi5csHGEG1CDpfIRCKopIRiAZQyQSmHMaqrBQQCWnMgnB4RwhOFyoWilY1FeFt6Yal999UuyGw2MZ1rBSIjnfkKLkPKez87fs2/91wKAmcBMLF34PIRSeeeYZXnvtNQAWNs7lXa1NaFlAgXRdjpf2/5aB/mIPGbunEkflu4kGZ6AoxUqJsxZ5mZN7C+WxB06k8Oo6jmvW03rdEp53HufljpfpTHQOG0+do47LatewzuRjTbgPR9trxZiQESIk42qixbmCrYWF/HdwFm/FT26GZjFrOHwWDKdO2KaScZoQTv0k64euKDTbLcPcLvMcVmbZzJgnKD5yuTDJZAvJ5DGSqWMkky3l2I5cLnSKo9VSGfSSW2WEm8WsV543/5UOZqyMZeGIRCLEYrEJnctqd2C2OcDsIFPKVBnImuhOq7TFBKHsqc0CigIzHXkWuRLMt0aYqYepVweoFgN4cn3Y0t2Y4t0o2RhCmE64VKg6KZ4jL6oQTOx6EFeTJQtHiJAaIp2LYKQjGIkYIhlFJBPoeRUMCwVFmYTgUIu9UhTXMNGhak7sDjfOyiq8tX5cVU6cvhNxG06fTIWVXBhIUXIec7z9IQ4e/DYAdXW3smD+P5DJ5Pjtb3/L4cPFCqWXNqxg4WEfCgp5n8GrnY/T0V2snGp1erF5LyUZn4OimFBVheaFNmZ2bsb448PloFWtspLMjZfz+yUZHgu/MKyEu1k1c7F/BeusNVyWTDKrYxdKx8kiJOFo5KBtGVsy83kkOJO2QuWw1xUF7D4rabdOym3C8JrBOrwzp0VVmGO3lC0eg+JjptUyofTafD5+QnCUxUdxy+fD4x6r676S6ChaPIa6WazWuvOmpf1gIbCxLBwTzVjRNA2L3YVqsZM32UhgJpLT6ckU3SpdKRVjAm3ca20FlrjiLLBHmaWHqdeC+MUA3lwvjnQ3pnhXSXCoFKgoiQz/iGyVQcExseZtKSVNnx6m3xQkpITI5CIU0hGMZLRo4UjGUfMqCDN5RSWnikkIjpGpsC40kxOHy4OrshpPXQBXpX1YZVGnz4LVqZ83wlUiOZNIUXKecqz1Po4c+R4AjY2fYu6cvyMUCvGLX/yC/v5+TCYTV7tW0dhVNEUfy7/N68efQCAw211YXO8im1mAouhousrcGQb1b/034tXnyu9hWjCPg+vncn/gAAeTx8rPNzjqebd7NpdlDS7uPoS9cycYw+3sMVs9b+lL2Jicy9PxOXRQPex13apheMykPTqG14xwm8sWEIuqMH+IxWNQhDTZzGinuHAXCmlSqdaS2GghmTpWEiAtZLP94x5rsdRgs83Abp+J3TYTm30GNmsTNlvDedPWPpPJjGvliEajEyoEZrbYihVHdXu54mh/tuhW6UgqpISJU92pKy0GS5xx5jtiNJvDNGhBAqIfX76vFMPRhZIOIwQIHKWg0RPb4O85qjFEJcoEanFklGzZwhEhRCYboZApCg6RiCHSCciBwExeUSYlOIpBooOCo2jpMJmdOJxeXNV+vHXVuCpKgsN3QnCYbTJ+QyKZKGfj/n1+RelNcYQQHG35Z44duxeAmTPvZPasr9Da2srDDz9MKpXCaXdwTXYZFV02CuR5tecJ2pMH0C0OzI7V5I3F5LI6FpvGnMoQ/pcfRN14oBiOqapk163g6dU6v7TsIC+OQhJsmoVrrfV8MBJm2dtvoBhbh40rbK5lh7aYp+JzeCm3gI70EBGigOYykxkUIF4zaVvRCmJSYLHDxnK3nWUuO8tcNhY4bOjjWD4MI0sq1U4yVbJ4lMRHMnmMTKZr3M9P1yuw22dht83Ebp+JrfzzDDTN/k6X5ayRyWQIh8PDtkEBEg6HJ5QmqygqZrsDxewgq1qJYyZUcqu0JxSihk4hrcEoZWAGcZgMlrjiLLTHaLaGadJCBBigotCHqyQ41NQAJEDETaWy5v6SW8VPUiwiKqrJCz95UY1yisBRhWItjmKGSpCoKFo48ukIRipatHKkUhh5gYGJnKKQH1VwmIdckQbFmYqiukZYOVyYLa5iwKg/gKe2GneFDYfXWnSr+GSxL4nkfEV+a08TQggOHf4Ox4/fD0Bz81eZOePz7Nixg8cffxzDMAi4q7lqYAGOgpl4IcwLXb8lXohg9axDKMspCAt2h0az6ShVz/8naqxYaVVxOWm9Yh4PzOtmr3lX6Q1hsaWaW+IJrj92AKc4UWl1wBTgNbGITal5vCYW0j5EhCi6SsGrY3gtCI8Zw6ODSUUF5jmsLHPZSyLExkUOG9ZRMl6EKJBOd5xs8UgeI5VuZ7zy6CaTuyw8bCWrh90+E7t91pS3eAzGc4wUHoNbKjWBBni6Gc3mpKDZSCoWInmd3oxKR0IhlNdJoyNSY4s+s2qwrCQ45lgjNJlC1NJPpdGPK9NTLPyV6ENJCUQSDDzDrBtpsYaEqCYrqsnjB+FBGceVMziSiBaj1xQirARJ58LkMmGMZAQjGUOkEuRzBQw0sqpCXhvN2qONIji0ExaOIQ3bLFY3Do8PT00AT001rgpbOR3W6SsGjJrMsneKRDIdmbQoeeGFF/j+97/Ptm3b6Orq4ne/+x0333zzGRja+YMQBfYf+AadnQ8DMG/e3dTX/RnPPPMMr7zyCgBz3I1c1tuMCY3jyQO83vskhmpHd9wGqh+nA2bHt1Hx9M/RSv1gck01vHipm581tJDSdwPg0izcWLBwS8ch5meLwbAFVLaI5TyRv4StxqJhIsRwmsoWEMNrRthNoBTjPwatH8tddha5bDi04Rd6w8gSj7cQjx8gnjhAInGoKDxSbeM2g9M0O7ZBsTFEdNhsM6d07Y50Oj2m4AiHw6SHFJ8bC023oFod5DQbCWEhmDfRlVLpSmkkhJls2gRjxKKqGCxwJlnsiDLXGmWGHqJWCVJl9OPO9mBLdaMmelAyBmQoZatUlUVHTiwmzZXkRDVZ/CCqUMTYTdwGV2HQrRJSQiQLIbKZYvGvQjIK6QS5TIa8UMgpClltDJeKPvi3M1xwFK0cRbGhqi4sNhdObyWemho8gSqcFSXrhteCoyQ4NJPMUJFILlQmLUoSiQTLli3jjjvu4JZbbjkTYzqvMIw8+/Z9le6exwCVhQvuobLyRn71q19x8OBBAC62zmNZbwMAOwee50D0dVR9PrpjPR6Hxoz2P1K55UnUUlGy3uWN/HJpjJfr+kApxlpcojq5pbeD9fEo1lLcwTZjLo8W1vGHwrsI4kaYFIwK8wkR4jGDrtJkNQ9zwSx12YcVHBNCkMl00R86cEKAxA+QSB4dU3yoqrkU4zHS6jELs7l6SgqPVCo1rntloqIDs52MaiMmzPRnTXSmVEJ5nYSwkEtrY4gOQcCcYZkrxAJ7lGY9RIMWpNrow5vrwZ7qRkt0o+TzEAERVjHwDYnlmEVMXEJWVJMhgMCPaoydHjv46Rd7qkQJqSHiRpBMNkw+FaGQimCk4hjZLOl8jpyqktVAKKPV+R88mzhx9rJlYzAl1oXV7sHhq8RbW4N3mOAoPtrcMkNFIpGMz6RFyfXXX8/1119/JsZy3mEYWd7a+yX6+p5BUUwsuugHWCyX8dOf/pTe3l40VeNyYxGzw9VkjBSv9DxKb6YDk309JvNimrs20nTwcRQEhs3Mmxf7eOiifrorirEXlYqFm2NRPhAKMqNUW+KwUcejhXU8ZlzKcRHA8Jkp+G0YlRaE00Sd1Vx0wbjsLHMXBUiFfmKZ8/kY8fgO2hMHiwIkvp9E4uCY1Us1zYnTOR+ncwFOx7yiCLHPwmKpmVJ9WQbTZcezdGQy49csAVB1SymA1ErE0OnLmOhOa8SFmbiwkE+P7jYwK3kWuWIsdsSYaw0z0xSkhn4q8704Mz1YEp0o2TgkgAQYwkahFLNRENUkxEVFwaH4KVCDWvCOGTw6NFk3qabo10LERJBULkw+HcZIRSmkYhiZNKlchqwCWQ0K6iiCQwXMCsOKyCn2stgY3Cw2d8nCUYu3tuhScfqsOCssuHxWKTgkEslp4YzHlGQymWE3g2h0/NLd5wtCCPbt+3pJkJhZsuTfSCXn8tBD/0EikcCu21gfX4RfeOjPdPJKz6OkhYbZ+VEcholF2/8Jd6yVeLWTx1cUeGpRlpR1ABWFy7OCW0L9vDuZQge6hY//W7iUxwrreEvMwKiwYgSsFAI2GjxWrqn0cEWFi+UuO35LMf3VMHIkky3Egwc4Ej9hAUmnO0adj6KYsNtnFwWIoyRCnPOxWGqnjNUjm80SCoWGbUNFx0TSZRXdQsFkJ4GFcF6nJ60SLhQFR1yYxxAdgjo9yVp3HwtsEWabwzSoA1QbfXiyPdhTXaiJXpScgDAIAQbesujIiUWkxJWk8JNValGMKjRj7MDdwREUKDCghYkoQZL5ELl0KT02FaOQTpHOpUmJPFlNIa+NEsejACO9N4qlFDB6QnCYrW4c3grcgQC+mhpcVQ5cvpKVo8IqS5pLJJKzxhkXJffccw/f/va3z/TbnHWOHv0B3T2PoigaS5f8Ox0dFfz+9z+jUChQpXtZH1uEEyuHotvYOfAc6HMxOzZQ272NeYd/Q8Qn+N41KtvmpBCqQr2h8oFQiJtjCQKFAlFh55HCFTxqrONVsZB8hQ0jYKPgt7KiysU1VW6urfKwwG4hm+slEd9JvHs/e+MHS/EfR8ZsGGex1AwTHw7nfBz22ajq2PEHZwPDMIjH4ycJj2AwSCgUmlD5c3Qrec1KXFiKJc8zJmIlK0diDNFhVbIsdsZY5OhmriXMDK1o5ajI9+HMdGNOdKLk02UrhxBasfIofvLCT0IsIC2qSSk1FKhBK1SgidFrowz9wsXUOCElSKIQIpMtxXGkYxjpJOlsikQhQ8YEOdUYI45j8IehcRzusthAdWI2u7G7K3D7A3hrA3j8nqKFw2cpPnpllVGJRDJ1+JPqlCiKcspA19EsJY2Njed1nZL2jl9w4MA3AFgw/zvs21fJSy+9BMBMNcDlyYWoQvBG/1O0xg9gsl+BRZvLggO/INC3nT+uUvjFFSqGrnB1IsEtsThr0hlywsRzxgoeLazjebGcVIULo8aGXmPjPQEv11Z5uLrChTPfQii0lWBoK5HItjGrmmqaE6djbll4FEXIfHTdc9Y+q5GMZu0Yup2y0ZtmJmeyERcWBnI6fVlT2coRF2YKI1weCgaNerRYBMwWZbY5RL1ywsphS3aipYPDjikGkFaXLB1+cqKahBIgq9SDqEbPu8fNWIFiLEdYjRARQVL5ELl0uGTliJPNpInnEiS1AllNjB7HcRJKKWD0RFqsbnZjc/mKdThqavAGfCW3iqVcj0OXWSoSieQ0MS3qlFgsFiwWy5l+m7NGf/9zHDhwNwBNTV/kxRcF+/cXBckyYxYXp2eRyEV4ufcRIvk8ZtdH8SUiXPT2d0haQvz9R1UOzlD4bDjCrdE4noJgq3ERXzPW8ZRxCeHKCowaGxX1Tm6s9XFNpZuLrSHS0VcJBrdy8Mir5HIDw8akKBp2+2wcjnnDLCBWa/1Zd70YhkEsFhtTdJza2qGQN9mIYyGY1wnmzMSEhZiwEBcWsiP+ZHXyzLGEucw5wHxbhFl6kDr6qcz34Mp0FYuAFbKQBJKDrhVXqfKon6S4jDh+0kodeaUGrVCJuTB6v5uhdqQcuWLwaCFIJlsUHSIVJ59JkswmiRpJ0ppBfjQrhwqM/EqMiOPQTC7sLh/OSj+emgC+QBWuKvsJC4dP1uGQSCTTD3lVmwSR6C72vPWXgEFV5U1setZKd/d+NEXlsswC5hq1dCYP82rfExRMs7G4rmRW60ZmtD3DliWCB69WWSQyPNIeJJZt4N7CjfzeWEtXVR2FgJW5M718rK6CK1w5GnNvEglvJXhwKztHFB1TVRte78VU+Nbi9b0Lp2MBmnb2hN9gkbDR3CzhcPiU1o6CqpPESiivEzbMxETR3RIruVjEkLu4gxQX2SNc5ehjnjVMkxakVvRRke/BkerElOxFQQxxragU8JVExwyCYjVxJUBWrUcIP+a8D5NxsptKZ4g3BEgpKSKiGMuRzYSKVo50gnQ2STyfJEqKjGaMka1y8hNFseEuZaq4sTq8OCuq8dTU4Kv14y71U3GVMlZkpVGJRHIhMmlREo/Hy71bAFpaWti5cycVFRU0NTWd1sFNJZLJVnbt+gyGkcJhX83GjQHi8R6sipkN6SX4DQ97wi/ydvg1TParcRk1LN75b4hCC9//oMqBZoW/CQaZE/HzxcL/ZGfFIpRaG5fMqeIzATOXqPuxJf5IsGcriWPH2D/kvRVFx+NZgc+3lgrfpbjdS894/Ec2m2VgYICBgQH6+/sZGBiYsLVDoJBWLIQLZqKGpWzpONnaIagkyhJnlEX2HpotYZq0fgJGH95sN/ZUJ1omUqzFFituQiilVFk/WbGQIFeQUOrIqfUohWoseS+aGO6y0OCkmqQxosSMIOlc0cphpGPkMkkS2SQRI05CzZJXC6PHcoz81pStHO5StVEPdm8Fbn8t3ho/vppKXBW2ouCosGB3mVFkpopEIpGcxKRjSjZv3syVV1550vO33347Dz744CmPPx9732SzQd7c9mFSqWNYLHN5+aW1pFKCCpxsSC/FXIBX+x6nJxNBd95AbX8L8w8+zBtzM/zntSoXixRf7k/xs8yHeGjGB7j64jqucPewKP8y2ciLxBMHRryjitu9BJ9vLT7fWryeVWja+KW+3wmFQoFwOFwWH0MFyKm6zeYUEzHDQsQYdK+cbO1QMahTQyx1Rlhoi9BsDtGg9lNd6MWT7caa7ETND68NMpi5khcBMiJAVKkhoTZQUOpRC1XYch40Mb6WLlAgSphkvuhaKaSiFDJx0tkUsXyCMHHSag5DGbvy7Am0YYJDM7mxOn04q6rx+ANU1tfgrnLhqjjhVpHVRiUSyXRkSsaUXHHFFRNqGjZdKBRS7Nr9OVKpY+h6Da+/toZUSlBjeLkmu4xEpp8tvY+SUuux2dez4MB/44y+wb+9T2X/fLi7v59sbAm3uj5D3aVe/tX1EPbwSxA2GBpe6XQuKIsQn3f1aSu5LoQgkUgMExyDWzAYxDDGvjFnMBE2rEQNK1FhJTrC2qFiUKOEWOKIcKWjjzl6kEa1ryg6Ml1Ykp0oRh6yFDcGRYeHvAgQEZcQVmpJmZooUItWqMSe9WAakbkyMsLDwCAqQkXRkQ5SSEfJZRIkswkiRoIIMbJqfnQrx0i9oNiGZaxYbF7s3ircfj++mhp8tVW4KqzlzeKQbhWJRCI5U8iYknEQosDevV8hGt2BprnZtesKolGFKsPFNdlltMfeZvvA8yi2y6lK21i05/vsqw9y30c03kOCL7VpfI8vsWvpGj5V+WMWsb0Y+wDY7bOGiJA1mM2Vf9JYh7pbRgqQ8YqG5YVSEhxWIuKEAIkIK3lU/IRY5Ahzsb2LOeYgTWo/AaMHT6YLa7ILxchBnnKDuKLocJMXAfpYS0StI2VqwhA1mPIVOLJe9CGiQwf0EZnLBgZxI0wyP0AuEyafjpLJJEjkEoSNOBFi5JTcyaJDYYToUMtWDlQXJt2NzVWBqyqA1+/HVx/AU+0uu1UcXousxyGRSCTnEClKxkAIwcGD/0Bf/0YUxczBAxsY6NfxGQ42ZJaya+BZWhLH0Z0fZnb7G9R0/pEH18PBhYL/NTDA7vh6PtT4Md47ZxPfVT6PhoHHczH1dbfi863Faq19R+NKp9P09PTQ3d1NX18fAwMD9PX1E4+P7W4RAuLCQlRYisKjJDpihgUHCRZYwyx2hJlnOVYSHb14s53YBkVHgWGl0w1hIy8C9HIxEbWBlGkGBWrQ8xU4cl7MQwJJbZwcz2FgkDQipHJBspkw+XSMdCZOrJAgLOJEjAh5dYzeOsM0g6lk5ShuZpsHh68at79YBKyyvhpXpb1s5TDb5J+7RCKRTGXkVXoM2tr+g/aO/wIUjrdtoLPTjtuwcW1mKTt6n6Q7b8Zlvp7Fe/6Lds9RvnaHwjVqnI+0V/Mt+9/gXZviO7a/xUUMl3MRs5vvorLi8gmb/oUQRKNRuru76ezqovV4Jz093aTiY1fETQvTCGuHBUXkqdOjLHVEudTSy0ytn4DoxZftwpbsQjVKZopS9krxvVVyVNKrLCSoNJDUZ1GgDlO+EkfWhyN/wqFiL20jSRhhUtkg+UyEXDpGMhcnWogTEXEiRpi8MnZDv7LwGKw+qnqKvVUcpRTZ6gC++hoqaqtwV9pwVVqLVg7ZyE0ikUjOa6QoGYXu7t9z+Mh3AejrvYKWlgocwsJ12eXs6n2GHmZQn8gx+8j3efjdGQ4vLfD3/Skey97KXy9cw2er/52ZSit2+2xmz/4O/urrxu0TUygU6OjuYd+R47S2dzLQ10M6GizW1xiFuDATNOyEhI2kYcJGilnWOIsdYeborTQoPVTlunAm29HyyeJBqdJWIi9s9CqNDKgNxM2zyVOPmq/CnvPhyXowlYJJXYArddIQSBtxkrkBcukQuUyMRCZOVCQIG1EiIkyBcUTHoC4rx3O4i64VdyXOSj/eQICKuloq6iqKvVUqrDJjRSKRSC4ApCgZQSj0Km/v+yoAkcgl7N/fgFXoXJdZzt7eTfQZjSxp3UVUfZVvfkLlRi3KquNL+UrdJ7m++Q/8o+lr2Cx1zJr1XWpqbkZVT3zEiUye/R0D7D96nI7OLiLBPvLxEJZ8HJXhwcMKYAiFsLCSFCZ0cjRYU8yzhpij91Bb6MGb7cSa6ikeMJg2W8IQKiGlml51IVFLM2mlEQpVWHIVeDJeXKUCYR7Akzz5cyiIfNG9kg2RTUdJZGNEjRjhQoywESTLKEplJIrjhGvFWgwgdVUVRUdlQy2+gAdXpRVnhRWLdK1IJBLJBY+8EwwhHj/I7j1/jhA5UsmL2L1rPmZh4rrMcg72baEv7+fit3/Pk6t6OLa8wN/0C36ifwXz6hh3O76JU3cxa+Y3sXk/yNH+LM+/3sHh1nb6u46Tj/Zhz8dxqSeCTocW7MoLBaGA15ShQY/SrPXQLFrwpo+jFUpps7nSViKBzhFtBkHTbBKmZgxRi56rwpHxUpH1YkIrWjpGER0AmUKMTCZINhMlmYkSNeKEjSjhQpCkEUFwiiwrxY6ielBUNxabr5S1EqCitpbKxlq8AVcxnsNnlf1VJBKJRHJKpCgpkc50s3PXHeTzMbLZGWzbthyTMHFddjlH+16mJ1vJ7LbH+acP9/F+Uxx3zwb+ce5aPl3zn1QqKVrSH2HL4Stoez6OLfNb6tQItWoMq5KnnFdTui/rSoFKLUGd2s8Mo42m/BG8SrTo1ciXttJDl2ahx9xMxDKflNqEkvdjzVbgTfuozPmwoDJWyGxB5MhkQ2QzEZKZKLFCjLARI2IEieWD5Mdo2FdGsZZFh9nmw+Gtxl0dwFdbQ1VjLb4aD67KYq8VGc8hkUgkkj8VKUqAfD7Grl2fJpPpolCoZtuba1ANnWtyS2nre43ujJemzsd5+sZOPpGs4sfOL7NhyVPcpfyA51veze62i6kwctSpW5mnZofVKzeRZ4bSxWzRQh09BOjDLjKQL/Z2DaoqbWYr282LielzyIk6tFwV9oyXqkwF1ekKAqgExhq7kSGTHiCdCRPLRYkUokSMMOH8AGkjNr61Q7EMyVwpig5XVVF0VDfV4av14a4sNnaTqbISiUQiOdNc8KLEMLLs2XMn8fh+hOFi27Z1FHJWNuSW0N23na60i7ru3/PGdT0Ush/m6eU5/tL+A9o757K97QYceQdr1c6yFUSlQAPdzKaN2bRRRzcDqkKbycxe2zxeNa0hX6jBlPXhTHuoyVbjT1ZQOU7X2UIhQzoTIpkNE8tHCRciRAoDRPP9pAvjlXzXSpYOD7q16F7xVNfgrQ1Q3VRPZV1lMXNFig6JRCKRTAEuaFEihGDf/v+PYOhlhDCzc+e7yaRcXJlbxEDfbjpSdqr7HmXvNQPsd3+O2+b8nFSvn517bySTcZbP46efGuU4ZlMnSVuMiG0We8Us9mdW40pV0JAJ0JAMUBsZu19NUXiESeSiRAsRwvkg0UI/sVxwfOFRiusw6V6s7ipclQG8NbVUNdRRPaMGT7UdV6UVky5Ln0skEolkanPBipJoOserO+9BS/4OQyi8/dZlxOOVvDu/kHj/PjqSFnyhR2m5Ksze6tu52fEHDm67ilTKg67EMdvayFkFqFYSWS+pzBVUZ2pZFPcPq1g6FMPIkcqEiOUiRAsRIvkBYvl+YrnQOMJDQ1ErUDQPFkcFTp8fd3UNFfW1BGY2UFHnw1VpxeoY/T0lEolEIjlfuKBEiRCCJ3Z38dDWYzgKf+DjC38FwOFDawiF6nlXbi7Z/qO0J3Qc0Ufpek+CAw0f4srMm7x16BLsqplZJjfN2QU0hGowndRIpUihkCOeDRLJhQjn+ojke4lm+0nkx8hoUewoWm3RxeKpxlUZwFdbS1VTHf6mWjx+Bw6PrNMhkUgkkunNBSNK9nZG+PZje7Eff55bqrfgX3oEgNbWpfR0z2VVbjZKfzutMTCnHqF/XZ5js9fz4e4Mczv/J+ZRPqp8IUssFyKc6yeSKwqPSK6fZH6UqquKA9VUh9VRhbOiBk+glqrGBmpmN1JZ78NVIdNmJRKJRHJhM+1FSTCR5QfPHODwG0/zddMvaag8xu7FHlAUurubaWtdypJ8E5aBHo7FCojco4TW6ITmruTOQ0uwh+eSK2Toz/USzfURyQ4QzfUTyfaTKozoN6PYUTUvNu9MnL4avDV1ReExp4mqBh9OnxVVWjskEolEIhmVaStK8gWDX7zexmNPP8sXCj/nDsceHqz0csmsKhxanlCwlsOH3sWCfD2egTAtkSw5HiGxyodpTiPXvj6LfQMt9KVfIJLrP3FixYpq8mF1zaTaF8BbU0d1UwO1c2dQ3ViF3WOWre0lEolEInkHTEtRsvXIAPc+toX3Bx/kO9ZX+M8qD/bQ+7kksA+H3kci4WHfvvcwO1+LfyDJ0XCShOkRooua8DYIjN9381omjMkSwOWfz9ym6/DPaKR27kz8M6qxOnUpPCQSiUQiOc1MK1HSEU7xz79/g+aD/5e/tT7LAwE7+/tuYF7nYuyLN+Lw9JHLmXl775U0ZOuoH8hxNBQjbH+EgfkX0VyXpy56E02fXsKsFXNwuG3nekoSiUQikVwwTAtRkskXeGDLfiJb/p2Pmn/P/6sxE+24mkviazhoCSFmvklNzRGEUNi/791UJGYwM6hwdCBMv/cR+uet5n/cuIwVc/9CWkAkEolEIjlHnPeipCuS4qc/+QGX5x/ksUrBi8fWcVXkSt6y97NfCeL1djF79jYAjh5diTm4gPlBE4f7e+mpfISe+e/hKx9az8z6m8/tRCQSiUQiucA5r0VJZyjJL+/7IlHzFva1reF9lmvZ7erjTbULAI8pzsIFL6Cogp6e2cSOr2BlyMaRvm56Ao/QddEGvnXrbVRWrD7HM5FIJBKJRHLeipKOUJJf3/dJfG066yz/yL6KIJu1FgA0A6qEYNGVO8gVssSilRw7cCmroz5au7vobvhvgkuv454PfwGns/kcz0QikUgkEgmcp6KkPRjnt/fdxsKjq0kFmnhGO4ihCBBQrdn5wCdvoz/2Xfr6jpHN2Ni390ouiTfR0dlK14zfkb34fXznlq9iNlec66lIJBKJRCIpcd6JkuP9Uf77J7ewtP0mjtQIOrRjAPjMDj708duob2ykpeVH9PU9hWGovP325SyJzaez4zhds36H9bIP8dX3/i2aZjm3E5FIJBKJRDKM80qUHO8L88i/3sSy8O3s9PcTUZOoAt534/tYsWoViqLQ17eRoy3/AhR72jQElxHs6KSv4XHqr/8Mf3b5/0RRZDl3iUQikUimGueNKGnvHeDRH36Yi3Kf4tWKdjJKHrNQ+cRn76ChoQGAePwgb+29C4COjvmYO9eQ7+wl6HmSxR/9K65b+dFzOQWJRCKRSCTjcF6IkrbOHp783meYafkYrzhbEYrArVr57FfuxOVyAZDLhdm1+3MYRpJwOEDk8OXU9CTpMW2i+VN/zXUrbznHs5BIJBKJRDIeU16UHDvexvP/6+/wVLyXbebjANQ7q/jklz6PrusAGEaePW/9Jen0cdJpB8f2rmdun5mu1LN4P/0JblwlBYlEIpFIJFOdKS1KDh49wBv3/Dv5wMUcMPUAsHzWQm76xEeGVV49evQHhEIvUyho7H/rKhb219A1sJn8x6/mz6741LkavkQikUgkkkkwZUXJ2/u2seefH6evtp6wGkITCtdcfjVrrrps2H49vU/S2vZ/ATh44FLm9y6kp/MNBm5ZwFffd9e5GLpEIpFIJJJ3wJQUJa1H9rHn3k201OqklSQWQ+O2j/0PZs0fXugsHj/A3r1/A8Dx4xdR176W4PF9dFxj4+9u+/a5GLpEIpFIJJJ3yJQUJS/+n99xtDKHoQjcBTN3fOULeCt8w/bJ5aLs2Pk5hEgTCtWgHbqGRFsbR98V5u7PPCAb60kkEolEcp4x5URJd2sL7V6BoQiqslY+9627MJvNw/YRwmD3nr8km20nnXYQ3HsDzrYghxcf4Jtf+pUUJBKJRCKRnIdMuSpiz9z7S9JqDpuh85E//7OTBAnAkSP/Qjj8IoWCRttb1+JtU2hteJ2vf/W/UNUpp7MkEolEIpFMgCknSiLmoqioSmr4mxpOer23byOtbfcC0HLwUgKttbR5nufLd/8K3WQ7q2OVSCQSiURy+phSouSP9/0HA6YkioDGBTUnvZ5IHGXP7i8D0Nk+H//hVbTxNHd8+/9hs7jO8mglEolEIpGcTqaUKOk4EgKgOmfnms98cthr+XycN9+8A5Q0kYgf69vX0xndxAf//j4qnNXnYLQSiUQikUhOJ1MmAKP3+HH6bFkAnNnssNeEEOzY8SXyheNkMjbiu99PtnMHl//j39NYOfMcjFYikUgkEsnpZspYSp760X+RUfLYDJ333nXHsNcOH/kR0dhmDEOl663rUQ73seCrH2Nx09JzM1iJRCKRSCSnnSljKYnqJiBLVUqjqq6u/Hxf32ZaW/8PigLHD12KfZ8Tyx2zuGzx1edusBKJRCKRSE47U8JS8sf7/pN+vRjg2rSgtvx8KtXGzh13oijQ0zkX557FxDaYuOXKT5zD0UokEolEIjkTTAlR0nEkCEBVzs6GT98OQKGQ4pWX/gzVlCYarULfsYH2ea187qN/dy6HKpFIJBKJ5Axxzt03QwNcXbkcUAxsfe2VvwCtg2zWSmbb++m07OSuL//XuRyqRCKRSCSSM8g5FyVP/eghMvZigOsNf1UMcN339o9I5V7AMBQG9ryXUHA/X/m3h87xSCUSiUQikZxJzrn7JqrrAFSlTFTW1NLT8yKdXf8KQM/hS0kfiPKF/3O/7GcjkUgkEsk055yKkj/+5D9OBLgurCWZ7GDnjj9HUQUDPc2IN2v4wD/fg8mkn8thSiQSiUQiOQucU1HScfREBderbr+VLRs/hMmcJh6roPDKOt71rU9T6ao4l0OUSCQSiURyljhnoqRvaAXXXIaNv/8YZlcvuZyZ1KvvpfFTlzK3Yf65Gp5EIpFIJJKzzDkTJc/e96tyBdemy5Ponh0IoRDedR3a6jouW7nhXA1NIpFIJBLJOeCciZKouRgnUmPqIWf9LQADh99FzOriwx/4/LkalkQikUgkknPEORMlQT2FriQIXPI8qmoQ7p1NuLWaz/7l35+rIUkkEolEIjmHnMM6JQWWLX4Z3ZIkmfASeW0xn//nfz53w5FIJBKJRHJOeUeWknvvvZeZM2ditVpZs2YNr7/++qTPsahxJzZfD/m8TvSly7lDChKJRCKRSC5oJi1KHn74Ye666y7uvvtutm/fzrJly7j22mvp7e2d1HncjYcACG+7kg//wz2THYZEIpFIJJJpxqRFyQ9/+EM++9nP8qlPfYqLLrqIn/zkJ9jtdu6///5Jv3no8MVc+ZlvYjZbJn2sRCKRSCSS6cWkREk2m2Xbtm2sX7/+xAlUlfXr17N169ZRj8lkMkSj0WEbQLy/kbmr/hJ/de2fMHyJRCKRSCTThUmJkv7+fgqFAoFAYNjzgUCA7u7uUY+555578Hg85a2xsREAi3ory1ate4fDlkgkEolEMt044ynBX//614lEIuXt+PHjAGz4wMfP9FtLJBKJRCI5j5hUSnBVVRWaptHT0zPs+Z6eHmpqakY9xmKxYLHImBGJRCKRSCTjMylLidlsZtWqVWzatKn8nGEYbNq0ibVr1572wUkkEolEIrlwmHTxtLvuuovbb7+diy++mNWrV/Mv//IvJBIJPvWpT52J8UkkEolEIrlAmLQoufXWW+nr6+Ob3/wm3d3dLF++nKeeeuqk4FeJRCKRSCSSyaAIIcTZfMNoNIrH4yESieB2u8/mW0skEolEInmHnI379zlryCeRSCQSiUQyFClKJBKJRCKRTAmkKJFIJBKJRDIlkKJEIpFIJBLJlECKEolEIpFIJFMCKUokEolEIpFMCaQokUgkEolEMiWQokQikUgkEsmUQIoSiUQikUgkU4JJl5n/UxksIBuNRs/2W0skEolEInmHDN63z2Qh+LMuSgYGBgBobGw8228tkUgkEonkT2RgYACPx3NGzn3WRUlFRQUAbW1tZ2xSU5FoNEpjYyPHjx+/oHr+yHnLeV8IyHnLeV8IRCIRmpqayvfxM8FZFyWqWgxj8Xg8F9RiDuJ2u+W8LyDkvC8s5LwvLC7UeQ/ex8/Iuc/YmSUSiUQikUgmgRQlEolEIpFIpgRnXZRYLBbuvvtuLBbL2X7rc4qct5z3hYCct5z3hYCc95mbtyLOZG6PRCKRSCQSyQSR7huJRCKRSCRTAilKJBKJRCKRTAmkKJFIJBKJRDIlkKJEIpFIJBLJlOCMiJJ7772XmTNnYrVaWbNmDa+//vq4+//mN79hwYIFWK1WlixZwpNPPnkmhnXGuOeee7jkkktwuVz4/X5uvvlmDhw4MO4xDz74IIqiDNusVutZGvHp4Vvf+tZJc1iwYMG4x5zvaw0wc+bMk+atKAp33nnnqPufr2v9wgsvcOONN1JXV4eiKDz66KPDXhdC8M1vfpPa2lpsNhvr16/n0KFDpzzvZK8PZ5vx5p3L5fja177GkiVLcDgc1NXV8YlPfILOzs5xz/lOvitnm1Ot9yc/+cmT5nDddded8rzn83oDo37XFUXh+9///pjnnOrrPZF7Vjqd5s4776SyshKn08kHP/hBenp6xj3vO70mDOW0i5KHH36Yu+66i7vvvpvt27ezbNkyrr32Wnp7e0fd/5VXXuGjH/0on/70p9mxYwc333wzN998M2+99dbpHtoZY8uWLdx55528+uqrbNy4kVwuxzXXXEMikRj3OLfbTVdXV3lrbW09SyM+fSxatGjYHF566aUx950Oaw3wxhtvDJvzxo0bAfjwhz885jHn41onEgmWLVvGvffeO+rr3/ve9/jXf/1XfvKTn/Daa6/hcDi49tprSafTY55zsteHc8F4804mk2zfvp1vfOMbbN++nUceeYQDBw7w/ve//5Tnncx35VxwqvUGuO6664bN4Ze//OW45zzf1xsYNt+uri7uv/9+FEXhgx/84LjnncrrPZF71le+8hUef/xxfvOb37BlyxY6Ozu55ZZbxj3vO7kmnIQ4zaxevVrceeed5d8LhYKoq6sT99xzz6j7f+QjHxE33HDDsOfWrFkjPv/5z5/uoZ01ent7BSC2bNky5j4PPPCA8Hg8Z29QZ4C7775bLFu2bML7T8e1FkKIL33pS6K5uVkYhjHq69NhrQHxu9/9rvy7YRiipqZGfP/73y8/Fw6HhcViEb/85S/HPM9krw/nmpHzHo3XX39dAKK1tXXMfSb7XTnXjDbv22+/Xdx0002TOs90XO+bbrpJXHXVVePuc76t98h7VjgcFrqui9/85jflffbt2ycAsXXr1lHP8U6vCSM5rZaSbDbLtm3bWL9+ffk5VVVZv349W7duHfWYrVu3Dtsf4Nprrx1z//OBSCQCcMqmRfF4nBkzZtDY2MhNN93E3r17z8bwTiuHDh2irq6O2bNn87GPfYy2trYx952Oa53NZvn5z3/OHXfcgaIoY+43HdZ6KC0tLXR3dw9bT4/Hw5o1a8Zcz3dyfTgfiEQiKIqC1+sdd7/JfFemKps3b8bv9zN//ny+8IUvlLu+j8Z0XO+enh7+8Ic/8OlPf/qU+55P6z3ynrVt2zZyudywtVuwYAFNTU1jrt07uSaMxmkVJf39/RQKBQKBwLDnA4EA3d3dox7T3d09qf2nOoZh8OUvf5l169axePHiMfebP38+999/P4899hg///nPMQyDSy+9lPb29rM42j+NNWvW8OCDD/LUU0/x4x//mJaWFt797ncTi8VG3X+6rTXAo48+Sjgc5pOf/OSY+0yHtR7J4JpNZj3fyfVhqpNOp/na177GRz/60XEbs032uzIVue6663jooYfYtGkT3/3ud9myZQvXX389hUJh1P2n43r/7Gc/w+VyndKNcT6t92j3rO7ubsxm80lC+1T38sF9JnrMaJz1LsHTnTvvvJO33nrrlP7DtWvXsnbt2vLvl156KQsXLuS+++7jH/7hH870ME8L119/ffnnpUuXsmbNGmbMmMGvf/3rCf0nMR346U9/yvXXX09dXd2Y+0yHtZacTC6X4yMf+QhCCH784x+Pu+90+K7cdttt5Z+XLFnC0qVLaW5uZvPmzVx99dXncGRnj/vvv5+PfexjpwxUP5/We6L3rLPFabWUVFVVoWnaSRG6PT091NTUjHpMTU3NpPafynzxi1/kiSee4Pnnn6ehoWFSx+q6zooVKzh8+PAZGt2Zx+v1Mm/evDHnMJ3WGqC1tZVnn32Wz3zmM5M6bjqs9eCaTWY938n1YaoyKEhaW1vZuHHjpNvXn+q7cj4we/ZsqqqqxpzDdFpvgBdffJEDBw5M+vsOU3e9x7pn1dTUkM1mCYfDw/Y/1b18cJ+JHjMap1WUmM1mVq1axaZNm8rPGYbBpk2bhv2nOJS1a9cO2x9g48aNY+4/FRFC8MUvfpHf/e53PPfcc8yaNWvS5ygUCuzZs4fa2tozMMKzQzwe58iRI2POYTqs9VAeeOAB/H4/N9xww6SOmw5rPWvWLGpqaoatZzQa5bXXXhtzPd/J9WEqMihIDh06xLPPPktlZeWkz3Gq78r5QHt7OwMDA2POYbqs9yA//elPWbVqFcuWLZv0sVNtvU91z1q1ahW6rg9buwMHDtDW1jbm2r2Ta8JYgzut/OpXvxIWi0U8+OCD4u233xaf+9znhNfrFd3d3UIIIT7+8Y+Lv/3bvy3v//LLLwuTyST+6Z/+Sezbt0/cfffdQtd1sWfPntM9tDPGF77wBeHxeMTmzZtFV1dXeUsmk+V9Rs7729/+tnj66afFkSNHxLZt28Rtt90mrFar2Lt377mYwjvir/7qr8TmzZtFS0uLePnll8X69etFVVWV6O3tFUJMz7UepFAoiKamJvG1r33tpNemy1rHYjGxY8cOsWPHDgGIH/7wh2LHjh3lLJP//b//t/B6veKxxx4Tu3fvFjfddJOYNWuWSKVS5XNcddVV4kc/+lH591NdH6YC4807m82K97///aKhoUHs3Llz2Pc9k8mUzzFy3qf6rkwFxpt3LBYTf/3Xfy22bt0qWlpaxLPPPitWrlwp5s6dK9LpdPkc0229B4lEIsJut4sf//jHo57jfFvvidyz/vzP/1w0NTWJ5557Trz55pti7dq1Yu3atcPOM3/+fPHII4+Uf5/INeFUnHZRIoQQP/rRj0RTU5Mwm81i9erV4tVXXy2/dvnll4vbb7992P6//vWvxbx584TZbBaLFi0Sf/jDH87EsM4YwKjbAw88UN5n5Ly//OUvlz+jQCAg3vve94rt27ef/cH/Cdx6662itrZWmM1mUV9fL2699VZx+PDh8uvTca0HefrppwUgDhw4cNJr02Wtn3/++VH/rgfnZhiG+MY3viECgYCwWCzi6quvPunzmDFjhrj77ruHPTfe9WEqMN68W1paxvy+P//88+VzjJz3qb4rU4Hx5p1MJsU111wjqqurha7rYsaMGeKzn/3sSeJiuq33IPfdd5+w2WwiHA6Peo7zbb0ncs9KpVLiL/7iL4TP5xN2u1184AMfEF1dXSedZ+gxE7kmnAqldGKJRCKRSCSSc4rsfSORSCQSiWRKIEWJRCKRSCSSKYEUJRKJRCKRSKYEUpRIJBKJRCKZEkhRIpFIJBKJZEogRYlEIpFIJJIpgRQlEolEIpFIpgRSlEgkEolEIpkSSFEikUgkEolkSiBFiUQikUgkkimBFCUSiUQikUimBFKUSCQSiUQimRL8/9jpNnNJYwDWAAAAAElFTkSuQmCC", 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" ] @@ -80,16 +80,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.10240661])" + "array([0.08516533])" ] }, - "execution_count": 6, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -100,12 +100,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -136,7 +136,7 @@ "\n", "portfolio_agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", "\n", - "portfolio_agent.T_sim = portfolio_agent.T_cycle + 1\n", + "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", "# Run the simulations\n", "portfolio_agent.initialize_sim()\n", "history = portfolio_agent.simulate()" @@ -144,16 +144,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "96" + "95" ] }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -164,12 +164,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "raw_data = {\n", - " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25 - 1,\n", + " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25,\n", " \"pIncome\": portfolio_agent.history[\"pLvl\"].flatten(),\n", " \"nrmM\": portfolio_agent.history[\"mNrm\"].flatten(),\n", " \"nrmC\": portfolio_agent.history[\"cNrm\"].flatten(),\n", @@ -186,12 +186,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -213,12 +213,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -269,7 +269,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/src/notebooks/WarmGlowPortfolio.ipynb b/src/notebooks/WarmGlowPortfolio.ipynb index 3aac949..b3bc89d 100644 --- a/src/notebooks/WarmGlowPortfolio.ipynb +++ b/src/notebooks/WarmGlowPortfolio.ipynb @@ -33,7 +33,7 @@ { "data": { "text/plain": [ - "(8.764682111110837, 43.77305416364993, 26.255634058683412)" + "(9.206778216146489, 26.1368726540768, 50.64405071849033)" ] }, "execution_count": 3, @@ -71,7 +71,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -91,7 +91,7 @@ "outputs": [ { "data": { - "image/png": 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enzNoZh6JpIQYIYQQ4lC1xyEG4Morr+TKK68c9txDDz005NjVV1/N1VdfvTcftROFviBj9W4r4FKdfZO6SfWGGC9go1sukpnuffCZQgghhPggyqm9k7Kr3SlgmYNDTCdO95K7tzKjWi7SRuTAN1EIIYQQB0ROhZi+Sdw2yoAQ44SWDvrHyCgoqLZG2o4djGYKIYQQ4gDIqRDTz8bqnZ2kK04lpi/EQG+QsXXSduKgtE4IIYQQ+19Ohhibod1J3dgoLrX3mAfV1siQOlhNFEIIIcR+llMhRhnwwt4pxCQ0FcWn9x7zoNgqGSV9EFophBBCiAMhp0LMQJZpAQNCjKqiep0Q41a9gEpGNUhmzIPVRCGEEELsRzkWYvqX7O3rTtJ7Q0xc1VC9zkrALtWDpagYlkk4LtUYIYQQ4lCUYyHG6VCyUbAtCwUVXXUBkNB1VF9/JcZSFTKmQk9EplkLIYQQh6KcCjHZKdaKjW2a2a4kgJSuo3j7x8SYqkImoxANdx6ElgohhBBif8upEDOQZZnZNWJsUli6O1uJcakeLBWMjEo8IiFGCCGEOBTlVIhRlIFjYqz+EKPEUDTPoIG9lgJWRiUpO1kLIYQQh6ScCjH9Y2LAtqz+7iQlDroLZcDAXlMBO6OSlhAjhBBCHJJyK8Qo/d8ty8Kl9HUnJXDr2uDuJAVIgxHvPihNFUIIIcT+lVMhpn/vpL5KTH+IcWn968Q4lRgbLa1gJroOTmOFEEIIsV/lVojpTTFDQoySxK2rg6ZY2wp40wqppIQYIYQQ4lCkH+wG7B0by7azC91ZJHBr6qAxMTYG/pSLdEZCjBBCCHEoyrFKTHb3pJ0qMalB3Ulu1Qt2Bn/aR8YMH5S2CiGEEGL/yqkQ0zcoxlacdWLcvSHGJIlrQHdSXyXGm/GSMWXFXiGEEOJQlFMhpq8S44yJsdGzISaNS1OylRhFUdABt+HHtGMHqbVCCCGE2J9yKsQwqDupf8VeixQeXUVxqdkncivgMb2YSgLbtod7NyGEEELksJwKMQMyDLZtoyvO5o8mGVxa76N4nO+6ouCyvNhqmnjaPNBNFUIIIcR+llshRnWaa2Nj2Saq4vxsQjbE9A/uVVAtL6aapjuROSjtFUIIIcT+k1Mhpq8UYytOJUbFmVJt2ANCjN+pzrhUDwpeDNUgHJcQI4QQQhxqcmqdGHWnKdZ9A30tFNy6E2I0vwuD/p2sU5iE46mD0VwhhBBC7Ec5VYlR1b7ZSTaWbfVXYlBwa865/lV7PWQ0F2lTJdYjC94JIYQQh5qcCjEofWNiANtG6f05gzJkTIxL9ZLRdTKGSiLSeTBaK4QQQoj9KKdCjKL1v7ZsKzuwNz0gxCjegZUYjUxGIRWVSowQQghxqMmpENMXWmzFdioxvc03BoyJUX19+yd5MTSw0iqZmIQYIYQQ4lCTWyFG6+9Osgd0J6VRcA/pTvJgqAp2RsWIy/5JQgghxKEmJ0MMgG1bqPSNiVFx6YMH9rpUD6ZqQ0bBTnYf8LYKIYQQYv/KqRCjaE5Xkd37/5TdjonxYioWWhrspFRihBBCiENNToUYVe0LMYBtZysxA0OM6ndCjEf1YSkm3rSCmZExMUIIIcShJrdCjN4/Pcm2rWwlJmVr2TExWsBZsdet+bDtNP6UC8PoPuBtFUIIIcT+lVMhRusNKha2s+1AtjtJHWbbATeqncGf9mFakYPTYCGEEELsN7kVYnQnoKAA9E+xTqOi967Yq3g17N7dCdyqgi/jBWJYln3A2yuEEEKI/SenQkxfd5K9UyUmNaASoygKisdJMW5FwW34UNQEkZRxcBothBBCiP0ip0KM3luJsektxvRKombHxAAofifsuFUNj+HF1lL0JGQnayGEEOJQklshxu3MPLKxUQbEmJStZbuTANSAGwCPqqNbXmw1RVhCjBBCCHFIyakQo7lc2dcDKzEDN4AE0PP6QowX1fZhqgbdcQkxQgghxKEkp0KM7u7rTrJRlP4Yk0Qb1J2k5XsB8Gh+FNtNRjUIx9MHtrFCCCGE2K9yKsS43B6gd0xM7xQk27YxFC277QD0rxXjUX3Yiou4CtGYTLMWQgghDiU5FmIGVmKcYxYmBiq62v8o6oAF7wxNJ2GpJCKdB7y9QgghhNh/cirE6G6nm2jg7CQbC3On7qTs1gOaj4yukTZU0jHZekAIIYQ4lORWiPE4A3ZtbNTeGGPZFgaDu5PUAd1JhqZhZBSMmGwCKYQQQhxKcivEuPpX7O3bN8nGwrC1Qd1Jmr+vO8mPoYKZUTHj3Qe6uUIIIYTYj3IrxLj7p1irvYUXG9OpxGi7qMSoYGdUSEolRgghhDiU5FSIUT0DQkxv0y3bAk0bNOVaDThjYnTV5aSdtIItIUYIIYQ4pORUiNFdWva12htabEwU1TXoOsWtYSnOho+apqOmwU52H7B2CiGEEGL/y6kQo+l69rWi9oUYC1vVB12nKAq2bgHg1nR8KQXbDJMxrQPXWCGEEELsV7kVYlz9YUXr607CRNX0Idfa2Z2sVfxpFy41SkdUVu0VQgghDhU5FWJUrb87SRnYnaS5hlyr+Pp2slbxp33oWpy2SOrANFQIIYQQ+11OhZi+LiQATdl1dxKA1rvgnVvV8KW9qGqc1kjywDRUCCGEEPtdToUYdcBaMEpvcNlVd5Lau5O1W3XhMXxoWkIqMUIIIcQhJKdCzMBp1Eq26SbqMN1Jet9O1qoLt+FFV+O0SogRQgghDhm5F2KcmdOoijPmxdrFmBhXyA+AR/Xisrxoapy2HulOEkIIIQ4VORVioH/jx74QY2OgDNOd5A75AGcTSMV2k9AgFm49UM0UQgghxH6WgyHGiTFKNsSYqLp7yHVa75gYj+pHsd1EVQWzp/nANVQIIYQQ+1XOhhi1dwNIFBNtmO6kvv2T3JoPGxdRVUWN7Dhg7RRCCCHE/pWDIcYxsDtp4Poxffr2T/JoPixNJ2qreBI7sG37QDVVCCGEEPtRzoWYPkrvdGsbC5c+TIjxO5UYTdGxdA9pQ6XE7qQzJqv2CiGEEIeCnAsx/WNieteJUQzc2tDHUFwqJqbz2u0hk1EpV7rY0SPTrIUQQohDQQ6GGIdK/5gYl6YMvU5RMBQDAE33kDR6Q4ys2iuEEEIcEnIwxPRVYvo3gNSHqcQAmJpTidF0FwlDpVTppFXWihFCCCEOCbkXYnrH5faFGFuxcO0ixFi9k5ZcmhtvUiFPC0t3khBCCHGI2KsQc8899zBmzBi8Xi9HHHEE8+fP3+31qVSKm266iVGjRuHxeBg3bhwPPPDAXjWYYdaJcQ/TnQSAxznu0nRCMbC0KK3h2F5+rhBCCCE+SIYudfsuHn30Ua699lruuecejjvuOH77299y5plnsmbNGkaOHDnsPZ/5zGfYsWMHf/jDHxg/fjytra0YhrFXDe6LKwMrMbvqTlL9OnSDS3VRELPp0FXiXbJqrxBCCHEo2OMQc9ddd3HZZZcxb948AO6++25efPFF7r33Xu64444h17/wwgv873//Y8uWLRQVFQEwevTovW7wzovdWey6O0nLdwM2HlWnIOamTdewI417/dlCCCGE+ODYo+6kdDrN4sWLOe200wYdP+2001i4cOGw9zzzzDMceeSR/PjHP6aqqoqJEyfyjW98g0QiscvPSaVS9PT0DPoaYkAlZlfdSa6gBwC36iIY99GmaajRlvfyqEIIIYT4gNujSkx7ezumaVJeXj7oeHl5OS0tw4eDLVu28Prrr+P1evnHP/5Be3s7V155JZ2dnbscF3PHHXdw6623Dnuuf4p17y7Wu+lOcoV8mCTxaG7yUx7WaRreRBumZaOpuxhHI4QQQoicsFcDexVlcACwbXvIsT6WZaEoCg8//DCzZ8/mrLPO4q677uKhhx7aZTXmhhtuIBwOZ7/q6+v7P9sePMXa3k13kqcgAIBb9eBP+WjVNcqULtqjMkNJCCGEyHV7FGJKSkrQNG1I1aW1tXVIdaZPRUUFVVVVhEKh7LHa2lps26ahoWHYezweD8FgcNDXEL2hyVKsYRe7A/AU5TnfVR9uw0ebqlFGFztkrRghhBAi5+1RiHG73RxxxBG89NJLg46/9NJLzJkzZ9h7jjvuOJqamohGo9ljGzZsQFVVqqur97jB2dlJvU23FBu3votKTGFviNF8pN35RExNth4QQgghDhF73J10/fXXc//99/PAAw+wdu1arrvuOurq6rjiiisApyvo4osvzl5/0UUXUVxczJe+9CXWrFnDa6+9xje/+U0uvfRSfD7fHjd46OwkG13dxeykPHfvtRoZbwFmQqVM6ZRKjBBCCHEI2OMp1hdccAEdHR3cdtttNDc3M23aNJ5//nlGjRoFQHNzM3V1ddnr8/LyeOmll7j66qs58sgjKS4u5jOf+Qy33377+2r44BV7h+9OUlwahpVBV12Y/iD5MfBoYdl6QAghhDgE7HGIAbjyyiu58sorhz330EMPDTk2efLkIV1QeysbVxQVbDDZdXcSQJoUOi4sTx5FUTAK47R3R3d5vRBCCCFyQ+7tndT3va87SbF22Z0EYKrOJpCK20tRBNo0jWS4eX83UwghhBD7Wc6FGOy+GOPsBGmh7LI7Cfo3gVR1N0URm1Zdw+6RECOEEELkupwLMX1xxeoLMYqFazfdSarPWRRP11UKowptmoYWk1V7hRBCiFyXcyFmZ5YN7l0sdgegB50ZSm5FIRQPsEPT8afaSBvWgWqiEEIIIfaDnAsx/Z1JTiXGxEbfzRYCrgI/AG5VJS8Vok3XGKF00RqRGUpCCCFELsu5ENObXbIhxobddid5Cp2tBzyKhtsqoF2RBe+EEEKIQ0HOhZjBw3rBfJfuJF+Js92BR/OQdodIpFTK6JK1YoQQQogcl4MhxokxdnZ2Eug7zU4yDCO7uaS3vAAAvx4k5g2gxlWKFdk/SQghhMh1e7XY3QdBXyXGgkG7WIfDYX7729+SSqWYN28eZUXFAPi0fBIeP4VR0D1hdkSkO0kIIYTIZTlXienTV4kxdupO2rJlC/F4HNM0WbRoEWqeC9M2nb2W/HkURSDpStPZ3X2QWi6EEEKIfSHnQoySHdjrMBVlUCWmtbU1+3r16tUYpkFac6ouuttHUdSmTdPIdMuCd0IIIUQuy7kQ08fuTTOmbQ8aE9PW1pZ9nUwm2bhxI6bHWRPG4/ZQGIFWTYOepgPbYCGEEELsUzkXYnZeJ8ZCHbYS07er9vLly1ELnL0HfJqLwpiPNl1Dj+84cI0WQgghxD6XcyFmZ5kBY2ISiQQ9PT0AnHrqqQBs3LgRq9AZvxzQ3OQnC9ihaeRn2kmkzYPTaCGEEEK8b7kXYnoHw1jZFXu1bHdSX1dSMBikpqaGiooKLMuinm4A/JoPr1lAm65RrnTLqr1CCCFEDsu5ENM/+qVvdpKS3XagryuprKwMgJkzZwKwudsZ/xLQ87HUEGHTWbW3JSwhRgghhMhVORti7OwBFUUZPsRMmzYNRVFo6WylW4nh1/OJ+4Nk4qqz9YCsFSOEEELkrJwLMX2yG0AqQwf19oWYvLw8xo8fD8AGtREAK1CAO64SUmTrASGEECKX5XCI6f2u9C86vHOIAZgxYwYA27V2AFRfPkUR0PQedoQTB6axQgghhNjnci7EKDvvYq06jxCNRonH4wCUlJRkrx8zZgwAYSVBigwut4+iKER0k3B35wFsuRBCCCH2pZwLMX36Q4wG9FdhioqKcLvd2evy8vIoKioCBVrVHjwuL0URmzZdI9nVeOAbLoQQQoh9IodDTO931elOGq4rqU9NTQ0AO9Ru/JqbwqiXVk0j3tGAbdtDrhdCCCHEB1/OhZidV+zduRKzuxDTqoQJ6D6CyRCtukZ+up1mmWYthBBC5KScCzEMKJxYtgWas6XAewoxag9ezY8/U0Cr5qwVs74lsv/bLIQQQoh9LvdCTC9LsXtDjI5t29kQU1paOuTa0tJSPG4PhmKScFmoahHtihNi1kmIEUIIIXJSzoWYgSv22lgoqk44HCadTqOqKsXFxUPuUVWV6ppqAHaoYay8MpJpJ8Rs2CEhRgghhMhFORdi+tiAbVsomk5LSwsAxcXF6Lo+7PX9XUphCBRgxlVGKi1SiRFCCCFyVA6HGBsLC0VzUVdXB/QHleFkZygpYTRvHoGYQoXazLbWMBnTOiBtFkIIIcS+k3MhRumdEt1XiUHVsyFm1KhRu7yvqqoKbIiqSWyvm6IIhF02VVYj29pjB6LpQgghhNiHci7E9I2KcSoxNoqi0dTk7FI9cuTIXd5lWS2EdGcmU8JrUxSxadJ1apU61su4GCGEECLn5GCI6avE2Ni2hW0ZWJZFfn4+BQUFw97R2PgIb7x5CpNnvQBYRN0ZCiNutrpcTFLrZZq1EEIIkYNyLsT0L3aHU4tJpwCnCqMoyrD3bNv+WwDcgSZqRq4irCcJJgvY7HIxWamTwb1CCCFEDsq5ENPHxlknxk45O1HvajyMYURJJuuzP48cuZJEfh0Bs5Stbp3JUokRQgghclJOhxgTEzPlDMrd1XiYaGw9AG53GcVFp6CqFhMmL8AqrGKzy0WV0k53ZxuxlHHA2i6EEEKI9y+HQwx0KXGwLDwez7DbDQBEI+sAyM+vpXbKDzHTXvLyulDmbCNlqHSqKhOVeja2Rg9g64UQQgjxfuVciFGyeyfZtOv9VRhVHf5RotG1AOTl1eJxlxBdewoA+ZNXMcW02Ox29XYp9ezvpgshhBBiH8q5ENOXYiygQ9t9VxJAJOpUYvLyJgPgbZ1Ga+toFNXmE9Vp1nvc1MrgXiGEECLn5F6IGVCJSagZAEpKSoa/1LaI9Y6Jyc+rdb77/WzZOJt02ktBwKa7xMVktU4G9wohhBA5JudCTHaKtQJpxQTA5/MNe20isR3TjKOqHny+0QB4S/IoyJTS1DQJgIIilYlKAxtbwvu76UIIIYTYh3IuxPSlGBsbQ3H2PPJ6vcNeGukdDxMITERVnY0hAzUFlFkhdrSMw7ag0ge2L4Un3kR7NLX/2y+EEEKIfSL3QkwvG5tMbyVmVyEmGnFCTF9XEkBwTBnlVoh0OkCkvRyAdZV+Z/sB6VISQgghckbOhZi+7iQDE6v3h12GmJ0G9QIUVJQTMpw9lJo7xgMQLnEzWdkug3uFEEKIHJJzIaZPUsn0vlJwu93DXhMZML26j7+gACsdJ2B7aO+owTAUVK9KbcEmNkiIEUIIIXJG7oWY3upLCmeFXc3lHnaNmEymm1SqGRhciVFVjR6rmwqrEMty0d1YCEDJiGbWyW7WQgghRM7IvRDTK9VbidFcw1dh+rqSvN4qFC2f++paWRR21pUx8iwqLSe8dDWPAyBRZtDU2oJl2cO+nxBCCCE+WHIuxPSNiUnihBjd7Rn2uoFdSf/p6OGWzU18YslGfrFtB96xRVSYTojZkRxPj6FguFTmhOZT3xXf788ghBBCiPcv50JMdop178q9rl2EmIGDetfFnJ2uLeCOrc38ZOIk0pqboOXDVnW2teUDMLtiCWubpUtJCCGEyAU5F2KUbC3G4fLsKsT0T6/eFHfWfzm2IIBPVXjTVPnccQES+TXOtdudbQvKS5pY19y0v5ouhBBCiH0o50LMgB0gAXC7h06vtiyDWGwj4FRiNveGmMuqSnn+iImM93to97n4zeG1bCirwY6MoicJimaTCL+0/59BCCGEEO9bzoWYnSsxnmEqMfH4FiwrjaYF8Hpr2JxIAjDO76E2z8eLR0zk2JY2LFVhwfjppHylrIg6a8eU6f/d/w8hhBBCiPct50IMyk4hZpiF7hKJbQAE/OPoMCx6DAsFGONzAk9A1/h6upOamEXK5WZNxWhWdVWCbTM6uJHW7m37+SGEEEII8X7lXIjZuRIz3Gq9qXQ7AG5PWXY8TI3XjVfrf9zqGeO4aHMUgOU14ynoGIne42xjsG7L4/ul7UIIIYTYd3IuxLBTiPF5h+5gnU61AeB2l2THw4zzD+52Khs3gdlbt1KUNIh7fHQVHk1ntzPeJh7+J7Yt68UIIYQQH2Q5GGIG8/mGjolJZ5xKjMddyqa4Mx5m/E4hxh8METc7+dw2J+SsGDmdFZF8bFPBZdcRiazazy0XQgghxPuRcyFGMa1BP/t977USM8wspkKdTzdY+DJpevx5vJN/Jr4O51fSsuPpfd10IYQQQuxDORdibN016OdhQ0ymAwCXu7g/xPg8Q7qI8ieX4zfhI/XO9RvKTmPkjjAALS3PYlnGPm+/EEIIIfaNnAsxqmdwiAkEhoYYw3AG7FpqPtuTKUib3PnX5Rx5+394e2tn9rrqGZPpSXdwQV0G3TTo8ZfxP/0w4mkfmUw7XV0L9u/DCCGEEGKv5VyI8eQNbnJewD/kGtN0thloNn2YNvg60qxqCNMRS/PFB9/mnW1OkCkfO46OVBMTU0GmNG0D4L7qi2nZUe3c3/zkfnwSIYQQQrwfORdidHXg7CQbv2foLtam6WziWJd2BvMWJPvH0cTTJl984G0WbevE4w8QUSLoaJy8rQnVstgUnEJ9dAIArW3/JpPp2n8PI4QQQoi9lnMhpnjkmOxrBQuXNvQRLMupxGxL6QDoMWdsy3c/Vstx44uJpU0ueeBtFm/vxC5xBvzWJvOZuKMOgMWlR1HXU4Vtp2lueWp/Po4QQggh9lLOhRivL5B9bSkZNHXwujG2bWe7k7amnMdL9qQBmF4V4v6Lj+LYsX1B5h06R9dgWBlq7GJm1W8E22Zx6QxeavsoAE1Nj8qaMUIIIcQHUM6FGDXdH1oMNYmy0zYElpUEnNCxNWmBYRGJOCFmQnk+PrfGH754JMeMLSKaMri3MUBruoVSO0RxNMrYdmcX68150zAsD7HYRsLhxQfm4YQQQgjxnuVciNEr+ysxppoZcr5vPAzA5kQGpbcrqSTPTVHAGT/jd+vcf8lRlAc9NEUMHrVTaKgUp70cVrcBgPqykbzcfhLgVGOEEEII8cGSeyEmr382kqkMF2KcrqS4UkhHxkSJOtdMKMsfdF2eR+dbp08G4J/eInqwGUEJpdEwI7u2Y6ka/7Q/DsCO1ufJZHr2y/MIIYQQYu/sVYi55557GDNmDF6vlyOOOIL58+e/p/sWLFiAruvMmjVrbz4WAO+Axe12V4nZoY0FIL93ZtKE8rwh1553WBWTR+STVHQeIcVoZQQAM+u2ANBVOoImsxbLSsoKvkIIIcQHzB6HmEcffZRrr72Wm266iaVLl3LCCSdw5plnUldXt9v7wuEwF198MaeccspeNxagoLAo+9pUrCHnzd6ZSS3UAOCNOTtTTyjPH3Ktqip8/bRJADxmp1HIRzcMKrvDTO3ZiKVpPBC5CJABvkIIIcQHzR6HmLvuuovLLruMefPmUVtby913301NTQ333nvvbu+7/PLLueiiizj22GP3urEA/gF7IPlM15DzfZWYZqUKACPS1500tBIDcGptGVNKPSQVeJgM1T06CjCjYRkA6/InEVdCRKNriURWvq+2CyGEEGLf2aMQk06nWbx4Maeddtqg46eddhoLFy7c5X0PPvggmzdv5vvf//57+pxUKkVPT8+grz4uTcu+rkgPs6lj75iYJnsEGBaxqDMzaeIwlRgARVH4zllTAXiaNP7eCk5+u83E2DZsXeMZ68sANDY+8p7aL4QQQoj9b49CTHt7O6ZpUl5ePuh4eXk5LS0tw96zceNGvvOd7/Dwww+j6/p7+pw77riDUCiU/aqpqelvsAKjcbquahj6mX2VmCa7NDszqTjgpqjldXjldkjHh9xzYm0F440wGeAVbwUYMVTbzSfqXgbgRetI0rjZ0fpcdl8mIYQQQhxcezWwd+e1WWzbHnIMwDRNLrroIm699VYmTpz4nt//hhtuIBwOZ7/q6+sHffbneIqv8CdKGLolgGkmsFBpsgpRok6ImVqqw+OXwms/gb99FsyhA4I/FnTe69+qjd7pVHP0jjjliTbSuovX9U9hmnF27HjuPT+HEEIIIfafPQoxJSUlaJo2pOrS2to6pDoDEIlEWLRoEVdddRW6rqPrOrfddhvLly9H13VeeeWVYT/H4/EQDAYHfQ3kwqScDtRhWm+acdopJYOOK+6EmLO8qyDRG3i2vApPfQWswYOCj581iqNNEwtYbU8DoNus4vKGxwB4yjwTE1XWjBFCCCE+IPYoxLjdbo444gheeumlQcdfeukl5syZM+T6YDDIypUrWbZsWfbriiuuYNKkSSxbtoyjjz76fTV+aO3HqcS04EyV9sWdmUlTla29DaoGVYeVj8EL34YBs41GTpvMxxPNKMAboRp2GGlAZVLLNnzpBB34eVM5iZ7ICiKRNe+r3UIIIYR4//a4O+n666/n/vvv54EHHmDt2rVcd9111NXVccUVVwBOV9DFF1/svLmqMm3atEFfZWVleL1epk2bRiAQ2N1H7RXTihPDeV+rd2ZSdXqzc/L4a+G83wIKvP07+N+d2ftKRo4mkKzjFJxxOxti1QAssydxfsOLAPxTuwgLhUapxgghhBAH3R6HmAsuuIC7776b2267jVmzZvHaa6/x/PPPM2rUKACam5vfdc2YfUUdphRjmgmS+MCwSMecEBMKr3NOjpgB0z8FZ/3E+fnVO+Ct3wKg6TqJQosv2S40YLNnFM2Wj4hdzJeb/oFimGw3i1jGEbS0PJVdGVgIIYQQB8deDey98sor2bZtG6lUisWLF3PiiSdmzz300EO8+uqru7z3lltuYdmyZXvzse+JacZJ4M/OTBrvT6BGWwAFyqc4F83+Msy9yXn9r2/BtgUATDj+KLT4Vs7CWX9mSboc24at5ihqG9YD8Jx6IYYZZUfrP/fbMwghhBDi3eXc3kkDDTMhCstMkMCXDTEnF+xwThSNBc+AtWJO/CbM+pzz+p9fBzPDpGPnsD26hi/iwWVDG6U0WUGWMpn/1/B3sGzW22NYxxS2b/8dlmXs5ycUQgghxK7kdogZ5lhfJUbt3fjxSG+Dc2LE9J1uVuC028FfDG1r4c17CZWNIBpIUmimOV9xdrxebFaSsv1MNRtwN0QAeFa9gHh8M80tT+yvRxNCCCHEu8ipEBOPxQb9vKvZSQm82TViJtrbnBM7hxgAfxF89AfO61d/BOFGpsw9ifr4ej6PG69l0WnlU2cVspaJnFD3JooNy+1pbGUMW7f+UsbGCCGEEAdJToUY00gP+nm47iTT6h0T01uJGRHf6JwYMWP4N535Wag5BjIxePEGpp50MtujqylE5dM41ZilRhVr7HF8Ir2QvE6nDc9rF5FKtVDf8Kd983BCCCGE2CM5FWIyxuCVdk2GbmNgmglipg8lYeIhja+nd3r1iGnDv6mqwsd+BooGa54m2L0Cu9RPzAhzkeojz0jRbfvYbJVRoUYw1nUA8KZ1GC1UsH37vWQy3fvyMYUQQgjxHuRUiDEyKQBuyVxMg13Cg3nzhlxjmnEisTwUYJa3GcW2nHEv+RW7fuMR0+CYrzivn/8mMz9yAtuja8hH4YKUU3lZalSylvEcHV/FBDQsFF5wXYJhRNi2ffc7eAshhBBi38upEGMazgq8D5lncHzql7S6KodeYyZIJJ3drWf7G52DI6bzWuN87nz7TsKp8PBvfvJ3nKDTuYWp+grqYs7aMp/xjyDfiBK1vfzXnMFcdTnJdc4WBv81j6SVchoa/kQy2bSPn1YIIYQQu5NTIWbn7iR12E0n46QyzliWyWx3Do6Yzg/f+iF/WfsXLv7XxTRFhwkcnnw4/YcAuN76NeVjAnSlWggoGhfv2AbAcqOSIi1N2/YepuguDFvhEdf1WFaaLVvu3mfPKYQQQoh3l1MhxjYHD+zVhtlN27ISpA1nrMwEcwsAbcVjaIw6VZkt4S18/vnPs65z3dAPmHoejJ0LZoq5ZWvYFl0FwMfzRuO3e0jg5mnzWKYo28is6MSlKLxtjGcJR9Lc8g+i0fX7+pGFEEIIsQs5FWKMzODF5XYuxNh2Gss2yRguFCxGZpwQs8LlhJrqvGrGF4ynLdHGF1/4IgubFjLkDc/6KWhu8toX4dIWYNkW+aFKzmlxVvV9xxjLXPc66ut7mOt2uq0e1r5KGp3NW362H55aCCGEEMPJqRBjmrvvTjLNBBncYNiMVFrxWgnQPCxPtQNwTOUx/PHMPzJ7xGximRhf/c9XeWbzM4M/pGQ8HPc1AE4csZ62xAYALg4H8Ks9pNDZaFYRJMbmhY1UuF20WEGeU86jvf1lurrf2U9PL4QQQoiBcivEGIMrMepOrXdW6/WBYVOr9G5CWVbL8vYVAMwomUHQHeTeU+/lrDFnYdgGN71+Ey/XvTz4jU74OhSMwk8PAe0vAHhGHc/09CsA/NeYyjzPKzS2Jzi1dy2ZZ/gkrZSzedOd2La9j59cCCGEEDvL7RAzTCUmgR/FsJisOiEmUzaFNR1rAJhZNhMAt+bmjhPu4IJJFwDw3de/S32kvv+NXD4488cATAguR7E34/YV8IUtTRSoPWTQWGmMpJQuXn11O8cX5JFB48/KPMI9S2lvf2m/PL8QQggh+uVUiNm5vjE0xPRWYjI2kxQnlGwsKCdpJsl35zM6OHrAvSrfnv1tZpXOIpqJ8p3XvkPGGtBdNekMmPQxNMUmT/8FYDPJdSxFQaca84o5nUu8r9MWSXFYj4VLUVjC4SzhSDZt/qlsDimEEELsZ7kVYkxnnZiLjh4JwNdOnTDovNm3g7VhZUPMct0JOjNKZ6Aqgx/Xpbq488Q7yXfls6J9Bfcu22nRujN/hKV5CLm24FNfxVU9m8Oa36RE78BE5YnU0dSwg8f+t40vVRQD8GdlHt3xelpantznzy+EEEKIfjkVYkzbqZT837nTWHXr6Rw+snDwecupxHgzCUYrOwBY0bslwMySmcO+Z2VeJTfPuRmA+1fez9vNb/efLBiJctK3AQjp9+NymZyzYxZ6xaPomGy1yzjCXUd3PEOgLkalx0UrpTzLuWzZ+gvZHFIIIYTYj3IqxFiGBYCiKOR5ht83KYGfiWY9qmJjeItY0e3MLppRuosNIIEzRp/BeePPw8bmhtdvoDvZnT2nzLmamF6OroYJ6X+hyn0UXnM7Y/OWA/Bc+nDG0cCfX9/GN2rKAXhWOZ/6lC2bQwohhBD7UU6FmL7upF2xzDhx20el6Uyp7igYRV3EGeA7vXT6bu/9zuzvMDo4mtZ4K99f+P3+GUa6G7N3Jd+A9jz+Cj9XL6uiY8TTVKjdGGhkVC/RlMHWla2cWJhHBhd/4lLZHFIIIYTYj3IqxJjvMljWNBPErQAFRAFY5XcWoxsbGkvQHSRdV4cViw17r9/l584T70RXdV6pf4XHNjyWPRc86lNsSo5CUSyKPb9lRlM1ZeEUpSUv4sagziqhilb+tHA711WU4lIUlipH8rYxiY0bfyhTroUQQoj9IKdCjGXsvhJjmnEiRn5/iHE5x2eWzqTnhRfZfPoZbDrlVDr/9GfsdHrI/VOKp3Dt4dcC8ON3fsymrk3Zc50zriZjuXGrGyia7uEbC8vYXrCMozwbAWimhJRh8tzCOi6vKQVwqjEtz9LY9Mj7fXQhhBBC7CSnQkzfwN5dnjcTRI0AIcWptqy0k4AzHqbt178C28bs7mbHD3/I5o99nJ7nnx9SJfnClC9wXOVxpMwU35r/LVJmCoApZ5zP661jASj0P8l4ewxHbdfoKH2Z0WonFioBkvzt7To+lR+k0uOiTSnnWc5lw4bb6O5etK9/HUIIIcSHWk6FGMvcfbeMacWJZfwUEMMEVhphAKbbVaQ3bQag7BtfRystIVNfT+P1X2fbZy4guWZN9j1UReX242+nyFvExq6N3LXoLgDyiorpGnU27akRqEqCsimbuXJhCS2+eqbnLcdHmhg+TNvmd//dxK3jqwB4VvkU6+xxrFx1FanUjv3wWxFCCCE+nHIqxNiWtdvzppkgbngJKTE2uVzEbYOAK0DZmmYAvDNmUDxvHuNfeIGSq69C9ftJrlxJ3aWXkd6+Pfs+Jb4Sbj/udgD+uu6v/K/+fwCcctk8nmsaiWXr+PQlVI2r5lPLfSwvfpvjXNt671Z4cmkjEy2Vj5WGyKDxc+VG6tI6K1d+FctK7fPfixBCCPFhlGMh5t3HxCQNHyGirPA6expNK5lG4s23AAgceywAaiBA6Ve/yrh/v4h32jTM7m7qr/gKZjicfa8Tqk/g87WfB+B7C75HW7yNUNkIRp9yEUu7JgJQUv4fLthQjmV2oofWMFFr7Wspd76wjl/VjmJWvp8IAX6qfJeGno1s2PCDffkrEUIIIT60civEvOvA3gRJw02BEmWFxwPAzJIZxBa+AUDg2GMGXa+XlFB9z2/QR4wgvXUrDV+7FjvTP+7muiOuY3LRZLpSXdz4+o1YtsWcz1zI27GxRDMj0JQeRh7RwFfeCLCkcDlH6VvJV5KAwivr2tjY1MOfpo+h2uuimRH8nG+zrekxGhv/tm9/MUIIIcSHUE6FGMvefXeSZcZJGx5CxFjrdioxM+MlGG1tKB4PvsMOG3KPq6yMmvvuRfX7ib/5Js233pod7OvW3Nx54p34dB9vNr/Jg6sexO31MffSy3mifhymHcDj2srZwQwjWmNsLdzI8a6tKL27PH37iRWUunX+MmMs+ZrKemUKv+dK1m24lXB46T7+7QghhBAfLjkWYt69EpMxNEJKjDqXs6Jvxdo2APxHHIHaW53ZmXfyZCrv+hmoKuHHn6Dj/vuz58aGxvLto5ytB3619Fcs2bGESXNOQKuezhvtp2DbKsHSzfx0o8Ka4BrK9DamaS0ArGuJ8OMX1zM54OMP08agK7BAOYkn7HNZufKrpFJt7/t3IoQQQnxY5VSIwXyXgb1WHCutkNGSJFQVBQX3knUA+HfqStpZ/sknU37DDQC0/ewuel54MXvu/Annc9aYszBtk2++9k26Ul2ceeVVvN3RQ3PykwBMqGng4g0xFhetZpbeSLHirFVz76ubeWZZIycW5XPnxBoAnlQu4D/pyaxcdRWWNXS9GiGEEEK8u5wKMfa7dCeljSQBI0FDbxWm0ltG8h1nfRbPUccy/9ENvPrwOratbMc0hr5X0Rc+T+HnncG8Td/+Nonlzv5IiqLw/WO/z5jQGFrjrdw4/0YKq6qYedrZvNgMMfNkFMXmGq0b1E0Y7i6Od21Dw/mM6/6+nMXbO/lcZTFXjywD4PfKlbwRTrBx4w/3ye9GCCGE+LDJqRDzbt1JMdOkIBNhh6YBMKsziBWNooZCbOsOseK/Daye38Q/f7OCB7/1Oi//cQ3bV3UMCjTlN3yHvJNOwk6lqL/yq6QbGgFnW4KfnfQzvJqXBU0LuH/l/Zzw2YuIaQmWds4lbY1Dd2W4v6GDpUVLKVQTHKY3AGBaNl/4w9vUdcS5YWwFnygrwETn53yLtxpfoanp8f30GxNCCCEOXTkVYrB2v9hdzIRCo4eu3hBTu8WZaRQ4+mi2rejIXucPuknFDda90cJzv17On29aSHuD0/2jaBqVP/sZnsmTMTs6aPjKFZiRCAATCidw0zE3AfCbZb9hWfcqPnLpl1nWuZDm5Dcw7RAj3Gm+2bmB1lA9U7UdTNOaAIinTc6/dwGRhMEvJo/kyKCfuJLHT7iJt9f/hJ6eFfv2dyWEEEIc4nIqxFjvsthd1FIIGVG6NOexRm3sAcA9ew4N67sAuOiWo7nkR8dx3tcPY/pJVfiCbmLhNM/8YildLc52BVpegJr77kUvKyO1cRON116XnXp97vhzOXf8uVi2xbfnf5vSI6YSGjWK5V3r6EjfgG1rnJKJM1b9D76CGEfojUzTnGpOezTN+fcuQLPhoeljGeV106aU8xP767yz4hrS6fb98nsTQgghDkU5FWJ2NybGsgzitk6BGaFT0/CkbUo3OaGgo3gqlmlTUO6ncEQAVVWonFDIiZ+dxOduOZqSmjwSkQxP372MnvYEAK4RI6i+9x4Un4/YggW03P5/2anXNx59I+MLxtOeaOeqV67ixCsuZVNkOa3JMrqNLwNwfVc32zxPkxeIc4TexHTN6Vra3Bbj8/e/RbFL4+GZYwnpKpuVidydvoDlK6/Bsna/P5QQQgghHIdQiEmQwE+BGaVLVZncYKMaFnplBfVOIYSxs0qG3Ofxu/jE12ZRWBEg1p3iqZ8vJdrlbBzpmzqVqp/9FBSF7kcfpeO3v3OO6z5+fvLPKfQUsqZjDTev/RETT/koizteImZ+jKjxUVTgzvY2FhS8iN+d4HC9mRlaPQBvb+vk6keWMt7v5aHpY3Er8I5yLPeFJ7Fp04/27S9NCCGEOETlVojZzZgY04yTsH0UWBG6NY1p25xrfUfPYfsqZzzMmJmlw97ry3NzzrWzCJX6iHQkefruZcR7nKnP+R/5COU33ghA29130/3kPwAYHRrNbz/6W/Ld+SxpXcJTNUvp1LvZFl1Dt3ElyfRoQpbFT9oaean8f3i1JIfpLczsDTLPrWjm1mdXc2xBHnfXjgLgn8o5PNjQxLZt9w7ZXVsIIYQQg+VUiGE3/7CbZpyImU+BEqNTU6mtd66NjD+WdNLEF3RTPjq4y/sDIQ/nXHcYeUUeunfEefrupSSjTtdO0Rc+T/GX5wHQ/L3vEX3tNQBqi2u599R78ek+3trxNsvOUFjS9V8ylk2ndStG2seETIYbOrfx36q3cKkZZuktzNSdIPPggm3c899NnF9eyLfHjADgIebxxJZXWLPm65hm8v3/zoQQQohDVG6FmN1sAGmaCXqMICElRpeqUexMKKIpUQjAmBklrHj5Xzz+f99jwd//Qt2q5WTSg3eUzi/ycu51h+EPuelsivHML5eRShgAlF5/PaFzPgGmScPXriWxwplNNLN0Jr855Td4NA9LEqt4bXYnK7pew6KYDus2LEvllHiCW9rWsKJiEZpiMlNryXYt/fjF9fz1re1cO6qcC0YUYSsaP1e+xcM74ixe/FmSqZZ9/VsUQgghDgk5FWJ2NybGNONEMgGCRAirCqEY2CjUb+8NITUWrzz4W7avWMqbT/yNx35wE7/50gU8eut3WPjYw0Q6nUHAoVI/53ztMLx5LtrqIvzz18vJpEwURaHi9tsJHH88diJB/eVXkN62DYCjRhzFz0/+Obqqs76ghYdG/5v14UVkqKUj810sU2NOMslt7YtpLl+KqtjM0luY2jtr6cZ/rOL5lc38ZFI1HysNYeDiAeUKfhI9kdff/gzhnuX79xcrhBBC5KCcCjG7m2BtmgliRgC3FsWXUNAtiARHEosYuDwa7XVvYplOJaf2+JPJKyzCNAwa1qzijccf4eEbr6ejwamOFFUG+MTXZuHx6zRvDvPPe1ZgpE0Ul4vqX9yNd+pUzK4u6uZ9GaPN2f/ohOoT+MmJP0FTNDZVR7lvxGNs6llKyp5Na+bHGGkPk9MZbm5fiFm8DFWBI/Rmxqs7ALjqkaUs2NDO/VNHc/O4SjRgvjKXGzJf55+Lr6W55an9+JsVQgghck9OhRjY/ZiYmOEDLU6hs24dHdVHA1AzpYD1C14F4Ozrb+Csq7/B/7v3j1x692/56P+7iuLqkcS6Onn01u/Qtn0rAKU1+Xz86pm4PBqN67v4130rMdImaiBAzW/vwzVyJJmGBur+3+UYnZ0AnDrqVH5w3A9QUFg3OsJvSx5lS2QFBpNoNX9FOp5HhWnyza7/UZi/GFWxOc61nRq1A9uGy/74Dgs3t3PlyDIemzWeUpdGvTKKm+zb+dOaR9m06U7sd1m1WAghhPiwyKkQs7utk0wzTsLwYWlJCqNO2GkvnApAfmE70a5OvIE8xh4+G3D2QyqsqGLGKWdwwS0/omzMOBI9Yf5+243s2LIJgBFjQnz8qhnobpW6NZ0895sVZFImekkJI3//O7SiIlJr17Lts58lXVcHwNnjzua7x3wXgFXjerg/9De2R1djKZW0cS+JnkKCls1XIq8x3vsWiqLwUdd6SpQwlg0XP/AOr6zbwZzCPP5z1GRmh/wklAA/V77Nj+riLFl+BYYR2V+/YiGEECJn5FSI2V2KMc04yYyHjJaiMAopd5CIqwQU6GxcDMCk405Cd7mG3OvLD/Lp7/0fFRMmkYxG+PttN9K0YS0AlRMKOfvqWdmKzHO/Xk46aeAeNYpRf/kLrqoqMtvr2HbhZ0msXAnAZyZ9hm8c+Q0Alk7q5oHAX6mLrsNWC+nQ7iPSMQI38PnkQo7WX8NSdD7pXkGIGKZlc9lDi/j1K5so97h4YtYELq9xpoY/p5zHNzpP4t/vfJF4fNs+/MUKIYQQuSe3Qsxuu5NiKGmIajYFUegsnAxASZWbrUveAmDqiR/Z5f3eQB6fuukHVNdOI52I8/jt36N+jRNKKicU8ImvzcLt1Wja2M2zv1xOKmHgGTuG0X97BM+UWszOTrZffAmRV18F4JKpl/DVWV8F4O0pXdwX/BN1sfWgBej2/orO5jEAnGks5gz1RdKKm4s9b1Oqx7GBn/57PWfc/RqRRJpbx1fx+6mjCaiwVpnG9Ymv8ud3vk1n54L3+wsVQgghclZOhZjdL3aXwJdJ06VpFEVtOotqAfD6t2OkUxRWVjNi/MTdvr/b5+f8G25h5PRZZFJJnrzjFrYtXwLAiLEhPnHtYXj8Oi1bwjzzi2UkYxn00lJG/enP2VlLDV+9iq7HHgPg8hmXc+m0SwGnInPbqN+xPr0SRfMRzf8pO+pqsYBjrLV8hqdJKh6+oM3no4EtgM26lghH//BlnlzSwNllBbx41GQm+HS6lSJuNb/FD5c/Tl3dH2VhPCGEEB9KORVi3q0S48+k6NRUCqIQzasBINyyFHCqMGY4TddTm+h6ehOxd1pIN0axjcFdVC6Pl/O+dTNjDz8KI53iqR/fxubFTiWnfHSQc649DG/AReu2HifIRDPOhpH33kPovPPANGn53s20/erXAFx7+LXcNuc2vJqXxtIEN076Ha+p81F1D6miH9C4+TBSQC3buITHSCo+RpktfC//eTyKQca0uf7vy/n8/W9Rpeu8cGQt55YGsRSNv3AJV2+KsWTNzVhWej/8voUQQogPrpwKMfbuQoyVIN9I0KVpFEZtkp4CLDNM2/Z1oChMPv5kOv64mtibzcTeaKbriY20/mopjd9fyI5fLKH7uS1YKWfmj+5284mv38iEo+dgGgbP/OyHrFvorNJbOjKfc68/DF++s47MUz9fSrwnjeJyUfHD/6Pkyq8A0P6b39D83e+CYXDehPP468f+ypjQGOJekx9NeIQ/BP8Guhu77EbqNxxNj6JSzQ4uUx8hnxj1mTJudD/FUV5n48jXN7Vz5P/9h2XbOrl36hh+OKEKHYu3lDnM23EMTy26VnbBFkII8aGSUyFmtwN7jThBI0aXqhKMezF1H1baGZw7cuoMPJ06meYYAIGjR+AZF0Lx6WDaZJpjRF9vpO33KzCjTkVD0118/GvfZvJxJ2GZJv/8xY9566nHsG2b4qo8zr3ucPxBNx2NUR674x1atoRRFIXSa65hxC23gKoSfuJJ6q/8KlYsxoTCCfztY3/j42M/jq3C41WvcUPVXfR4Ddwjvk7Dujk0aRrFVpgvqQ9TTRNblVEcw0ZuzPsXGiaxlMnn7n+bbzy2nC9UFPP04ZMod1k0K1VcF/0Cd715O5HI2v3+ZxBCCCE+CHIrxOxGwkgQNKJ0qhpesxDbtrEyawCYcuJHiMx3VsfNO76KwvMmUPrlGVTefAwjvn0URRdOQvXrZBqitN23AqMjAYCqaZx51fUcftY5ALz+yB956Xe/wjQMiioDnPf1wyko9xPtSvGPny1hxX8bsG2bwgsvoPrXv0bxeonNn8/2iy/BaG/H7/Lzw+N/yC3H3oJua6wIbuHK0bezJtRE3oiriKw5ha26j6CV5os8yhnay6Rx0WQU8xPvw0zQWwF4Ykkjc370Cr6YycuzZzAnqJFSfNxtfpGrFj1DXdMTu13dWAghhDgU5FaI2c0A1qhhUmBGSGdUTFchttmCZXbj8ngZXTOT1KZuUCHvuMrsPYqioBd68c8qo/QrM9EKPBjtCVrvXU660VkxT1U15l7yZT7ypctRFJWVr/ybf9x5K6l4jIJyP5/+zpGMO6wUy7SZ/+gGXvrDatJJg/yPzGXUHx9CKywkuXo12y78LMl161AUhU9O/CR/O/tRSq0QXe4I3x71cx6vXIi7/GIyLx1GnVmODhxjruBK5QFmspYVTOZT+ttcG3gZBYu2SIqP/XI+D7yyiUdnTeXqmhAAL3I6F60zeOTNy2lv/68M+hVCCHHIyqkQs7t/kKOmSYEVgbhC0lOIZTQDUDNtBsm3nbEivhml6IXeYe93lfopu3ImrooAVjRD229XkNzYlT1/2Blnc843v4vu8bB9xVL+dvO36Glvxe3TOf3/TeO4T41HVRU2Lmrl8R8torM5hm/mTEY/8ldcNTVkGhrY+qlP0/qLX2Cl00wqnsSzF73ArPQ4LMXmwbKnuG3Cw6SPuwpj7YVsXT+FZtVDyE5yHi9yKY9ioRI1fdzj+wMj1G5s4J5XN3P6z1/jkuJy/jhtJPmqwSZlEtcnv8olKzby2DvX0NX9zr77IwghhBAfEIdQiLHw2RECMYWUpxDbdIJLRcV4Eiuc/Y3yT6je7ftrQQ+ll8/AMzaEnTZpf2g18WWt2fPjjpjNhbfcSaCwiPb67fz1pq+zY8smFEVh1qkjOff6wwiE3HS1xHnsR4vY+M4O3KNHM/pvj5B36ilgGHTcex9bzz2P+JIlBDx5/PHSJzg/fhyaqbAobzVXjf0RG6ePRS39DpFXTmR1bAxxRWEkzVzOw3yU11luT+Zrrue5xLcAsNnSHuOkn7xK84Yw/zlqGueXBlCxWazM5prYZXxhyWKeXPwtIpE17/+PIIQQQnxA5FSI2Z2ejIpb66EwCklvIVZviClP1oAFnvEFuKvy3vV9VK9OyaXT8M0oAdOm82/ribzWkD1fPnY8F93+M0pGjibW3cXfbvk2mxY5U7ArxhfwmZtmUzWpACNl8u8/rOa1RzeghAqp/tWvqLr7brTiYtJbtrD9c5+n5bYfYCeS3HL5PXzL9QWCURcdrm6+NernPFm9CO8xV+Nrvpj6N2exRA2hYnM0y7iah/ArKYJ2ivv992VX+r3l2TVc88Aivj+yhv/NruWcEi8KNouUY7iy5yIueud/PLPsFuLxrfvnjyCEEEIcQDkWYnZdielM6aDFKIjhVGKsMLrixlWnAJB/QtV7/hRFVym6cHJ2/Ez4+a10P7clu9hesKSUC2/9MaNnHo6RSvH0T2/nzSf+hmWa+INuPnHNLA4/YxQAK//bwD9+toRYd4rgGacz7p/POevJ2DZdf/0rW84+m9j8+Vz0hW/y1zP+TG24HEuxeKD8KW4c+Uu2jS/DN+VG8l8/jbdbprBV0wmQ4Bxe4jP8k81WDXd6/szZ7sWAzfKGMMfe8TK/eXYtPxo3lldn1/LxIhcKNu8ox/L/us7ls2+9wPMr7ySZatnLv4MQQghx8Cl2Doz87OnpIRQK8fvbv8q8m3497DXf+ufXmLbyVdpXZgip36E7/XcmBo/ksOJT0Mv8lF93OIqiEIvF6OjooKysDK93+PExfWzbJvpaI+F/OZULz4QCii6cjBZw9l8yDYNXHriPFS+/AEDF+Emc8dXrKap0AtPWFe3858E1pBMG3jwXp102lZraIgCiCxbQcvP3yTQ6s6aCH/845TfegBoK8ctnb+ehzicwNedPM6dnJl9oP5uqxhjJTX9nywlbOFlvJWA7K+csYgarmUCV2s71iUuI2M5zKcDsMUXces5UyHdx58aN/KvLmbWk2BbHKG/xlREGc8d/HpercO/+OEIIIcQu9P37HQ6HCQaD+/z9cyrE3H/7lVx202+GnLdtiyuf/g5HbXwae4FGsuhGUrFH+djIKwhoQQrOH0/e7ApaWlp48MEHSaVSAJSWllJTU0N1dTU1NTWUlJSgKMqQ948vb6Xr8Y3YGQst5KH487W4a/J7P9tm7euv8soD95GKx9DdHk78/JeYddrHUBSFcFuCF363kvb6KCgw/aRqZn98DN48F1Y8Ttsvfknnn/8MloVWWEj5jTcQ/PjH2dS8lh88dwPL3FuwFVBshZN6juRzrWdQsuItWvzvkJyxkVPTcaeNePkvxxKwo7ytTueh5InEbXf2GcaUBPj2GZMZNTrEHRvX8u9upwin2BbHKm9xdaWbE8ZdiK4H9vnfTwghxIeThBgGVGJ+8BXmffeeIecNI8aXnvoZx9U9RMkLRTRV/z8KzeXMrbgQ1a9TccNsbE3h/vvvp6mpaZefM378eM4//3z8fv+Qc5mWGB1/WYvRngBNoeDscQSOHpENPT3tbbx4793UrVoOwKgZh3H6FV8jv7gEI20y/9ENrFngzJjy+HWO+tgYpp1UhaarJFasoPmm75LauBGAwEknUnHLLbgqKnj9nef52fw72VTcCYBqq5zWfSyf3n4koSXP8vZRWzi2oIXxhrNIXyPlvMTxlNntNBPifuNMms1Q9jkK/C6+fMJYjj9sBD/evIaXe5ygo9gmx6nvcG1NiDljzkdVPXv8dxJCCCEGkhDDwBBzBfO+e++Q86lUG5978iGOa/0Vk5+axIZRpzPWFeWw4lPwTSum+PNTePvtt3n++efxeDxcddVVADQ0NNDQ0EB9fT2NjY2YpklhYSEXXngh5eXlQz7HShp0PraB5OoOAPyHl1Fw7nhUtwaAbVksffGfzH/4QYxMGk8gwCmXfoXJx52Eoig0rOvk9cc30dHgrEETKvNx3CfHM3pGCWQytN9/Px333oedyaD6/ZR+/XoKP/tZTNPk8Sd/zR/r/kZDmVN50S2ds7qO5/wlQVLdC1l17BY+Z0QI2CY2sIRp/JdjGGPXo1gJ/sjHWJ6pyT6LW1P5xKxKzjtpFL+qW8OrESe4KbbJidpirhtdztEjP46iaPvuDymEEOJDRUIM7x5i4vHtXPL3v1MbuYeTHzuSFaMnMSuvjPHBw8ifW4M6p5hf//rXpFIpzjrrLGbPnj3kPVpaWvjb3/5Gd3c3LpeL8847jylTpgy5zhkn00D4hW1gg6siQPHna9GLfdlrOhrreeE3d9Gy2amsTDzmeE6ddyW+/CCWZbNuYTNvPrOFRI9TPamaVMjxn55ASXUeqU2baP7ezSSWOhtXeqdMofjL88j/6Efpbm/l/gdu4yXvEpqLnVWFPZabs1vn8PH/7mD9hGYKqzZxZtIJOgYaaxnPImagYVBqNfOsfTwvZGZh9Y7pVoBjxhZx0amj+XP7Jl6LOd1kqm1ykr6Ub4wdzeFVpwzbzSaEEELsjoQYBoSY2y5n3vfuG3I+ElnL5Y88Q0Xsd5zx7CmsqvRyYvFsyn2jKPz0RP61dT6rVq2isrKSefPmoarDT8qKx+M8/vjjbNmyBYATTzyRk08+edjrk5u76XxkHVY0g+LVKPrMJHxTirPnLdPkrX/8nTefdGYtBQoKOe3yaxh7+FEApJMGi1/YzvL/1GMaFooCtcdVcvQnxuLL0+n66yO03XUXVtwJJK7qaoq++EVC553LhqWL+OMTP2PJmHaaQxEA/KaXc7ZP5cyXNpOaZlBatZlxdirbnjaKWMx0GiljlF3HG9ZkHs7MJUn/uJmxJQE+Pbea/xgNLIgXAE6Y+YhrBV8bXc5hFSfJmBkhhBDvmYQYBgzsve1yLhsmxHR3L+K6h18kP/xHTpt/HhsKWvh41QX49Xwi54R49MUnURSFL3/5y1RWVg7zCf1M0+Sll17izTffBGDSpEmcd955w85kMsMpOh5eS7rOCRK+GSUUfHwsWrB/PEnL5o386zd30dlYD8DUk05lzmcuIlhS5jxbe4I3/rGZTYudRfVcXo0jzxzNjI9UQyRM18N/pevhhzG7uwHQQiEKLvosgfPP4/Xnn+HF1c+wanKS5oCzunDQCHDe9pmc/no7rrxtpKZ1MtHdha93erqBxmomsIYJFNsdbDaLecD8GG12/39chX4XHz+ujBX+dt5KlmSPT2Ajc/2dnFM1hlkVJ6Bp/dUnIYQQYmcSYhhQibn1/zHv5t8OOd/e/l9u/suruDoe5fhVl9DkWcInR1+DgcnTI5bR1d3NMcccwxlnnPGeP3PZsmU8++yzmKZJaWkpF154IcXFxUOusw2L8IvbiL7eCDYoHo3gR0eRd2wliuZ0wWTSKRb87U8s/ufTAKiazvSPfJTZ5346G2aaNnWz4LGNtG53AlF+sZc5549n3OGl2Mkk4aeeouPBh8jU1QGguN2EzjkH65STeeGpx1mvN7J0QjctXmcAcFEmxGda5jJ3cRKtZQGpwyIEy1oYTTrb9laKWUot2NBpufmzeTprrAHjZnSVE2cFaR4RY0mmBHvAskKj2cZcfwfnVo7myMrj0TQZCCyEEGIwCTG8e4hpbn6Snz38FmbLsxxWfwWGezmnVX2RJb5tLLE3k5+fz1VXXYXHs2f/0DY0NPDoo48SiUTwer2ce+65TJ48edhr041Rup/aRLreCSGuigAF547HM6r/j9a4fi0L//5n6latAPrCzGm9YaYU27LZ8HYLb/xjM7GwEzYqxoc49txxjBgXAssi8p+X6XjgDySXr8i+b97cubRNmcSCpW+zpSTM4vEddHmcwcNBI8DxPYdxXF0FE5eswA5uJjWtg8meCF6cNWMyaKxmIu12IUnb4knzRF4xZw16vuICD5NmKOwImKy1SrJjagCqaeRkfzvnVo7i2Mpj0LTdr78jhBDiw0FCDAO7k77MZd/73ZDz2+vu5w9/XY6x/RVGRa+kyLWNY8s+wd8Db9JjxvjkJz/J9OnT9+qzI5EIjz76KA0NztYDM2fO5IwzzsDnG9qVYls2sUUthP+1DTthABA4agTBM0ZnF8gDaFizioWP/5X61U4Q0XSdaR85naPP/TT5xSVkUiZL/72dpf+uw8g4QaO4KsDUE6qYePQI3F6NxJIldPzhAaKvvJJ9X+/MmZhzT2JJZwv/Cc9n1bgeIp7+cTGlmUJO6JrJcastRq5bQvKwborLO6khmb1mByVsooa0pfBv8zCeME8kTX/bAVSXyuTpFslShfWUYaJnz5Wzg7n+ds6pqOHEqqOlQiOEEB9iEmIYWIn5MvNuHhpiNm3+CX/7+yq8axbh1q5goifGhMLZ/Nn7GgDf/va3hw0d75VhGLzyyissXLgQgPz8fD7xiU8wYcKEYa83o2nCL2wjvmgHAKpfJ3TmGPxHlKOo/bN86levYOHjf6VhzSrACTPTTzmd2ed+mvyiEqJdSd5+bisb3t6B2RtmdLfKhCPLmXpCFWWj80lv3Urngw8Rfvpp7LRTvXGNHEn+hZ9hW9DHc0ueZn1eC1vLIyT1TPazRyZHcFzLOI5/ZwclbCY9vYcpnjDu3upMGp31jCFhu3nNrOWvxql0MvA/QBtQQLWpGJ3CO0Znk15BZsBA4WI6OcnfxjkjavhI9ZG4NDdCCCE+PD6QIeaee+7hJz/5Cc3NzUydOpW7776bE044Ydhrn3zySe69916WLVtGKpVi6tSp3HLLLZx++unv+fP6Zyd9mXnDVGLWrLmBp55eR+miZuJ553BUfjF6cATPu5dQUFDAtddeu6ePOKy6ujqeeuopOjudcSeHH344p59++i67qVLbwnT9YxPGDmeGkXtkPgXnjsddOXgjyvrVK1j42F9pWDswzJzB7HM/RX5RCclYhvVvtbB6fhNdzbHsfSU1eU51ZnY5arSbzocfpuuvj2CFw877FBZScNFnyRx1BIuXvMl/N71EQ2WGTcXtGKqZfZ+JsZEct7WA4xZvQpvUQ1V5DxVEs+ebKWU1o9hgVrPZquBNczKNlO70tDYoUDAiRnCCSp23hqTS360UIsyJ/nbOGVHJR6uOxKO7EEIIcWj7wIWYRx99lC984Qvcc889HHfccfz2t7/l/vvvZ82aNYwcOXLI9ddeey2VlZXMnTuXgoICHnzwQX7605/y1ltvcdhhh72nz8yGmFvmMe/7vx9y/o3lV/Hav9Yz8nWdHYUzOaXkSBr8Kd5ybWTy5MlceOGFe/KIu5VOp3n55Zd56y1n5+pQKMQ555zD2LFjh73eNi2iC5voeakOO22CCoHZFeSfUDVobRnbtqlfvZKFjz1M47rVAGguFzNOOYPZ53yKvKJibNumeXOY1fMb2by4DdPorc54NCYeVc7UEyopKdXpfuJJOh96KLsvE4B3+nT8J51EW1GQN1e+wypjE/XVBptCLViK85+AaitM7x7FCasMZnU14J2UYoq7HRdO4EnjYhOj2EoFDeTTZJSy2hzFUnsCJv2L4qlYWCgES2Lkj7VoLhhJXOmfmp1HlON9HXxiRCVn1czCq0mgEUKIQ9EHLsQcffTRHH744dx7b/+ic7W1tZx77rnccccd7+k9pk6dygUXXMDNN9/8nq5/txDz/DuXsfK/a5n0ymi2Foc4r+rTvO7ZwCathblz53LSSSe9t4fbA9u2beOpp56iu3fq81FHHcWpp566y6qMEU4Rfm4LiZXtzgEFvLXF5B1XiWdsKLuYnBNmVvSGmTUA6C43tSfOZcoJc6maNAVFVUlGM6x7s5nV85vo7q30AJSNymfqCVWMP6yY5Guv0Pnnv5BYsmRQW1wjR+I6ejYNXhdvbF9NS7nFxoowWwLN/ddYOofvGMGJy9s43B+nsqKLIj0+6H3C5LGFGrYqxWy0ytmaqeEdaxIdhAZdp2PgC6XIG5WhrbyaqNp/3keCOb4OPjGigo9XzySg6wghhDg0fKBCTDqdxu/389hjj3Heeedlj3/ta19j2bJl/O9//3vX97Asi9GjR/Otb30ru/z/zlKpVHaTRnB+CTU1Nfz+1nnMu3loiPnj/C/S9PZyZvz7WOpLTT5e/Xked79JtxrjoosuYuLEie/1EfdIKpXipZdeYtGiRQAUFhZyxhlnMHHixF2ucJvc1E3ktQZSG7qyx1wjAuQdX4l/ZhmKy5n1Y9s2dSuXs/Cxh2nasDZ7bX5JKZOPO4na40+mdORobNumaUO3U51Z2oZlOn9Ol1dj0uwRTD2xkpA7QfS/rxJ55WXib7yZHTsDoBUUYE+fxjYVVme6aRnjZUVZE43e1uw1ftPDUS0jOWVLN0eHt+IaZVAYiKIr1qBna6OIzVSxTilhtTGG1cZ41tijB13jIoMnL01eZYauqhGE3f3T1t2kOcbbyskFLo4uGc3M4vHoqmx7IIQQueoDFWKampqoqqpiwYIFzJkzJ3v8hz/8IX/84x9Zv379u77HT37yE370ox+xdu1aysrKhr3mlltu4dZbbx1y/Pe3Xsa8m+8fcvxHL16GsmwBk1/7JJGSMMdXfIo/el/FBr7+9a+Tn58/6PpweBld3W+RF5hAfnAGHnfJkPfcE5s3b+aZZ54h3DsWpa/7bPz48bsMM5nWONGFTcQX78DuHbSrBlwEjqkg75gKtHxnEKxt2zSsWcnq115h41sLSSf6qyElI0dTe/zJTD7uRIIlZSQiada+0cya+U2E2xLZ68rHBJlwVDkjpxQRzLOJLVhI9JWXibz6v+z4GQDF4yYzdixbbJOm8kK2jtN4p2gj7a7u7DWFmSCzu2qZ1OLhiOb1VGhb8FSmKfRGGPioFgpNlLFeqWClXcmizBSWmrXE6O9CU7HI88bxlBtEq0royitj4Jt4SDFOa2O63+TwUCHHlI5nYqhCtkAQQogc8YEMMQsXLuTYY4/NHv+///s//vznP7Nu3brd3v/II48wb948nn76aU499dRdXrfrSsylzLv5D4OutSyD65+5juqVz1O57EsECtPUlM7mGc8iAoEA3/zmNwdd39j4COs3fB/b7h/Y6vFUEAzOIJg/g2BwBgUFR6GqezZOI5lM8vrrr/PWW2+RyTizgKqrq5k7dy5jx47d5T+8VjxD7J0WogubMcO9z6wp+GeUknd8Fe6q/kHAmXSKrUveYe3rr7J16SJMw8ieq5o8ldrjT2biscfj9efRsKGL1a81sXVZG5bV/yfOL/JSM6WIkVOKqByfj7VmBZFXXib68iuDxtCgKCTLy2j2+KgbVcTyCRmWFW0jqveHI4DydDGTIiOZ2OrmsLbNjFEbyC+LUeCKDbrOQGM7I1ihVLLYHMf8zBFst6sGXRPQ4/iKM5gjfHSWjCClD51R5ifOBK2DmXkqRxaWcUzZBGr8IQk2QgjxAfSBCjHvpzvp0Ucf5Utf+hKPPfYYH/vYx/aokf1TrIeGmFS6neue+CljVj5O4dZ5jCvwkikqZ6FrPePHj+fzn/884FQ0tmz5Gdu2O2N5CkJHkTG6icU2AYN/BXl5tUyb+gsCgXF71E6AaDTKwoULefvttzF6Q8bIkSOZO3cuY8aM2eV9tmmTWN1OdEET6e092ePu0UHyj6/CO6V40PTsZDTKhrcWsO71V6lfuwp6/4yqpjN61uHUHn8y446YTSalsv6tFupWd9C0qRvL6H9WRXGqNDW1RdRMKSKUbiH231eIvvwKydWrB7dvRDmdI0pZHEyyYpRFc2GcBn8HtrLT7870MzEykkkdHmZ01jFFaaGsoIc8bXD4SeJmvVLJEkayMDOdV42jBk3P1jAp0cO48zJYBTqxonzag5UYw4w5Ctk9jNXCzMx3c3xZFceUTqBkDxc2FEIIse99oEIMOAN7jzjiCO65557ssSlTpnDOOefscmDvI488wqWXXsojjzzCueeeu8eN7B/Yeynzvj84xESjG/jOow9Rvepx8tsv5eiiSjbmJ1mvN3HCCSdwyimnYFkp1qz9Djt2PAPAmDFfY8zoq1EUBcOIEomspieygp6eFXR2LsAwwqiqj0kTb6Gi4pN79b/yI5EICxYs4J133sE0narP6NGjmTt3LqNGjdrtven6CJEFjSRWtENvFUUr8JA3pxLfjFL0gsH/QEc62lm34H+sXfA/2rZtyR53eX1MOOoYao8/mZHTZ2Ea0LSxm7o1HdSv6aSrZfBAXY9fp3pSITVTiqgoMWHJfKIvv0Ls7bdhQNVHLSrCmlpLk9/FMruDLcVpWooSbM9rI6VmBr2ny9IZG69iUqeHqZEdzLJbGevvwqukB13Xg5/lSg1v2JP4d/poNlnDB75CrQd/XgorpBMtDNIRLMfyuQd1QwEU212MVqNM9fs4uWosx5eNJOiSQcNCCHEgfeBCTN8U6/vuu49jjz2W3/3ud/z+979n9erVjBo1ihtuuIHGxkb+9Kc/AU6Aufjii/nFL37B+eefn30fn89HKBTa1ccM0h9ivsS87z8w6FxX15v88OEnKFr1Ar7Epzmz9HBe8q+nXY3wmc98hgkTqlix8it0d7+FouhMnvx/VFZ8apeflUrtYPWar9PV9QYA5eVnM3nSD9D1/F3e825tnz9/PkuWLMmGmbFjxzJ37lxqamp2e6/ZkyL6RjOxt5qx4v0hwlUZwDelGO+UYlwVgUEhq6OhjrWvv8ra1/9HT9uO7HF/qICJxxzHyGkzqa6dhi8/SKQzSf3aTupWd9KwrpPUgM8AKCj3UzOliOoxPoLNK0j97xWir72GFY0Ouk4NBrFHVtOd52cjcTaU2myrNNia30bYNfhagJpEORPDHmrjXRxptjNd68CtDP7sdruAtYxkrVLOaruGlcZ4tpijgKGB0q8mCQbiWCGNaEGIrmAZdkCHAZUrxbYosTupIcFkXx7HlY/lozVVBN0SbIQQYn/5wIUYcBa7+/GPf0xzczPTpk3j5z//OSeeeCIAX/ziF9m2bRuvvvoqACeffPKw3UyXXHIJDz300Hv6vOy2A7d+ictuHhxidrT+i/v+/ByBFStwW8dybtXHeMjzKpZic/XVV7Nt+9V0d7+FpuUxffpvKC46/l0/z7ZNtm//HVu2/hzbNvF6a5g27ReEgjPfU3uH093dzfz581m6dCmW5QzkHTNmDLNmzWLy5Mm73dfJzpjEl7YRW7yDdF3PoN4vrcCDt7YI35RiPGNCKHr/7KamDetY+/qrbHhjPolIz6D3LKkZRfWUaVTXOl++YAGt23uoX9NJ/ZpOWrb2YA8YS6NqChXjQ9RMDFFiNeFeMZ/UihUk164dNNspq6iQZEkJTS7YWqSwfozGhpIumjztQy9N5zM+4mdKIsoxRgezrTZcytD/LBO2h0ZK2UQ5q+1KVtljWJGZRAeFQ67VFIOQPw5BlWgoRCRYiJ3vAr1/zyfVNik1OxhhpRjrDnJUyWhOrSmlJuSTMTZCCLEPfCBDzIHWH2K+yGU3PzjoXEPDwzz8l+cILY3h8k7myMqT+YfnbTweD5dfPpelyz6Hqno48ognyM+v3aPPDYeXsGr1tSSTjSiKzrix1zNy5JdRFPXdb96Frq4uXnvtNZYtW0bfr97lclFbW8uMGTMYO3Ysqrrr9zejaZLrOkms6SS1sSs7swlA8Wp4JxXhm1KEd2IRqs+pMpiGwfYVS9my5G0a1q6mo6FuyPsWVlRRPWUaNbXTqKqdhsdfQON6p+upbk0nkY7koOt9+S5qaouoHB8kZHXgbVpPevUKkitXkdq0CSxryGeYJcV0+f00hnS2jvSwdHSCDfnNmDtN1faZHibHC6hNWExPRJhqtjOSLrRhgg1Aj+2njnI2MIJVdg0rjQmss8YQwT/k2jxvHDWoEAsGSQSDWEEXePqncet2hqJ0B6FUimJTp1QvZHSgiPFBP9WFfipCXipCPnxumfothBDvRkIMuw8xW7f+iif//hylb43AW1BFScVMXnOtZfTo0cyZU099w0NUVHyKKbV37tVnZzI9rFt/E62tzwNQVHg8tbV34PVWvq9n6urqYvny5axYsSK7jQFAXl4e06dPZ+bMmYwYMWK372GlTVKbukms6SC5rhMrOmA8iqrgGRvq7XYqQi/o3wIgHu6mYd1qGtauomHNKtrqtmUHBvcJlZVTXTud6tqpVNVOA4I0rOuibk0njeu7yKTMQderqkJhhZ+S6nyKy93kZ9rxt6zHWreM5MpVZHo30BzIVlWSoQLagj4aK32sHGWysLqVuCs15No800dNqoCKtEZVOsWoTJhao4PJZhfuXRRNOuwQWyhnrV3JKmsMa8xxbLKrSDK46uVxpdGCEM/PJx30Ywdd2H590Dgb3c4QNLvxpuNoiTSulIrPDFCiFzLa72dUgY/KkI8RIW/2u1vf+7ArhBCHAgkxDJyd9EXm7RRiVqz/Af9+5jlq5h9NsGIEkdIy1ugNHHvsMeTn30kiWceM6fdSWnraXn++bds0Nf+dDRtuw7KSqKqbqsqLGDX6K+97jRnbtmloaGDFihWsWrWKRKJ/Fk9ZWRkzZ85k+vTp7/rHty2bdH2E5NoOEms6MFoHzwZyVQTwTinGN6UYV+XgcTTJaJTG9aupX7OKxrWr2LF1M/ZOlZS84hKqJ0+lZsp0KiZOIZXIo35tFzu2hmmvjw4ZT5O9r9BDSXUehcU6QaMN/44N6BuWkFy5EnNAeOtjuVxEioI0FLlYX2GzeHSSTaVRbHX4pKLaKhWZIJVpF1XpNKMyUWqNLmqNHgota8gIGsuGForZbI9gtV3DanMc6+2RbLUryAzYjVtTTVz5Fma+m4Q3gOVzYfs0bJ8OHnXIQGK/FSU/04OeSkHcJB3V0FN5lLn8jPR7qAr5qCxwqjh938vyPeiaBB0hxKFLQgwDQ8wlzLv5oUHnXlr+XZb8+0lGvf4JqmtGs7IowQ41zNlnH0l3+GsoipsTT1iErgeGf/M9EI1tZP2679EdfgcAVfVRU30xo0Z9GZdr6LiMPWUYBps2bWL58uVs2LAhOxAYnMHAM2bMoLa2drfjZ/pk2hMk1ziBJr198DgaNejGU5OPqyYfd3U+7uo8VG//P+DpRJzG9WuzlZqWzRuxzMEhxR8q6B1PM7W3+6mMzuYE7fUROhqitDVE6WkbHKT6uDwaxVUBiopUgkY7/raNeDYsIrN6BVY8PvSG/Dxi5YV0eBWafSaNIZOtJQbrRsQI+zJDr++7zfRQmfZQnTYZlYlTa3Qz0YhRkzHYeRUgw1apt0vZYFex2hrNensU6+1q6uxyLAYHDUWxcHlM8KlkfB4yXl9vwNGwvTq2Txs0qFi1DQrMbvLSEdREhlRMI97jJhXzUKbqVOV5qCzwURkaHHIqCryUBDyouwhwQgjxQSchht2HmN+/fhNd/32U0hUXcnj1dJ4LbiCjmHz600FadvyG4qITmTXrweHfeC/Ytk1n1wK2bLmLnp7lAGhaHiNrvsTIkZft9SymnSUSCVavXs2KFSuoq+sfw+JyuZg0aRLjx49n1KhRFBa+e3gyY5necTQdpDYMHkfTRy/1OYGmxvlyVQSyg4QzqSTNG9dTv2YVDWtX0rxxPWZmcHjQdJ3CiiqKqkdSXFVDcXUNwZJKDDNId0uK9voI7Q1ROhpj2Y0rB1IUZzZUYQiCVgeBjk14Ny7GXrcMMrsOKlZ+HtGQj06fSku+TUOhyZbSDBvK4vT47SEVEwDNVijPeKhO24zOJJlk9DAhk2BMxqBgpwpUytaps0dQb5fSaBdTZ5fRbBfTbBfRYhexg0IMhs5w0twm+DQyXo9TxfFq2UqO7dXA1R+MfFacokwngVQULZ4hEdVpD4dIx1XcaYuKYG+4CXmpGBB2RoS8VBb4KPS7ZCCyEOIDSUIMA8bE3HYJl33voUHnbn3uu3jmP0qw/jPMqTmGp3yL0XWdM85YQbhnMZMm3kp19ef3eZts26aj479s3vJzotHejRr1EKNGzqO6+pJ9Uvnp09nZycqVK1m+fPmg8TPg7KI9evTo7FdBQcFu/0GzMybp+gjphijphgjp+ghm19AxKGgKropAb6UmH3dNHnqpH0VVMDIZWjatp2GtM66maf1aMqnk0PcAFFWloLyC4uoaiqtHUlhRhctbhmEE6W7J0N7ghJtEZPig4st3UVQAIT1KMN2Kt3M7rqaNsH0TZu/mm7tietxEgz468zR2BKGu2GRTaZL6YoPOINjD/J7yTY3qtMLoTJqJmQjjjDRj0hmqjKHVGwDLVugin1a7gGa7mHq7hCa7JBtymilih11Eeqe7Fd0Gr4rh8/RWcHaq5LidLivFtigyuyjKdJKXiKDHMiQjbsIRH11xH+m0jltTCPncFPhdFPpdFPjdFPhcFAb6jjk/F/jdFAacn0M+F16XDE4WQuxfEmIYOLD3Yi67+Y+Dzt3w8LcoeP1ZQrFPUls9i/+6V1FTU8ToMb8CLI6bM/99D8LdHdu2aGv7N1u23k0sthEAl6uI0aOuoKLi07hc++6PZts2jY2NrF27lu3bt9PY2MjOf75gMDgo1BQWFr7r/0o3o2kn1NRHyDRESDdEsGJDx7gobg1XVZ5Tral2vmsFHrBtetrb6Giso6Ohns7GejoanNcD93oa/GYKwZIyiqtrKKqqIb+oAkUrJpMO0t1q0l4fpbs1vvNiyllur0ZegZuA18SvJPCmuvBEmnG3bkNr2IDauGWYFWX6WZpKNN9LZ75OSwFsLzHYUpKkpVChLQSGPvhup3rjosS0KTENyowUFWacctOg1DQpMU3KDJOAbQ/7uV12HjvsIhrtYhrtEifg2EW0UJQNPIMGHKs2tlcbXMXx6tAXeLxOl5XHTlKeaaUs3UZBugdvKoWeyqAkbIyERjqpkc5oxNNuuu08uu08IvixUPG5tP7Q0xd2/K4Br92953sDUG/40aR7SwjxHkmIYWAl5mIu+15/iLFtmxt/dy1lC5ZSop1IoGocK/TtHH20jdvzF/LyJnP07H8ekDbatsmOHc+xZesvSCS2A6AoboqLjqes7CxKS0/dZ11NfVKpFPX19Wzbto1t27bR1NSUXYOmT1+oGTVqFKNHj6aoqOhdQ41t25hdKadS01utyTRGsdNDu4HUgCsbalw1+bgr8lDzne4N27aJdXXS0VBPR2Ndb7hxAs7O69YMlFdYRFH1SAorqnH7S0EpIp0I0t1qE25LkIzuunupj+5SCeSrBNwGfjuKJ9GBu7sJd8tm9MaNuGMdKLtISLYCsTwP7UGN5kKbbcUZmoosWgoVdhRA0rPr35/HUig2VIpNizIzwwgzSbmZocQwKTPNbODJt4aGnbAdoLk36LT0dVkNCDnNdjFx+meZ2R7V6Z7KVnKcrirbrYJLdb5rSraik2dHKTS7KTTDhIwI+ekoeek4vmQSPWmgJGystIKRUkilNXrsAN12HmECdNn5hAlgohH06r1Vnt6Kz4CQMzAEDfw5z6NLl5cQH0ISYuj/Jfzh1i9w6c1/yh43jCi33nM9o+d3UVYwg+YRIZq0Tj5yyiYymTcYPepKxo37+gFtq2UZtLT8g7r6P2QrM9AbaIpPcAJNySn7PNCAs7fVwFDT2Ng4JNTk5+cPCjXFxcXv6R8X27IxWuPZUJNuiJJpjmW3RRhIcavoRT70Yi9asfPd+fKhhTwoqkK8J0xnb7jp6A03nQ11RLuGzljq48sP9nZJVeMLlaPpQSw7gJnxk4zpRLvSRDqSxMLDLL63E1WFQEDBr6XwmT14Y624OhtwNW/GE27Ck+pGtYeGNoC0300soBPxqXT7bDr8Fm35Bh15Jt0BCAcUwgEI+4dWdPp4LIUi06bUMCk3007QMZ2gU2KYlJoWpaZBcKew02P7esfkFA/qsuoLOS12ERF8ZFc2VsB2q9gu5wu3iu12xuTYfUFn5++9wSfPihCywhQZYYqNborT3YTSEfKScXypJK64gZqxsFMWRkYlYvkJE8hWfLrJo9sOECYPW3NLl5cQH0ISYhjYnfQFLhsQYhKJeu649xtMeq2QmqrpvF7SRUZNceJJT2NZMY484glCoVkHrd3R2EZaW//Fjh3/JB7flD3uBJoTKSs7c78FGnBCTUNDQzbUNDQ0DAk1gUCAsrIySktLs19lZWX4/UMXituZnbFIN0fJ9I2xqY9gdCR22QUEgKagF3mdr2IfWm+40Yu96IVe0unEgMpNQ7ZbauAWCsNRVJVAYRH5RcXkFRbj9heguYMoah6WGSCT9pGKuYl2GUS7U4NWIx72/RTweSz8agJvuhtvpAVXex2ezga8yQ68yS5Ue/hp5QOlvDoxv0aPT6ErYNORZ9KeZxEO4AQev5J9nXENDTxuC4p7A025aVBqONWcUtMc9Do0YDp5zPbSZod6Q0Qe3fRVVJxQ0R8w+oNGT2+VBZxKVF+gyQYflwpubXDwGXhOV/DbMfKtHgrMMEVGF8XpbsrT3YxIdhNKRvAnk7jSBkrawE7bxCxvb5sGBx4nBAXoJg/F5Sfg0cnzaPjdOnkeHb9HI+DWCQx7TCfg1pzvA8/3HvPoqlSEhDiAJMQwsDvpC1z2vf4QE+5Zzl2/+ybjF0xn3NjD+Xf+RoqKWpk67UVcrmJOOP7N97W67r4UjW5wAk3r84MCjaq6KSo6kfKysygp+ch+CzTQH2q2b9+eDTUDp3EPFAgEBoWavteBwO4HLNuGhdGVxOhIYnQkMHu/Gx1JjK4kmLv5z00BLeRBL/FlQ05fNYcAdLU309HojLnpbGog2tFBpKuDWGcn9i6qJjvzBUPkFxXjzS/C7Quh6fmg5GGaftJJL4mYh1i3NWi3713xeiy8moHHTuIyYrjSPbhiXWiRDrTuHbiSYdyZKK5MFFcmtsvuqz4pt0rUrxH2Q2fAojMbdnorOwGFbj+EA5B0M2jmlduye7uwjEEBp9AyyTctglbfl03QssizLHaucfTY/mx4GBx0AoTtwYGomzznGAEM9EHBZ2gA0nYKQ853r5Ygz+4h3wwTMsIUZpxqT3mym6pkJzWJTgrTUTyZJN50CtNSieMhZnuJ4yWGl7jt6f3e+zNeYrZnwPnB16VUHy63h4DHhd+t9QYcfafQ8+7hKNB3n1uTtX6E2I39HWJybPe7wf8IZNKduJNhkm4XCY/zf0iqq53uiJKSuR+YAAOQlzeRvLyJjBlzDbHYwECzmfb2/9De/h8UxU1+/lRCoVmEgocRCh2Gx1Oxz/6Xo9vtZuzYsYwdOxaATCbDjh07aGtry361trYSDoeJxWLEYjG2bds26D38fv+gUNP3ui/cKLqKq9SPq3RoJce2bMzuFEZnb6jZKejYGQuzO4XZnWKY+VKo+S5KiooYUVyJPvPkbBeVEnKRyESIdXU6waaznWhnB5EO53u00zlmZjIkesIkesLAlmE+weHxB/CHCvHmFeLyhFD0fLADGIafVMJDIurBSLtIplSSuAE3EASlAvJwviqGPD1u3cKjZnBbSVxGFFeyBz3Wid7ThisRxpWJ4s5EqeyIMKolhmaZ2Xt3ltEVevwq3QGbzjw7233VHXARDrhoDChOpScP4h6GnWoesGzyTYuQZZKfDTkm+VYXQauDfMui0LQY1XsuvzcABS0Lz4ABzBHb19+NlArQneoPOH1jaYbrYkopLiIulR53IY2u4iHdW7a/v/qDS0V3ZXBpSbwk8dgJPFYcj5XAZ8bxWnH8RpyAmSAv080II05BOkpBOkZRJkaREaPYiFGaiRHMJEknXcSSA0OOlxhO+IkPCEGdA45nw9NOIcnQ/ahuP36vi4C7v+oTeI/hKBumPM53n0uTapEQ71GOhZjBkulOPNE4Lp+fdjUK2ARD/7+9c4+x5KoP9HceVXVvv2am5+F52ePxLosTbMDYSaRghQAR2cUJQZGihA3BLH85iokdJAKCSERRiK39A0WRElBQhHbXRI4QTkQiFGEScIIiYdaeIbbJYgj22Di2x/Pqvn1f9Ti//eNU1a17+3ZPzzA9PT0+n3R0nlW3us7cqe/+6vUMAHv2vG1rN24NlFLMzb2WubnXcvToPXS7T/PyyS9z8uSX6fV+wPLyMZaXj/E8/tk2cbyvlJo3srBwCwsLN2NM+5JsSxRFHD58mMOHD4+1D4dDTp06VUtNJTjnzp2j1+vVp6eazMzMrIra7N27l7m5udHfrkenkvjP49siIriVbBS1aeTFmQGul+M6GWkn8w/vm8QozFzEwnybXXP/CT13I+a1MWYuQs/H6LmITKX00mW6K2fonDnNytkJ0Tl9irTfY9jrMux1gdWvSqg/LoqZWdhFMreLpL0DGy+AnkFcjHMJRRaTppG/O6hvUcqS5oYUA7SAnT5rAbunf4ZVBbFKvfRkHaL+kpee/lni1Ed39nQ7HDjnoz0270+N9uRG0ZnVrLSgmwgridBLvNx0W9BLbF0/nUAvUfRa1G3DiFUSFImwULhafnzeYcEtedEpHPud47+4yTGOOScYoCcJZ5ljKS3lZ/KUUn36a76OAJ1Vc3RNQtfOIGYWrEKMP52FUYj11/SIUf5ln3MKsb7s2/w4azIiPSTWPRIGxK5P5EoxKvq0ih7tvMdM0WM2W2Iue5G9WZcdeY9duZeixbzL7qzHnOTMOKHoxgy6DdkhmRohOjMtklRLkh8j0SwqnqXVatUSNDtFjtqxIbGGVqRJrCGxmlY0PU8iTcsaknJsuMsscDWwrSRm8sfJi70OcccwHyW8qJdpt5dR6hRKxSzuOv/bqreaptDccPRe+v3nWF4+ztLyMZaWjrOy8m+k6UleeeUrvPLKV8plDHNzN7KwcAs7Ft7Ijh1vpN2+/pL+ckuShEOHDnHo0KGx9jRNx6I2VTp79iy9Xo8TJ05w4sSJVetaWFiYmubn51lYWKDd9m+NNvMxZj4muX7Hqm1yvYz8TFNuRmXXSaEQiqWUYinlfPcuxVazb243B+YPoOcizDVecsx8jIuFgevRTzt0+mdYWT7NyplTpeycoXPmFP3lJYospXP6ZTqn179Wp8ImLZL2LFFrFhvPYKIZlG6DSkASnEvIs5g8taSDCJGETLXIVZsebYh2QQSsE41VOGJS4qJHlHaI+uew/XM+wpP6KM9sZ4XDZ1aweQ+bDzDFEMX6p+IK5e/I6iXQTaCXiBedBHqJptfSdf35pJIf5QVpZrUIKRHmyqiOF5s+867LgnupFp0jheNm1zwNNpIgcRFLMstK1qaXjaInXVr0G1LQjKj49vGxdfTFtDlnFr0AVRJkFZim+GgkUtAqRamSJqOJTIo1Q2I1wOoBkepjxUtRXPSJih6twstRO+vSzjvMZS+zUHQ5kPfYkfto0c6ix5xztMUx44S2CHbFUHRi0olIUL+WpIQhMR0iThExlIghEUNin0uZV0l834AIp2OwLcS2ULaFihKSyJZC1JCfhiRN5pNiNF6fLlRWqxBpClwytpXETL6k8AdnekTdvcy3E55SSxzY7X8579r1U5f0YXOXA6UUMzNHmJk5wv79vwRAUQzodJ6spWZ56RjD9GU6nafodJ7ihRceACCKdrGw8AYWFt7AzMxRZtrXMzNz/SW/viaO4zXlporcNKM3Z8+eZTgc1u1rYa1dU3CqNDs7evDeJJI7ipUU18koOiluxefFyqhc5TIsIB+dtloLA+wkYVd8GD13A2YhRh/0oqPahsykDIsu3axDt3+W5c4r9HrLDFY6ZVqhv9JhuLKCiCMfDsiHA+D0Be1zEyXErVls4sVHmzZKJQgtXBFT5DFZFuGyGHSLgWoz0Auo9iK0j2zsM8ixZFiXYoohJutj0y467WKzPqYY1MKTFANm+gPsyqDRPmgI0fTrfgoF/WQU3alEp5cYn1q+/h8JfH+sfyRC1XVA8RQBaovQdq7MvQDsFsehslz3lf0tEdri6rHaaYZFQreo5CcZj46UEZV+fU3OhDyV7V1a9FRC1+zkjDlAZqOxKNCqqFEy3m51SqxTIj3Eqj6R7qMZYKWPdj2MDDDOi1Fc9EjyHknep12cY8YNabkh7SJl1g3Z64bMFkNaIsQiJGWKGuW46nNCPBBUzyLik5OIXEZCNJgqRBEd4jGBGtQC1ZCpRn+mYsQkiG2BbaEiL1FRFJFEdqoQnU+MNiJb4aLuq5PtJTETT9Y4eXqZZHANanEWp86xd89/ALBnz9u3YuMuOca02LnzNnbuvK1uGwxeZGn5GMtLPmLT6TxJlp3l9Omvc/r018eWj6LdXoza19OeuZ6Z9pE6v5SCE8cxBw8e5ODB8YcKpmnK0tISy8vLdep0OmP1Xq9HnuecOXNm1dOIm2ita7GZFJwqzR3wD+BbD5cWXmhWUlwnpVjJfN4sl7lkDkkdxZkBxZnpTySeRTPLbvaxG5UYzHyMPuxlR7ctKjE448hVTu6GZG7AsBgwyLoMBiv0B8v0eksMeiv0O8sMVlZqCRJxFNmQfjaEztr7Zur+sjFRJT62jVL+3JVzMa5IKPIIkRaoFk61yFSEUhHYGbB7UTMX95+9kUqGBpi8j0nLiE8+wBYj4ZkvBuxaGWLO9bHFcFX/tAiRU9MkaHQqrNeCfqw4F0EaQWp9BGgYQWoVaQTDeNSeWj+uenJzJTotJ7RlQFv6ZdlLUKuUoBkn7G1IUGuKJLXFj48KjeQRubQYTIsS1ULkJajPKEpUidIKLfq6Tdcu0tFterZFapPVp9Cs8k96Ngo0/qWpWmF0gVEFRuVY5YXV6IyIDKNSLClaUgwZyqUoyRDJwKUgGUqGKMlA/DglQzQpsaQkRZfEnaXthrRcyowbMlMM2elSWuJGkiTUIhVPSlUhJLlge6BkJFHiIpxEZOtIkY9ArSdZ4+ML0wKblKkSqAQdtYkjO0WMmqfjfB6ZKiliq4mrui3bjCZujIuNJrLKlxvjw+m8S8P2khg1/itv5fQ52sU8gyTC2iFz8z60v2f3lXk9zKWg1TpAq3WAa/a9EwDnUlZW/h9LS8fodJ6i13+Wfv8EaXqKLDvN0tJplpYeX7UeLzjXl4JzpI7etNtHsHZu1fiLIY7j+tqYtciyrBabScGp0srKCs45lpaWWFpaWvczZ2dna9GZmZmh3W7TarVot9vjaa5Na88MM60WWk+/ANwNi1JqUopOhltJJyI9Wd1PLsiwIB/24dT0F1+Cj/DMoJlhHpgHDoDyT0PWsxa9x6BaXn6IGAmQpGRuSFoMGGY9BmkpQINlVrrn6HWXGJQSJOJwecowT4Fz552n1ShMFGNsgrYJWkcoHaN0jD+nFSES4ZxFnKEo/EFHqZiCiFRFKB1B0ka1FvwyKkapC3vmiyllyGZ9TBXxKQaYfIgt+sTFkJl0gOk3IkJ1/0iItMtQUqz5BOfUeJkZRk3B0aSRYmhX9/UsnCv7mjLkhUn5esxo2TKPldCW1EdLxI1HhSqBEmF/Q4KqSFFrQpJsqlDDiMLFiGuRS0yf9kiEpEWfmJSIFMtQItLyYJ6WB3SvL42DvooYqoRUzzPUMUMdMdQxaSPHav9yU60QjS+bsm4bZU0pTs6LU0OgLClWZb4sKaaSKEnBlfLkMsSlODLfJinKDUFSjMqwDLGqU544y2i5YRmJSmmJsNAQpXVFKheSTIh6PhqnxEIZhRJnQSKcWC9SVSSp/CtyDD2xLJV1/5cZMqnqpm7PsKRix+o5FtEWTAwmQnQMNgITo0xc5hE68vXI2jFZGhcqTWxGolSNq9omBSoyquzXjWXUVCG70qNX20tiJuj2znDQLHLO9tm1+AJKCXOzr6XdPnT+ha8StI5ZWHg9CwuvH2vP8w69/gn6vRNebHrP0uufoNd7liw73RCcx1atM4730K6kpnWYONlHEu8ljvfUSev4kmx/FEUsLi6yuLi45piiKOh2u1MFpxnhqcZ1u11efPHFDW/DpORMk55Wq0X7QJv2DW3a7UXm2m2iyL8PScQLTNEpT2mVUR43KHD9HDfIkWGjPChwA18mFxD88sOCYg1HM4BB02IGmAH2jA9og95lvQDFCrHgrKNQBTkZuaRegPIeg7TLYNih1+/Q7Z+j2z1Lv99hOOyWKxOKbEiRrX267WJQ2qBN7JOOQcc++kPkT184/+tb8FGhnAhUhLIxRBGKWVC7fJvyfRBt7C5EcWjJMS5DFym6yNAu8/UqFWlZz9EuJXEZbZdheo0xzWWmrcNlmCJDuxTt8rHTa4UqpaeUpGFDcFKrGn3Qi+BsM4I0RZhSqxgmVVuBMssYs4TV0GK1JMX4g/iMCDvqA7lvi0SIGG+r26u2XCAzZaTEIBKB83khFocllXhMllKxI3GqBWqirbqOR1lSHTNUc7VA5Tom1ZaBTkhN5HMdURhbR5y8NI0u2m7Kk1EFlqJUj9wLFBlWhlhXypAboiX1sutScCniMlwpUnkZhTJkKIYYyTB00SrHkKPJMDrHSo7ROUYLWkGE33cWaIswL0Ikvt2WZUu1j/FtCFYgKoQo98tG4tu0gBIDYvChNoNyBicGwYJoXBnFKmT0V1cy1ZdaIcclS8aFK63bfBJtcdpLlpgIdATWf38xMcpGKOO/p8rGaJtgTFSKkMala7x65hKxrSVGOq8w0z7Kc2qZQ4v+epgr9a6ky4218yzM38TC/E2r+kaC8yy9no/c9PrPloJzhjQ9RZqeYmnp/66z/p0kSVNs9pKUeVWPk73E0a4L/gU+iTGmPmW0FiJCr9cbk5rBYEC/35+aBoMBaeqf7DsYDBgMBpw9e/aCtstaOz3KU0nPfJv2vqptlna7zWyrRasR/ZHM1ULTlBsZTJOeAhnkvn04KiOAA9fLoTf+AD4NxChiEmZIgImLpqs7xHcBVqMTDYmGSCFaEC045XCqoKDASU4uOYXL/OmxIiXLh2T5kDTrk2Z9hmmf4bDLcNBlMOyRZj0Kl+NcQeH6FNnakaqLQekIbWKUikHZ8g8qhUhFqFKGvPz4PoxBGQvK+LqKUSw06gZF1W/K9V74rc+qlpzcy9MqcfJ57DJaLkMPM3S/EqEqeSGqBczlGJdOEaesjjpVkjR2Ws1CWkaKhhFkFnIDK9rnuYHM+LvZqvrUVI7PrCK3VXtObnKU7oMWtBaU9k/GNkqIS0HyB/aRJMUizIqws2qjKU/j4yIRIgdxIZhhdUD3QqVKqarkypWRwkJi8ono05B4LDpVtQ2VZahiUjVbRqJiUhVRlEI1NBHDUqIGJiE1XrDQPjolCtAKrVyZxOc4DL5uKDC4Mi8wUuU5Fi/aVjKM5BjJ0ZJhKsGSDC05yuUgBbgCXI5IgYjPneQ4lyPkgEOTocm9fNFFk6OVFy9bSZjOsUpQStDGz5dVQpsJyaqkTKil1wpEmWCzUtCkFDRAnEaJpjdQfOpSfuEn2GYSM346aebcWZJWmxVzhl2LV9f1MJvJeQWn92wZvTnBYPACaXqKYfoKafoKaXoakYw8P0eenxt7tcJ0NHG8u5YdH9GpBGc3cbK3brN24aJDl0opZmdnmZ2d5cCBVQ9pmUqe56tEZy3xmWwXEfI8Z2VlhZWVlQve3iiKiON4VT61bddk2+xYPcJincYWCp0rZFggfS89XoIa5SntMiyfR5M7XO6gO76tuky2jAfRfFHlNGyZJq+tVzTu6hEwlIJUJnIKKSgkI3cZeZGSFV6QsqKUpLSSpB65Sykko5Cc3GUU0qeQnGIDT1K+WJQyoC1KGVQpNrXkiEGkqvu8HlPJkTYoPS5HijaoOUbyNJImlC1/BDTazidT4iaiRQ0JcinW5cRFiskzdJqX/ZUE5WhXoKRAS4FyBUryuuzzfKxfS44S5yNPzXEyWlehCnLjKPS4EGWrJGlcoPoaOlPHThEtDblx5GZIZoa+zYLToLT4f8SmkivBlKKlynIlUHPAroY8xTI6QFdj4kKIct+mnf+GqFKmlNNoMSjRaNEIPnriyn8fPlkKvIAVGAqJyDFkpd5kGPIyZWJHZQyZtuTEZMqSK1vmpkxlXVsyZRrlMi/LmTbkKiI3fplCab9favkSjHJopJSwAq2cFy0cRnyEy4uW81Eoyf3Wu9xHqyTDuBxnOsD/3LTv5DaTmBHOZbSX/EvvFna8jLUZUbS46rRK4MKwdp6FhZtZWLh5ar8/eC95qRm+Ukdt0vSVUnRG9TQ9DbiyvPbdSRVKGYyZw9p5rF0o83msnRuvm3lstODzeozv1zrZsAhZa5mbmxt7ls1GEBGGw+EFi0+/3yfL/A3gWZbV5UvNunK0EBPtXi1QkbJYMVjRRGK8FGGwzmDR2EJjCgW58xc8Zw5JC1xVzor6QmjJfV/VXl+fK0AmkI0elOcP2ZUcRev/YVGZNnDjoWhBDEgtST6SVEmOTxm5yylcSu5SsiKjcClZnlIUGXnuy04KnyhzcWWe43A4yXBu0Bjn++U8T2j+kZiMFDEuOuPyVNaNAWsnBCsGNVPW11tHeaDGlLfKl21KM9Lc8986rVyGFjcSoVqY8ob8FBhXEJXCpF2BKprC1FiuzJvCNC5f/rNGbas/q5IvkQKnvXA5XZCrnEIXPhnxkmRLgdIwNNBdU8YgN1JKlSPX2Zi4FdrLVVEmMXjJ1wLKSxaq3NVKUMrLli7H6LJuAKuEWINSglX+tJQVMOVpK4OXLYPvMwLW+TE2L+siKFEoNIhCiS7LPqlaxsq5FoOIHkkZFifanxbGIM7nhVi6A+ELl/Lf/gTbSmKa348sX6K1soPhNYbFxRcA2LP7rT/yqYvA+iiliKKdRNFOmH3NumOdy8mys6XEVKJTCk4pQFU9z5cQKcjzJfJ8/Yt319++qCE286uFaJUATQiSnT/v9T5KKVrlaaFdu3Zd0PZV0Z80TUnTlCzLVpUn84305fko+lC1d7vddbbkwvFzPy5A1lqMMdi2xcyZum7MeNkojcFg0Bg0GoUW7SXGabQojPNt2oHOy3qBT7lC5/iU4aNGtUyVstR4pYVyCuWA8lPPK0jVcRjOG2jaKII/EImSMjkcgnjNQRgJUVEKUFGeDijKlBcZRZkcTYHyUlUtV8sVzXo6pW0kWc36paPckWNyo8v/l0v5afap8mBZLVMlPaWPch1lVEsRr/qcqq7Q5bjVn6nWWGbU1/yc6qAtGJmUJF+2riCWRhSqKOVLHGoNYRqXLzcW7VLiUDifiwNx5Vgp81Ffc5wS/29KEJwqECU4ilrkRTmKMneqwGk/zikpxU1wWtVi1UxeuByFdr6sVouYL6tRuRzTz6a/2uZSsa0kpnk6KR2eIhruYsmm9fNhwqmkKwutLUmylyRZ++6kCueGZNkSed4p0/LqctEhz8p8yjgQRDKy7AxZdmG3JDdRKkLrBGPaaN3CmFaZt8fbdQttqryN0UmZtxrLtRtjWhjdJopbtFoJWi+g9aX5CjrnLlqIzje+er+WiNRtW43WeiRKDXmyxqCVwWiD1RqjSoFSBqMUXqUqgSrlSRTa6TIv6+WpAOPAiPbtTqGdFyRT6FKwFNoJqlCowh8yVRlnUigQUDKKO52XKshxmX+Lealyo7yWqmK87PI69zKUj8SKwj95G4dItR4vbWvVm+Md0mjPa+FbNdZNW9bXR+MFVy/n6z96ZGw98SnlR5XyMylhk2JX99kpgqbKiNd6gqaool9lCGdV22hcs339cV6U/H5SQl1X4rACMQ5FQ6acQxWVaDVTUY/rpz3gYz/ivl+b7SUxjUjMyyuvMGv38/Ls87TbHZSKWFx889ZtW+BHQuuEJNlHkuy7qOVFHEXRbcjNZGqK0PKECPlUFN1yXdWv3wu/1uVCUcqOCU8tTKsEqYU2CUavFiulY7SK/K3QKkLrmCSJaLdtXVdqruy3ZR7X4/31HWufBiiKoj79NSk9eZ5TFAVFUdTlyfxi+5pjJl9U6pyrxW3LqY4zZbBHaz1KSmO0RmmNVgqtdCOpUY6/ldUfoprlsi7+8KVQaPFJoVCiyrtWlJcr8REoJfgxRdVHKWCgitG6q/UpJtqURmPRRBhRRM2+aptKXVOs/W/nSqMWm4YgVfLkpClbbg0hGpe09evSEKxRDG79sfnayzaWG9/GifqUsU2R822+nbq8UcFTjIvThEjhb7FXDUkabOI1arDdJKbBv/3w34nbO5nb8ygAO3f+1CV7vklg+6GUrk8HXSzO5RTFCkXRx7kBRTHAuX6ZDyjcAFf0KdwQVwwoXL/My/6ij3PDieWn91WIVJ+5+cK0Hk2p0TpCqwilbd2uy2e/eAmK63qVR1FEnESr2tfO22PSNZKrqh6hta23BwzOQVE4nFPkhcMVCufchiRoswRrGpVgXXFsUpSnKWQKhVGlhKnytGHZ7ssjAWrKUFOg/LUZlGVG9UaZUtKqSJfva45prGO9sqw3TqGxVJqmy17dEMoNfc6qzzj/MltBU26qMhPSc8HtCJ1BF3hw07Z7e0lMQxa//9z3ODizj93lrdV794ZTSYEfDa0tWpfX+2wiIoJzw4bcDCZEqSlMvj4mQk15KgY4l+KkfDiY5IhUz7jI/HURLitvwSzbZXX0QiSlKLb+NNGFo1DKopQey0GjlQ/VV+3a2PLR843xlOPV5HgznhivU+X+vlqkvnbC/wIVNCIKRCNUub9oUkSVF0VS5mosOVE4B+Kqui9XeRm9xzlwTpW5T4UT/7b4ApyTseQFsKq7sVQUxaq2yf618KeTADb32oc1qQIBVxG1JKnzSU8leJNyxni+rlStJ3H4C32rsv/GjG0j9TKMt5Sf3TObGy3dXhJTPrFXRFj57vdIZ3ewe4e/6+Vqfkpv4OpCKYUx/pTQZgvTNET8tUMiOc5lIwEqZWckRNnooV+S46TxEDDJvDCtGpeVd+tkY9I0llefudH2KdLV+GvKvwXg0j6c70pGaTD6RwmqjK7lUOU1EkpVdxjpVfXJ6zDGr7eo2punFsokykvclLqfs0ablI8GbC4jNMaXplIJ37RlhLKvzIV6jJR9fkxjnPOfIVKVR8uO1V21nBfGuiyjsjgpy1KOkcY4KX+Ij7Z5vMxEWa3TXm5zuW3lv4rRPl2jvbme84+vytPbN2qOQ7e538ttJjE++/Z3P8Puf1fkt3zPP6BHXUu7fXhrty0Q2CYopcqHvsWYbXAzn4h/mp+PJq2fU15sijhGDwGbGFuui/OsS+rP9LffUuarEtPbV41fY9zY2HXGjI1fNe5Crzvw10n4/XuJJ2yzKW89vsqCL9sWkabQVLf4jdq63c09tbq9JEbgkW99nuf+1zMs3jbD/JFvAnDw4H/d4g0LBAKbhSqfSxIen7A+IpWYuIb4baQujfZR3V/46a9rYI26iFuzD5FG+0R91eeOtmdjy44uVl378y9gexr1qftkvb+/MZbGtSH1+ilfLVKXq3bq5UfrWHtsc31+f0mjfL52GuXqMy8NSlWfWX3GZH+QmJr2tc9y5un/w753PYu1PsQsxSzXXffft3jLAoFAYGvx1/no8w8MBGCDwlO1M0WWXHl6qiqX583GZMqxtLQMTH946qVgW0nMzM6XmJ09CcCwu5Mj172f177uf4S7kgKBQCAQuACUGp3y2cwXVWfZ8uatnG0mMUv//gbSJGFx/5v4b+/+cPjVEQgEAoHAq5htJTG/9IH/ve6bjAOBQCAQCLx6CKGMQCAQCAQC25IgMYFAIBAIBLYlQWICgUAgEAhsS4LEBAKBQCAQ2JYEiQkEAoFAILAtCRITCAQCgUBgWxIkJhAIBAKBwLYkSEwgEAgEAoFtSZCYQCAQCAQC25IgMYFAIBAIBLYlQWICgUAgEAhsS4LEBAKBQCAQ2JYEiQkEAoFAILAt2RZvsRYRAJaXl7d4SwKBQCAQCGyU6rhdHccvNdtCYk6fPg3Atddeu8VbEggEAoFA4EI5ffo0O3bsuOTr3RYSs7i4CMBzzz23KTshsHGWl5e59tpref7551lYWNjqzXlVE+biyiHMxZVFmI8rh6WlJa677rr6OH6p2RYSo7W/dGfHjh3hH+QVwsLCQpiLK4QwF1cOYS6uLMJ8XDlUx/FLvt5NWWsgEAgEAoHAJhMkJhAIBAKBwLZkW0hMkiR84hOfIEmSrd6UVz1hLq4cwlxcOYS5uLII83HlsNlzoWSz7nsKBAKBQCAQ2ES2RSQmEAgEAoFAYJIgMYFAIBAIBLYlQWICgUAgEAhsS4LEBAKBQCAQ2JZc8RLzZ3/2Zxw9epRWq8Wtt97KP//zP2/1Jl313HffffzET/wE8/Pz7Nu3j3e/+91897vfHRsjIvz+7/8+Bw8epN1u87M/+7M89dRTW7TFrx7uu+8+lFLce++9dVuYi8vLCy+8wHvf+152797NzMwMb3zjG3nsscfq/jAfl4c8z/m93/s9jh49Srvd5oYbbuAP/uAPcM7VY8JcbA7/9E//xC/+4i9y8OBBlFL8zd/8zVj/Rvb7cDjkgx/8IHv27GF2dpZ3vetd/PCHP7zwjZErmAcffFCiKJLPfvaz8p3vfEfuuecemZ2dlRMnTmz1pl3V/PzP/7x87nOfkyeffFKOHz8ud9xxh1x33XWysrJSj7n//vtlfn5evvjFL8oTTzwhv/qrvyoHDhyQ5eXlLdzyq5tHH31Urr/+enn9618v99xzT90e5uLycebMGTly5Ii8//3vl29+85vyzDPPyFe/+lX5/ve/X48J83F5+MM//EPZvXu3/N3f/Z0888wz8oUvfEHm5ubkj//4j+sxYS42hy9/+cvy8Y9/XL74xS8KIH/913891r+R/X7XXXfJoUOH5OGHH5bHH39c3vrWt8ob3vAGyfP8grblipaYn/zJn5S77rprrO3GG2+Uj370o1u0Ra9OTp48KYA88sgjIiLinJP9+/fL/fffX48ZDAayY8cO+cxnPrNVm3lV0+l05DWveY08/PDD8pa3vKWWmDAXl5ePfOQjcvvtt6/ZH+bj8nHHHXfIBz7wgbG2X/7lX5b3vve9IhLm4nIxKTEb2e/nzp2TKIrkwQcfrMe88MILorWWv//7v7+gz79iTyelacpjjz3GO97xjrH2d7zjHfzLv/zLFm3Vq5OlpSVg9CLOZ555hpdeemlsbpIk4S1veUuYm03it37rt7jjjjv4uZ/7ubH2MBeXly996Uvcdttt/Mqv/Ar79u3jlltu4bOf/WzdH+bj8nH77bfzD//wDzz99NMAfPvb3+Yb3/gG73znO4EwF1vFRvb7Y489RpZlY2MOHjzITTfddMFzc8W+APLUqVMURcE111wz1n7NNdfw0ksvbdFWvfoQET70oQ9x++23c9NNNwHU+3/a3Jw4ceKyb+PVzoMPPsjjjz/Ot771rVV9YS4uLz/4wQ/49Kc/zYc+9CE+9rGP8eijj/Lbv/3bJEnC+973vjAfl5GPfOQjLC0tceONN2KMoSgKPvnJT/Ke97wHCN+NrWIj+/2ll14ijmN27dq1asyFHt+vWImpUEqN1UVkVVtg87j77rv513/9V77xjW+s6gtzs/k8//zz3HPPPXzlK1+h1WqtOS7MxeXBOcdtt93GH/3RHwFwyy238NRTT/HpT3+a973vffW4MB+bz1/91V/xwAMP8Jd/+Ze87nWv4/jx49x7770cPHiQO++8sx4X5mJruJj9fjFzc8WeTtqzZw/GmFVWdvLkyVWGF9gcPvjBD/KlL32Jr33taxw+fLhu379/P0CYm8vAY489xsmTJ7n11lux1mKt5ZFHHuFP/uRPsNbW+zvMxeXhwIED/PiP//hY24/92I/x3HPPAeG7cTn58Ic/zEc/+lF+7dd+jZtvvpnf+I3f4Hd+53e47777gDAXW8VG9vv+/ftJ05SzZ8+uOWajXLESE8cxt956Kw8//PBY+8MPP8xP//RPb9FWvToQEe6++24eeugh/vEf/5GjR4+O9R89epT9+/ePzU2apjzyyCNhbi4xb3/723niiSc4fvx4nW677TZ+/dd/nePHj3PDDTeEubiMvPnNb171uIGnn36aI0eOAOG7cTnp9XpoPX4IM8bUt1iHudgaNrLfb731VqIoGhvz4osv8uSTT1743FzU5ciXieoW67/4i7+Q73znO3LvvffK7OysPPvss1u9aVc1v/mbvyk7duyQr3/96/Liiy/Wqdfr1WPuv/9+2bFjhzz00EPyxBNPyHve855w6+Jlonl3kkiYi8vJo48+KtZa+eQnPynf+9735POf/7zMzMzIAw88UI8J83F5uPPOO+XQoUP1LdYPPfSQ7NmzR373d3+3HhPmYnPodDpy7NgxOXbsmADyqU99So4dO1Y//mQj+/2uu+6Sw4cPy1e/+lV5/PHH5W1ve9vVd4u1iMif/umfypEjRySOY3nTm95U3+Yb2DyAqelzn/tcPcY5J5/4xCdk//79kiSJ/MzP/Iw88cQTW7fRryImJSbMxeXlb//2b+Wmm26SJEnkxhtvlD//8z8f6w/zcXlYXl6We+65R6677jpptVpyww03yMc//nEZDof1mDAXm8PXvva1qceIO++8U0Q2tt/7/b7cfffdsri4KO12W37hF35BnnvuuQveFiUictFxo0AgEAgEAoEt4oq9JiYQCAQCgUBgPYLEBAKBQCAQ2JYEiQkEAoFAILAtCRITCAQCgUBgWxIkJhAIBAKBwLYkSEwgEAgEAoFtSZCYQCAQCAQC25IgMYFAIBAIBLYlQWICgUAgEAhsS4LEBAKBQCAQ2JYEiQkEAoFAILAtCRITCAQCgUBgW/L/AZR/xAyzNg7OAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -115,7 +115,7 @@ "\n", "portfolio_agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", "\n", - "portfolio_agent.T_sim = portfolio_agent.T_cycle + 1\n", + "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", "# Run the simulations\n", "portfolio_agent.initialize_sim()\n", "history = portfolio_agent.simulate()" @@ -128,7 +128,7 @@ "outputs": [], "source": [ "raw_data = {\n", - " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25 - 1,\n", + " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25,\n", " \"pIncome\": portfolio_agent.history[\"pLvl\"].flatten(),\n", " \"nrmM\": portfolio_agent.history[\"mNrm\"].flatten(),\n", " \"nrmC\": portfolio_agent.history[\"cNrm\"].flatten(),\n", @@ -150,7 +150,7 @@ "outputs": [ { "data": { - "image/png": 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", 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1axcA58+f58CBA1mKkZzUrl2bqlWrsmTJEvO2JUuWULp0adq2bWvelpKSwsSJE83jtjLf52JjY/P8PIiCIWNkRK7Ur1+fqlWr8ttvvzF+/Hh+++03FEXJ0iPw4YcfMmHCBEaOHMnUqVPx9PTExsaGcePG5TgoNTe9MQaDgQ4dOhATE8M777xD1apVcXZ25urVqwwfPjzH+82rzPt4+umnsx0fzxQaGprr+2vcuDEVKlRg3LhxhIeHM3jw4Ic+7ptvvvnAHoGKFSvm+nEfZOHChQwfPpxevXrx1ltv4ePjg1arZcaMGZw/fz7b/pmF6f3u/Q933LhxdO/enRUrVrB27VomTJjAjBkz2LRpE3Xr1n3g72Jra8uxY8ce+3fKSW5yW6rcZO/fvz///fcfb731FnXq1MHFxQWj0UinTp0K5O+gIDI+ipubGx06dKBDhw7Y2dmxYMEC9uzZQ6tWrR44xudB8wo96P2je/fuODk5sXTpUpo1a8bSpUuxsbExD9Z+mAEDBjB9+nSio6NxdXXl77//ZtCgQdja3v2oHDNmDPPmzWPcuHE0bdoUd3d3NBoNAwcOLJLnQWQnhYzItSFDhjBhwgSOHj3KokWLqFSpUpYBdX/88Qdt2rThxx9/zHK72NhYSpcuna/HPHbsGGfOnGHBggVZTv9ev359jvsbjUYuXLhg7oUBOHPmDMADB6F6e3vj6uqKwWB44IR2eTVo0CCmTZtGtWrVsg2mzZTZc2RnZ/fIxw0ODubs2bPZtp8+ffqRWf744w/Kly/P8uXLs3xYZPb65FeFChV44403eOONNzh79ix16tRhzpw5LFy4MMf9nZycaNu2LZs2beLy5csEBQU99P6Dg4PZsGEDCQkJWXplTp06Zb4+L7y9vXF0dMx3O+ZlMG1wcDBHjx7FaDRm6ZXJb/bbt2+zceNGpkyZwsSJE83bc/pdcpsPcv69T506RenSpXF2ds7XfedWgwYNWLBgAdevXwfu9vDExsZm2e/+3qtHcXZ2plu3bvz+++988sknLFmyhBYtWmQ72SAnAwYMYMqUKSxbtgxfX1/i4+MZOHBgln3++OMPhg0bxpw5c8zbUlNTs+UWRUcOLYlcy+x9mThxIocPH842PkOr1Wb77+z33383j/PIj8z/Au+9X0VR+L//+78H3ubLL7/Msu+XX36JnZ2deSxPTo/Rt29fli1bluMcJlFRUXnO/eyzzzJp0qQsb3b38/HxoXXr1nz77bfmN/MHPW6XLl3YvXs3e/fuzXL9r7/++sgsObXhnj17zF3veZWcnJztcECFChVwdXUlLS3tobedNGkSiqLwzDPPkJiYmO36AwcOmE+B7dKlCwaDIcvzCfDpp5+i0Wjo3LlznnJrtVo6duzIihUriIiIMG8/efIka9eufeTtMz/Yc/OB1aVLF27cuJHlMEVGRgZffPEFLi4u5kOjeckO2Xs/PvvsszzdTyZ/f3/q1KnDggULsvw+x48fZ926dXTp0iVf93u/5OTkB77OMsc4ZR7eyhzftG3bNvM+BoOB7777Ls+PO2DAAK5du8YPP/zAkSNHHnlYKVO1atWoVasWS5YsYcmSJfj7+9OyZcss++T0PvfFF19Y7IzUJYH0yIhcCwkJoVmzZvz1118A2QqZbt268cEHHzBixAiaNWvGsWPH+PXXXx84r0NuVK1alQoVKvDmm29y9epV3NzcWLZs2QOPyzs4OLBmzRqGDRtG48aN+ffff1m1ahXjx4/H29v7gY8zc+ZMNm/eTOPGjXnuueeoXr06MTExHDx4kA0bNhATE5On3MHBwblay+mrr77iiSeeoFatWjz33HOUL1+emzdvsmvXLq5cuWKeg+ftt9/ml19+oVOnTrz66qvm068z//N/mG7durF8+XJ69+5N165dCQ8PZ+7cuVSvXj3HYuJRzpw5Q7t27ejfvz/Vq1fH1taWP//8k5s3b2b77/V+zZo146uvvuLll1+matWqWWb23bJlC3///TfTpk0DTIcI2rRpw/vvv8/FixepXbs269at46+//mLcuHFZBvbm1pQpU1izZg0tWrTg5ZdfNhcXNWrUeGQ71qlTB61Wy6xZs4iLi0On09G2bdssc6Fkev755/n2228ZPnw4Bw4coFy5cvzxxx/s3LmTzz77LNcDnjO5ubmZxyLp9XrKlCnDunXrCA8Pz9P93Oujjz6ic+fONG3alFGjRplPv3Z3dy+wdciSk5Np1qwZTZo0oVOnTgQFBREbG8uKFSvYvn07vXr1Mh+KrFGjBk2aNOG9994jJiYGT09PFi9eTEZGRp4ft0uXLri6uvLmm2+a/1HJrQEDBjBx4kQcHBwYNWpUtnFO3bp145dffsHd3Z3q1auza9cuNmzYUCAzm4t8KuKzpISV++qrrxRAadSoUbbrUlNTlTfeeEPx9/dXHB0dlebNmyu7du3Kdmpq5mmJv//+e7b7yOmUxbCwMKV9+/aKi4uLUrp0aeW5554zn1o7b948837Dhg1TnJ2dlfPnzytPPvmk4uTkpPj6+iqTJk3Kdlo4951+rSiKcvPmTWX06NFKUFCQYmdnp/j5+Snt2rVTvvvuu0e2S+bp1w+T0+nXiqIo58+fV4YOHar4+fkpdnZ2SpkyZZRu3bopf/zxR5b9jh49qrRq1UpxcHBQypQpo0ydOlX58ccfH3n6tdFoVD788EMlODhY0el0St26dZWVK1cqw4YNU4KDg837ZZ4KnNNp1fe2V3R0tDJ69GilatWqirOzs+Lu7q40btxYWbp06SPbKdOBAweUwYMHKwEBAYqdnZ1SqlQppV27dsqCBQuyPFcJCQnKa6+9Zt6vUqVKykcffaQYjcZs+UaPHp3tce4/xVZRFGXr1q1K/fr1FXt7e6V8+fLK3LlzzacYP+q233//vVK+fHnz6dqZr9P721xRTK+nESNGKKVLl1bs7e2VWrVqZXm9Kkru21xRFOXKlStK7969FQ8PD8Xd3V3p16+fcu3atWz75fb0a0VRlA0bNijNmzdXHB0dFTc3N6V79+5KWFhYln0y2+be6Qty+zh6vV75/vvvlV69eplff05OTkrdunWVjz76KNu0AOfPn1fat2+v6HQ6xdfXVxk/fryyfv36HE+/rlGjxkN/tyFDhiiA0r59+xyvz+n5VRRFOXv2rAIogLJjx45s19++fdv8vLq4uCgdO3ZUTp06le3+5PTroqNRFCsYCSeEEEIIkQMZIyOEEEIIqyWFjBBCCCGslhQyQgghhLBaUsgIIYQQwmpJISOEEEIIqyWFjBBCCCGsVrGfEM9oNHLt2jVcXV3zNMW4EEIIIdSjKAoJCQkEBARkm5jwXsW+kLl27doj13QRQgghhGW6fPkygYGBD7y+2BcymVOBX758GTc3N5XTFF96vZ5169bx5JNPYmdnp3acEkfaXz3S9uqS9ldXYbZ/fHw8QUFBj1zSo9gXMpmHk9zc3KSQKUR6vR4nJyfc3NzkzUQF0v7qkbZXl7S/uoqi/R81LEQG+wohhBDCakkhI4QQQgirJYWMEEIIIaxWsR8jI4QQJZ3BYECv16sdo1Do9XpsbW1JTU3FYDCoHafEeZz2t7OzQ6vVPnYGKWSEEKKYUhSFGzduEBsbq3aUQqMoCn5+fly+fFnmClPB47a/h4cHfn5+j/XcSSEjhBDFVGYR4+Pjg5OTU7H8oDcajSQmJuLi4vLQSdNE4chv+yuKQnJyMpGRkQD4+/vnO4MUMkIIUQwZDAZzEePl5aV2nEJjNBpJT0/HwcFBChkVPE77Ozo6AhAZGYmPj0++DzPJsy6EEMVQ5pgYJycnlZMI8WCZr8/HGcMlhYwQQhRjxfFwkig+CuL1KYWMEEIIIayWFDJCCCFKpPnz5+Ph4aF2DPGYpJARQghhUYYPH45Go+HFF1/Mdt3o0aPRaDQMHz686IPdZ8uWLWg0mmJ9ers1kEJGCFEgDEaFq7EpXI1N4XpcCjfiUomMTyVVL5OUibwLCgpi8eLFpKSkmLelpqayaNEiypYt+9j3r+YEgQaDAaPRqNrjP4w1TpwohYwQokA8/cMems/cRPOZm2g6YxNNZmyk0YcbqTphDY2mb6DvN/8xbvEhPll3mkMRt9WOKyxcvXr1CAoKYvny5eZty5cvp2zZstStWzfLvhs2bKBly5Z4eHjg5eVFt27dOH/+vPn6ixcvotFoWLJkCa1atcLBwYFff/0122NGRUXRoEEDevfuTVpaGkajkRkzZhASEoKjoyO1a9fmjz/+MN9nmzZtAChVqtRDe4kyD2H9/fffVK9eHZ1OR0REBGlpabz55puUKVMGZ2dnGjduzJYtW8y3u3TpEt27d6dUqVI4OztTo0YNVq9ebb5+69atNGrUCJ1Oh7+/P++++y4ZGRnm68uVK8dnn32WJUudOnWYPHmy+WeNRsM333xDjx49cHZ2Zvr06QD8888/NGzYEAcHB0qXLk3v3r3Nt7k3t6urK+3bt89T7oIm88gIIR5bbHI6uy7cAsBea4OCglEBo6KgKBCZkEZkQhoHLpkKmM83naNRiCcvtapA6yrecmZNEVEUhRSVesgc7bR5fp5HjhzJvHnzGDJkCAA//fQTI0aMyPKhCZCcnMy4ceOoU6cOiYmJTJw4kd69e3P48OEsc5u8++67zJkzh7p16+Lg4MDatWvN112+fJkOHTrQpEkTfvzxR7RaLdOnT2fhwoXMnTuXSpUqsW3bNp5++mm8vb154oknWLZsGX379uX06dO4ubmZ50XJSXJyMrNmzeKHH37Ay8sLHx8fXnnlFcLCwli8eDEBAQH8+eefdOrUiWPHjlGpUiVGjx5Neno627Ztw9nZmbCwMFxcXAC4evUqXbp0Yfjw4fz888+cOnWK5557DgcHhyyFSm5MnjyZmTNn8tlnn2Fra8uqVavo3bs377//Pj///DPp6elZCpF7c/v5+bF48WK6dOmSq9yFQQoZIcRjO3olDoByXk5seauNebuiKNxO1nM5JpnLt5O5HJPCyevx/Hv8OnvDY9gbHkMVX1deaFWeHrUDsNVKJ3FhStEbqD5x7aN3LARhH3TEyT5vHzlPP/007733HpcuXQJg586dLF68OFsh06NHD9zc3MxFy08//YS3tzdhYWHUrFnTvN+4cePo06dPtsc5ffo0HTp0oHfv3nz22WdoNBrS0tL48MMP2bBhA02bNgWgfPny7Nixg2+//ZZWrVrh6ekJgI+PzyMHDev1er7++mtq164NQEREBPPmzSMiIoKAgAAA3nzzTdasWcO8efP48MMPiYiIoG/fvtSqVcv8+Jm+/vprgoKC+PLLL9FoNFStWpVr167xzjvvMHHixDxNTjd48GBGjBhh/nngwIEMHDiQKVOmmLc9KLfRaGTMmDFs3bo1V7kLgxQyQojHduRyLAChgR5Ztms0Gjyd7fF0tqd20N3rrsdVZd7OiyzaE8Hpmwm8vvQIP++6xJz+tangXXj/uQnr4u3tTdeuXZk/fz6KotC1a1dKly6dbb/z58/z0UcfsXfvXqKjo83jTyIiIrIUMg0aNMh225SUFFq0aMHgwYOzHIY5d+4cycnJdOjQIcv+6enp2Q5t5Ya9vT2hoaHmn48dO4bBYKBy5cpZ9ktLSzPPxDx27Fheeukl1q1bR/v27enbt6/5Pk6ePEnTpk2z9HI1b96cxMRErly5kqdxRPe3y+HDh3nuuedy3PdxcxcGKWSEEI/tyJ0emXuLlYfxd3dkfJdqjG5TkYW7LzF363kOX46l6+fbeadTVYY1LYeNzd036KS0DLadieLkjQRuJaYRk5TOraR0biel07xiad7vWg076c15JEc7LWEfdFTtsfNj5MiRvPLKKwB89dVXOe4zaNAgypUrx/fff2/uJahZsybp6elZ9nN2ds52W51OR/v27Vm5ciVvvfUWZcqUASAxMRGAVatWmbfde5u8cnR0zFJ0JCYmotVqOXDgQLap+TMPwzz77LN07NiRVatWsW7dOmbMmMGcOXMYM2ZMrh7TxsYGRVGybMtpMO/97fKwQ2T35753rSU3N7cCyZ1XUsgIIR6LoigcuRILQO1A9zzd1t3RjtFtKtK7bhneWXaU7WejmfJPGOvDbjK+SzWOX41jXdhNdpyLJj0j57M8zkYmEpmQyucD68qhqUfQaDR5Pryjtk6dOpGeno5Go6Fjx+xF2K1btzh79izff/89rVq1AmDHjh25vn8bGxt++eUXBg8eTJs2bdiyZQsBAQFZBuVm3u/97O3tAdNZSHlVt25dDAYDkZGRtGjR4oH7BQUF8eKLL/Liiy/y3nvv8f333zNmzBiqVavGsmXLUBTFXCDt3LkTV1dXAgMDAVOP1vXr1833FR8fT3h4+COzhYaGsnHjxiyHmx6U22g0Eh8fn+XQ3sNyFwbrekULISzOjfhUohLS0NpoqBGQt0ImU4CHIz+PbMTC3Zf4cPUp/jt/i25fZP0wCvZyolkFL7xdHfBytsfLxZ7E1Awm/nWC1cduoLU5wqf9a0sxU8xotVpOnjxp/v5+pUqVwtPTk++//54yZcoQERHBu+++m+fH+PXXXxk0aBBt27Zly5Yt+Pn58eabb/Laa69hNBp54okniIuLY+fOnbi5uTFs2DCCg4PRaDSsXLmSLl264OjomOtBrZUrV2bIkCEMHTrUPAA5KiqKjRs3EhoaSteuXRk3bhydO3emcuXK3L59m82bN1OtWjUAXn75ZT777DPGjBnDK6+8wunTp5k0aRKvv/66uaBo27Yt8+fPp3v37nh4eDBx4sRcLcw4adIk2rVrR4UKFRg4cCAZGRmsXr2ad955J1vu2rVrc/HiRfbs2UPt2rUfmbswSCEjhHgsmeNjKvu64mifv8MHYOoteKZpOZ6o5M3bfxxh38Xb1A5058kafnSo7kslH5ccz3op7aLjpV8P8M+Ra2g1MKd/HbQ2chZUcZJ5yCInNjY2/Pjjj4wfP56aNWtSpUoVPv/8c1q3bp2nx7C1teW3335jwIAB5mJm6tSpeHt7M2PGDC5cuICHhwf16tVj/PjxAJQpU4YpU6bw7rvvMmLECIYOHcr8+fNz/Zjz5s1j2rRpvPHGG1y9epXSpUvTpEkTunXrBph6ekaPHs2VK1dwc3OjU6dOfPrpp+bHXr16NW+99Ra1a9fG09OTUaNG8b///c98/++99x7h4eF069YNd3d3pk6dmqsemdatW/P7778zdepUZs6ciZubGy1btnxgbi8vL5o0aUL37t0fmbswaJT7D6AVM/Hx8bi7uxMXF/fQPwbxePR6PatXr6ZLly7Y2dmpHafEUbP9Z605xTdbzjOoURAz+hTcgL70DCP2trnrXVl74gajfz1IhlGhT70yTOxW3TyO5lZiOql6A0/W8C2UwyqW+tpPTU0lPDyckJAQHBwc1I5TaB50aEMUjcdt/4e9TnP7+S09MkKIx/KgM5YeV26LGICONfz4fFBdxvx2iOUHr7L84NVs+zSv6MW84Y3ydL9CCMsnf9FCiHwzGhWOZZ6xVMCFTF51qeXPZwPq4Hzn8JaLzpZgLyfqlvXAyV7LznO3eHf50WxncQghrJv0yAgh8u1CdBIJaRk42NlQ2Vf9+V+61w6gQ3VfABzuOd138+lInl2wn+UHrxJYyonXO1R+0F0IIayM9MgIIfLt6J3TrmsGuFvM2UIOdtosRQxAmyo+TOtlmhjt841nWbrvshrRhBCFwDLeeYQQVqmwxscUhkGNyvJKm4oAvPfnMbaeiVI5kRCiIEghI4TIt7sz+uZv/pii9saTleldtwwGo8LLCw+w8eRNtSMJIR6TFDJCiHxJzzASdi0eUH+gb25pNBpm9Q2leUUvktINjFqwn0l/HSdVpRWhhRCPTwoZIUS+nL6RQLrBiLujHcFeTmrHyTV7Wxt+Gt6QUU+EALBg1yV6frmT0zcSVE4mhMgPKWSEEPly+M5A39BA9xxn3LVkOlstE7pVZ/6IhpR2sef0zQS6f7mDhbsvqR1NCJFHUsgIIfLl6J2BvnVyueK1JWpdxYd/X21J6yrepGcY+d+K4/x1OPtkekJYCo1Gw4oVK9SOYVGkkBFC5MvROwN9reGMpYfxdtUxb3hDnmthOtT03vJjnIuUw0yW4MaNG4wZM4by5cuj0+kICgqie/fubNy4Ue1ohW7y5MnUqVMn2/br16/TuXPnog9kwaSQEULkWVJaBmfvfNjXDrSOM5YeRqPR8G7najSr4EVyuoEXFx4kKS1D7Vgl2sWLF6lfvz6bNm3io48+4tixY6xZs4Y2bdowevRoteOpxs/PD51Op3YMiyKFjBAiz45fjcOogL+7Az5uxWNBQq2Nhv8bWBcfVx3nIhN5/89jspyBil5++WU0Gg179+6lb9++VK5cmRo1avD666+ze/duACIiIujVqxeBgYF4eHjQv39/bt68e0p9Zq/GL7/8Qrly5XB3d2fgwIEkJNztcfvjjz+oVasWjo6OeHl50b59e5KSkgDTKtDjxo3LkqtXr14MHz7c/HO5cuWYNm0aQ4cOxcXFheDgYP7++2+ioqLo2bMnLi4uhIaGsn//fvNt5s+fj4eHBytWrKBSpUo4ODjQsWNHLl++bL5+ypQpHDlyBI1Gg0ajMa+qff+hpWPHjtG2bVtz/ueff57ExETz9cOHD6dXr158/PHH+Pv74+XlxejRo9Hr9Y/1/FgSVQuZbdu20b17dwICAnI87qcoChMnTsTf3x9HR0fat2/P2bNn1QkrhDA7cmegr7Wcdp1b3q46vhxcD62NhhWHr7Fob4TakQqWokB6kjqXPBSFMTExrFmzhtGjR+Ps7Jzteg8PD4xGIz179uT27dusXLmStWvXcuHCBQYMGJBl3/Pnz7NixQpWrlzJypUr2bp1KzNnzgRMh2kGDRrEyJEjOXnyJFu2bKFPnz55LmA//fRTmjdvzqFDh+jatSvPPPMMQ4cO5emnn+bgwYNUqFCBoUOHZrnf5ORkpk+fzs8//8zOnTuJjY1l4MCBAAwYMIA33niDGjVqcP36da5fv57t9wJISkqiY8eOlCpVin379vH777+zYcMGXnnllSz7bd68mfPnz7N582YWLFjA/PnzzYVRcaDqWktJSUnUrl2bkSNH0qdPn2zXz549m88//5wFCxYQEhLChAkT6NixI2FhYcV6WXohLNnxq3Es3mv6zzHUSibCy4tGIZ683bEKM/49xZS/wwgt40GtYnD4DAB9MnwYoM5jj78G9tmLkpycO3cORVGoWrXqA/fZuHEjx44d4/z587i7u+Pm5sbPP/9MjRo12LdvHw0bNgTAaDQyf/58XF1dAXjmmWfYuHEj06dP5/r162RkZNCnTx+Cg4MBqFWrVp5/tS5duvDCCy8AMHHiRL755hsaNmxIv379AHjnnXdo2rQpN2/exM/PDwC9Xs+XX35J48aNAViwYAHVqlVj7969NGrUCBcXF2xtbc3752TRokWkpqby888/mwu+L7/8ku7duzNr1ix8fU3rjpUqVYovv/wSrVZL1apV6dq1Kxs3buS5557L8+9qiVTtkencuTPTpk2jd+/e2a5TFIXPPvuM//3vf/Ts2ZPQ0FB+/vlnrl27JiO2hVBBqt7A7DWn6PnVTi5EJ1HKyY7uoSp9KBay51uWp0N1X9INRl5ceIDIhFS1I5UouekROXnyJEFBQQQFBZm3Va9eHQ8PD06ePGneVq5cOXMRA+Dv709kZCQAtWvXpl27dtSqVYt+/frx/fffc/v27TznDQ0NNX+fWTzcWxBlbst8XABbW1tzsQVQtWrVbNkf5eTJk9SuXTtLr1Xz5s0xGo2cPn3avK1GjRpotXfXH7u3DYoDi139Ojw8nBs3btC+fXvzNnd3dxo3bsyuXbvMXXD3S0tLIy0tzfxzfLxp5lG9Xl+sjglamsy2lTZWR2G3/4FLtxm/4gQXopMB6FzDl4ndqlLaxa7YPucze1Xn7M0ELt5KZtT8fSwc2QAn++xvmZb62tfr9SiKgtFoxGg0mjZqHeDdK+oE0jpAZo5HqFChAhqNhpMnT9KzZ88c98ksdu79mvl7Zv7OiqJgZ2d39/e/I/N6jUbD2rVr+e+//1i/fj1ffPEF77//Prt27SIkJAQbG5us7Qekp6dneSwwFSX3P4ZWqzVvy8yYkZGR5f7uv8392fOzT05tkFO++3+v/Mqp/fMiM6Ner89SbEHu/6YstpC5ceMGcLeSzeTr62u+LiczZsxgypQp2bavW7cOJyfrmX3UWq1fv17tCCVaYbT/5msa/rpkg4IGNzuFp0KM1Ha7yt5txX++laeD4NM4LceuxjPkyw2MqmLE5gFz/1naaz/zsERiYiLp6elqx4HU3J/SbmtrS9u2bfnqq68YNmxYtnEycXFxlC1blsuXL3Py5EkCAwNJSEjg1KlTxMbGEhwcTHx8PGlpaRgMBvM/tACpqakYjcYs22rVqkWtWrV49dVXCQ0NZfHixYwePRoPDw8uX75s3tdgMHDs2DFatGhh3mY0GklNTc1yfwApKSnmbZmDb5OSkoiPjyc1NZWMjAy2bt1K/fr1ATh79iyxsbGULVuW+Ph4jEYj6enp2e733vsuV64c8+fP5/r16+Y2Wr9+PTY2NgQEBBAfH49erycjIyPL/aSnp2fb9rjuHUCdF+np6aSkpLBt2zYyMrKeKZicnJyr+7DYQia/3nvvPV5//XXzz/Hx8QQFBfHkk0/i5uamYrLiTa/Xs379ejp06ICdnZ3acUqcwmr/77aHs+KSaYB977oBvN+5Cu6OJev5rdUwlmfm7ef4bThEOSZ0yTpuw1Jf+6mpqVy+fBkXFxerHFM4d+5cWrRowZNPPsnkyZMJDQ0lIyODDRs2MHfuXI4fP06tWrV46aWXmDZtGvb29owZM4ZWrVrRqlUrAHQ6HVqtNst7v4ODAzY2Nri5ubFnzx42bdpEhw4d8PHxYc+ePURHR1OnTh3c3Nzo0KEDb775Jtu3b6dChQp8+umnxMfHY2dnZ75PGxsbHBwcsn2+ODo6mre5uLgA4OzsjJubGw4ODtjZ2TF+/Hg+++wzbG1tGTt2LE2aNKFNmzYAVKlShYiICC5cuEBgYCCurq7m064z73vUqFHMmjWLsWPHMmnSJKKionjvvfd4+umnqVjRtNK7nZ0dtra2WfLZ29tn25ZfiqKQkJCAq6trvmb4Tk1NxdHRkZYtW2Z7nea20LLYQiZzgNPNmzfx9/c3b79582aOkwRl0ul0OZ5jb2dnZ1FvMsWVtLO6CrL9v9x0lo/XmYqYce0rMa595QK5X2vTuII3n/avw+hFB/l5dwTlSrsw8s46TfeytNe+wWBAo9FgY2ODjY31zbRRsWJFDh48yPTp03nrrbe4fv063t7e1K9fn2+++QatVstff/3FK6+8QteuXbGxsaFTp0588cUX5t8384P13t//3m0eHh5s376d//u//yM+Pp7g4GDmzJlD165dAXj22Wc5duwYw4cPx9bWltdee402bdqY2/Xe+7y/je9t93u/Zl6cnJx45513ePrpp7l69SotWrTgxx9/NO/br18/VqxYQbt27YiNjWXevHnm074z78PFxYW1a9fy6quv0rhxY5ycnOjbty+ffPJJljbIKe/97ZJfmYeTcmqD3LCxsUGj0eT495PrvyfFQgDKn3/+af7ZaDQqfn5+yscff2zeFhcXp+h0OuW3337L9f3GxcUpgBIXF1eQccV90tPTlRUrVijp6elqRymRCrr9P11/Wgl+Z6US/M5K5fMNZwrkPq3d3C3nlOB3Virl3l2pLPgvXNFnGBRFsdzXfkpKihIWFqakpKSoHaVQGQwG5fbt24rBYFA7Sq7NmzdPcXd3VztGgXjc9n/Y6zS3n9+q9sgkJiZy7tw588/h4eEcPnwYT09PypYty7hx45g2bRqVKlUyn34dEBBAr1691AstRDGmKAqfrj/D55tMf5dvd6rCy60rqpzKMjzfsjwRMcn8uieCiX+d4Mcd4YxpW4muNbzVjiZEiaZqIbN//37z8UDAPLZl2LBhzJ8/n7fffpukpCSef/55YmNjeeKJJ1izZo1VHu8Vwhr8feSauYgZ36Uqz7esoHIiy6HRaPigZ01CSjvzzZbzXLqVzJu/H+HLTU48UUrDkwYjFnRkSYgSQ6MoxXsO7vj4eNzd3YmLi5PBvoVIr9ezevVqunTpYlHjBEqKgmj/2OR02n+ylejEdMa0rcgbT1Yp4JTFR3J6Bj/vusS3W89zO9l0imighwOjWpRnQMOgHE/TLmqpqamEh4cTEhJSrP/5yzwDyc3NzSrHAlm7x23/h71Oc/v5Lc+6EAKAWWtOEZ2YTkUfF8a0raR2HIvmZG/Li60qsOOdtrzZoRLOtgpXYlOZ8k8YTWds4uO1p4lKSHv0HQkhHpsUMkII9l+M4bc7yw582LsW9rby1pAbzjpbXmgZwuR6BqZ0r0Y5LyfiUvR8ufkcrT/azPGrcWpHlIUvhUUriNenvFsJUcLpDUbe//M4AAMaBNEoxFPlRNbHXguDGwWx8Y3WzH26HtX83UhKNzB1ZZhqhUTmIcbcTiomhBoyX5+PMyRB/QO5QghVfb/9AqdvJuDpbM+7nR+8SJ94NK2Nhk41/akV6EGbj7ewJzyGTaciaVfN99E3LugsWi0eHh7mNXWcnJzyNWGZpcucATc1NVXGyKggv+2vKArJyclERkbi4eGRbXmCvJBCRogSLOJWMp9vNE16936XapRytlc5UfFQxsORkc1DmLv1PDP+PUWryt7Yaov+QzZzYtHitEDg/RRFISUlBUdHx2JZqFm6x21/Dw+Ph67wnRtSyAhRQhmNCv/76zipeiNNy3vRp14ZtSMVKy+1rsDifRGci0zk9wNXGNSobJFn0Gg0+Pv74+PjY3GLWhYUvV7Ptm3baNmypZwxqYLHaX87O7vH6onJJIWMECVQZhGz7UwU9lobpvWuKf/NFjB3RzvGtK3E1JVhfLL+DD1qB+CsU+ctV6vVFsgHhiXSarVkZGSY1y8SRcsS2l8OKApRwiiKwsS/j7NoTwQaDcx6qhYVvF3UjlUsPdMkmLKeTkQlpPHD9nC14whRLEkhI0QJoigKk/8+wcLdpiLmo6dq07tuoNqxii17Wxve6miaWPDbbeeJTEhVOZEQxY8UMkKUEIqi8MHKMBbsugTArD6hPFVfipjC1i3Un9qB7iSnG/i/DWfVjiNEsSOFjBAlQKrewIS/jjNv50UAZvapRf+GQeqGKiE0Gg3ju1QD4Le9Eew8F61yIiGKFylkhCjmjl+No8eXO1i4OwIwzdw7UIUzaEqyxuW96FsvEKMCL/96kPDoJLUjCVFsSCEjRDGVYTDyxcaz9PpqJ2duJlLaxZ7vhzZgcGMpYtQwvXdN6gR5EJeiZ9SCfcSlFM/ToYUoalLICFEMXbqVxFNzdzFn/RkyjAqdavixdlxLOlQv+hlmhYmDnZbvhtbH392BC1FJjPntEBkGo9qxhLB6UsgIUcxcjkmm/7e7OHw5FlcHWz4dUJtvnq6Hl4tO7Wglno+rA98PbYCDnQ3bzkTx4epTakcSwupJISNEMXIzPpUhP+zhZnwalX1dWDuuJb3rBspkdxakZhl35vSrA8BPO8NZsi9C3UBCWDkpZIQoJpL0MGLBASJikinr6cTCUY0J8HBUO5bIQddQf8a1rwTApL9PcDU2ReVEQlgvKWSEKAYSUjP45qSWs5FJ+Lk58OuzjfFxc1A7lniIV9tVolGIJ6l6I9NXhakdRwirJYWMEFYuVW/ghV8PcTlJQyknOxY+24ggTye1Y4lH0Gg0TOlRA62NhtXHbrDjrMwvI0R+SCEjhJWb8s8J9l28jYNWYd6w+lT0cVU7ksilav5uPNMkGIBJfx8nPUPOYhIir6SQEcKKbT0TxW97LwMwsoqRGgFuKicSefVah8qUdrHnfFQS8/+ThSWFyCspZISwUnEpet754ygAQ5uUpYq7onIikR/ujna806kqAP+34Sw342VhSSHyQgoZIazUB/+EcSM+lZDSzrzZoZLaccRj6FsvkLplPUhKN/Dh6pNqxxHCqkghI4QV2hB2k2UHr6DRwMf9QnG016odSTwGGxsNH/SoiUYDfx2+xp4Lt9SOJITVkEJGCCtzOymd9/48BsBzLcpTP9hT5USiINQKdGfwncU8p64KQ1HkUKEQuSGFjBBWZtLfJ4hKSKOijwuvd6isdhxRgF7vUBlney3Hr8az9sRNteMIYRWkkBHCiqw+dp2/j1xDa6NhTr/aONjJIaXixMtFx8gnQgD4ZP1pDEbplRHiUaSQEcJKRManMv7OIaWXWlWgdpCHuoFEoXi2RXncHGw5czORlUevqR1HCIsnhYwQVkBRFN5edpTYZD01AtwY207OUiqu3B3teKFVBQA+XX8GvUEmyRPiYaSQEcIK/Longi2no7C3teGzAXWwt5U/3eJseLNyeDnbc/FWMssPXlE7jhAWTd4NhbBw4dFJTF9lmlvk7Y5VqOQrSxAUd846W15qbeqV+XzjOdIyDConEsJySSEjhAXLMBh5felhUvQGmpb3YmTzELUjiSLydJNg/NwcuBqbwuI7y1AIIbKTQkYICzZ363kORcTiqrPl4/61sbHRqB1JFBEHOy2vtK0IwJebz5GSLr0yQuREChkhLNTC3Zf4ZP0ZAKb0rEEZD0eVE4mi1r9BEIGlHIlKSGOeLCgpRI6kkBHCwhiNCjP/PcX/VhzHqMDgxmXpXbeM2rGECuxtbcyTHn69+TxRCWkqJxLC8kghI4QFScsw8OqSw8zdeh4wzfQ6vVdNNBo5pFRS9apThtBAdxLTMvh0wxm14whhcaSQEcJCxCXreebHvfxz5Bq2Nho+7lebse0qSRFTwtnYaPhf1+oALN4bwakb8SonEsKySCEjhAXIMBgZ+tMe9obH4KqzZf6IRjxVP1DtWMJCNArxpHNNP4wKTF91UhaUFOIeUsgIYQHm7bzIkStxuDva8ftLTXmiUmm1IwkL827nqthrbdh+Npotp6PUjiOExZBCRgiVXY5JNp+dNL5LVar6uamcSFiiYC9nhjcvB8C0VWGydIEQd0ghI4SKFEVh4l/HSdEbaBTiSf8GQWpHEhZsdJuKeDrbcz4qid/2RqgdRwiLIIWMECpafewGm09HYafV8GHvWjKwVzyUu6Mdr7U3LRj66fozxKXoVU4khPqkkBFCJXEpeib/cwKAl1pXpKKPi8qJhDUY1KgslXxcuJ2s5/82nFU7jhCqk0JGCJV8tPYUUQlplC/tzMt3FggU4lFstTZM6GY6HfvnXRc5F5mgciIh1CWFjBAqOHDpNr/uMY1xmN67Fg52WpUTCWvSsrI37av5kmFUmPJPmJyOLUo0KWSEKGKpegNv/3EERYF+9QNpWsFL7UjCCk3oVs18OvaGk5FqxxFCNVLICFHEPlp7mvNRSXi76hjfpZracYSVCvZyZlSLEACmrgwjVS+rY4uSSQoZIYrQ7gu3+GmnaRXj2X1DKeVsr3IiYc1Gt6mIj6uOiJhkftwhq2OLkkkKGSGKSGJaBm/+bjqkNLBhEG2q+qgdSVg5F50t73auCsBXm89xIy5V5URCFD0pZIQoItNXhXHldgqBpRz5352zToR4XL3qlKFuWQ+S0w3MWnNK7ThCFDkpZIQoAptPRfLb3stoNPBxv9q46GzVjiSKCRsbDZO71wDgz0NXZcZfUeJIISNEIYtNTuedZUcBGNk8hCbl5SwlUbBqB3kwpm1FAN7/8xjrTtxQOZEQRUcKGSEK2WcbzhKZkEYFb2fe6lhF7TiimHq9Q2X6NwjEqMCY3w6x72KM2pGEKBJSyAhRiCLjU81d/ZN71JCJ70Sh0WhM63W1q+pDWoaRUfP3cfqGzPorij8pZIQoRN9tu0BahpF6ZT14omJpteOIYs5Wa8OXg+tRr6wH8akZDPtpL1djU9SOJUShkkJGiEISnZjGwj2XABjbrpKsbC2KhKO9lp+GN6Sijws34lN54Zf9GI2yhIEoviy6kDEYDEyYMIGQkBAcHR2pUKECU6dOlXVFhFX4fvsFUvVGage606qyt9pxRAni4WTPzyMb4aqz5fjVeNbK4F9RjFl0ITNr1iy++eYbvvzyS06ePMmsWbOYPXs2X3zxhdrRhHiomKR0ftll6o15tb30xoiiF+DhyIjm5QD4v41npVdGFFsWXcj8999/9OzZk65du1KuXDmeeuopnnzySfbu3at2NCEe6scdF0hON1CzjBttqsgMvkIdI58IwVVny6kbCdIrI4oti56Vq1mzZnz33XecOXOGypUrc+TIEXbs2MEnn3zywNukpaWRlpZm/jk+Ph4AvV6PXq8v9MwlVWbbShtDbLKe+f9dBGB0q/JkZGQU+mNK+6vHktve2U7D0KZl+WrLBT7bcIa2lb2wsSlevYOW3P4lQWG2f27vU6NY8IATo9HI+PHjmT17NlqtFoPBwPTp03nvvfceeJvJkyczZcqUbNsXLVqEk5NTYcYVAoDVl21Ye8WGMk4Kb4UakKNKQk1JevjgkJZUg4YRlQ3U8bLYt3whskhOTmbw4MHExcXh5ub2wP0supBZvHgxb731Fh999BE1atTg8OHDjBs3jk8++YRhw4bleJucemSCgoKIjo5+aEOIx6PX61m/fj0dOnTAzs5O7TiqiU/R0/qT7SSkZvDFwNp0quFbJI8r7a8ea2j7zzae46stF6ji68LfLzctVr0y1tD+xVlhtn98fDylS5d+ZCFj0YeW3nrrLd59910GDhwIQK1atbh06RIzZsx4YCGj0+nQ6XTZttvZ2cmLvAiU9Hb+bsM5ElIzqOzrQtfQMkX+gVHS219Nltz2z7WswM+7Ijh9M5FNZ27RuZa/2pEKnCW3f0lQGO2f2/uz6MG+ycnJ2NhkjajVajEajSolEuLBLsckM2/HRQDe7Vy1WP3XK6ybh5O9nMEkii2LLmS6d+/O9OnTWbVqFRcvXuTPP//kk08+oXfv3mpHEyKb2WtPk24w0ryil5ypJCyOnMEkiiuLLmS++OILnnrqKV5++WWqVavGm2++yQsvvMDUqVPVjiZEFgcjbvPPkWtoNPB+l+oyb4ywOPf2yny64QwG6ZURxYRFFzKurq589tlnXLp0iZSUFM6fP8+0adOwt7dXO5oQZoqiMG1lGABP1QukeoAMKheWadQT5XF1sOXMzURWHr2mdhwhCoRFFzJCWIPVx25wMCIWRzstb3asonYcIR7I3cmO51uUB+CzDWfJMMh4Q2H9pJAR4jGkZRiYueYkAC+0Ko+vm4PKiYR4uBFPhFDKyY7w6CSWH7qqdhwhHpsUMkI8hgX/XeRyTAq+bjqeb1le7ThCPJKLzpaXWlcA4P82nCU9Q3plhHWTQkaIfLoRl8oXm84B8MaTVXCyt+hpmYQwe6ZJOXxcdVyNTWHJ/stqxxHisUghI0Q+GI0Kb/1xhITUDGoHutO3XqDakYTINUd7La+0rQjAl5vOkqo3qJxIiPyTQkaIfPhl9yW2n43Gwc6GTwbUQSuT3wkrM6BhEGU8HLkZn8bC3ZfUjiNEvkkhI0QenYtM5MPVpgG+47tUo4K3i8qJhMg7na2Wse1MvTLfbDlPUlrhr9IuRGGQQkaIPNAbjLy25DBpGUZaVvbmmSbBakcSIt/61AuknJcTt5LS+XWP9MoI65SnQiYpKYmJEydSs2ZNXFxccHV1JTQ0lA8++IDk5OTCyiiExfhi41mOXY3D3dGOj54KlRl8hVWz09rwQivTGUyL915GUWS2X2F9cl3IpKen06pVK2bPnk2lSpUYM2YMo0ePJiQkhOnTp9OuXTv0en1hZhVCVQcu3ebLzaazlD7sXUvmjBHFQvfaATjZa7kQncT+S7fVjiNEnuX6fNFvvvmGK1eucOTIEapUyTp76alTp2jdujVz585lzJgxBR5SCLWl6g28+fsRjAr0rluGrqH+akcSokC46GzpFurP0v1XWLz3Mg3LeaodSYg8yXWPzPLly5kwYUK2IgagatWqvP/++/zxxx8FGk4ISzF363nCo5PwcdUxuUcNteMIUaAGNCwLwOpj14lPlZ51YV1yXciEhYXRunXrB17fpk0bwsLCCiKTEBblQlQiX28+D8DE7tVxd7RTOZEQBateWQ8q+riQojfwzxFZTFJYl1wXMrGxsXh5eT3wei8vL+Li4goklBCWQlEUJvx1nHSDkVaVvelaSw4pieJHo9EwsGEQAEv3yUy/wrrkupAxGo1otdoH35GNDQaDzA4pipe/j1xj57lb6Gxt+KBnDTlLSRRbveuWwU6r4ciVOE5ej1c7jhC5luvBvoqi0K5dO2xtc75JRoZMpiSKl7hkPVNXmg6XjmlbkWAvZ5UTCVF4vFx0dKjuy+pjN1iy77KMBRNWI9eFzKRJkx65T9++fR8rjBCWZPbaU0QnplPB25nnZGVrUQL0bxDE6mM3+PPQVd7tXBUHuwf3wgthKQq0kBGiuDgYcZtFeyMAmN67FjpbeUMXxV+LSt4EuDtwLS6VdWE36VE7QO1IQjzSYy9RsHXrVlavXs3t2zKRkigeDEaFCSuOoyjQt14gTco/eJC7EMWJ1kbDUw1Mg36X7ItQOY0QuZPrQmbWrFlMmDDB/LOiKHTq1Ik2bdrQrVs3qlWrxokTJwolpBBFadGeS5y4Fo+bgy3vdamqdhwhilS/+oFoNLDz3C0u3UpSO44Qj5TrQmbJkiXUrFnT/PMff/zBtm3b2L59O9HR0TRo0IApU6YUSkghisqtxDQ+WnsagDc7VqG0i07lREIUrSBPJ1pW8gbgh+3hKqcR4tFyXciEh4cTGhpq/nn16tU89dRTNG/eHE9PT/73v/+xa9euQgkpRFGZveY08akZVPd3Y0hjWdlalEwv3llIcsn+y0QmpKqcRoiHy3Uhk5GRgU5397/TXbt20axZM/PPAQEBREdHF2w6IYrQ4cuxLNlvmgzsg5410NrInDGiZGpS3pN6ZT1IzzDy4w7plRGWLdeFTIUKFdi2bRsAERERnDlzhpYtW5qvv3LlykNn/hXCkhmMChP/Og5An3plaCAL54kSTKPRMLpNRQAW7rpEbHK6yomEeLBcFzKjR4/mlVdeYdSoUXTu3JmmTZtSvXp18/WbNm2ibt26hRJSiMK2ZN9ljl6Jw1Vny3udq6kdRwjVta3qQ1U/V5LSDSz475LacYR4oFwXMs899xyff/45MTExtGzZkmXLlmW5/tq1a4wcObLAAwpR2G4npTN77SkAXutQGW9XGeArxL29MvP+CycpTWZvF5Yp1xPiAYwcOfKBxcrXX39dIIGEKGpfbT5HbLKeqn6uDG0qA3yFyNSllj9z1p3m4q1kftsbwbMtZIZrYXlyXcgcPXo0x+3u7u6ULVtWFtMTVikqIY2Fe0zd5u92roqt9rHniBSi2NDaaHipdQXeWXaM77Zd4JmmwTLLtbA4uS5k6tSpg0ajQVGULNs1Gg0ODg6MGzeODz744KErZAthab7ffoFUvZHaQR60quytdhwhLE7vuoF8tuEs1+NS+ePAFZmWQFicXBcy4eE5n4IXGxvLgQMHmDBhAqVKleLNN98ssHBCFKboxDR+2WXqjRnXrpL0KgqRA3tbG55rUZ4PVoYxd+t5BjQIkp5LYVFyXcgEB+dchQcHB1O7dm3c3NyYMmWKFDLCany//QIpegOhge60riK9MUI8yKBGZflq8zkux6Tw1+Fr9K0fqHYkIcwKrKyuX7/+A3tthLA0MUnp5t6YV6U3RoiHcrTXmgf6frn5HBkGo8qJhLirwAqZGzdu4O0t/9UK6/D99gskpxuoVcadtlV91I4jhMUb2jSYUk52hEcnsfLodbXjCGFWIIVMVFQUEyZMoE2bNgVxd0IUqpikdBb8dxGAsdIbI0SuOOtszb0yX2w6i8GoPOIWQhSNXI+RqVu3bo5v+HFxcVy5coUqVaqwcOHCAg0nRGH44U5vTI0AN9pXk94YIXJraNNgvtt2gfNRSaw6dp0etQPUjiRE7guZXr165bjdzc2NKlWq0LFjRzn1Wli8W4lp0hsjRD65Otgx6okQPll/hi82nqVbLX9sZHFVobJcFzKTJk0qzBxCFDqjUeG1pUdISjdQ3d+NJ6v7qh1JCKszrFk5vt9+gbORifx7/AZdQ/3VjiRKOJkMQJQY32w9z7YzUehsbZjTv7b0xgiRD+6OdoxoHgKYxsoYZayMUJkUMqJE2H3hFnPWnQbgg541qObvpnIiIazXqOYhuOhsOXUjgXVhN9WOI0o4KWREsReVkMbY3w5hVKBPvTL0bxCkdiQhrJq7kx3Dm5UD4PONZ7MtXSNEUcpVIRMfH1/YOYQoFAajwrglh4hMSKOSjwvTetWUQ0pCFIBRT4TgZK8l7Ho8W85EqR1HlGC5KmRKlSpFZGQkAG3btiU2NrYwMwlRYL7YdJad527haKfl6yH1cLLP9fh2IcRDlHK2Z0jjsgB8vfmcymlESZarQsbFxYVbt24BsGXLFvR6faGGEqIgHL8ax+cbzwIwvXdNKvm6qpxIiOLl2RblsdfasO/ibfaGx6gdR5RQufr3tH379rRp04Zq1aoB0Lt3b+zt7XPcd9OmTQWXToh8MhgV3v/zGEYFuob606eeLHInREHzdXOgb/1AftsbwddbztEopJHakUQJlKtCZuHChSxYsIDz58+zdetWatSogZOTU2FnEyLfftsbwZErcbjobJnYrbracYQotl5sVZ4l+yLYcjqK41fjqFnGXe1IooTJVSHj6OjIiy++CMD+/fuZNWsWHh4ehZlLiHyLSkhj9ppTALzxZGV83RxUTiRE8RXs5Uz32gH8dfga32w5z1dD6qkdSZQweT79evPmzeYiRlEUOe1OWJwZ/54kPjWDGgFuPNMkWO04QhR7L7WuAMDq49e5EJWochpR0uRrHpmff/6ZWrVq4ejoiKOjI6Ghofzyyy8FnU2IPNt94RbLD15Fo4HpvWthq5WpkoQobFX9TAuwKgrM3Xpe7TiihMnzu/wnn3zCSy+9RJcuXVi6dClLly6lU6dOvPjii3z66aeFkVGIXEnPMPK/FccBGNyoLHWCPNQNJEQJ8nKbigAsP3iVq7EpKqcRJUmeJ9X44osv+Oabbxg6dKh5W48ePahRowaTJ0/mtddeK9CAQuTWDzsucC4ykdIu9rzdsaracYQoUeqVLUXT8l7sunCL77ddYHKPGmpHEiVEnntkrl+/TrNmzbJtb9asGdevXy+QUELk1fGrcXy23jRnzPgu1XB3slM5kRAlz+g7vTK/7Y0gMj5V5TSipMhzIVOxYkWWLl2abfuSJUuoVKlSgYQSIi8SUvW8sugg6QYjHar70rtuGbUjCVEiNa/oRb2yHqRlGPlu2wW144gSIs+HlqZMmcKAAQPYtm0bzZs3B2Dnzp1s3LgxxwJHiMKkKArj/zzOxVvJlPFw5KOnQmUtJSFUotFoGNuuEsPn7WPhnku80KoC3q46tWOJYi7PPTJ9+/Zlz549lC5dmhUrVrBixQpKly7N3r176d27d2FkFOKBFu+7zD9HrmFro+GLwXXxcMp5xmkhRNFoVdmb2kEepOqN/LBdemVE4cvXCnr169dn4cKFBZ1FiDw5eT2eyX+fAOCtjlWoV7aUyomEEBqNhlfbVWTk/P38vOsSz7csj5eL9MqIwiOTbAirlJSWwehFB0nLMNKmijfPtSivdiQhxB1tqvhQq4w7KXoDP+4IVzuOKOYsvpC5evUqTz/9NF5eXjg6OlKrVi3279+vdiyhshn/nuRCVBJ+bg7M6V8HGxsZFyOEpcgcKwOw4L+L3E5KVzmRKM4supC5ffs2zZs3x87Ojn///ZewsDDmzJlDqVJyCKEkO30jgUV7IgD4ZEBtPJ1lXIwQlqZ9NR+q+buRlG7gp53SKyMKT77GyBSVWbNmERQUxLx588zbQkJCVEwkLMGHq09iVKBzTT+aVSitdhwhRA4yx8q8uPAg83de5Nknysv8TqJQPHYhEx8fz6ZNm6hSpQrVqlUriExmf//9Nx07dqRfv35s3bqVMmXK8PLLL/Pcc8898DZpaWmkpaVlyQeg1+vR6/UFmk/cldm2hd3G289Gs/VMFHZaDW90qCjP6R1F1f4iO2n7B2tTyYvKPi6ciUzki01neKdj5QJ/DGl/dRVm++f2PjVKHpev7t+/Py1btuSVV14hJSWF2rVrc/HiRRRFYfHixfTt2zdfgXPi4OAAwOuvv06/fv3Yt28fr776KnPnzmXYsGE53mby5MlMmTIl2/ZFixbh5ORUYNlE0TMqMPuIluspGlr7G+ldzqh2JCHEI5y4reG7U1psNApvhxrwl7dhkUvJyckMHjyYuLg43NzcHrhfngsZPz8/1q5dS+3atVm0aBGTJk3iyJEjLFiwgO+++45Dhw49dvhM9vb2NGjQgP/++8+8bezYsezbt49du3bleJucemSCgoKIjo5+aEOIx6PX61m/fj0dOnTAzq5wuo+X7L/C//4Kw93Rlg3jWuAh3dRmRdH+ImfS9o/20q+H2HAqioblSvHryAYFOmmltL+6CrP94+PjKV269CMLmTwfWoqLi8PT0xOANWvW0LdvX5ycnOjatStvvfVW/hPnwN/fn+rVq2fZVq1aNZYtW/bA2+h0OnS67HMW2NnZyYu8CBRWOyemZfDZxvMAjG1XGW93+bcuJ/I6V4+0/YNN7lmTnee3se/ibf45Fknf+oEF/hjS/uoqjPbP7f3l+ayloKAgdu3aRVJSEmvWrOHJJ58ETGcYZR4KKijNmzfn9OnTWbadOXOG4ODgAn0cYfnmbjlPdGIa5byceKaJPP9CWJPAUk7m07E/XH2SuGQZzyIKTp4LmXHjxjFkyBACAwMJCAigdevWAGzbto1atWoVaLjXXnuN3bt38+GHH3Lu3DkWLVrEd999x+jRowv0cYRluxabwvd3pjp/t3NV7G0tetYAIUQORj0RQkUfF24lpfPRulNqxxHFSJ4/EV5++WV27drFTz/9xI4dO7CxMd1F+fLlmTZtWoGGa9iwIX/++Se//fYbNWvWZOrUqXz22WcMGTKkQB9HWC5FUZj89wnSMow0KudJxxp+akcSQuSDva0NU3vWBODXPREcuRyrbiBRbOTr9OsGDRrQoEGDLNu6du1aIIHu161bN7p161Yo9y0s37/Hb7Au7Ca2Nhqm9KwhK1sLYcWaVvCid90y/HnoKv9bcZwVo5ujlVm5xWPKVSHz+uuv5/oOP/nkk3yHEeJescnpTPzLtCjkS60rUM1fzjoTwtqN71KNDSdvcuxqHKuPXad77QC1Iwkrl6tC5v5Tqg8ePEhGRgZVqlQBTANwtVot9evXL/iEosSavuok0YlpVPB25pW2FdWOI4QoAN6uOkY0D+HzjWf5eddFKWTEY8tVIbN582bz95988gmurq4sWLDAvObR7du3GTFiBC1atCiclKLE2XE2mt8PXEGjgVl9Q9HZatWOJIQoIEMal+XrzefYd/E2J67FUSPAXe1IworlebDvnDlzmDFjRpaFG0uVKsW0adOYM2dOgYYTJVNyegbv/XkUgKFNgmlQzlPlREKIguTr5kCnmqaB+7/suqRyGmHt8lzIxMfHExUVlW17VFQUCQkJBRJKlGyfrDvD5ZgUAtwdeKtTVbXjCCEKwbBm5QBYcfgqscnp6oYRVi3PhUzv3r0ZMWIEy5cv58qVK1y5coVly5YxatQo+vTpUxgZRQly/GocP+0MB2B6n1q46Cx6gXYhRD41CC5FNX83UvVGft9/Re04worluZCZO3cunTt3ZvDgwQQHBxMcHMzgwYPp1KkTX3/9dWFkFCWEoihMXRmGUYHutQNoU8VH7UhCiEKi0WgY1tQ0S/cvuy9hMOZp2T8hzPJcyDg5OfH1119z69YtDh06xKFDh4iJieHrr7/G2dm5MDKKEmLtiRvsCY9BZ2vDu53lkJIQxV3POmVwd7QjIiaZrWci1Y4jrFS+53p3dnYmNDSU0NBQKWDEY0vLMPDhatO05c+3LE8ZD0eVEwkhCpujvZYBDYMAmP+fDPoV+ZPnAQhJSUnMnDmTjRs3EhkZidFozHL9hQsXCiycKDkW/HeRiJhkfFx1vNiqgtpxhBBF5OnGwXy//QLbzkRxISqR8t4uakcSVibPhcyzzz7L1q1beeaZZ/D395cp48Vji05M44uN5wB4q2MVnGWArxAlRlkvJ9pW8WHjqUh+2X2JSd1rqB1JWJk8f2L8+++/rFq1iubNmxdGHlECfbr+DAlpGdQs40bfeoFqxxFCFLGhzcqx8VQkf+y/wug2FSntolM7krAieR4jU6pUKTw9ZYIyUTBO30jgt70RAEzoWh0bWUBOiBKnRcXS1AhwIyEtg0l31lcTIrfyXMhMnTqViRMnkpycXBh5RAmiKArTVplOt+5c04/G5b3UjiSEUIGNjYZZfUOxtdGw6th1/j12Xe1Iwork+dDSnDlzOH/+PL6+vpQrVw47O7ss1x88eLDAwonibdnBq2w/G4291ob3OldTO44QQkU1y7jzUusKfLHpHBP+Ok7j8l54OturHUtYgTwXMr169SqEGKKkuRabwpS/TV3I4zpUoqyXk8qJhBBqe6VtRdaeuMGZm4lM+ecE/zewrtqRhBXIcyEzadKkwsghShBFUXhn2VES0jKoW9aD51uUVzuSEMIC6Gy1fPRUbXp/vZO/Dl+jW2gAHar7qh1LWLh8T4gnRH79uieC7WejcbCzYU6/2thq5WUohDCpHeTBcy1N/9y8/+cx4pL1KicSli7PnyAGg4GPP/6YRo0a4efnh6enZ5aLEA9z6VYSH64+CcDbHavK5FdCiGxea1+Z8t7ORCakMX11mNpxhIXLcyEzZcoUPvnkEwYMGEBcXByvv/46ffr0wcbGhsmTJxdCRFFcGIwKb/1+lOR0A41DPBnerJzakYQQFsjBTstHT4UCsHT/FY5fjVM5kbBkeS5kfv31V77//nveeOMNbG1tGTRoED/88AMTJ05k9+7dhZFRFBPzdoaz92IMzvZaPu5XW+aMEUI8UP1gT3rWCQBgxr8nURRZHVvkLM+FzI0bN6hVqxYALi4uxMWZKuVu3bqxatWqgk0nio1Lt5L4eN1pAN7vWp0gTzlLSQjxcG8+WQV7rQ07z91iy5koteMIC5XnQiYwMJDr102TFVWoUIF169YBsG/fPnQ6mVZaZKcoCv9bcZxUvZFmFbwY1ChI7UhCCCsQ5OnE8OblAJi5+hQGo/TKiOzyXMj07t2bjRs3AjBmzBgmTJhApUqVGDp0KCNHjizwgML6rThsmvhOZ2vDh71ryUKjQohcG926Iu6Odpy+mcCyA1fUjiMsUJ7nkZk5c6b5+wEDBlC2bFl27dpFpUqV6N69e4GGE9YvJimdqStNZymNbVeJcqWdVU4khLAm7k52jGlbkWmrTjJn/Wm61w7A0V6rdixhQfJcyNyvadOmNG3atCCyiGJo+qqTxCSlU8XXledbysR3Qoi8e6ZpMPP/u8iV2yn8uOMCr7StpHYkYUHyfGhpwYIFWQb1vv3223h4eNCsWTMuXbpUoOGEddt5LpplB6+g0cCMvrWwk4nvhBD5oLPV8lbHKgDM3XqB6MQ0lRMJS5LnT5YPP/wQR0dHAHbt2sWXX37J7NmzKV26NK+99lqBBxTWKVVvYPyfxwAY2iSYemVLqZxICGHNuocGUKuMO4lpGXy+8azacYQFyXMhc/nyZSpWrAjAihUreOqpp3j++eeZMWMG27dvL/CAwjp9uuEMl24l4+fmwJt3/pMSQoj8srHRML5LNQAW7YngfFSiyomEpchzIePi4sKtW7cAWLduHR06dADAwcGBlJSUgk0nrNK+izF8t+0CAFN71cTVwU7lREKI4qBpBS/aV/Mhw6gw699TascRFiLPhUyHDh149tlnefbZZzlz5gxdunQB4MSJE5QrV66g8wkrk5CawWtLDqMo0K9+oKxcK4QoUO92rorWRsO6sJvsDY9RO46wAHkuZL766iuaNm1KVFQUy5Ytw8vLC4ADBw4waNCgAg8orMuH/57myu0UAks5MrF7dbXjCCGKmYo+rgxsaJpUc/qqMIwySV6Jl+fTrz08PPjyyy+zbZ8yZUqBBBLW62iMhj9OX0WjgU/615FDSkKIQjGufWVWHLrKkStxrD5+I+//kYtiJc+FzLZt2x56fcuWLfMdRliv6MQ0Fp83vZ0837I8jUI8VU4khCiuvF11vNiqAnPWn2HO+rO8JucTlGh5LmRat26dbdu9U84bDIbHCiSsj6IojF9xgqQMDVV9XXi9Q2W1IwkhirlnW5Rn4Z5LXIlNZdsNDT3UDiRUk+ceudu3b2e5REZGsmbNGho2bGheQFKULL/vv8Lm09FoNQofP1ULna1MHy6EKFyO9lrefNLUFbPuig23k9NVTiTUkudCxt3dPculdOnSdOjQgVmzZvH2228XRkZhwSLjU5m6KgyArkFGqvi5qpxICFFS9KkXSFVfF1IMGj5eJ5PklVQFNkbK19eX06dPF9TdCSsx6e8TJKRmUDPAjdYBcvaAEKLoaG00TOxmmiRv6YGr7DgbrXIioYY8j5E5evRolp8VReH69evMnDmTOnXqFFQuYQXWHL/Bv8dvoLXRML1XdS4e2qF2JCFECdOwXCla+BrZftOGd5cfZe24ljjrHns9ZGFF8vxs16lTB41Gg6Jk/e+7SZMm/PTTTwUWTFi2uBQ9E/86DsALLctT3d+Ni4dUDiWEKJG6BRu5kObEldspfLT2NJN71FA7kihCeS5kwsPDs/xsY2ODt7c3Dg4OBRZKWL4Zq08SmZBG+dLOjG1XCTCqHUkIUUI5aGFqz+qMXHCQBbsu0i3UnwblZAqIkiLPY2SCg4OzXIKCgqSIKWH+Ox/N4n2XAZjRpxYOdnKWkhBCXS0qlqZ/g0AUBd7+4yipepkKpKTI12DfrVu30r17dypWrEjFihXp0aOHrHxdQqSkG3hv+TEAhjQuS+PyXionEkIIk/e7VsfHVceF6CQ+2yBnMZUUeS5kFi5cSPv27XFycmLs2LGMHTsWR0dH2rVrx6JFiwojo7AgH64+yaVbyfi5OfBO56pqxxFCCDN3Rzum964FwHfbznP8apzKiURRyHMhM336dGbPns2SJUvMhcySJUuYOXMmU6dOLYyMwkJsPhXJL7svATD7qVDcZC0lIYSF6VDdl26h/hgV+N+K47KoZAmQ50LmwoULdO/ePdv2Hj16ZBsILIqP6MQ03vrjCAAjmpejZWVvlRMJIUTOJnSrjovOlsOXY1m6/7LacUQhy3MhExQUxMaNG7Nt37BhA0FBQQUSSlgWRVF4d9lRohPTqezrwjud5JCSEMJy+bo5MK59JQBmrjlFTJIsX1Cc5fn06zfeeIOxY8dy+PBhmjVrBsDOnTuZP38+//d//1fgAYX6ftt7mQ0nI7HX2vB/A+vKWUpCCIs3vFk5/jhwhVM3Epi95hQz+4aqHUkUkjwXMi+99BJ+fn7MmTOHpUuXAlCtWjWWLFlCz549CzygUNeFqESmrjStpfR2pypU83dTOZEQQjyardaGqb1q0m/uLhbvu0z/hkHUK1tK7ViiEORrHufevXvTu3fvgs4iLIzeYOS1JYdJ0RtoXtGLkc1D1I4khBC51rCcJ33rBbLs4BUmrDjO3688gdZGo3YsUcDyvWhkeno6V65cISIiIstFFB9fbz7PkStxuDnY8nG/2tjIG4AQwsq816Uqbg62nLgWz8I7Z12K4iXPhczZs2dp0aIFjo6OBAcHExISQkhICOXKlSMkRP5jLy6OXYnji02mCaWm9qqJv7ujyomEECLvSrvoeOvOCQofrz3N1dgUlROJgpbnQ0vDhw/H1taWlStX4u/vj0Yj/6UXN6l6A68vPUyGUaFrLX961A5QO5IQQuTb4EZlWX7wCociYnlj6WEWPdtEepiLkTwXMocPH+bAgQNUrSqn4BZXn6w/w9nIREq76Jjaq6YUq0IIq6a10fBJ/zp0/Xw7uy/E8MOOCzzfsoLasUQByfOhperVqxMdHV0YWYQF2Bsew/fbLwAws08tPJ3tVU4khBCPL6S0MxO6VQfg47VnOHk9XuVEoqDkqpCJj483X2bNmsXbb7/Nli1buHXrVpbr4uPlhWHNktIyePP3IygK9KsfSPvqvmpHEkKIAjOwYRDtq/mSbjAybvFhWSG7mMhVIePh4UGpUqUoVaoUHTp0YPfu3bRr1w4fHx/z9sx9CtPMmTPRaDSMGzeuUB+npPpw9UkiYpIp4+HIxO7V1Y4jhBAFSqPRMLNvLUq72HP6ZgIfrT2tdiRRAHI1Rmbz5s2FneOR9u3bx7fffktoqMzOWBh2nI3m1z2m0+c/eioUV1kQUghRDJV20TH7qVBGzt/PjzvCaVvVh+YVS6sdSzyGXBUyrVq14oMPPuDNN9/EycmpsDNlk5iYyJAhQ/j++++ZNm1akT9+cZeYlsE7y44CMLRpMM3kj1oIUYy1rerLkMZl+XVPBK8tOczKsU/g4+qgdiyRT7k+a2nKlCm8+OKLqhQyo0ePpmvXrrRv3/6RhUxaWhppaWnmnzPH7ej1evR6faHmtFYfrgrjamwKgR4OvN6uQr7aKfM20sbqkPZXj7S9uvLb/m8/WZG94bc4G5nEK78eZMHw+thq8z1HbIlVmK//3N5nrgsZRVHyHeZxLF68mIMHD7Jv375c7T9jxgymTJmSbfu6detUKcIs3dk4DYvCTItA9gxIYuvGdY91f+vXry+IWCKfpP3VI22vrvy0f78AmHNLy96Ltxn97Tp6ljMWQrKSoTBe/8nJybnaL0/zyBT1fCKXL1/m1VdfZf369Tg45K7b77333uP11183/xwfH09QUBBPPvkkbm6y4OG9ktIy+OirXUAKgxoGMq5H/gf46vV61q9fT4cOHbCzk/E1RU3aXz3S9up63Pb3r3qTMYuPsOm6Db1b1aVTDTlbMy8K8/Wf2zOh81TIVK5c+ZHFTExMTF7u8qEOHDhAZGQk9erVM28zGAxs27aNL7/8krS0NLRabZbb6HQ6dDpdtvuys7OTN5n7fLr6NFdup1DGw5H3u9XAzi5fa4hmIe2sLml/9Ujbqyu/7d+9TiDHriXw3bYLvPfnCaqX8aCCt0shJCzeCuP1n9v7y9Mn15QpU3B3d89XoPxo164dx44dy7JtxIgRVK1alXfeeSdbESNyb/eFWyzYZVpAbWbfWrjoHr+IEUIIa/R2xyocuRzLnvAYXvzlACtGN8dZ3hOtRp6eqYEDB+Lj41NYWbJxdXWlZs2aWbY5Ozvj5eWVbbvIvbQMA+P/NBWIAxsG0aKSt8qJhBBCPbZaG74YXJdun+/gbGQiry4+xDdP18dOBv9ahVw/S7LeTvHxw/ZwLkQlUdpFx3tdqqkdRwghVOfj6sA3T9dDZ2vDhpORvL70CAajOie5iLyx+LOW7rdlyxa1I1i1yzHJfL7xLAD/61oNd0c5pi+EEAD1gz2Z+3R9nv9lP/8cuYazvZYZfWrJP/IWLtc9MkajsUgPK4nCMeWfE6RlGGlS3pOedQLUjiOEEBalTVUf/m9gXWw0sHjfZaauPGkx/8iLnMkBwBJkfdhNNpyMxE6rYVqvmvJfhhBC5KBLLX9mP1UbgJ92hvPp+jMqJxIPI4VMCZGcnsHkv08A8FyL8lT0cVU5kRBCWK6n6gcypUcNAD7fdI6fdoSrnEg8iBQyJcQXm85xNdY0Z8yYtpXUjiOEEBZvWLNyvNWxCgBTV4Wx7sQNlROJnEghUwKcvZnA99suADC5Rw0c7WX+HSGEyI2XW1dgUKOyKAq8uvgwR6/Eqh1J3EcKmWLOYFR4e9lRMowK7av50KG6TL8thBC5pdFomNqzBq0qe5OiNzBy/n6u3M7dGkCiaEghU8z9tCOcQxGxuOpsmdpLJhEUQoi8stXa8OXgulT1cyU6MY0R8/YRlyKrnVsKKWSKsfNRiXy87jQA/+tWDX93R5UTCSGEdXJ1sGPeiIb4uuk4G5nIy78eIC3DoHYsgRQyxZbBqPD2H0dJyzDSolJp+jcIUjuSEEJYNX93R34a3hBney07z93ipYUHpZixAFLIFFPz/7vIgUu3cdHZMrNvqMwZI4QQBaBGgDvfDW2Ag50Nm05F8uIvB0jVSzGjJilkiqGL0Ul8tPYUAOO7VKOMhxxSEkKIgtK8Yml+GtYQBzsbNp+O4gUpZlQlhUwxY7xzSClVb+SJiqUZ1EgOKQkhREFrVrE084Y3wtFOy9YzUTz3834pZlQihUwx88vuS+y9GIOTLHYmhBCFqmkFL+aNaIijnZbtZ6MZtWAfiWkZascqcaSQKUYuxyQza43pkNK7nasS5OmkciIhhCjempT3Yv6IhjjdGQA86LvdRCWkqR2rRJFCpphQFIV3lx8lOd1A4xBPnm4crHYkIYQoERqX92LRc03wdLbn2NU4+n7zH+HRSWrHKjGkkCkmFu+7zM5zt3Cws2FW31BsbOSQkhBCFJU6QR4se6kZZT2diIhJpu83/3H4cqzasUoEKWSKgWuxKUxfdRKAN5+sQrnSzionEkKIkiektDPLXmpGzTJuxCSlM+i73awPu6l2rGJPChkrpygK4/88RmJaBnXLejCieYjakYQQosTydtWx+PmmtLyzNtNzP+9n6sowmTivEEkhY+WWH7zKltNR2Gtt+OipULRySEkIIVTlorPlx2ENGN6sHAA/7ginz9f/cT4qUd1gxZQUMlbsWmwKU/45AcCr7StR0cdV5URCCCEA7LQ2TO5Rgx+GNqCUkx0nrsXT7fMdLN1/GUVR1I5XrEghY6UMRoVxSw4Tn5pB7UB3nm9ZXu1IQggh7tO+ui9rxrWkWQUvUvQG3v7jKK8sOkRscrra0YoNKWSs1DdbzrE33DTx3f8NrIudVp5KIYSwRL5uDvwyqjFvdayCrY2GVceu0/GzbWw/G6V2tGJBPv2s0MGI23y64SwAH/SsKWcpCSGEhdPaaBjdpiLLX25GeW9nbsan8cyPe5n89wlZ2uAxSSFjZRJS9by6+BAGo0L32gH0rVdG7UhCCCFyKTTQg1VjWjC0qWnS0vn/XaTr59v599h1DEYZO5MfUshYmYl/neByTAplPByZ1qumrKUkhBBWxtFeywc9azJ/REN8XHWcj0ripV8P0vrjzfy0I1zWa8ojKWSsyIpDV/nz0FVsNPD5oDq4O9qpHUkIIUQ+ta7iw9pxLRnTtiKlnOy4HJPCByvDaPrhRmasPkl8ql7tiFZBChkrEZOUzqS/75xq3a4y9YM9VU4khBDicZVytueNJ6vw37vt+LB3LSp4O5OQlsG32y7Q4ZOtrDl+Q+2IFk8KGSsxe80p4lL0VPVzZXSbCmrHEUIIUYAc7bUMblyW9a+14oehDSjn5cTN+DReXHiA537ez/W4FLUjPpDaQ3ukkLEChyJus2T/ZQCm9qqJrZxqLYQQxZKNjcY898wrbSpia6NhfdhN2s/Zynfbzlvc+Jmd528x55iWyIQ01TLIJ6KFMxgVJv51AkWBPvXK0LCcHFISQojizsFOy5sdq7BqbAvqlfUgKd3Ah6tP0fTDjXzwTxgRt5LVjsi2M1G8sPAQV5I0zN16QbUcUshYuMX7Ijh2NQ5XnS3vda6mdhwhhBBFqIqfK3+82IxZfe+On/lpZzitPt7Mcz/vZ+e5aIwqHNvZfDqSZ3/eT1qGkZqljLzTqUqRZ8hkq9oji0eKSUpn9prTALz+ZGW8XXUqJxJCCFHUbGw0DGhYln71g9h2Nop5Oy+y9UwU68Nusj7sJuW8nBjYqCxP1Q+ktEvhf05sPHmTlxYeJN1gpEM1Hzq5XUNnq16/iPTIWLB7B/g+0yRY7ThCCCFUZGOjoXUVHxaMbMSG11vyTJNgXHS2XLyVzMx/T9F0xkZGLzrIltORZBiMhZJh3YkbvLjwAOkGI51r+vF/A0JRsYYBpEfGYh2+HCsDfIUQQuSooo8rU3vV5L0uVfnnyDUW7b3MkcuxrDp6nVVHr+PtqqNn7QB61ytDdX+3x548NTY5nR+2hzN363kyjApdQ/35bEAdMKq/vIIUMhYow2Dk/T+PyQBfIYQQD+Vkb8uAhmUZ0LAsJ67FsWTfZf45co2ohDR+2BHODzvCqezrQotK3jQO8aRxiBfuTrmfTDU2OZ0fd4Qzb+dF8xlTPesEMKdfbWy1NuilkBE5+WX3JU5ci8fNQQb4CiGEyJ0aAe580NOd/3WtztYzUfx56AobTkZy5mYiZ24m8uOOcDQaqObnRr1gD8p6OhFYyokyHo4ElnJEo9FwIy6Vmwmp3IxL5VxkIkv2XSbhTgFT1c+Vce0r07GGr0UtjyOFjIW5GZ/KnHVnAHi7U1UZ4CuEECJP7G1t6FDdlw7VfYlL0bPldCR7wmPYfeEWF6KSCLseT9j1+Fzfn6mAqcST1f2wsbGcAiaTFDIW5oOVYSSmZVAnyIPBjcqqHUcIIYQVc3e0o2edMvSsUwaAyIRU9lyI4eT1eK7GpnDldgpXbidzM940oZ2nsz2+bg74uenwc3egVWVviy1gMkkhY0G2noli1dHr2GhgWq+aFv3CEUIIYX18XB3oXjuA7rUDsmxPyzCNddHZatWI9VikkLEQqXoDE/86DsDwZiHULOOuciIhhBAlhTUWMJnknF4L8fWW81y6lYyvm47Xn6ysdhwhhBDCKkghYwEuRCUyd8t5ACZ1r4GLTjrKhBBCiNyQQkZlRqPCe8uPkW4w0qqyN51r+qkdSQghhLAaUsiobOn+y+wJj8HRTsu0XjUt6tx8IYQQwtJJIaOiyPhUPlx9EoA3nqxMkKeTyomEEEII6yKFjIom/3OC+NQMQgPdGd6snNpxhBBCCKsjhYxK1ofdZPWxG2htNMzsEyqLQgohhBD5IJ+eKkhI1TNhhWnOmOdalKd6gJvKiYQQQgjrJIWMCmavOc2N+FSCvZwY176S2nGEEEIIqyWFTBE7cjmWhXsuATCjdy0c7Kx3NkUhhBBCbVLIFCFFUZjyzwkUBfrULUOziqXVjiSEEEJYNSlkitDfR65xMCIWJ3st73SuqnYcIYQQwupJIVNEktMzmPnvKQBebl0BXzcHlRMJIYQQ1k8KmSLy7dYLXI9LpYyHI8+2KK92HCGEEKJYkEKmCFyLTeHbbaZFIcd3qSYDfIUQQogCIoVMEZj57ylS9UYalfOkSy1ZFFIIIYQoKBZdyMyYMYOGDRvi6uqKj48PvXr14vTp02rHypP9F2P4+8g1NBqY2L26LAophBBCFCCLLmS2bt3K6NGj2b17N+vXr0ev1/Pkk0+SlJSkdrRcMRoVPlgZBkD/+kHULOOuciIhhBCieLFVO8DDrFmzJsvP8+fPx8fHhwMHDtCyZUuVUuXeymPXOXolDmd7LW92rKJ2HCGEEKLYsehC5n5xcXEAeHp6PnCftLQ00tLSzD/Hx8cDoNfr0ev1hRvwHukZRj5aYzrd+tknyuHhYFOkj1/UMn+34vw7WjJpf/VI26tL2l9dhdn+ub1PjaIoSoE/eiEwGo306NGD2NhYduzY8cD9Jk+ezJQpU7JtX7RoEU5OToUZMYtt1zUsu6jF1U5hQl0DOjlRSQghhMi15ORkBg8eTFxcHG5uD15c2WoKmZdeeol///2XHTt2EBgY+MD9cuqRCQoKIjo6+qENUZASUjNo9+l2bifrmdK9GoMbBRXJ46pJr9ezfv16OnTogJ2dndpxShxpf/VI26tL2l9dhdn+8fHxlC5d+pGFjFUcWnrllVdYuXIl27Zte2gRA6DT6dDpdNm229nZFdmLfP7mC9xO1lO+tDODm5TDTmvRY6oLVFG2s8hO2l890vbqkvZXV2G0f27vz6ILGUVRGDNmDH/++SdbtmwhJCRE7UiPFBmfyvfbwwF4u1OVElXECCGEEEXNoguZ0aNHs2jRIv766y9cXV25ceMGAO7u7jg6OqqcLmf/t/EsKXoDdct60LGGTH4nhBBCFCaL7i745ptviIuLo3Xr1vj7+5svS5YsUTtajs5HJbJ432UA3u1UVSa/E0IIIQqZRffIWMk4ZLM5605jMCq0q+pD4/JeascRQgghij2L7pGxJqduxLP62A00Gni7U1W14wghhBAlghQyBeSLTecA6FLTnyp+riqnEUIIIUoGKWQKwNmbCaw+dh2AV9pWVDmNEEIIUXJIIVMAvtx8DkWBjjV8qeZfNJPuCSGEEEIKmcd2PiqRf45cA2Bsu0oqpxFCCCFKFos+a8kafLXpHEYF2lfzpUaAu9pxhBDW7v6zNTUa0zZFAcUIisH01Wi4+7PRaPo+IwVS4yEtAdLufDUaQGsLNnagtQMbW9PF/L0d2NqDsw84lwYbWRhOWBcpZB7DxegkVhy+CsDYdjI2RogSx2iE5Fu4plxBE74NUm9Bwg1IvAkpsWDMuFNoZJgKiow0SE+C9ARISzR9n5Fqut6gv7u/WjRacPEBVz9TYePoAQ4e4OBuutz7c+b3Tp5g76xeZlHiSSHzGL7abOqNaVPFm9BAD7XjCCEexWg0FQ4ZqaYiIi0eUuPuXOIhPdFUbGSk3v2qTwF9EqQngz7ZdLvkGEiKguRo7BQjbQFOqf3LAVodOLiBzhV0d77aaE1FlEEPRv2dr5nFlR4MGaaenORbpiIq4brpkhe2jqbeHCcv01dn73u+3rk4eYJTadN2OydTT5MQBUAKmXy6HJPM8kOZvTEyNkaIImM03CkkIiEx0nT4JCPN9GGsTzUVG0lRpl6RhBumS1KUabshvVAipWudsSsViMbVF1z8TL0aTp6mwzY2WtMhHI0N2OrA3sVUYNg7m763dbhz6Mf27v6a+4YvKsqd7RrTdZrM77V3tmvB5jGHPBoyTO2UcP1Om0WairvUOEiNNfUwmYu+zJ9jTW2akQJxl02X3LB1AFd/8K0BvjXBr6bpq0fw4/8eosSRQiafvt5yDoNRoWVlb+qWLaV2HCGsi9Fo6uVISzT1gqQn3u3x0Cebvk+Lv1OM3ITEG3e+3oTkaNN4kMeltb97yETnZvpq7wx2jqaCQ6szfbVzNPUg2Dubvto5mYqUOz0Nent3/l27ni5dulj36staW3DzN11yS1FMPVRJUaYenaRoc0+V+Xvz5ZZpuyHd1NN1O9x0ObXy7v3Z2Jna1cUbXHxNh7fcA8Gz/N2Lk6f05ogspJDJp1plPNjiHsWrMjZGCJPblyBiN6Tczvofe+Z/8ff+R5+eCDzmEiROXqYPPQd303/4do53vzp5mcZ5uPiB650PRHOR4nC3F6Qg6PUFcz/WSKMBnYvp4hny6P0VxfTcJ0Wbem9uHIebx+HGMYg6ZSpyEq6ZLg+icwevClC6EnhVQlOqPK4p103FkTUXkiLfpJDJp8GNy9KvQSB2WukGFSWc0QC7v4GNH4AhLW+31diYDq/Yu4C9U9aeD52L6b9yF9+7RYmLz53DNqULrhARRUejuTN+x9VU+IS0vHudQW/qcUu8c8gwKdL0c2wExIRDzAWIvwppcXDtoOmC6UOsLaCcnmA6NFW6sqnI8a5iOnTlU91UwIpiS94JHoMUMaLEi7kAK0ZDxH+mn/3rQKlyd89oyemsF537nQ8zFxn0Ke7S2pkOI7kHPngffYqpqLl1DqLPwK1zGKPOYLh5EjtD8t3DVWfX3r2Nxga8KprG4PhUNxU5pSuBZwWwcyj830sUOilkhBB5ZzTCgXmwboJprIu9C3T8EOoNlcJEFB47R/CtbrrcYdDrWb1qFV1aNcAuNtxU4ESfhcgwuHnCNC4n+ozpcmL5PXemAY8gU0HjGQKlQkxFuGcIuJUBx1LyWrYSUsgIIR7MaDCNe7mwxdTFH3/VNLYh/trdM4CCn4BeX5k+BIRQg0ZjOgRZKhBCWtzdriimw1M3jsPNYxB12lTk3DprGqsVG2G6XNic/T61OtP4Kld/cAsA72p3zq6qAe5l5ewqCyKFjBAiq4w0CN8GJ/+GU6tN/9HmxN4F2rwPjV+UN3VhmTQa0/gqVz+o1P7udkUxDTiOPmM6FBUTfs/Xi5ASYxrvlVnoAPDn3dvbu4JPtTsDjiuaBx7jGWI6000UKSlkhBCm+ULOrYdTq+DsetOpz5kc3KFSR9Mbt3sQuJcxjWNw9TeNaxDC2mg0d07x9oZyzbNfr0+9M/D4pmlendgIuBlmOsMq6pRpZuYre02XrHdsOizlGQKlgk2Hq9yD7hRT/qavOlc5ZFXApJARoiRSFNOAyfCtcHqN6eu9k8W5+EHVrlCtO5R7QgoWUbLYOdwpRIKzX2fQm/52Ik/eGXR851BV9DlTgRN/xXS5uP0B9+2ctbBxvXM2ns71ztl7zqaLg7vp79DZW87QewRpHSFKAkWBW+dNZxeFbzcdOkq8kXUfr4pQtZvpUqa+HC4SIidaO1PvpE+1rNszD1fde4jqdrhpPFnmDNNpcabB8THnTZdc0ZiKGVc/0/IO954F6OBu2u4eZBq47BpgWgC0hJFCRojiKCkarh6AK/tNX68eME1Ody+tDoIaQYU2puLFu4oqUYUoFu49XBXUKOd90pPuFjWZS0Ek3jDNm5OedGeG6yTTJeW2abtiMM2pkxSZmxCmnh7PkDuXe2ZE9qpYbBf3lEJGCGuXloDm8gEq3FyNdvkyuH7ongGK99DqIKCu6ayOci1Mb7YyUZgQRcfe2TQrsVeF3O1vNJiWfki4blqiI/nW3bWuUuNMxU78NYi7YroY0u7OjHxpZ/b7cy97d7JAn2oQ2BBKV7H63lcpZISwJunJpvkxrh2Cq3dmN406jS0KNQHundndqxIENjAdJgpsAD41SmS3sxBWy0Z7dzbrRy2BZTSazjCMjTAd1oq5cPdy65ypCIqLMF3Ob7x7O53bnfeIhqZ/coKbmx7XikghI4QlMmSY3nBunTdN6nXjmOly62yOCyYqbmW4rvHHt15ntEENIaCO6fi5EKJksLG5W/QENsh+fXLMnXl0TkPUGbhx1PTPUFq8aR6dC5th22zTumTVe0LNPhDUxCp6a6SQEUJNqXGmN5WoU6ZL9FnTIMDbF8GYkfNtnL1NSwGUqQcB9aBMPTJ0pdi3ejVdmnVBKwvnCSHu5+QJwU1Nl0yGDFMP75W9cHkvnFlrGouz73vTxdUfGoyCpqNNa6FZKClkhChsGWl3unfvnKlw6/zdnx+2yq9WZxqk510Z/ELBvzb41TKdpXC/krwCsxAif7S24B9qujR81nRq+YUtcHy5aU6phOuweRocmA8dpkDNvhY5B44UMkIUBKPRVJTcvmg69TJzvZfo06ZtORwOMnMNMA2+865yd6ZQzwqmibWsoFtXCFFMaO2gUgfTJSMNwv4yrWofdxmWjYI930KnmRBYX+2kWUghI8SjKIrpLIH466YzBOKv3pkb4prp6+1LEHsp64Ry97N3vXu2guc9X70ry1gWIYTlsdVBaH/TpJi7voTtn5oOQf3QFuo+DR2mmg5XWQApZETJZtCb5nLILEzund8hIbNwuQb65Effl42taWIqz5A7669Uvntx9bPILlkhhHgoO0do+RbUedrUO3NkERxaCKf/Na14X62P2gmlkBHFTGbvSdIt06mISdGmBeCSY0xzLmR+n3DD1LOSePPhh33u5VjKdBjILfNSxvTVPdBUvLgFylTiQojiyc0fen8D9YfBP+Mg6iT8+QLaQwtxduymajR51xWWR1FMM1umxd+Z/OnO15QY01wIybfuFCYxd66Pu2e/2Aef7fMgNnamP1K3MvetgeJvurgFmL5a8Kh9IYQoEmWbwAvbTIebts7C5uJ22mh2wX4NNH1BlUhSyIgCo1EyTAVFcprpUEzmlNtpCZCWaFpQzfx90p2fE+/OVJkSe3fGyrwWI/ezdwVnL3C6c3H0NB3PdfQERw9ToeJ2ZxVnp9IyqFYIIXLL1h5avA41emNc+TraC5vI8AhSL45qjyyKjqKYxoLo76zhkZ585/tk0KdARorpq/7Oz/oUyEi95/sU07L2mdsyUu+uB6JPhvREbNOT6WHUw+ECzK3R3lkYzc00+6TTPYXJvUWJzu3ufg4epuvtHAowiBBCiGw8QzAMXMLO3z+jScUOqsWQQkZtimI6ze3+BcPMRUeSqRfj3u0ZaaZiIiMta9GRkXZf0ZF85/uU3I8Dyacsw1htbE1L1ds7mZal17nevdi7gM7lnq93tjt6mIqQzK8O7qZ1SWSArBBCWC6NhhgXdReclUKmICmKafxG4k3TqqWJkabvk6NNA02TY0yHT1Jum8Z/pCWYipfHPYySFzZ2pgIjs9CwczKNSrdzNH1v63Bnm8Od7x3vfrVzBFtH03V2zqZC406xotfYs37LTjp07YmdQ/FcYVUIIYTlkUImv4xG0yyt149kvaTG5v8+7ZxMxYHdnZ6MzB4Ne+e7X80Fhc5UYNg63Pk+s8C4p9i4/6udc+EtGqjXo7d1Bq0sSiiEEKLoSCGTX78Pg5N/53ydkxe4+JoW73L2Ma2N41TKdPpu5sXB/c5hFZe7RYqVrTgqhBBCqE0KmfzyqQZn14Nfzbvr4PjXNm231amdTgghhCgRpJDJr+avQsu3ZQI0IYQQQkXyKZxf9jKgVQghhFCbzAImhBBCCKslhYwQQgghrJYUMkIIIYSwWlLICCGEEMJqSSEjhBBCCKslhYwQQgghrJYUMkIIIYSwWlLICCGEEMJqSSEjhBBCCKslhYwQQgghrJYUMkIIIYSwWlLICCGEEMJqSSEjhBBCCKtV7Fe/VhQFgPj4eJWTFG96vZ7k5GTi4+Oxs7NTO06JI+2vHml7dUn7q6sw2z/zczvzc/xBin0hk5CQAEBQUJDKSYQQQgiRVwkJCbi7uz/weo3yqFLHyhmNRq5du4arqysajUbtOMVWfHw8QUFBXL58GTc3N7XjlDjS/uqRtleXtL+6CrP9FUUhISGBgIAAbGwePBKm2PfI2NjYEBgYqHaMEsPNzU3eTFQk7a8eaXt1Sfurq7Da/2E9MZlksK8QQgghrJYUMkIIIYSwWlLIiAKh0+mYNGkSOp1O7SglkrS/eqTt1SXtry5LaP9iP9hXCCGEEMWX9MgIIYQQwmpJISOEEEIIqyWFjBBCCCGslhQyQgghhLBaUsiIfJs5cyYajYZx48aZt6WmpjJ69Gi8vLxwcXGhb9++3Lx5U72QxczVq1d5+umn8fLywtHRkVq1arF//37z9YqiMHHiRPz9/XF0dKR9+/acPXtWxcTFh8FgYMKECYSEhODo6EiFChWYOnVqlnVgpP0LzrZt2+jevTsBAQFoNBpWrFiR5frctHVMTAxDhgzBzc0NDw8PRo0aRWJiYhH+FtbrYe2v1+t55513qFWrFs7OzgQEBDB06FCuXbuW5T6Kqv2lkBH5sm/fPr799ltCQ0OzbH/ttdf4559/+P3339m6dSvXrl2jT58+KqUsXm7fvk3z5s2xs7Pj33//JSwsjDlz5lCqVCnzPrNnz+bzzz9n7ty57NmzB2dnZzp27EhqaqqKyYuHWbNm8c033/Dll19y8uRJZs2axezZs/niiy/M+0j7F5ykpCRq167NV199leP1uWnrIUOGcOLECdavX8/KlSvZtm0bzz//fFH9ClbtYe2fnJzMwYMHmTBhAgcPHmT58uWcPn2aHj16ZNmvyNpfESKPEhISlEqVKinr169XWrVqpbz66quKoihKbGysYmdnp/z+++/mfU+ePKkAyq5du1RKW3y88847yhNPPPHA641Go+Ln56d89NFH5m2xsbGKTqdTfvvtt6KIWKx17dpVGTlyZJZtffr0UYYMGaIoirR/YQKUP//80/xzbto6LCxMAZR9+/aZ9/n3338VjUajXL16tciyFwf3t39O9u7dqwDKpUuXFEUp2vaXHhmRZ6NHj6Zr1660b98+y/YDBw6g1+uzbK9atSply5Zl165dRR2z2Pn7779p0KAB/fr1w8fHh7p16/L999+brw8PD+fGjRtZ2t/d3Z3GjRtL+xeAZs2asXHjRs6cOQPAkSNH2LFjB507dwak/YtSbtp6165deHh40KBBA/M+7du3x8bGhj179hR55uIuLi4OjUaDh4cHULTtX+wXjRQFa/HixRw8eJB9+/Zlu+7GjRvY29ubX8iZfH19uXHjRhElLL4uXLjAN998w+uvv8748ePZt28fY8eOxd7enmHDhpnb2NfXN8vtpP0Lxrvvvkt8fDxVq1ZFq9ViMBiYPn06Q4YMAZD2L0K5aesbN27g4+OT5XpbW1s8PT3l+ShgqampvPPOOwwaNMi8cGRRtr8UMiLXLl++zKuvvsr69etxcHBQO06JYzQaadCgAR9++CEAdevW5fjx48ydO5dhw4apnK74W7p0Kb/++iuLFi2iRo0aHD58mHHjxhEQECDtL0osvV5P//79URSFb775RpUMcmhJ5NqBAweIjIykXr162NraYmtry9atW/n888+xtbXF19eX9PR0YmNjs9zu5s2b+Pn5qRO6GPH396d69epZtlWrVo2IiAgAcxvff5aYtH/BeOutt3j33XcZOHAgtWrV4plnnuG1115jxowZgLR/UcpNW/v5+REZGZnl+oyMDGJiYuT5KCCZRcylS5dYv369uTcGirb9pZARudauXTuOHTvG4cOHzZcGDRowZMgQ8/d2dnZs3LjRfJvTp08TERFB06ZNVUxePDRv3pzTp09n2XbmzBmCg4MBCAkJwc/PL0v7x8fHs2fPHmn/ApCcnIyNTda3TK1Wi9FoBKT9i1Ju2rpp06bExsZy4MAB8z6bNm3CaDTSuHHjIs9c3GQWMWfPnmXDhg14eXllub5I279Ahw6LEufes5YURVFefPFFpWzZssqmTZuU/fv3K02bNlWaNm2qXsBiZO/evYqtra0yffp05ezZs8qvv/6qODk5KQsXLjTvM3PmTMXDw0P566+/lKNHjyo9e/ZUQkJClJSUFBWTFw/Dhg1TypQpo6xcuVIJDw9Xli9frpQuXVp5++23zftI+xechIQE5dChQ8qhQ4cUQPnkk0+UQ4cOmc+KyU1bd+rUSalbt66yZ88eZceOHUqlSpWUQYMGqfUrWZWHtX96errSo0cPJTAwUDl8+LBy/fp18yUtLc18H0XV/lLIiMdyfyGTkpKivPzyy0qpUqUUJycnpXfv3sr169fVC1jM/PPPP0rNmjUVnU6nVK1aVfnuu++yXG80GpUJEyYovr6+ik6nU9q1a6ecPn1apbTFS3x8vPLqq68qZcuWVRwcHJTy5csr77//fpY3bmn/grN582YFyHYZNmyYoii5a+tbt24pgwYNUlxcXBQ3NzdlxIgRSkJCggq/jfV5WPuHh4fneB2gbN682XwfRdX+GkW5Z1pKIYQQQggrImNkhBBCCGG1pJARQgghhNWSQkYIIYQQVksKGSGEEEJYLSlkhBBCCGG1pJARQgghhNWSQkYIIYQQVksKGSGEEEJYLSlkhBAWadeuXWi1Wrp27ap2FCGEBZOZfYUQFunZZ5/FxcWFH3/8kdOnTxMQEKB2JCGEBZIeGSGExUlMTGTJkiW89NJLdO3alfnz52e5/u+//6ZSpUo4ODjQpk0bFixYgEajITY21rzPjh07aNGiBY6OjgQFBTF27FiSkpKK9hcRQhQ6KWSEEBZn6dKlVK1alSpVqvD000/z008/kdl5HB4ezlNPPUWvXr04cuQIL7zwAu+//36W258/f55OnTrRt29fjh49ypIlS9ixYwevvPKKGr+OEKIQyaElIYTFad68Of379+fVV18lIyMDf39/fv/9d1q3bs27777Lqv9v5/5BkonjOI5/IEEhwaDdSXATCpSWaAlbgwZxLcEtEAcRoiknaYv+DAfRIohISy6BEOiiDqYgZENDTg0VmJtomzxHS8NT5+953i+44fjeHd/f9uF337ubG3W73dn1BwcHyuVyent709LSkhKJhBYWFnRxcTG7plaraWNjQ6PRSB6Px4llAfgB7MgAmCsPDw9qNBqKx+OSJJfLpVgsJsuyZvVwOGy7JxKJ2M7v7+91eXkpr9c7O7a2tjSZTPT09PQ7CwHwK1xONwAAf7IsS+Px2DbcO51O5Xa7dXJy8q1nfHx8KJlMan9//0vN7/f/tV4BOI8gA2BujMdjXV1d6fj4WNFo1Fbb3t5WoVBQMBhUpVKx1ZrNpu18dXVVvV5PgUDgx3sG4CxmZADMjevra8ViMb28vMjn89lqmUxG1WpVxWJRwWBQqVRKe3t7arfbSqfTGgwGen9/l8/nU6fT0dramnZ3d5VIJLS4uKher6fb29tv7+oAMAMzMgDmhmVZ2tzc/BJiJGlnZ0etVkvD4VClUknlclmhUEhnZ2ezr5bcbrckKRQK6e7uTv1+X+vr61pZWdHh4SH/ogH+QezIADBeLpfT+fm5np+fnW4FwC9jRgaAcU5PTxUOh7W8vKx6va58Ps8/YoD/FEEGgHEeHx91dHSk19dX+f1+pdNpZbNZp9sC4ABeLQEAAGMx7AsAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjPUJvDoaBjClkPEAAAAASUVORK5CYII=", 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" ] @@ -177,7 +177,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -228,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/src/notebooks/WealthPortfolio.ipynb b/src/notebooks/WealthPortfolio.ipynb index ec00ee9..f35515f 100644 --- a/src/notebooks/WealthPortfolio.ipynb +++ b/src/notebooks/WealthPortfolio.ipynb @@ -34,7 +34,7 @@ { "data": { "text/plain": [ - "(3.437921707793278, 1.0, 0.5295360307565933, 0.0)" + "(5.35399091577092, 1.0, 0.1710302407154898, 0.0)" ] }, "execution_count": 3, @@ -71,7 +71,7 @@ "outputs": [ { "data": { - "image/png": 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FxVgsllAv77mkSe+C3EcEJoIgCEJIjYxcpa7+XSYmmgCIjt5Cft4PCA/PDPHK5tbk5CQXLlzg6tWreL3KQz4vL4/du3eTkJAQ4tU9n2/cw9iFLnqrGxfkfiIwEQRBEELC7R6iqen36On9CACdLpbcpe9gtR78WpXDejwerl69yoULF3A6nQCkp6dTWlpKevribQjns7txnO9k/EoPskdCdvkW5L4iMBEEQRAWlCxLdPccpanp9/F6RwEVKSn/mOysf49OFxXq5c0Zn8/HnTt3qKmpweFwABAfH09paSm5ubmLNvjy2pw4znYyfr0XvDIAupQIYtanwh/P//1FYCIIgiAsmLGxeurqv8Po6E0AIiKWk5/3HlFRq0O7sDkkyzKPHj2iqqqKoSFl0nFUVBS7du2isLBw0fYi8Q5N4qjpZPxWH/iUgESfbsa8Ox1jbnQguJpvIjARBEEQ5p3PN0Fr63+jveN/IsteNBoTWVn/jtSUX0Ct/vo8ilpbW6msrKSrqwuAsLAwduzYwfr169FqF+fv6emfwFHdwcTdfpCUa4asKMy70zFkRS34zs7ifJcEQRCEr42BwSoa6r+H09UNQHz8q+QufQejMSnEK5s7PT09VFVV0dSkJPDqdDo2b97Mli1bMBqNIV7dl3N3j+Go7mDy/iAoGyQY86Ix70rDsCR0R2oiMBEEQRDmhdPZTUPDDxgYPA2A0ZhKXu73iIvbFeKVzZ3h4WGqq6upra0FQK1Ws27dOnbs2IHZvDhn+Lg7HNjPtON8NBy4ZlweS2RJGvrU0K9ZBCaCIAjCnJIkDx2df0Nr63/B55tApdKSnv7PyVzyb9FowkK9vDkxNjbGuXPnuHHjBpKknH+sXLmSXbt2ERsbG+LVfTlX6yj2M+24Gm3KBRWEFcYTuSsNXeLiaegmAhNBEARhzoyO3qKu/juMjdUBEBVVRH7ee0RE5IZ4ZXPD6XRy+fJlLl26hMfjASA7O5vdu3eTnJwc4tX9PFmWcTXZsJ9px91qVy6qIXyNFXNxKrr48NAu8EuIwEQQBEGYNY/HRlPzH9Dd/TMAdLpocrJ/h6SkN1CpFmcVynR4vV5u3LjBuXPnmJiYACA5OZnS0lKysrJCvLqfJ8syzrphHGc6cHf4q2k0KkxFVsw709DGLM68FxCBiSAIgjALsizT2/sxjU3/Lx6PkrOQlHSEnOzfQq+PCfHqZk+SJGpra6mursZmswEQGxtLSUkJy5cvX3S9SGRJZvLBII4zHXh6xpWLWjURGxOJ2JGKNmrxt/cXgYkgCIIwI+PjzdTXv8uI7QoAJtNS8vLeI9qyPsQrmz1ZlmlsbKSyspL+/n4AIiIiKC4uZs2aNWg0mhCv8FmyT2by3gD26na8/ZMAqPQaIjYnEbEtBY35qzOlWAQmgiAIwrT4fE7aHv+Ex4//DFn2oFYbycz8NdLTfgm1+qvzAHyejo4OTp8+TXt7OwAGg4Ft27axceNG9PrF9fvJXomJ2/3YazrwDSnt7lVGDRFbU4jYkozGpAvxCqdPBCaCIAjClA0NX6C+/jtMTioP7djYXeTlfo+wsNQQr2z2+vv7qaqqor6+HgCtVsuGDRvYtm0b4eGLK0lU9kiM3+jFcbYTn80FgNqkJWJbKhGbk1Abv7qP96/uygVBEIQF43IN0Nj0I/r6jgNgMCSSu/Rd4uP3Lro8i+kaHR2lurqau3fvIssyKpWK1atXU1xcTFTU4prdI7l9jF/twXGuE8mhVAWpzTrMO1IxbUxCrV9cR0wzIQITQRAE4blkWaKr6+9obvkDvF4HoCYt9f8iK+vfodVGhHp5szIxMcH58+e5du0aPp8yOTc/P5/du3cTHx8f4tU9S3J6Gbvcw9iFTqRxLwCaKAPm4lRMRYmodF/9yqcnRGAiCIIgfCmH4yF19d/Bbr8DgNlcQH7+D4k0rwztwmbJ7XZz5coVLl68iMulHINkZGRQWlpKWlpaiFf3LGnCg+NiN2MXu5Gd/oAk1khkcRrhaxJQab8+AckTIjARBEEQnuH1jtPa+l/o6PxrZNmHRhNBdva/JzXln6BSfXWPCnw+H7du3eLs2bOMjY0BYLVaKS0tJScnZ1EdSfnG3Iyd72Lscg+yW9nN0SaEEbkrnbDCeFSahV2r1+3mfs3pBbmXCEwEQRCEgIGB09Q3fB+XqweAhITXyF36DgaDNcQrmzlZlnnw4AFnzpxheFjptWKxWCgpKWHlypWo1Ytn18E36sJxrpPxa73IHqXVvS7JhLkknbAVsajUCxuQTNhHufP5Se6cOsnI0OCC3FMEJoIgCAJOZzf1Dd9ncLASAKMxjfy87xMbuzPEK5udlpYWKisr6e5WJhuHh4ezc+dO1q1bh1a7eB6B3mEnjrMdjN/oA58y6lefZsZckoYxP2bBd3OGuzu5efJjHp49g9fjBiAiNm5B7r14/lQEQRCEBSdJXjo6//oLA/f+BZlLvvWVHrjX3d1NZWUlLS0tAOj1erZs2cLmzZsxGBZP91PP4CSO6g4mbveBskGCPjOSyJJ0DDmWBQ1IZFmm69EDbpw8RvPNayArAZI1aylFB14ncXkhv/EXfzvv6xCBiSAIwjfU6Oht/8C9RwBYotaTl/eDr/TAvaGhIc6cOcODBw8AUKvVrF+/nu3btxMRsXiqiDx949jPdDB5bwCU5z+GpRYid6VjyFrYEmXJ56Ph6kVunjhGb3Nj4HrWug2sP/AGKctWoFKpsNvtC7IeEZgIgiB8w3g8dppb/oCurr8DZLRaC0tzfoekpDe/sgP3HA4HZ8+e5datW0iSsvVQWFjIrl27iI6ODvHqgtxdYzjOtDP5YChwzbgsBvOuNAzpkQu7lskJas+c5tan5dgHlLb7Wp2e5TtKWLv/ELEpoalQEoGJIAjCN4Qsy/T1Haex6Ue43UoiY1Lim+Tk/M5XduCe0+nk4sWLXLlyBY9HaTiWk5NDaWkpiYmJIV5dkKvdjuNMB846JfkWFYStjMO8Kw198sLu5DiGBrn92XHuVX6Ga0IZ9BdmjmT1KwdY/cp+wiND21ROBCaCIAjfABMTbdTXf5fhkQsAhIdnk5/3HtHRG0O8spnxeDxcv36d8+fPMzmpDK1LTU2ltLSUJUuWhHZxT3G1jGI/046ryaZcUEH4qnjMu9LQWU0Lupb+thZunDhG/aVzSP6GctFJKRQdOMyyHbvQ6RdH7s20ApOf/OQn/OQnP6GtrQ2AFStW8O6777Jv3775WJsgCIIwS5Lk4vHjn9L2+E+RJDdqtYElS75FRvo/R61eHA+i6ZAkibt371JdXR3IeYiLi2P37t3k5+cvil4ksizjarJhr2rH3ebPy1CrCF+bQGRxGtq4hUsqlmWZtru3uHH8I9rv3w1cT12+kqIDh8lasx7VIiqXhmkGJqmpqfze7/0eOTk5APzN3/wNhw4d4vbt26xYsWJeFigIgiDMzMjIFerqv8PEhFKZEhOznbzc7xMenhHilU2fLMvU19dTVVXFwMAAAGazmV27drFq1So0mtA3fpNlGWfdMI4zHbg7HMpFjQrT+kTMO1LRxhgXbC1ej4dHF6q5eeJjhjqVgYsqtZrcTdsoOnCYxOylC7aW6VLJsr8eaIZiYmL4gz/4A375l395St9vt9uJiopidHSUyMiFTfQRBEH4JnC7h2hs+jG9vccA0OvjWLr0HawJBxbFjsJ0PX78mMrKSjo6OgAwGo1s376dDRs2oNPpQrw6kCWZyQdDOM604+lRcjZUOjWmDUpAoolauJ2pSYedu6c/5c7nJxi3jQCgM4ZRuHsva/cdIjI+YcavvVDP7xnnmPh8Po4ePcr4+DibN29+7ve5XK7ALAJgwcqNBEEQvmlkWaKn5wMam34fr9cGqEhJ+SdkZ/17dLqv3gfBvr4+qqqqaGhoAECr1bJp0ya2bt1KWFjoe6zIPpnJ2gHsZzrw9k8AoNJrMG1OwrwtBY1Zv2BrsfX2cPOTj7lfU4nX/8yNiIll7b4yCna/gtG0eEqlX2bagUltbS2bN2/G6XQSERHBsWPHWL58+XO//8c//jHf//73Z7VIQRAE4cXGxhqoq/8Oo6M3AIiIWE5+3ntERa0O7cJmwGazUV1dzd27Sk6ESqVi7dq17Ny5c1HstMs+iYnb/TiqO/AOOQFQGTVEbE0hYksyGtPC7eJ01T/i5oljNF6/HGiIFr8ki/UHDpO7eTuaRdTddqqmfZTjdrtpb2/HZrPx4Ycf8hd/8RecPXv2ucHJl+2YpKWliaMcQRCEOeDzTdLa9ie0t/8FsuxFowknK/PfkZr6f6FWf7UeSuPj45w/f57r16/j81eNLF++nJKSEuLiFqYd+ovIXonxG304ajrw2ZTnmjpcS8T2FCI2J6M2Lsz7LUk+mq5f4caJY/Q01AWuZ64poujAYdJWFM7Lkd1CHeXMOsektLSU7Oxs/uzP/mxK3y9yTARBEObG4GA19Q3fw+nsBCA+bg+5ue9iNCaHeGXT43K5uHLlChcvXsTtVuayZGZmUlpaSkpKSohXB5Lbx/i1XhznOpHsyvrUETrMO1IxbUxCbViYxFuP08n9mtPc/KSc0b5eADRaLcu2l1B04HViU9Pn9f422yjR0ZbFm2PyhCzLz+yICIIgCPPL6eqlseGH9A98CoDBkERe7veIjy8N8cqmx+v1cuvWLc6ePcv4uJI0mpiYGPjAG+pEXcnlZfxKD47zXUhjSvM2TaQe885UTBsSUekWJiAZGxnmzucnuHvqE5zjYwAYI8ys3vsaq185gMkyv51tnWMeHl7s5vqphnm9zxPTCkx+93d/l3379pGWlobD4eBnP/sZNTU1fPbZZ/O1PkEQBMFPln10dv5vmlv+CJ9vDJVKQ1raL5G55NfQahe2WddsSJLEgwcPOHPmDCMjSuVIdHQ0JSUlrFixAnWI+2pIk17GLnUzdrELacILgCbagLk4DdM6KyrtwqxvsL2NGyc+pu5iDT6vsg5LYhLrXnudFTt3ozPOb/nxQLuDezWdNF7vw+eRmHQ75/V+T0wrMOnr6+MXfuEX6OnpISoqisLCQj777DP27NkzX+sTBEEQALu9lrr6d3A47gMQGbmG/Lz3MJuXhXhlUyfLMs3NzVRWVtLbqxxFmEwmdu7cydq1a9GGOFHTN+5h7EIXY5e6kV1Kjos2LgzzrjTCV8ej0sx/QCLLMu21d7lx4iPa7t4KXE/OW07RwcNkr9uAWj1/OzU+r0TzrX5qazrpbQlW0canm8lcnwJ/NW+3DpjW34K//Mu/nK91CIIgCF/C63XQ3PJHdHb+b0BCq40kO/s/kJL8D79SA/c6OzuprKwMdA7X6/Vs3bqVTZs2YTCEtgOtz+HGcb6T8Ss9yG5lAKDWGk5kSRphBfGo1PN/pOTzeqi7eI6bJ44x0N4GgEqlZumGzaw7cJjk3Px5vf+4zcX98108ON/N5JM8Go2K7LUJFO5KxZoZicPhmNc1PPHVStkWBEH4hpBlmf6Bz2hseA+Xuw8Aq7WMpUu/jUEf+gqVqRocHKSqqopHjx4BoNFoWL9+Pdu3b8dkCu3xk3fUxdjZTsau9YJXCUh0ySYiS9IxLo9dkIDEOT7GvcrPuP1pBWMjyoA/ncHIypI9rN13CIt1/gYRyrJMT9MotTWdtNweQJKUWhhTlJ4VO1JYvi0Z0wI2h3tCBCaCIAiLzORkB/UN32NoqAaAsLAM8vPeIyZma2gXNg12u52amhpu377Nk+LPVatWsWvXLiwWS0jX5h124qjpYPxmH/iUtenTzZhL0jHmRS9I0u1ofx+3Pimntvo0HqcyhNAUHcOaVw+yqnQfxoj5a4jmcftovNbHvZpOhjrHAteTcqIoKE4la008mgU4tnoeEZgIgiAsEpLkob39L2lt+29IkhOVSs+SjF8hI+Nfo9F8NQbuTU5OcuHCBa5evYrXn7CZm5vL7t27sVqtIV2bZ2ACR00nE7f7QNkgQZ8ZReTuNAzZlgUJSHqa6rlx4mMar1xElpVFxKUvoejAYfK37kCjnb/mbKMDk9w/28mjSz24/Em9Wp2a3I2JFBSnEJdqnrd7T4cITARBEBYBm+0GdfXvMD7eCIDFspH8vPcwmbJDvLKp8Xg8XL16lQsXLuB0KtUbaWlplJaWkpER2qGBnt5x7NUdTN4bAH/nLsNSC5El6Rgyo+b9/rIk0XzrOjeOf0RX3YPA9YzCNRQdOExG4Zp5C4pkSabj0TC1NZ203R8K/P6RcUZW7kxl2ZYkjAvYqXYqRGAiCIIQQh7PCE1N/4nunvcB0OliWJrzuyQmvh7yPh5T4fP5uHv3LtXV1YHkyPj4eEpLS8nNzQ3p7+DuGsN+ph3ng6HANeOyGCJL0tGnzf/ugMft4uHZM9w8+TEjPV0AqDValm3bybr9rxOfkTlv93ZNeqm71EPt2U5G+ycD19NXxFBQnEr6iljUC5BDMxMiMBEEQQgBWZbp7T1GY9OP8XiUpMfkpLfJyfltdDpLaBc3BbIs8+jRI86cOcPg4CAAUVFR7Nq1i8LCwpD2InG123Gc6cBZp7yvqCBsZRzmXWnok+d/mN2EfZQ7n5/kzucnmHQoJbeGcBOFe/ax5tUDmGPmL3l5qHuM2pou6q/24vWXPOuNGvK3JFGwMxWLNXze7j1XRGAiCIKwwMbHW6iv/w4jtisAmEy55Oe9h8VSFOKVTU1rayuVlZV0dSm7AGFhYezYsYOioiJ0utAdC7habNjPdOBqsikXVBC+Kh7zrjR01vmvABru7uTmyY95ePYMXo9SchsZn8C61w6xctce9GHzExRIPonWe4PU1nTSVW8LXI9JNlFQnEruBiv6BZrjMxe+OisVBEH4ivP5XLQ9/lMeP/4psuxGrTaSmflrpKf9M9TqxXXO/2V6e3uprKykqakJAJ1Ox+bNm9myZQvGee5C+jyyLONqtGE/0467zd8QTK0ifG0CkcVpaOPC5v3+XXUPuHHiGM03rwUm/FqzlrK+7A2WbtiCWjM/DdEmHW4eXuzm/tkuxkaU0TAqFWSujqewOJXk3IVJ6J1rIjARBEFYAEPDF6ivf5fJyccAxMYWk5f7PcLC0kK8spcbHh6murqa2tpaANRqNevWrWPHjh2YzaGp5JBlGeejYezVHXg6/I2/NCpM6xMx70hFGzO/gZLk89F47RI3Thyjtyk4QyZr3QbWH3iDlGUr5i0o6GuzU1vTSeONPiSvEggZI3Ss2JbMih0pmOf5d59vIjARBEGYRy7XAI1NP6Kv7zgABr2VpbnfISH+1UX/aXZsbIxz585x48YNJEkpbV25ciW7du0iNjY2JGuSJZnJB4M4znTg6VEG/6l0akwblIBEM88NwdzOSe5Xn+bmyXLsA0rjO41Ox4odu1m7/xCxKfMTaPo8Ek03+7hX00V/W7BVfEKGmcJdqWSvS0C7QEMF55sITARBEOaBLEt0df+M5ub/hNfrANSkpv4C2Vn/Dq12cfSLeB6Xy8WlS5e4dOkSHo8yVTc7O5vdu3eTnJwckjXJPpnJewPYqzvw9k8AoNJrMG1OwrwtBY1ZP6/3Hxse4vZnx7lb+Sku/yTkMHMkq1/Zz+q9+wmPsszPfUec3D/XxcML3Uw6lD8LtVbF0nVWCoqVVvFfNyIwEQRBmGMOxyPq6t/Bbr8DgNm8kvy8HxIZWRDahb2E1+vlxo0bnDt3jokJ5eGfnJxMaWkpWVlZIVmT7JOYuNWPo6YD75DSH0Vl1BCxNYWILclo5rkHx5MJv48u1CD5lKZk0UnJrNv/Ost3lKAzzP2xiSzLdDfYlFbxdweR/a3iI6INSqv4rcmER85vIPZF3pERht9/f0HuJQITQRCEOeL1jtPa+l/o6PxrZNmHRhNBdtZvkpr6T1GpFu82uyRJ1NbWUl1djc1mAyA2NpaSkhKWL18ekiMn2SsxfqMPR00HPpuS2KkO1xKxPYWIzcmo57HK5HkTflPyl1N04A2y121ANQ/l0G6nl4ZrfdTWdDLcPR68b66FguJUMlfFoV7AVvGyJDFx7Rq2ox/gOHUKh79x3nwTgYkgCMIcGBg4TX3D93G5egBISHiNpUu/jdEwf0PYZkuWZRobG6mqqqKvT8mXiIiIoLi4mDVr1qCZp2qSF5HcPsav9eI414n0ZMpthA7zjlRMG5NQG+ZvTT6vh/pL57lx/KNnJ/xu3ELRgcMkLc2bl/va+ia4f7aLR5d7cE/6W8Xr1eRtSqJgZwqxKfPfe+Vpnv5+Ro99jO3DD/G0tweu63Nzoalx3u8vAhNBEIRZcDq7qW/4PoODlQAYjank5X6PuLhdIV7Zi3V0dFBZWcnjx0qVkMFgYNu2bWzcuBG9fmGPCQAkl5fxKz04znchjSm5FJpIPeadqZg2JKKax8TOwITfz44zNqx0idUZjKzctYe1r83PhF9Zknn8YIjamk7aHwwHrkfFh1FQnEr+5kQM4QtXQi77fIydP4/tgw8Yq64Bn9KcTW0yEXngAJa33sKdngYLMIBRBCaCIAgzIEleOjv/hpbWP8bnm0Cl0pKe/s/JXPJv0Wjmt3fGbAwMDFBVVUVdXR0AGo2GjRs3sm3bNsLDF74rqDTpZexSN2MXu5D8g+U00QbMxWmY1llRaefv6MI+0M+tT8u5V3UqOOHXEs2aVw9SuGcfYRFzn6TsHPdQd7mH2rNd2Af8reJVkLEyVmkVvywG1QK2ivd0dWH78CNsH32Et7c3cD1s9WosR44Q+eorqE1KczqP3f68l5lTIjARBEGYptHRO9TVv8PY2CMAoqLWkZ/3HhER87PVPxdGR0epqanhzp07yLKMSqVi9erVFBcXExU1/4Psvsg37mHsQhdjl7qR/a3TtXFhmHelEb46HtU85lL0Njdy48QxGq5cQPaXQcemplN08A3yt+5EOw/dawc7x6g920nD1V68buWehnAt+VuSWLkjBUvCwgWFstuNo7oG29GjjF+8GGgKp4mKIur1Q0S9+SbG3NwFW88XicBEEARhijweO80tf0hX198CMlqthaU5v01S0luoVKGbDfMik5OTXLhwgatXr+L1KjsS+fn57N69m/j4+AVfj8/hxnG+k/ErPcj+B7TWGk5kSRphBfHztlsgSxItt29w88QxOh7WBq6nF6ym6MBhlqxaO+dJvj6fROsdpVV8d6MtcD025Umr+ER085gz80WullZsH37A6Mfl+IaCgw3DN23CcuQtzKWlqA3z2wdmKkRgIgiC8BKyLNPXd5zGph/hdisD65IS3yAn53fQ60PTaOxlPB4P165d4/z58zj91RTp6ens2bOHtLSF7zbrtbkYO9fJ2LVe8CoBiS7ZRGRJOsblsfMWkHjdbh6er+bmiWMMd3cCoNZoyNuyg6IDh0lYMvdl0BN2Nw8vdHP/XBfj/ooilVpF1uo4CnelkpSzcK3iJacTx6lT2N4/ysSNG4Hrmvg4LIffwPLmG+gzMhZkLVMlAhNBEIQXmJhoo77+uwyPXAAgPDyL/Lz3iI7eFOKVfTlJkrh79y7V1dXY/TkBCQkJlJaWsnTp0gUv/fUOO3HUdDB+sw98ypGBPt2MuSQdY170vK1nwj7K3dOfcOfzk0yM2pT7hoVTWPoqa/eVYY6d+wm/fa3+VvE3g63iw8w6VmxPYcX2FCKiF243wllfj+39o4weP470JDdErSZi+3Ysbx8hYscOVCEcuPgiIjARBEH4EpLk4vHjn9L2+E+RJDdqtZ4lGf+GjIx/iVod+u3uL5Jlmfr6eqqqqhgYGAAgMjKSkpISCgsLUc9D340X8QxM4KjuYOJOPygbJOgzo4jcnYYhe/52DEZ6urh5spwHZ6vwupXdCnNcvH/C714Mc5zg+7xW8dbMSAqKU8lZm4BGtzDvvW9sHPsnJ7Ed/QBnbfC4SpecTNRbb2J54w10iYu3fP0JEZgIgiB8wcjIFerq32ViohmAmOit5OV9n/DwzBCv7Mu1t7dz+vRpOjo6ADAajezYsYP169ejW+BPxZ7ecezVHUzeGwBl0wDDUguRJekYMucvybar/hE3jn9E040rgWTOhMxsig6+Qe7GrWi0c/u4e26r+CJ/q/glC9MqXpZlnPfuMXL0KPZPPkX2d+xFp8NcUoLlyBFMWzbPS0O4+SICE0EQBD+3e4impt+jp/cjAPT6OJbmfBur9eCiHLjX399PVVUV9fX1AGi1WjZt2sTWrVsJC1vYkmV31xj2M+04HwSTKo3LYogsSUefNj+zgSTJR9P1K9w4/hE9jfWB61lr11N04DCpywvm9M9NlmW6G/2t4u+EtlW8z2ZjtOI4tqNHcTUGm57plyzBcuQIUa8fQjvHgxY9kmdOX+95RGAiCMI3nixL9PR8QGPT7+P12gAVKSn/mOys/wedbvENSfuy0t81a9ZQXFxMZOTCrtfVbsdR1Y6zfkS5oIKwlXGYd6WhT56fjqUep5P7Nae5+Uk5o31K7w2NVsvyHSWs23+Y2NS5Te71uHw0XOultqaToa5gq/jkpRYKdy1cq3hZlpm4dh3b0aM4Tp1CdiudcVUGA5GvvoLlyBHC1q2b8yC6briO8qZyKh5UzOnrPo8ITARB+EYbG2ugrv47jI4qFQsREcvIz3uPqKg1IV7Zz1sspb+yLONuHcV+pgNXk025qILwVfGYd6Whs5rm5b5jI8Pc+fwEd099gnN8DABjhJnVe19j9SsHMFmi5/R+owOT1J7tpO5SD66Jp1rFb0ykoDh1wVrFewcHsR07hu2DD/A8DraIN+TnYznyFlEHDqCZ4140g5ODnGw5SUVzBQ0jDQD4/P1m5psITARB+Eby+SZpbfsT2tv/Aln2otGEk5n566Sl/iJq9eL6p3GxlP7Ksoyr0Yb9TDvuJ4meahXhaxOILE5DGzc/x0eDHY+5efJjHp2vxucPyCzWJNbtf50VO3ejM87dhF9Zkul4NMy9mk4e3x8K5MlExodRsDOF/M1JGOd5ojEoLeLHL15Udkeqa8D/e6vDw5UW8UeOYFy5Yk53R1w+FzUdNVQ0V3Cx6yI+WQlEdGodxWnFlCaUsp/9c3a/51lc//UJgiAsgMGhGurrv4fTqSSLxsWVkpf7XYzG5BCv7FnPK/3dvXs3ubm5C5b3IssyzkfD2Ks78HQ4lIsaFab1iZh3pKKNmbvA4Ol7djy4x43jH9F652bgenLuMooOHia7aCNq9dw1J3NNeqm73MP9s13Y+iYC19NXxFBQnErGivnrtfI0T3d3sEV8T0/getiqVVjePkLkq68GWsTPBVmWuTd4j4qmCj5t+xSH2xH4WmFcIWXZZbya+SpRhqjA38H5JgITQRC+MVyuPhoa3qN/4FMADIYk8nK/S3z8nhCv7FmLpfRXlmQmHwziONOBp0fJrVDp1Jg2KAGJJmruy6Z9Xi8Nl89z/cQxBtpalIsqFUs3bKbowGGSc5fN6f2Gu8epPdtJ/ZVePP6jCr1RQ/6WJAp2pmKxzn+reNnjwVFdje3oB4xfuPBMi/jIQ2VY3nprzlvE9473crz5OBXNFbTZ2wLXreFWDmYf5GD2QbKi5r753FSIwEQQhK89WfbR2fl/aG75z/h8Y6hUGtJSf5HMzF9Hq52ffIiZWgylv7JPZvLeAPbqdrz9yqA5lV6DaXMS5m0paMxzX3nimhintupzbn5awdiQ0l1XazCwsriUta8dIjpx7nazJEmm7Z7SKr6zbiRwPTrJRGFxCrkbE9Eb5//x6G5rw/bBB9iOffxsi/iNG7EcOYJ5z9y2iJ/wTFDVXkV5cznXeq4h+8+pwrRh7E7fTVl2GRsSN6CZw52omRCBiSAIX2t2x33q6t7B4VAaTkVGriY/7z3M5uUhXtmzFkPpr+yVmLjdj6OmA++QksuiMmqI2JpCxJZkNPOQW2EfHODWpxXUVn2Ge1IJgsKjLKx59SCr9uwjzDx3VUbOMQ8PL3Zz/2wXjmH/76eCzFXxFBSnkDKPnWifkFyuYIv469cD1zVxcVgOH8by1ptz2iJekiVu9t2kvKmc049PM+ENHlMVWYsoyy5j75K9mHSLJ0AXgYkgCF9LXq+D5pY/orPzfwMSWq2Z7Kz/QErKP0SlCu0nwqcthtJf2SMxfrMXR00nPv9sF3W4lojtKURsTkY9D7sHfS1N3DhxjPrL5wMTfmNS0ig6cJhl24rR6uduV2ag3cG9mk4ar/fh8yj3Mpp0LN+WzIodyUTGzn/g56xvwHbU3yJ+dFS5qFZj2r6N6CNHiNi5c05bxLfb26loruB483G6x7sD19PMacpRTdZBUs2pU3/BwUa4/L/mbH0vIgITQRC+VmRZZmDgcxoafoDL3QeA1XqQpTnfxmBY+Gm6z7MYSn8lt4/xa704znUi2ZWeGOoIHeYdqZg2JqGe48m3siTRevcmN08co/3+vcD1tBWFFB08TOaqdXPWodTnlWi5PcC96k56W0YD1+PTzRQUp7K0KAGtfn4DVGl8HPunnzJy9CjOu8HfV5uchOVNf4v4pKQ5u5/dbefzts+paKrgzsCdwPUIXQSvLHmFsuwy1iSsmfqukKMP7n8I9/4eeu6AS56ztb6ICEwEQfjamJzspL7hewwNVQMQFpZOXu4PiI3dHuKVBS2G0l/J5WXscg9j57uQxpVunppIPeadqZg2JKLSze0D2+vx8OhCNTdPfMxQp9KHQ6VWk7d5O0UHDmPNypmze42PunhwvpsH57uYGPUHWxoV2WsTKNyVijUzcl6Pa2RZxnn/Prb3j2I/eRLpSYt4rfbZFvGauXmPvZKXy92XqWiuoLqjGpfPv+OlUrM5aTNl2WWUpJdg1E6xcsrlgEfH4d770HoWZP+gI5UGsrYD899kTQQmgiB85UmSh46Ov6Kl9b8gSU5UKh0ZGf+SJRn/Bo1m7ktZZ2IxlP5Kk17GLnbhuNiNPKns0miiDZiL0zCts6LSzm21z6TDzt3Tn3L7s+NPTfgNo6DkFda+VkZkXMKc3EeWZXpblMm+zTf7kfyt4sOj9KzckcLybcmY5qGC6Gk+u53R48exHf0AV11d4Lo+IwPL20eIOnQIbdzcTTRuHGmkormCky0nGZgcCFzPseRQll3G/qz9JIRP8f31eaCpStkZqf8UvJPBr6Wuh4K3YcVhBmwyIjARBEF4idHR29TVv8PYmPIwsFg2kJ/3HibT3H0Knw1ZlmloaKCysjJkpb++cQ9jF7oYu9SN7C+J1caFYd6VRvjqeFRz3E7d1tvDzU8+5n5NJV6X8gk+IiaWta8donD3KxjC5ybR0uv20Xijj9qaLgbag/03krKjKNiVStbqeDRzHGw9TZZlJm/exHb0KPbPPkf2/64qvR7zK69gOfIW4evXz1nQOewc5tPWTylvKufR8KPAdYvBwmuZr1GWU8bymOVTu58sQ8dVZWfkwTGYHA5+LTZHCUYK3mI0LJ3PHvRQ/nfNXHrUMSe/x8uIwEQQhK8kj8dOc8sf0tX1t4CMVmth6dL/SFLim4tm4F57ezuVlZW0tyvHFwtd+utzuHGc62T8Sg+yP+lTaw0nsiSNsIL4OW8Y1t3wiBsnjtF47XKgF0f8kizWHzhM7ubtczbh1z40yYNzXTy80IPzyVGUTk3uemWyb3z6/AwNfMI7PMzox+XYPvgAd0tL4Lph6VJlgF7ZQTQWy5zcy+PzcK7zHOXN5ZzvPI9X9rfGV2nZkbqDspwydqTsQKeZ4t+ngXolGKk9CrbHweumBFj5JhS+zWRcIVX1/ZSf7OZsfQNun/J3R1qYFBMRmAiC8NUiyzL9/Z/Q0PgebreyA5GU+AY5Ob+DXj+301RnKtSlv95RF46aDsav94JXeZrokk1ElqRjXD63HUwlyUfzjavcOH6M7obgp/jM1etYd+Aw6StXzUmgKMsyXfUj3KvupO3e4JO4B3OMkZU7lcm+xoj5C/ZkSWLiyhVGjh7FUVkFHiUgUoWHE/naPqKPHMFYWDhnv+vDoYeUN5fzaeun2Fy2wNeWxy6nLLuM1zJfI9o4xdlA9p5gEmtvMAkXfQTkH4DCt/FkbOdCi42KC92celDJuDs4FycizIcnAWxx8fDHs/71XkoEJoIgfGVMTnZQ3/BdhobOAhAenkle3nvERG8O8coUoS799Q47cZztYPxGH/iUJ7c+3Yy5JB3jHPfo8LicPKip4uYnH2PrVVqna7Ra8rcVU7T/deLSl8zJfdxOLw1Xe7lX08VIT3Cyb2p+NAXFqSwpjEM9j63iPX39jD4ZoNfZGbhuXLkSy5EjRO5/DU3E3Azz65/o50TLCSqaKmgebQ5cjw+L50DWAcqyy8iJnuIRpdPuT2L9e2g9R2Doj1oL2buh8G2k3H3c6HZRcbeLk39bw8iEJ/DjYXoJ4n3YMlJwmpWAT+UQLekFQRAAJbm1veN/0tr6X/3JrXqWZPwrMjL+FRrN/CY1TkWoS3+9g5PYazqYuNUf2G/XZ0YSWZKOIccypwHJuG2EO6dOcufUJzj9DyqjKYJV/gm/EdExc3IfW99EYLKv2+kfJmfQkL8pkZXFqcQkzV9DMNnrZez8eWxHP2Ds7FnwKfdXm81EHfQP0Fs2N63xnV4nZ9rPUNFcweWey0j+KhiDxkBJWgllOWVsStqEdiqDJb1uaKpUgpGGz8DrDH4tbSMUHEFecZiHozoq7nZz/MQVukeD32PQyWhiPdjSEnFGG5Xuc0D6yAiZQy2snDjPu3PyW7+YCEwEQVjURkdvUVf3DmPjyrGIxbKR/LwfYjKFZo7H00Jd+uvpn8BR3cHEnf7AB2JDjkUJSLKi5vReQ50d3Dx5jIfnq/H5jzGiEqysfe11Vu4qRW+c/RGVLMk8fjBEbU0n7Q+CyZgWazgFxSnkb0pCHzZ/jy1PVxe2Dz/E9uFHePv6AtfD1q3DcuQtIl95BfUcHMXJsszt/ttUNFfwedvnjHnGAl9bk7CGsuwyXlnyCmb9FHJlJMmfxPr38PBjmAy22CcuN5DE2iYlUHG3m4o/e0BTf/B+Oo2MPtrNaLoVZ2w4+HefkkdHyRxso8h3jazo+8RkdjPpr+SabyIwEQRhUVKSW/+Arq6/A2R0umiW5vwuiYmHQ57cGurSX0/vOPYz7UzWDgYCEmNeNOaSdAwZc3dkJMsynQ9ruXHiGC23gu3Tk3LyKDp4mJwNm+dkwq9z3EPd5R5qazqxD/o/watgycpYCnalkpYfM2+TfWW3G0d1DbajRxm/eDE4QM9iIer117EceQtDdvac3KvT0RkYnNc5FjwWSolI4WD2QcqyykiLnGJA2//In8T6AYy2B69HWGHlW1D4Nv2mPI7X9lLxd93c7XgY+BaNSsZocWNPi8WZEAka5b2NHxsja6CdtZ7r5EXdJSajE40mmGtiMmUDbbN5C6ZEBCaCICwqSnLrSX9yqzLMLSnpLXKyfxu9fm6OCWaztlCW/rq7xnCcaWfyQXDgm3FZDJG709Gnzl0lis/rpeHqRW6eOEZfS5NyUaUip2gjRQfeIDlv2ZwEX0NdY9yr6aThai9et/8II1zLsi1JrNyZSlT8/CUKu1pbsX3wAaMflz87QG/zJqVFfGkp6jloiz/uGedU2ynKm8u52XczeB9tOHuX7KUsu4x11nWoVVP4u2PvVgKR2vehtzZ4XW+GZQeh8G1GrZv57JFSUXO55UwgSVgFhEe6GEu1MJ5oYVyn3C96YpLswQ5WOW+y3HyLuNQOtNqnck2M6VitB7BaDyBJScDc7sR9GRGYCIKwaExOtlNX/y7Dw+cBCA/PIj/vPaKjN4V4ZaEt/XV3OLBXteOs8x9vqCBsZRzmXWnok+cm8RLAPTlB7ZlT3PykHMegEnhpdXpWFJeybv8hopNSZn0PySfReneQe9WddDfaAtdjU0wUFKeSuzER3Ty1in/eAD1tfDxRb7yB5c030Kenz/o+PsnHtd5rVDRXUPm4EqfPPzAQFRuTNlKWXcbu9N2E68Jf/mLOUXhYoQQjred5Jol16V4oOMJk5l6qmu2UX+ympv4MHl+wrjfc5GYyOYLJlBgm/SMGIlwucjo7KZi4S6HpGvGJ7eh0rsDPGAyJWBP2Y7UewGwuCAShT3YH55sITARBCDlJ8tDe/pe0tv1XJMmlJLcu+TcsyfiXqNWhTW4NZemvq20U+5kOXA3+vAEVhBXGE1mShs46d8mfjqFBbn1awb3Kz3BPKi3UwyKjWPPKAVbtfY3wyNl/Sp6wu3l4QWkVPzbib0SmVpG1Op7CXSkkzXGS7tOcDQ3Yjn7AaEXFMwP0IrZvx/K2f4DeHPRYaR1tDQzO65sI5qgsiVzCoZxDHMg6QKIp8eUv5HVB42klGKn/DHzBoIH0zVBwBE/+IS50SVTc7ebU+xeeKe8NM3pwJxmZSIvD6c/JCXN7yO7uZMVYLWvCrpAQ34bBEOzwqtPFYk14Dav1AFFRa1FNZQdnnojARBCEkLLZblBX/w7j440AREdvJj/vPcLDM0O6rlCW/rpabNir2nE1P3mIQvjqBMy70tDFT+FT9hT1t7UoE34vnUPyV55EJ6dSdOB1lm3fhU4/+6Cwr81ObXUnjTf7kPw9VcLMOlZsT2HF9mQioudnZMCTAXq2ox8wefdu4PpcD9AbdY3yedvnlDeVc28w2CPErDcr3VizyyiIK3h50CVJ0H5ZCUYefAxOW/BrcXlKee/Kt7gxGkn5nS4++fTWM+W9Br0Xn1XLeFpCoLxX7/WS3dtFvuMB6/SXSUpoxpgULLnWaiNJiH8Vq/UAFstG1FOp/FkAi2MVgiB843g8ozQ1/ye6u38GgE4X409ufT2kya2hKv2VZRlXkxKQuNv8W+ZqFeFrE4jclYY2dm52Z2RZpu3uLW6cOEZ77Z3A9dTlKyk68AZZa4pmPeHX55FoutXPvepO+tuC2//WzEgKilPJWZuARjf3n8iVAXoPlBbxJ08ijfsfwoEBem9h2rJl1gP0vJKXS92XKG8qp6ajBrekDAvUqDRsTdlKWXYZxWnFGKZSyt73UAlGaj+A0adavpuTYOWbyAVHeCgvoeJuD8f/rPmZ8l6d1ocqHhzpiTijdKBSofX5yOzvJXe0jvX6i6TENRFuDf4ZqNXhJMTvwWo9QEzMNtTq2efRzDURmAiCsKBkWaav7zgNjT/E41GSDpOSjrA057fR6abYyXIehKr0V5ZlnPUjOKracXf4571oVJjWJ2LemYp2jnYUvB4PdRfPcvPEMQY7lFbkKrWa3E3bKDpwmMTspbO+x9iIMzDZd9KhfJpXa1UsLVJaxVuXzM9Ok8/hCA7QexTsPqvLSCf6yBGiXn99TgboNYw0UNFUwYmWEww5gwmzS6OXcij7EPuz9hMXNoX7jHbB/Q+Uqpq++8HrhkhYVgaFR2iLWEtFbR/lf9dF88DFwLdo1RKaWJ8SjMQYQK1CLUlkDg+SbWtgo/oSaXH1RMQHy4ZVKj1xcSVYrQeIiy1Go5n/7sOzIQITQRAWzMTEY+obvvtUcmu2P7l1Y8jW9GWlv/Hx8ZSWls5r6a8syTgfDWE/04Gny99XQqsmYoMSkGjmaBquc2yMu6c/4fbnJxgfUZJndcYwCkr2snZfGVEJ1lm9vizL9DTZuFfdRcudAWR/g7eIaAMrdiit4sMj5/5TuSzLTN66he3oB9g/+wzZH0yq9HrMe/diOXKE8A2zH6A37Bzmk5ZPqGiueGZwXrQhmv1Z+ynLLiM/Jv/l95m0wcNyZUZN2wWCSaw6JYm18G36E3dy/JGNik+6uNt5PvCjapWMLtrDWGoCzoTwQHlv2sgIOSONbOASS2IfEZk1GPgZlUpLTMx2rNYDxMftRqud3/lBc0kEJoIgzDtJctPe/he0tv0JkuRCrdazJONbZGT8i5Alt4aq9FeWZCbvD+I404GnVzlqUOnUmDYnYd6eisY8Nw9xW18vtz4p5371aTwu5aEdER3Dmn1lFJa+itE0u2oej9tHw9Veamu6GOoKNuxKXmqhcFcqmaviUM/x1GIA78hIcIBec7Btu2Fpjn+AXtmsB+g9d3CeWsvO1J0cyj7EttRt6NQvqcbyuqDhc+WopuFz8LmDX8vYCgVHsGfu59MWJ+WXurnccump8l4ZQ6SHsdRYvEkR4J+SnGQfJXuohfXyZXKi7xO1pI9gTKQmOnoTVusBEuL3hnQHcjZEYCIIwrz6+eTWLeTn/SCkya2hKP2VJZnJewPYz7Tj7VeqIVQGDRGbk4nYlowmYm4Ckp7GemXC79VLyP725vHpS1h34DD5W3eg0c7u9xsdmOT+2U4eXerBNeF/YOvV5G5MpLA4ldiUuStffkKWJCauXsV29CiO05XITwbohYUFB+itmt2wwBcNzlsRu4Ky7DL2Ze57+eA8SYL2S/5OrOVKue8T8cug8G2c+W9wultPxd1uao7deKa812ByM5kSjTvZHCjvjR8bI2uwjSLfVfItd4jO6EGlCv5MVNQ6fzCyD4Nh/kcgzDcRmAiCMC88Hps/ufXvAX9y69Jvk2g9FLLk1oGBAaqqqqirqwMWpvRX9klM3B7AUdOBd9AfkBg1RGxNwbw1GXX47AMhWZJovnWdG8c/oqvuQeB6RuEaig6+QUbB6tk9tCWZjrphaqs7abs/FDiFiIwzUlCcSv7mJIymuQ/oPP39jB77WBmg1xFMDDWuWKEM0Duwf9YD9AYmBjjRcoLypvLZDc7rexDsxGoPdnXFnAwFb+FZ8RYX7ImU3+3m1Ok6Jp4q79Ub3biSzbhTLYHyXsvEJNntj1nnuc7yyJvEpnWhVkvBlzWvVBqfJezHaEye1Xuw2IjARBCEOfVlya3JSW+Tk/NbIdtaDkXpr+yVGL/Vh6OmE9+wcpSiDtcSsS2FiC3JqI2z/+fX63bz8PwZbpz4mJFu5WGo1mhZtm0n6/a/TnzG7Hal3JNe6q70UFvTha1vInA9fUUMBcWpZKyInfNW8bLPFxygV1MTHKAXEUHkwQPK7sjy5bO6h9PrpLqjmvLmci53z2Jw3miXkjNSe/Tnk1iXH0JaeYQbqhWU3+3hk7/sYWQiGLDo9B48ieG4UqMD5b1ml4uszjbWuG6yKuIqsclfbAm/FGvCAazW/SEvp59PIjARBGHOTEy0UV//LsMjShVBeHgO+fk/JNqyPiTrCUXpr+yRGL/RqwQko0pjLLVJh3lHCqZNSagNs/9nd9Jh5+4pJaF1YtQGgD4snFV79rFm30HMMbOrQBnuGed+TSd1V3rxuJQHo96oIX9LEgU7U7FY566XyhPKAL2PsH30Ed7e3sD1sLVrld2RV2c3QE+WZe4O3KW8uZzPWz/H4XEEvrY6fjWHcg6xd8leIvUvCVQnbfCoQtkd+WISa+4ryAVHeGTeTPn9YY6/30336NXAj2q1XiSrnsnU2EB5b5jHQ1Z3G4WTd1gXfol4a/uzLeHD0v3ByAEiIvJm/Pt/lYjARBCEWZMkN4/b/5y2tj9BktxKcuuSf0tG+r8ISZ+EUJT+Sm4f49d6cZzrRLIrSY5qsx7zzlRMGxJRz0GbdVtfLzdPfsz9mtN4XUrQY46NZ93+Q6zctRdD+MwDBkmSabs3SG1NJ511wVLT6CQThcUp5G5MRD8HuzxPkz0eHNXV2I5+wPiFC88O0Dt0SBmglzOFY5QX6BnrUbqxthznsf1x4HqSKUkZnJddRkZkxotf5Ekn1nt/709ifaoTqz+JtT1xDx/XT1L+WRfNAzcCX9aofRCvZjI1LlDeq/d6yerrZOXEfYoMF0mMa0Gnf3lL+G8KEZgIgjArI7br1NW9w8SEMuwtJnoreXk/IDx8yYKvJRSlv5LLx/iVHhznO5HGlE+6mig95uI0TEWJqOagkVhPUz03jn8hoXVJFusPvkHupm1oZtFO3Tnm4eHFbu6f7cLhP3JSqSBzVTwFxSmk5EXP+fvmbmvD9sEH2I59/OwAvU2bsBx5C/OePbMaoDfhmaCyvZKKpgqu9V5D9u9qhGnD2JOxh0PZhyhKLHrx4LxnOrEe+9Ik1sHMg1S0aSm/2s3djmB3WZVKQhMjM5EWjxRnBI3S+CxrqJfljgds0F8gOa4Jg3VxtoQPNRGYCIIwIx6Pjaam36e7531A+Yc1d+k7WK0HF/wT3vNKf3ft2sWqVavmpfRXcnoZu9zN2PkuJH91iibaoAQk66yotLO7pyxJtNy+zo3jx+h8FMxfWLJ6HUUHDpO+cnZVKAPtDmprOmm43ofPowQ7RpOO5duSWbEjmcg56jT7hDJA7zS2o0eZuHYtcF0TF4fl8GEsb72JPuMlOxcven1Z4mbfTT5u+pjTj08z6Q0+9DckbqAsu4w9GXtePjiv/5GyM/JlnVgL3sKR9yaf9sVSfq+by580IAWKY2R0UT7G0+KRrEbQqlFLEktGBsi317FJc560+HqMcYu/JXyoiXdBEIRpkWWZ3r5yGht/hMejNOxKTv4H5GT/FjqdZcHXs9Clv9KEB8fFbsYudiM7/eWysUbMu9IIX5OAapa9O5SE1mpunjjG8BcTWg8cJj59yYxf2+eTaLk1QG1NJz3NwR2A+HQzBcWpLC1KQDvHk31djY2MHD2KvbwC31MD9EzbtxF9xD9AbxZ/Tu329sDgvO7x7sD1NHMaZdllHMw+SErES6Yi27uVQOTe+9BXG7xuiITlZbiXv0Xl5FLK7/ZSfbYXty94H12Eh4mUGHxJJpz+8t5U2zC5tgY2qc6TGfeQ8JivVkv4UBOBiSAIUzYx0Upd/buMjFwClCqB/LwfYrEULfhaFrr01zfuYexCF2OXupH9CaHa+DAiS9IJK4xHpZndLtHkmENJaP3s+JwntI6PugKt4idG/fkvahXZ6xIo3JWKNTNyTne5pIkJZYDe+0efHaCX5B+g9+bsBug53A4+b/uciuYKbvffDlyP0EXwypJXOJRziNXxLymRdo7CwwrlqKb1PF/sxOpbeYRLmiKO3R/i1P/pY8z11O9h9OBKseBNjsAZrjxGE+2jLO1tYpN8kZzYe0RkfHVbwoeaCEwEQXgpSXLx+PFPaXv8p/7kVgOZS36V9PRfXvBPfAtd+utzuHGc72L8SjeyWzny0CWGYy5JJ2xl3KzLZUf7e7l5spza6lPPJLSufa2MgpJXZpzQKssyfa127lV30nyrH8nfxCs8Us+KHcpkX9Mctb1/YvLJAL0TJ54doLerGMuRI5i2bp3xAD2f5ONKzxXKm8s5034Glz8BVa1SszlpM2XZZZSkl2DUvmC2kNcNTf4k1vrPnk1iTd+MXPA2d6N2cezROCfLexgcC04L1uq9uJMi8KREBsp7Y8fHyOloYaPvMssst4lMHwh8v9ISfhtW68GvXEv4UBOBiSAILzQyco26+neYmFCaT8XEbCcv9/uEh888H2AmFrr012d34Tjbyfi1XmR/DoYu2URkSTrG5bPv39Hb1MD1E8dovHIxmNCakakktG7ePuOEVq/HR+P1fmprOhloD5bEJmVHUVCcStaaeDSzzH95ms/hwH7iBCNHj+J6+OwAPctbb2F5/XW0s/jzabY1U95czsnmk/RP9geuZ0dlU5ZTxoGsAySEJzz/BSQJOq4qwciDY+C0Bb8WlweFb9Oc+BoftqipONNN58jDwJc1Gi8+axiu1CicFj2oVERNTpLd0cQG31VWRl7HktL3VBdWldISPuEACQmvfGVbwoeaCEwEQfhSHs8IjU2/T0/PUSB0ya3PK/0tLS0lPT19zu/ntTlx1HQyfqMXvMoDR5dmJnJ3OsZZVqgoCa03uHH8o2cTWletpejAG6QXzDyh1THs5P7ZLh5e6MY57q8O0qnJXa9M9o1Pn7tP7LIsM3n7Nrb3jz47QE+ne3aA3gyTjm1OG5+2fUpFUwX3h4LvU5Qhin1L9nEo5xArYle8+L3qr1OOae4dhdH24PWIRCh4i96MMj7qiaHiVg91va2BL6vVPuR4Pa4UC1KsUt5rcrvJ6mmiyH2DNRGXiU7uRq1+qiV85BqlJXzCaxgMLwiShCkRgYkgCM+QZZne3mM0Nv34qeTWf+hPbo1asHUsdOmvd9iJo6aD8Zt94D/20C+JJLIkHcNSy6zu53W7eXShhhsnjjHcpVR6qDUa8rfsoOjgGzPu0CrLMl0NNmqrO2m9OxAYAGeOMbJypzLZ1xgxdwnA3pERRsv9A/Sagu3b9TnZRB85QmRZGdrome0SeCQPFzovUNFcQU1nDV7Jn1is0rItdRuHsg+xI3UHes0Ljg7tPXDfn8TaGzyGQW+G5WWMLj1MxWgWH9/t42b1IKBM41WpJFTRapxp0UjxRtCoMXo8ZPa3scZ5h6LwC8QldDzThTUiYjmJ1gMkJOwnLCx1Rr+z8OVEYCIIQoCS3PodRkYuA6FJbpVlmaamJk6fPk1/v7J1P5+lv57BSRzVHUzc7gP/KBJDVhTm3ekYsqJmFZBMjjm4d/pTbn1a8UxCa2Hpq6zdV4Y5dmYJrR6Xj/qrvdTWdDLcHSw/Tc2PpqA4lSWFcajnqFW8LElMXLuG7f2jOE6ffnaA3r59WI68Rdjqmc/iqRuuo7ypnE9aP2HYORy4nh+TT1l2Ga9lvkZsWOzzX8BpD3ZibT1HMIlVC0v34lz2Jp95VnOsdpgL/98gPqnuyW+GJkrGmRqDzxoGOjU6n4/soW4KJ+6yyXiOhNg2tFpv4Fbh4VlYrQexJuzHZMqe0e8rvJwITARBQJJctD3+KW1tf4osP0lu/TXS0//Zgia3dnV1cfr0adra2gCl9Hf79u1s2LBhzkt/PX3j2Ks7mLw7EHiWGXKjiSxJw7BkdjtDo/293PyknPtnTuNxKcccEbFxrNtXRsHuVzCEm2b2ul822degIX9TIgU7U4lJntnrfpnAAL0PP8TTHjwKMS5fjuXtI0Tu34/GPLPjocHJQU62nKSiuYKGkYbA9RhjDPuz9nMo+xB5MS9ov+51Q1OlclRT/yl4ncGvpW3Cs/II53Vb+fDRJJUf9OHy1ge+rDH5lGAkMQyMGjSSxJLhfgrG77NJf5aU2CZ08e7g72tM8beEP0hERP43rgtrKIjARBC+4UZGrvqTW1sAJbk1P+8HhIXNff7G8wwPD1NVVcWDB8pkXI1Gw4YNG9i+fTvhs2iz/mXcPeM4zrQzeX8wEJAY82OI3J2OPm12eRi9zY3cOP4RDV9IaC06+AZ5M0xolWWZzkcj3KvppK02uObI+DAKi1PJ35KEIWxu/imXfT7GL1xg5OhRxqprggP0TCYiDx7AcuQIYStWzOi13T43NR01VDRXcKHrAj5ZeW2dWkdxWjGHsg+xJWULOvVzAlBZfjaJdTJYjktcLlLB29yOKuX9Jg2ffNqDw9kS+LLG6MOVHIUv2YRs0qKSZdJsQyzvf8BmzTky4urQxwaDG70+PtASPjJydpOZhekTgYkgfEO53cM0Nf0ePb0fAqDXx7F06TtYEw4s2D/E4+PjnD17lhs3biBJyoO8sLCQkpISLBbLnN7L3TWGvaod58NgC3TjilgiS9LRp0TM+HVlSaL1zk1uHP+IjofB5lwZhWsoOvgGGQUze7C5nV7qryjHNSO98zvZ19PdHRyg19MTuB62Zk1wgN4MAkRZlrk/eJ/y5nI+bf0UuzvYaKwgroBD2Yd4NfNVogwv2KEaqFeOaWrfB9vTSaxW5JVv0Wjdx886YjhxoYd+R7DxmVrnw5NkxptiQjYrA/OSRkfI76hns+os2bEPMFqe7sJqISFB6cIabdmASjW3jeaEqROBiSB8wyjJrR/5k1uVT50pKf+Y7Kz/gE43931Avozb7eby5ctcvHgRt1vZNs/OzmbPnj0kJibO6b1c7XYcZzpw1vnzF1QQVhBHZEk6usSZH314PR4eXajm5omPGepUHphPElrXHThMwpKsGb2urX+C2ppO6i714Hb6dxWMGvI3J1GwM4XoWaz5aYEBeh98wPj5pwboRUUR9fohLG+9hWHp0hm9dt94H8dbjlPRXEHraLDiJSE8gYNZBynLKSMr6gXvj6NX6cRa+z70BBuboY+AZWV0pR/k/aFMyu/20lZjB5SAR63x4bWa8KREIEcr5b3xYw5yO++zWXWevOg7hKcGgyONxkR8/B6s1oPERG9F/bzdGmFBicBEEL5BxsdbqKt/B5tNGcVuMuWSn/9DLFHrFuT+Pp+PO3fuUF1dzdjYGABJSUns2bOHrKyZPcifx9U6iv1MO65Gm3JBBeGrEzDvSkOXMPPjIefYGHdPKx1ax21KYKcPC6OwdB9rXj1IZNz0e3bIkkz7o2Fqqzt5fD+4o2OxhlNQnEr+pkT0c3Rc4378ODhAb3AwcD1840YsR45g3lOK2jD9xmuT3knOtJ+hvKmcKz1XAoPzjBojJeklHMo5xMbEjWjUz9mJcNqh7oRyVNN6DvxHYai1kFPKSM7rfDRewLH7I9y/ageUoxqVWkKKM+BJMSsD89QqLJMTLO2+zybpIistN4hIDR77qNUG4uJ2Y004QGzsTjSaFzRkE0JCBCaC8A2gJLf+GW1tP/EntxrJzPw10tP+2YJ8SpRlmbq6Oqqqqhj0PwwtFgu7d+9mxYoVc1ZpI8syruZRHGfacbU8mcuiInyNPyCJm3kr8NH+Pm59Uk7tmVPBhNaYWNa+dojCGSa0uie9PLrcQ21NJ6P9/qFzKshYGUthcSppy2Lm5LhGcrlwnK5UBuhdvRq4PtsBerIsc6v/FhXNFXze9jnjnuDRyNqEtRzKOcTejL1E6J9zVOZ1Q3OVclRT/8kXklg3MpH3Bp/Km3n/4QTXjg0jy4+f3BlitLhTIpESlIF5ES4XOb31bPBdYXXkZaKSg0GXSqUlNmaH0hI+bjda7cyP7oT5JwITQfiaGxm54k9uVbbUY2N2kJf3A8LC0hbk/u3t7Zw+fZqODqV/R1hYGDt37qSoqAjtDLubfpEsy7gabdir2nE/9m/Va1SY1lkxF6ehjZn5p+K+liauH/+IhisXkP15MHHpS1h/8A3ytmxHo51+YDfSO05tTRd1l3vw+Ofu6I0alm1NZuXOFCyz2NF5mquxEdsHHzD6cXlwgJ5KhWn7NmV3pLh4RgP0Oh2dHG9Wjmo6xzoD11MiUjiYfZCyrDLSIp/z90uWoeOackxz/yOYDJYIE7sU94q3OGso5meNGs59OoDHF3x9VaQKd2oUPqsR9BrCPG6yBloo8t6gKOI80Ym9BNN51EoXVusBEuJfCcmASWFmpvWvwo9//GM++ugj6urqCAsLY8uWLfz+7/8+eXkvKOsSBCEklOTWH9PT+xGgVBrkLv0OCQmvLUhy6+DgIJWVlc8M2du8eTNbt27FaJyb7XNZlnE+GsZ+ph1Pp3I0hFaFaX0i5p1paC0zmwUjSxKtd29y4/gxOh4EG3WlF6xm/cE3yChcM+33UJZkHt8f4l5NJx0Pgw/j6MRwCnelkrsxEb1x9oGaMkDvM2xHjzJ5507gujYpCcsbbygD9JKTp/26455xTrWdoqK5ght9NwLXw7Xh7F2yl7LsMtZZ16FWPWf3a6BBCUZqj8JIW/C6KQHfyje5FbWH//M4mlPV/Ux6gsdZqnBZCUYSwyBMi87rJXu4k7Wum2wMP0e8tfOplvAQFbXOH4zsw2CY+1EFwvyb1n8FZ8+e5Vvf+hbr16/H6/Xy7W9/m7179/Lw4UNMprmrnxcEYeZkWaan50Mam36M12sDVKSk/BOys/79giS3OhwOampquHXr1rwN2ZMlGefDISUg8TcYU+nUmDYmYd6RiiZyZr1XvB4Pdf4OrU8ntOZt2UHRDBNaXRMeHl3qofZsF/aB4HHNkoI4CktSSZ1lm/snJh88GaB3Esmfv4NGQ8SuYqKPHMG0bdu0B+hJssS13muUN5VT1V7FpHfSv3wVG5I2cCj7ELvTdxOue84Oj6MP7n+o5I303Ale10cg5x+gPmEf/6c/gxPXBrBNOgGlIkhtkHCnROFLCkOO0KGRJLKGe1k9eJvNxhoSY9tQq6XAy5nNK/zzafYTFpYyrd9RWHxUsizLL/+2LzcwMEBCQgJnz55lx44dU/oZu91OVFQUo6Oj8zIJVBC+ycbHm6mr/04guTUiIp/8vB8SFbVm3u/tcrm4ePEily9fxuPvDpqbm0tpaSkJCXMzP0SWZCZrB7Gfacfbp5TQqvRqIjYnE7E9BU3EzAIS59gYdys/VRJaR5TdDH1YGAW7lQ6tM0loHe4e515NJ/VXe/H6j2sM4VqWbU2mYGcKkbPId3nCNzaG/cQJbO8fxfkwOHxOl64M0It6/RC6Gbz3baNtVDRXcLzlOL3jvYHrSyKXUJatDM5Likj68h92OeDRCWV3pKXmmSRWOXs3nWkH+dnoSj66P0zPaDCnRKWV8CRH4EsyIUfpUAFpI/0UTNSyVV9NakwTGs3TXVhzSLQewGo9QHj4zFr6C9OzUM/vWe0bjvrPLGNiYp77PS6XC5crOFr6ycwLQRDmjs/n4vHjn9D2+H8gyx7UaiNZmb9OWtovzXtyq9fr5ebNm5w9e5aJCSVYSE1NZc+ePWTMIKHyy8g+mYl7AzjOtOP17zqoDBoitiYTsTUFjWlmv6N9oJ+bTxJancrrRkTHKAmtpa9OO6FVkmTa7g1SW9NJZ12wEiQm2RQ4rtHpZ9cfQ5ZlnHfvMnL0KPZPPkWe9L8fOh3mPXuwvH2E8A0bpj1Az+6281nrZ5Q3l3NvIHh8ZdaZeTXzVQ7lHKIwrvDLd3d8Hmg+o+yM1H0C/p0VAFLXM5T1Oh+5NvCzhxM0144DSr8RlUbCazXhSw5HijH4e40MsbLrAVt11SyJrkMX/XQX1jSs/mAkwpQnGp99Tc04MJFlmd/8zd9k27ZtrFy58rnf9+Mf/5jvf//7M72NIAgvMTx8ibr67zA52QZAbGwxebnfn/fBYrIs8+DBA6qqqhgZUR7CMTExlJaWsmzZsjl5aMg+iYlb/dhrOvAN+SfYhmkx+wMS9QxLaPtamrhx4hj1l88/k9BadOAw+Vt3TDuh1Tnu4eHFbu6f7cLxZJ0qyFwdT2FxKsm5sxsCCOAbHWW04ji2o0dxNQTbuOuzs7EceYuoQ4emPUDPK3m51H2JiuYKqturcUtKEKBWqdmavJWynDJ2pe3CoPmSXB1Zhs7rSkXNg49gIpgXQmwOY7lv8KlqO/+nUcPdUzZgQPmaSkaKN+JNNinlvRoV8Y5RlnffZav6LEtj7qKPfLoLawJW636s1oNEmp8TGAlfKzM+yvnWt77FyZMnuXDhAqmpz/8H8Mt2TNLS0sRRjiDMkts9RGPTj+ntPQYo/4Dn5r5LQvyr8/6Pd2trK6dPn6a7W/nkazKZKC4uZu3atWimmcfwZWSvxPjNPhw1HfhGlH8/1CYtEdtTidiUhHoGSaKyLNN25yY3TnxE+/0vJLQeOEzGqrXTft+Gusa4V91Jw9VevB4lwDGYtKzYlsyKHSlExs7uuEaWZSZv3cL2/vvYP/sc2f9vqcpgUAbovX2EsDXTT8RtGGmgoqmCk60nGZwMltXmWHI4lH2I/Vn7iQ9/zvHVYJNyTHPvfRgJNk/DFI8r/w3OGYv567ZoLrcMIwWeLjJyjB5PsgnJGgZaNdETY+TbGtjCeZZH38BoDHa31WmjSbDuw5pwAIulSHRhXSQW9VHOr/7qr1JRUcG5c+deGJQAGAwGDDNo1iMIwpeTZZme3g9pbAwmt6am/FOys/89Wu3sZr28TF9fH5WVlTQ2NgKg0+nYunUrmzdvnpP/zmWPxPj1XhxnO/CN+j+9R+gw70jFtCkJ9QyOQXxeD48unOXmiWMMdih9MFRqdaBDqzVzelNiJZ9E671B7p3ppPtJ8zYgLi2CguJUctdb0c7yuMY7MsJoeTm2ox/gbm4OXDfk5mJ5+22iDh5AEzW9QYMjzhE+af2E8qZyHg0/Cly3GCzsz9pPWXYZy2Kes9M11u9PYn0fum8Fr+tM+PL2czNqD3/Vk0HllSE8PhnwVx1FapS8kcQwMGgwOyfJHXjIZvkiqyyXCU9yBF5Ko4kgIX6v0hI+eovowvoNNq3ARJZlfvVXf5Vjx45RU1NDZqZIOBKEhTQx0Upd3TuM2K4AT5Jbf0RU1Op5ve/o6CjV1dXc8ZefqtVq1q1bx86dO4mImH2zKtnjY+xqL46znUgOf0ASqce8M5WIDYmodNN/0DvHx7hX+Rm3P61gzJ/QqjOGUbj7Fda+VkZk3PSSQifH3Dy8oBzXjPl3cVRqFVmr4yksSSUpO2pWO1WyLDNx7Tq299/HceoUsj+BWBUWRuT+14g+cgRj4fSOMjw+D+e6zlHRVMG5znN4Zf9EYpWWHak7KMspY0fKDnSaLwkCXGNQd1LJG2mpAf/QPVQapOwS6hP28TfDyzleO8q4ywf4d17CVUoX1sQw5HAt4W43OSNNbPRdZp35AubEp7uwGomLK8FqPUBsTDGaLzsyEr5xphWYfOtb3+Jv//ZvKS8vx2w209urZGtHRUURFjb7DHNBEL6cJLl5/PintD3+70iS25/c+mukzXPn1snJSS5cuMDVq1fxepWH2vLly9m9ezexsbGzfn3J7WP8ao8SkIwpD2KNxYC5OBXTukRUuul3hLUP9HPr03LuVT2b0LpmXxmFpa9iNE0vkBrocFBb3UnD9T58/uMaY4SOFduTWbkjhYjo2fVk8Q4NMfrxx9jeP4r78ePAdePy5VjePkLkgQNophH8ybLMw+GHVDRV8Gnrp4y4goHAsphlHMo5xGuZrxFt/JJ8FJ8HmquVo5q6k+AJHq/IKUV0pB7kZ+Pr+PtHTobuu3myM6Iy4K+oCUc26zB4vWQNt1Nku8om01ksCQOB11GptMTGFmNN2O/vwipaTQjPmlaOyfMi9b/6q7/iF3/xF6f0GqJcWBCmxzZ6k7q6bzM+rhyfxMRsJz/vB4SFpc/bPb1eL9euXeP8+fNM+qs+MjIy2LNnz0uPb6dCcvkYv9KD41wn0rg/IIk2YN6VhmmtFZV2+gFJX2szN45/9GxCa1oGRQffmHZCq88n0XJ7gNqaTnqaRgPX49PNFO5KJacoAe0MdnGekCWJ8cuXsR39AEdVFfh3R9Th4UQePIjlyBHCVq6Y1msOTAxwsuUk5c3lNNmaAtfjwuI4kHWAsuwylkZ/yVA+WYaum8rOyP2PYCKYc0JMFoNZr3PMs5m/adDSORKstlFpZTxJEUqvEYserSSxZKSLde6bbAk7Q2xU9zNdWGOit2C1HiA+fi863fSOoYTFYVHmmMyi5YkgCNPk8dhpbvkDurr+FgCdLobcpe9gtZbNW3KrJEnU1tZy5syZQDuA+Ph4SktLyc3NnfV9JZeXscs9jJ3vRBpXdmA0MUYid6URvjYBlWZ6AYksy7TdvcWN4x/Rfj84hTZ95SqKDr7BkmkmtE7Y/cc157oYt/mTbtUqstfGU1iShjUzclbvgae/n9FjH2M7ehRPZ7DVurGwkOi3jxC5bx/qaTSrdPlcVLdXU95czqXuS0j+niF6tZ6S9BLKssvYnLwZrfpL/qkfalZyRu79/bNJrOFxOJYe4nPNDv68OZr6C2OAR/mfWsaXGI4vMRwp1oAamXRbH2t6brPFWE1yTOsXurAWkWg9SHzCqxj0cdN9u4RvKDErRxAWGVmW6R/4jIaGH+B29wOQlHSEpTm/jU43vXLQ6WhqaqKysjJwRGs2m9m1axerVq2adaWN5PQydqmbsQtdSBP+PIdYI+aSdMJXx087IPF5PdRdPMeN4x89k9Cat3k7RQcOY83Kmdbr9T+2c6+6k8YbfUhe5cEaFqlXjmu2p2CaYWt7ANnnY/ziRWxHj+I4Uw0+JVdDHRFBVFkZlrePYMzPn/rryTL3Bu9R3lTOZ22f4XAHE0hXxa+iLLuMV5a8QpThS3YlxvqVXZHa95Vdkid04Thz9nEhrISfdqZz7eqT1xxTynvjjHiTw5HijajUKpJHBynsvcg2fRXp0Y2oo5/uwlqg9BpJeA2jcfqt7wVBBCaCsIg4nd3UN3yPwcEqAMLClpCf/0NiojfP2z17eno4ffo0LS3KGHmDwcC2bdvYuHEjev3MOqk+IU16GbvYheNCN7LTH5DEhykBSWE8Ks30dh9cE+PcPf3plyS07mXtvkNExk89odXnlWi+3U9tdSe9LcHGjwlLIpXjmrUJaGaQ4/KEp68P24cfYvvgA7zdPYHrYWvWYHn7bSJffQX1NHLz+sb7ON5ynPKmctrsbYHriaZEDmYdpCy7jCVRS37+B93jwSTW5upnkli9mcXctuzlz/uXUXlnzF/e6wBkpGgDvuRwfNYw0Kmx2kdY2X+TbdozZEc/QBPlC9zCZFqKNeFJF9YvWYMgTIMITARhEZBlHx2d/4uWlv+MzzeBSqUjI+NXWJLxb+atUmFkZIQzZ85QW1sLKJU2GzZsYPv27bOefSVNeHBc7GbsYheyU3mAaRPCiCxJJ6wwHpV6egGJfXCAW5+UU3vmc9z+nBdTdAxr95VRuPtVjNNIDh0fdfHgfDcPznUxYfdXAGlU5BQlUFisHNfMlOz1MnbuPLajRxk7exb8uS7qqCiiDpURfeQIhqVfkufxHE6vkzPtZyhvLudy92VklN0co8bInow9lOWUsSFxw88PzvN5oaVaOaqpO/FMEquUvFapqBldy0f1Htw+CVBm68hmLd5kk1Lea9QQM25nxdB1tqlryI++jdbsCbyO0ZhGovWg0oU1QgxyFeaOCEwEIcQcjgc8qvs2DocSIERFrSM//0dEmKb+AJuOiYkJzp07x/Xr1/H5jxUKCgooKSkhepqdQ7/IN+5h7EIXY5e6kf3zYbTWcCJ3pxO2Mm7aAUl/W0sgoVXyrzU2Nd2f0LoTrW7qCa29raPUVnfSdLMfyac84MOj9KzckcLybcmYomYeAHq6upTdkQ8/wtvXF7geXlSE5R+8jXnvXtRT7PMiyzJ3B+5S3lzOZ62fMeYZC3xtbcJaXs95nT0Ze4jQR3zxB6HrlrIz8uAjGA9WwsjRmUpFjXMT/6tBw1iLD/A3vgxTK43PksKRTVoiJydYNnqPLaPnKLBcxWANNsg0GKyBnRGzuUB0YRXmhQhMBCFEfL4JWlr/Kx0d/xNZ9qHVmsnO/i1Skv8hqueNjp8Ft9vN1atXuXDhQqAbc2ZmJnv27CE5eXa5AL4xtz8g6UF2KwGELtGEeXc6YStipxWQyLLM47u3uH7iGO21dwLX01YUsv7gGyxZvW7KD0SfR6LpZh/3qjvpfxzMxUjMiqJwVypZa+LRzKACCED2eHDU1GA7epTx8xeUwADQREcT9frrWI68hSFr6tOIe8d7Od58nPLmch7bg2XDyaZkynLKKMsqIy0y7ed/cKgZao8quyPDwWZscngcQ0v2Uy5t5yeNUQxef7Lb4QO9StkZSQpDNusId7vJG61n89gF1kVdwJjwVBdWXTQJCa9htR7EErVuXv5uCsLTRGAiCCEwNHSWuvp3cTqVyoyEhNfIXfodDIa5mcL7NEmSuHPnDtXV1TgcysPZarWyZ88esrOzZ/Wp1zfmxnGui/Er3chu5dhCl2Qicnc6xuXTC0gCCa0njjHY3gYoCa25m7ax/uAb00poHbe5uH+uiwfnu5h0+EtxtSpyi6wU7EolIWPmxzXujg5sRz/AduwjfAPB0trwzZuIPnKEiNJS1FPMzZn0TipHNU3lXOm5EjiqCdOGsSdjD4eyD1GUWPTzRzXjg0oS672/h64bwevaMByZr1Kp3cl/bUuh9daTYMQDWvAmhiu9RqL1GLxe8m2tbJy8xEZzDab4p7uwmklIeAVrwgGiozej/rKqHkGYJ+JvmyAsIJd7kMbGH9LXdxwAgyGJ/LwfEBdXMuf3kmWZhoYGKisrGRhQtvWjoqIoKSmhoKAA9TSnzz7N53DjONvJ+NUeZH/TMV1KhBKQLIuZVrDjmhjnXuVn3Pq0grFhZRCczmCkYPcrrN1XRlSCdUqvI8syvS127lV30HJrAMk/qMVkMbByZwortiUTZp5ZMq/sduM4cwbb+0cZv3QpcF0TG4vljTewvPUm+ilOUpZlmTsDdyhvKufzts+fOapZZ13HoexD7F2yF5PuC3k+7gmo/0QJRpqqnkpiVeNM38klUwn/rTuP27Ve/w94QA2+hDB8SWFIcUZ0ssTSkXY29F9ls+kMlrjg4D21Ooz4uN1KF9bYHajVogurEBoiMBGEBSDLMj09R2ls+j283lFATVraL5KV+Rvz0vmys7OT06dP89jfSdRoNLJjxw7Wr1+Pbhp5GV/ks7twnO1k7GoveP0BSWoEkaUZGPOipxWQOIYHufVJBfcqP8M9qRwdmCzRrNlXxqrSfVNOaPV6fDRe76e2ppOB9uCn/qScKAp3pZG5Og7NNMuRn3C1tmL74ANGj32Mb9g//0WlwrR1K5YjRzDvKkY1xd2RnrEejrccp6K54pmjmpSIFMqyyziYfZA08xeOaiQftJ5TjmkeVYA7GMR4E1dzN/oV/sdgIZX1Kv9eixeQ8cUblV4jCUbUasiwdVHUf5Nt4aeJi+0NvIZKpSM2dieJ1oPExZWg0YTP6H0ShLkkAhNBmGfj4y3U1b+DzXYVAHPECvLzf0RkZMGc32toaIiqqioePnwIgEajYdOmTWzbtm1WYyO8oy4cNR2MX+8Ff58PfbqZyN3pGHKnF5AMdbZz/fhHPDpfg+RTPt3HpKRRdPAwy7btmnJCq2PYyf1zXTy80I3zSTt7nZrcDVYKilOJT5vZQEPJ5cJxuhLb++8zce1a4Lo2Pp6ot97E8uZb6FNTpvRak95JqtqrKG8q52rP1Z87qnk953XWWdc9e1Qjy9B3H+7+DGo/gLFgICFZMmi0vsbfjG3g71sN+NqeWrdF7y/vNaLSqUkZ7WPt4Fl2hFWSFP3UN6IhJsbfhTVuLzqd6MAtLC4iMBGEeSJJLtoe/5S2tj9Flt2o1WFkZf0Gaam/OOdn9mNjY5w9e5abN28i+UtUV69eza5du4ia5hTap3ltThw1nUpA4q9k0WdEElmajiHHMuWARJZluh494PrxD2m5dT1wPSV/BevL3iRrTRGqKRwtybJMT5ONe9WdtNwZRPYf10TEGCjYmcryrckYI2a2I+Rqbsb2/lFGP/4Yn7/rLWo1Edu3Y3n7CBE7d6LSvvzPTZZlbvffprxZOaoZ94wHvlZkLeJQziH2ZOz5+aOa0c5gEmv/w+DrGS10p+7jA/cWftISizMYpyBHaPEmhyvlvWFarPZB1oxcYYe+kvSoRlRRT7qwqrBY1mNNOEBCwqvo9bOfcyQI80UEJoIwD0Zs16mr+zYTE0qVRGzsTvJyf0BY2OznzDzN5XJx+fJlLl26hNut9ORYunQppaWlWK1Ty834Mt5hJ46zHYzf6AsGJJmRRO7OwDCNKbqS5KP5+lWuV3xIT1O9clGlYun6zRQdfIPk3Kl1PPW6fTRcV6prhjqDxxkpuRYKd6WxpDAW9QyOaySnE8fnnzPy/lEmbwY7oWoTE7G89RaWN99Al5Q0pdfqGeuhormCiuYK2h3twTVGpHAo+xAHsw+Sav7Cn79zFB5WKHkjbRfAv6Mia/TYUkv4RF3Mf25NY+h+8P2WjWp8TypqInTEjtlYZb/C9okqciz3UZuDXVgjzYVYrQdJSNiH0Ti130MQQk0EJoIwhzyeUZqa/xPd3T8DQKeLJS/3XRIS9s9pzwefz8etW7eoqalhfFz5RJ6cnMyePXvIzMyc8et6hyaVHZKbffjbgGLIilJ2SLIsU34dj9vFw7NnuHHiI2y9StdTjU7Hip27Wbf/MDHJUzsKsQ9Ncv9sFw8vduPyz9bR6tTkbkqksDiV2JTpTQp+wlnfgO3oUUYrKpDs/q6vGg0RxcVEv30E07ZtqKbQhn/CM6Ec1TSXc63n2jNHNXsz9nIo59DPH9V43dBUqQQj9Z+CL9gnZCJ5E+eMJfxBex7N9U/t/OhUeJP8FTVROqKc46x03Ga7s5rllptoTMEurBGmPKzWAyQk7Cc8fGoJuYKwmIjARBDmgCzL9PefpKHxPdxupYQ0OeltcnJ+G53OMqf3efToEVVVVQwNKRUV0dHR7N69mxUrVsw4+PEOTmKv7mDidh/4P3AbcixKDknm1I+CJscc3P38JLc+O86kXTkOMZoiWLV3P2tePYDJ8vIGbrIs09Vg496ZDtruDT5pD4I51kjBzlSWbU3CaJr+cY00MYH908+wvf8+k3eDA/90KSlYjrxF1OE30FlfXq79oqOaDYkbKMsuY0/GHsJ14U//EHReD07wnRwOfMkTs5Rbllf4495CLrc8FWhpwGcNw5cUjhRjwORxscz+iG1DNayOuowuPtiFNSwswz+f5gAREbnTfm8EYTERgYkgzNLkZBf1De8yNFQDQHh4Fvl5PyI6esOc3ufx48ecPn2aTv9U2vDwcHbu3Mm6devQTiH34ct4BiZwVHcwcac/GJDkRisByTR6fYz293Hzk4+5f+Y0HpcTAHNcPEX7X2dlyV70xpcn3npcPuqv9lJb08lwd/Bhn5ofTeGuVDIK4lBPs3MsgPPhQ0aOHsV+/ATSmP8YSKvFXFKC5e23MW3ZPKX8lu6x7sBRTYejI3A9JSKFQzmHKMsuIyXiCztBQ81KMHLv72GkLXBZMiVQH/8qf24r4lh3DHK3//4q8MUZlfLeBCMGycey0SY2D11gQ2QNxjhn4DUMhkSsCfuxWg9iNq8UXViFrw0RmAjCDEmSl87Ov6G55Y+QpElUKj1LlvxrlmT8ypz2gBgYGKCyspL6eiVHQ6fTsXnzZrZs2YLRaJzRa3r6J3CcaWfi7sCTtAaMedGYd6djSJ96QNLf1sL1ig+pv3we2Z90G78ki/UH3yB30zY0UwiY7IOT1NZ08uhSD64nk4cNGvI3JlJQnEpM8vTLqX1j49hPnsR29CjO+/cD13Xp6ViOvIXl8GG0cXEvfZ3AUU1TOVd7rwauh2nDeGXJKxzKPsRa69pnj2qe0/xM1pnoStrN305u5s87U/EMBY+KpGg9viSlokargaWjj9k0fJnNEZWYY4MDBkUXVuGbQAQmgjADdsd96up+F4fjAQCWqPXk5/8Qk2nq3UlfxuFwUF1dze3bt5FlGZVKxdq1aykuLsZsnlkprKdvHPuZDibvPRWQLIshcnc6+tSpvaYsy7TX3uX68Q95fO924Hp6wWrWl71JRsHql356l2WZzvoR7p3ppK12MLCWyPgwCotTyd+ciCF8esc1sizjvH9fqaw5eRJ5wt9WXacjck8plrffJnzDhpfujsiyzM2+m5Q3l3Oq7RQT3mB79o2JGynLKaM0vfTZo5pA87P3lfwRf/MzWaVhOHEbJ9jGH7VnY2sIBpKSWYcvKQxfYhhqg5oMexfrR6+zPfwUMdHBOTcaTQQJ8XuxWg/6u7DOvA+NIHwViMBEEKbB6x2ntfW/0N7xV4CEVhtJTs7vkJx0ZM4+vbpcLi5dusSlS5fweJQ8gvz8fHbv3k18fPyMXtPTO469qp3J+8EgwLg8VglIpphAKvl81F+5wPWKDxloawGUlvF5m7dTdPANrJnZL1+H20fD1V7uVT97XJO+PIaCXalkTHOuDoDP4WD0+HFsRz/A9ehR4Lo+MxPLkSNEvX4IbUzMS1+na6xLOappqqBzrDNwPTUilUM5SlXNM0c1kg/azivByMMKcAebu43HFVJt2MUfdubT1hrM0ZGNGnzJSt6IHKEjxd7HurHz7PSdIjEqeDykVhuIiy3Baj1IbGzxvE2YFoTFSAQmgjBFg4PV1Dd8F6ezCwBrwgGWLn0Hg2FmwcIX+Xw+bt++TXV1daDSJjU1lb1795Kenj6j13R3j+GoamfyQbD1eNjKWMwl6eiTpxaQeJxOaqtPcfPkx9gH+gHQGgwUlOxl3WuvT6llfKC65kL3M8c1yzYlUrArlejE6R3XyLLM5J072N4/iv3TT5GdSu6FSq/H/OorRB85QlhR0Ut3biY8E5x+fJqK5gqu9QabqYVrw5WjmpxDrE1Y++zr9NYqxzS1H4CjJ3DZY07jemQpf9RTyPXO4Hsi61T4EsOV8l6LnvjxEdZMKsHIkshG8G9UqVRaYmK2YbUeJD6uFK12ZhVHgvBVJwITQXgJl2uAhsYf0N//CQBGYwp5eT8gLrZ4Tl7/yUyb06dPMzioVPRER0dTWlrK8uXLZ5TU6O4aw17VjvOhPyBRQVhBHJEl6eimGARMjNq4/dlx7pz6BOeYshsQFhnFmlcPsHrvfsLML85FedIM7e6ZTlrvDASqayLjjBQUp7JsazKGsOn9E+QbHWW0vALb0aO4GhsD1w1Lc7AceZuosoNoLJYXvoYkS8pRTVM5px6fYtI7CYAKFRuSNnAo+xC703c/e1Qz2vVU87MHwdcyWKiL3c1PhtZyfGAJDPj/rNRPVdTEGrA4x1g1cZMdtkryou6hMj3d+GyDUt4b/yp6/ct3dgTh604EJoLwHLIs0d39Pk3Nv4/XawfUpKf9EllZvzFnM0W6uro4depUYKZNWFgYO3fupKioaEaVNu4OhxKQ1D2Z6wJhhfFElqShs04tIBnp7ebmiWM8qKnC61GatlkSkyg6cJjlO3ej07/4WMHr8dFw7eeboaXmR1NYkkbGythpVdfIsszkzZuMvP8+js9PIbuUvh8qo5HIffuwHDlC2JqX57V0Ojo53nyc8uZyusa6AtfTzGmBBmjJEcnBH3COwqPjSmv4LzQ/64jfyf8ZW8/fDObiGvXPynm6oibeiMnnYqXjAdtHqymMvIom7Esan1lfw2hInPJ7IQjfBCIwEYQvMT7exKO6bzM6qlRVmM0rlfk25pVz8vojIyNUVVVx318xMtuZNq52O46qdpz1I8oFFYSvTsC8Kw1dwtSCqJ7Geq4f/5DGa5d5sr2RmJPL+rI3yVm/CbX6xQ3Hxkac3D/bxYOnZtdodWry/Mc1sVM8OnrCOzLC6Mfl2I4exd3SErhuyM/H8vYRog4cQBP54l2bCc8Epx6foqK5guu9wVb4Jp0pUFWzJmFNMKjxuqG5Ktj8zBsszx2KK+KYdyv/vW8FI23B3+XpGTUGlY8V9ga22s+xPvIcuthgrxGTaSnWhANYrQcID18yrfdCEL5JRGAiCE/x+Vw8fvwT2h7/D2TZg0YTTlbWb5Ka8gtzMt9mcnKSc+fOce3aNXw+pXKjsLCQkpISLC85gvgyrsd27JWPcTXalAvqpwKS+JcHJLIk0XrnJtcrPqTzUbCsNmvtetYffJOUZS9u2ibLMr0tdu5Vd9B8a+DZ2TXF/tk102iGJssyE1evYXv/fRynTyP7k39V4eFE7X8Ny9tvY1z54p4dT45qPm76mNOPTz9zVLMxaSOHcpSjmjBt2JObQsd1uPezn2t+NhGZzWltMX/cV0BrZ7D5mhShVcp7k8LQ6iHX3spmxwW2mKsIi5kMfJ/RmIrVepBE60FMplzRa0QQpkAEJoLgNzJylbr6d5iYUD6dx8buIi/3+4SFTa19+ot4vV6uXbvGuXPncPoTNTMzM9m7dy9JU5zF8jRX6yj2qnZcTTblghrC11qJLE5DG/fyHRef18OjC2e5cfwjhjqVuS5qjZZl24opOniYuLQXtzL3eSSabvZx90wnA+3BapTkpRYKS1LJLIyb1uwa79AQo8eOMXL0KJ7HwTkzxhUrsLz9NpH796OJePFRVIejg+PNx6lornjmqCbdnK5U1WQdJCniqfd6qFnJGbn39zDSGrjsCYvniqmE/9ZXyLX+JYASTMhGjVLemxSOyqQh097Bhokr7FCdItJiC/y8Xh9PQsJrJFoPEhn58iMmQRCeJQIT4RvP47HR1PT7dPe8DygPltzcd0mI3zfrh4okSTx48ICqqipsNhsACQkJ7Nmzh5ycnGm/vrPZhqOqHVfLk+m3KkxFVszFaWhjXt5szTUxzr3Kz7j1STljI8rOgD4sjMLSfax9rQxzzIubjo2Purh/rosH57qYdCi7GRqtmtwNVgpLUombYi8UUHZrxi9fxvb+URxVVeBVqnXUJhORBw8ouSMrVrx4PZ5xTrWdory5nJt9wSF8Jp2JV5e8yqGcQ6yOfyo4eE7zM0kbzkPLTv5saDWfjCzHN6IcWz1TUROlI3Wsj/Xus+zUfEZ8VF/g57XaKBLiX8FqPUB09CZUqpfP2REE4cuJwET4xpJlmb6+4zQ0/hCPR6leSUn+R2Rn/xY63dS7nz5PW1sbp06doru7GwCz2cyuXbtYvXo16im0QH96na5mG/aqdtytTwbOPRWQRL88IHEMD3LrkwruVX6Ge1JpGGaKjmHtvjJW7dmHIfzFuxF9rcpxTdPNfiT/tGGTxUBBcQrLtyUTFqGf8u/jHRjA9tExbEeP4ukM9gsxriok+u23iXz1VdSm569HkiVu9N6gvLn8545qNiVt4lDOIUrSS4JHNZ7JZ5ufSUoAJKs0dERv4n851vH/OVYzOaa8j7IapIQwfMlKRY11Yoi1rrMUT35OqrktsA61Ooz4+FKl10jMdtTqqb8HgiA8nwhMhG+kyckO6uvfZWj4HADh4Tksy/8RFkvRrF97YGCA06dP09DQAIBer2fr1q1s3rwZvX7qDy9ZlnE1+gOSx08FJOsTlYDE8vKmW0Od7Vw//hGPztcg+ZQHcmxqOkUHDpO/rRit7vn5Hz6vRPPtfu6d6aSvNdgWPSk7ioJdqWStiUczxeMaWZIYv3RZyR05cya4O2I2E1VWhuXtIxjz8l74Gh32DipalAZo3ePdgesZkRmBqppEk7/CRfJBy1llZ+QLzc+GolbwoWsjP7UVMThpCf6+cQalvDfBSLTbwerJa+wcP8XSiEfgj5NUKh2xsTtJtB4kLq5kzqqzBEEIEoGJ8I0iSV46Ov+KlpY/RpKcqFR6Mpf8GzIy/uWs59s4HA5qamq4detWoIX8unXrKC4uJiJi6hUpsizjbBjBUdWO+0n+hlZFxIYkzDtT0US9eJ2yLNNV94DrFR/ScitYiZKSv4L1ZW+StabohW3ZJ+xuHl7oovZsFxOjSrmwWqtiaZGVwl2pJExjuJ93cDC4O9IR7GwatmaNkjvy6iuoX1CF9OSo5uOmj7nVfytwPUIXwauZr3Io+xCr4lcFj2p67z/V/CwYvEyEp/CZahv/Y2QdDX2pgeuSRR9oCx8huygcv8+OiUpWRNxEHViWmpjozUrjs/i96HRTn7YsCML0icBE+Maw2+/xqO7bjI09BMBi2Uh+3g8xmbJm9bput5tLly5x8eLFQAv5vLw8SktLp9VC/klAYq9sx9PxJCBRE7ExEfPONDSRL95tkf7/9u47PKrzTPj/d3pRGfUZdQkEKhRRhQQIgQSigzA22E68TtndJL/NZp2yWcfZvE72TWKnbJI3yaau02PTEZgukOgdRAehhgqSEOp1ZjRzzu+PI0aWkSg2RcDzuS5dV3x0zpnnzNFkbp5z3/cjuSk9cYwTm9dTW6Is+IdKxYjJaUxa/BxhIxPuePzNynbO5Vdx9eQNJJfyuMbsq2d0Rjij0sMx3+X1PdchSXQdPUrz6jX9c0d8fLAsXYrfCy9gjB85+HXIEifqTrCpZBO7K3f3e1STFpbG0uHKoxqjtvcR1iDNz3r0Fg4bpvPbpvEcaUpARgnGblXUSDYTBp2b0R1XmN61l4k+B9H69/UasVgmKL1Gguc/sO6+giDcnQhMhKee291FadlPqar6E8r6NhZGxH2D0NDnP1Zyq9vt5syZMxQUFNDRoTQSCw8PJzs7m+joO1e1fNBAAYlKp8YrNRSfGRFofO4cEPQ4HVzal8+prRtprlVmCTQ6HaMyspi4cBkBYYNXFUluibIzDZwrqKK2pNWzPSTGl+TMCIZPCEGjvbfHNa6GBlo2bqRl7Tp6Kvsqa0zjximzI/Pn3XF2pLKtUlmrpnQztZ19rd5jfGNYGreURcMW9T2qsbcpsyLnVkP5AW41P5PUei54p/JO83i2t03AifKoSjZqcNuUvBGtCRI7SklzHCBNn4/Bz+l5LW/vRKzWxVhDFmIy9c2sCILw6IjARHiqNTbu50rRt7DblSRLq3UJI0d8E73+7kveD0aWZYqLi8nLy+PmTWUVWH9/f7Kyshg16s59Pz58HkdvQOL8YECS1huQ3CWhtLujnbM7t1K4cwtdrS0AGL28Sc5eyPh5i/Dy8x/0WHtHDxcPXufCvut0NCudVNVqFcMnhjA2MwJb7L09rvDMjtyqrOmdMVJ7eyu5IytX3nF2pMPZwa6KXWwq2dTvUY2Pzkd5VBO3lLFBY5X31N0DRTt6m59t69f8rMJ7HH/rmMDqrlTaupTHZrJO5WkLr7JoGdZRSaprCzPkXXhZ+jrSmkzRH+g18uBWhxYE4aMRgYnwVOrpaeZq8Xepq8sFwGgIIyHhuwQGZnys89bU1LBr1y6uXbsGKC3kZ8yYweTJk++5hfwdA5L0u8+QtNbf4NS2XC7k59HjUL6cfYKCmbQwh9GZ2eiNg89KNFR3cL6giqLjN3D3KI8tTD46RqWHM3pGOF73kFALH+g7smZt/9mR5OS+2RHzwImhkixxvO648qimYjd2d+8CfKiYGjaVpXFLmRU5S3lUI8tQfVIJRi5ugK6+xQibzLGss0/mz13TuG5XHrV4KmpCTUiBBqK6aknpKWCmazv+vn2N0wx6K1brIqzWxfj43LlhmyAIj5YITISnSl8J8P+lp6cJUBEZ8SrDhn0Frfb+VrD9oJaWFvbs2cP58+cBpYX8lClTSE9Pv+cW8p4qm90VnqTW+3lkU3+tjBOb11N05ACypAQVwdGxTF6ynJGp09EMEhhJksy1cw2cy6/i+tUWz/bgKB/GZkYQNzEEre7ufTdkSaLr+HGaV6+mffeHZ0cWK11ZEwbPY6lqq2JT6aZ7e1TTWNqbN7Iamvra0XfrA9khp/JORxoX7LGAClkFUmBfRY3N0cgkZwEznTsI9e5rtKbT+RMSsgBryCL8/CahUt17ybYgCI+OCEyEp4bdXsOVom/R2LgXAC+vkSQmvIXFMu4jn7O7u5sDBw5w7NgxTwv5MWPGkJWVdc8t5D9OQCLLMpXnz3Li/fVUnCv0bI8aM47JS5YTPWbwzqL2zh4uH6rl/L5q2ht7ZyXUKoaPD2bsrAhswy33NFPgamykNTeX5jVr+ndlvdV3ZP78QWdHbq1Vk1uS268Bmo/Oh/mx81kat5QxQWOUcXQ2wvHfK8FIdV81kUtj5pB2Mn9on8JB+xjcKEGU5KdX8kZsJgKkdibYjzHLuYMYcwn0Dkej8SI4OBubdTH+/lNRq++9Pb4gCI+HCEyEJ54su6m+/ndKS3+M293ZWwL8L70lwB+t6ZXL5eLEiRPs37+f7m6lKiQmJobs7GzCwsLucvStcd0ekKBV453aW/Z7h4BEcrspOnqQE5vXc/OaMmOgUquJT0tn0uLnsMYOH/TYpppOzu2tpuhoLS6nMrNi9NKRlB7G6Bnh+NxDh1hlzZpjtKxZQ1ve7r7ZES8vfJcsxn/FCoyJiYMee7r+NLkluey8tvO2Bmg5cTl9VTU93XBxY2/zs7wPND9Tc0E/jj93TmarfQrdKGOWvHorakJNeGscJHedZWbPbhK8zqLqvSyVSk9QUCY262ICA2ei0dz9egVBGDpEYCI80To6rnLlyhu0timzCRbLJBITvo+X1+Bf3Hciy7KnhXxzs7JSb3BwMHPmzGHEiBH3NMPwcQKSHrud8wW7OLV1E203lZbnWoOBMZnZTFyQgyXEOvBrSjIVFxo5V1BF1eVmz/bAcG/GZkYwcrIVrf7uj2tcTU20bsylZc0anBUVnu3GMWPwX7kC3wULBp0dqeusY3PpZjaVbKKyvW9m5dZaNUuGL1Ee1UgSXDugBCOXNvVrflZlGMG7XZNZ1zOdm73Nz2SD2rNgnsHoJrnrMunufMabjqLWy71HaggMnI41ZDHBwbPRau+9Nb4gCEOLCEyEJ5IkObh27Tdcq/h17yrA3sQN/zrh4S995NyBiooKdu3axfXrSl6Ct7e3p4W8RnMPORiyjKOkRUlqvdWp9R4Dkq62Vgp3vM+ZnVuxdyhf1CZfC+PnLWJc9kJMPgM3NXN0u7hyuJZze6tpu9k7M6GC2ORgxmZGEDbC767BlCzLdB0/Qcvq1f1W9L21Zo3/ihUYk5IGPNbuspNfmU9uSS5Ha48i95btmrQm5sbMJScuhwkhE5Qx1F+Gw79Wckfa+nI/WnQ2Njgn8XdnBqV2pbRZ1qpw20xIoWY0PiqSuoqZxj6maPehs7h6j1Th5zcFq3URIcHz0OsD7nidgiA8GURgIjxxWltPc/nKG3R2FgMQFJRF/MjvYDTe/yq9AA0NDezevZsrV64AoNPpPC3kDYa7V6kMGpDcQ2O01vo6Tm7ZyIWC3bicStmunzWUSYuXkZSRhU4/8Ou33OjiXEE1V47U0uNQcl8MZi1J08IYnRGO7z2sMOxqbu6bHemtMgJldsRvxQtYFiwYcM0aWZa50HCB3JJctpdvp72nb8ZjknUSS+OWkh2djVlnhvYbcPRXcHYV1J3z7GfX+LBLmshf7DM4ZR+JjFqpqLEqFTWqAB1xXddIk3OZrt6NybevNNjHZww262JCQhZ85HsuCMLQJQIT4YnhcnVQWvZjqqv/BsjodIHEj3yTkJAFH6ncs6Ojg71793Lq1ClPC/kJEyYwc+ZMfHzu/ijg4wQkNyvKOb5pXb8KG+uwEaQsXU5cShpq9e0zNLIkU3m5iXP51VRe7Cub9Q/1YuysCOKn2NAZ7jyz45kdWbOG9l27+mZHzGZ8Fy/Gb8XgK/o2dDewpXQLuSW5lLaWeraHeoWyZPgSlg5fSqRvJDi74Mo2OPselBaArARObpWWY6pk/mKfTr6kND+T6auokUMMRDtqSHXnMUPegcWnb30es3k4NutirNZFmM2xd7xGQRCebCIwEZ4IDQ0FXCn6Fg6HUmYaGvo8I+K+gU7nd9/ncjqdHDlyhEOHDuF0Kl0/R44cyZw5c+6phbxntd/dlTiv3QpIVHhPCb1jQCLLMtWXL3Bi0zrKz/RVqESPHU/K0ueJHDV2wADLaXdRdLSOcwXVtNxQVgZGBTFjghg7K4KIBP+7Bmau5mZaczcpsyPl5Z7txlGj8Fu5At8FC9F43z470uPuYV/1PnJLcjl4/SDu3iDDoDGQFZVFTlwOU0KnoJZR8kby37pt0byrmhH83Z7GJvc0WlACPsmiU/JGbCZCXQ2kuPYwS95GsFd939iMEZ5eI95e8aLXiCA8I0RgIgxpTmcDV4u/y40b7wNgMkaRkPBdAgKm3fe5JEnytJBvb1e+OMPCwpgzZw6xsXf/V/igAUlKKD4zI9D4DvzYRZYkSk4e5cSmvjVsVCo1I1OnMXnJcqzDBu422nqzi/N7r3P5UA1OuxIQ6I0aEqeGMWZWOJbgO69sK8sy3SdPKmvW7NzZf3Zk0SL8VqzANHrg2ZGipiJyS3LZWraVZkdfMu3Y4LHkxOUwN2YuvnpfJW9k93duyxu5qQlhjTOVta4MrsnK4xbJrFUan4WaCVC3M8lxhFlsJ9Lcl2Sr0wVhtS7AZl2Mr+94EYwIwjNIBCbCkCTLMnV1G7la/D1crhZATVTUZxgW+xoazb01NPvguUpKSsjLy6O+XvkXuZ+fn6eFvPoOK+3eOt5R2qpU2dxHQOJ29XDpQAEnN2+gqUZpia/R6Rg9czaTFj2Hn+32/AhZlqkuauZcfjXXzjfcWgIGP6tZeVyTakNvvPPH1tXcTOumTbSsWYuzrK85mTEpCb+VK/FdOPDsSIu9ha3lW9lUsonLTZc924NMQSwevpic4TkM8xsGHfVw6m9wbhXUnvXs16X2YptrIqt6ZnJSjgdUyHq1Z40aL4ODiY6zzFTvZITxkqe8V6PxxRoyD6t1Ef7+qahUd080FgTh6SUCE2HI6e6u4krRt2hqOgAoC6slJryFr++Y+z5XbW0teXl5lPV+QRuNRmbMmEFKSspdW8jLsoyjrDcgKf9QQJIRgcYycEDi7O7i3O4dnNqaS0ez0gbdYPYiOXsBE+YvGXANmx6Hm6JjyuOa5tpOz/aoUYGMzYwgKjEAlXrw2QPP7MiatcrsSO8jKpXZjGXhQvxWrhxwdsQluThcc5jcklwKqgpw9fYR0aq1zIqcRU5cDlPDpqJ1OZX1abZ8HUrz++WNHJJH8a5zFgXSOBzokTUqpBAj7jAzeh8Yb79Ehi6PZMMJ1AYl0lKrTQQHZWG1LiYwMB21+t5a4QuC8PQTgYkwZMiym6qqP1Na9hMkqRu1Wk9szL8RFfXZ++7Y2draSn5+PmfPKv+i12g0pKSkkJ6ejnmQPhwfZC9tue+ApLOlWSn53bUVR6cSXHj7BzBhYQ5js+ZhGOB125vsnN9bzaWDNTi6lKBAZ9CQkBbK2FkR+FnvPFZ3SwutmzbRvGYtztK+hFRDUiL+K1biu2ghGm/v244ray0jtySXLaVbuNl907M9MSCRpXFLWRi7ED+9L1QchM3/dlu/kcvE8l7PDN53p9GMr9IWPsiAO8yMOlBHgr2E6eoC0vT70OqV61KhJTBwBlar0mtEo7n7fRAE4dkjAhNhSGjvuMKVy9+grV0pKfXzm0JiwvfuuwLD4XBw8OBBjhw5gsulfCGOHj2arKws/P0HX233FvutHJLyVmWDRoVXig3fmZGDBiQtN+o4+f4GLu7djatHmanwD4tg8uLnSEyfhVbXP6iSZZkb5W2c3VNFaeFNZEmZRfANNjF2ZgQJU0MxmAb/aMqyTPfp08qaNTs+PDuyQFmzZvTtC9O1O9vZcW0HuSW5nLvZV7rrb/Bn4bCF5MTlEB8QD/VX4ODP4NxaaKv27HdDFcyanqlscM+g/FbeSG8Sq2Q1MMxVzTR5G9M1u/Dy6e49Suk1opT3zkOnu/s9EATh2SYCE+GxcrsdXLv2Syoqf4csu9BqfYiL+wZhoS/cV6M0t9tNYWEhBQUFdPbOVkRHR5OdnU14ePhdjx8sIPGZGYl2kIDkRnkpJzat4+rRQ8iyUvJrixtJytLniZuUiupDuStut0Tp6XrO7qmm/lpfKWx4vD/JWZFEjw5EfYfHNe6WFlo3b6Z5zRqcJR+YHUlMVLqyLlp02+yIJEscqz1Gbkkueyr34HA7ei9PQ3p4OkvjlpIRkYGuuwUurIOz/9Q/b0Rl5n3XZNa6Mjx5I5JZ09sW3kwYN0l172KWdhv+hr4kWR/vUVhtS7CGLBS9RgRBuC8iMBEem+bm41wpeoOuLqV8NTh4LvEj38RgGLjt+mBKSkrYtWuXJ7E1ICCA7Oxs4uPvXmLqKFMCEkfZvQUksixTdfEcxzet67eoXsy4iaQsWU5E0pjbXtPe0cPFg9c5X1BNZ6syu6HRqhmZYmVsZiRBEbc/avng63WfPq2sWbNjJ7JDCSxUJhO+CxcoXVnH3P6aVe1VbCq5fSXf4Zbh5MTlsGj4IoK0XnBlK6x6GUr29OWNoGG/NJo1rpnkS+OVvJEPJLH669tJ6TlMpm474bq+GRWTKRqbdQlW6+KPvCSAIAiCCEyER87laqek5Adcr3kPAL0+hPiR3yYkZO59nae+vp5du3ZRUlICKImtM2fOZNKkSXdNbB0wIJncG5D43R6QSJKbkhNHObFpHXWlSsdZlUpN/NR0Ji9ZTkjMsNuOaazp4FxBNUVH63D3KDMqZl89ozPCGZUejvkODdjcra20btpM85rV/WdH4uPxW7kCy+LFaD7UBK6rp4u8ijxyS3I5eeOkZ/utlXxz4nIYHZCEqvIQ7HzztryRC3IMq10z2eJOVfJGNCqkUCPuUDMmHxdTnIVkGrYTpy/yVNTo9cFYrYuxWRfj43N7gCQIgnC/RGAiPFI3b+6iqOjbOJzKAnVhYSuJG/46Ot3Aa8EM5MMdW9VqNSkpKcyYMeOuia23qmzuNSBx9fRwaf8eTr6/keZapU+HVqdn1Kw5TFq0DD+rrd/+siRTcbGRc/n9F9MLjvIhOTOCuIlWNLqBH1HJskx34RlaVq+mbceOvtkRoxHfBQvwX7kC49j+TdhureS7qWQTO6/tpMulNGBToSItLI2cuBxmRc7C2FyhtIU//xK0VnmOryOQta7pnrwRTxJrqBldoJpxzotkaPNINpz0VNT0lfcuxt9/iijvFQThgRKBifBIOBw3uXr1O9Tf3A6AyRRDYsL38PdPvedz9PT0cOzYMfbv3+/p2JqYmMjs2bMJDAy88+uX9wYkpfcWkDi6ujibt43T2zfT2Vvya/TyZtzchYyftxizxa/f/gN1Z1WpYNi4YMZmRRI63DLobIK7tZXWze/TsmYNjuJiz3bDyJHK7MiSJbfNjtzTSr6yRskb2TUXas949unExPuuFNa7MzgpK+vU3EpiJURPgquUdM0eUvUH0PVW1KjVRoKCsrBZFxMYOEOU9wqC8NCIwER4qGRZprZ2LcUlb+FytaFSaYiK+mdiY76IRmO853NcuHCB3bt309qqBBahoaHMnTuXmJiYOx47YEAyyYrPrEi0fre/fmdLM6e3beJs3nYcXb0lvwGBTFyYw9isuehN/Wdk2pvsnC+o5tKhvnJfvVFD0vQwxsyMGHQxPVmWsZ87R/Oq1bRt23b77MiKFzAmJ/cLZhxuh2cl3yM1RwZeydc/EdXV7ZD7JSjZ7ckbcaFhv3s0a919eSO3klhlm4lYVTVT2U66IQ8v462KGg2BgRlYrUsIDpqNVjt4LowgCMKDIgIT4aHp6rrGlSvfpLnlKAA+PqNJTHgLH5+kez5HVVUVO3fupLpaSbL09fUlKyuLMWPG3LFjq+Naq5JDUtKibLhLQNJcV8PJzRu4uH8P7t7W7QFhEUxe+jyJ0zPQaPtKfmVZpq5MKfctO9NX7msJNjE2M5KEtMG7s7o7OmnbsoXm1atxXO7rrmoYMQK/lSuxLFmMxrfvsVa/lXyvbafd2X8l35y4HOZEZmGuKYSjf1DWqXH0Vfycl2JY685gizuNJnz7klhDzdj0DUyV8php2kGAuslzjMUyCZt1CSEh89Dr7zwTJQiC8KCJwER44CTJRVXVO5SV/z8kyYFabWTYsC8TGfEp1Op7+5Nrbm5m9+7dXLx4EQCdTsf06dNJS0tDrx88aXTQgGRmJFr/2wOSG2UlHN+0juJjhz0lv6Ej4klZ+gLDJ6b0K/l1u26V+1ZRX9EXIEQk+JOcqZT7Dtad1X7lCs2rVtG2+X2krt5HPXo9vvPnK11Zx4/rNztyayXfTaWbKGkp8Wzvt5Kvo1tpC//+N/vljdTKAax1zyDXPZ0yOaxfEqvFu4sp7qNkGbcRpulb28bbKwGbTamoMRrDBn1/BUEQHjYRmAgPVFv7Ba5cfoP2DiWgCPCfRkLCdzGZou7peLvdzoEDBzh69Chut/IYYvz48WRmZuLzoTyLD3JUtNGWV3FPAYksy1SeP8vxzeuoPH/Gsz12/CRSljxPeOKofkFCd4eTiwdquLD3Q+W+U6wkZ0YSGD7wIw7Jbqdtxw5aVq2m+0zf6+hjYvB7cSWWpUvRfqDpW4+7h/3V+8ktyeXA9QP9VvKdHT2bpcOXMsVnGOqLG2HVK1DTV67cgYktrilscKdzQo5HUqmRApUkVqO/xBRXIZnGHcRpr3DryozGSE8w4u01YtD3VhAE4VESgYnwQLjd3ZSX/5zKqneQZTdarYURI94g1Lb8nkpI3W43p0+fpqCggK7eGYXY2Fjmzp2LzWYb9DhndTtteRXYi3orYNQfeGTzoYBEktwUHzvMic3ruVGmzEKo1GoSpmUweclygqNi+u3feL2Dc/lVFB2/0a/cd8xMpdzX5DPwzI2jrJyW1atpyc1F6s2JQavFZ/Zs/F9ciXnKlH7vyd1W8p0XPhOf8gNQ8NMP5Y2o2eseywb3DPZIE5S8kd4kVm2whmTpErP0uxirO40aZfw6XSBW6yJs1iX4+iaL8l5BEIYcEZgIH1tz81EuX/kG3d1KdUhIyEJGjvw/GPRBdz1WlmWKi4vZtWsXDQ0NAAQFBZGdnc2IESMG/eJ01nYqAcmlRmWDGswTrPhmRqEN6B+QuJxOLu7bw8ktG2ipU5qNafUGxmRmM3FhDpaQvoZut8p9z+6povrKh8p9syKJmxiCRnt7bovsdNK+Zw/Nq1bTdeyYZ7suLAy/FSvwW/4c2uBgz/ZWRytby7aSW5I78Eq+w5YwrLVOKfFd/5V+eSPnpBjWuzN4vzdvRDJrkELNyFYDCepSZmjySTEcRI+SK6PReBMSMg+bdQl+flPu+XGaIAjC4yD+H0r4yFyuDkpKf8T1638DwGCwET/yOwQHz76n42/cuMHOnTs9K/+aTCZmzZrFxIkT0WgG7o3RU99F2+4Kus8pQQwqMI8LwTcrCu2HKmAcXZ2c2bWN09s20dXaAoDR24dxcxcxft4izL4Wz763yn3P5lfRWq9UpahUMGx8MMmZkdgGKfd1Vl+nZe1aWtavx90bWKFW452Rgf+LK/GaPh1V77VIssTRmqNsLNnInso99EhK4NBvJV9dMNoLa+FPy6C1rwz4uhzABvcMct3TKJXDlSTWcBPuUBMxuhrSVTtIN+3BzK1SZT3BQfOx2hYTGDATjUaU9wqC8GQQgYnwkTQ2HeTKlTew25UEyrCwFxkR9zpa7eB5ILe0t7dTUFBAYWEhsiyj0WiYMmUK6enpmEwDl9e6Grpp21NJ15l6eqtkMY0Nwnd2NLqQ/iW8HU2NnNq2iXO7t+PsVoIMn8BgJi7MYUxWNnpj32u0NXZzfu91Lh2swdndW+5r0irlvhnhA5b7ym43Hfv207x6FZ37D4Dc23gsOAi/55/H/4UX0IX1JZBe77hObkkum0o29WsPnxCQQE5cDgutqfgV74Ztb0LN6b7rkI1scaey0Z3OcTkeSaNBsilJrFZzE2nsZpZpBwGqWzM7GgIC0rFZFxMcnH1P90IQBGGoEYGJcF9crnaKi79PTe0aAIzGCBITvk9AwLS7HtvT08ORI0c4ePCgp0FaUlISs2fPJiAgYODXa7LTll9J1+kb9KZJYEwKxHdONPpQr377NtVc5+T767m0Px9378rCgRFRTF6ynIRpGWh629TLskxdaStn86soK7x5K67AEmIiOTOS+NSBy317btTTsn4dLWvX4artCzC8pqbht/JFfDJnoepdSdjuspNfmc+Gkg0cq+17tOOr92XhsIUsi1lI4s1SKFwNJV8CSRmvCzX73MlscKezW5qAXaX3JLH6WhykcoxM43bC1X1r1Pj6TsBmW0xIyIJ7enwmCIIwlInARLhnDQ0FXCn6TxyOOgAiIl5h+LB/R6v1uuNxkiR5GqS1tSm5EuHh4cydO5eoqIGrdVytDtrzK+k8eQPcSuRgjPdXApKID3VBLS3m+Ka1FB8/4pm9CItPImXpcoaNn+wp+XW7JEpO1XMuf4By36xIokfdXu4rSxJdR4/SvGo17Xv2QG+lkMbPD8tzz+G/4gX0vU3eZFnmYuNFNhZvZFv5Nk/PERUqpoRO4bm4ZWTijeHCWshf9KG8kVjWu2f05Y30JrEagiAFZY2aEZoiT0WNl9fI3gXzFmEyRd7x/RcEQXiSiMBEuKuenhauFn+XurqNAJhMUSQm/AB//5S7HltRUcHOnTupqakBwGKxMHv2bEaNGjVggzR3u5P2gio6jteCSwkyDHF++M6JxhDdv/FY9eULHNu4pt8qv8MmTGby0ueJSBjl2dbd7uTigeuc33edrg+U+8ZPUVb3Hajc19XcTOuGjTSvWU1PRV+uh2nCBPxfXInP3LmoDUreRou9ha3lW9lYvJGi5iLPvmFeYeTE5bAkeALhxQXw/jeg+Zrn99flADa609nonk6pHI5k0iCFmVGHaBmrucRM3S7G6grR9E4VGQxh2GxLsVkX4+0df9f3XhAE4UkkAhPhjm7ezONK0bdwOm8CKqIiP8OwYV9Goxk4F+SWpqYm8vLyuNzb3VSv15Oenk5qaio6ne62/d0dTtr3V9N5pBa5tzRXH+uLZU4MhmF9SaqyLFN2+gTHctdQe/UK0Ffym7JkOUEfKPltvN7B2fwqrh67gdvVW+5r0TMmI4JR6WG3lfsqi+gV0rxqFe07diL3Pm5Se3lhWboUv5UrMcaPVMYruTl0/RAbSzaSX5nvSWTVq/VkRWWxLHouUxqrUReuhorXPa/RKRvY6k5lg5TOMSkBSa/FHWYCm4GR+nIytGtJ0R9Gj/LaWq0/NtsirNbFWHwniPJeQRCeeiIwEQbkdDZx9ep3uFG/BQCzeThJiW9jsUy443Hd3d3s37+fY8eOIUkSKpWKCRMmMGvWLLy9b5+ZkLp6aD9wnY5D15GdvQFJlI8yQxLn5/kiliQ3V48c5HjuWm5WXgNAo9MxeuYcJi95DkuI0utElmQqLjRyNr9/uW9ItA9jMwcu93W3t9O6eTMtq1b3W0TPmJSE30svYlmwALWX8riqur1aSWQt3URdZ51n38SARJbFLWUB3lguvQ8HPgEuJfFWklUclEaz3p3OLmkSXWoTUogRd6iJaK860jU7mG4owAtlbR612ouQkIXYrEvw958qynsFQXimiP/HE25zo34bRUVv0tPTBKiJjv5nYmO+dMeSU7fbzcmTJ9m7dy/dvZUww4cPJzs7G6vVetv+kt1Fx8HrtB+4juxQ8jZ04d74zonGGO/vCUhcPT1c2p/Pic3rPD1IdEYTyXPmM3FhDt7+StKs0+7iypE6zuVX0Xrz3sp9uy9cpGX1Klq3bEXuHbPKaMR30UL8V76IacxoQElk3V22hdziXI7VDZDIGjCOxGtHYdv/hfa+pNhiKYz1vSW+tQQiBRhwh5kIsbQxVVPATOMOAmnqHauOoKBsrNYlBAXOuucFDgVBEJ42IjARPBzOBoqK3uTmzR2AkmCZlPgDfH3HDnqMLMtcvXqVXbt20dioNDsLDg72NEj7MMnhpuNwDe37q5F7y3N1NrMSkCQFeoKHHrudc3t2cnLLBjqalPMavX2YMH8J4+YtwuStJMC2NXRzfm81lw7V3l7uOzMc38D+j5ykri7atm+nedVq7OfPe7br44bjv/JFLEuXoPH17Z/IWraN9p6+RNbU0FSei5rDrOZ6DGfWQO0PPOdplr3Y5J7GBnc65+RhSN463GFmvALdTNEeJ8u4jUjVrXVt1Pj7T8NmXUJwcDY6nS+CIAjPOhGYCMiyzI0bmym6+l+4XC2oVFpior9ATMwXUKsHnyWpra1l165dlJeXA2A2m8nMzGT8+PG3NUiTe9x0HKmlfV81UmdvY7FgE75zojGNDvJUw9g7Ojizcwuntm/G3q5UrXj5BzBp0TLGzp7n6UFSV97KmbwqygrrPeW+flYzY2dFDFju6ygupnn1Glo3bUJq7w0ydDp85s7F/8WVmCZORKVS0WJvYculv7GxZCNXm696jg/zCiNn2GKWqn0Ju7wDjn7eU+LbI6spkMaz3j2DfGk8Tr0ed7gZjU3LeN15sgw7SNRcQN3bgMXHZzQ2Ww7WkIUYDCH3f8MEQRCeYiIwecbZHXUUFf0fGhr2AODtnURS4g/w8Uka9JiOjg727NlDYaFSDaPRaEhLS2P69OkYjR9aMM8l0Xm8jraCSqT23oAk0IjP7GjMycGegKSzpZlT2zZxdtdWT1M0i9VGytLnSZqRhVanQ5JkSgvrObu7itrSVs9rDFbuKzmdtO/cRfPqVXSfPOXZrouKwn/lCizLlqENCMAtuTlcc5gNxRsoqCq4LZH1uYAxpFScQZ33Y+juy1u5VeK72Z1Gk8aCZDUi2YwkeJczU7eKSdpjnrbwRmMkobYcrNYleHkNu8+7JAiC8OwQgckzSpZlamvXU1zyXVyudlQqHbExXyQ6+nOo1bdXzQC4XC6OHTvGvn37PA3SRo8eTVZWFv4fWCUXegOSUzdoz6/EfatE18+Ab1YU5glWVBolgGitv8GJ9zdwsSAPV4+yX1BkNCnLVhCfOh21RoPT7uLcwSrO7qmircEOgFqjYuRkK8mzowiK6J9U66yspGXNGlrWb8Dd3BtIaDT4ZM7Cb+WLeE1NQ6VWU9VeRW7h39lUsokbXTc8xycGJLIsYhYLWluwnN8IDb/1/K5O9ifXPZ317nSuyhGe5mcRlptk6HYyXb8XbzqAvooamzVHLJgnCIJwj0Rg8gyy22u4fOUNmpoOAODrM5bExLcH7Y1xa6G9HTt20NSkJGuGhYUxb9682xqkyW6ZrsIbtO2pxN3sAEDjq8cnMwqvSVZUvRUxjdVVHN+0lssH9yJLSjVOaFw8U55b4WmK1tHs4Pzeci4eqMHRpTw2MZi1jJ4RzphZEXhZ+h4zyS4X7QUFtKxaTeehQ57tWqsVvxUv4Pf88+isVuwuO1uvbWNj8UaO1x337Oer92VRzFyWqfxIuJqPfOobqHofvXTLenZKk9jgTuegNAaXtx53mBlLkJ103QFmGXYSzE0A1GoTIcFLsdqWEOA/bdAgTxAEQRiYCEyeIbIsc73mPUpKfoDb3YFarWdY7GtERn520JLUmzdvsnPnTkpKSgDw8vJi9uzZJCcn92uQJksy3Wdv0ranEleD8ihG7a3DZ1Yk3imhqHTKvjfKSji2cQ3FJ/q6tEaNGceUnBVEjhqDSqXiZlU7Z3ZXUnKiHklS9rEEm0jOiiQhLRSdoS9/pae2lpa162hZtw5Xfb2yUaXCK306/i++iPeMGaDRcKnxEhuO/I7t5dv7JbKmhaayzC+JWdeLMBT8Fpwdvb+DY1IC693pbHNPod3gjTvcjD5ExTTTSTL124lVlfV2YlUTGDATm20pwcGz0Wj6r90jCIIg3DsRmDwjursruXzlDZqbjwBg8R1PYuIP8PIaPsj+3ezbt4/jx48jSRJqtZq0tDTS09P75ZHIkkz3xQba8ipx1Ssr26q9tPhkROKVGopar0GWZaounb+tS2vc5FRScl4gNC7e03/kzO5Krhe1ePYJG+FHclYksWP7EmRlSaLz0CGaV62mo6AAemdcNAEB+C1fjt+KF9BHRtJsb+bvV1exoWQDxc19/UnCvcNZGjaDnPYOQi+9Dy2rPb+rkELY4E5ngzSdSrUNKcQINj1jvS+TadjJGPVZTydWX9/x2GxLsYYsQK8P/Di3RxAEQeh134HJ/v37+dGPfsSpU6eora1l48aN5OTkPIShCQ+CLEtUV/+VktIfIUndqNVGhg//GpER/4BKpbltf0mSOH36NPn5+XR1KYFGfHw82dnZBAYGfuC8MvbLTbTlVdBTqzQGUxm1+GSE4z01DLVB29eldeMaaq4qHWBVajWJ0zKYvPR5giKjcTndXDxwnbN7qmiu6+rdR0XcxBDGzY4k5ANt6N0tLbRs2EjzqlX0VPa1iTenpCht4mfPRtJqOFJ7hA17f0pBVQGu3soZvVrP7IgMlqkspJQeRH2+r8S3TTax1Z3Kenc6J+R4pEAjUqiR4X7VzDKsJ0VzBCPKYymTKZZQ21Ks1iWYzdEP5B4JgiAIfe47MOns7CQ5OZlPf/rTLF++/GGMSXhAurrKuXz5G7S0ngDAzy+FxIS3MJtjBtz/2rVr7Nixg7o6paNpUFAQ8+bNIy4uzrOPLMs4rjbTmldBT3XvYw+DBu/p4fikh6M2apEkN1cO71e6tFYopcQf7tLa1ebk2PtlXNh3HXtHbxWMUUNSejhjZ0XgE9A3K9N94SLN771L25atyA4lQFD7+GBZloP/iy9iGDaMqvYq/nzxt7clsiYFJLLMksj8ujJ8D/4NlVs53i2rOCCN9XRj7fY24w4zExLYykzjdtJ1BVhQKn90uiBstk9gsy7Bx2e0SGIVBEF4iO47MJk/fz7z589/GGMRHhBZdlNZ9UfKyn6CJDnQaMzEDf8PwsNfRqW6feG8lpYW8vLyuHjxIgBGo5GZM2cyefLkfv1IHNdaad1xDec1pb+ISqfGe1oY3ukRaLx0uF09nM/fxfFNawft0tpU00n+Xy/3W7/GJ8DI2MwIkqaFoTcpf5KSw0H7jh00vfsu9rPnPGMwJCTg//JLWBYtwqFXsatiNxt3fpcTdSc8+1gMFhZZ01jW2U38lV3QsdPzuytSJOvd6WxyT+OGIRB3uBmvEBdzzIeZqd9FGMpig2q1GWvIcmy2pfj7pw44uyQIgiA8eA89x8ThcODo/Vcu4Fn2Xng4OjtLuHT5ddralFyOAP9pJCR8H5Mp4rZ9nU4nhw4d4tChQ7hcLlQqFRMnTmTWrFl49a4NA+Cs6aBt5zXsRb2lt1o13qmh+MyMQOOtp8du5+y2bZzYspGOxgagf5dWo5c31VeaKfjbWSovNnrOGxLjy7jZkQwfH4xaowRMzurrtKxeRcu69X2lvjodvnPn4v/yy5jGj+NS0yV+efa/2Va+jY6eW8mqKqZaJ5Gj9iOz7Bj6K7/zvE6j7MNm91TWuWdwQR2LZDWhsWqZ5HuGLP0ORqqKepNYtQQFzsYWupSgwEzRFl4QBOExeOiByVtvvcV3vvOdh/0yzzxJclFZ+XvKyn+OLDvRaLwZMeINwkJX3PboQZZlLly4QF5enidQjI6OZv78+dhsNs9+rsZuWvMq6D6jlMKiBq/JNnwzo9BYDNg7OzixfsOgXVo1WgPFJ25wZvdlGq8rAQQqGDYumHFZfevXyJJEx4EDNP/9XTr27fNU62hDQ/FfuRK/55fT7Wtgc9lWNmx5i8tNlz1jDPcKI8eSwNL6KmzHclHJyro7TlnDHmkC690zKJCScQZ6IYcaSPIrIdO4i/Hq02hR8k8slsmE2pYSEjIfnc7vgd8bQRAE4d6pZPlWQ++PcLBKddfk14FmTCIjI2ltbcXXV6wN8iB0dpZw8dLXaG9X1n4JDMwgIf67GI1ht+1bU1PDjh07qOxNHrVYLGRnZ5OUlOQJYNxtTtryK+k8Xge95bqm5GB850SjCzLdtUurywkX9l/n/N5qunqbq2kNGhKnhpKcGYElWCmnHSyZ1WvqVPxffgmvjAzONJ1nffF6dl3bhd2tNFfTqXXMDp7A8u4eJhXtRWNv8RxbKMWx3p3OFncqTT4BuENNRAXeINO8m1TNIcwoCbYmUxxhocuwWhdjMoU/sHshCILwtGpra8NisTz07++HPmNiMBgwGAZfb0X46GRZoqr6z5SW/ghJcqDV+jJyxH9isz132yxJR0cH+fn5nD59GgCdTsf06dOZOnUqOp3SBEzq6qF9fzUdh2qQe5T8D2O8P77ZMejDvWm7Wc/+P/yJC/kDd2lta3RweF0Zl4/U4nIqx3tZ9IzNjCRpehhGL+V1ui9epPndwZJZX6IrzI8NpZvZsPV5ylrLPNcQ5xPNcr2NhdfO4F+61rO9Rg5go3s6G9zplOgjcUea8QvuZolPPjO0+QTS2HvNVsJCP4nVthQf74QHfj8EQRCEj0/0MXlC2e01XLr8dU9fkoCAdBIT38ZosPXbz+Vycfz4cfbt2+eZuRozZgyzZ8/GYrEAIDl7V/zdW41s7y2vjfbFMjcGwzALLTfqKPjNH7i0fw+SW3lUcqtLa+y4SdSVtbPjdxcpP9dAb7NUgiK9GTc7iriJIWi0aiSHg9ZNmwZNZvVZuIATref57+JfsefQHs96NSaNkXn+STzX3MjY84dQy0rA0yUb2C5NZr17BkdUo3BZvdBbZTIsx5ipzyOKClSAWu2NzboSm20pfn6TB0z+FQRBEIaO+w5MOjo6PF1AAcrLyzlz5gwBAQG3tScXHjxZlqmry6Xo6rd7u7eaGBH3jd6Km/6zJFevXmXnzp00NiozBqGhocyfP99zn2SXROeJOtryP7DAntWMZW4MxsQAWupqKPjVH7l0IN/TNj5qdDJTlq0kPGEUZYUNrP/haeor2j2vGT0mkHFZkYTH+6NSqXBWX6dx0GTWl+iIj2Bd6SY27HiB6o5qz3mSfGNZLpmZV3IU35Jcz/YT0kjWujPY4k6lPcgPtU1Hsv9FZpt2ksgl1EiAjuDgeYTalhIYmHHHFZIFQRCEoeW+c0z27t3LrFmzbtv+6quv8qc//emuxz+qZ1RPI6eziStF/8nNm0r5q6/vOEYl/RizObbffg0NDezcuZPiYqXbqZeXF1lZWYwbNw61Wq10az13k9ZdFbiblLwNjb8B3+wYzMnBNNdd5+iG1Vw5uA+5d4YiZtxE0pa/SGDkCC4fquFsfhUdTb1r4ejUxKfaSM6MJCDUq68z67vv0bF3b18yq82G/4sr8X4uh2OOItYVr+NA9QHcvQmr3lovFnrH8FztNZLq+hJca+UANrins86dQalXJFKYieFBVcz22slE1QkMOAEVfn6phNpyCAmZi1br89DugyAIwrPoUX1/f6zk149CBCYfTUNDPpevfAOnswGVSkts7JeIjvpcvzVu7HY7+/bt49ixY5428qmpqcyYMQOj0ah0ay1qpm3HNXrqlG6tam8dvllReE220VRXrQQkh/d7golhEyaTuvxFvPyjOFdQzaWDNfTYlUDC5KNjzMwIRs8Ix+SjV5JZN+bSvOo9eio+mMyahv/LL9M2KZ6N5ZvYWLKR+q56z+/H+8Sw3O5mdulxvNzKzI1D1pInTWKtO4P9mmR6Qr0ICukg07KH6Zq9WFCqgMzmBMLClmG1LrrtMZYgCILw4Dw1ya/Cx+NydVBc/D1qatcA4OU1gqSkH+PrM9qzjyRJFBYWsmfPHk8b+ZEjR5KdnU1QUBDQ2xxt+zWcFb3N0QwafGZG4D0tnMa6Kgp++SOuHj3oCUiGT5pC2vKX0OitnN5ZScmpo8i9FTr+oV6Mmx3JyBQrWp2G7osXqXn3Xdq2bkO2KzMwt5JZfVY8zyHtNTYUr+PwpsPIvUkofjofluhDWFZ5ibjy/Z5rOSfFstadwSZpKs3BQehtkB5wjEz9LiKpAkCnsxEe9glsthy8vPq60gqCIAhPPhGYDGEtLSe5eOlr2O1VgIqoyM8wbNhX0Wj6ciYqKirYvn17vzbyc+fOZcSIEcAgzdGmheGbEUFDfRV7f/kDio8d9pxvRMpUpixbgasnkONbK6i82DfzEZHgz7g5UUQlBSA7nbRv23J7Mmt8PP4vv0xLxljerd7GplP/RJO9yfP7KV6RLG9tJav8AnqUTrMNsi+57mmsdWdw2Wc4hBoYFXyVT5tXM5rzqJFQq8xYbS8QalsmklgFQRCeYuJRzhAkSQ7Kyn5GReXvARmjIYykpB/h75/q2aetrY1du3Zx4cIFQCnLnjlzJikpKWg0mjs2R2torOLohlWUnDiq/E6lYuSUaaTkrKCj2ZvTOys8Ca0qFQyfGMKE7GiCo3wG78yanY3Xyuc5EFDP+pINnLpxyjPWYL0vObI3SyvOEe1QZnRcspoCaTxr3TPYo52II8yX8OCbZPvtJEV1BDPdgIaAgHTCQp8jKChLdGIVBEF4jMSjnGdUe8cVLl36Kh0dVwAItS1n5MhveZI53W43J06cID8/H6dT6SUyceJEMjMz8fLywt3mpDm/fMDmaI0tVWz+zduUne5dV0alIj4tnclLXqChWk/eHytprb8GKAmtiVNDGTc7Ct9AA52HDlH11sDJrA2zx/OXxnzeL/0K7ZeVgEaNmulGG8vqq5jZeMHzh1YkRbDWncFGeTr1ITa8rD3MCdzLTG0BwShBlJc5ifDw57FaF6HX961oLAiCIDz9xIzJECHLbior/5fSsp8hy050ugASEr5LSPBczz7V1dVs2bLF89gmPDychQsXEhYWpjRH21dNx+Hbm6M1dFZzdP17lJ9RZjFUKjUJ02YwfsFyaopVnMuvpqtNCXIMZi1jZkYwZmYEBrWT1g0bafr7325LZjWveI590V2sL8vlfMN5z+/CdBZyHDLLqi9j6+150iqb2eyeylp3Bmd84lGH6ZkUcobZxp3EUYwK0GmthIc/h822DC+v4Q/1vRYEQRDun6jKeYZ0d1dy8dK/09p6EoCgoCwSEr6PQR/U+/tudu/ezalTSmBhNBqZPXs2EyZMAJc8aHO0Bud1jqx/j4pzyoJ+KrWaxOkzGTt7GRUX3Fw4cN1TYePtbyA5S+nQSl01TX9/l9YNG5A6b1XveGNZlsPNeZNY7zjC9vLtdLmUxzJalZpZ2gByakqZ1tGKBpBkFQekMax1Z7BLn0J3qC/DQ6qZa9nOeE6iw4VKZcZqXUBY6HMib0QQBGGIE49yngGyLFNTu4bi4u/hdnei0XgxcsS3CA19XlncTpY5e/Ysu3bt8lTbJCcnM2fOHLyMZjqPDdwcrVGuYfPff0jlhbOAEpAkzcgkcfpiSgudbP75NSS3Eo8GhHkxPjuKuEkh2I8d5ca/fZ+O/X3lwvphwzC9tJz9o9SsrX6fqxdWecYfrfUlp62dZTcrCZSuAVAuWVnrzmC9PIMaazgB1g6WBO1mqvqAUuIra/APmE542HKRNyIIgiDcRsyYPCYOx02uXHmDhsZ8APz8UkhK/CEmUyQA9fX1bN26lYqKCgCCg4NZuHAh0dHRdF9opHVHOe7GDzRHmxNNo7aOIxvfo/qSkhCr1mgYlZFF7MT5FJ/o7tcyPjTOwoTsaCKHmWjb8j5Nf/0bztJSz/i8ZsygZek0VvtcYWflLhxupZmaXqVhtmxieW0Zk+12VECnbGCrO5W17gyOWcagC1MzLeQoWfrdRPSW+JpNiUREvCDyRgRBEJ5Q4lHOU6y+fidXir5JT08zKpWe4cO/QlTkZ1CpNDidTvbt28eRI0eQJAmdTkdGRgapqalItd20bC3DeU3pRaL21uGTGUmTqZ4juau4fkUpv1VrtIyeNYewhCyKjnVSW9Lqee3Y5CDGZ0cTZOyg6e/v0rJuHVJb7/nMZsw5izmVbuVv7Xsoai7yHDdC482yhnqWtDVi6U2qPSolstaVwVZ9Gl1hFkbZrjLXewejuIAaCa0mhPCI5YSKvBFBEIQnnniU8xRyudopuvpt6upyAfD2TmRU0n/j7R0PwJUrV9i+fTutrUogER8fz/z58/HGSNu6Erp6S39VOjXe6eE0+zfw/uafUntVqeDRaLWMmpVNcHQGl490cPVUDQBqjYqRU2yMmx2JsfoSzT/7FiV79kDv+je6qChcy+awMb6N3LoddFYqeSUGlYY5DpkXG2oY63CiAq7LgfzJPYN1cgblthjCrQ28ELiFKaojmLCjwozV+hxhYSJvRBAEQbh/YsbkEWlqOsyly1/H4agF1ERHf45hsf+KWm2gubmZ7du3c/XqVQAsFgsLFixgRMxw2vdW037gOriUIMI0PoS2iDaObFtFbYkyo6HV6Rk1MxufkGlcOdJGR7Py2EVn0DBqRjhjpocgHdxN01//iuPKFc+YTGlTKJ2TyJ99z1PYeNazPVpl4PnGmyxrb8MiSdhlHTukyax1Z3DQbzzmUImZ1n1kaPcqJb6yGj+/aUREPE9Q0GyRNyIIgvAUEo9ynhKS5KS09MdUVr0DgMkYRVLSj/Dzm4TL5eLIkSPs27cPl8uFWq1m6tSppE9Lp+dcE215FUgdSmKrPtaCM0niQN671BRdAkCrNzAqIxuDTwpFx9pxdClVOSZfPcmZEcQn6OnKXUPL6jWeZmgqoxHN/FnsSTHxN8c+Wh3K7IwWFRl2iZea60mxO1ABF6Vo3nNnkqufQUe4hYm2s2SbdzG8t8TXZEwgMvJW3kjQo31jBUEQhEdKPMp5CnR3V3Ph4pdoa1NmI8LDXiIu7htotV6Ul5ezdetWGhoaAIiOjmbhwoX4tuho/s0FXDd6S3GDTMgTjBw4soZrvzytbNPpSUjPRqOfRMnpDtw9StBhCTYxbk4U0eZ62t79BVVf2wUuJVjRhobSuCCFv8fVsbctj9418LCh4/mmBp7raCfYLdEum3jXncW7cibnbEnEhl7nZf/1TFApJb4adTAREV8gNFTkjQiCIAgPnghMHpKbN/O4dPnruFxtaLW+JCX+kODgOXR0dLBr1wbOnVPWlzGbzcydO5fEkOG0brlGw9XemQ2TFu0kH45feZ+rvz0EKFU2cSmzUGsnU3rGgSwr0UVItA/jM8MJvHGKll/+jOpzfWvXaMePpXBmGL/zPcMN51ZoAxUw1e7mpZZGpnfb0QAnpZH80D2Tzd4zUIdpyAwt4NPaP+JPC2AiJGQpEeHLRd6IIAiC8FCJwOQBkyQnJaU/oqrqDwD4+iYzetTPMRjCOHHiBHv27MHeuwLvpEmTmDklnZ4D9dSvKlRKeTUqdMkWzt0o4PxfdyPLEqhUDBs/DbSpVF6RACWHJDIpgORUP0wnttH6ta9Sd/NWcqyO7szJvD/BzXpVIZJ8CZwQIKtZ1trCC+3thLvcNMve/Mk9n/fUmRSHDic57BKv+fyaERSBrMLXO4Wo6JcIDpoj8kYEQRCER0IEJg9Qd/f13kc3ZwCIivwsw4d/jbq6BrZufYfr168DEBoaysK58/Eth9ZfXER2Kt1XdSN9KXKe5FTu+0hu5RFM5KhJaAxTqSnXAhKoIG5iCKPiXKh3rKbts1vp6lHyUNTBgVzLSuD3sRUUcVwZlAwT7S5ebGshq7MLHXDQPYq33LPYHpCOf1gH80N28GX1zzDiQKsOIyr6q4SGLsNoDH2Ub58gCIIgiMDkQbnZsIdLl/4dl6vV8+jGxyedHTt2c+LECWRZxmAwMGvWLEbrY2h/r5K2VmXmQxNqospUzJGCdbicyjZb3Gh05uncrDIDyiq/IyaHkOBzHXfu27T/uG/1XnfScA5MtfCHoEvYVccA8JFVLG1r5YX2Dob1uLgh+/Fb9xxW6zKpjQ5jWtgR/sv4f7FRB7KRkJBFREauxOI7AZVK9YjfPUEQBEFQiMDkY5KkHkpLf+Spurn16Ka6upu//OV/aG9XVtsdPXo0sxLTcBXU01JdDIDaV8fNoDr2H34XZ7eS7BoYGYfBO52mOn9oBJVaRfykIOJcF+h59y06qquVF9ZqaJqayKox7ez1rfCMZ5Sjh5Vtbczr7EIvQYE0ju9JmeQHpxIbUcUy//cZozqHSpbxNk0kJvbrBAdno9GYHuG7JgiCIAgDE4HJx6A8uvk32tqURfIiIz9DRPi/kpe3l9OnlQqagIAA5qXPIfCiTPdfywBQ6dW0hbWz7+Tf6TzbW1FjjcToO4OWmyF0dqiUpmjj/RnWcoSe3/6Zrt6ma7LFh0vpkfwuroJak9KTxCjDwo4OXmhrZ5SzhyopmP9xL2CNVxbd4V7MtuXzU92/40MHapWVqKh/JTz8eYzGsEf9lgmCIAjCHYnA5CPq/+jGh6TEH9LWNpzf/vYdT+fWKRMnM0kagWNdPd1uGVRgD+vhwJU1NBUpMx/eAVaMlnTamyNxNKjQ6FTEj/EhujqPnl+sxu5QHu302ALJm2ri3WG1OHVKI7ZhThcr29pY3NGJQVKzS5rE98nisG0cEyIu8Dmfd4ihHFkyEBQwj+iYl/CzTBKPagRBEIQhSwQm90mSeigt+zGVlf8LgK/PWEaO/DEHDlzm5Mm/AuDv70923DQsp3pwdN0AwBUsc6zqfaoPXgbA5BOAyW8ane3DcbWo0erUxCfqibici+tnW3D29r1rG25lzSQHeTEtyOpWdLKKBR2drGjrYILDQZkUyv9zL2W9JQtDhIOFITt4Rf0HdLgw6pOJHfYjrNZ5aDTmx/BuCYIgCML9EZ1f74PdXsP5C1/qe3QT8Sm02pVs3ryNlpYWACYkJjPhRgTUKCXBkq+Kc817Kao8CoDe7IPZbyrdXfGoVFq0OjUjYiXCCt9DPnHQ81rXx9r449hmzkW4QKUiyOXmxfZ2nm/rwMutYauUyrua2ZwPG0lGxGHmGHcSSBMqOYiIqJVERqzAZIp4tG+QIAiC8NQSnV+HmIaGfC5e+prn0c2IuO9x5oyK48ffBcDi60tWSAoBZ2SQ7cg6KHGfpfDsTmRktAYTZr9UHI5R2Lv16AxqRoR2YTv8J1R555EBWavh/IQA/jS2iepgpSPsGLuTT7S1k93ZRYkUyU+l5WwKmEVYZC2LArbyBdUvQNITYJlN7LBPigZogiAIwhNNBCZ3oTy6+W8qK38PgI/PGAL8X2fduuM0NTUBkBybxPjqMLQXlMmnRn0dB0vWYXd3otHqMflNoqcnGafThN6oJs6vEeu+36PepVTTuMx69k40sDa5i2afZjQyzO/o5BNt7STZ3WyXUnhRN5+SqEjmhu/hLd0bmLCjU49i2Ii3sVkXoNV6PZ43SBAEQRAeIBGY3IHdXsOFC1+itffRTXjYP1BRMZFtW3cA4OPtw0xzMtbLBkCmR+/kcPVm6jpLUak1mP0n4ZYm4HJ7YzCpGW6sIiT/t2jaGwHo8jexaaKbHWPddBu68XdL/FNLByvbOpBcFt51L+GzAXOxRd0gxz+XWNU1cAcQFvoPxMS8hMkU9ZjeGUEQBEF4OERgMoiGhoLeRzctaLU+BAf/O3l5TTQ2ngRglG0EE6rDMTRokFUyV7tOc+5aAZLsRu81DDTpSARiMKkYxlWC976DzqH0NGkINbNqop1DSU7cGhUjHT188mY78zu7OOlK4v9o5nI0YgxZEXv5tuE7GCUn3qZ0RsR/h4CAqeJRjSAIgvDUEoHJh8iyTFn5z7h27ZcAeHuPpq11JWvXFCPLMt4mL9IZRfg1HwCaqedI1Sbae5rQGgLQ6Gag0sZiNKqI7TpLyO6/oHUribAlw4ysmeTkzDAHalTM7Ormk23tjOxWs8E9gwW+89FGO3guKJcXVH9B5Q4mKvILREe/iF4f9NjeE0EQBEF4VERg8gGS5OTylTeoq9sIgL/fco4eDae+XukbEm+JIeVGFAZ09Kh7OFW3g4rOS6g1BrSmGWgM49Fp1cS2niD04Cq0bgeySsWxUTo2TpYoC3Xh44ZX29p5sa2ddmcYfyGHXaFppEYd4zXTTzBLDrwM0xiZ+CYBAami54ggCILwTBGBSS+Xq53z579IU/NBQAN8gvffB1luxqw3Mb0nnqgbgQCUdZ7nTMMeeiQnGsNotMZpqDVmIjvOE3n2PQzONnp0anaMU7Nlsop6f5kYp4tvNrQzv93OPvdkXjPNpzHei+esm/iBOhe5J4So8M8TG/sSen3AY30vBEEQBOFxEYEJ4HDc4MzZf6Sj4xJqlYnKqvmUlykVNnHGCFJbYjGip83dxPEb22h0XEetC0fvNRO11orVXkrM2b/h1V2P3aBm7TQVOyapaDermNbVzXfq2ontMrNKms/8kFkkRV/mk95/xEvqwqSbRuLovxMQMEXMjgiCIAjPvGc+MOnoLObsmc9gd9SgVlk4c2YGra1emLQG0rpHMsweghsXZxoLuNp2EjRe6LwWoNbF4+++wbDT/41fWxl2vYr1U1VsSVEhGWBJRwcvV7dTa4/nz4Z/oGhYJItDt/Id7ZvIzmCiQv+Z4XEvo9P5Pe63QBAEQRCGjGc6MGluOcG5c/+My9WGLFs5djwNu92HCHUQGR2JmNBzvauY0w15dEndaIwpaI2T8Za6iL34e4IbzuLUqdiUqmLzFDUag8RnW1tZeMPFLtd0vhCQTUhSLTmWTbzg7sComUrSuPcI8Bfr1QiCIAjCQJ7ZwORG/TYuXfoqkuTE6Yzk1MlUXC4jY13RTHINx+7u4EDD+9R0laDWjcTgNQODWkdM8XrCag/h0khsnaxiU5oavcHN51tbmHZDz99UOXwiYjyzI/bwNd0PkezBRIZ8mhHx/4BO92S14BcEQRCER+2ZDEwqq/5IcfH3AJm2tuGcP5eCWjIwy5nAcMlGUesJLjQfwK32Q++9Ap3WRmTFLqKqdiPjYOcEFblpGkxGN19qbSbhhj9/0L/K+vggVoasZqq0C52cxuix7xIYKGZHBEEQBOFePVOBiSxLFJe8RVXVHwCoq0uk+OoEvGUzs51j8XFp2X9zLbX2WrSmDPS6JMLrjhJ77X/QuNooSFaxIU2Dj8nFV1pbCK6P4A9en2TL2B6e99+AvltPmN8nSUr+LFqtz2O+WkEQBEF48jwzgYnb7eDS5a9RX78NgPKyCVRXJxHq9iezZwyd9np21m/GqYnG4PtpgpuKGF72fYzdN9g3RsX6aRr8zD283tKM3JDAn/3/mdDJ11jh9RekjigSov8vMcMWi66sgiAIgvAxPBOBSU9PK+fOf56WluPIsoaiK2ncvBnLKFckU1xxFLUc53zLCbTm2QTatcSd/RW+baUcGKVi3TQNIWYn32jpoKlxIr+zzmbi6GN8Rvcb6JrIhDFrCApOftyXKAiCIAhPhac+MLHbayg882m6ukpwu/VcvDiDjuZwZrjiiXb6cfDmem70uDF4rWBERT5h1Xs5mghrV2oIMzt4o6Wb4tZ0fh2RQvb47fyz+/eYVNlMnnoQsznkcV+eIAiCIDxVnurApL39MmfOfgansx6n08z5c5nQaWOhcwyq7nZ21v+VHv04At0Wkk7/gmLbTX72j2qivOx8vcXFqc65/DZmGDmhG/hsVxkhppeYMPXzaDTGx31pgiAIgvBUemoDk6amQ5w7/wXc7k46Oy1cuJCFX3cYs51juNZymottl9GZ5hNfeRRL4x7+d44KXbSDL7do2dezmHeHe7EkIJfElnJGWL9B/OilorpGEARBEB6ypzIwuVG/nYsXX0OWXbS0WLl0cSYjHDFMcERy8uZm6iU/AuRpjCr8X47F3WDvEolPdkrs7VrJ9lFtzDbvRGpOZmL83wmNFPkjgiAIgvCoPHWBSUvrKS5e/Aqy7KK+PoaSK9NI7UkkqBN231yLW59GfN1F9O0/5edLZKZYuklpz+T0KInZ2q1oumcyLXUvPr4if0QQBEEQHrWnKjDp6qrgzJl/RpadNDREUHl5FvOdyTQ1X2ZfRw0WOZWEC6vYM6aWhnEOMtuGcSp4HHMC9uDlWMyM7F+j04n8EUEQBEF4XJ6awKSnp5XCws/gdrfQ3h7AzUvzmd+dxPn6PTQSTVyDGpfz//Hb5yQy1VrqdSswTSwgu6mdrIwCzF6Wx30JgiAIgvDMeyoCE0lycubs57A7rmG3m6k6v4DJreEcashDLyUyriyXrZOqCYp1Eu2eQ1tSJbOaSpg+7l2CQ4c97uELgiAIgtDriQ9MZFnm0uU3aGs7gculo+zCXMbcDOdYcznDGow0aX9D7rIuot2jaIgKYlxPOclR3yJu4bTHPXRBEARBED7kiQ9Mysv/hxs3NiLLKkouz2R0XRJFjdUkVR5ie1olwwK8kIOzCPU5Q5r/SlKyXn7cQxYEQRAEYRBPdGBSV7eF8ms/BaCsOIXEqimU1JWjcu7gxKI2dIbZeEVdYYEzhFk5eWi1T/TlCoIgCMJT74n9plbKgr8KKrhenUh02RzK6spB3kZ9SgzGOEhrgXlzN2M0mx/3cAVBEARBuAdPZGDS3V3J6VOfBZWLxoYIAi7lUF1zDTfbKZ02nkmmTjLTfkRQaPjjHqogCIIgCPfhiQtMenpaOXb8FWTaaW8PQHNuBQ01VThV2ymfOp7nrDNJfU7kkQiCIAjCk+iJCkwkycmJE5/F7a7G4TDTfWYFzsqbdKp2cH3aeL6U8f8RkSBayAuCIAjCk+qJCUxkWabwzNfothficmlpOJODuqyTDs0Obkwbx7defAuTd8DjHqYgCIIgCB/DExOYFBX9jJaWrciyirqL89AV62nSbaMlbRzf/vTPUav1j3uIgiAIgiB8TOrHPYB7UVm1ges1vwSgpngahktWGrU7cGek8c3P/EoEJYIgCILwlBjyMyaNjccpKnodtRpuVI/GdCaJOvUWgnKW8NL8/3jcwxMEQRAE4QEa0oGJ3V7PyROfQat309QQif7odK6rtzLmHz9H5oRPPu7hCYIgCILwgA3ZwESWZfLeX4k5sJvODj9UR+ZTpd7OrC+/wbjh2Y97eIIgCIIgPARDNjDZvfErmAMrkSQ1PacWUOnaw9I33mZ4WMrjHpogCIIgCA/JkAxMaq+fBp+tALRenUZ1wzk++d1fEuI/8jGPTBAEQRCEh2nIBSZut4NTJz6PyddNe1MELYVaPvOz3+LrZXvcQxMEQRAE4SEbcuXCuX96CZNvIz09BhwHp5H5X18SQYkgCIIgPCOGVGByYMvPscScBaDjXCbSzGCGhY59zKMSBEEQBOFRGTKBSUf7DTrU/4tKBS01CVQ0dfFSzpcf97AEQRAEQXiEhkxgkrfpFfTGTuzdPrQejeEr3//fxz0kQRAEQRAesSERmKz5xT/iG1aKLKvoPD6Lxd/9JirVkBiaIAiCIAiP0GP/9j93aCuW+EMAtJROwjw1mWC/sMc8KkEQBEEQHofHGphIkotrdW+h1TrpbA2hpsKPRXM/9TiHJAiCIAjCY/RYA5N1v3kZL/9a3G4tLUcm8qXv/eZxDkcQBEEQhMfssQUm6/7nP/EfWQhA64XpPP9fbz+uoQiCIAiCMER8pMDkV7/6FbGxsRiNRiZOnMiBAwfu+xw+w/ajVku018diSZiN2ej9UYYiCIIgCMJT5L4Dk9WrV/Paa6/xzW9+k8LCQtLT05k/fz6VlZX3dR6DuRWnw0TdxUSy5790v8MQBEEQBOEppJJlWb6fA6ZMmcKECRP49a9/7dmWmJhITk4Ob7311l2Pb2trw2KxsGlzDB1n5/Dyf/7u/kctCIIgCMIjdev7u7W1FV9f34f2Ovc1Y+J0Ojl16hTZ2dn9tmdnZ3P48OEBj3E4HLS1tfX7AWirSuKFr//yIw5bEARBEISn0X0FJg0NDbjdbqxWa7/tVquVurq6AY956623sFgsnp/IyEgAfPyXodPrP+KwBUEQBEF4Gn2k5FeVStXvv2VZvm3bLd/4xjdobW31/FRVVQEwa+GKj/LSgiAIgiA8xbT3s3NQUBAajea22ZH6+vrbZlFuMRgMGAyGjz5CQRAEQRCeGfc1Y6LX65k4cSJ5eXn9tufl5TF16tQHOjBBEARBEJ499zVjAvCVr3yFV155hUmTJpGWlsbvfvc7Kisr+fznP/8wxicIgiAIwjPkvgOTlStX0tjYyH/9139RW1vL6NGj2bZtG9HR0Q9jfIIgCIIgPEPuu4/Jx/Wo6qAFQRAEQXhwhmQfE0EQBEEQhIdJBCaCIAiCIAwZIjARBEEQBGHIEIGJIAiCIAhDhghMBEEQBEEYMkRgIgiCIAjCkCECE0EQBEEQhgwRmAiCIAiCMGSIwEQQBEEQhCHjvlvSf1y3Gs22tbU96pcWBEEQBOEjuvW9/bAbxj/ywKSxsRGAyMjIR/3SgiAIgiB8TI2NjVgslod2/kcemAQEBABQWVn5UC9sqGlrayMyMpKqqqpnao0gcd3iup8F4rrFdT8LWltbiYqK8nyPPyyPPDBRq5W0FovF8kzd0Ft8fX3FdT9DxHU/W8R1P1ue1eu+9T3+0M7/UM8uCIIgCIJwH0RgIgiCIAjCkPHIAxODwcCbb76JwWB41C/9WInrFtf9LBDXLa77WSCu++Fet0p+2HU/giAIgiAI90g8yhEEQRAEYcgQgYkgCIIgCEOGCEwEQRAEQRgyRGAiCIIgCMKQ8cADk1/96lfExsZiNBqZOHEiBw4cuOP++/btY+LEiRiNRoYNG8ZvfvObBz2kh+6tt95i8uTJ+Pj4EBISQk5ODkVFRXc8Zu/evahUqtt+rly58ohG/fF9+9vfvm38Npvtjsc8Dfc7JiZmwHv3L//yLwPu/6Te6/3797N48WLCwsJQqVTk5ub2+70sy3z7298mLCwMk8nEzJkzuXjx4l3Pu379epKSkjAYDCQlJbFx48aHdAUfzZ2uu6enh//4j/9gzJgxeHl5ERYWxj/8wz9QU1Nzx3P+6U9/GvBvwG63P+SruXd3u9+f+tSnbht/amrqXc/7JN9vYMD7plKp+NGPfjToOYf6/b6X76zH+fl+oIHJ6tWree211/jmN79JYWEh6enpzJ8/n8rKygH3Ly8vZ8GCBaSnp1NYWMgbb7zBl770JdavX/8gh/XQ7du3j3/5l3/h6NGj5OXl4XK5yM7OprOz867HFhUVUVtb6/kZMWLEIxjxgzNq1Kh+4z9//vyg+z4t9/vEiRP9rjkvLw+AF1544Y7HPWn3urOzk+TkZH75y18O+Psf/vCH/OQnP+GXv/wlJ06cwGazMWfOHNrb2wc955EjR1i5ciWvvPIKZ8+e5ZVXXmHFihUcO3bsYV3GfbvTdXd1dXH69Gm+9a1vcfr0aTZs2MDVq1dZsmTJXc/r6+vb7/7X1tZiNBofxiV8JHe73wDz5s3rN/5t27bd8ZxP+v0Gbrtnf/jDH1CpVCxfvvyO5x3K9/tevrMe6+dbfoBSUlLkz3/+8/22JSQkyK+//vqA+3/961+XExIS+m373Oc+J6empj7IYT1y9fX1MiDv27dv0H0KCgpkQG5ubn50A3vA3nzzTTk5Ofme939a7/e//du/ycOHD5clSRrw90/DvQbkjRs3ev5bkiTZZrPJb7/9tmeb3W6XLRaL/Jvf/GbQ86xYsUKeN29ev21z586VX3zxxQc+5gfhw9c9kOPHj8uAXFFRMeg+f/zjH2WLxfJgB/cQDXTdr776qrx06dL7Os/TeL+XLl0qZ2Zm3nGfJ+1+f/g763F/vh/YjInT6eTUqVNkZ2f3256dnc3hw4cHPObIkSO37T937lxOnjxJT0/PgxraI9fa2gpwTwsdjR8/ntDQULKysigoKHjYQ3vgiouLCQsLIzY2lhdffJGysrJB930a77fT6eRvf/sbn/nMZ1CpVHfc90m/1x9UXl5OXV1dv/tpMBjIyMgY9PMOg/8N3OmYoa61tRWVSoWfn98d9+vo6CA6OpqIiAgWLVpEYWHhoxngA7R3715CQkIYOXIk//RP/0R9ff0d93/a7veNGzfYunUrn/3sZ++675N0vz/8nfW4P98PLDBpaGjA7XZjtVr7bbdardTV1Q14TF1d3YD7u1wuGhoaHtTQHilZlvnKV77C9OnTGT169KD7hYaG8rvf/Y7169ezYcMG4uPjycrKYv/+/Y9wtB/PlClT+Mtf/sLOnTv5/e9/T11dHVOnTqWxsXHA/Z/G+52bm0tLSwuf+tSnBt3nabjXH3brM30/n/dbx93vMUOZ3W7n9ddf5+WXX77jYm4JCQn86U9/YvPmzbz33nsYjUamTZtGcXHxIxztxzN//nz+/ve/k5+fz3//939z4sQJMjMzcTgcgx7ztN3vP//5z/j4+PDcc8/dcb8n6X4P9J31uD/fD3x14Q//q1GW5Tv+S3Kg/Qfa/qT44he/yLlz5zh48OAd94uPjyc+Pt7z32lpaVRVVfHjH/+YGTNmPOxhPhDz58/3/O8xY8aQlpbG8OHD+fOf/8xXvvKVAY952u73O++8w/z58wkLCxt0n6fhXg/mfj/vH/WYoainp4cXX3wRSZL41a9+dcd9U1NT+yWKTps2jQkTJvCLX/yCn//85w97qA/EypUrPf979OjRTJo0iejoaLZu3XrHL+qn5X4D/OEPf+ATn/jEXXNFnqT7fafvrMf1+X5gMyZBQUFoNJrbIqP6+vrbIqhbbDbbgPtrtVoCAwMf1NAemX/9139l8+bNFBQUEBERcd/Hp6amDsmI+l55eXkxZsyYQa/habvfFRUV7N69m3/8x3+872Of9Ht9q/rqfj7vt46732OGop6eHlasWEF5eTl5eXl3nC0ZiFqtZvLkyU/030BoaCjR0dF3vIan5X4DHDhwgKKioo/0eR+q93uw76zH/fl+YIGJXq9n4sSJngqFW/Ly8pg6deqAx6Slpd22/65du5g0aRI6ne5BDe2hk2WZL37xi2zYsIH8/HxiY2M/0nkKCwsJDQ19wKN7dBwOB5cvXx70Gp6W+33LH//4R0JCQli4cOF9H/uk3+vY2FhsNlu/++l0Otm3b9+gn3cY/G/gTscMNbeCkuLiYnbv3v2RgmpZljlz5swT/TfQ2NhIVVXVHa/habjft7zzzjtMnDiR5OTk+z52qN3vu31nPfbP932lyt7FqlWrZJ1OJ7/zzjvypUuX5Ndee0328vKSr127JsuyLL/++uvyK6+84tm/rKxMNpvN8pe//GX50qVL8jvvvCPrdDp53bp1D3JYD90XvvAF2WKxyHv37pVra2s9P11dXZ59PnztP/3pT+WNGzfKV69elS9cuCC//vrrMiCvX7/+cVzCR/LVr35V3rt3r1xWViYfPXpUXrRokezj4/PU329ZlmW32y1HRUXJ//Ef/3Hb756We93e3i4XFhbKhYWFMiD/5Cc/kQsLCz3VJ2+//bZssVjkDRs2yOfPn5dfeuklOTQ0VG5ra/Oc45VXXulXlXfo0CFZo9HIb7/9tnz58mX57bfflrVarXz06NFHfn2DudN19/T0yEuWLJEjIiLkM2fO9Pu8OxwOzzk+fN3f/va35R07dsilpaVyYWGh/OlPf1rWarXysWPHHsclDuhO193e3i5/9atflQ8fPiyXl5fLBQUFclpamhweHv5U3+9bWltbZbPZLP/6178e8BxP2v2+l++sx/n5fqCBiSzL8v/8z//I0dHRsl6vlydMmNCvZPbVV1+VMzIy+u2/d+9eefz48bJer5djYmIGvfFDGTDgzx//+EfPPh++9h/84Afy8OHDZaPRKPv7+8vTp0+Xt27d+ugH/zGsXLlSDg0NlXU6nRwWFiY/99xz8sWLFz2/f1rvtyzL8s6dO2VALioquu13T8u9vlXm/OGfV199VZZlpaTwzTfflG02m2wwGOQZM2bI58+f73eOjIwMz/63rF27Vo6Pj5d1Op2ckJAw5AK0O113eXn5oJ/3goICzzk+fN2vvfaaHBUVJev1ejk4OFjOzs6WDx8+/Ogv7g7udN1dXV1ydna2HBwcLOt0OjkqKkp+9dVX5crKyn7neNru9y2//e1vZZPJJLe0tAx4jiftft/Ld9bj/HyregcpCIIgCILw2Im1cgRBEARBGDJEYCIIgiAIwpAhAhNBEARBEIYMEZgIgiAIgjBkiMBEEARBEIQhQwQmgiAIgiAMGSIwEQRBEARhyBCBiSAIgiAIQ4YITARBEARBGDJEYCIIgiAIwpAhAhNBEARBEIYMEZgIgiAIgjBk/P8/yDcX3wMpOAAAAABJRU5ErkJggg==", + "image/png": 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", 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" ] @@ -91,7 +91,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -119,7 +119,7 @@ "\n", "portfolio_agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", "\n", - "portfolio_agent.T_sim = portfolio_agent.T_cycle + 1\n", + "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", "# Run the simulations\n", "portfolio_agent.initialize_sim()\n", "history = portfolio_agent.simulate()" @@ -132,7 +132,7 @@ "outputs": [], "source": [ "raw_data = {\n", - " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25 - 1,\n", + " \"Age\": portfolio_agent.history[\"t_age\"].flatten() + 25,\n", " \"pIncome\": portfolio_agent.history[\"pLvl\"].flatten(),\n", " \"nrmM\": portfolio_agent.history[\"mNrm\"].flatten(),\n", " \"nrmC\": portfolio_agent.history[\"cNrm\"].flatten(),\n", @@ -190,7 +190,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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-925,256 +925,253 @@ z - + - + - + +" clip-path="url(#p16f8d705a1)" style="fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #d62728; stroke-width: 1.5"/> + diff --git a/src/setup.py b/src/setup.py deleted file mode 100644 index 976657c..0000000 --- a/src/setup.py +++ /dev/null @@ -1,12 +0,0 @@ -import sys - -from setuptools import find_packages, setup - -sys.path[0:0] = ["estimark"] - -setup( - name="estimark", - version="0.1.0", - description="Local package for estimark", - packages=find_packages(include=["estimark"]), -) diff --git a/tests/test_package.py b/tests/test_package.py new file mode 100644 index 0000000..8026afa --- /dev/null +++ b/tests/test_package.py @@ -0,0 +1,9 @@ +from __future__ import annotations + +import importlib.metadata + +import estimark as m + + +def test_version(): + assert importlib.metadata.version("estimark") == m.__version__ From 7bb04d1c8b940c4e77096336d769083246171f96 Mon Sep 17 00:00:00 2001 From: alanlujan91 Date: Fri, 20 Sep 2024 16:11:12 -0400 Subject: [PATCH 4/7] update notebooks --- content/paper/math.ipynb | 532 +-- .../WarmGlowPortfolio_estimate_results.csv | 4113 ++++++++++++++--- content/tables/parameters.tex | 6 +- src/msm_notebooks/FinAssets_Cov.ipynb | 2 + .../MSM Full Bequest model.ipynb | 2 + src/msm_notebooks/MSM LCIM model.ipynb | 2 + src/msm_notebooks/MSM TRP model.ipynb | 2 + .../MSM Term Bequest model.ipynb | 2 + .../MSM Warm Glow Bequest model.ipynb | 2 + src/msm_notebooks/NetWorth_Cov.ipynb | 2 + src/msm_notebooks/savres.ipynb | 2 + src/notebooks/IndShock.ipynb | 7 +- src/notebooks/Model_Comparisons.ipynb | 35 +- src/notebooks/Portfolio.ipynb | 10 +- src/notebooks/SCF_notebook.ipynb | 7 +- src/notebooks/WarmGlow.ipynb | 7 +- src/notebooks/WarmGlowPortfolio.ipynb | 18 +- src/notebooks/WealthPortfolio.ipynb | 19 +- src/notebooks/median_share.pdf | Bin 17055 -> 17054 bytes src/notebooks/median_share.svg | 134 +- src/notebooks/median_wealth.pdf | Bin 18241 -> 18256 bytes src/notebooks/median_wealth.svg | 230 +- src/notebooks/msm.ipynb | 2 + src/notebooks/parse_tables.ipynb | 28 +- src/notebooks/testing_notebook.ipynb | 2 + 25 files changed, 4087 insertions(+), 1079 deletions(-) diff --git a/content/paper/math.ipynb b/content/paper/math.ipynb index 680151b..316af62 100644 --- a/content/paper/math.ipynb +++ b/content/paper/math.ipynb @@ -1,268 +1,270 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 421, - "metadata": {}, - "outputs": [], - "source": [ - "from sympy import *\n", - "from sympy.plotting import plot" - ] - }, - { - "cell_type": "code", - "execution_count": 422, - "metadata": {}, - "outputs": [], - "source": [ - "c, a, x = symbols(\"c a x\")\n", - "rho, delta = symbols(\"rho delta\")" - ] - }, - { - "cell_type": "code", - "execution_count": 423, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}}{1 - \\rho}$" - ], - "text/plain": [ - "(a**delta*c**(1 - delta))**(1 - rho)/(1 - rho)" - ] - }, - "execution_count": 423, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "u = (c ** (1 - delta) * a**delta) ** (1 - rho) / (1 - rho)\n", - "u" - ] - }, - { - "cell_type": "code", - "execution_count": 424, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho} \\left(1 - \\delta\\right)}{c}$" - ], - "text/plain": [ - "(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c" - ] - }, - "execution_count": 424, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uc = simplify(u.diff(c))\n", - "uc" - ] - }, - { - "cell_type": "code", - "execution_count": 425, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\delta \\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}}{a}$" - ], - "text/plain": [ - "delta*(a**delta*c**(1 - delta))**(1 - rho)/a" - ] - }, - "execution_count": 425, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ua = simplify(u.diff(a))\n", - "ua" - ] - }, - { - "cell_type": "code", - "execution_count": 426, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho} \\left(1 - \\delta\\right)}{c} - \\frac{\\delta \\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}}{a}$" - ], - "text/plain": [ - "(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c - delta*(a**delta*c**(1 - delta))**(1 - rho)/a" - ] - }, - "execution_count": 426, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f = simplify(uc) - simplify(ua)\n", - "f" - ] - }, - { - "cell_type": "code", - "execution_count": 427, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left(a^{- \\delta} \\left(\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}\\right)^{- \\frac{1}{\\rho - 1}}\\right)^{- \\frac{1}{\\delta - 1}}$" - ], - "text/plain": [ - "(1/(a**delta*((a**delta*c**(1 - delta))**(1 - rho))**(1/(rho - 1))))**(-1/(delta - 1))" - ] - }, - "execution_count": 427, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "g = ((f * c * a / (-a * delta + a - c * delta)) ** (1 / (1 - rho)) / a**delta) ** (\n", - " 1 / (1 - delta)\n", - ")\n", - "simplify(g)" - ] - }, - { - "cell_type": "code", - "execution_count": 428, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle 0.00025 \\left(\\frac{c^{1.0} - 0.001 c^{2.0}}{0.0158113883008419 c^{0.5} - 1.58113883008419 \\cdot 10^{-5} c^{1.5}}\\right)^{2.0}$" - ], - "text/plain": [ - "0.00025*((c**1.0 - 0.001*c**2.0)/(0.0158113883008419*c**0.5 - 1.58113883008419e-5*c**1.5))**2.0" - ] - }, - "execution_count": 428, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "g_1 = g.subs({a: 1000, delta: 0.5, rho: 2})\n", - "simplify(g_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 429, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":1: RuntimeWarning: invalid value encountered in scalar power\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 429, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot(g_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 430, - "metadata": {}, - "outputs": [], - "source": [ - "util = x ** (1 - rho) / (1 - rho)" - ] - }, - { - "cell_type": "code", - "execution_count": 431, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left(x^{- \\rho}\\right)^{- \\frac{1}{\\rho}}$" - ], - "text/plain": [ - "(x**(-rho))**(-1/rho)" - ] - }, - "execution_count": 431, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simplify(simplify(util.diff(x) ** (-1 / rho)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "estimatingmicrodsops", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "orig_nbformat": 4 + "cells": [ + { + "cell_type": "code", + "execution_count": 421, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from sympy import *\n", + "from sympy.plotting import plot" + ] + }, + { + "cell_type": "code", + "execution_count": 422, + "metadata": {}, + "outputs": [], + "source": [ + "c, a, x = symbols(\"c a x\")\n", + "rho, delta = symbols(\"rho delta\")" + ] + }, + { + "cell_type": "code", + "execution_count": 423, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}}{1 - \\rho}$" + ], + "text/plain": [ + "(a**delta*c**(1 - delta))**(1 - rho)/(1 - rho)" + ] + }, + "execution_count": 423, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "u = (c ** (1 - delta) * a**delta) ** (1 - rho) / (1 - rho)\n", + "u" + ] + }, + { + "cell_type": "code", + "execution_count": 424, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho} \\left(1 - \\delta\\right)}{c}$" + ], + "text/plain": [ + "(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c" + ] + }, + "execution_count": 424, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uc = simplify(u.diff(c))\n", + "uc" + ] + }, + { + "cell_type": "code", + "execution_count": 425, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\delta \\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}}{a}$" + ], + "text/plain": [ + "delta*(a**delta*c**(1 - delta))**(1 - rho)/a" + ] + }, + "execution_count": 425, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ua = simplify(u.diff(a))\n", + "ua" + ] + }, + { + "cell_type": "code", + "execution_count": 426, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho} \\left(1 - \\delta\\right)}{c} - \\frac{\\delta \\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}}{a}$" + ], + "text/plain": [ + "(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c - delta*(a**delta*c**(1 - delta))**(1 - rho)/a" + ] + }, + "execution_count": 426, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f = simplify(uc) - simplify(ua)\n", + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left(a^{- \\delta} \\left(\\left(a^{\\delta} c^{1 - \\delta}\\right)^{1 - \\rho}\\right)^{- \\frac{1}{\\rho - 1}}\\right)^{- \\frac{1}{\\delta - 1}}$" + ], + "text/plain": [ + "(1/(a**delta*((a**delta*c**(1 - delta))**(1 - rho))**(1/(rho - 1))))**(-1/(delta - 1))" + ] + }, + "execution_count": 427, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g = ((f * c * a / (-a * delta + a - c * delta)) ** (1 / (1 - rho)) / a**delta) ** (\n", + " 1 / (1 - delta)\n", + ")\n", + "simplify(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 0.00025 \\left(\\frac{c^{1.0} - 0.001 c^{2.0}}{0.0158113883008419 c^{0.5} - 1.58113883008419 \\cdot 10^{-5} c^{1.5}}\\right)^{2.0}$" + ], + "text/plain": [ + "0.00025*((c**1.0 - 0.001*c**2.0)/(0.0158113883008419*c**0.5 - 1.58113883008419e-5*c**1.5))**2.0" + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_1 = g.subs({a: 1000, delta: 0.5, rho: 2})\n", + "simplify(g_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 429, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: RuntimeWarning: invalid value encountered in scalar power\n" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "data": { + "image/png": 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FRQoJdGreqBj1aNHQ6miwAMUOAIAaKjM3X9M+2KYV2zMkSX3bNNKcEZ3UsK7T4mSwCsUOAIAaKCn1lCYsTtLhU7ny8nDo4YFtdec1UfLg1GutRrEDAKAGKSoy+vfX+/XCF8kqKDJq1sBP88d0Uedm9ayOhmqAYgcAQA1xMselKe9tVULycUnSjR3DNPOWjgr09bY4GaoLih0AADXAhp9OaNKSLTqW7ZLTy0NPDemgMVc3k8PBqVf8H4odAADVWEFhkV76ap/mf7VXxkgtG9fVgtti1DY00OpoqIYodgAAVFPpmbl6YMkWfZfysyRpZNemmj60g+r48PSNsrFnAABQDX25+6imvrdVp87my9/HU88N76hhnZtYHQvVHMUOAIBqJK+gSLM+36OF61MkSdFNAjV/TBdFBftbnAw1AcUOAIBq4uDJM5qwOEnbDmdKksb1jNS0wW3l9PK0OBlqCoodAADVwPKtRzTtg+3KcRUoyM9bc0Z00g3tQ6yOhRqGYgcAgIVy8wr19Cc7tfi7Q5KkrhH19dKYGIXX87M4GWoiih0AABb58Wi24t75QT8ezZHDId3fp6UmXd9KXp4eVkdDDUWxAwDAzYwxWrL5kGYs36lz+UUKruvUvFGddU2rYKujoYaj2AEA4EbZ5/I17YPt+mRbuiSpV6tgzR3ZWY0CnBYngx1Q7AAAcJNth08r7p0kpf58Vp4eDk3t30Z3X3uFPDz4WjBUDoodAABVzBijhetTNOvzPcovNGpSz08vjYlRbER9q6PBZih2AABUoZ/P5Okv723Vl3uOSZIGdAjR7Fs6KaiOt8XJYEcUOwAAqsim/Sf1wJItysg6Jx9PDz1+Uzvd3j1CDgenXlE1KHYAAFSywiKjBV/t09+//FFFRroi2F/zb4tRh/Agq6PB5ih2AABUoqNZ5zRpyRZt3H9SkjQ8pomeuTla/k6eclH12MsAAKgkCcnHNOXdrTp5Jk91fDz1zLBo3RLb1OpYqEUodgAAXKb8wiLN+SJZ/1q3X5LUNjRA//hTF7VoVNfiZKhtKHYAAFyGQz+f1YTFSdpy6LQk6fbuEXrsxnby9fa0NhhqJYodAACXaMX2dD28bJuyzxUowNdLs2+5UoM6hlkdC7UYxQ4AgIt0Lr9Qz3yyS29vSpUkxTSvp5dGx6hZgzoWJ0NtR7EDAOAi7DuWo7h3ftCejGxJ0j29W2hK/9by9vSwOBlAsQMA4IIYY/R+4mE9+dFO5eYXqqG/j+aO6qzerRtZHQ0oRrEDAOA8clwFeuLDHYpPSpMk/aFlQ704srMaB/panAwoiWIHAEAFdqRlasLiJKWcOCMPhzT5hta6t09LeXrwtWCofnhBAAC3mT59uhwOR4lLaGhohXPWrl2r2NhY+fr66oorrtArr7ziprSo7Ywxev2bFA3/5walnDijsCBfLb27h+Kua0WpQ7XFETsAbtWhQwetXr26+LqnZ/mf9ZWSkqLBgwdr/Pjxeuutt/TNN9/ovvvuU6NGjXTLLbe4Iy5qqdNn8/TQ+9u0ctdRSdL17UL0wq1Xqr6/j8XJgIpR7AC4lZeX13mP0v3qlVdeUfPmzTVv3jxJUrt27fT9999rzpw5FDtUmcSDP2vi4i1KO50rb0+Hpg1qp//9Q6QcDo7SofrjVCwAt9q7d6/Cw8MVFRWl0aNHa//+/eWO3bhxo/r3719i2YABA/T9998rPz+/qqOilikqMvrHmn0a+a9vlXY6V5EN6+iDe/+gP18TRalDjcEROwBu061bN7355ptq3bq1jh49qr/+9a/q2bOndu7cqYYNG5Yan5GRoZCQkBLLQkJCVFBQoBMnTigsrOxP+He5XHK5XMXXs7KyKndFYDvHs12a/O4Wfb33hCRpWOdw/fXmaAX4elucDLg4FDsAbjNo0KDif3fs2FE9evRQixYt9MYbb2jy5Mllzvn9kRJjTJnLf2vmzJmaMWNGJSRGbfD13uN6cOlWnchxydfbQ08PjdaIrk05SocaiVOxACzj7++vjh07au/evWXeHhoaqoyMjBLLjh07Ji8vrzKP8P1q2rRpyszMLL4cOnSoUnPDHgoKizT78z2647XvdCLHpTYhAVoed41GXtWMUocaiyN2ACzjcrm0e/du9erVq8zbe/TooeXLl5dYtnLlSnXt2lXe3uWfInM6nXI6nZWaFfaSdjpXExcnKfHgKUnSmKub66kh7eXrXf67tIGagCN2ANxm6tSpWrt2rVJSUrRp0ybdeuutysrK0tixYyX9cqTtjjvuKB5/zz336ODBg5o8ebJ2796t1157TQsXLtTUqVOtWgXYwBc7MzT4718r8eApBTi9tOC2GM0c3pFSB1vgiB0Atzl8+LDGjBmjEydOqFGjRurevbu+/fZbRURESJLS09OVmppaPD4qKkorVqzQgw8+qH/84x8KDw/XSy+9xEed4JK4Cgo1c8Uevb7hgCSpU9MgzR/TRc0b1rE2GFCJHObXVyIDgE1lZWUpKChImZmZCgwMtDoOLLD/eI4mLE7SziO/vEN6fK8o/WVAW/l4ceIK9sIROwCArcUnHdbj8Tt0Jq9Q9et4628jO+m6tiHnnwjUQBQ7AIAtnc0r0JMf7dT7iYclSd2iGujvo2MUGuRrcTKg6lDsAAC2szs9S3Hv/KCfjp+Rh0OacF0rTezXSp4efIwJ7I1iBwCwDWOM3t6Uqqc/2aW8giKFBDo1b1SMerQo/3MPATuh2AEAbCEzN1/TPtimFdt/+VDrvm0aac6ITmpYl880RO1BsQMA1HhJqac0YXGSDp/KlZeHQw8PbKs7r4mSB6deUctQ7AAANVZRkdG/v96vF75IVkGRUbMGfpo/pos6N6tndTTAEhQ7AECNdDLHpSnvbVVC8nFJ0o0dwzTzlo4K9C3/6+YAu6PYAQBqnA0/ndCkJVt0LNslp5eHnhrSQWOubiaHg1OvqN0odgCAGqOgsEgvfbVP87/aK2Oklo3rasFtMWobyjeKABLFDgBQQ6Rn5uqBJVv0XcrPkqSRXZtq+tAOquPDUxnwK34bAADV3pe7j2rqe1t16my+/H089dzwjhrWuYnVsYBqh2IHAKi28gqKNOvzPVq4PkWSFN0kUPPHdFFUsL/FyYDqiWIHAKiWDp48owmLk7TtcKYkaVzPSE0b3FZOL0+LkwHVF8UOAFDtLN96RNM+2K4cV4GC/Lw1Z0Qn3dA+xOpYQLVHsQMAVBu5eYV6+pOdWvzdIUlS14j6emlMjMLr+VmcDKgZKHYAgGrhx6PZinvnB/14NEcOh3R/n5aadH0reXl6WB0NqDEodgAASxljtHTzIU1fvlPn8osUXNepeaM665pWwVZHA2ocih0AwDLZ5/L1aPwOLd96RJLUq1Ww5o7srEYBTouTATUTxQ4AYIlth09rwuIkHTx5Vp4eDk3t30Z3X3uFPDz4WjDgUlHsAABuZYzRa98c0POf7VZ+oVGTen56aUyMYiPqWx0NqPEodgAAt/n5TJ7+8t5WfbnnmCRpQIcQzb6lk4LqeFucDLAHih0AwC027T+pB5ZsUUbWOfl4eeiJG9vpf7pHyOHg1CtQWSh2AIAqVVhktOCrffr7lz+qyEhXBPtr/m0x6hAeZHU0wHYodgCAKnM065wmLdmijftPSpKGd2miZ4ZFy9/J0w9QFfjNAgBUiYTkY5ry7ladPJOnOj6eemZYtG6JbWp1LMDWKHYAgEqVX1ikOV8k61/r9kuS2oUFasFtMWrRqK7FyQD7o9gBACrNoZ/PasLiJG05dFqSdEePCD06uJ18vT2tDQbUEhQ7AEClWLE9XQ8v26bscwUK9PXS7Fuv1MDoMKtjAbUKxQ4AcFnO5RfqmU926e1NqZKkmOb19NLoGDVrUMfiZEDtQ7EDAFyyfcdyFPfOD9qTkS1Juqd3C03p31renh4WJwNqJ4odAOCiGWP0fuJhPfnRTuXmF6qhv4/mjuqs3q0bWR0NqNUodgCAi5LjKtATH+5QfFKaJOkPLRvqxZGd1TjQ1+JkACh2AIALtiMtUxMWJynlxBl5OKTJN7TWvX1aytODrwUDqgOKHQDgvIwxemPDAT23Yo/yCosUFuSrl8bE6KrIBlZHA/AbFDsAQIVOn83TQ+9v08pdRyVJ17cL0Qu3Xqn6/j4WJwPwexQ7AEC5Eg/+rImLtyjtdK68PR2aNqid/vcPkXI4OPUKVEcUOwBAKUVFRi+v/UlzV/2owiKjyIZ1NH9MF3VsGmR1NAAVoNgBAEo4nu3S5He36Ou9JyRJwzqH6683RyvA19viZADOh2IHACj29d7jenDpVp3IccnX20NPD43WiK5NOfUK1BB8NDgAt5k5c6auuuoqBQQEqHHjxrr55puVnJxc4ZyEhAQ5HI5Slz179rgpde1QUFik2Z/v0R2vfacTOS61CQnQ8rhrNPKqZpQ6oAbhiB0At1m7dq3uv/9+XXXVVSooKNBjjz2m/v37a9euXfL3969wbnJysgIDA4uvN2rENxxUlrTTuZq4OEmJB09JksZc3VxPDWkvX29Pi5MBuFgUOwBu8/nnn5e4vmjRIjVu3FiJiYm69tprK5zbuHFj1atXrwrT1U5f7MzQQ+9vU2ZuvgKcXpp5S0fddGW41bEAXCJOxQKwTGZmpiSpQYPzf8htTEyMwsLC1K9fP61Zs6aqo9meq6BQ0z/eqbv/m6jM3Hx1ahqkTyf2otQBNRxH7ABYwhijyZMn65prrlF0dHS548LCwvTqq68qNjZWLpdL//3vf9WvXz8lJCSUe5TP5XLJ5XIVX8/Kyqr0/DXZ/uM5mrA4STuP/LJdxveK0l8GtJWPF//XB2o6hzHGWB0CQO1z//3369NPP9X69evVtGnTi5o7ZMgQORwOffzxx2XePn36dM2YMaPU8szMzBKv06uN4pMO6/H4HTqTV6j6dbz1t5GddF3bEKtjAagkFDsAbjdhwgR9+OGHWrdunaKioi56/rPPPqu33npLu3fvLvP2so7YNWvWrFYXu7N5BXryo516P/GwJKlbVAP9fXSMQoN8LU4GoDJxKhaA2xhjNGHCBMXHxyshIeGSSp0kJSUlKSwsrNzbnU6nnE7npca0nd3pWYp75wf9dPyMPBzShOtaaWK/VvL04GNMALuh2AFwm/vvv1/vvPOOPvroIwUEBCgjI0OSFBQUJD8/P0nStGnTlJaWpjfffFOSNG/ePEVGRqpDhw7Ky8vTW2+9pWXLlmnZsmWWrUdNYYzR25tS9fQnu5RXUKSQQKfmjYpRjxYNrY4GoIpQ7AC4zcsvvyxJ6tOnT4nlixYt0rhx4yRJ6enpSk1NLb4tLy9PU6dOVVpamvz8/NShQwd9+umnGjx4sLti10iZufma9sE2rdj+S3nu26aR5ozopIZ1OZIJ2BmvsQNge1lZWQoKCqo1r7FLSj2lCYuTdPhUrrw8HHp4YFvdeU2UPDj1CtgeR+wAwCaKioz+/fV+vfBFsgqKjJo18NP8MV3UuVk9q6MBcBOKHQDYwMkcl6a8t1UJycclSTd2DNPMWzoq0Nfb4mQA3IliBwA13IafTmjSki06lu2S08tDTw3poDFXN5PDwalXoLah2AFADVVQWKSXvtqn+V/tlTFSy8Z1teC2GLUNtf/rCAGUjWIHADVQemauHliyRd+l/CxJGtm1qaYP7aA6PvxZB2oz/gIAQA3z5e6jmvreVp06my9/H089N7yjhnVuYnUsANUAxQ4Aaoi8giLN+nyPFq5PkSRFNwnU/DFdFBXsb3EyANUFxQ4AaoCDJ89owuIkbTucKUka1zNS0wa3ldPL0+JkAKoTih0AVHPLtx7RtA+2K8dVoHp1vPXCrZ10Q/sQq2MBqIYodgBQTeXmFerpT3Zq8XeHJElXRdbX30fHKLyen8XJAFRXFDsAqIZ+PJqtuHd+0I9Hc+RwSHF9W+qBfq3k5elhdTQA1RjFDgCqEWOMlm4+pOnLd+pcfpEaBTg1b1Rn/aFlsNXRANQAFDsAqCayz+Xr0fgdWr71iCSpV6tgzR3ZWY0CnBYnA1BTUOwAoBrYdvi0JixO0sGTZ+Xp4dDU/m1097VXyMODrwUDcOEodgBgIWOMXvvmgJ7/bLfyC42a1PPTS2NiFBtR3+poAGogih0AWOTUmTxNfW+rvtxzTJI0sEOoZt1ypYLqeFucDEBNRbEDAAt8l/KzJi5OUkbWOfl4eeiJG9vpf7pHyOHg1CuAS0exAwA3Kiwy+seafZq3+kcVGemKYH/Nvy1GHcKDrI4GwAYodgDgJkezzmnSki3auP+kJGl4lyZ6Zli0/J38KQZQOfhrAgBukJB8TFPe3aqTZ/JUx8dTzwyL1i2xTa2OBcBmKHYAUIXyC4s054tk/WvdfklSu7BALbgtRi0a1bU4GQA7otgBQBU59PNZTVicpC2HTkuS7ugRoUcHt5Ovt6e1wQDYFsUOAKrAiu3penjZNmWfK1Cgr5dm33qlBkaHWR0LgM1R7ACgEp3LL9Qzn+zS25tSJUkxzevppdExatagjsXJANQGFDsAqCT7juUo7p0ftCcjW5J0T+8WmtK/tbw9PSxOBqC2oNgBwGUyxuj9xMN68qOdys0vVEN/H80d1Vm9WzeyOhqAWoZiBwCXIcdVoCc+3KH4pDRJ0h9aNtSLIzurcaCvxckA1EYUOwC4RDvSMjVhcZJSTpyRh0OafENr3dunpTw9+FowANag2AHARTLG6I0NB/Tcij3KKyxSWJCvXhoTo6siG1gdDUAtR7EDgItw+myeHnp/m1buOipJur5diF649UrV9/exOBkAUOwA4IIlHvxZExdvUdrpXHl7OjRtUDv97x8i5XBw6hVA9UCxA4DzKCoyenntT5q76kcVFhlFNqyj+WO6qGPTIKujAUAJFDsAqMDxbJcmv7tFX+89IUka1jlcf705WgG+3hYnA4DSKHYAUI6v9x7Xg0u36kSOS77eHnp6aLRGdG3KqVcA1RbFDgB+p6CwSHNX/aiX1/4kY6Q2IQFacFuMWoUEWB0NACpEsQNsKD8/XxkZGTp79qwaNWqkBg34GI4LlXY6VxMXJynx4ClJ0pirm+upIe3l6+1pcTIAOD+KHWATOTk5evvtt7V48WJ99913crlcxbc1bdpU/fv31//7f/9PV111lYUpq7cvdmboofe3KTM3XwFOL828paNuujLc6lgAcMEodoANvPjii3r22WcVGRmpoUOH6pFHHlGTJk3k5+enn3/+WTt27NDXX3+tG264Qd27d9f8+fPVqlUrq2NXG66CQs1csUevbzggSerUNEjzx3RR84Z1rA0GABfJYYwxVocAcHlGjBihJ598Uh07dqxwnMvl0sKFC+Xj46O77rrLTemsl5WVpaCgIGVmZiowMLDEbfuP52jC4iTtPJIlSRrfK0p/GdBWPl4eVkQFgMtCsQNsZv369QoICFCnTp2sjlJtlFfs4pMO6/H4HTqTV6j6dbz1t5GddF3bEAuTAsDl4b+kgM1MmDBBiYmJpZb/+OOPyszMtCBRaf/85z8VFRUlX19fxcbG6uuvv65w/Nq1axUbGytfX19dccUVeuWVVy7r8c/mFWjqe1v14NKtOpNXqG5RDfTZA9dS6gDUeBQ7wGaSk5PVu3fvUsvXrFmjMWPGWJCopKVLl2rSpEl67LHHlJSUpF69emnQoEFKTU0tc3xKSooGDx6sXr16KSkpSY8++qgmTpyoZcuWXdLj707P0pD56/V+4mF5OKQH+rXSO+O7KzTI93JWCwCqBU7FAjYTGhqq5cuXl3r3665du3TttdfqxIkTFiX7Rbdu3dSlSxe9/PLLxcvatWunm2++WTNnziw1/uGHH9bHH3+s3bt3Fy+75557tHXrVm3cuPGCHvPXU7Gvrt6uF9YcUl5BkUICnZo3KkY9WjS8/JUCgGqCI3aAzQwdOlRz5swptdzDw0N5eXkWJPo/eXl5SkxMVP/+/Uss79+/vzZs2FDmnI0bN5YaP2DAAH3//ffKz8+/oMfNzP1l3DOf7FZeQZH6tmmkFRN7UeoA2M4FfdyJMUbZ2dlVnQVAJZg2bZp69OihPn366LnnnlN0dLTOnTunZ555Ru3bt1dWVpZl2dLT01VYWKi6deuWyBEUFKS0tLQys6WlpalPnz4lbqtbt64KCgqUkpKi0NDQUnNcLlfx5/ilnT6n+97aLEnyyM/V5P6tdUePSHkUuZSV5So1FwCqq4CAgPN+peEFnYr99TQGAAAArFHWRzb93gUVO3cfscvKylKzZs106NCh865AbcE2KY1tUrbfbpesrCxt27ZN3t7eio2NtfyrxfLy8hQSEqI333xTQ4YMKV7+0EMPafv27frss89KzRk4cKCuvPJKzZ49u3jZ8uXLNXbsWB09elTe3t6l5vz2iJ0kbd17UDddd4127dqlJk2aVPJa1Uz8/pSN7VIa26Q0q7bJhRyxu6BTsQ6Hw5IfZmBgIDvR77BNSmOblC0wMFBNmzZV+/btrY5SQteuXfXNN9/oT3/6U/GydevWadiwYWX+HHv16qXly5eXuG39+vW66qqr1LDhxb1GLiAggH3ld/j9KRvbpTS2SWnVcZvw5gnABsr7qJDypKWlVVGS85s8ebL+85//6LXXXtPu3bv14IMPKjU1Vffcc4+kX14jeMcddxSPv+eee3Tw4EFNnjxZu3fv1muvvaaFCxdq6tSpVq0CAFRbFDvABq666iqNHz9e3333XbljMjMz9e9//1vR0dH64IMP3JiupFGjRmnevHl6+umn1blzZ61bt04rVqxQRESEpF/eYPHbohoVFaUVK1YoISFBnTt31jPPPKOXXnpJt9xyi1WrAADV1gWdinU3p9Opp556Sk6n0+oo1QbbpDS2yf8ZNmyYAgICNHDgQHl7e6tVq1aaNGmS/P39derUKe3atUs7d+5U165d9cILL2jQoEGW5r3vvvt03333lXnb66+/XmpZ79699cMPP1zy4/26j7Cv/B9+f8rGdimNbVJadd4mfEAxYAM+Pj7FL+INCQnRyJEjdfLkSeXm5io4OFgxMTEaMGCAoqOjrY5qifK+KxYA7KZaHrEDcHGaNGmipKQkDRw4UDk5OXruuefUuHFjq2MBANyM19gBNjB16lQNHTpUPXv2lMPh0Ntvv63NmzcrNzfX6mgAADfiVCxgEzt37tRHH32kxx9/XFdccYUOHDggh8Ohli1bqlOnTurcubM6depk+evrrMCpWAC1BcUOsJmWLVvq22+/lb+/v7Zt26YtW7YUX3bs2FErvx6QYgegtrDkVOyzzz6rnj17qk6dOqpXr16ZY1JTUzVkyBD5+/srODhYEydOPO8XmLtcLk2YMEHBwcHy9/fX0KFDdfjw4SpYg6qXkJAgh8NR5mXz5s3lzhs3blyp8d27d3dj8qoVGRlZav0eeeSRCucYYzR9+nSFh4fLz89Pffr00c6dO92UuGodOHBAd955p6KiouTn56cWLVroT3/6kwIDA+Xn56du3brp7rvv1ssvv6yNGzcWf9+qHfeTf/7zn4qKipKvr69iY2P19ddfVzh+7dq1io2Nla+vr6644gq98sorbkpa9WbOnKmrrrpKAQEBaty4sW6++WYlJydXOKe8vzl79uxxU+qqN3369FLrV9Z3Df+WnfcTqey/qQ6HQ/fff3+Z4+24n6xbt05DhgxReHi4HA6HPvzwwxK3X+pzyLJly9S+fXs5nU61b99e8fHxVbQGJVlS7PLy8jRixAjde++9Zd5eWFioG2+8UWfOnNH69eu1ZMkSLVu2TFOmTKnwfidNmqT4+HgtWbJE69evV05Ojm666SYVFhZWxWpUqZ49eyo9Pb3E5a677lJkZKS6du1a4dyBAweWmLdixQo3pXaPp59+usT6Pf744xWOnz17tubOnasFCxZo8+bNCg0N1Q033GCLI1d79uxRUVGR/vWvf2nnzp168cUX9corr+jRRx8tc/xvv4rGTvvJ0qVLNWnSJD322GNKSkpSr169NGjQoHI/uDklJUWDBw9Wr169lJSUpEcffVQTJ07UsmXL3Jy8aqxdu1b333+/vv32W61atUoFBQXq37+/zpw5c965ycnJJfaLVq1auSGx+3To0KHE+m3fvr3csXbfTyRp8+bNJbbHqlWrJEkjRoyocJ6d9pMzZ86oU6dOWrBgQZm3X8pzyMaNGzVq1Cjdfvvt2rp1q26//XaNHDlSmzZtqqrV+D/GQosWLTJBQUGllq9YscJ4eHiYtLS04mWLFy82TqfTZGZmlnlfp0+fNt7e3mbJkiXFy9LS0oyHh4f5/PPPKz27u+Xl5ZnGjRubp59+usJxY8eONcOGDXNPKAtERESYF1988YLHFxUVmdDQUPP8888XLzt37pwJCgoyr7zyShUktN7s2bNNVFRUhWPstp9cffXV5p577imxrG3btuaRRx4xxhiTmZlpJBX//XjooYdM27ZtS4y/++67Tffu3d0T2M2OHTtmJJm1a9eWO2bNmjVGkjl16pT7grnZU089ZTp16nTB42vbfmKMMQ888IBp0aKFKSoqKvN2u+8nkkx8fHzx9Ut9Dhk5cqQZOHBgiWUDBgwwo0ePrvTMv1ct3xW7ceNGRUdHKzw8vHjZgAED5HK5lJiYWOacxMRE5efnq3///sXLwsPDFR0drQ0bNlR55qr28ccf68SJExo3btx5xyYkJKhx48Zq3bq1xo8fr2PHjlV9QDeaNWuWGjZsqM6dO+vZZ5+t8BR9SkqKMjIySuwXTqdTvXv3tsV+UZbMzEw1aNDgvOPssp/k5eUpMTGxxM9Ykvr371/uz3jjxo2lxg8YMEDff/+98vPzqyyrVTIzMyXpgvaLmJgYhYWFqV+/flqzZk1VR3O7vXv3Kjw8XFFRURo9erT2799f7tjatp/k5eXprbfe0p///OfzftG83feTX13qc0h5+447nneqZbHLyMhQSEhIiWX169eXj4+PMjIyyp3j4+Oj+vXrl1geEhJS7pyaZOHChRowYICaNWtW4bhBgwbp7bff1ldffaW//e1v2rx5s6677jq5XC43Ja1aDzzwgJYsWaI1a9YoLi5O8+bNK/cbDCQV/+x/vz/ZZb/4vZ9++knz588v/t7V8thpPzlx4oQKCwsv6mdc1t+YkJAQFRQU6MSJE1WW1QrGGE2ePFnXXHNNhR9QHRYWpldffVXLli3TBx98oDZt2qhfv35at26dG9NWrW7duunNN9/UF198oX//+9/KyMhQz549dfLkyTLH16b9RJI+/PBDnT59usIDCLVhP/mtS30OKW/fccfzTqV9QPH06dM1Y8aMCsds3rz5vK8P+1VZ/1swxpz3fxGVMacqXcp2Onz4sL744gu9++67573/UaNGFf87OjpaXbt2VUREhD799FMNHz780oNXoYvZJg8++GDxsiuvvFL169fXrbfeWnwUrzy/3weq237xe5eynxw5ckQDBw7UiBEjdNddd1U4tybuJ+dzsT/jssaXtbymi4uL07Zt27R+/foKx7Vp00Zt2rQpvt6jRw8dOnRIc+bM0bXXXlvVMd3itx/107FjR/Xo0UMtWrTQG2+8ocmTJ5c5p7bsJ9IvBxAGDRpU4mzZ79WG/aQsl/IcYtXzTqUVu7i4OI0ePbrCMZGRkRd0X6GhoaVeYHjq1Cnl5+eXasC/nZOXl6dTp06VOGp37Ngx9ezZ84Ie1x0uZTstWrRIDRs21NChQy/68cLCwhQREaG9e/de9Fx3uZx959d3cu7bt6/MYvfrO94yMjIUFhZWvPzYsWPl7kvVwcVukyNHjqhv377q0aOHXn311Yt+vJqwn5QnODhYnp6epf4nXNHPODQ0tMzxXl5eFf4HoaaZMGGCPv74Y61bt05Nmza96Pndu3fXW2+9VQXJqgd/f3917Nix3P2+tuwnknTw4EGtXr1aH3zwwUXPtfN+cqnPIeXtO+543qm0YhccHKzg4OBKua8ePXro2WefVXp6evGGXLlypZxOp2JjY8ucExsbK29vb61atUojR46UJKWnp2vHjh2aPXt2peSqDBe7nYwxWrRoke644w55e3tf9OOdPHlShw4dKrFDVjeXs+8kJSVJUrnrFxUVpdDQUK1atUoxMTGSfnkdydq1azVr1qxLC+wGF7NN0tLS1LdvX8XGxmrRokXy8Lj4V1jUhP2kPD4+PoqNjdWqVav0xz/+sXj5qlWrNGzYsDLn9OjRQ8uXLy+xbOXKleratesl/Z5VN8YYTZgwQfHx8UpISFBUVNQl3U9SUlKN3CculMvl0u7du9WrV68yb7f7fvJbixYtUuPGjXXjjTde9Fw77yeX+hzSo0cPrVq1qsRZppUrV7rnQFOVvz2jDAcPHjRJSUlmxowZpm7duiYpKckkJSWZ7OxsY4wxBQUFJjo62vTr18/88MMPZvXq1aZp06YmLi6u+D4OHz5s2rRpYzZt2lS87J577jFNmzY1q1evNj/88IO57rrrTKdOnUxBQYHb17GyrF692kgyu3btKvP2Nm3amA8++MAYY0x2draZMmWK2bBhg0lJSTFr1qwxPXr0ME2aNDFZWVnujF0lNmzYYObOnWuSkpLM/v37zdKlS014eLgZOnRoiXG/3SbGGPP888+boKAg88EHH5jt27ebMWPGmLCwMFtsk7S0NNOyZUtz3XXXmcOHD5v09PTiy2/ZfT9ZsmSJ8fb2NgsXLjS7du0ykyZNMv7+/ubAgQPGGGMefPDBEu+K3b9/v6lTp4558MEHza5du8zChQuNt7e3ef/9961cjUpz7733mqCgIJOQkFBinzh79mzxmEceecTcfvvtxddffPFFEx8fb3788UezY8cO88gjjxhJZtmyZVasQpWYMmWKSUhIMPv37zfffvutuemmm0xAQEDxfvL7bWL3/eRXhYWFpnnz5ubhhx8udVtt2E+ys7OLe4ik4ueZgwcPGmMu7Dnk9ttvL34XvjHGfPPNN8bT09M8//zzZvfu3eb55583Xl5e5ttvv63y9bGk2I0dO9ZIKnVZs2ZN8ZiDBw+aG2+80fj5+ZkGDRqYuLg4c+7cueLbU1JSSs3Jzc01cXFxpkGDBsbPz8/cdNNNJjU11Y1rVvnGjBljevbsWe7tksyiRYuMMcacPXvW9O/f3zRq1Mh4e3ub5s2bm7Fjx9b4bfCrxMRE061bNxMUFGR8fX1NmzZtzFNPPWXOnDlTYtxvt4kxv7xd/amnnjKhoaHG6XSaa6+91mzfvt3N6avGokWLyvxd+v3/2WrDfvKPf/zDREREGB8fH9OlS5cSH+0xZsyYEsXOGGMSEhJMTEyM8fHxMZGRkebll1+2InaVKG+f+O3vxdixY03v3r2Lr8+aNcu0aNHC+Pr6mvr165trrrnGfPrpp+4PX4VGjRplwsLCjLe3twkPDzfDhw83O3fuLL7999vEGHvvJ7/64osvjCSTnJxc6rbasJ/8+hEuv7+MHTvWGHNhzyG9e/cuHv+r9957z7Rp08Z4e3ubtm3buq388pViAGyPrxQDUFtUy487AQAAwMWj2AEAANgExQ4AAMAmKHYAAAA2QbEDAACwCYodAACATVDsAAAAbIJiBwAAYBMUOwAAAJug2AEAANgExQ6ArR0/flwTJ06UJDVu3FidOnXSunXrLE4FAFXDy+oAAFBVDh48qO7du6tbt26SpI0bNyoxMVEBAQEWJwOAquEwxhirQwBAVRg8eLByc3P14Ycfql69esrMzFRgYKDVsQCgynDEDoAtpaam6rPPPtMPP/wgh8NhdRwAcAteYwfAlpKSkuTj46OYmBirowCA21DsANiSt7e3CgoKdPbsWaujAIDbUOwA2FK3bt0UFBSke++9V8nJyZKkhQsXas+ePRYnA4CqQ7EDYEsNGzbU8uXLtXfvXvXp00eS9OmnnyokJMTaYABQhXhXLADby8rKUlBQEO+KBWB7HLEDAACwCYodAACATVDsAAAAbIJiBwAAYBMUOwAAAJug2AEAANgExQ6AWxw4cEB33nmnoqKi5OfnpxYtWuipp55SXl5ehfPGjRsnh8NR4tK9e3c3pQaAmsXL6gAAaoc9e/aoqKhI//rXv9SyZUvt2LFD48eP15kzZzRnzpwK5w4cOFCLFi0qvu7j41PVcQGgRqLYAXCLgQMHauDAgcXXr7jiCiUnJ+vll18+b7FzOp0KDQ2t6ogAUONxKhaAZTIzM9WgQYPzjktISFDjxo3VunVrjR8/XseOHatwvMvlUlZWVokLANQGFDsAlvjpp580f/583XPPPRWOGzRokN5++2199dVX+tvf/qbNmzfruuuuk8vlKnfOzJkzFRQUVHxp1qxZZccHgGqJ74oFcFmmT5+uGTNmVDhm8+bN6tq1a/H1I0eOqHfv3urdu7f+85//XNTjpaenKyIiQkuWLNHw4cPLHONyuUoUv6ysLDVr1ozvigVge7zGDsBliYuL0+jRoyscExkZWfzvI0eOqG/fvurRo4deffXVi368sLAwRUREaO/eveWOcTqdcjqdF33fAFDTUewAXJbg4GAFBwdf0Ni0tDT17dtXsbGxWrRokTw8Lv7VICdPntShQ4cUFhZ20XMBwO54jR0Atzhy5Ij69OmjZs2aac6cOTp+/LgyMjKUkZFRYlzbtm0VHx8vScrJydHUqVO1ceNGHThwQAkJCRoyZIiCg4P1xz/+0YrVAIBqjSN2ANxi5cqV2rdvn/bt26emTZuWuO23L/VNTk5WZmamJMnT01Pbt2/Xm2++qdOnTyssLEx9+/bV0qVLFRAQ4Nb8AFAT8OYJALaXlZWloKAg3jwBwPY4FQsAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgfAbSIjI+VwOEpcHnnkkQrnGGM0ffp0hYeHy8/PT3369NHOnTvdlBgAahaKHQC3evrpp5Wenl58efzxxyscP3v2bM2dO1cLFizQ5s2bFRoaqhtuuEHZ2dluSgwANQfFDoBbBQQEKDQ0tPhSt27dcscaYzRv3jw99thjGj58uKKjo/XGG2/o7Nmzeuedd9yYGgBqBoodALeaNWuWGjZsqM6dO+vZZ59VXl5euWNTUlKUkZGh/v37Fy9zOp3q3bu3NmzYUO48l8ulrKysEhcAqA28rA4AoPZ44IEH1KVLF9WvX1/fffedpk2bppSUFP3nP/8pc3xGRoYkKSQkpMTykJAQHTx4sNzHmTlzpmbMmFF5wQGghuCIHYDLMn369FJviPj95fvvv5ckPfjgg+rdu7euvPJK3XXXXXrllVe0cOFCnTx5ssLHcDgcJa4bY0ot+61p06YpMzOz+HLo0KHLX1EAqAE4YgfgssTFxWn06NEVjomMjCxzeffu3SVJ+/btU8OGDUvdHhoaKumXI3dhYWHFy48dO1bqKN5vOZ1OOZ3O80UHANuh2AG4LMHBwQoODr6kuUlJSZJUorT9VlRUlEJDQ7Vq1SrFxMRIkvLy8rR27VrNmjXr0gIDgI1xKhaAW2zcuFEvvviitmzZopSUFL377ru6++67NXToUDVv3rx4XNu2bRUfHy/pl1OwkyZN0nPPPaf4+Hjt2LFD48aNU506dXTbbbdZtSoAUG1xxA6AWzidTi1dulQzZsyQy+VSRESExo8fr4ceeqjEuOTkZGVmZhZff+ihh5Sbm6v77rtPp06dUrdu3bRy5UoFBAS4exUAoNpzGGOM1SEAoCplZWUpKChImZmZCgwMtDoOAFQZTsUCAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBcIuEhAQ5HI4yL5s3by533rhx40qN7969uxuTA0DN4WV1AAC1Q8+ePZWenl5i2RNPPKHVq1era9euFc4dOHCgFi1aVHzdx8enSjICQE1HsQPgFj4+PgoNDS2+np+fr48//lhxcXFyOBwVznU6nSXmAgDKxqlYAJb4+OOPdeLECY0bN+68YxMSEtS4cWO1bt1a48eP17Fjxyoc73K5lJWVVeICALWBwxhjrA4BoPYZPHiwJGnFihUVjlu6dKnq1q2riIgIpaSk6IknnlBBQYESExPldDrLnDN9+nTNmDGj1PLMzEwFBgZefngAqKYodgAuS3kl6rc2b95c4nV0hw8fVkREhN59913dcsstF/V46enpioiI0JIlSzR8+PAyx7hcLrlcruLrWVlZatasGcUOgO3xGjsAlyUuLk6jR4+ucExkZGSJ64sWLVLDhg01dOjQi368sLAwRUREaO/eveWOcTqd5R7NAwA7o9gBuCzBwcEKDg6+4PHGGC1atEh33HGHvL29L/rxTp48qUOHDiksLOyi5wKA3fHmCQBu9dVXXyklJUV33nlnmbe3bdtW8fHxkqScnBxNnTpVGzdu1IEDB5SQkKAhQ4YoODhYf/zjH90ZGwBqBI7YAXCrhQsXqmfPnmrXrl2ZtycnJyszM1OS5Onpqe3bt+vNN9/U6dOnFRYWpr59+2rp0qUKCAhwZ2wAqBF48wQA28vKylJQUBBvngBge5yKBQAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxA1Apnn32WfXs2VN16tRRvXr1yhyTmpqqIUOGyN/fX8HBwZo4caLy8vIqvF+Xy6UJEyYoODhY/v7+Gjp0qA4fPlwFawAANR/FDkClyMvL04gRI3TvvfeWeXthYaFuvPFGnTlzRuvXr9eSJUu0bNkyTZkypcL7nTRpkuLj47VkyRKtX79eOTk5uummm1RYWFgVqwEANZrDGGOsDgHAPl5//XVNmjRJp0+fLrH8s88+00033aRDhw4pPDxckrRkyRKNGzdOx44dU2BgYKn7yszMVKNGjfTf//5Xo0aNkiQdOXJEzZo104oVKzRgwIALypSVlaWgoCBlZmaW+TgAYBccsQPgFhs3blR0dHRxqZOkAQMGyOVyKTExscw5iYmJys/PV//+/YuXhYeHKzo6Whs2bKjyzABQ03hZHQBA7ZCRkaGQkJASy+rXry8fHx9lZGSUO8fHx0f169cvsTwkJKTcOdIvr8tzuVzF17Oysi4jOQDUHByxA1Cu6dOny+FwVHj5/vvvL/j+HA5HqWXGmDKXV+R8c2bOnKmgoKDiS7NmzS7q/gGgpuKIHYByxcXFafTo0RWOiYyMvKD7Cg0N1aZNm0osO3XqlPLz80sdyfvtnLy8PJ06darEUbtjx46pZ8+e5T7WtGnTNHny5OLrWVlZlDsAtQLFDkC5goODFRwcXCn31aNHDz377LNKT09XWFiYJGnlypVyOp2KjY0tc05sbKy8vb21atUqjRw5UpKUnp6uHTt2aPbs2eU+ltPplNPprJTcAFCTcCoWQKVITU3Vli1blJqaqsLCQm3ZskVbtmxRTk6OJKl///5q3769br/9diUlJenLL7/U1KlTNX78+OJ3qqalpalt27b67rvvJElBQUG68847NWXKFH355ZdKSkrS//zP/6hjx466/vrrLVtXAKiuOGIHoFI8+eSTeuONN4qvx8TESJLWrFmjPn36yNPTU59++qnuu+8+/eEPf5Cfn59uu+02zZkzp3hOfn6+kpOTdfbs2eJlL774ory8vDRy5Ejl5uaqX79+ev311+Xp6em+lQOAGoLPsQNge3yOHYDaglOxAAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYBMUOAADAJih2AAAANkGxAwAAsAmKHQAAgE1Q7AAAAGyCYgcAAGATFDsAAACboNgBAADYhMMYY6wOAQBVyRij7OxsBQQEyOFwWB0HAKoMxQ4AAMAmOBULAABgExQ7AAAAm6DYAQAA2ATFDgAAwCYodgAAADZBsQMAALAJih0AAIBN/H+pNY51LHZMZwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 429, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot(g_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 430, + "metadata": {}, + "outputs": [], + "source": [ + "util = x ** (1 - rho) / (1 - rho)" + ] + }, + { + "cell_type": "code", + "execution_count": 431, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left(x^{- \\rho}\\right)^{- \\frac{1}{\\rho}}$" + ], + "text/plain": [ + "(x**(-rho))**(-1/rho)" + ] + }, + "execution_count": 431, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simplify(simplify(util.diff(x) ** (-1 / rho)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "estimatingmicrodsops", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv index 4c88d3b..229e4a5 100644 --- a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv +++ b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv @@ -1,11 +1,11 @@ -CRRA,9.206778216146489 -BeqShift,50.64405071849033 -BeqFac,26.1368726540768 -time_to_estimate,189.59267950057983 -params,"{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}" -criterion,0.6411981580830574 -start_criterion,0.6327697843473623 -start_params,"{'CRRA': 9.206831729527266, 'BeqShift': 51.48987537348443, 'BeqFac': 25.99186716077577}" +CRRA,9.206775856414323 +BeqShift,45.64298427855443 +BeqFac,23.05054873023735 +time_to_estimate,236.11751127243042 +params,"{'CRRA': 9.206775856414323, 'BeqShift': 45.64298427855443, 'BeqFac': 23.05054873023735}" +criterion,0.6411981344087744 +start_criterion,0.6327696850981256 +start_params,"{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}" algorithm,multistart_tranquilo_ls direction,minimize n_free,3 @@ -14,19 +14,19 @@ success, n_criterion_evaluations, n_derivative_evaluations, n_iterations, -history,"{'params': [{'CRRA': 9.128106374273193, 'BeqShift': 48.86990017445058, 'BeqFac': 23.900475106498696}, {'CRRA': 10.493158284578906, 'BeqShift': 51.32070255175769, 'BeqFac': 19.898866377479305}, {'CRRA': 5.987775253161292, 'BeqShift': 51.15666958513776, 'BeqFac': 20.93539879336119}, {'CRRA': 4.974971863108593, 'BeqShift': 51.00296190014448, 'BeqFac': 25.344148489629724}, {'CRRA': 8.661161951547838, 'BeqShift': 44.00535479239297, 'BeqFac': 23.871620945700617}, {'CRRA': 8.396810731754107, 'BeqShift': 47.04878789780259, 'BeqFac': 28.37612346061257}, {'CRRA': 4.826328077748139, 'BeqShift': 46.585777956700284, 'BeqFac': 24.30067582180679}, {'CRRA': 11.312762441066148, 'BeqShift': 53.22479889559116, 'BeqFac': 23.51994070721196}, {'CRRA': 13.029705730643922, 'BeqShift': 47.61158506757852, 'BeqFac': 21.240242600721672}, {'CRRA': 8.480895627642582, 'BeqShift': 52.21694645187303, 'BeqFac': 27.40205630602071}, {'CRRA': 7.934315470817556, 'BeqShift': 46.532760058680935, 'BeqFac': 19.77793817049746}, {'CRRA': 12.63230013568631, 'BeqShift': 45.830064534694614, 'BeqFac': 25.437581737917117}, {'CRRA': 12.907750238863407, 'BeqShift': 50.164708878465945, 'BeqFac': 26.714801688023883}, {'CRRA': 9.308015155295243, 'BeqShift': 50.37166272775331, 'BeqFac': 28.558762945887366}, {'CRRA': 9.26556410189128, 'BeqShift': 50.1466458784944, 'BeqFac': 26.074204341762798}, {'CRRA': 9.306771879635068, 'BeqShift': 49.57690311934757, 'BeqFac': 24.92860973348605}, {'CRRA': 9.175898510068395, 'BeqShift': 50.75145652974846, 'BeqFac': 25.98737882491696}, {'CRRA': 9.263797896208295, 'BeqShift': 50.372350451393935, 'BeqFac': 24.823779893176457}, {'CRRA': 9.154459573389804, 'BeqShift': 51.29445206167982, 'BeqFac': 26.26698771558419}, {'CRRA': 9.202041402977615, 'BeqShift': 50.559384394363455, 'BeqFac': 26.260409837048027}, {'CRRA': 9.147471035061725, 'BeqShift': 50.44050518128637, 'BeqFac': 26.85791599392265}, {'CRRA': 9.224646740112595, 'BeqShift': 50.85076502735223, 'BeqFac': 26.351855097884435}, {'CRRA': 9.21160542953121, 'BeqShift': 50.64537719999909, 'BeqFac': 26.134551637500216}, {'CRRA': 9.219093926094676, 'BeqShift': 50.94264606429679, 'BeqFac': 26.204649473853145}, {'CRRA': 9.212549269175398, 'BeqShift': 50.727550535520294, 'BeqFac': 25.993350101828756}, {'CRRA': 9.236903417946987, 'BeqShift': 50.583802104763535, 'BeqFac': 26.090455076807366}, {'CRRA': 9.22993549756282, 'BeqShift': 50.67331246866706, 'BeqFac': 26.153301020113346}, {'CRRA': 9.196966234964316, 'BeqShift': 50.63824802900686, 'BeqFac': 26.14451605163276}, {'CRRA': 9.21208615266317, 'BeqShift': 50.62817806751546, 'BeqFac': 26.14282082081475}, {'CRRA': 9.226942686055207, 'BeqShift': 50.63836473001979, 'BeqFac': 26.143496718423}, {'CRRA': 9.207870685334894, 'BeqShift': 50.6616428001297, 'BeqFac': 26.143820002177318}, {'CRRA': 9.225078947624299, 'BeqShift': 50.6431264679526, 'BeqFac': 26.12121675238415}, {'CRRA': 9.212084595052097, 'BeqShift': 50.645954903477566, 'BeqFac': 26.153626681529083}, {'CRRA': 9.228728760115736, 'BeqShift': 50.653800971826904, 'BeqFac': 26.135053895894423}, {'CRRA': 9.204438664808913, 'BeqShift': 50.65036356536122, 'BeqFac': 26.117575351407574}, {'CRRA': 9.194567266866448, 'BeqShift': 50.653981557748885, 'BeqFac': 26.13425721207449}, {'CRRA': 9.215955769137937, 'BeqShift': 50.62999911014034, 'BeqFac': 26.124110873964387}, {'CRRA': 9.214460374939259, 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50.643965368292854, 'BeqFac': 26.13636749731083}, {'CRRA': 9.206226751341177, 'BeqShift': 50.644175383761606, 'BeqFac': 26.136243764551427}, {'CRRA': 9.206139999933411, 'BeqShift': 50.64434589304829, 'BeqFac': 26.136339414991006}, {'CRRA': 9.20692991957421, 'BeqShift': 50.64378902036765, 'BeqFac': 26.13696828979672}, {'CRRA': 9.206030525524028, 'BeqShift': 50.6444124709626, 'BeqFac': 26.13695405708431}, {'CRRA': 9.206033510436992, 'BeqShift': 50.64400644035142, 'BeqFac': 26.13700040210868}, {'CRRA': 9.206910984777197, 'BeqShift': 50.64373643105872, 'BeqFac': 26.136616281363658}, {'CRRA': 9.20619643081797, 'BeqShift': 50.644622612155665, 'BeqFac': 26.136535077074925}, {'CRRA': 9.20642878297644, 'BeqShift': 50.6444960576583, 'BeqFac': 26.137246459012054}, {'CRRA': 9.207127002456316, 'BeqShift': 50.64430039244862, 'BeqFac': 26.1367999605648}, {'CRRA': 9.207114719797469, 'BeqShift': 50.64419857360247, 'BeqFac': 26.13692076455369}, {'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}], 'criterion': [0.6419131545898231, 0.6992405625490731, 1.1240845476970416, 1.577376990569092, 0.6524642769901355, 0.6664887878432892, 1.6647770568284477, 0.7958273217414884, 1.1577117413885285, 0.6612605763524525, 0.7056652645879914, 1.051463755129049, 1.1229714048823773, 0.6417128790724664, 0.6415610317693744, 0.6416973103739135, 0.64141783218841, 0.6415718165830668, 0.6414915855271336, 0.6412798714314748, 0.6416690245253375, 0.6413838490259491, 0.6412533792921618, 0.6413209357438362, 0.6412698132156118, 0.6414892120617004, 0.641391882855679, 0.6412912681201268, 0.6412600044422002, 0.6414014712692039, 0.641212422800454, 0.64138920463213, 0.6412599767231901, 0.6413894182130144, 0.641240011408127, 0.6412958845272176, 0.6412931073773732, 0.6412860311722518, 0.6412856091678842, 0.641299080615386, 0.6412654340898993, 0.6412040518376516, 0.6412798376316433, 0.6412586069868993, 0.6412003819983737, 0.6411989063826031, 0.641198632121119, 0.641198805868094, 0.6412003147825824, 0.6412028895752717, 0.6412042395231233, 0.6412001224290423, 0.641205979879279, 0.6412059242494071, 0.6411998771542384, 0.6412033613190102, 0.6411998007566294, 0.6412026671203108, 0.6412025111397647, 0.6411981580830574], 'runtime': [0.0, 1.6225000619888306, 1.8208623389946297, 2.0214831299963407, 2.3780758359935135, 2.6056774169846904, 2.8214367309992667, 3.026432433980517, 3.247528671985492, 3.475298994977493, 3.716090304980753, 3.926765499985777, 4.1696615399851, 5.700570738990791, 7.090572686982341, 8.46942359500099, 9.826952342991717, 11.1874410599994, 12.670953125983942, 14.073503133986378, 15.415775064000627, 16.777498332987307, 18.136912931979168, 19.44478028998128, 20.9249066929915, 22.26256391199422, 23.626468014990678, 25.251971355988644, 25.453611488977913, 25.659738533984637, 25.865622649987927, 26.112973237002734, 26.36346254197997, 26.55055605797679, 26.754232481995132, 26.958369220985333, 27.18434452699148, 27.420230353978695, 27.644893939985195, 29.20647358399583, 30.686982325976714, 32.02958239999134, 33.3786810519814, 34.71376580098877, 36.05778275799821, 37.38048776300275, 39.191983733995585, 39.40957244997844, 39.614045912981965, 39.824004255002365, 40.0257615079754, 40.23395056297886, 40.45599366599345, 40.66358802097966, 40.90082527400227, 41.12791417900007, 41.331096677982714, 41.55513457299094, 43.058433758997126, 44.375435253983596], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26]}" -convergence_report,"{'one_step': {'relative_criterion_change': 3.3680601244564846e-08, 'relative_params_change': 0.15745854181285046, 'absolute_criterion_change': 2.159593948114491e-08, 'absolute_params_change': 5.969365090497884}, 'five_steps': {'relative_criterion_change': 8.826656507099289e-07, 'relative_params_change': 0.15745854181285046, 'absolute_criterion_change': 5.659635894383896e-07, 'absolute_params_change': 5.969365090497884}}" -multistart_info,"{'start_parameters': [{'CRRA': 9.206831729527266, 'BeqShift': 51.48987537348444, 'BeqFac': 25.99186716077577}, {'CRRA': 9.275122078746548, 'BeqShift': 46.36962222316765, 'BeqFac': 22.931039835503583}, {'CRRA': 9.128106374273193, 'BeqShift': 48.86990017445058, 'BeqFac': 23.900475106498696}], 'local_optima': [Minimize with 3 free parameters terminated. +history,"{'params': [{'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 10.556800566878689, 'BeqShift': 48.18211054060359, 'BeqFac': 19.257926550618446}, {'CRRA': 6.326950770325419, 'BeqShift': 48.028109248006054, 'BeqFac': 20.231068322997192}, {'CRRA': 5.376086840779585, 'BeqShift': 47.88380177945927, 'BeqFac': 24.370194536190226}, {'CRRA': 8.836842627116226, 'BeqShift': 41.31414348521892, 'BeqFac': 22.987721550281236}, {'CRRA': 8.588658189640931, 'BeqShift': 44.17145102421629, 'BeqFac': 27.216744679593038}, {'CRRA': 5.236533581737813, 'BeqShift': 43.73675712771144, 'BeqFac': 23.390536953045526}, {'CRRA': 11.326280642245544, 'BeqShift': 49.96975911041845, 'BeqFac': 22.657548826930118}, {'CRRA': 12.938221774324816, 'BeqShift': 44.69982951667233, 'BeqFac': 20.51726896510742}, {'CRRA': 8.667600751746502, 'BeqShift': 49.02354334490217, 'BeqFac': 26.30224801483706}, {'CRRA': 8.154447497289702, 'BeqShift': 43.68698161615405, 'BeqFac': 19.144393883554894}, {'CRRA': 12.565120095009513, 'BeqShift': 43.02726045627806, 'BeqFac': 24.457913738642898}, {'CRRA': 12.823724647554654, 'BeqShift': 47.096813337304226, 'BeqFac': 25.657023449945957}, {'CRRA': 9.311678364660667, 'BeqShift': 47.23783920054173, 'BeqFac': 27.400544008848303}, {'CRRA': 9.333051913639503, 'BeqShift': 43.638384441015646, 'BeqFac': 23.525261669970227}, {'CRRA': 9.268767404448845, 'BeqShift': 46.9682573257543, 'BeqFac': 22.64867805102556}, {'CRRA': 8.831533831545041, 'BeqShift': 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45]}" +convergence_report,"{'one_step': {'relative_criterion_change': 3.5828565118959094e-08, 'relative_params_change': 0.1730113235913555, 'absolute_criterion_change': 2.297320911281986e-08, 'absolute_params_change': 5.876738890647083}, 'five_steps': {'relative_criterion_change': 3.5828565118959094e-08, 'relative_params_change': 0.1730113235913555, 'absolute_criterion_change': 2.297320911281986e-08, 'absolute_params_change': 5.876738890647083}}" +multistart_info,"{'start_parameters': [{'CRRA': 9.20677821614649, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 9.128116958674036, 'BeqShift': 48.90833875417502, 'BeqFac': 23.98172788815444}], 'local_optima': [Minimize with 3 free parameters terminated. -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. +The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 7.038e-07* 7.038e-07* -relative_params_change 4.652e-05 4.652e-05 -absolute_criterion_change 4.513e-07* 4.513e-07* -absolute_params_change 0.001257 0.001257 +relative_criterion_change 1.182e-08* 2.384e-08* +relative_params_change 7.164e-07* 7.164e-07* +absolute_criterion_change 7.578e-09** 1.529e-08* +absolute_params_change 3.435e-05 3.435e-05 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. @@ -35,10 +35,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 7.979e-07* 3.031e-05 -relative_params_change 3.723e-05 0.0001754 -absolute_criterion_change 5.116e-07* 1.944e-05 -absolute_params_change 0.001147 0.001754 +relative_criterion_change 8.029e-09** 7.847e-06* +relative_params_change 2.558e-07* 0.0001336 +absolute_criterion_change 5.148e-09** 5.032e-06* +absolute_params_change 5.08e-06* 0.00307 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. @@ -47,23 +47,110 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 7.393e-07* 2.225e-05 -relative_params_change 2.367e-05 0.0004532 -absolute_criterion_change 4.74e-07* 1.426e-05 -absolute_params_change 0.0004571 0.01895 +relative_criterion_change 5.493e-08* 6.761e-06* +relative_params_change 1.236e-05 5.445e-05 +absolute_criterion_change 3.522e-08* 4.335e-06* +absolute_params_change 0.0006154 0.000671 -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.206831729527266, 'BeqShift': 51.48987537348443, 'BeqFac': 25.99186716077577}, {'CRRA': 9.368749999999999, 'BeqShift': 39.375, 'BeqFac': 18.75}, {'CRRA': 8.778125, 'BeqShift': 63.4375, 'BeqFac': 28.125}, {'CRRA': 9.959375, 'BeqShift': 6.5625, 'BeqFac': 84.375}, {'CRRA': 8.1875, 'BeqShift': 26.25, 'BeqFac': 62.5}, {'CRRA': 10.549999999999999, 'BeqShift': 35.0, 'BeqFac': 50.0}, {'CRRA': 7.596874999999999, 'BeqShift': 50.3125, 'BeqFac': 71.875}, {'CRRA': 7.00625, 'BeqShift': 13.125, 'BeqFac': 31.25}, {'CRRA': 11.73125, 'BeqShift': 30.625, 'BeqFac': 6.25}, {'CRRA': 12.321874999999999, 'BeqShift': 67.8125, 'BeqFac': 96.875}, {'CRRA': 6.415625, 'BeqShift': 19.6875, 'BeqFac': 15.625}, {'CRRA': 12.9125, 'BeqShift': 8.75, 'BeqFac': 87.5}, {'CRRA': 5.824999999999999, 'BeqShift': 52.5, 'BeqFac': 75.0}, {'CRRA': 13.503124999999999, 'BeqShift': 45.9375, 'BeqFac': 3.125}, {'CRRA': 5.234375, 'BeqShift': 59.0625, 'BeqFac': 9.375}, {'CRRA': 14.093749999999998, 'BeqShift': 56.875, 'BeqFac': 43.75}, {'CRRA': 14.684375, 'BeqShift': 24.0625, 'BeqFac': 59.375}, {'CRRA': 4.64375, 'BeqShift': 21.875, 'BeqFac': 93.75}, {'CRRA': 15.274999999999999, 'BeqShift': 17.5, 'BeqFac': 25.0}, {'CRRA': 4.053125, 'BeqShift': 10.9375, 'BeqFac': 53.125}, {'CRRA': 15.865624999999998, 'BeqShift': 54.6875, 'BeqFac': 65.625}, {'CRRA': 3.4625, 'BeqShift': 43.75, 'BeqFac': 37.5}, {'CRRA': 16.45625, 'BeqShift': 48.125, 'BeqFac': 81.25}, {'CRRA': 17.046875, 'BeqShift': 15.3125, 'BeqFac': 21.875}, {'CRRA': 2.871875, 'BeqShift': 32.8125, 'BeqFac': 46.875}, {'CRRA': 2.28125, 'BeqShift': 65.625, 'BeqFac': 56.25}, {'CRRA': 17.6375, 'BeqShift': 61.25, 'BeqFac': 12.5}, {'CRRA': 18.228125, 'BeqShift': 28.4375, 'BeqFac': 78.125}, {'CRRA': 18.81875, 'BeqShift': 4.375, 'BeqFac': 68.75}, {'CRRA': 19.409375, 'BeqShift': 41.5625, 'BeqFac': 34.375}], 'exploration_results': array([0.64119885, 0.64211564, 0.64842422, 0.66164008, 0.68191739, +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 9.368749999999999, 'BeqShift': 39.375, 'BeqFac': 18.75}, {'CRRA': 8.778125, 'BeqShift': 63.4375, 'BeqFac': 28.125}, {'CRRA': 9.959375, 'BeqShift': 6.5625, 'BeqFac': 84.375}, {'CRRA': 8.1875, 'BeqShift': 26.25, 'BeqFac': 62.5}, {'CRRA': 10.549999999999999, 'BeqShift': 35.0, 'BeqFac': 50.0}, {'CRRA': 7.596874999999999, 'BeqShift': 50.3125, 'BeqFac': 71.875}, {'CRRA': 7.00625, 'BeqShift': 13.125, 'BeqFac': 31.25}, {'CRRA': 11.73125, 'BeqShift': 30.625, 'BeqFac': 6.25}, {'CRRA': 12.321874999999999, 'BeqShift': 67.8125, 'BeqFac': 96.875}, {'CRRA': 6.415625, 'BeqShift': 19.6875, 'BeqFac': 15.625}, {'CRRA': 12.9125, 'BeqShift': 8.75, 'BeqFac': 87.5}, {'CRRA': 5.824999999999999, 'BeqShift': 52.5, 'BeqFac': 75.0}, {'CRRA': 13.503124999999999, 'BeqShift': 45.9375, 'BeqFac': 3.125}, {'CRRA': 5.234375, 'BeqShift': 59.0625, 'BeqFac': 9.375}, {'CRRA': 14.093749999999998, 'BeqShift': 56.875, 'BeqFac': 43.75}, {'CRRA': 14.684375, 'BeqShift': 24.0625, 'BeqFac': 59.375}, {'CRRA': 4.64375, 'BeqShift': 21.875, 'BeqFac': 93.75}, {'CRRA': 15.274999999999999, 'BeqShift': 17.5, 'BeqFac': 25.0}, {'CRRA': 4.053125, 'BeqShift': 10.9375, 'BeqFac': 53.125}, {'CRRA': 15.865624999999998, 'BeqShift': 54.6875, 'BeqFac': 65.625}, {'CRRA': 3.4625, 'BeqShift': 43.75, 'BeqFac': 37.5}, {'CRRA': 16.45625, 'BeqShift': 48.125, 'BeqFac': 81.25}, {'CRRA': 17.046875, 'BeqShift': 15.3125, 'BeqFac': 21.875}, {'CRRA': 2.871875, 'BeqShift': 32.8125, 'BeqFac': 46.875}, {'CRRA': 2.28125, 'BeqShift': 65.625, 'BeqFac': 56.25}, {'CRRA': 17.6375, 'BeqShift': 61.25, 'BeqFac': 12.5}, {'CRRA': 18.228125, 'BeqShift': 28.4375, 'BeqFac': 78.125}, {'CRRA': 18.81875, 'BeqShift': 4.375, 'BeqFac': 68.75}, {'CRRA': 19.409375, 'BeqShift': 41.5625, 'BeqFac': 34.375}], 'exploration_results': array([0.64119816, 0.64211564, 0.64842422, 0.66164008, 0.68191739, 0.7044766 , 0.74714868, 0.84823283, 0.8626059 , 0.97951322, 0.98996599, 1.12431839, 1.18221304, 1.30406645, 1.43892243, 1.52011014, 1.78024917, 1.78131285, 2.0971452 , 2.24344059, 2.48116605, 2.89787 , 2.94379242, 3.4976294 , 3.77080322, 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637, 48.86990017, 23.90047511]), radius=4.886990017445058, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6419131545898231, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=4.5881190537852365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6414954627541696, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=0, candidate_x=array([ 9.27523039, 45.88119054, 23.01481109]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=4.5881190537852365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5891883650231197, linear_terms=array([-0.00968953, -0.00117103, -0.00391564]), square_terms=array([[ 2.04288848e+00, -7.00381948e-03, -4.70750143e-03], + [-7.00381948e-03, 3.59762915e-05, 4.06366460e-05], + [-4.70750143e-03, 4.06366460e-05, 1.41716415e-04]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -145,12 +232,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=0, candidate_x=array([ 9.12810637, 48.86990017, 23.90047511]), index=0, x=array([ 9.12810637, 48.86990017, 23.90047511]), fval=0.6419131545898232, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.12810637, 48.86990017, 23.90047511]), radius=4.886990017445058, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5838053927491154, linear_terms=array([-0.0784591 , -0.00113294, -0.00430834]), square_terms=array([[ 2.37622930e+00, -9.44902309e-03, -6.59043823e-03], - [-9.44902309e-03, 5.65537660e-05, 7.53932158e-05], - [-6.59043823e-03, 7.53932158e-05, 2.21756861e-04]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=13, candidate_x=array([ 9.31167836, 47.2378392 , 27.40054401]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=-0.061531136949691, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=2.2940595268926183, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5838720419935737, linear_terms=array([-0.00815151, 0.00777168, -0.0017958 ]), square_terms=array([[ 5.11958440e-01, 4.80442275e-03, -1.12028161e-03], + [ 4.80442275e-03, 3.51638936e-04, -1.06350122e-04], + [-1.12028161e-03, -1.06350122e-04, 3.29771720e-05]]), scale=2.2940595268926183, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -232,12 +319,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=13, candidate_x=array([ 9.30801516, 50.37166273, 28.55876295]), index=13, x=array([ 9.30801516, 50.37166273, 28.55876295]), fval=0.6417128790724664, rho=0.03368406179865712, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=4.897683486158282, relative_step_length=1.0021881503082781, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.30801516, 50.37166273, 28.55876295]), radius=2.443495008722529, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), model=ScalarModel(intercept=0.6096236821586578, linear_terms=array([0.02840541, 0.00191562, 0.02107148]), square_terms=array([[5.95634493e-01, 4.34268068e-04, 1.74066775e-02], - [4.34268068e-04, 2.73557040e-05, 2.15252435e-04], - [1.74066775e-02, 2.15252435e-04, 2.09032328e-03]]), scale=2.443495008722529, shift=array([ 9.30801516, 50.37166273, 28.55876295])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=14, candidate_x=array([ 9.33305191, 43.63838444, 23.52526167]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=-0.02774497157353281, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 4, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=1.1470297634463091, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 14]), model=ScalarModel(intercept=0.592708784872857, linear_terms=array([ 0.00152966, -0.00076228, 0.00025576]), square_terms=array([[ 1.29771760e-01, -7.60521227e-04, 2.29395773e-04], + [-7.60521227e-04, 6.85822163e-06, -1.35713831e-06], + [ 2.29395773e-04, -1.35713831e-06, 6.87591617e-07]]), scale=1.1470297634463091, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -319,12 +406,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=14, candidate_x=array([ 9.2655641 , 50.14664588, 26.07420434]), index=14, x=array([ 9.2655641 , 50.14664588, 26.07420434]), fval=0.6415610317693744, rho=0.00737246118082768, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), old_indices_discarded=array([ 1, 10]), step_length=2.4950884015831862, relative_step_length=1.0211145890114302, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2655641 , 50.14664588, 26.07420434]), radius=1.2217475043612644, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 3, 5, 6, 7, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5911089857829682, linear_terms=array([0.00422151, 0.00795934, 0.0161826 ]), square_terms=array([[0.15195804, 0.00362337, 0.00870881], - [0.00362337, 0.00041071, 0.00091153], - [0.00870881, 0.00091153, 0.00203185]]), scale=1.2217475043612644, shift=array([ 9.2655641 , 50.14664588, 26.07420434])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=15, candidate_x=array([ 9.2687674 , 46.96825733, 22.64867805]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=-0.048166814746878124, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 14]), old_indices_discarded=array([ 4, 11, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=0.5735148817231546, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6406974663539128, linear_terms=array([ 2.50388822e-03, -3.55470721e-05, 1.59802574e-05]), square_terms=array([[ 2.95828916e-02, -9.48578252e-06, 1.81188466e-06], + [-9.48578252e-06, 2.35871761e-08, 1.80023429e-08], + [ 1.81188466e-06, 1.80023429e-08, 6.07710911e-08]]), scale=0.5735148817231546, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -406,12 +493,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=15, candidate_x=array([ 9.30677188, 49.57690312, 24.92860973]), index=14, x=array([ 9.2655641 , 50.14664588, 26.07420434]), fval=0.6415610317693744, rho=-0.007719959864419043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 5, 6, 7, 9, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2655641 , 50.14664588, 26.07420434]), radius=0.6108737521806322, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15]), model=ScalarModel(intercept=0.6412075612871301, linear_terms=array([ 5.18605429e-03, -5.96658930e-04, 8.53007592e-05]), square_terms=array([[ 3.43820818e-02, -5.19387823e-05, 5.05459631e-06], - [-5.19387823e-05, 6.87957385e-07, -9.67077712e-08], - [ 5.05459631e-06, -9.67077712e-08, 1.41737452e-08]]), scale=0.6108737521806322, shift=array([ 9.2655641 , 50.14664588, 26.07420434])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=28, candidate_x=array([ 9.22693263, 46.40381893, 22.77679268]), index=28, x=array([ 9.22693263, 46.40381893, 22.77679268]), fval=0.6414015158827587, rho=0.6515618478573166, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.5763036284627624, relative_step_length=1.0048625534026752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22693263, 46.40381893, 22.77679268]), radius=1.1470297634463091, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 15, 16, 17, 18, 20, 21, 22, 24, 25, 27, 28]), model=ScalarModel(intercept=0.6408041108663739, linear_terms=array([ 0.00065102, 0.00049794, -0.00019907]), square_terms=array([[ 1.18433148e-01, 1.13521966e-04, -6.65681444e-05], + [ 1.13521966e-04, 8.00378364e-07, -1.25371889e-07], + [-6.65681444e-05, -1.25371889e-07, 3.16174660e-07]]), scale=1.1470297634463091, shift=array([ 9.22693263, 46.40381893, 22.77679268])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -493,12 +580,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=16, candidate_x=array([ 9.17589851, 50.75145653, 25.98737882]), index=16, x=array([ 9.17589851, 50.75145653, 25.98737882]), fval=0.6414178321884101, rho=0.14527765423064729, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.6175552708890588, relative_step_length=1.0109376424909002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17589851, 50.75145653, 25.98737882]), radius=1.2217475043612644, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.6052901828442077, linear_terms=array([-0.00707459, 0.00285906, 0.00874449]), square_terms=array([[1.45487940e-01, 8.88677433e-04, 3.95549893e-03], - [8.88677433e-04, 3.27781430e-05, 1.13238367e-04], - [3.95549893e-03, 1.13238367e-04, 4.03782515e-04]]), scale=1.2217475043612644, shift=array([ 9.17589851, 50.75145653, 25.98737882])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=29, candidate_x=array([ 9.22191036, 45.33875132, 23.20257127]), index=29, x=array([ 9.22191036, 45.33875132, 23.20257127]), fval=0.6413556371977909, rho=0.08543637029916488, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18, 20, 21, 22, 24, 25, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 23, 26]), step_length=1.1470316679451833, relative_step_length=1.0000016603744164, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22191036, 45.33875132, 23.20257127]), radius=0.5735148817231546, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 29]), model=ScalarModel(intercept=0.6408171976152108, linear_terms=array([-6.73581006e-05, -4.04978148e-04, -3.80738956e-05]), square_terms=array([[ 2.95973821e-02, -3.63270062e-05, 1.65796041e-06], + [-3.63270062e-05, 4.23472225e-07, 4.40106549e-08], + [ 1.65796041e-06, 4.40106549e-08, 5.10964732e-08]]), scale=0.5735148817231546, shift=array([ 9.22191036, 45.33875132, 23.20257127])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -580,12 +667,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=17, candidate_x=array([ 9.2637979 , 50.37235045, 24.82377989]), index=16, x=array([ 9.17589851, 50.75145653, 25.98737882]), fval=0.6414178321884101, rho=-0.016345474533435187, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17589851, 50.75145653, 25.98737882]), radius=0.6108737521806322, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6408680252047284, linear_terms=array([ 1.22608587e-03, -1.52831904e-04, -7.93126783e-05]), square_terms=array([[ 3.46548267e-02, 3.74686901e-06, -1.56196185e-05], - [ 3.74686901e-06, 4.51202398e-08, 1.43733385e-08], - [-1.56196185e-05, 1.43733385e-08, 1.99575801e-08]]), scale=0.6108737521806322, shift=array([ 9.17589851, 50.75145653, 25.98737882])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=30, candidate_x=array([ 9.22388626, 45.90974892, 23.25621668]), index=29, x=array([ 9.22191036, 45.33875132, 23.20257127]), fval=0.6413556371977909, rho=-0.04787806421435067, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 29]), old_indices_discarded=array([14, 15, 19, 26, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22191036, 45.33875132, 23.20257127]), radius=0.2867574408615773, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 21, 22, 24, 25, 27, 29, 30]), model=ScalarModel(intercept=0.6410097550441288, linear_terms=array([ 1.39651156e-05, -2.88874413e-04, 1.60170799e-04]), square_terms=array([[ 7.40015236e-03, -8.47567918e-06, 4.66545233e-06], + [-8.47567918e-06, 1.73700052e-07, -8.46835155e-08], + [ 4.66545233e-06, -8.46835155e-08, 6.84200563e-08]]), scale=0.2867574408615773, shift=array([ 9.22191036, 45.33875132, 23.20257127])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -667,12 +754,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=18, candidate_x=array([ 9.15445957, 51.29445206, 26.26698772]), index=16, x=array([ 9.17589851, 50.75145653, 25.98737882]), fval=0.6414178321884101, rho=-0.3807981173026105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17589851, 50.75145653, 25.98737882]), radius=0.3054368760903161, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18]), model=ScalarModel(intercept=0.6414178321884101, linear_terms=array([-7.14351462e-04, 3.57051786e-06, -4.34650781e-06]), square_terms=array([[ 8.31583940e-03, -5.01950042e-06, -1.11678712e-06], - [-5.01950042e-06, 3.13345575e-08, 6.31491818e-09], - [-1.11678712e-06, 6.31491818e-09, 1.42073640e-09]]), scale=0.3054368760903161, shift=array([ 9.17589851, 50.75145653, 25.98737882])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=31, candidate_x=array([ 9.22175171, 45.58988885, 23.06332445]), index=31, x=array([ 9.22175171, 45.58988885, 23.06332445]), fval=0.6413538895520631, rho=0.005285332353177379, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 21, 22, 24, 25, 27, 29, 30]), old_indices_discarded=array([19, 23, 26, 28]), step_length=0.2871580766830809, relative_step_length=1.0013971244139295, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22175171, 45.58988885, 23.06332445]), radius=0.14337872043078864, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 21, 24, 25, 27, 29, 30, 31]), model=ScalarModel(intercept=0.6407199525901757, linear_terms=array([-3.92919336e-06, -1.65929664e-04, 1.55155143e-04]), square_terms=array([[ 1.84527109e-03, -5.57596163e-06, 4.23287767e-06], + [-5.57596163e-06, 7.39326117e-08, -6.46121591e-08], + [ 4.23287767e-06, -6.46121591e-08, 7.38892314e-08]]), scale=0.14337872043078864, shift=array([ 9.22175171, 45.58988885, 23.06332445])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -754,12 +841,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=19, candidate_x=array([ 9.2020414 , 50.55938439, 26.26040984]), index=19, x=array([ 9.2020414 , 50.55938439, 26.26040984]), fval=0.6412798714314748, rho=3.766886757176218, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.3348448739735063, relative_step_length=1.0962817530732418, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2020414 , 50.55938439, 26.26040984]), radius=0.6108737521806322, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6411413456382316, linear_terms=array([ 3.14313654e-03, 2.74625666e-05, -1.27910543e-04]), square_terms=array([[ 3.47663383e-02, 2.63538243e-05, -2.12337211e-05], - [ 2.63538243e-05, 5.29791904e-08, -3.77184755e-08], - [-2.12337211e-05, -3.77184755e-08, 4.60609078e-08]]), scale=0.6108737521806322, shift=array([ 9.2020414 , 50.55938439, 26.26040984])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=32, candidate_x=array([ 9.22250536, 45.69461821, 22.9654026 ]), index=31, x=array([ 9.22175171, 45.58988885, 23.06332445]), fval=0.6413538895520631, rho=-0.02756030763036017, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 21, 24, 25, 27, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22175171, 45.58988885, 23.06332445]), radius=0.07168936021539432, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 17, 29, 31, 32]), model=ScalarModel(intercept=0.6413730871630696, linear_terms=array([-1.63989532e-04, 3.65884738e-05, 5.21973410e-05]), square_terms=array([[ 4.60761375e-04, -7.31726922e-07, -1.08851257e-06], + [-7.31726922e-07, 2.10367260e-08, 3.07816346e-08], + [-1.08851257e-06, 3.07816346e-08, 4.50672893e-08]]), scale=0.07168936021539432, shift=array([ 9.22175171, 45.58988885, 23.06332445])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -841,12 +928,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=20, candidate_x=array([ 9.14747104, 50.44050518, 26.85791599]), index=19, x=array([ 9.2020414 , 50.55938439, 26.26040984]), fval=0.6412798714314748, rho=-1.4403818757718805, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2020414 , 50.55938439, 26.26040984]), radius=0.3054368760903161, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6413874688839755, linear_terms=array([-6.33748043e-04, -5.73186109e-05, -1.79357454e-05]), square_terms=array([[8.46968425e-03, 2.56443878e-06, 1.01117391e-07], - [2.56443878e-06, 9.68297991e-09, 2.15723240e-09], - [1.01117391e-07, 2.15723240e-09, 1.06370393e-09]]), scale=0.3054368760903161, shift=array([ 9.2020414 , 50.55938439, 26.26040984])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=33, candidate_x=array([ 9.24400879, 45.54869625, 23.00457613]), index=31, x=array([ 9.22175171, 45.58988885, 23.06332445]), fval=0.6413538895520631, rho=-2.1461414457605144, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 29, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22175171, 45.58988885, 23.06332445]), radius=0.03584468010769716, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6413717608202565, linear_terms=array([ 2.00924894e-04, -1.09372237e-05, -5.43208484e-06]), square_terms=array([[1.15625621e-04, 3.96142165e-08, 3.87212268e-09], + [3.96142165e-08, 8.40304844e-10, 2.22207615e-10], + [3.87212268e-09, 2.22207615e-10, 3.03437454e-10]]), scale=0.03584468010769716, shift=array([ 9.22175171, 45.58988885, 23.06332445])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -928,12 +1015,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=21, candidate_x=array([ 9.22464674, 50.85076503, 26.3518551 ]), index=19, x=array([ 9.2020414 , 50.55938439, 26.26040984]), fval=0.6412798714314748, rho=-1.2442024876135518, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2020414 , 50.55938439, 26.26040984]), radius=0.15271843804515806, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6413861775924228, linear_terms=array([-1.34889295e-04, -1.17794331e-05, 1.72033189e-05]), square_terms=array([[ 2.12047970e-03, 7.30611322e-07, -4.58026451e-07], - [ 7.30611322e-07, 1.91216963e-09, 3.03077874e-10], - [-4.58026451e-07, 3.03077874e-10, 3.30402692e-09]]), scale=0.15271843804515806, shift=array([ 9.2020414 , 50.55938439, 26.26040984])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=46, candidate_x=array([ 9.18863529, 45.60031418, 23.0722383 ]), index=46, x=array([ 9.18863529, 45.60031418, 23.0722383 ]), fval=0.6413227801302601, rho=0.2209034862216151, accepted=True, new_indices=array([34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_used=array([31, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.03584468010769763, relative_step_length=1.000000000000013, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.18863529, 45.60031418, 23.0722383 ]), radius=0.07168936021539432, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=0.6412728088877506, linear_terms=array([1.47083725e-04, 4.86568931e-05, 4.73713244e-06]), square_terms=array([[4.62518943e-04, 5.01529807e-07, 3.15449154e-07], + [5.01529807e-07, 7.95596362e-09, 2.36322212e-09], + [3.15449154e-07, 2.36322212e-09, 4.02093856e-09]]), scale=0.07168936021539432, shift=array([ 9.18863529, 45.60031418, 23.0722383 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1015,12 +1102,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=22, candidate_x=array([ 9.21160543, 50.6453772 , 26.13455164]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=1.0576140080519945, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.15273021841934944, relative_step_length=1.0000771378645708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.3054368760903161, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6413638550561954, linear_terms=array([-2.14082253e-04, -4.27982472e-05, -1.00993546e-05]), square_terms=array([[8.50926454e-03, 4.22952742e-06, 1.16007861e-06], - [4.22952742e-06, 1.36024420e-08, 5.92380689e-09], - [1.16007861e-06, 5.92380689e-09, 4.14150417e-09]]), scale=0.3054368760903161, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=47, candidate_x=array([ 9.16814755, 45.53125705, 23.0656262 ]), index=46, x=array([ 9.18863529, 45.60031418, 23.0722383 ]), fval=0.6413227801302601, rho=-1.5874902190178886, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 34, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46]), old_indices_discarded=array([ 0, 29, 32, 33, 40, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.18863529, 45.60031418, 23.0722383 ]), radius=0.03584468010769716, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 46]), model=ScalarModel(intercept=0.6412601275794009, linear_terms=array([ 6.58420832e-05, 2.37502856e-06, -1.69911469e-06]), square_terms=array([[1.16248591e-04, 7.39108563e-08, 8.34825523e-08], + [7.39108563e-08, 8.95644154e-10, 2.55046365e-10], + [8.34825523e-08, 2.55046365e-10, 1.63561948e-09]]), scale=0.03584468010769716, shift=array([ 9.18863529, 45.60031418, 23.0722383 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1102,12 +1189,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=23, candidate_x=array([ 9.21909393, 50.94264606, 26.20464947]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-1.4513449695755447, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.15271843804515806, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6413399722336997, linear_terms=array([-1.41886991e-05, -1.22506996e-05, 2.10449721e-05]), square_terms=array([[ 2.12392194e-03, 8.98039493e-07, -4.74231704e-07], - [ 8.98039493e-07, 2.50309927e-09, 5.64263262e-12], - [-4.74231704e-07, 5.64263262e-12, 3.29687601e-09]]), scale=0.15271843804515806, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=48, candidate_x=array([ 9.1689186 , 45.57597523, 23.09042949]), index=46, x=array([ 9.18863529, 45.60031418, 23.0722383 ]), fval=0.6413227801302601, rho=-4.814845868541751, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 46]), old_indices_discarded=array([32, 33, 39, 45, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.18863529, 45.60031418, 23.0722383 ]), radius=0.01792234005384858, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 34, 35, 36, 37, 38, 40, 41, 43, 44, 46, 48]), model=ScalarModel(intercept=0.641301357069062, linear_terms=array([ 1.13424122e-05, -2.86544384e-06, 8.47454926e-06]), square_terms=array([[2.91095694e-05, 2.83004622e-08, 8.04819438e-09], + [2.83004622e-08, 4.86209411e-10, 4.53100349e-10], + [8.04819438e-09, 4.53100349e-10, 1.09578749e-09]]), scale=0.01792234005384858, shift=array([ 9.18863529, 45.60031418, 23.0722383 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1189,12 +1276,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=24, candidate_x=array([ 9.21254927, 50.72755054, 25.9933501 ]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.6299099565848705, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.07635921902257903, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6412636200468966, linear_terms=array([-1.85982564e-04, 2.34556335e-05, 1.68033597e-05]), square_terms=array([[ 5.32130898e-04, -3.65300183e-08, -4.57058856e-08], - [-3.65300183e-08, 2.84261585e-09, 1.19188371e-09], - [-4.57058856e-08, 1.19188371e-09, 8.22139690e-10]]), scale=0.07635921902257903, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=49, candidate_x=array([ 9.18334295, 45.60584936, 23.05592332]), index=46, x=array([ 9.18863529, 45.60031418, 23.0722383 ]), fval=0.6413227801302601, rho=-8.05386603687764, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 34, 35, 36, 37, 38, 40, 41, 43, 44, 46, 48]), old_indices_discarded=array([39, 42, 45, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.18863529, 45.60031418, 23.0722383 ]), radius=0.00896117002692429, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 34, 38, 40, 41, 43, 46, 48, 49]), model=ScalarModel(intercept=0.6413208790980892, linear_terms=array([-2.60118911e-05, -1.28021483e-05, -5.38316365e-06]), square_terms=array([[7.31639774e-06, 1.22085331e-08, 1.54293572e-08], + [1.22085331e-08, 3.60772518e-10, 1.00381975e-10], + [1.54293572e-08, 1.00381975e-10, 4.51829443e-10]]), scale=0.00896117002692429, shift=array([ 9.18863529, 45.60031418, 23.0722383 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1276,12 +1363,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=25, candidate_x=array([ 9.23690342, 50.5838021 , 26.09045508]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-3.865430938711674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.038179609511289514, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 22, 24, 25]), model=ScalarModel(intercept=0.6412400317662156, linear_terms=array([-1.51811080e-04, -1.33574181e-04, -8.96571588e-05]), square_terms=array([[1.33253248e-04, 3.18742679e-07, 2.22600759e-07], - [3.18742679e-07, 7.45315433e-08, 5.15360719e-08], - [2.22600759e-07, 5.15360719e-08, 3.57710222e-08]]), scale=0.038179609511289514, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=50, candidate_x=array([ 9.19472049, 45.60608042, 23.07540427]), index=50, x=array([ 9.19472049, 45.60608042, 23.07540427]), fval=0.6412954271044503, rho=1.047714915274639, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([31, 34, 38, 40, 41, 43, 46, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=0.008961170026924032, relative_step_length=0.9999999999999711, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.19472049, 45.60608042, 23.07540427]), radius=0.01792234005384858, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 34, 35, 38, 40, 41, 43, 44, 46, 48, 49, 50]), model=ScalarModel(intercept=0.6413235472013079, linear_terms=array([5.87195972e-06, 8.56723531e-07, 3.37566941e-06]), square_terms=array([[2.91564679e-05, 1.82177678e-08, 3.36634221e-08], + [1.82177678e-08, 9.65257547e-10, 1.21388748e-09], + [3.36634221e-08, 1.21388748e-09, 2.34389772e-09]]), scale=0.01792234005384858, shift=array([ 9.19472049, 45.60608042, 23.07540427])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1363,12 +1450,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=26, candidate_x=array([ 9.2299355 , 50.67331247, 26.15330102]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.6957078218562044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.019089804755644757, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6412949003105258, linear_terms=array([ 7.79166084e-05, -1.08575400e-05, -7.85152239e-06]), square_terms=array([[ 3.29424647e-05, 2.40761818e-08, -1.09908809e-08], - [ 2.40761818e-08, 3.71198501e-10, 2.17963621e-10], - [-1.09908809e-08, 2.17963621e-10, 2.48160791e-10]]), scale=0.019089804755644757, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=51, candidate_x=array([ 9.19151691, 45.60170146, 23.05809991]), index=51, x=array([ 9.19151691, 45.60170146, 23.05809991]), fval=0.6412865692543276, rho=2.1901531589690073, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([31, 34, 35, 38, 40, 41, 43, 44, 46, 48, 49, 50]), old_indices_discarded=array([36, 37, 39, 42, 45, 47]), step_length=0.01813502723366537, relative_step_length=1.0118671545779045, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.19151691, 45.60170146, 23.05809991]), radius=0.03584468010769716, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 34, 37, 38, 40, 41, 43, 46, 48, 49, 50, 51]), model=ScalarModel(intercept=0.6413084866973564, linear_terms=array([-9.43455614e-05, -2.44499112e-05, 8.66979268e-06]), square_terms=array([[ 1.16998712e-04, 1.44344558e-07, 1.33852174e-07], + [ 1.44344558e-07, 2.46069903e-09, -2.10488309e-10], + [ 1.33852174e-07, -2.10488309e-10, 7.49795177e-09]]), scale=0.03584468010769716, shift=array([ 9.19151691, 45.60170146, 23.05809991])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1450,12 +1537,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=39, candidate_x=array([ 9.19367375, 50.65189736, 26.13394921]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.7356352461674331, accepted=False, new_indices=array([27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_used=array([22, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.009544902377822378, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.6413009360685263, linear_terms=array([ 2.98872405e-05, -9.59030521e-06, -4.27533832e-06]), square_terms=array([[8.22114603e-06, 2.13676999e-08, 1.22020370e-08], - [2.13676999e-08, 2.01378174e-10, 1.00233499e-10], - [1.22020370e-08, 1.00233499e-10, 1.15290113e-10]]), scale=0.009544902377822378, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=52, candidate_x=array([ 9.21413393, 45.62863914, 23.04842051]), index=52, x=array([ 9.21413393, 45.62863914, 23.04842051]), fval=0.6412854794294208, rho=0.0191505676819926, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([31, 34, 37, 38, 40, 41, 43, 46, 48, 49, 50, 51]), old_indices_discarded=array([32, 33, 35, 36, 39, 42, 44, 45, 47]), step_length=0.036480939414014354, relative_step_length=1.017750452909763, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21413393, 45.62863914, 23.04842051]), radius=0.01792234005384858, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 35, 36, 38, 40, 43, 44, 46, 49, 50, 51, 52]), model=ScalarModel(intercept=0.6413111545180797, linear_terms=array([ 6.07234400e-05, -2.93353077e-05, 3.62911874e-06]), square_terms=array([[ 2.91163686e-05, -3.60530090e-09, 3.27467461e-08], + [-3.60530090e-09, 4.70892615e-09, -1.70722796e-09], + [ 3.27467461e-08, -1.70722796e-09, 9.04180163e-10]]), scale=0.01792234005384858, shift=array([ 9.21413393, 45.62863914, 23.04842051])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1537,12 +1624,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=40, candidate_x=array([ 9.20314317, 50.64928872, 26.1365999 ]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.4286316408626921, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39]), old_indices_discarded=array([26, 32, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.004772451188911189, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 40]), model=ScalarModel(intercept=0.6412973906154201, linear_terms=array([ 1.75398422e-05, -5.99250886e-06, -2.51011929e-06]), square_terms=array([[2.05767865e-06, 4.69879566e-09, 2.64315982e-09], - [4.69879566e-09, 6.35990905e-11, 1.90355049e-11], - [2.64315982e-09, 1.90355049e-11, 1.83491578e-11]]), scale=0.004772451188911189, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=53, candidate_x=array([ 9.20208962, 45.64138552, 23.0447226 ]), index=53, x=array([ 9.20208962, 45.64138552, 23.0447226 ]), fval=0.641279581055819, rho=0.10563477010312446, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([31, 35, 36, 38, 40, 43, 44, 46, 49, 50, 51, 52]), old_indices_discarded=array([34, 37, 39, 41, 42, 45, 48]), step_length=0.017922340053846738, relative_step_length=0.9999999999998972, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20208962, 45.64138552, 23.0447226 ]), radius=0.03584468010769716, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 35, 36, 38, 40, 43, 46, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6412690149568342, linear_terms=array([ 7.72982084e-05, -4.84958394e-05, 8.58288716e-08]), square_terms=array([[ 1.16667259e-04, -2.39765872e-08, 2.87804376e-07], + [-2.39765872e-08, 1.59054285e-08, -6.18270308e-09], + [ 2.87804376e-07, -6.18270308e-09, 5.46381768e-09]]), scale=0.03584468010769716, shift=array([ 9.20208962, 45.64138552, 23.0447226 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1624,12 +1711,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=41, candidate_x=array([ 9.20724051, 50.64712891, 26.13536112]), index=41, x=array([ 9.20724051, 50.64712891, 26.13536112]), fval=0.6412040518376516, rho=2.769849739315419, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 40]), old_indices_discarded=array([32, 36, 39]), step_length=0.004772451188911595, relative_step_length=1.000000000000085, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20724051, 50.64712891, 26.13536112]), radius=0.009544902377822378, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.6412558733517228, linear_terms=array([-1.69255546e-05, -4.39895767e-06, -2.08824813e-06]), square_terms=array([[8.21955555e-06, 2.05881180e-08, 6.90866621e-09], - [2.05881180e-08, 1.57618492e-10, 7.22730875e-11], - [6.90866621e-09, 7.22730875e-11, 5.92353119e-11]]), scale=0.009544902377822378, shift=array([ 9.20724051, 50.64712891, 26.13536112])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=54, candidate_x=array([ 9.18560691, 45.67519773, 23.04475912]), index=53, x=array([ 9.20208962, 45.64138552, 23.0447226 ]), fval=0.641279581055819, rho=-1.483262913370219, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([31, 35, 36, 38, 40, 43, 46, 49, 50, 51, 52, 53]), old_indices_discarded=array([32, 33, 34, 37, 39, 41, 42, 44, 45, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20208962, 45.64138552, 23.0447226 ]), radius=0.01792234005384858, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([35, 36, 38, 40, 43, 46, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.6413288335696544, linear_terms=array([ 2.99084627e-05, 3.03647513e-06, -4.81804264e-06]), square_terms=array([[ 2.91926184e-05, -1.94104060e-08, 8.16030631e-08], + [-1.94104060e-08, 1.14242551e-10, -1.09412828e-10], + [ 8.16030631e-08, -1.09412828e-10, 1.22342917e-09]]), scale=0.01792234005384858, shift=array([ 9.20208962, 45.64138552, 23.0447226 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1711,12 +1798,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=42, candidate_x=array([ 9.21317423, 50.65319986, 26.13972458]), index=41, x=array([ 9.20724051, 50.64712891, 26.13536112]), fval=0.6412040518376516, rho=-5.9786318496293385, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), old_indices_discarded=array([26, 29, 31, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20724051, 50.64712891, 26.13536112]), radius=0.004772451188911189, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 30, 32, 34, 35, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.6412588576917913, linear_terms=array([-6.83670642e-06, -1.11357466e-06, -5.72399169e-07]), square_terms=array([[2.05052218e-06, 6.90899197e-09, 1.39972212e-09], - [6.90899197e-09, 9.18556326e-11, 2.86773343e-11], - [1.39972212e-09, 2.86773343e-11, 1.40014692e-11]]), scale=0.004772451188911189, shift=array([ 9.20724051, 50.64712891, 26.13536112])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=55, candidate_x=array([ 9.18793277, 45.63512574, 23.0547363 ]), index=53, x=array([ 9.20208962, 45.64138552, 23.0447226 ]), fval=0.641279581055819, rho=-3.0850952098020277, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 36, 38, 40, 43, 46, 49, 50, 51, 52, 53, 54]), old_indices_discarded=array([31, 34, 37, 39, 41, 44, 45, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20208962, 45.64138552, 23.0447226 ]), radius=0.00896117002692429, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([38, 43, 49, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.6412927018713143, linear_terms=array([-3.95830918e-05, 1.71611401e-06, -1.75745674e-05]), square_terms=array([[ 7.34282847e-06, -1.66027178e-09, 3.06944699e-08], + [-1.66027178e-09, 4.31041762e-11, 1.22267555e-10], + [ 3.06944699e-08, 1.22267555e-10, 1.11863146e-09]]), scale=0.00896117002692429, shift=array([ 9.20208962, 45.64138552, 23.0447226 ])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1798,12 +1885,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=43, candidate_x=array([ 9.21199791, 50.6471936 , 26.13498794]), index=41, x=array([ 9.20724051, 50.64712891, 26.13536112]), fval=0.6412040518376516, rho=-9.46039138436527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 30, 32, 34, 35, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([28, 29, 31, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20724051, 50.64712891, 26.13536112]), radius=0.0023862255944555946, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 40, 41, 42, 43]), model=ScalarModel(intercept=0.6412405831157186, linear_terms=array([ 4.52303257e-06, 2.81196588e-05, -2.35968467e-05]), square_terms=array([[ 5.12190353e-07, -7.84167461e-09, 9.55156040e-09], - [-7.84167461e-09, 1.32218333e-09, -1.32903857e-09], - [ 9.55156040e-09, -1.32903857e-09, 1.49501369e-09]]), scale=0.0023862255944555946, shift=array([ 9.20724051, 50.64712891, 26.13536112])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=56, candidate_x=array([ 9.21052789, 45.64202864, 23.04766956]), index=56, x=array([ 9.21052789, 45.64202864, 23.04766956]), fval=0.6412388217057458, rho=1.0275942194681258, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([38, 43, 49, 51, 52, 53, 54, 55]), old_indices_discarded=array([], dtype=int64), step_length=0.008961170026924889, relative_step_length=1.0000000000000668, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.01792234005384858, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([35, 38, 43, 46, 49, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.6413235030118442, linear_terms=array([3.04274084e-05, 4.74538518e-06, 1.23753508e-05]), square_terms=array([[ 2.91839759e-05, -9.35564134e-09, 7.99806758e-08], + [-9.35564134e-09, 2.75030515e-10, 1.05398303e-09], + [ 7.99806758e-08, 1.05398303e-09, 5.78042311e-09]]), scale=0.01792234005384858, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1885,12 +1972,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=44, candidate_x=array([ 9.20694996, 50.64530232, 26.13689391]), index=44, x=array([ 9.20694996, 50.64530232, 26.13689391]), fval=0.6412003819983737, rho=0.09857365047199054, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.002402151221322381, relative_step_length=1.0066739820844222, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20694996, 50.64530232, 26.13689391]), radius=0.0011931127972277973, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 40, 41, 43, 44]), model=ScalarModel(intercept=0.641203279969248, linear_terms=array([7.68329827e-06, 2.09695197e-05, 2.72233817e-06]), square_terms=array([[ 1.26006786e-07, -3.28572663e-09, -8.55594767e-11], - [-3.28572663e-09, 5.92079298e-10, 1.02968247e-10], - [-8.55594767e-11, 1.02968247e-10, 6.19322113e-11]]), scale=0.0011931127972277973, shift=array([ 9.20694996, 50.64530232, 26.13689391])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=57, candidate_x=array([ 9.19871772, 45.6369924 , 23.03461107]), index=56, x=array([ 9.21052789, 45.64202864, 23.04766956]), fval=0.6412388217057458, rho=-2.034547178334105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 43, 46, 49, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([31, 34, 36, 37, 39, 40, 41, 44, 45, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.00896117002692429, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([35, 38, 43, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.641336784215586, linear_terms=array([ 3.37194784e-05, 1.47646655e-05, -4.94622101e-06]), square_terms=array([[ 7.27205053e-06, -8.46098600e-09, 2.41038200e-08], + [-8.46098600e-09, 7.14551454e-10, 5.67261002e-10], + [ 2.41038200e-08, 5.67261002e-10, 1.36351391e-09]]), scale=0.00896117002692429, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -1972,12 +2059,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=45, candidate_x=array([ 9.20654349, 50.64418718, 26.13674915]), index=45, x=array([ 9.20654349, 50.64418718, 26.13674915]), fval=0.6411989063826031, rho=0.06546551794973687, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.001195701055462824, relative_step_length=1.0021693323892262, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20654349, 50.64418718, 26.13674915]), radius=0.0005965563986138987, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412016028005694, linear_terms=array([-2.55858927e-06, -7.29526994e-08, -7.87710385e-07]), square_terms=array([[3.41315492e-08, 1.04426797e-11, 7.35805731e-11], - [1.04426797e-11, 2.63347216e-13, 5.28969670e-13], - [7.35805731e-11, 5.28969670e-13, 2.04887560e-12]]), scale=0.0005965563986138987, shift=array([ 9.20654349, 50.64418718, 26.13674915])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=58, candidate_x=array([ 9.2028289 , 45.63774774, 23.04931335]), index=56, x=array([ 9.21052789, 45.64202864, 23.04766956]), fval=0.6412388217057458, rho=-0.9305753868686838, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 38, 43, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.004480585013462145, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 53, 56, 57, 58]), model=ScalarModel(intercept=0.6412438622916211, linear_terms=array([-9.52899239e-06, -1.52791403e-05, 4.25097244e-07]), square_terms=array([[1.82495925e-06, 6.38394061e-10, 4.11144666e-10], + [6.38394061e-10, 4.31291655e-10, 1.19384986e-10], + [4.11144666e-10, 1.19384986e-10, 5.63993449e-10]]), scale=0.004480585013462145, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2059,12 +2146,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=58, candidate_x=array([ 9.20711472, 50.64419857, 26.13692076]), index=45, x=array([ 9.20654349, 50.64418718, 26.13674915]), fval=0.6411989063826031, rho=-1.3540135264197082, accepted=False, new_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_used=array([44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20654349, 50.64418718, 26.13674915]), radius=0.00029827819930694933, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([45, 46, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.6412021137195624, linear_terms=array([-9.14153413e-07, 4.95084351e-07, -4.46123326e-07]), square_terms=array([[ 8.50301604e-09, -1.62114960e-11, 2.28942335e-11], - [-1.62114960e-11, 1.09677906e-12, 8.83835907e-14], - [ 2.28942335e-11, 8.83835907e-14, 1.02199200e-12]]), scale=0.00029827819930694933, shift=array([ 9.20654349, 50.64418718, 26.13674915])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=59, candidate_x=array([ 9.21278674, 45.6460377 , 23.04755793]), index=56, x=array([ 9.21052789, 45.64202864, 23.04766956]), fval=0.6412388217057458, rho=-1.9232326674259503, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([52, 53, 56, 57, 58]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.0022402925067310725, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([53, 56, 58, 59]), model=ScalarModel(intercept=0.6412388217057453, linear_terms=array([-2.10242301e-05, 3.20216675e-05, 2.46938450e-05]), square_terms=array([[ 4.58766424e-07, -3.09316980e-09, -1.53270498e-09], + [-3.09316980e-09, 1.89755517e-09, 1.49970806e-09], + [-1.53270498e-09, 1.49970806e-09, 1.24593067e-09]]), scale=0.0022402925067310725, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2146,12 +2233,99 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=59, candidate_x=array([ 9.20677822, 50.64405072, 26.13687265]), index=59, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830574, rho=0.6634184250305409, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([45, 46, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([44, 47, 49]), step_length=0.0002982781993076994, relative_step_length=1.0000000000025147, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 60 entries., 'multistart_info': {'start_parameters': [array([ 9.20683173, 51.48987537, 25.99186716]), array([ 9.27512208, 46.36962222, 22.93103984]), array([ 9.12810637, 48.86990017, 23.90047511])], 'local_optima': [{'solution_x': array([ 9.20658168, 51.49004107, 25.99177342]), 'solution_criterion': 0.6411987240466468, 'states': [State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=5.148987537348444, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6411988508284691, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=60, candidate_x=array([ 9.21155599, 45.64044686, 23.04644973]), index=56, x=array([ 9.21052789, 45.64202864, 23.04766956]), fval=0.6412388217057458, rho=-0.3079080046126188, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([53, 56, 58, 59]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21052789, 45.64202864, 23.04766956]), radius=0.0011201462533655363, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.6412388437802616, linear_terms=array([1.64312695e-05, 4.65279188e-07, 1.07229961e-07]), square_terms=array([[ 1.08773373e-07, 6.78393854e-10, -2.26235629e-10], + [ 6.78393854e-10, 2.77167620e-11, -8.32587980e-12], + [-2.26235629e-10, -8.32587980e-12, 3.11734525e-12]]), scale=0.0011201462533655363, shift=array([ 9.21052789, 45.64202864, 23.04766956])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=73, candidate_x=array([ 9.20940815, 45.64200068, 23.04765841]), index=73, x=array([ 9.20940815, 45.64200068, 23.04765841]), fval=0.6412231729718957, rho=0.9551430358606648, accepted=True, new_indices=array([61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), old_indices_used=array([56, 59, 60]), old_indices_discarded=array([], dtype=int64), step_length=0.0011201462533656243, relative_step_length=1.0000000000000786, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20940815, 45.64200068, 23.04765841]), radius=0.0022402925067310725, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 61, 62, 63, 64, 65, 67, 68, 69, 71, 72, 73]), model=ScalarModel(intercept=0.6412226294004633, linear_terms=array([ 3.24586330e-05, 5.92811032e-07, -3.43705302e-08]), square_terms=array([[4.28909220e-07, 2.16458658e-11, 5.94629399e-10], + [2.16458658e-11, 2.77111185e-12, 8.02947612e-13], + [5.94629399e-10, 8.02947612e-13, 7.42708381e-12]]), scale=0.0022402925067310725, shift=array([ 9.20940815, 45.64200068, 23.04765841])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2233,12 +2407,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=0, candidate_x=array([ 9.20683173, 51.48987537, 25.99186716]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=5.148987537348444, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.577099034852349, linear_terms=array([-0.05437769, -0.00133237, -0.00445358]), square_terms=array([[ 2.64363755e+00, -1.20940035e-02, -7.41335774e-03], - [-1.20940035e-02, 7.53100996e-05, 9.59315557e-05], - [-7.41335774e-03, 9.59315557e-05, 2.83957639e-04]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=74, candidate_x=array([ 9.20716808, 45.64197452, 23.04767586]), index=74, x=array([ 9.20716808, 45.64197452, 23.04767586]), fval=0.641203166149811, rho=0.6204008749724397, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([56, 61, 62, 63, 64, 65, 67, 68, 69, 71, 72, 73]), old_indices_discarded=array([53, 58, 59, 60, 66, 70]), step_length=0.002240292506730774, relative_step_length=0.9999999999998668, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20716808, 45.64197452, 23.04767586]), radius=0.004480585013462145, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 61, 63, 64, 65, 66, 67, 68, 69, 71, 72, 74]), model=ScalarModel(intercept=0.6411998091125397, linear_terms=array([ 5.18943226e-05, -1.60828779e-06, 1.35217729e-06]), square_terms=array([[ 1.73015705e-06, 3.77827059e-09, -1.90012334e-09], + [ 3.77827059e-09, 9.22190115e-11, -2.17780041e-11], + [-1.90012334e-09, -2.17780041e-11, 2.01557834e-11]]), scale=0.004480585013462145, shift=array([ 9.20716808, 45.64197452, 23.04767586])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2320,12 +2494,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=13, candidate_x=array([ 9.33401434, 53.19570811, 30.87305492]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.09810705368372, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=2.574493768674222, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5758510743673294, linear_terms=array([-0.0284062 , -0.00246908, -0.00205258]), square_terms=array([[ 6.60453986e-01, -3.47466598e-03, -1.80209326e-03], - [-3.47466598e-03, 4.17175646e-05, 4.54497838e-05], - [-1.80209326e-03, 4.54497838e-05, 6.70501402e-05]]), scale=2.574493768674222, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=75, candidate_x=array([ 9.20269645, 45.64218582, 23.04748719]), index=74, x=array([ 9.20716808, 45.64197452, 23.04767586]), fval=0.641203166149811, rho=-1.365460544825835, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([56, 61, 63, 64, 65, 66, 67, 68, 69, 71, 72, 74]), old_indices_discarded=array([52, 53, 55, 57, 58, 59, 60, 62, 70, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20716808, 45.64197452, 23.04767586]), radius=0.0022402925067310725, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 61, 62, 64, 66, 67, 68, 69, 70, 72, 73, 74]), model=ScalarModel(intercept=0.6411999998265243, linear_terms=array([ 2.50884881e-05, 1.46068594e-06, -1.10174093e-06]), square_terms=array([[ 4.30036003e-07, -3.60509007e-10, 3.77466081e-10], + [-3.60509007e-10, 1.02282903e-11, -9.39314439e-12], + [ 3.77466081e-10, -9.39314439e-12, 1.07931716e-11]]), scale=0.0022402925067310725, shift=array([ 9.20716808, 45.64197452, 23.04767586])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2407,12 +2581,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=14, candidate_x=array([ 9.33189635, 53.49930647, 27.60036285]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.13056351453261902, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 7, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=1.287246884337111, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=0.5893107379228858, linear_terms=array([-0.01558663, -0.00510477, 0.0007044 ]), square_terms=array([[ 1.57482213e-01, -2.47144499e-03, 4.20037179e-04], - [-2.47144499e-03, 2.07713086e-04, -1.46683639e-05], - [ 4.20037179e-04, -1.46683639e-05, 1.89757814e-06]]), scale=1.287246884337111, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=76, candidate_x=array([ 9.20493572, 45.64182612, 23.04779201]), index=74, x=array([ 9.20716808, 45.64197452, 23.04767586]), fval=0.641203166149811, rho=-0.9986451321394485, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([56, 61, 62, 64, 66, 67, 68, 69, 70, 72, 73, 74]), old_indices_discarded=array([53, 58, 59, 60, 63, 65, 71, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20716808, 45.64197452, 23.04767586]), radius=0.0011201462533655363, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 62, 64, 66, 67, 68, 69, 70, 72, 73, 74, 76]), model=ScalarModel(intercept=0.6412213950497655, linear_terms=array([ 3.86823120e-06, -1.33070744e-06, -3.80294539e-06]), square_terms=array([[1.11795853e-07, 3.74223299e-10, 8.85352147e-10], + [3.74223299e-10, 5.72514104e-12, 1.55351185e-11], + [8.85352147e-10, 1.55351185e-11, 4.57607236e-11]]), scale=0.0011201462533655363, shift=array([ 9.20716808, 45.64197452, 23.04767586])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2494,12 +2668,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=15, candidate_x=array([ 9.34996065, 52.76373404, 25.81051104]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.11385902017059198, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 14]), old_indices_discarded=array([ 6, 7, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.6436234421685555, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6402147674526484, linear_terms=array([-0.00034196, 0.00014716, 0.00011216]), square_terms=array([[3.74756501e-02, 2.15030692e-05, 1.57785121e-05], - [2.15030692e-05, 6.58166572e-08, 3.83598315e-08], - [1.57785121e-05, 3.83598315e-08, 5.78693093e-08]]), scale=0.6436234421685555, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=77, candidate_x=array([ 9.20639987, 45.64224413, 23.04844635]), index=77, x=array([ 9.20639987, 45.64224413, 23.04844635]), fval=0.641200197514102, rho=0.5336222775676067, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([56, 62, 64, 66, 67, 68, 69, 70, 72, 73, 74, 76]), old_indices_discarded=array([60, 61, 63, 65, 71, 75]), step_length=0.001120930115522763, relative_step_length=1.000699785545746, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20639987, 45.64224413, 23.04844635]), radius=0.0022402925067310725, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 64, 66, 67, 68, 69, 70, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=0.6412241577746707, linear_terms=array([-4.24139607e-06, -8.38654247e-06, -2.38208500e-05]), square_terms=array([[4.54621752e-07, 4.15189024e-09, 5.17693770e-09], + [4.15189024e-09, 1.53039993e-10, 2.26888691e-10], + [5.17693770e-09, 2.26888691e-10, 7.63852052e-10]]), scale=0.0022402925067310725, shift=array([ 9.20639987, 45.64224413, 23.04844635])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2581,12 +2755,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=28, candidate_x=array([ 9.21313161, 50.97798884, 25.60175485]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.4308702401838545, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.32181172108427775, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.640144273855586, linear_terms=array([-2.05822944e-04, -1.09807039e-04, 6.94001699e-05]), square_terms=array([[ 9.36041691e-03, -3.38163136e-06, 9.06517069e-06], - [-3.38163136e-06, 4.99770966e-08, -3.22003356e-08], - [ 9.06517069e-06, -3.22003356e-08, 4.04731428e-08]]), scale=0.32181172108427775, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=78, candidate_x=array([ 9.20676711, 45.64298428, 23.05054873]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=0.07874134829834548, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([56, 64, 66, 67, 68, 69, 70, 73, 74, 75, 76, 77]), old_indices_discarded=array([53, 58, 59, 60, 61, 62, 63, 65, 71, 72]), step_length=0.0022589155167016327, relative_step_length=1.0083127582289395, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=0.0011201462533655363, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([56, 61, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78]), model=ScalarModel(intercept=0.6412027938426249, linear_terms=array([ 5.40552063e-06, -5.81510678e-07, -4.60889875e-06]), square_terms=array([[ 1.11417104e-07, -1.28705802e-10, 6.93508248e-10], + [-1.28705802e-10, 1.32519455e-11, 1.07286462e-11], + [ 6.93508248e-10, 1.07286462e-11, 3.37418922e-11]]), scale=0.0011201462533655363, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2668,12 +2842,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=29, candidate_x=array([ 9.21407231, 51.76171238, 25.81970303]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.6555276541732982, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([15, 20, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.16090586054213887, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 29]), model=ScalarModel(intercept=0.6401375835808683, linear_terms=array([-1.21532046e-04, 4.55539802e-05, 5.12717895e-05]), square_terms=array([[2.33891702e-03, 2.43888922e-06, 2.77356802e-06], - [2.43888922e-06, 1.42208051e-08, 1.00057806e-08], - [2.77356802e-06, 1.00057806e-08, 1.51206072e-08]]), scale=0.16090586054213887, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=79, candidate_x=array([ 9.20592325, 45.64307551, 23.0512797 ]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-1.3780848715897542, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([56, 61, 66, 67, 68, 69, 70, 73, 74, 76, 77, 78]), old_indices_discarded=array([62, 64, 71, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=0.0005600731266827681, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=0.6412009708000245, linear_terms=array([-6.35493630e-07, -2.15627085e-07, 3.36056584e-07]), square_terms=array([[ 2.97074267e-08, 1.30526313e-11, -3.63146602e-11], + [ 1.30526313e-11, 1.58264530e-13, 1.04142450e-13], + [-3.63146602e-11, 1.04142450e-13, 1.34699673e-12]]), scale=0.0005600731266827681, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2755,12 +2929,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=30, candidate_x=array([ 9.21519997, 51.38314652, 25.87173596]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-1.2479692844655803, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 29]), old_indices_discarded=array([20, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.08045293027106944, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.6413807790403127, linear_terms=array([-6.87288309e-05, -3.64270026e-05, 8.78179857e-06]), square_terms=array([[ 5.86255249e-04, 2.16800229e-07, -5.03319249e-07], - [ 2.16800229e-07, 5.36195721e-09, -2.57378877e-09], - [-5.03319249e-07, -2.57378877e-09, 3.38987925e-09]]), scale=0.08045293027106944, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=92, candidate_x=array([ 9.20726383, 45.64311556, 23.05032577]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-8.383049611305378, accepted=False, new_indices=array([80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), old_indices_used=array([77, 78, 79]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=0.00028003656334138407, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([78, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92]), model=ScalarModel(intercept=0.641199936976986, linear_terms=array([ 1.68215172e-06, -3.60432889e-07, -3.25130121e-07]), square_terms=array([[7.25448649e-09, 1.27253368e-11, 1.53337190e-11], + [1.27253368e-11, 2.83381020e-13, 2.85430374e-13], + [1.53337190e-11, 2.85430374e-13, 3.32440347e-13]]), scale=0.00028003656334138407, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2842,12 +3016,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=43, candidate_x=array([ 9.21565369, 51.56787536, 25.97317301]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-2.2350763518376344, accepted=False, new_indices=array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.04022646513553472, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.6413827428619181, linear_terms=array([-5.68830793e-05, -6.97428000e-05, -5.78950371e-06]), square_terms=array([[1.46566718e-04, 1.91014436e-07, 1.56910312e-07], - [1.91014436e-07, 1.58064073e-08, 3.63198578e-09], - [1.56910312e-07, 3.63198578e-09, 7.76262350e-09]]), scale=0.04022646513553472, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=93, candidate_x=array([ 9.20649824, 45.64304241, 23.05060121]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-0.6000813756488979, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([78, 80, 81, 82, 83, 84, 86, 88, 89, 90, 91, 92]), old_indices_discarded=array([79, 85, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=0.00014001828167069203, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([78, 80, 81, 82, 83, 84, 86, 88, 90, 91, 92, 93]), model=ScalarModel(intercept=0.6412000970879008, linear_terms=array([ 7.73526022e-07, -1.53366108e-07, -2.08033084e-07]), square_terms=array([[1.81747170e-09, 2.20820579e-12, 5.45451394e-12], + [2.20820579e-12, 4.71590434e-14, 7.11155001e-14], + [5.45451394e-12, 7.11155001e-14, 1.49659707e-13]]), scale=0.00014001828167069203, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -2929,12 +3103,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=44, candidate_x=array([ 9.21725848, 51.52873506, 25.99507102]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-1.3484993659269786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42]), old_indices_discarded=array([30, 39, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.02011323256776736, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 44]), model=ScalarModel(intercept=0.6413942783338986, linear_terms=array([-3.20943867e-05, -3.91002224e-05, -1.05813254e-05]), square_terms=array([[3.67028934e-05, 9.25969229e-08, 7.70162092e-08], - [9.25969229e-08, 6.16329040e-09, 2.79926237e-09], - [7.70162092e-08, 2.79926237e-09, 2.51326114e-09]]), scale=0.02011323256776736, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=94, candidate_x=array([ 9.20663436, 45.64301073, 23.05058457]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-0.5169576261346827, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([78, 80, 81, 82, 83, 84, 86, 88, 90, 91, 92, 93]), old_indices_discarded=array([85, 87, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=7.000914083534602e-05, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 78, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106]), model=ScalarModel(intercept=0.6411983657280034, linear_terms=array([-1.03793979e-07, 7.27301433e-08, 7.34858031e-08]), square_terms=array([[ 4.67614996e-10, -8.23635135e-13, -9.30032955e-13], + [-8.23635135e-13, 1.57774271e-14, 1.60155267e-14], + [-9.30032955e-13, 1.60155267e-14, 1.64466615e-14]]), scale=7.000914083534602e-05, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3016,12 +3191,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=45, candidate_x=array([ 9.21483693, 51.50785987, 25.99672395]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-1.8697559665017334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 44]), old_indices_discarded=array([34, 39, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.01005661628388368, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412559421570525, linear_terms=array([-9.51327476e-06, 1.32879771e-05, 8.73910006e-06]), square_terms=array([[ 9.24100090e-06, -4.73635941e-09, -1.50247903e-08], - [-4.73635941e-09, 2.89689424e-10, 7.47144718e-11], - [-1.50247903e-08, 7.47144718e-11, 1.64721337e-10]]), scale=0.01005661628388368, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=107, candidate_x=array([ 9.20681662, 45.64294947, 23.05051355]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-3.3493252311935513, accepted=False, new_indices=array([ 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106]), old_indices_used=array([78, 93, 94]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=3.500457041767301e-05, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 78, 95, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107]), model=ScalarModel(intercept=0.6411983574243617, linear_terms=array([ 1.23497155e-07, 1.30854524e-08, -3.22669068e-08]), square_terms=array([[ 1.14966940e-10, -7.98896006e-14, 1.97016633e-13], + [-7.98896006e-14, 4.88353748e-16, -1.20420334e-15], + [ 1.97016633e-13, -1.20420334e-15, 2.96937558e-15]]), scale=3.500457041767301e-05, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3103,12 +3278,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=58, candidate_x=array([ 9.21046974, 51.48202748, 25.98670836]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-2.2048759160947164, accepted=False, new_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_used=array([ 0, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.00502830814194184, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412532241492871, linear_terms=array([-3.16145510e-06, 3.88469830e-06, 1.93075386e-06]), square_terms=array([[ 2.30697473e-06, -2.42890651e-09, -2.37176868e-09], - [-2.42890651e-09, 3.85116505e-11, 7.38815414e-12], - [-2.37176868e-09, 7.38815414e-12, 3.61043992e-11]]), scale=0.00502830814194184, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=108, candidate_x=array([ 9.20673342, 45.6429807 , 23.05055755]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-0.9176254431425854, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 78, 95, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107]), old_indices_discarded=array([ 94, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=1.7502285208836504e-05, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 78, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107, 108]), model=ScalarModel(intercept=0.6411983851403613, linear_terms=array([ 6.38639428e-08, 5.43914697e-09, -4.07015582e-08]), square_terms=array([[ 2.87288275e-11, -1.65702288e-14, 1.24010042e-13], + [-1.65702288e-14, 8.43754307e-17, -6.31386781e-16], + [ 1.24010042e-13, -6.31386781e-16, 4.72470794e-15]]), scale=1.7502285208836504e-05, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3190,12 +3365,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=59, candidate_x=array([ 9.20905677, 51.48583314, 25.98985865]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-4.375996109540071, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57]), old_indices_discarded=array([45, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.00251415407097092, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=0.641257540030709, linear_terms=array([-1.56329417e-06, 5.26882951e-06, 4.74793679e-06]), square_terms=array([[ 5.76813598e-07, -1.21814516e-09, -1.24595987e-09], - [-1.21814516e-09, 3.52205771e-11, 3.14265057e-11], - [-1.24595987e-09, 3.14265057e-11, 4.30058797e-11]]), scale=0.00251415407097092, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=109, candidate_x=array([ 9.20675239, 45.64298302, 23.05055811]), index=78, x=array([ 9.20676711, 45.64298428, 23.05054873]), fval=0.6411981649680742, rho=-0.6772843375754085, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 78, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107, 108]), old_indices_discarded=array([ 98, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20676711, 45.64298428, 23.05054873]), radius=8.751142604418252e-06, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 78, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121]), model=ScalarModel(intercept=0.6411981649678726, linear_terms=array([-3.05612962e-08, 1.46809760e-13, -1.70649995e-13]), square_terms=array([[ 7.38373402e-12, 7.56759673e-19, -8.94606680e-19], + [ 7.56759673e-19, 1.33945952e-25, -1.57431020e-25], + [-8.94606680e-19, -1.57431020e-25, 1.85043423e-25]]), scale=8.751142604418252e-06, shift=array([ 9.20676711, 45.64298428, 23.05054873])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3277,12 +3453,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=60, candidate_x=array([ 9.20735201, 51.48797467, 25.99015438]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.8818739691224265, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([46, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.00125707703548546, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.6412049552732294, linear_terms=array([ 3.72377171e-06, -1.82881098e-06, -5.03992414e-07]), square_terms=array([[1.46537462e-07, 7.87116195e-10, 2.29063663e-10], - [7.87116195e-10, 1.39179684e-11, 3.03878402e-12], - [2.29063663e-10, 3.03878402e-12, 8.63730755e-13]]), scale=0.00125707703548546, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=122, candidate_x=array([ 9.20677586, 45.64298428, 23.05054873]), index=122, x=array([ 9.20677586, 45.64298428, 23.05054873]), fval=0.6411981344087744, rho=1.0000555009711634, accepted=True, new_indices=array([110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_used=array([ 78, 108, 109]), old_indices_discarded=array([], dtype=int64), step_length=8.75114260381017e-06, relative_step_length=0.999999999930514, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 123 entries., 'multistart_info': {'start_parameters': [array([ 9.20677822, 50.64405072, 26.13687265]), array([ 9.27523039, 45.88119054, 23.01481109]), array([ 9.12811696, 48.90833875, 23.98172789])], 'local_optima': [{'solution_x': array([ 9.20676928, 50.64405258, 26.1368695 ]), 'solution_criterion': 0.6411981573819835, 'states': [State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=5.064405071849033, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6411981580830596, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3364,12 +3540,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=73, candidate_x=array([ 9.20572253, 51.49044404, 25.99203004]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-2.968221572845718, accepted=False, new_indices=array([61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), old_indices_used=array([ 0, 59, 60]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.00062853851774273, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.6412066960749747, linear_terms=array([ 9.00045805e-07, -5.31489227e-07, 8.19697450e-07]), square_terms=array([[ 3.69602827e-08, 8.29347553e-11, -5.74182412e-11], - [ 8.29347553e-11, 1.14617076e-12, -7.27051952e-13], - [-5.74182412e-11, -7.27051952e-13, 1.51026310e-12]]), scale=0.00062853851774273, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=0, candidate_x=array([ 9.20677822, 50.64405072, 26.13687265]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=5.064405071849033, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5789960151511114, linear_terms=array([-0.05106117, -0.00134537, -0.00447129]), square_terms=array([[ 2.54921349e+00, -1.10137535e-02, -6.96996937e-03], + [-1.10137535e-02, 6.67996913e-05, 8.61725402e-05], + [-6.96996937e-03, 8.61725402e-05, 2.58016928e-04]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3451,12 +3627,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=74, candidate_x=array([ 9.20641163, 51.49013045, 25.99147378]), index=0, x=array([ 9.20683173, 51.48987537, 25.99186716]), fval=0.6411988508284691, rho=-0.8803124320987271, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 73]), old_indices_discarded=array([60, 65, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683173, 51.48987537, 25.99186716]), radius=0.000314269258871365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74]), model=ScalarModel(intercept=0.6412058603389366, linear_terms=array([ 8.89707068e-07, -5.84463472e-07, 3.30323888e-07]), square_terms=array([[ 9.19824922e-09, 3.54441471e-11, -1.06455208e-11], - [ 3.54441471e-11, 8.88211681e-13, -3.88896604e-13], - [-1.06455208e-11, -3.88896604e-13, 2.67769208e-13]]), scale=0.000314269258871365, shift=array([ 9.20683173, 51.48987537, 25.99186716])), vector_model=VectorModel(intercepts=array([ 0.04848873, 0.12360457, 0.14833762, 0.19329553, 0.21683843, - 0.23172151, 0.23264264, 0.0658259 , -0.08093415, -0.06795128, - -0.41008598, -0.4187298 , -0.12365674, -0.09727986, -0.0878388 , - -0.09147924, -0.09757891]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=13, candidate_x=array([ 9.32828881, 52.30084005, 30.93946475]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.09392165572328727, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=2.5322025359245166, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5757857577586193, linear_terms=array([-0.02957024, -0.00320448, 0.00216017]), square_terms=array([[ 6.34961854e-01, -4.80911376e-03, 1.79236742e-03], + [-4.80911376e-03, 8.80608690e-05, -2.30795032e-05], + [ 1.79236742e-03, -2.30795032e-05, 1.02105624e-05]]), scale=2.5322025359245166, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3538,12 +3714,99 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=5.148987537348444, shift=array([ 9.20683173, 51.48987537, 25.99186716])), candidate_index=75, candidate_x=array([ 9.20658168, 51.49004107, 25.99177342]), index=75, x=array([ 9.20658168, 51.49004107, 25.99177342]), fval=0.6411987240466468, rho=0.11404565445920974, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 61, 62, 63, 64, 66, 67, 69, 70, 71, 72, 74]), old_indices_discarded=array([65, 68, 73]), step_length=0.00031426925887290564, relative_step_length=1.0000000000049023, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 76 entries., 'history': {'params': [{'CRRA': 9.206831729527266, 'BeqShift': 51.48987537348444, 'BeqFac': 25.99186716077577}, {'CRRA': 10.645065744515577, 'BeqShift': 54.07206826772235, 'BeqFac': 21.775727287046788}, {'CRRA': 5.898143611067358, 'BeqShift': 53.899241292871714, 'BeqFac': 22.867829475179303}, {'CRRA': 4.831042590899532, 'BeqShift': 53.73729315064104, 'BeqFac': 27.512937642034817}, {'CRRA': 8.714853844435495, 'BeqShift': 46.36453575591438, 'BeqFac': 25.961466093079782}, {'CRRA': 8.436330431999226, 'BeqShift': 49.57113103737977, 'BeqFac': 30.707460504454108}, {'CRRA': 4.674429830162727, 'BeqShift': 49.08329856628939, 'BeqFac': 26.41352312655501}, {'CRRA': 11.508609883596527, 'BeqShift': 56.07824554848442, 'BeqFac': 25.59093184630501}, {'CRRA': 13.317600605658994, 'BeqShift': 50.164100452681794, 'BeqFac': 23.18901633239281}, {'CRRA': 8.524923222134564, 'BeqShift': 55.01636090094755, 'BeqFac': 29.681172416770217}, {'CRRA': 7.949040233708621, 'BeqShift': 49.02743831382652, 'BeqFac': 21.648315970788577}, {'CRRA': 12.898889610055392, 'BeqShift': 48.28707042221219, 'BeqFac': 27.611379961190053}, {'CRRA': 13.189106930605947, 'BeqShift': 52.854100357866734, 'BeqFac': 28.95707323702586}, {'CRRA': 9.334014335528215, 'BeqShift': 53.19570810758337, 'BeqFac': 30.87305491840859}, {'CRRA': 9.331896347838615, 'BeqShift': 53.499306466487155, 'BeqFac': 27.600362850409002}, {'CRRA': 9.34996064759815, 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51.875183342908095, 'BeqFac': 25.79941044203952}, {'CRRA': 8.813607918284164, 'BeqShift': 51.15149494486946, 'BeqFac': 25.610914597966836}, {'CRRA': 9.213131607257464, 'BeqShift': 50.97798883637481, 'BeqFac': 25.601754852662797}, {'CRRA': 9.214072305669143, 'BeqShift': 51.76171237766068, 'BeqFac': 25.819703029938704}, {'CRRA': 9.215199966232754, 'BeqShift': 51.383146519484995, 'BeqFac': 25.871735957845736}, {'CRRA': 9.216716853924463, 'BeqShift': 51.45554902814732, 'BeqFac': 26.063955026055925}, {'CRRA': 9.209001964927008, 'BeqShift': 51.57027756477344, 'BeqFac': 25.99000928472032}, {'CRRA': 9.260948249030404, 'BeqShift': 51.459961040179294, 'BeqFac': 25.940396693816776}, {'CRRA': 9.164115003882157, 'BeqShift': 51.48828419604994, 'BeqFac': 25.923709817123495}, {'CRRA': 9.152415514988588, 'BeqShift': 51.51127945983985, 'BeqFac': 25.936609449352183}, {'CRRA': 9.258945844608023, 'BeqShift': 51.43617836453795, 'BeqFac': 26.021420912293166}, {'CRRA': 9.13765155127113, 'BeqShift': 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51.49072642980771, 'BeqFac': 25.99112610425305}, {'CRRA': 9.207689904457064, 'BeqShift': 51.48974575980235, 'BeqFac': 25.992776544499193}, {'CRRA': 9.206213144709368, 'BeqShift': 51.489361553265844, 'BeqFac': 25.99283338242693}, {'CRRA': 9.205722528052792, 'BeqShift': 51.49044404355336, 'BeqFac': 25.99203003838284}, {'CRRA': 9.206411630301693, 'BeqShift': 51.49013045462568, 'BeqFac': 25.99147378047824}, {'CRRA': 9.206581683381966, 'BeqShift': 51.49004107027674, 'BeqFac': 25.991773423561632}], 'criterion': [0.6411988508284691, 0.7136701554488605, 1.1553085127439813, 1.6619035528363348, 0.6504948960140015, 0.6639451678306449, 1.7607975696784457, 0.8253755448011229, 1.2439318019269798, 0.6588184500529425, 0.7041559181497187, 1.1206043415760254, 1.2046592051476548, 0.6417198728389927, 0.6417144055001947, 0.6418917111895248, 0.6486031736486461, 0.6417108151257079, 0.6542291407170552, 0.6452036132572929, 0.6441713894463951, 0.6416200619812618, 0.6452949238884177, 0.642921991646109, 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0.6412000146663274, 0.6411987240466468], 'runtime': [0.0, 1.7674728150013834, 1.971279171993956, 2.1699622180021834, 2.4078327319875825, 2.6349312469828874, 2.8703876420040615, 3.0949717510084156, 3.331537355988985, 3.5810636919923127, 3.7896313270030078, 4.032132853986695, 4.268133122008294, 5.7164638779941015, 7.061364024004433, 8.404509437998058, 10.2552733909979, 10.469754358986393, 10.700817857985385, 10.935404510004446, 11.156862239993643, 11.354435465007555, 11.626386560994433, 11.879323094006395, 12.171966446010629, 12.425181676982902, 12.670657834009035, 12.888932864996605, 14.365909608983202, 15.711069712007884, 17.080602344998624, 18.97552325000288, 19.160451968986308, 19.375511763006216, 19.60766480700113, 19.85564505698858, 20.04778404100216, 20.298691281990614, 20.54271566699026, 20.797930906002875, 21.058459894004045, 21.340124403999653, 21.56825467100134, 23.25028569099959, 24.63702903498779, 26.02356418099953, 27.74768948700512, 27.94462815899169, 28.154294089006726, 28.355741470004432, 28.71203763000085, 28.931574468006147, 29.164977904001717, 29.363440007989993, 29.5702488120005, 29.8512691789947, 30.101471366011538, 30.310745497001335, 32.004218265006784, 33.50335030199494, 34.96903544300585, 36.74690315898624, 36.96303538099164, 37.17741673300043, 37.41295213898411, 37.66523804501048, 37.88733551898622, 38.11805283499416, 38.390439902985236, 38.58656119299121, 39.043486090988154, 39.249697869003285, 39.467651521990774, 41.00770620300318, 42.36208155300119, 43.689884113002336], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20]}}, {'solution_x': array([ 9.20676289, 45.59590683, 22.9510348 ]), 'solution_criterion': 0.6411981796789968, 'states': [State(trustregion=Region(center=array([ 9.27512208, 46.36962222, 22.93103984]), radius=4.6369622223167655, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6414958683212747, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=14, candidate_x=array([ 9.34375658, 52.7552565 , 24.74194456]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.12278291672953444, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 1, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=1.2661012679622583, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=0.574745021508885, linear_terms=array([-0.01448034, 0.00480865, -0.00061109]), square_terms=array([[ 1.59580540e-01, 2.04832341e-03, -4.08843446e-04], + [ 2.04832341e-03, 1.90184721e-04, -4.96063564e-05], + [-4.08843446e-04, -4.96063564e-05, 1.36817947e-05]]), scale=1.2661012679622583, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=15, candidate_x=array([ 9.3343088 , 49.38777224, 26.2936056 ]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.09328618662280141, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14]), old_indices_discarded=array([ 1, 4, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.6330506339811292, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6403286802894971, linear_terms=array([-3.81858233e-04, 7.65007951e-05, -4.10301280e-05]), square_terms=array([[ 3.62463318e-02, -4.15487066e-06, -2.26071914e-05], + [-4.15487066e-06, 3.00843205e-08, 2.19045970e-08], + [-2.26071914e-05, 2.19045970e-08, 7.32549810e-08]]), scale=0.6330506339811292, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3625,12 +3888,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=0, candidate_x=array([ 9.27512208, 46.36962222, 22.93103984]), index=0, x=array([ 9.27512208, 46.36962222, 22.93103984]), fval=0.6414958683212746, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.27512208, 46.36962222, 22.93103984]), radius=4.6369622223167655, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5881261163934798, linear_terms=array([-0.01066396, -0.00125612, -0.00395789]), square_terms=array([[ 2.09022435e+00, -7.43148824e-03, -4.96555300e-03], - [-7.43148824e-03, 3.88929180e-05, 4.45740439e-05], - [-4.96555300e-03, 4.45740439e-05, 1.50833837e-04]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=28, candidate_x=array([ 9.21355497, 50.0866759 , 26.43770107]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.9705446696147382, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.3165253169905646, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27]), model=ScalarModel(intercept=0.6404706538725462, linear_terms=array([-5.67345184e-04, 2.74265158e-04, 9.56828002e-05]), square_terms=array([[9.01546888e-03, 1.04773684e-05, 2.47603794e-06], + [1.04773684e-05, 1.74310737e-07, 6.31897341e-08], + [2.47603794e-06, 6.31897341e-08, 2.70525471e-08]]), scale=0.3165253169905646, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3712,12 +3975,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=13, candidate_x=array([ 9.31433205, 47.82012341, 27.33908379]), index=0, x=array([ 9.27512208, 46.36962222, 22.93103984]), fval=0.6414958683212746, rho=-0.06744979380670008, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27512208, 46.36962222, 22.93103984]), radius=2.3184811111583827, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12]), model=ScalarModel(intercept=0.5921591562782017, linear_terms=array([ 2.93089963e-03, -4.76516299e-03, 4.03398872e-05]), square_terms=array([[ 5.30904188e-01, -5.61712335e-03, 5.76543930e-04], - [-5.61712335e-03, 1.61886965e-04, 6.50802775e-06], - [ 5.76543930e-04, 6.50802775e-06, 3.52139536e-06]]), scale=2.3184811111583827, shift=array([ 9.27512208, 46.36962222, 22.93103984])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=29, candidate_x=array([ 9.22643862, 50.34519023, 26.0326932 ]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.6661027359823584, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27]), old_indices_discarded=array([15, 24, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.1582626584952823, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 29]), model=ScalarModel(intercept=0.6404784832040219, linear_terms=array([-3.15520037e-04, 1.63853796e-04, 9.20223976e-05]), square_terms=array([[2.25226929e-03, 3.23332981e-06, 1.70120380e-06], + [3.23332981e-06, 6.16654786e-08, 3.52690733e-08], + [1.70120380e-06, 3.52690733e-08, 2.15873630e-08]]), scale=0.1582626584952823, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3799,12 +4062,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=14, candidate_x=array([ 9.28679021, 48.6892354 , 22.90614115]), index=0, x=array([ 9.27512208, 46.36962222, 22.93103984]), fval=0.6414958683212746, rho=-0.024819974400623245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12]), old_indices_discarded=array([11, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27512208, 46.36962222, 22.93103984]), radius=1.1592405555791914, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14]), model=ScalarModel(intercept=0.5957412470858591, linear_terms=array([ 0.00248444, -0.00089163, -0.00022855]), square_terms=array([[ 1.32806446e-01, -8.94595704e-04, 1.75001583e-04], - [-8.94595704e-04, 1.45793768e-05, -1.42593512e-06], - [ 1.75001583e-04, -1.42593512e-06, 6.46034765e-07]]), scale=1.1592405555791914, shift=array([ 9.27512208, 46.36962222, 22.93103984])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=30, candidate_x=array([ 9.22747482, 50.50608883, 26.05940528]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.9680679386475163, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 29]), old_indices_discarded=array([24, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.07913132924764114, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.6414058023823169, linear_terms=array([-5.22656412e-05, 8.88275468e-06, -2.13975506e-05]), square_terms=array([[ 5.67797994e-04, -1.08779162e-07, -6.52765214e-07], + [-1.08779162e-07, 1.75466701e-09, 2.24298365e-10], + [-6.52765214e-07, 2.24298365e-10, 3.40606226e-09]]), scale=0.07913132924764114, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3886,12 +4149,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=15, candidate_x=array([ 9.26068266, 47.48958436, 23.2307316 ]), index=0, x=array([ 9.27512208, 46.36962222, 22.93103984]), fval=0.6414958683212746, rho=-0.08922847434749884, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14]), old_indices_discarded=array([ 2, 11, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27512208, 46.36962222, 22.93103984]), radius=0.5796202777895957, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6406308340870236, linear_terms=array([ 2.46442757e-03, 2.37028145e-04, -1.79444704e-05]), square_terms=array([[ 3.02073781e-02, 1.65031915e-05, 5.41504947e-06], - [ 1.65031915e-05, 1.22265702e-07, -8.95972132e-09], - [ 5.41504947e-06, -8.95972132e-09, 3.91122560e-08]]), scale=0.5796202777895957, shift=array([ 9.27512208, 46.36962222, 22.93103984])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=43, candidate_x=array([ 9.21387432, 50.61173749, 26.21500275]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-3.2197933365114784, accepted=False, new_indices=array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.03956566462382057, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6414136708984267, linear_terms=array([-2.98689783e-05, 9.90591607e-06, -1.50732791e-05]), square_terms=array([[ 1.41976539e-04, 2.95346995e-09, -1.05937432e-07], + [ 2.95346995e-09, 5.73210469e-09, -2.07901137e-10], + [-1.05937432e-07, -2.07901137e-10, 1.02469176e-09]]), scale=0.03956566462382057, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -3973,12 +4236,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=28, candidate_x=array([ 9.2285044 , 45.73915809, 22.98019649]), index=28, x=array([ 9.2285044 , 45.73915809, 22.98019649]), fval=0.6413920207839838, rho=0.2897773418727435, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.6340935281815585, relative_step_length=1.0939809259256745, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2285044 , 45.73915809, 22.98019649]), radius=1.1592405555791914, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.6408049220810728, linear_terms=array([ 2.84779294e-04, -7.53428271e-04, -4.79516493e-05]), square_terms=array([[ 1.20811694e-01, -7.93957752e-05, -3.19967655e-05], - [-7.93957752e-05, 1.20566655e-06, 6.57148266e-08], - [-3.19967655e-05, 6.57148266e-08, 1.19721232e-07]]), scale=1.1592405555791914, shift=array([ 9.2285044 , 45.73915809, 22.98019649])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=44, candidate_x=array([ 9.21417122, 50.6226981 , 26.16941327]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-4.183452628096946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42]), old_indices_discarded=array([30, 40, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.019782832311910286, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 39, 41, 42, 44]), model=ScalarModel(intercept=0.6414104317520569, linear_terms=array([-1.71941430e-05, 3.00680387e-06, -1.01831737e-05]), square_terms=array([[ 3.55162006e-05, 1.08509300e-08, -1.40682694e-08], + [ 1.08509300e-08, 8.26274781e-10, 9.37031761e-11], + [-1.40682694e-08, 9.37031761e-11, 4.64529280e-10]]), scale=0.019782832311910286, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4060,12 +4323,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=29, candidate_x=array([ 9.22656371, 46.89606098, 23.05376338]), index=28, x=array([ 9.2285044 , 45.73915809, 22.98019649]), fval=0.6413920207839838, rho=-0.015254435543014998, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2285044 , 45.73915809, 22.98019649]), radius=0.5796202777895957, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28]), model=ScalarModel(intercept=0.6409384183159138, linear_terms=array([ 1.61766905e-04, -5.10943921e-04, 6.04893594e-05]), square_terms=array([[ 3.01982385e-02, -4.69113992e-05, 8.73747171e-06], - [-4.69113992e-05, 5.89438354e-07, -8.80750748e-08], - [ 8.73747171e-06, -8.80750748e-08, 4.25622534e-08]]), scale=0.5796202777895957, shift=array([ 9.2285044 , 45.73915809, 22.98019649])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=45, candidate_x=array([ 9.21415784, 50.63844304, 26.1558486 ]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-5.991408867597847, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 39, 41, 42, 44]), old_indices_discarded=array([38, 40, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.009891416155955143, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412572249847813, linear_terms=array([-5.53927555e-06, 7.39160776e-06, 1.37452164e-05]), square_terms=array([[ 8.92072166e-06, -1.53858821e-08, -1.99561660e-08], + [-1.53858821e-08, 1.22760088e-10, 1.48921582e-10], + [-1.99561660e-08, 1.48921582e-10, 2.83311686e-10]]), scale=0.009891416155955143, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4147,12 +4410,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=30, candidate_x=array([ 9.22635001, 46.31476639, 22.91208979]), index=28, x=array([ 9.2285044 , 45.73915809, 22.98019649]), fval=0.6413920207839838, rho=-0.023574344078806802, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28]), old_indices_discarded=array([15, 19, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2285044 , 45.73915809, 22.98019649]), radius=0.28981013889479784, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 21, 23, 24, 25, 27, 28, 30]), model=ScalarModel(intercept=0.6409846094096279, linear_terms=array([ 7.15842404e-05, -2.42504599e-04, 9.71440835e-06]), square_terms=array([[ 7.54356286e-03, -1.39281443e-05, -4.55681710e-07], - [-1.39281443e-05, 1.45840224e-07, -4.80821837e-09], - [-4.55681710e-07, -4.80821837e-09, 1.00622502e-08]]), scale=0.28981013889479784, shift=array([ 9.2285044 , 45.73915809, 22.98019649])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=58, candidate_x=array([ 9.20896905, 50.63947734, 26.12836693]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-1.3730446696271148, accepted=False, new_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_used=array([ 0, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.0049457080779775715, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412595140316975, linear_terms=array([-2.27742736e-06, 7.78396411e-06, 1.06086832e-06]), square_terms=array([[ 2.22987352e-06, -4.48397478e-09, -4.11582136e-09], + [-4.48397478e-09, 1.10331897e-10, -8.93732402e-12], + [-4.11582136e-09, -8.93732402e-12, 4.14066802e-11]]), scale=0.0049457080779775715, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4234,12 +4497,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=31, candidate_x=array([ 9.22635723, 46.0287285 , 22.96858695]), index=28, x=array([ 9.2285044 , 45.73915809, 22.98019649]), fval=0.6413920207839838, rho=-0.04985791456251465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 21, 23, 24, 25, 27, 28, 30]), old_indices_discarded=array([19, 22, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2285044 , 45.73915809, 22.98019649]), radius=0.14490506944739892, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 25, 27, 28, 30, 31]), model=ScalarModel(intercept=0.6408971198477394, linear_terms=array([3.58738624e-04, 8.59942564e-05, 2.59392791e-05]), square_terms=array([[1.90666144e-03, 1.38680699e-06, 1.32065566e-06], - [1.38680699e-06, 1.93796783e-08, 5.05745577e-09], - [1.32065566e-06, 5.05745577e-09, 3.87895449e-09]]), scale=0.14490506944739892, shift=array([ 9.2285044 , 45.73915809, 22.98019649])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=59, candidate_x=array([ 9.20787121, 50.63927105, 26.1362217 ]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-1.7596015269275105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([45, 47, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.0024728540389887857, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 59]), model=ScalarModel(intercept=0.6412535531548258, linear_terms=array([-3.45240007e-06, 7.06290806e-06, -1.22931862e-06]), square_terms=array([[ 5.59064982e-07, -2.23550765e-09, -4.22062020e-10], + [-2.23550765e-09, 7.86359074e-11, -1.84433937e-11], + [-4.22062020e-10, -1.84433937e-11, 1.28062020e-11]]), scale=0.0024728540389887857, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4321,12 +4584,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=32, candidate_x=array([ 9.20259338, 45.60122914, 22.93885113]), index=32, x=array([ 9.20259338, 45.60122914, 22.93885113]), fval=0.6412746242553712, rho=0.9575028532009756, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 25, 27, 28, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.14630521557629694, relative_step_length=1.0096625061789593, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20259338, 45.60122914, 22.93885113]), radius=0.28981013889479784, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 23, 25, 27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.6411605508243214, linear_terms=array([-8.00766779e-04, -1.87173267e-04, -9.01420202e-05]), square_terms=array([[ 7.52619899e-03, -1.73444797e-05, -9.14217214e-06], - [-1.73444797e-05, 1.82296565e-07, 7.89294303e-08], - [-9.14217214e-06, 7.89294303e-08, 5.07950448e-08]]), scale=0.28981013889479784, shift=array([ 9.20259338, 45.60122914, 22.93885113])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=60, candidate_x=array([ 9.20781463, 50.64177496, 26.13726886]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-1.6880414428879142, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 59]), old_indices_discarded=array([47, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.0012364270194943929, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.6412050560114186, linear_terms=array([ 2.34403636e-06, -1.25504945e-06, -6.72756376e-07]), square_terms=array([[1.43031335e-07, 2.72638134e-10, 1.85921639e-10], + [2.72638134e-10, 3.58667531e-12, 2.26234004e-12], + [1.85921639e-10, 2.26234004e-12, 4.96051423e-12]]), scale=0.0012364270194943929, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4408,12 +4671,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=33, candidate_x=array([ 9.23332219, 45.86150172, 23.06431621]), index=32, x=array([ 9.20259338, 45.60122914, 22.93885113]), fval=0.6412746242553712, rho=-0.6705794028601634, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 23, 25, 27, 28, 30, 31, 32]), old_indices_discarded=array([19, 21, 22, 24, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20259338, 45.60122914, 22.93885113]), radius=0.14490506944739892, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 17, 25, 27, 28, 30, 31, 32, 33]), model=ScalarModel(intercept=0.6410877940168466, linear_terms=array([ 9.26976569e-06, -6.82886493e-06, 3.85534656e-05]), square_terms=array([[ 1.90630255e-03, -7.29591096e-07, 1.56080907e-06], - [-7.29591096e-07, 3.03219948e-09, -2.68050753e-09], - [ 1.56080907e-06, -2.68050753e-09, 5.16628251e-09]]), scale=0.14490506944739892, shift=array([ 9.20259338, 45.60122914, 22.93885113])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=73, candidate_x=array([ 9.20573979, 50.64464056, 26.13719284]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-4.720045973331271, accepted=False, new_indices=array([61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72]), old_indices_used=array([ 0, 59, 60]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.0006182135097471964, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.6412044150956402, linear_terms=array([-1.02496555e-07, 3.31982306e-07, -1.51152871e-07]), square_terms=array([[ 3.59804504e-08, -2.07244738e-11, 2.86410518e-11], + [-2.07244738e-11, 4.97044676e-13, -5.27497115e-13], + [ 2.86410518e-11, -5.27497115e-13, 7.64804027e-13]]), scale=0.0006182135097471964, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4495,12 +4758,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=34, candidate_x=array([ 9.20202687, 45.62648809, 22.79616498]), index=32, x=array([ 9.20259338, 45.60122914, 22.93885113]), fval=0.6412746242553712, rho=-0.13620936166915193, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 17, 25, 27, 28, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20259338, 45.60122914, 22.93885113]), radius=0.07245253472369946, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([28, 32, 33, 34]), model=ScalarModel(intercept=0.6412746242553713, linear_terms=array([ 1.51821685e-04, 1.72405095e-05, -2.51853925e-07]), square_terms=array([[4.74437325e-04, 9.62985593e-08, 5.90606931e-08], - [9.62985593e-08, 3.24675398e-09, 3.85599517e-10], - [5.90606931e-08, 3.85599517e-10, 1.13736902e-10]]), scale=0.07245253472369946, shift=array([ 9.20259338, 45.60122914, 22.93885113])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=74, candidate_x=array([ 9.206938 , 50.64348138, 26.13713187]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-5.245952537715729, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([60, 61, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.0003091067548735982, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 62, 63, 64, 65, 67, 68, 69, 70, 71, 73, 74]), model=ScalarModel(intercept=0.6412044828872996, linear_terms=array([-1.27443731e-07, 2.43052887e-07, 2.03585292e-07]), square_terms=array([[ 9.00189158e-09, -9.44681221e-12, -4.57998397e-12], + [-9.44681221e-12, 1.49391685e-13, 6.17187987e-14], + [-4.57998397e-12, 6.17187987e-14, 9.80244824e-14]]), scale=0.0003091067548735982, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4582,12 +4845,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=35, candidate_x=array([ 9.18023587, 45.52896803, 22.9399869 ]), index=32, x=array([ 9.20259338, 45.60122914, 22.93885113]), fval=0.6412746242553712, rho=-3.7502853225457637, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 32, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20259338, 45.60122914, 22.93885113]), radius=0.03622626736184973, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([28, 32, 34, 35]), model=ScalarModel(intercept=0.6412746242553709, linear_terms=array([-6.64869792e-04, 1.39932037e-04, 2.60605822e-05]), square_terms=array([[ 1.29547503e-04, -1.26581071e-06, -2.36967452e-07], - [-1.26581071e-06, 1.14410595e-07, 2.17757353e-08], - [-2.36967452e-07, 2.17757353e-08, 4.15737620e-09]]), scale=0.03622626736184973, shift=array([ 9.20259338, 45.60122914, 22.93885113])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=75, candidate_x=array([ 9.206892 , 50.64382791, 26.13668602]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-4.277350855267876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 62, 63, 64, 65, 67, 68, 69, 70, 71, 73, 74]), old_indices_discarded=array([61, 66, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=0.0001545533774367991, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), model=ScalarModel(intercept=0.6411989618472214, linear_terms=array([ 7.46758928e-07, -6.31964096e-08, 1.47212322e-07]), square_terms=array([[ 2.23261497e-09, 1.62015486e-12, -4.18063334e-12], + [ 1.62015486e-12, 1.10921156e-14, -2.69297169e-14], + [-4.18063334e-12, -2.69297169e-14, 6.72153720e-14]]), scale=0.0001545533774367991, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4669,12 +4932,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=36, candidate_x=array([ 9.23849715, 45.59719351, 22.94149216]), index=32, x=array([ 9.20259338, 45.60122914, 22.93885113]), fval=0.6412746242553712, rho=-0.40687960405411, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 32, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20259338, 45.60122914, 22.93885113]), radius=0.018113133680924865, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6413039707695232, linear_terms=array([ 1.78846812e-05, -2.43776919e-05, 6.28993170e-07]), square_terms=array([[ 2.97194280e-05, 1.17262526e-07, -9.61426736e-08], - [ 1.17262526e-07, 2.47166769e-09, 2.00862617e-10], - [-9.61426736e-08, 2.00862617e-10, 1.63762035e-09]]), scale=0.018113133680924865, shift=array([ 9.20259338, 45.60122914, 22.93885113])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=88, candidate_x=array([ 9.20662716, 50.64406369, 26.13684266]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.5859822328217689, accepted=False, new_indices=array([76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), old_indices_used=array([ 0, 74, 75]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=7.727668871839955e-05, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 76, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), model=ScalarModel(intercept=0.6411990081265565, linear_terms=array([3.25333457e-07, 3.30925020e-08, 1.33673466e-07]), square_terms=array([[ 5.59584480e-10, -6.31985828e-13, -1.91478111e-12], + [-6.31985828e-13, 6.07754261e-15, 1.67927703e-14], + [-1.91478111e-12, 1.67927703e-14, 5.52741188e-14]]), scale=7.727668871839955e-05, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4756,12 +5019,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=49, candidate_x=array([ 9.19659884, 45.61914756, 22.93836602]), index=32, x=array([ 9.20259338, 45.60122914, 22.93885113]), fval=0.6412746242553712, rho=-0.6855188838995444, accepted=False, new_indices=array([37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_used=array([32, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20259338, 45.60122914, 22.93885113]), radius=0.009056566840462433, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48]), model=ScalarModel(intercept=0.6412899966619717, linear_terms=array([-8.75824082e-06, -4.68883192e-07, -4.22220147e-07]), square_terms=array([[ 7.43343085e-06, 7.40486950e-10, -2.50861630e-08], - [ 7.40486950e-10, 4.06402636e-10, 3.14282337e-10], - [-2.50861630e-08, 3.14282337e-10, 4.97087023e-10]]), scale=0.009056566840462433, shift=array([ 9.20259338, 45.60122914, 22.93885113])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=89, candidate_x=array([ 9.20670708, 50.64404344, 26.13684336]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.6137415283588851, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 76, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), old_indices_discarded=array([75, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=3.863834435919978e-05, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 76, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89]), model=ScalarModel(intercept=0.6411989723184431, linear_terms=array([1.71778699e-07, 6.81085265e-09, 6.62630558e-08]), square_terms=array([[ 1.39760194e-10, -8.24931696e-14, -4.76972134e-13], + [-8.24931696e-14, 4.93577895e-16, 2.09595584e-15], + [-4.76972134e-13, 2.09595584e-15, 1.37545094e-14]]), scale=3.863834435919978e-05, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4843,12 +5106,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=50, candidate_x=array([ 9.21071609, 45.59986349, 22.94261649]), index=50, x=array([ 9.21071609, 45.59986349, 22.94261649]), fval=0.6412423314571071, rho=6.484931348405917, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([32, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48]), old_indices_discarded=array([36, 45, 49]), step_length=0.00905656684046146, relative_step_length=0.9999999999998925, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21071609, 45.59986349, 22.94261649]), radius=0.018113133680924865, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 37, 38, 39, 42, 43, 44, 45, 47, 48, 49, 50]), model=ScalarModel(intercept=0.6412781189403443, linear_terms=array([-2.11788424e-05, -3.70967270e-06, -2.31489028e-05]), square_terms=array([[2.98553111e-05, 2.06031515e-08, 1.49854249e-08], - [2.06031515e-08, 2.15439326e-09, 1.93523563e-09], - [1.49854249e-08, 1.93523563e-09, 2.51993300e-09]]), scale=0.018113133680924865, shift=array([ 9.21071609, 45.59986349, 22.94261649])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=90, candidate_x=array([ 9.2067422 , 50.64404928, 26.13685874]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.5098285694817388, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 76, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89]), old_indices_discarded=array([77, 78, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=1.931917217959989e-05, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, + 101, 102]), model=ScalarModel(intercept=0.6411982071451217, linear_terms=array([-6.05426987e-09, 8.12416294e-09, -5.19020430e-08]), square_terms=array([[ 3.55347134e-11, -2.91079862e-14, 1.85924509e-13], + [-2.91079862e-14, 1.88258283e-16, -1.20265313e-15], + [ 1.85924509e-13, -1.20265313e-15, 7.68292653e-15]]), scale=1.931917217959989e-05, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -4930,12 +5194,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=51, candidate_x=array([ 9.21780334, 45.60262928, 22.9599168 ]), index=50, x=array([ 9.21071609, 45.59986349, 22.94261649]), fval=0.6412423314571071, rho=-2.280933910129145, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 37, 38, 39, 42, 43, 44, 45, 47, 48, 49, 50]), old_indices_discarded=array([35, 36, 40, 41, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21071609, 45.59986349, 22.94261649]), radius=0.009056566840462433, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 37, 38, 39, 42, 44, 45, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.6412840152428855, linear_terms=array([ 1.53216145e-06, 8.28819200e-07, -1.52798571e-06]), square_terms=array([[7.46594783e-06, 5.70100161e-09, 7.34594153e-09], - [5.70100161e-09, 3.61079251e-10, 8.20620599e-11], - [7.34594153e-09, 8.20620599e-11, 1.00086054e-10]]), scale=0.009056566840462433, shift=array([ 9.21071609, 45.59986349, 22.94261649])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=103, candidate_x=array([ 9.20678043, 50.64404775, 26.13689162]), index=0, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830596, rho=-0.5410025828097851, accepted=False, new_indices=array([ 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102]), old_indices_used=array([ 0, 89, 90]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20677822, 50.64405072, 26.13687265]), radius=9.659586089799944e-06, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102]), model=ScalarModel(intercept=0.6411982194422793, linear_terms=array([ 7.59408746e-08, -1.57717395e-08, 2.68111814e-08]), square_terms=array([[ 8.61862000e-12, 2.46903340e-14, -4.19719973e-14], + [ 2.46903340e-14, 7.09360329e-16, -1.20587550e-15], + [-4.19719973e-14, -1.20587550e-15, 2.04992535e-15]]), scale=9.659586089799944e-06, shift=array([ 9.20677822, 50.64405072, 26.13687265])), vector_model=VectorModel(intercepts=array([ 0.04848823, 0.12360338, 0.14833629, 0.19329327, 0.2168361 , + 0.23171846, 0.23263925, 0.06582157, -0.08093788, -0.06795554, + -0.41009007, -0.4187331 , -0.12365488, -0.09727806, -0.08783687, + -0.09147742, -0.09757722]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5017,12 +5281,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=52, candidate_x=array([ 9.20919782, 45.59544441, 22.95078009]), index=52, x=array([ 9.20919782, 45.59544441, 22.95078009]), fval=0.6412216190982429, rho=10.70802381239341, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([32, 37, 38, 39, 42, 44, 45, 47, 48, 49, 50, 51]), old_indices_discarded=array([36, 40, 41, 43, 46]), step_length=0.009406262300279364, relative_step_length=1.0386123644839214, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20919782, 45.59544441, 22.95078009]), radius=0.018113133680924865, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 37, 39, 42, 43, 44, 45, 47, 48, 50, 51, 52]), model=ScalarModel(intercept=0.6412682696446266, linear_terms=array([-1.06086822e-05, 3.89954644e-05, -2.40744738e-05]), square_terms=array([[ 2.98828945e-05, -1.49158925e-08, 5.43108663e-08], - [-1.49158925e-08, 4.82998378e-09, -1.07526917e-09], - [ 5.43108663e-08, -1.07526917e-09, 1.09420463e-09]]), scale=0.018113133680924865, shift=array([ 9.20919782, 45.59544441, 22.95078009])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=5.064405071849033, shift=array([ 9.20677822, 50.64405072, 26.13687265])), candidate_index=104, candidate_x=array([ 9.20676928, 50.64405258, 26.1368695 ]), index=104, x=array([ 9.20676928, 50.64405258, 26.1368695 ]), fval=0.6411981573819835, rho=0.0085433573499533, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102]), old_indices_discarded=array([ 90, 100, 103]), step_length=9.659586090723239e-06, relative_step_length=1.0000000000955833, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Relative criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 105 entries., 'history': {'params': [{'CRRA': 9.20677821614649, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 10.62138634777998, 'BeqShift': 53.18382590637229, 'BeqFac': 21.98999134872144}, {'CRRA': 5.952441947753802, 'BeqShift': 53.01383796182259, 'BeqFac': 23.064153564193404}, {'CRRA': 4.902870205831076, 'BeqShift': 52.85455014320962, 'BeqFac': 27.63295649658987}, {'CRRA': 8.722882056285245, 'BeqShift': 45.6029051018246, 'BeqFac': 26.106970984998675}, {'CRRA': 8.448933950499514, 'BeqShift': 48.75682561319241, 'BeqFac': 30.775002902649977}, {'CRRA': 4.748830124351252, 'BeqShift': 48.27700677057874, 'BeqFac': 26.551602073439014}, {'CRRA': 11.470745039368346, 'BeqShift': 55.157047694543834, 'BeqFac': 25.74252350769399}, {'CRRA': 13.250019454248527, 'BeqShift': 49.34005431447091, 'BeqFac': 23.380064281957736}, {'CRRA': 8.536071426058779, 'BeqShift': 54.112606635851016, 'BeqFac': 29.76557365879496}, {'CRRA': 7.969648472765358, 'BeqShift': 48.222064134995385, 'BeqFac': 21.864673019323945}, {'CRRA': 12.838186628009867, 'BeqShift': 47.49385827352681, 'BeqFac': 27.72978170286549}, {'CRRA': 13.123636545974042, 'BeqShift': 51.98586556654233, 'BeqFac': 29.05336926291726}, {'CRRA': 9.328288811521322, 'BeqShift': 52.30084004630286, 'BeqFac': 30.93946474925285}, {'CRRA': 9.343756575818412, 'BeqShift': 52.75525650026588, 'BeqFac': 24.741944555771756}, {'CRRA': 9.334308801392456, 'BeqShift': 49.387772243797045, 'BeqFac': 26.2936056024445}, {'CRRA': 8.760571973596782, 'BeqShift': 50.30760657564745, 'BeqFac': 26.434292278581136}, {'CRRA': 9.328683110257387, 'BeqShift': 50.30510627782382, 'BeqFac': 26.65745787103651}, {'CRRA': 9.748277486657958, 'BeqShift': 50.38059642564924, 'BeqFac': 26.332125005670985}, {'CRRA': 9.077214335092005, 'BeqShift': 51.26219335559197, 'BeqFac': 26.180069715400887}, {'CRRA': 9.746359418873869, 'BeqShift': 50.66350940890234, 'BeqFac': 25.80637819086352}, {'CRRA': 9.125051342937237, 'BeqShift': 50.86969918511925, 'BeqFac': 26.722668346180594}, {'CRRA': 9.635965911796614, 'BeqShift': 51.045910261006476, 'BeqFac': 26.371521189497667}, {'CRRA': 8.845999643449264, 'BeqShift': 50.74567724438737, 'BeqFac': 25.62671191871925}, {'CRRA': 8.621845204500444, 'BeqShift': 50.86614021326763, 'BeqFac': 26.23321959684178}, {'CRRA': 9.389211075575094, 'BeqShift': 50.20828259860967, 'BeqFac': 25.715473196696642}, {'CRRA': 9.342351647280148, 'BeqShift': 51.040336661189684, 'BeqFac': 25.66218285897537}, {'CRRA': 8.84744649935771, 'BeqShift': 50.19329116801906, 'BeqFac': 25.87524381363203}, {'CRRA': 9.213554965716096, 'BeqShift': 50.08667589790345, 'BeqFac': 26.437701072116173}, {'CRRA': 9.226438620871706, 'BeqShift': 50.34519022669459, 'BeqFac': 26.032693199582265}, {'CRRA': 9.227474819586835, 'BeqShift': 50.50608883094321, 'BeqFac': 26.059405283428845}, {'CRRA': 9.176666394120378, 'BeqShift': 50.60774138445433, 'BeqFac': 26.20040750832727}, {'CRRA': 9.241289824494036, 'BeqShift': 50.71388261079487, 'BeqFac': 26.122936263764718}, {'CRRA': 9.245558372919803, 'BeqShift': 50.65869736632045, 'BeqFac': 26.06946833361827}, {'CRRA': 9.189054653966641, 'BeqShift': 50.62580677815847, 'BeqFac': 26.061940676568373}, {'CRRA': 9.135981171707446, 'BeqShift': 50.64428166197232, 'BeqFac': 26.101524493837513}, {'CRRA': 9.249436594210819, 'BeqShift': 50.600627715943894, 'BeqFac': 26.187434229401074}, {'CRRA': 9.142396507953219, 'BeqShift': 50.668142570925106, 'BeqFac': 26.17606889834683}, {'CRRA': 9.167513685915457, 'BeqShift': 50.575350486736966, 'BeqFac': 26.13628776838735}, {'CRRA': 9.260284160849391, 'BeqShift': 50.5894087163105, 'BeqFac': 26.116546986861948}, {'CRRA': 9.175696389543402, 'BeqShift': 50.71474954331307, 'BeqFac': 26.11962842338367}, {'CRRA': 9.216088797081158, 'BeqShift': 50.6865232054343, 'BeqFac': 26.202987464852843}, {'CRRA': 9.281865975890593, 'BeqShift': 50.661945692436305, 'BeqFac': 26.154290045172583}, {'CRRA': 9.213874319135748, 'BeqShift': 50.611737492880735, 'BeqFac': 26.215002753008797}, {'CRRA': 9.214171216471302, 'BeqShift': 50.62269809700905, 'BeqFac': 26.16941326628907}, {'CRRA': 9.214157837580217, 'BeqShift': 50.638443043460285, 'BeqFac': 26.155848596443303}, {'CRRA': 9.20990514348597, 'BeqShift': 50.64951793673829, 'BeqFac': 26.144499708790974}, {'CRRA': 9.20972149939538, 'BeqShift': 50.65049077209843, 'BeqFac': 26.12996592498034}, {'CRRA': 9.215876886913987, 'BeqShift': 50.64159021353402, 'BeqFac': 26.133872617544522}, {'CRRA': 9.205348256892893, 'BeqShift': 50.63442148307648, 'BeqFac': 26.13511961230662}, {'CRRA': 9.214295617997292, 'BeqShift': 50.650310796388496, 'BeqFac': 26.138335598946806}, {'CRRA': 9.200390663072893, 'BeqShift': 50.64445776884281, 'BeqFac': 26.129331196040532}, {'CRRA': 9.202014556109866, 'BeqShift': 50.652580506743, 'BeqFac': 26.135326572258037}, {'CRRA': 9.200934030677926, 'BeqShift': 50.64794479033814, 'BeqFac': 26.14383841420929}, {'CRRA': 9.203546425206072, 'BeqShift': 50.63840182372465, 'BeqFac': 26.14432152169638}, {'CRRA': 9.197255600983837, 'BeqShift': 50.64139339601146, 'BeqFac': 26.137186582854696}, {'CRRA': 9.213212829206885, 'BeqShift': 50.637823009454294, 'BeqFac': 26.14107402391244}, {'CRRA': 9.208575115270197, 'BeqShift': 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50.64404928146958, 'BeqFac': 26.136858744231954}, {'CRRA': 9.206779304489483, 'BeqShift': 50.64406401256287, 'BeqFac': 26.13685867861407}, {'CRRA': 9.206796029438973, 'BeqShift': 50.64405197691585, 'BeqFac': 26.13686528295817}, {'CRRA': 9.206777620452703, 'BeqShift': 50.6440356023814, 'BeqFac': 26.13686063830327}, {'CRRA': 9.206777035014948, 'BeqShift': 50.644069273987114, 'BeqFac': 26.136867407279585}, {'CRRA': 9.206781076491463, 'BeqShift': 50.6440511835528, 'BeqFac': 26.136853553486304}, {'CRRA': 9.206784978578296, 'BeqShift': 50.64406730274076, 'BeqFac': 26.136879897181625}, {'CRRA': 9.206763642611994, 'BeqShift': 50.64403811399626, 'BeqFac': 26.136871250782363}, {'CRRA': 9.206767485336034, 'BeqShift': 50.64404308963533, 'BeqFac': 26.13688679199362}, {'CRRA': 9.20678768834861, 'BeqShift': 50.64403898233073, 'BeqFac': 26.136860580537196}, {'CRRA': 9.206783870255052, 'BeqShift': 50.64403584262765, 'BeqFac': 26.13686170098683}, {'CRRA': 9.206763358828995, 'BeqShift': 50.64404818940974, 'BeqFac': 26.136884741023557}, {'CRRA': 9.206787797369545, 'BeqShift': 50.64406700488012, 'BeqFac': 26.13686863113188}, {'CRRA': 9.206780427125146, 'BeqShift': 50.64404774951627, 'BeqFac': 26.136891621670163}, {'CRRA': 9.206769277589977, 'BeqShift': 50.64405257528418, 'BeqFac': 26.13686949779299}], 'criterion': [0.6411981580830596, 0.7112487684247719, 1.1362144678464896, 1.618853894223651, 0.6502215795184842, 0.6631744082852923, 1.7126505134440446, 0.8194333786332193, 1.223261450990546, 0.6582251219118612, 0.7021232581674297, 1.1040120628042465, 1.1852970059241923, 0.6416931580694478, 0.641782582874636, 0.6417211210625294, 0.6488353412030428, 0.6416954307095081, 0.651691541037559, 0.6422030835568494, 0.651618212235352, 0.6419173117452164, 0.6477684829517987, 0.6464258162559123, 0.6543086081644518, 0.6423192435683919, 0.6417621507791378, 0.6463765541833686, 0.6412844681152551, 0.6414038946914774, 0.6414017609573774, 0.6414155041931758, 0.6415467160080115, 0.6415638678381014, 0.6413162380876546, 0.6418396023619015, 0.641584500720459, 0.641744153519646, 0.6414432213900426, 0.641577579379121, 0.6414178472692312, 0.6412939928191069, 0.6415295735512484, 0.6412857689611132, 0.6412854285151801, 0.6412854467718407, 0.6412291869379874, 0.6412270033627487, 0.6412925901777091, 0.641218002929169, 0.6412855560081665, 0.6412899163125819, 0.6412800332048714, 0.6412853463812271, 0.6412583454978733, 0.6412893601766347, 0.641280345193484, 0.6412183587368531, 0.6412204559669911, 0.6412124284401708, 0.6412118209784493, 0.6412084161441574, 0.6412059184461134, 0.641211007134014, 0.6412008117851546, 0.6412028790315208, 0.6412114804337654, 0.6412124687093406, 0.6411991708717584, 0.6412013840163135, 0.6412004615671938, 0.6412111691890182, 0.6411995200607582, 0.641210861154327, 0.6412002270587622, 0.6411996312912115, 0.6412000500964, 0.641198317315905, 0.6411985678810963, 0.6411984331103835, 0.6411989523728293, 0.6412000795936361, 0.6411986082676019, 0.6411985579964115, 0.6411994463372177, 0.6411987258501974, 0.6411985812034867, 0.6411998069125714, 0.6411986050019289, 0.6411983747594618, 0.6411982519839904, 0.641198172170226, 0.6411983886648773, 0.6411981503726285, 0.6411981427949761, 0.6411981951066646, 0.6411982456153995, 0.6411981770619077, 0.6411981636410624, 0.6411982806914908, 0.6411982312691004, 0.6411981780530825, 0.6411982821027037, 0.6411981867013665, 0.6411981573819835], 'runtime': [0.0, 1.741092655999637, 1.9511895269997694, 2.150650272999883, 2.364944698999352, 2.612510132999887, 2.848578797999835, 3.0754288159996577, 3.3325294259993825, 3.6003782119996686, 3.9060686829998303, 4.234876341999552, 4.505377958999816, 6.018324919999941, 7.404840139999578, 8.7243116869995, 10.5747867759992, 10.764527328999975, 10.963280076999581, 11.157741928999712, 11.376654062999478, 11.634703972999887, 11.925132875999225, 12.19573415099967, 12.40920004399959, 12.62536381099926, 12.82942404699952, 13.033726185999512, 14.471324528999503, 15.804259841999738, 17.20924835599999, 18.9265819809998, 19.123914522999257, 19.340027077999366, 19.530735990999347, 19.76851599099973, 20.117825712000013, 20.34463118199983, 20.594751979999273, 20.819601956000042, 21.0492069539996, 21.248313412999778, 21.498196271999404, 23.018266518999553, 24.32021637299931, 25.641691206999894, 27.373447652999857, 27.590111434999926, 27.80535423099991, 28.033494462999442, 28.257874373999584, 28.493307226999605, 28.706003990999307, 28.927894646999448, 29.14462390099925, 29.39457991299969, 29.590416320999793, 29.813295047999418, 31.324800030000006, 32.843812919999436, 34.198620593999294, 35.88395878599931, 36.08372781499929, 36.2910078799996, 36.51809983599924, 36.717287655999826, 36.96291232799922, 37.17789251799968, 37.42308125399995, 37.63693642999988, 37.859192443999746, 38.06126696899992, 38.28311381399999, 39.81690756599983, 41.19128538099994, 42.54764601899933, 44.324595346000024, 44.53997478399924, 44.76005411599999, 44.962844829999995, 45.16980529899956, 45.378680891999466, 45.59922934199949, 45.831447233999825, 46.04689413199958, 46.273181599000054, 46.49314191299982, 46.72520658299982, 48.32034132699937, 49.62858914699973, 50.94919961699998, 52.57772140799989, 52.931281673999365, 53.154263189999256, 53.35307114199986, 53.572895979999885, 53.821950480999476, 54.066166519000035, 54.32157505699979, 54.51904240299973, 54.75313015099982, 54.949440847000005, 55.18286510300004, 56.762109994999264, 58.09238937800001], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 23, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 27]}}, {'solution_x': array([ 9.20677586, 45.64298428, 23.05054873]), 'solution_criterion': 0.6411981344087744, 'states': [State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=4.5881190537852365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6414954627541696, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5104,12 +5368,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=53, candidate_x=array([ 9.21171307, 45.58016496, 22.96021119]), index=52, x=array([ 9.20919782, 45.59544441, 22.95078009]), fval=0.6412216190982429, rho=-0.7048640670081282, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 37, 39, 42, 43, 44, 45, 47, 48, 50, 51, 52]), old_indices_discarded=array([35, 36, 38, 40, 41, 46, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20919782, 45.59544441, 22.95078009]), radius=0.009056566840462433, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 37, 39, 42, 44, 45, 47, 48, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6412725618779757, linear_terms=array([-1.25153127e-06, 1.15292460e-05, -7.79485816e-06]), square_terms=array([[7.46927647e-06, 6.54768333e-09, 7.38429733e-09], - [6.54768333e-09, 6.79583317e-10, 1.04654917e-11], - [7.38429733e-09, 1.04654917e-11, 1.48042845e-10]]), scale=0.009056566840462433, shift=array([ 9.20919782, 45.59544441, 22.95078009])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=0, candidate_x=array([ 9.27523039, 45.88119054, 23.01481109]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=4.5881190537852365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5891883650231197, linear_terms=array([-0.00968953, -0.00117103, -0.00391564]), square_terms=array([[ 2.04288848e+00, -7.00381948e-03, -4.70750143e-03], + [-7.00381948e-03, 3.59762915e-05, 4.06366460e-05], + [-4.70750143e-03, 4.06366460e-05, 1.41716415e-04]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5191,12 +5455,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=54, candidate_x=array([ 9.20972781, 45.58795377, 22.95584422]), index=52, x=array([ 9.20919782, 45.59544441, 22.95078009]), fval=0.6412216190982429, rho=-0.3919786874561503, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 37, 39, 42, 44, 45, 47, 48, 50, 51, 52, 53]), old_indices_discarded=array([36, 38, 40, 41, 43, 46, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20919782, 45.59544441, 22.95078009]), radius=0.004528283420231216, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([32, 37, 42, 44, 45, 47, 48, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.6412695031423565, linear_terms=array([-5.52474194e-07, 6.85950407e-06, -5.63927150e-06]), square_terms=array([[ 1.86711180e-06, 1.32728269e-09, 2.80810292e-09], - [ 1.32728269e-09, 1.78679129e-10, -2.22631529e-11], - [ 2.80810292e-09, -2.22631529e-11, 4.90120302e-11]]), scale=0.004528283420231216, shift=array([ 9.20919782, 45.59544441, 22.95078009])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=13, candidate_x=array([ 9.31167836, 47.2378392 , 27.40054401]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=-0.061531136949691, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=2.2940595268926183, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5838720419935737, linear_terms=array([-0.00815151, 0.00777168, -0.0017958 ]), square_terms=array([[ 5.11958440e-01, 4.80442275e-03, -1.12028161e-03], + [ 4.80442275e-03, 3.51638936e-04, -1.06350122e-04], + [-1.12028161e-03, -1.06350122e-04, 3.29771720e-05]]), scale=2.2940595268926183, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5278,12 +5542,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=55, candidate_x=array([ 9.20943004, 45.59195102, 22.95365198]), index=52, x=array([ 9.20919782, 45.59544441, 22.95078009]), fval=0.6412216190982429, rho=-0.1964321529222822, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([32, 37, 42, 44, 45, 47, 48, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20919782, 45.59544441, 22.95078009]), radius=0.002264141710115608, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([37, 42, 50, 52, 54, 55]), model=ScalarModel(intercept=0.6412192039265117, linear_terms=array([-1.70585100e-05, -1.91702725e-05, -2.07860044e-05]), square_terms=array([[4.77656571e-07, 4.70371007e-09, 6.34361770e-09], - [4.70371007e-09, 6.95489985e-10, 7.38921113e-10], - [6.34361770e-09, 7.38921113e-10, 8.02053934e-10]]), scale=0.002264141710115608, shift=array([ 9.20919782, 45.59544441, 22.95078009])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=14, candidate_x=array([ 9.33305191, 43.63838444, 23.52526167]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=-0.02774497157353281, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 4, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=1.1470297634463091, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 14]), model=ScalarModel(intercept=0.592708784872857, linear_terms=array([ 0.00152966, -0.00076228, 0.00025576]), square_terms=array([[ 1.29771760e-01, -7.60521227e-04, 2.29395773e-04], + [-7.60521227e-04, 6.85822163e-06, -1.35713831e-06], + [ 2.29395773e-04, -1.35713831e-06, 6.87591617e-07]]), scale=1.1470297634463091, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5365,12 +5629,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=56, candidate_x=array([ 9.21035845, 45.59676801, 22.95221521]), index=52, x=array([ 9.20919782, 45.59544441, 22.95078009]), fval=0.6412216190982429, rho=-0.4353628908997743, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([37, 42, 50, 52, 54, 55]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20919782, 45.59544441, 22.95078009]), radius=0.001132070855057804, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.6412231453306527, linear_terms=array([9.79372427e-06, 8.30626977e-07, 4.41893963e-07]), square_terms=array([[ 1.08649853e-07, -1.87783273e-10, -4.03006738e-11], - [-1.87783273e-10, 4.59613271e-12, 1.92334535e-12], - [-4.03006738e-11, 1.92334535e-12, 8.81906257e-13]]), scale=0.001132070855057804, shift=array([ 9.20919782, 45.59544441, 22.95078009])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=15, candidate_x=array([ 9.2687674 , 46.96825733, 22.64867805]), index=0, x=array([ 9.27523039, 45.88119054, 23.01481109]), fval=0.6414954627541696, rho=-0.048166814746878124, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 14]), old_indices_discarded=array([ 4, 11, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=0.5735148817231546, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6406974663539128, linear_terms=array([ 2.50388822e-03, -3.55470721e-05, 1.59802574e-05]), square_terms=array([[ 2.95828916e-02, -9.48578252e-06, 1.81188466e-06], + [-9.48578252e-06, 2.35871761e-08, 1.80023429e-08], + [ 1.81188466e-06, 1.80023429e-08, 6.07710911e-08]]), scale=0.5735148817231546, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5452,12 +5716,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=69, candidate_x=array([ 9.20807174, 45.59534284, 22.95072352]), index=69, x=array([ 9.20807174, 45.59534284, 22.95072352]), fval=0.6412143824581863, rho=0.739578726104902, accepted=True, new_indices=array([57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), old_indices_used=array([52, 55, 56]), old_indices_discarded=array([], dtype=int64), step_length=0.0011320708550585452, relative_step_length=1.0000000000006546, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20807174, 45.59534284, 22.95072352]), radius=0.002264141710115608, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 57, 58, 59, 60, 61, 62, 63, 65, 67, 68, 69]), model=ScalarModel(intercept=0.6412131794518777, linear_terms=array([ 1.94153193e-05, 3.55006418e-07, -8.32585471e-08]), square_terms=array([[ 4.34534062e-07, -3.23553362e-10, 9.72098765e-10], - [-3.23553362e-10, 1.06006138e-12, -2.24653152e-12], - [ 9.72098765e-10, -2.24653152e-12, 7.26875070e-12]]), scale=0.002264141710115608, shift=array([ 9.20807174, 45.59534284, 22.95072352])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=28, candidate_x=array([ 9.22693263, 46.40381893, 22.77679268]), index=28, x=array([ 9.22693263, 46.40381893, 22.77679268]), fval=0.6414015158827587, rho=0.6515618478573166, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.5763036284627624, relative_step_length=1.0048625534026752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22693263, 46.40381893, 22.77679268]), radius=1.1470297634463091, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 15, 16, 17, 18, 20, 21, 22, 24, 25, 27, 28]), model=ScalarModel(intercept=0.6408041108663739, linear_terms=array([ 0.00065102, 0.00049794, -0.00019907]), square_terms=array([[ 1.18433148e-01, 1.13521966e-04, -6.65681444e-05], + [ 1.13521966e-04, 8.00378364e-07, -1.25371889e-07], + [-6.65681444e-05, -1.25371889e-07, 3.16174660e-07]]), scale=1.1470297634463091, shift=array([ 9.22693263, 46.40381893, 22.77679268])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5539,12 +5803,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=70, candidate_x=array([ 9.20580884, 45.59527628, 22.95075811]), index=70, x=array([ 9.20580884, 45.59527628, 22.95075811]), fval=0.6412099333022344, rho=0.23173453857046095, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([52, 57, 58, 59, 60, 61, 62, 63, 65, 67, 68, 69]), old_indices_discarded=array([37, 42, 50, 54, 55, 56, 64, 66]), step_length=0.00226414171011655, relative_step_length=1.0000000000004161, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20580884, 45.59527628, 22.95075811]), radius=0.004528283420231216, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 70]), model=ScalarModel(intercept=0.6412064122584149, linear_terms=array([ 2.26657859e-05, -3.18696222e-06, 2.54922192e-06]), square_terms=array([[ 1.82543914e-06, 1.06656974e-08, -3.02946807e-09], - [ 1.06656974e-08, 2.19743985e-10, -5.76034095e-11], - [-3.02946807e-09, -5.76034095e-11, 2.90254095e-11]]), scale=0.004528283420231216, shift=array([ 9.20580884, 45.59527628, 22.95075811])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=29, candidate_x=array([ 9.22191036, 45.33875132, 23.20257127]), index=29, x=array([ 9.22191036, 45.33875132, 23.20257127]), fval=0.6413556371977909, rho=0.08543637029916488, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 15, 16, 17, 18, 20, 21, 22, 24, 25, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 23, 26]), step_length=1.1470316679451833, relative_step_length=1.0000016603744164, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22191036, 45.33875132, 23.20257127]), radius=0.5735148817231546, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 29]), model=ScalarModel(intercept=0.6408171976152108, linear_terms=array([-6.73581006e-05, -4.04978148e-04, -3.80738956e-05]), square_terms=array([[ 2.95973821e-02, -3.63270062e-05, 1.65796041e-06], + [-3.63270062e-05, 4.23472225e-07, 4.40106549e-08], + [ 1.65796041e-06, 4.40106549e-08, 5.10964732e-08]]), scale=0.5735148817231546, shift=array([ 9.22191036, 45.33875132, 23.20257127])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5626,12 +5890,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=71, candidate_x=array([ 9.20139767, 45.59606286, 22.9501037 ]), index=70, x=array([ 9.20580884, 45.59527628, 22.95075811]), fval=0.6412099333022344, rho=-3.284681929947907, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([52, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 70]), old_indices_discarded=array([32, 37, 42, 44, 45, 47, 48, 50, 51, 53, 54, 55, 56, 65, 66, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20580884, 45.59527628, 22.95075811]), radius=0.002264141710115608, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 57, 58, 60, 62, 63, 64, 65, 66, 68, 69, 70]), model=ScalarModel(intercept=0.6412079518224751, linear_terms=array([ 8.65500838e-06, 3.77131685e-07, -1.47696736e-06]), square_terms=array([[ 4.64008756e-07, -3.22180177e-10, 3.94868556e-09], - [-3.22180177e-10, 1.69138274e-12, -1.39191756e-12], - [ 3.94868556e-09, -1.39191756e-12, 7.67133561e-11]]), scale=0.002264141710115608, shift=array([ 9.20580884, 45.59527628, 22.95075811])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=30, candidate_x=array([ 9.22388626, 45.90974892, 23.25621668]), index=29, x=array([ 9.22191036, 45.33875132, 23.20257127]), fval=0.6413556371977909, rho=-0.04787806421435067, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 29]), old_indices_discarded=array([14, 15, 19, 26, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22191036, 45.33875132, 23.20257127]), radius=0.2867574408615773, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 20, 21, 22, 24, 25, 27, 29, 30]), model=ScalarModel(intercept=0.6410097550441288, linear_terms=array([ 1.39651156e-05, -2.88874413e-04, 1.60170799e-04]), square_terms=array([[ 7.40015236e-03, -8.47567918e-06, 4.66545233e-06], + [-8.47567918e-06, 1.73700052e-07, -8.46835155e-08], + [ 4.66545233e-06, -8.46835155e-08, 6.84200563e-08]]), scale=0.2867574408615773, shift=array([ 9.22191036, 45.33875132, 23.20257127])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5713,12 +5977,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=72, candidate_x=array([ 9.20359224, 45.5951304 , 22.95119601]), index=70, x=array([ 9.20580884, 45.59527628, 22.95075811]), fval=0.6412099333022344, rho=-5.550713913269525, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([52, 57, 58, 60, 62, 63, 64, 65, 66, 68, 69, 70]), old_indices_discarded=array([37, 50, 54, 55, 56, 59, 61, 67, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20580884, 45.59527628, 22.95075811]), radius=0.001132070855057804, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 57, 58, 60, 62, 63, 64, 65, 66, 69, 70, 72]), model=ScalarModel(intercept=0.6412309039854828, linear_terms=array([-4.88020617e-06, 1.30537143e-06, 2.03420926e-06]), square_terms=array([[ 1.18870178e-07, -4.72177435e-10, 1.32325984e-10], - [-4.72177435e-10, 4.20642500e-12, 1.33038186e-12], - [ 1.32325984e-10, 1.33038186e-12, 7.96019463e-12]]), scale=0.001132070855057804, shift=array([ 9.20580884, 45.59527628, 22.95075811])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=31, candidate_x=array([ 9.22175171, 45.58988885, 23.06332445]), index=31, x=array([ 9.22175171, 45.58988885, 23.06332445]), fval=0.6413538895520631, rho=0.005285332353177379, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 20, 21, 22, 24, 25, 27, 29, 30]), old_indices_discarded=array([19, 23, 26, 28]), step_length=0.2871580766830809, relative_step_length=1.0013971244139295, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22175171, 45.58988885, 23.06332445]), radius=0.14337872043078864, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 16, 17, 18, 21, 24, 25, 27, 29, 30, 31]), model=ScalarModel(intercept=0.6407199525901757, linear_terms=array([-3.92919336e-06, -1.65929664e-04, 1.55155143e-04]), square_terms=array([[ 1.84527109e-03, -5.57596163e-06, 4.23287767e-06], + [-5.57596163e-06, 7.39326117e-08, -6.46121591e-08], + [ 4.23287767e-06, -6.46121591e-08, 7.38892314e-08]]), scale=0.14337872043078864, shift=array([ 9.22175171, 45.58988885, 23.06332445])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5800,12 +6064,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=73, candidate_x=array([ 9.20681941, 45.59500922, 22.95032335]), index=73, x=array([ 9.20681941, 45.59500922, 22.95032335]), fval=0.6411986913191434, rho=2.082556410838997, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([52, 57, 58, 60, 62, 63, 64, 65, 66, 69, 70, 72]), old_indices_discarded=array([37, 56, 59, 61, 67, 68, 71]), step_length=0.0011320708550586268, relative_step_length=1.0000000000007268, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20681941, 45.59500922, 22.95032335]), radius=0.002264141710115608, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 57, 60, 62, 63, 64, 65, 66, 68, 69, 70, 73]), model=ScalarModel(intercept=0.6412078927766048, linear_terms=array([1.22594415e-05, 1.61897835e-06, 1.54845803e-06]), square_terms=array([[4.62123158e-07, 3.86924729e-10, 1.77696551e-09], - [3.86924729e-10, 3.00481457e-11, 2.10497761e-11], - [1.77696551e-09, 2.10497761e-11, 3.85820221e-11]]), scale=0.002264141710115608, shift=array([ 9.20681941, 45.59500922, 22.95032335])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=32, candidate_x=array([ 9.22250536, 45.69461821, 22.9654026 ]), index=31, x=array([ 9.22175171, 45.58988885, 23.06332445]), fval=0.6413538895520631, rho=-0.02756030763036017, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 21, 24, 25, 27, 29, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22175171, 45.58988885, 23.06332445]), radius=0.07168936021539432, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 17, 29, 31, 32]), model=ScalarModel(intercept=0.6413730871630696, linear_terms=array([-1.63989532e-04, 3.65884738e-05, 5.21973410e-05]), square_terms=array([[ 4.60761375e-04, -7.31726922e-07, -1.08851257e-06], + [-7.31726922e-07, 2.10367260e-08, 3.07816346e-08], + [-1.08851257e-06, 3.07816346e-08, 4.50672893e-08]]), scale=0.07168936021539432, shift=array([ 9.22175171, 45.58988885, 23.06332445])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5887,12 +6151,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=74, candidate_x=array([ 9.20458071, 45.59476332, 22.95009077]), index=73, x=array([ 9.20681941, 45.59500922, 22.95032335]), fval=0.6411986913191434, rho=-3.1170682302761343, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([52, 57, 60, 62, 63, 64, 65, 66, 68, 69, 70, 73]), old_indices_discarded=array([37, 42, 50, 54, 55, 56, 58, 59, 61, 67, 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20681941, 45.59500922, 22.95032335]), radius=0.001132070855057804, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([52, 60, 62, 63, 64, 65, 66, 68, 69, 70, 73, 74]), model=ScalarModel(intercept=0.6412191013317629, linear_terms=array([ 2.28385122e-07, -3.55775509e-06, -2.81988253e-06]), square_terms=array([[1.17199564e-07, 1.00094460e-09, 1.07118366e-09], - [1.00094460e-09, 7.51571130e-11, 4.49726145e-11], - [1.07118366e-09, 4.49726145e-11, 3.36729964e-11]]), scale=0.001132070855057804, shift=array([ 9.20681941, 45.59500922, 22.95032335])), vector_model=VectorModel(intercepts=array([ 0.04902536, 0.12501084, 0.1503711 , 0.19587102, 0.21991184, - 0.23522003, 0.23681939, 0.07111459, -0.07579182, -0.06299477, - -0.40502842, -0.41381428, -0.12605813, -0.09953675, -0.09019229, - -0.09362869, -0.09977548]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), candidate_index=33, candidate_x=array([ 9.24400879, 45.54869625, 23.00457613]), index=31, x=array([ 9.22175171, 45.58988885, 23.06332445]), fval=0.6413538895520631, rho=-2.1461414457605144, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 29, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22175171, 45.58988885, 23.06332445]), radius=0.03584468010769716, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6413717608202565, linear_terms=array([ 2.00924894e-04, -1.09372237e-05, -5.43208484e-06]), square_terms=array([[1.15625621e-04, 3.96142165e-08, 3.87212268e-09], + [3.96142165e-08, 8.40304844e-10, 2.22207615e-10], + [3.87212268e-09, 2.22207615e-10, 3.03437454e-10]]), scale=0.03584468010769716, shift=array([ 9.22175171, 45.58988885, 23.06332445])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, + 0.23522539, 0.2368249 , 0.07112057, -0.07578243, -0.06298703, + -0.4050205 , -0.41380612, -0.12606189, -0.09954036, -0.09019588, + -0.09363218, -0.09977924]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -5974,12 +6238,2711 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.6369622223167655, shift=array([ 9.27512208, 46.36962222, 22.93103984])), candidate_index=75, candidate_x=array([ 9.20676289, 45.59590683, 22.9510348 ]), index=75, x=array([ 9.20676289, 45.59590683, 22.9510348 ]), fval=0.6411981796789968, rho=0.11112145416657117, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([52, 60, 62, 63, 64, 65, 66, 68, 69, 70, 73, 74]), old_indices_discarded=array([37, 55, 56, 57, 58, 59, 61, 67, 71, 72]), step_length=0.001146756546700263, relative_step_length=1.0129724138527612, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 76 entries., 'history': {'params': [{'CRRA': 9.275122078746548, 'BeqShift': 46.36962222316765, 'BeqFac': 22.931039835503583}, {'CRRA': 10.570335311635066, 'BeqShift': 48.69503684389947, 'BeqFac': 19.134161094454562}, {'CRRA': 6.295456329035357, 'BeqShift': 48.539396118889606, 'BeqFac': 20.117662521200636}, {'CRRA': 5.33446990558365, 'BeqShift': 48.393552414267866, 'BeqFac': 24.300852115669155}, {'CRRA': 8.832067429490081, 'BeqShift': 41.75395632558529, 'BeqFac': 22.903661908988543}, {'CRRA': 8.581240925807608, 'BeqShift': 44.64168154817753, 'BeqFac': 27.17770542399812}, {'CRRA': 5.193431021763925, 'BeqShift': 44.2023600849078, 'BeqFac': 23.31076551360062}, {'CRRA': 11.348006945364338, 'BeqShift': 50.50171596182066, 'BeqFac': 22.569974306929595}, {'CRRA': 12.977108118727214, 'BeqShift': 45.17568493385298, 'BeqFac': 20.40690993390085}, {'CRRA': 8.661023876930996, 'BeqShift': 49.54542718478037, 'BeqFac': 26.25347341580519}, {'CRRA': 8.142407810677877, 'BeqShift': 44.152054684376026, 'BeqFac': 19.01941980679372}, {'CRRA': 12.600034557255288, 'BeqShift': 43.485310412977014, 'BeqFac': 24.389505139528513}, {'CRRA': 12.861392104130983, 'BeqShift': 47.598186027107054, 'BeqFac': 25.601380063989815}, {'CRRA': 9.314332050636349, 'BeqShift': 47.82012340696157, 'BeqFac': 27.339083794903384}, {'CRRA': 9.286790206926275, 'BeqShift': 48.68923540045349, 'BeqFac': 22.906141148379305}, {'CRRA': 9.260682662358942, 'BeqShift': 47.48958435724096, 'BeqFac': 23.230731601854455}, {'CRRA': 8.884860033418006, 'BeqShift': 46.09463299157837, 'BeqFac': 23.25972832771286}, {'CRRA': 9.394091716518384, 'BeqShift': 45.89500772579352, 'BeqFac': 23.24175991949499}, {'CRRA': 9.799290964180724, 'BeqShift': 46.20789703773276, 'BeqFac': 23.11826058852961}, {'CRRA': 9.05584322903144, 'BeqShift': 46.85085343957124, 'BeqFac': 22.69377844199164}, {'CRRA': 9.673777950336097, 'BeqShift': 46.33257567041187, 'BeqFac': 22.511920860269846}, {'CRRA': 9.175638277663463, 'BeqShift': 46.53393888797886, 'BeqFac': 23.477906065410195}, {'CRRA': 9.610018211187157, 'BeqShift': 46.67443184082566, 'BeqFac': 23.292834451113585}, {'CRRA': 9.069289561933921, 'BeqShift': 46.404237254025126, 'BeqFac': 22.39030474224053}, {'CRRA': 8.760043806956176, 'BeqShift': 46.61570755733012, 'BeqFac': 23.031518931162726}, {'CRRA': 9.416898024796081, 'BeqShift': 45.88359595609334, 'BeqFac': 22.648838284727447}, {'CRRA': 9.62777705046149, 'BeqShift': 46.78317233198733, 'BeqFac': 22.729618771945535}, {'CRRA': 8.856100860488962, 'BeqShift': 46.02392354863563, 'BeqFac': 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0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=0, candidate_x=array([ 9.12811696, 48.90833875, 23.98172789]), index=0, x=array([ 9.12811696, 48.90833875, 23.98172789]), fval=0.641913108278745, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.12811696, 48.90833875, 23.98172789]), radius=4.890833875417503, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.583716765604152, linear_terms=array([-0.07859534, -0.0011287 , -0.00430102]), square_terms=array([[ 2.38031357e+00, -9.48468248e-03, -6.59715077e-03], + [-9.48468248e-03, 5.68338479e-05, 7.56457573e-05], + [-6.59715077e-03, 7.56457573e-05, 2.22800606e-04]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=13, candidate_x=array([ 9.30817323, 50.41072581, 28.64399966]), index=13, x=array([ 9.30817323, 50.41072581, 28.64399966]), fval=0.6417154484942845, rho=0.033276725821794646, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=4.901669630475602, relative_step_length=1.002215523024113, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.30817323, 50.41072581, 28.64399966]), radius=2.4454169377087513, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), model=ScalarModel(intercept=0.6095839952420734, linear_terms=array([0.02847601, 0.00192014, 0.02111076]), square_terms=array([[5.96661659e-01, 4.33856639e-04, 1.74507155e-02], + [4.33856639e-04, 2.74994283e-05, 2.16161701e-04], + [1.74507155e-02, 2.16161701e-04, 2.09801764e-03]]), scale=2.4454169377087513, shift=array([ 9.30817323, 50.41072581, 28.64399966])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=14, candidate_x=array([ 9.26566463, 50.18540399, 26.1572868 ]), index=14, x=array([ 9.26566463, 50.18540399, 26.1572868 ]), fval=0.641560429358155, rho=0.007512522107510051, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), old_indices_discarded=array([ 1, 10]), step_length=2.497262046774645, relative_step_length=1.02120092826153, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.26566463, 50.18540399, 26.1572868 ]), radius=1.2227084688543757, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 3, 5, 6, 7, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5910305640762153, linear_terms=array([0.00422937, 0.00796844, 0.01620315]), square_terms=array([[0.1522213 , 0.00363233, 0.0087327 ], + [0.00363233, 0.00041228, 0.00091517], + [0.0087327 , 0.00091517, 0.00204036]]), scale=1.2227084688543757, shift=array([ 9.26566463, 50.18540399, 26.1572868 ])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6061,12 +9024,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=0, candidate_x=array([ 9.12810637, 48.86990017, 23.90047511]), index=0, x=array([ 9.12810637, 48.86990017, 23.90047511]), fval=0.6419131545898232, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 9.12810637, 48.86990017, 23.90047511]), radius=4.886990017445058, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.5838053927491154, linear_terms=array([-0.0784591 , -0.00113294, -0.00430834]), square_terms=array([[ 2.37622930e+00, -9.44902309e-03, -6.59043823e-03], - [-9.44902309e-03, 5.65537660e-05, 7.53932158e-05], - [-6.59043823e-03, 7.53932158e-05, 2.21756861e-04]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=15, candidate_x=array([ 9.30699788, 49.61506971, 25.01041036]), index=14, x=array([ 9.26566463, 50.18540399, 26.1572868 ]), fval=0.641560429358155, rho=-0.007886315339199876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 5, 6, 7, 9, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.26566463, 50.18540399, 26.1572868 ]), radius=0.6113542344271878, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15]), model=ScalarModel(intercept=0.6412073334062556, linear_terms=array([ 5.19349520e-03, -5.98359323e-04, 8.59183085e-05]), square_terms=array([[ 3.44371233e-02, -5.21030805e-05, 5.09651589e-06], + [-5.21030805e-05, 6.91566502e-07, -9.74269049e-08], + [ 5.09651589e-06, -9.74269049e-08, 1.42970270e-08]]), scale=0.6113542344271878, shift=array([ 9.26566463, 50.18540399, 26.1572868 ])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6148,12 +9111,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=13, candidate_x=array([ 9.30801516, 50.37166273, 28.55876295]), index=13, x=array([ 9.30801516, 50.37166273, 28.55876295]), fval=0.6417128790724664, rho=0.03368406179865712, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=4.897683486158282, relative_step_length=1.0021881503082781, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.30801516, 50.37166273, 28.55876295]), radius=2.443495008722529, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), model=ScalarModel(intercept=0.6096236821586578, linear_terms=array([0.02840541, 0.00191562, 0.02107148]), square_terms=array([[5.95634493e-01, 4.34268068e-04, 1.74066775e-02], - [4.34268068e-04, 2.73557040e-05, 2.15252435e-04], - [1.74066775e-02, 2.15252435e-04, 2.09032328e-03]]), scale=2.443495008722529, shift=array([ 9.30801516, 50.37166273, 28.55876295])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=16, candidate_x=array([ 9.17594703, 50.79063701, 26.07002062]), index=16, x=array([ 9.17594703, 50.79063701, 26.07002062]), fval=0.6414177609144429, rho=0.14441039761596372, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.6180385419257706, relative_step_length=1.0109336079185673, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17594703, 50.79063701, 26.07002062]), radius=1.2227084688543757, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.6052290080578056, linear_terms=array([-0.00708001, 0.00285835, 0.00875437]), square_terms=array([[1.45734146e-01, 8.89361268e-04, 3.96564545e-03], + [8.89361268e-04, 3.28047912e-05, 1.13464106e-04], + [3.96564545e-03, 1.13464106e-04, 4.05115526e-04]]), scale=1.2227084688543757, shift=array([ 9.17594703, 50.79063701, 26.07002062])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6235,12 +9198,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=14, candidate_x=array([ 9.2655641 , 50.14664588, 26.07420434]), index=14, x=array([ 9.2655641 , 50.14664588, 26.07420434]), fval=0.6415610317693744, rho=0.00737246118082768, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13]), old_indices_discarded=array([ 1, 10]), step_length=2.4950884015831862, relative_step_length=1.0211145890114302, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2655641 , 50.14664588, 26.07420434]), radius=1.2217475043612644, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 3, 5, 6, 7, 9, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5911089857829682, linear_terms=array([0.00422151, 0.00795934, 0.0161826 ]), square_terms=array([[0.15195804, 0.00362337, 0.00870881], - [0.00362337, 0.00041071, 0.00091153], - [0.00870881, 0.00091153, 0.00203185]]), scale=1.2217475043612644, shift=array([ 9.2655641 , 50.14664588, 26.07420434])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=17, candidate_x=array([ 9.26389375, 50.41169957, 24.90532478]), index=16, x=array([ 9.17594703, 50.79063701, 26.07002062]), fval=0.6414177609144429, rho=-0.01627040853300433, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17594703, 50.79063701, 26.07002062]), radius=0.6113542344271878, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6408669351182988, linear_terms=array([ 1.22927317e-03, -1.53440992e-04, -7.90629366e-05]), square_terms=array([[ 3.47101203e-02, 3.61136658e-06, -1.55842854e-05], + [ 3.61136658e-06, 4.50052366e-08, 1.45178126e-08], + [-1.55842854e-05, 1.45178126e-08, 1.99338003e-08]]), scale=0.6113542344271878, shift=array([ 9.17594703, 50.79063701, 26.07002062])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6322,12 +9285,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=15, candidate_x=array([ 9.30677188, 49.57690312, 24.92860973]), index=14, x=array([ 9.2655641 , 50.14664588, 26.07420434]), fval=0.6415610317693744, rho=-0.007719959864419043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 3, 5, 6, 7, 9, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2655641 , 50.14664588, 26.07420434]), radius=0.6108737521806322, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15]), model=ScalarModel(intercept=0.6412075612871301, linear_terms=array([ 5.18605429e-03, -5.96658930e-04, 8.53007592e-05]), square_terms=array([[ 3.43820818e-02, -5.19387823e-05, 5.05459631e-06], - [-5.19387823e-05, 6.87957385e-07, -9.67077712e-08], - [ 5.05459631e-06, -9.67077712e-08, 1.41737452e-08]]), scale=0.6108737521806322, shift=array([ 9.2655641 , 50.14664588, 26.07420434])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=18, candidate_x=array([ 9.15447068, 51.33486012, 26.34827514]), index=16, x=array([ 9.17594703, 50.79063701, 26.07002062]), fval=0.6414177609144429, rho=-0.37962116512774374, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17594703, 50.79063701, 26.07002062]), radius=0.3056771172135939, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18]), model=ScalarModel(intercept=0.6414177609144434, linear_terms=array([-7.19667964e-04, 3.27673764e-06, -4.38632601e-06]), square_terms=array([[ 8.32991536e-03, -4.95785367e-06, -1.12565541e-06], + [-4.95785367e-06, 3.12359635e-08, 6.32213746e-09], + [-1.12565541e-06, 6.32213746e-09, 1.43003510e-09]]), scale=0.3056771172135939, shift=array([ 9.17594703, 50.79063701, 26.07002062])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6409,12 +9372,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=16, candidate_x=array([ 9.17589851, 50.75145653, 25.98737882]), index=16, x=array([ 9.17589851, 50.75145653, 25.98737882]), fval=0.6414178321884101, rho=0.14527765423064729, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.6175552708890588, relative_step_length=1.0109376424909002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17589851, 50.75145653, 25.98737882]), radius=1.2217475043612644, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=0.6052901828442077, linear_terms=array([-0.00707459, 0.00285906, 0.00874449]), square_terms=array([[1.45487940e-01, 8.88677433e-04, 3.95549893e-03], - [8.88677433e-04, 3.27781430e-05, 1.13238367e-04], - [3.95549893e-03, 1.13238367e-04, 4.03782515e-04]]), scale=1.2217475043612644, shift=array([ 9.17589851, 50.75145653, 25.98737882])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=19, candidate_x=array([ 9.20227374, 50.61564005, 26.34665317]), index=19, x=array([ 9.20227374, 50.61564005, 26.34665317]), fval=0.64127879028253, rho=3.7790387049118928, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.3283939779394031, relative_step_length=1.0743165236995338, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20227374, 50.61564005, 26.34665317]), radius=0.6113542344271878, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6411417972530091, linear_terms=array([ 3.16072074e-03, 2.78118281e-05, -1.27986623e-04]), square_terms=array([[ 3.48228413e-02, 2.65152618e-05, -2.12954966e-05], + [ 2.65152618e-05, 5.39158692e-08, -3.83514408e-08], + [-2.12954966e-05, -3.83514408e-08, 4.64226978e-08]]), scale=0.6113542344271878, shift=array([ 9.20227374, 50.61564005, 26.34665317])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6496,12 +9459,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=17, candidate_x=array([ 9.2637979 , 50.37235045, 24.82377989]), index=16, x=array([ 9.17589851, 50.75145653, 25.98737882]), fval=0.6414178321884101, rho=-0.016345474533435187, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 2, 3, 5, 7, 9, 11, 12, 13, 14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17589851, 50.75145653, 25.98737882]), radius=0.6108737521806322, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.6408680252047284, linear_terms=array([ 1.22608587e-03, -1.52831904e-04, -7.93126783e-05]), square_terms=array([[ 3.46548267e-02, 3.74686901e-06, -1.56196185e-05], - [ 3.74686901e-06, 4.51202398e-08, 1.43733385e-08], - [-1.56196185e-05, 1.43733385e-08, 1.99575801e-08]]), scale=0.6108737521806322, shift=array([ 9.17589851, 50.75145653, 25.98737882])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=20, candidate_x=array([ 9.14744387, 50.49524578, 26.94433444]), index=19, x=array([ 9.20227374, 50.61564005, 26.34665317]), fval=0.64127879028253, rho=-1.438980527261934, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20227374, 50.61564005, 26.34665317]), radius=0.3056771172135939, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6413841070992645, linear_terms=array([-6.53726515e-04, -5.99067852e-05, -1.84848863e-05]), square_terms=array([[8.48604787e-03, 2.74233289e-06, 1.50336569e-07], + [2.74233289e-06, 1.06911664e-08, 2.45419451e-09], + [1.50336569e-07, 2.45419451e-09, 1.17491524e-09]]), scale=0.3056771172135939, shift=array([ 9.20227374, 50.61564005, 26.34665317])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6583,12 +9546,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=18, candidate_x=array([ 9.15445957, 51.29445206, 26.26698772]), index=16, x=array([ 9.17589851, 50.75145653, 25.98737882]), fval=0.6414178321884101, rho=-0.3807981173026105, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.17589851, 50.75145653, 25.98737882]), radius=0.3054368760903161, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18]), model=ScalarModel(intercept=0.6414178321884101, linear_terms=array([-7.14351462e-04, 3.57051786e-06, -4.34650781e-06]), square_terms=array([[ 8.31583940e-03, -5.01950042e-06, -1.11678712e-06], - [-5.01950042e-06, 3.13345575e-08, 6.31491818e-09], - [-1.11678712e-06, 6.31491818e-09, 1.42073640e-09]]), scale=0.3054368760903161, shift=array([ 9.17589851, 50.75145653, 25.98737882])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=21, candidate_x=array([ 9.22555433, 50.90760858, 26.4370058 ]), index=19, x=array([ 9.20227374, 50.61564005, 26.34665317]), fval=0.64127879028253, rho=-1.3571447197113051, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20227374, 50.61564005, 26.34665317]), radius=0.15283855860679696, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6413867749395041, linear_terms=array([-1.15334196e-04, -1.03339480e-05, 1.89663138e-05]), square_terms=array([[ 2.12361077e-03, 7.37109646e-07, -4.72884217e-07], + [ 7.37109646e-07, 2.13322450e-09, 4.55845370e-10], + [-4.72884217e-07, 4.55845370e-10, 3.49467194e-09]]), scale=0.15283855860679696, shift=array([ 9.20227374, 50.61564005, 26.34665317])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6670,12 +9633,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=19, candidate_x=array([ 9.2020414 , 50.55938439, 26.26040984]), index=19, x=array([ 9.2020414 , 50.55938439, 26.26040984]), fval=0.6412798714314748, rho=3.766886757176218, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18]), old_indices_discarded=array([], dtype=int64), step_length=0.3348448739735063, relative_step_length=1.0962817530732418, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2020414 , 50.55938439, 26.26040984]), radius=0.6108737521806322, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6411413456382316, linear_terms=array([ 3.14313654e-03, 2.74625666e-05, -1.27910543e-04]), square_terms=array([[ 3.47663383e-02, 2.63538243e-05, -2.12337211e-05], - [ 2.63538243e-05, 5.29791904e-08, -3.77184755e-08], - [-2.12337211e-05, -3.77184755e-08, 4.60609078e-08]]), scale=0.6108737521806322, shift=array([ 9.2020414 , 50.55938439, 26.26040984])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=22, candidate_x=array([ 9.21043634, 50.68853093, 26.21255295]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=1.6828512349311813, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.1528482217948772, relative_step_length=1.0000632248051036, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.3056771172135939, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6413655868487599, linear_terms=array([-2.08473659e-04, -3.95761353e-05, -7.61346768e-06]), square_terms=array([[8.52016057e-03, 4.18394593e-06, 1.05230575e-06], + [4.18394593e-06, 1.37295451e-08, 5.81549864e-09], + [1.05230575e-06, 5.81549864e-09, 4.01248942e-09]]), scale=0.3056771172135939, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6757,12 +9720,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=20, candidate_x=array([ 9.14747104, 50.44050518, 26.85791599]), index=19, x=array([ 9.2020414 , 50.55938439, 26.26040984]), fval=0.6412798714314748, rho=-1.4403818757718805, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 9, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2020414 , 50.55938439, 26.26040984]), radius=0.3054368760903161, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6413874688839755, linear_terms=array([-6.33748043e-04, -5.73186109e-05, -1.79357454e-05]), square_terms=array([[8.46968425e-03, 2.56443878e-06, 1.01117391e-07], - [2.56443878e-06, 9.68297991e-09, 2.15723240e-09], - [1.01117391e-07, 2.15723240e-09, 1.06370393e-09]]), scale=0.3054368760903161, shift=array([ 9.2020414 , 50.55938439, 26.26040984])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=23, candidate_x=array([ 9.21772683, 50.98870086, 26.27022546]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-1.6364205448497149, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.15283855860679696, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6413359796696954, linear_terms=array([-1.81109576e-05, -1.14485767e-05, 2.37026915e-05]), square_terms=array([[ 2.12663363e-03, 8.91621394e-07, -5.38885654e-07], + [ 8.91621394e-07, 2.74292807e-09, 6.77799196e-11], + [-5.38885654e-07, 6.77799196e-11, 3.59250963e-09]]), scale=0.15283855860679696, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6844,12 +9807,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=21, candidate_x=array([ 9.22464674, 50.85076503, 26.3518551 ]), index=19, x=array([ 9.2020414 , 50.55938439, 26.26040984]), fval=0.6412798714314748, rho=-1.2442024876135518, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2020414 , 50.55938439, 26.26040984]), radius=0.15271843804515806, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6413861775924228, linear_terms=array([-1.34889295e-04, -1.17794331e-05, 1.72033189e-05]), square_terms=array([[ 2.12047970e-03, 7.30611322e-07, -4.58026451e-07], - [ 7.30611322e-07, 1.91216963e-09, 3.03077874e-10], - [-4.58026451e-07, 3.03077874e-10, 3.30402692e-09]]), scale=0.15271843804515806, shift=array([ 9.2020414 , 50.55938439, 26.26040984])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=24, candidate_x=array([ 9.21165679, 50.75953193, 26.06549617]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-0.5890207447246926, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.07641927930339848, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6412542210909683, linear_terms=array([-2.23995338e-04, 2.54535201e-05, 2.11746359e-05]), square_terms=array([[ 5.33098874e-04, -5.21340692e-08, -6.08486471e-08], + [-5.21340692e-08, 2.75567603e-09, 1.19347943e-09], + [-6.08486471e-08, 1.19347943e-09, 8.72229962e-10]]), scale=0.07641927930339848, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -6931,12 +9894,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=22, candidate_x=array([ 9.21160543, 50.6453772 , 26.13455164]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=1.0576140080519945, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int64), step_length=0.15273021841934944, relative_step_length=1.0000771378645708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.3054368760903161, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6413638550561954, linear_terms=array([-2.14082253e-04, -4.27982472e-05, -1.00993546e-05]), square_terms=array([[8.50926454e-03, 4.22952742e-06, 1.16007861e-06], - [4.22952742e-06, 1.36024420e-08, 5.92380689e-09], - [1.16007861e-06, 5.92380689e-09, 4.14150417e-09]]), scale=0.3054368760903161, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=25, candidate_x=array([ 9.24063024, 50.63073733, 26.16448842]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-3.849990572292386, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.03820963965169924, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 22, 24, 25]), model=ScalarModel(intercept=0.641219258129736, linear_terms=array([-0.00032591, -0.00024411, -0.00013343]), square_terms=array([[1.34077944e-04, 5.43533915e-07, 3.15951718e-07], + [5.43533915e-07, 1.95813700e-07, 1.10514267e-07], + [3.15951718e-07, 1.10514267e-07, 6.24967287e-08]]), scale=0.03820963965169924, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7018,12 +9981,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=23, candidate_x=array([ 9.21909393, 50.94264606, 26.20464947]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-1.4513449695755447, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.15271843804515806, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([14, 16, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6413399722336997, linear_terms=array([-1.41886991e-05, -1.22506996e-05, 2.10449721e-05]), square_terms=array([[ 2.12392194e-03, 8.98039493e-07, -4.74231704e-07], - [ 8.98039493e-07, 2.50309927e-09, 5.64263262e-12], - [-4.74231704e-07, 5.64263262e-12, 3.29687601e-09]]), scale=0.15271843804515806, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=26, candidate_x=array([ 9.23584043, 50.71471982, 26.22686584]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-0.5673497189202965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.01910481982584962, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6412969075158591, linear_terms=array([ 8.32111914e-05, -3.85237511e-06, -1.89858710e-05]), square_terms=array([[ 3.30302534e-05, -1.92352742e-09, 1.34216817e-08], + [-1.92352742e-09, 2.03912494e-10, 1.41492124e-10], + [ 1.34216817e-08, 1.41492124e-10, 7.26728056e-10]]), scale=0.01910481982584962, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7105,12 +10068,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=24, candidate_x=array([ 9.21254927, 50.72755054, 25.9933501 ]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.6299099565848705, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 16, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.07635921902257903, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6412636200468966, linear_terms=array([-1.85982564e-04, 2.34556335e-05, 1.68033597e-05]), square_terms=array([[ 5.32130898e-04, -3.65300183e-08, -4.57058856e-08], - [-3.65300183e-08, 2.84261585e-09, 1.19188371e-09], - [-4.57058856e-08, 1.19188371e-09, 8.22139690e-10]]), scale=0.07635921902257903, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=39, candidate_x=array([ 9.19299773, 50.68734198, 26.22026498]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-0.8735221855116919, accepted=False, new_indices=array([27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_used=array([22, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.00955240991292481, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.6412994242831694, linear_terms=array([ 2.85233747e-05, -1.63095244e-05, -8.02189878e-07]), square_terms=array([[ 8.22961168e-06, 8.93803337e-09, 1.13412880e-08], + [ 8.93803337e-09, 5.36069130e-10, -1.49130908e-10], + [ 1.13412880e-08, -1.49130908e-10, 3.21289345e-10]]), scale=0.00955240991292481, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7192,12 +10155,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=25, candidate_x=array([ 9.23690342, 50.5838021 , 26.09045508]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-3.865430938711674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.038179609511289514, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([16, 19, 22, 24, 25]), model=ScalarModel(intercept=0.6412400317662156, linear_terms=array([-1.51811080e-04, -1.33574181e-04, -8.96571588e-05]), square_terms=array([[1.33253248e-04, 3.18742679e-07, 2.22600759e-07], - [3.18742679e-07, 7.45315433e-08, 5.15360719e-08], - [2.22600759e-07, 5.15360719e-08, 3.57710222e-08]]), scale=0.038179609511289514, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=40, candidate_x=array([ 9.20281168, 50.69426121, 26.21307923]), index=22, x=array([ 9.21043634, 50.68853093, 26.21255295]), fval=0.6412372943065084, rho=-1.1237580039409194, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39]), old_indices_discarded=array([26, 28, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21043634, 50.68853093, 26.21255295]), radius=0.004776204956462405, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40]), model=ScalarModel(intercept=0.6412963629457158, linear_terms=array([ 1.62960573e-05, -7.58208714e-06, -1.23880890e-06]), square_terms=array([[ 2.06057449e-06, 2.04005372e-09, 2.61576683e-09], + [ 2.04005372e-09, 1.27885790e-10, -2.61931144e-11], + [ 2.61576683e-09, -2.61931144e-11, 6.68359271e-11]]), scale=0.004776204956462405, shift=array([ 9.21043634, 50.68853093, 26.21255295])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7279,12 +10242,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=26, candidate_x=array([ 9.2299355 , 50.67331247, 26.15330102]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.6957078218562044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 19, 22, 24, 25]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.019089804755644757, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6412949003105258, linear_terms=array([ 7.79166084e-05, -1.08575400e-05, -7.85152239e-06]), square_terms=array([[ 3.29424647e-05, 2.40761818e-08, -1.09908809e-08], - [ 2.40761818e-08, 3.71198501e-10, 2.17963621e-10], - [-1.09908809e-08, 2.17963621e-10, 2.48160791e-10]]), scale=0.019089804755644757, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=41, candidate_x=array([ 9.20624584, 50.69077625, 26.21301162]), index=41, x=array([ 9.20624584, 50.69077625, 26.21301162]), fval=0.6412025926118272, rho=2.0188340128829463, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40]), old_indices_discarded=array([28, 34, 39]), step_length=0.004776204956462672, relative_step_length=1.000000000000056, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20624584, 50.69077625, 26.21301162]), radius=0.00955240991292481, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.6412523356275223, linear_terms=array([-2.97875373e-05, -3.12914020e-06, -1.35700767e-05]), square_terms=array([[8.24295698e-06, 1.83108503e-08, 2.74598078e-08], + [1.83108503e-08, 1.20423462e-10, 1.79372343e-10], + [2.74598078e-08, 1.79372343e-10, 4.82089539e-10]]), scale=0.00955240991292481, shift=array([ 9.20624584, 50.69077625, 26.21301162])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7366,12 +10329,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=39, candidate_x=array([ 9.19367375, 50.65189736, 26.13394921]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.7356352461674331, accepted=False, new_indices=array([27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_used=array([22, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.009544902377822378, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.6413009360685263, linear_terms=array([ 2.98872405e-05, -9.59030521e-06, -4.27533832e-06]), square_terms=array([[8.22114603e-06, 2.13676999e-08, 1.22020370e-08], - [2.13676999e-08, 2.01378174e-10, 1.00233499e-10], - [1.22020370e-08, 1.00233499e-10, 1.15290113e-10]]), scale=0.009544902377822378, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=42, candidate_x=array([ 9.21535452, 50.69006497, 26.21579992]), index=41, x=array([ 9.20624584, 50.69077625, 26.21301162]), fval=0.6412025926118272, rho=-3.058119371836011, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), old_indices_discarded=array([26, 29, 31, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20624584, 50.69077625, 26.21301162]), radius=0.004776204956462405, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 30, 32, 34, 35, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.641260958677883, linear_terms=array([-8.54001386e-06, -4.00908422e-06, -5.87995380e-06]), square_terms=array([[2.06075442e-06, 4.78854877e-09, 6.77613271e-09], + [4.78854877e-09, 5.18473074e-11, 6.78590681e-11], + [6.77613271e-09, 6.78590681e-11, 1.04111696e-10]]), scale=0.004776204956462405, shift=array([ 9.20624584, 50.69077625, 26.21301162])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7453,12 +10416,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=40, candidate_x=array([ 9.20314317, 50.64928872, 26.1365999 ]), index=22, x=array([ 9.21160543, 50.6453772 , 26.13455164]), fval=0.6412533792921618, rho=-0.4286316408626921, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39]), old_indices_discarded=array([26, 32, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.21160543, 50.6453772 , 26.13455164]), radius=0.004772451188911189, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 40]), model=ScalarModel(intercept=0.6412973906154201, linear_terms=array([ 1.75398422e-05, -5.99250886e-06, -2.51011929e-06]), square_terms=array([[2.05767865e-06, 4.69879566e-09, 2.64315982e-09], - [4.69879566e-09, 6.35990905e-11, 1.90355049e-11], - [2.64315982e-09, 1.90355049e-11, 1.83491578e-11]]), scale=0.004772451188911189, shift=array([ 9.21160543, 50.6453772 , 26.13455164])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=43, candidate_x=array([ 9.20961343, 50.69268469, 26.21580971]), index=41, x=array([ 9.20624584, 50.69077625, 26.21301162]), fval=0.6412025926118272, rho=-2.1744638005385846, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 30, 32, 34, 35, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([28, 29, 31, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20624584, 50.69077625, 26.21301162]), radius=0.0023881024782312025, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 40, 41, 42, 43]), model=ScalarModel(intercept=0.6411961332281664, linear_terms=array([ 8.04706243e-05, 1.32742578e-04, -1.57116889e-04]), square_terms=array([[ 4.93024268e-07, -1.40881505e-08, 2.11157299e-08], + [-1.40881505e-08, 2.10191501e-08, -2.50091707e-08], + [ 2.11157299e-08, -2.50091707e-08, 2.99012604e-08]]), scale=0.0023881024782312025, shift=array([ 9.20624584, 50.69077625, 26.21301162])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7540,12 +10503,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=41, candidate_x=array([ 9.20724051, 50.64712891, 26.13536112]), index=41, x=array([ 9.20724051, 50.64712891, 26.13536112]), fval=0.6412040518376516, rho=2.769849739315419, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 40]), old_indices_discarded=array([32, 36, 39]), step_length=0.004772451188911595, relative_step_length=1.000000000000085, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20724051, 50.64712891, 26.13536112]), radius=0.009544902377822378, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.6412558733517228, linear_terms=array([-1.69255546e-05, -4.39895767e-06, -2.08824813e-06]), square_terms=array([[8.21955555e-06, 2.05881180e-08, 6.90866621e-09], - [2.05881180e-08, 1.57618492e-10, 7.22730875e-11], - [6.90866621e-09, 7.22730875e-11, 5.92353119e-11]]), scale=0.009544902377822378, shift=array([ 9.20724051, 50.64712891, 26.13536112])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=44, candidate_x=array([ 9.2053765 , 50.68933969, 26.21471197]), index=41, x=array([ 9.20624584, 50.69077625, 26.21301162]), fval=0.6412025926118272, rho=-0.06665297495399475, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20624584, 50.69077625, 26.21301162]), radius=0.0011940512391156012, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 40, 41, 43, 44]), model=ScalarModel(intercept=0.6412340229860928, linear_terms=array([ 3.52986518e-07, 8.56291047e-06, -9.16081742e-06]), square_terms=array([[ 1.28888785e-07, -6.98266228e-10, 1.36219560e-09], + [-6.98266228e-10, 9.60382236e-11, -9.67779110e-11], + [ 1.36219560e-09, -9.67779110e-11, 1.24968104e-10]]), scale=0.0011940512391156012, shift=array([ 9.20624584, 50.69077625, 26.21301162])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7627,12 +10590,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=42, candidate_x=array([ 9.21317423, 50.65319986, 26.13972458]), index=41, x=array([ 9.20724051, 50.64712891, 26.13536112]), fval=0.6412040518376516, rho=-5.9786318496293385, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 28, 30, 32, 34, 35, 37, 38, 39, 40, 41]), old_indices_discarded=array([26, 29, 31, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20724051, 50.64712891, 26.13536112]), radius=0.004772451188911189, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 27, 30, 32, 34, 35, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.6412588576917913, linear_terms=array([-6.83670642e-06, -1.11357466e-06, -5.72399169e-07]), square_terms=array([[2.05052218e-06, 6.90899197e-09, 1.39972212e-09], - [6.90899197e-09, 9.18556326e-11, 2.86773343e-11], - [1.39972212e-09, 2.86773343e-11, 1.40014692e-11]]), scale=0.004772451188911189, shift=array([ 9.20724051, 50.64712891, 26.13536112])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=45, candidate_x=array([ 9.20621228, 50.68995703, 26.21388804]), index=41, x=array([ 9.20624584, 50.69077625, 26.21301162]), fval=0.6412025926118272, rho=-0.04141465744419389, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20624584, 50.69077625, 26.21301162]), radius=0.0005970256195578006, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412034259450424, linear_terms=array([-8.11970601e-06, -2.39531003e-07, 1.81597890e-07]), square_terms=array([[ 3.51047344e-08, -2.56898246e-11, 4.88710489e-11], + [-2.56898246e-11, 5.62665624e-13, -7.44694288e-13], + [ 4.88710489e-11, -7.44694288e-13, 1.18184915e-12]]), scale=0.0005970256195578006, shift=array([ 9.20624584, 50.69077625, 26.21301162])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7714,12 +10677,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=43, candidate_x=array([ 9.21199791, 50.6471936 , 26.13498794]), index=41, x=array([ 9.20724051, 50.64712891, 26.13536112]), fval=0.6412040518376516, rho=-9.46039138436527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 27, 30, 32, 34, 35, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([28, 29, 31, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20724051, 50.64712891, 26.13536112]), radius=0.0023862255944555946, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 40, 41, 42, 43]), model=ScalarModel(intercept=0.6412405831157186, linear_terms=array([ 4.52303257e-06, 2.81196588e-05, -2.35968467e-05]), square_terms=array([[ 5.12190353e-07, -7.84167461e-09, 9.55156040e-09], - [-7.84167461e-09, 1.32218333e-09, -1.32903857e-09], - [ 9.55156040e-09, -1.32903857e-09, 1.49501369e-09]]), scale=0.0023862255944555946, shift=array([ 9.20724051, 50.64712891, 26.13536112])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=58, candidate_x=array([ 9.20684239, 50.69079511, 26.21299702]), index=58, x=array([ 9.20684239, 50.69079511, 26.21299702]), fval=0.6411989888451124, rho=0.44448642236637376, accepted=True, new_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_used=array([41, 44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.000597025619557705, relative_step_length=0.9999999999998398, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20684239, 50.69079511, 26.21299702]), radius=0.0011940512391156012, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 58]), model=ScalarModel(intercept=0.6411961236662362, linear_terms=array([-1.48124284e-05, -2.26730670e-07, -2.78872623e-07]), square_terms=array([[1.40386495e-07, 1.43891086e-11, 9.74123226e-11], + [1.43891086e-11, 2.49639098e-13, 5.84477295e-13], + [9.74123226e-11, 5.84477295e-13, 1.67606530e-12]]), scale=0.0011940512391156012, shift=array([ 9.20684239, 50.69079511, 26.21299702])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7801,12 +10764,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=44, candidate_x=array([ 9.20694996, 50.64530232, 26.13689391]), index=44, x=array([ 9.20694996, 50.64530232, 26.13689391]), fval=0.6412003819983737, rho=0.09857365047199054, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=0.002402151221322381, relative_step_length=1.0066739820844222, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20694996, 50.64530232, 26.13689391]), radius=0.0011931127972277973, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([22, 40, 41, 43, 44]), model=ScalarModel(intercept=0.641203279969248, linear_terms=array([7.68329827e-06, 2.09695197e-05, 2.72233817e-06]), square_terms=array([[ 1.26006786e-07, -3.28572663e-09, -8.55594767e-11], - [-3.28572663e-09, 5.92079298e-10, 1.02968247e-10], - [-8.55594767e-11, 1.02968247e-10, 6.19322113e-11]]), scale=0.0011931127972277973, shift=array([ 9.20694996, 50.64530232, 26.13689391])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=59, candidate_x=array([ 9.20803626, 50.69080762, 26.21301375]), index=58, x=array([ 9.20684239, 50.69079511, 26.21299702]), fval=0.6411989888451124, rho=-1.024937804675889, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([41, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 58]), old_indices_discarded=array([22, 40, 43, 44, 45, 51, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20684239, 50.69079511, 26.21299702]), radius=0.0005970256195578006, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([41, 46, 47, 48, 49, 51, 52, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.6411968517699711, linear_terms=array([-6.43261951e-06, 2.03543544e-07, 1.62100697e-07]), square_terms=array([[ 3.49331176e-08, -1.80338542e-11, -5.22765309e-13], + [-1.80338542e-11, 2.50756952e-13, 1.87739614e-13], + [-5.22765309e-13, 1.87739614e-13, 1.76049815e-13]]), scale=0.0005970256195578006, shift=array([ 9.20684239, 50.69079511, 26.21299702])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7888,12 +10851,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=45, candidate_x=array([ 9.20654349, 50.64418718, 26.13674915]), index=45, x=array([ 9.20654349, 50.64418718, 26.13674915]), fval=0.6411989063826031, rho=0.06546551794973687, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([22, 40, 41, 43, 44]), old_indices_discarded=array([], dtype=int64), step_length=0.001195701055462824, relative_step_length=1.0021693323892262, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20654349, 50.64418718, 26.13674915]), radius=0.0005965563986138987, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.6412016028005694, linear_terms=array([-2.55858927e-06, -7.29526994e-08, -7.87710385e-07]), square_terms=array([[3.41315492e-08, 1.04426797e-11, 7.35805731e-11], - [1.04426797e-11, 2.63347216e-13, 5.28969670e-13], - [7.35805731e-11, 5.28969670e-13, 2.04887560e-12]]), scale=0.0005965563986138987, shift=array([ 9.20654349, 50.64418718, 26.13674915])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=60, candidate_x=array([ 9.20743902, 50.6907779 , 26.21298366]), index=58, x=array([ 9.20684239, 50.69079511, 26.21299702]), fval=0.6411989888451124, rho=-1.202423070496879, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([41, 46, 47, 48, 49, 51, 52, 54, 55, 56, 57, 58]), old_indices_discarded=array([44, 45, 50, 53, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20684239, 50.69079511, 26.21299702]), radius=0.0002985128097789003, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([41, 46, 47, 48, 49, 51, 54, 55, 56, 57, 58, 60]), model=ScalarModel(intercept=0.6412011641332256, linear_terms=array([-4.16514126e-07, 5.09594074e-07, -4.79341866e-07]), square_terms=array([[ 8.42771432e-09, -2.43642549e-11, 2.79372778e-11], + [-2.43642549e-11, 7.61207584e-13, -8.07048298e-13], + [ 2.79372778e-11, -8.07048298e-13, 8.96443906e-13]]), scale=0.0002985128097789003, shift=array([ 9.20684239, 50.69079511, 26.21299702])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -7975,12 +10938,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=58, candidate_x=array([ 9.20711472, 50.64419857, 26.13692076]), index=45, x=array([ 9.20654349, 50.64418718, 26.13674915]), fval=0.6411989063826031, rho=-1.3540135264197082, accepted=False, new_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_used=array([44, 45]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20654349, 50.64418718, 26.13674915]), radius=0.00029827819930694933, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([45, 46, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.6412021137195624, linear_terms=array([-9.14153413e-07, 4.95084351e-07, -4.46123326e-07]), square_terms=array([[ 8.50301604e-09, -1.62114960e-11, 2.28942335e-11], - [-1.62114960e-11, 1.09677906e-12, 8.83835907e-14], - [ 2.28942335e-11, 8.83835907e-14, 1.02199200e-12]]), scale=0.00029827819930694933, shift=array([ 9.20654349, 50.64418718, 26.13674915])), vector_model=VectorModel(intercepts=array([ 0.0478985 , 0.12198145, 0.14591097, 0.19026117, 0.21318417, - 0.22770334, 0.22810381, 0.06014365, -0.08704274, -0.07443179, - -0.41631926, -0.42444642, -0.1208239 , -0.09458291, -0.08510185, - -0.08890017, -0.09498552]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=61, candidate_x=array([ 9.2069943 , 50.69060731, 26.21317367]), index=58, x=array([ 9.20684239, 50.69079511, 26.21299702]), fval=0.6411989888451124, rho=-2.4147290290986723, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([41, 46, 47, 48, 49, 51, 54, 55, 56, 57, 58, 60]), old_indices_discarded=array([45, 50, 52, 53, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20684239, 50.69079511, 26.21299702]), radius=0.00014925640488945016, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=array([41, 47, 48, 51, 57, 58, 60, 61]), model=ScalarModel(intercept=0.6412008661256092, linear_terms=array([ 6.35130769e-07, 2.39823317e-07, -1.15441192e-07]), square_terms=array([[ 2.06563718e-09, -4.66680942e-12, 3.66227593e-12], + [-4.66680942e-12, 2.32998555e-13, -1.96650017e-13], + [ 3.66227593e-12, -1.96650017e-13, 2.15322317e-13]]), scale=0.00014925640488945016, shift=array([ 9.20684239, 50.69079511, 26.21299702])), vector_model=VectorModel(intercepts=array([ 0.04789857, 0.12198169, 0.14591124, 0.1902616 , 0.21318464, + 0.22770393, 0.22810445, 0.06014447, -0.08704184, -0.07443106, + -0.41631848, -0.42444566, -0.12082428, -0.09458328, -0.08510221, + -0.08890051, -0.09498588]), linear_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], @@ -8062,7 +11025,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [[0., 0., 0.], [0., 0., 0.], - [0., 0., 0.]]]), scale=4.886990017445058, shift=array([ 9.12810637, 48.86990017, 23.90047511])), candidate_index=59, candidate_x=array([ 9.20677822, 50.64405072, 26.13687265]), index=59, x=array([ 9.20677822, 50.64405072, 26.13687265]), fval=0.6411981580830574, rho=0.6634184250305409, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([45, 46, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([44, 47, 49]), step_length=0.0002982781993076994, relative_step_length=1.0000000000025147, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 60 entries., 'history': {'params': [{'CRRA': 9.128106374273193, 'BeqShift': 48.86990017445058, 'BeqFac': 23.900475106498696}, {'CRRA': 10.493158284578906, 'BeqShift': 51.32070255175769, 'BeqFac': 19.898866377479305}, {'CRRA': 5.987775253161292, 'BeqShift': 51.15666958513776, 'BeqFac': 20.93539879336119}, {'CRRA': 4.974971863108593, 'BeqShift': 51.00296190014448, 'BeqFac': 25.344148489629724}, {'CRRA': 8.661161951547838, 'BeqShift': 44.00535479239297, 'BeqFac': 23.871620945700617}, {'CRRA': 8.396810731754107, 'BeqShift': 47.04878789780259, 'BeqFac': 28.37612346061257}, {'CRRA': 4.826328077748139, 'BeqShift': 46.585777956700284, 'BeqFac': 24.30067582180679}, {'CRRA': 11.312762441066148, 'BeqShift': 53.22479889559116, 'BeqFac': 23.51994070721196}, {'CRRA': 13.029705730643922, 'BeqShift': 47.61158506757852, 'BeqFac': 21.240242600721672}, {'CRRA': 8.480895627642582, 'BeqShift': 52.21694645187303, 'BeqFac': 27.40205630602071}, {'CRRA': 7.934315470817556, 'BeqShift': 46.532760058680935, 'BeqFac': 19.77793817049746}, {'CRRA': 12.63230013568631, 'BeqShift': 45.830064534694614, 'BeqFac': 25.437581737917117}, {'CRRA': 12.907750238863407, 'BeqShift': 50.164708878465945, 'BeqFac': 26.714801688023883}, {'CRRA': 9.308015155295243, 'BeqShift': 50.37166272775331, 'BeqFac': 28.558762945887366}, {'CRRA': 9.26556410189128, 'BeqShift': 50.1466458784944, 'BeqFac': 26.074204341762798}, {'CRRA': 9.306771879635068, 'BeqShift': 49.57690311934757, 'BeqFac': 24.92860973348605}, {'CRRA': 9.175898510068395, 'BeqShift': 50.75145652974846, 'BeqFac': 25.98737882491696}, {'CRRA': 9.263797896208295, 'BeqShift': 50.372350451393935, 'BeqFac': 24.823779893176457}, {'CRRA': 9.154459573389804, 'BeqShift': 51.29445206167982, 'BeqFac': 26.26698771558419}, {'CRRA': 9.202041402977615, 'BeqShift': 50.559384394363455, 'BeqFac': 26.260409837048027}, {'CRRA': 9.147471035061725, 'BeqShift': 50.44050518128637, 'BeqFac': 26.85791599392265}, {'CRRA': 9.224646740112595, 'BeqShift': 50.85076502735223, 'BeqFac': 26.351855097884435}, {'CRRA': 9.21160542953121, 'BeqShift': 50.64537719999909, 'BeqFac': 26.134551637500216}, {'CRRA': 9.219093926094676, 'BeqShift': 50.94264606429679, 'BeqFac': 26.204649473853145}, {'CRRA': 9.212549269175398, 'BeqShift': 50.727550535520294, 'BeqFac': 25.993350101828756}, {'CRRA': 9.236903417946987, 'BeqShift': 50.583802104763535, 'BeqFac': 26.090455076807366}, {'CRRA': 9.22993549756282, 'BeqShift': 50.67331246866706, 'BeqFac': 26.153301020113346}, {'CRRA': 9.196966234964316, 'BeqShift': 50.63824802900686, 'BeqFac': 26.14451605163276}, {'CRRA': 9.21208615266317, 'BeqShift': 50.62817806751546, 'BeqFac': 26.14282082081475}, {'CRRA': 9.226942686055207, 'BeqShift': 50.63836473001979, 'BeqFac': 26.143496718423}, {'CRRA': 9.207870685334894, 'BeqShift': 50.6616428001297, 'BeqFac': 26.143820002177318}, {'CRRA': 9.225078947624299, 'BeqShift': 50.6431264679526, 'BeqFac': 26.12121675238415}, {'CRRA': 9.212084595052097, 'BeqShift': 50.645954903477566, 'BeqFac': 26.153626681529083}, {'CRRA': 9.228728760115736, 'BeqShift': 50.653800971826904, 'BeqFac': 26.135053895894423}, {'CRRA': 9.204438664808913, 'BeqShift': 50.65036356536122, 'BeqFac': 26.117575351407574}, {'CRRA': 9.194567266866448, 'BeqShift': 50.653981557748885, 'BeqFac': 26.13425721207449}, {'CRRA': 9.215955769137937, 'BeqShift': 50.62999911014034, 'BeqFac': 26.124110873964387}, {'CRRA': 9.214460374939259, 'BeqShift': 50.661387434724546, 'BeqFac': 26.12455452209819}, {'CRRA': 9.198197575121423, 'BeqShift': 50.63489104375454, 'BeqFac': 26.125909264391545}, {'CRRA': 9.193673745180618, 'BeqShift': 50.6518973553509, 'BeqFac': 26.133949212278544}, {'CRRA': 9.203143169947426, 'BeqShift': 50.64928871593105, 'BeqFac': 26.136599897066976}, {'CRRA': 9.207240508661233, 'BeqShift': 50.64712891383064, 'BeqFac': 26.13536111560476}, {'CRRA': 9.213174227403073, 'BeqShift': 50.65319985633544, 'BeqFac': 26.139724577578736}, {'CRRA': 9.211997907592666, 'BeqShift': 50.647193599914004, 'BeqFac': 26.134987938075483}, {'CRRA': 9.206949955411677, 'BeqShift': 50.64530232098181, 'BeqFac': 26.136893913002943}, {'CRRA': 9.2065434947578, 'BeqShift': 50.64418718230116, 'BeqFac': 26.13674914949305}, {'CRRA': 9.20661679269972, 'BeqShift': 50.643932653337664, 'BeqFac': 26.137283679150954}, {'CRRA': 9.2065595869972, 'BeqShift': 50.644783362471536, 'BeqFac': 26.136735373390117}, {'CRRA': 9.206944767351247, 'BeqShift': 50.643965368292854, 'BeqFac': 26.13636749731083}, {'CRRA': 9.206226751341177, 'BeqShift': 50.644175383761606, 'BeqFac': 26.136243764551427}, {'CRRA': 9.206139999933411, 'BeqShift': 50.64434589304829, 'BeqFac': 26.136339414991006}, {'CRRA': 9.20692991957421, 'BeqShift': 50.64378902036765, 'BeqFac': 26.13696828979672}, {'CRRA': 9.206030525524028, 'BeqShift': 50.6444124709626, 'BeqFac': 26.13695405708431}, {'CRRA': 9.206033510436992, 'BeqShift': 50.64400644035142, 'BeqFac': 26.13700040210868}, {'CRRA': 9.206910984777197, 'BeqShift': 50.64373643105872, 'BeqFac': 26.136616281363658}, {'CRRA': 9.20619643081797, 'BeqShift': 50.644622612155665, 'BeqFac': 26.136535077074925}, {'CRRA': 9.20642878297644, 'BeqShift': 50.6444960576583, 'BeqFac': 26.137246459012054}, {'CRRA': 9.207127002456316, 'BeqShift': 50.64430039244862, 'BeqFac': 26.1367999605648}, {'CRRA': 9.207114719797469, 'BeqShift': 50.64419857360247, 'BeqFac': 26.13692076455369}, {'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}], 'criterion': [0.6419131545898231, 0.6992405625490731, 1.1240845476970416, 1.577376990569092, 0.6524642769901355, 0.6664887878432892, 1.6647770568284477, 0.7958273217414884, 1.1577117413885285, 0.6612605763524525, 0.7056652645879914, 1.051463755129049, 1.1229714048823773, 0.6417128790724664, 0.6415610317693744, 0.6416973103739135, 0.64141783218841, 0.6415718165830668, 0.6414915855271336, 0.6412798714314748, 0.6416690245253375, 0.6413838490259491, 0.6412533792921618, 0.6413209357438362, 0.6412698132156118, 0.6414892120617004, 0.641391882855679, 0.6412912681201268, 0.6412600044422002, 0.6414014712692039, 0.641212422800454, 0.64138920463213, 0.6412599767231901, 0.6413894182130144, 0.641240011408127, 0.6412958845272176, 0.6412931073773732, 0.6412860311722518, 0.6412856091678842, 0.641299080615386, 0.6412654340898993, 0.6412040518376516, 0.6412798376316433, 0.6412586069868993, 0.6412003819983737, 0.6411989063826031, 0.641198632121119, 0.641198805868094, 0.6412003147825824, 0.6412028895752717, 0.6412042395231233, 0.6412001224290423, 0.641205979879279, 0.6412059242494071, 0.6411998771542384, 0.6412033613190102, 0.6411998007566294, 0.6412026671203108, 0.6412025111397647, 0.6411981580830574], 'runtime': [0.0, 1.6225000619888306, 1.8208623389946297, 2.0214831299963407, 2.3780758359935135, 2.6056774169846904, 2.8214367309992667, 3.026432433980517, 3.247528671985492, 3.475298994977493, 3.716090304980753, 3.926765499985777, 4.1696615399851, 5.700570738990791, 7.090572686982341, 8.46942359500099, 9.826952342991717, 11.1874410599994, 12.670953125983942, 14.073503133986378, 15.415775064000627, 16.777498332987307, 18.136912931979168, 19.44478028998128, 20.9249066929915, 22.26256391199422, 23.626468014990678, 25.251971355988644, 25.453611488977913, 25.659738533984637, 25.865622649987927, 26.112973237002734, 26.36346254197997, 26.55055605797679, 26.754232481995132, 26.958369220985333, 27.18434452699148, 27.420230353978695, 27.644893939985195, 29.20647358399583, 30.686982325976714, 32.02958239999134, 33.3786810519814, 34.71376580098877, 36.05778275799821, 37.38048776300275, 39.191983733995585, 39.40957244997844, 39.614045912981965, 39.824004255002365, 40.0257615079754, 40.23395056297886, 40.45599366599345, 40.66358802097966, 40.90082527400227, 41.12791417900007, 41.331096677982714, 41.55513457299094, 43.058433758997126, 44.375435253983596], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 9.20683173, 51.48987537, 25.99186716], + [0., 0., 0.]]]), scale=4.890833875417503, shift=array([ 9.12811696, 48.90833875, 23.98172789])), candidate_index=62, candidate_x=array([ 9.20670483, 50.69074294, 26.21302219]), index=62, x=array([ 9.20670483, 50.69074294, 26.21302219]), fval=0.6411983826225262, rho=0.8814327915604036, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([41, 47, 48, 51, 57, 58, 60, 61]), old_indices_discarded=array([], dtype=int64), step_length=0.00014925640488986044, relative_step_length=1.000000000002749, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 63 entries., 'history': {'params': [{'CRRA': 9.128116958674036, 'BeqShift': 48.90833875417502, 'BeqFac': 23.98172788815444}, {'CRRA': 10.494242549435306, 'BeqShift': 51.36106880807309, 'BeqFac': 19.97697169708566}, {'CRRA': 5.985315812705749, 'BeqShift': 51.1969068214584, 'BeqFac': 21.014319396685387}, {'CRRA': 4.971715802980925, 'BeqShift': 51.043078237812104, 'BeqFac': 25.426536791395705}, {'CRRA': 8.660805261197643, 'BeqShift': 44.039967167954444, 'BeqFac': 23.952851032139954}, {'CRRA': 8.396246116169749, 'BeqShift': 47.08579408316602, 'BeqFac': 28.460896559794314}, {'CRRA': 4.822955101973545, 'BeqShift': 46.62241996197595, 'BeqFac': 24.382243381005484}, {'CRRA': 11.314491364896664, 'BeqShift': 53.26666281731726, 'BeqFac': 23.600894179849416}, {'CRRA': 13.032785114804481, 'BeqShift': 47.64903392059222, 'BeqFac': 21.319402978691425}, {'CRRA': 8.480397148963819, 'BeqShift': 52.25801764808977, 'BeqFac': 27.486063253484424}, {'CRRA': 7.9333870799667014, 'BeqShift': 46.56936036277259, 'BeqFac': 19.855948374122228}, {'CRRA': 12.63506694080353, 'BeqShift': 45.86611213420074, 'BeqFac': 25.520043529527573}, {'CRRA': 12.91073369903237, 'BeqShift': 50.2041658889099, 'BeqFac': 26.79826807593395}, {'CRRA': 9.308173225823834, 'BeqShift': 50.410725810642916, 'BeqFac': 28.64399965624611}, {'CRRA': 9.26566462573952, 'BeqShift': 50.1854039894783, 'BeqFac': 26.157286800670093}, {'CRRA': 9.306997881559322, 'BeqShift': 49.615069712990945, 'BeqFac': 25.0104103583992}, {'CRRA': 9.17594702814447, 'BeqShift': 50.79063700650143, 'BeqFac': 26.070020615394284}, {'CRRA': 9.263893754437193, 'BeqShift': 50.411699572494896, 'BeqFac': 24.905324775611366}, {'CRRA': 9.154470681554113, 'BeqShift': 51.334860122284155, 'BeqFac': 26.34827514398519}, {'CRRA': 9.2022737448878, 'BeqShift': 50.61564004625755, 'BeqFac': 26.346653174283844}, {'CRRA': 9.14744387487816, 'BeqShift': 50.49524578449314, 'BeqFac': 26.944334437017478}, {'CRRA': 9.22555433331291, 'BeqShift': 50.907608583114424, 'BeqFac': 26.437005802787674}, {'CRRA': 9.210436344325563, 'BeqShift': 50.688530931156656, 'BeqFac': 26.21255295140968}, {'CRRA': 9.217726825417827, 'BeqShift': 50.98870086262041, 'BeqFac': 26.270225457732973}, {'CRRA': 9.21165679169444, 'BeqShift': 50.75953192732864, 'BeqFac': 26.065496173292587}, {'CRRA': 9.240630244877064, 'BeqShift': 50.63073732595818, 'BeqFac': 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26.200369749671346}, {'CRRA': 9.21372456765587, 'BeqShift': 50.70129794667283, 'BeqFac': 26.198725983458683}, {'CRRA': 9.19974450203932, 'BeqShift': 50.67657389121503, 'BeqFac': 26.20217471240017}, {'CRRA': 9.192997732492683, 'BeqShift': 50.68734198391714, 'BeqFac': 26.220264978535024}, {'CRRA': 9.20281168271835, 'BeqShift': 50.694261210798274, 'BeqFac': 26.21307922676431}, {'CRRA': 9.206245840892574, 'BeqShift': 50.690776245300945, 'BeqFac': 26.213011622487578}, {'CRRA': 9.215354520147347, 'BeqShift': 50.690064972324855, 'BeqFac': 26.215799917330614}, {'CRRA': 9.20961343427463, 'BeqShift': 50.692684691391385, 'BeqFac': 26.215809707925725}, {'CRRA': 9.205376500062254, 'BeqShift': 50.689339688986, 'BeqFac': 26.214711974901615}, {'CRRA': 9.206212275198338, 'BeqShift': 50.68995702786194, 'BeqFac': 26.213888042490407}, {'CRRA': 9.206383278949096, 'BeqShift': 50.69067950951654, 'BeqFac': 26.21358450341678}, {'CRRA': 9.206400227873939, 'BeqShift': 50.69123681484353, 'BeqFac': 26.212664520273762}, {'CRRA': 9.206689440773921, 'BeqShift': 50.69071232573907, 'BeqFac': 26.2126171950224}, {'CRRA': 9.20618393419009, 'BeqShift': 50.69053519001226, 'BeqFac': 26.212468944407686}, {'CRRA': 9.20576293304336, 'BeqShift': 50.69078575068171, 'BeqFac': 26.2126606962171}, {'CRRA': 9.20673050637059, 'BeqShift': 50.690447165097176, 'BeqFac': 26.21312671021201}, {'CRRA': 9.205828536576984, 'BeqShift': 50.69086264611442, 'BeqFac': 26.213429751173546}, {'CRRA': 9.205780959370562, 'BeqShift': 50.69041417871431, 'BeqFac': 26.213107708635093}, {'CRRA': 9.20627061442872, 'BeqShift': 50.69018127553888, 'BeqFac': 26.212968764063078}, {'CRRA': 9.205906137770373, 'BeqShift': 50.691248116513165, 'BeqFac': 26.21287605354478}, {'CRRA': 9.206264599299153, 'BeqShift': 50.6912740083395, 'BeqFac': 26.213340742025192}, {'CRRA': 9.206740583228921, 'BeqShift': 50.69105929799355, 'BeqFac': 26.213189247891858}, {'CRRA': 9.206842389809529, 'BeqShift': 50.69079510897646, 'BeqFac': 26.212997023109683}, {'CRRA': 9.208036258240758, 'BeqShift': 50.69080762400932, 'BeqFac': 26.213013753456305}, {'CRRA': 9.207439017630938, 'BeqShift': 50.69077789545306, 'BeqFac': 26.212983661682323}, {'CRRA': 9.206994297388489, 'BeqShift': 50.69060731086223, 'BeqFac': 26.21317367153902}, {'CRRA': 9.206704829898786, 'BeqShift': 50.69074294245607, 'BeqFac': 26.21302219053918}], 'criterion': [0.641913108278745, 0.6993311991335497, 1.124943967609028, 1.5791773626837113, 0.6524834670210764, 0.6665191810333042, 1.6667604759808738, 0.7961007397690689, 1.1585873846733912, 0.6612905946584647, 0.7057495806215178, 1.0521938471112793, 1.1238080511804318, 0.6417154484942845, 0.641560429358155, 0.6416998320784174, 0.6414177609144429, 0.6415711874417671, 0.6414914762412312, 0.64127879028253, 0.6416696999087816, 0.6413977471143772, 0.6412372943065084, 0.6413072315419228, 0.6412538999213911, 0.6415430863538073, 0.6414664674742304, 0.6412979995415883, 0.6412373371379366, 0.6413868788250133, 0.6412202166633335, 0.641396414807483, 0.6412215977920664, 0.6414031336058099, 0.6412860652057653, 0.6412967396322089, 0.6412973336434422, 0.6412858131106813, 0.6412920971046923, 0.6412981117429888, 0.6412709825026036, 0.6412025926118272, 0.6412893760616618, 0.6412255366339068, 0.6412173207334658, 0.6412031147929009, 0.6412004554687146, 0.6412001919407437, 0.641198436458276, 0.641203555772694, 0.6412105615497572, 0.6411982928429767, 0.6412096286482352, 0.6412103281753796, 0.6412022072754073, 0.6412082550345554, 0.641202300831791, 0.6411982576237238, 0.6411989888451124, 0.6412141028559148, 0.641206708865415, 0.6412009570817677, 0.6411983826225262], 'runtime': [0.0, 1.6423095520003699, 2.040658591000465, 2.2376682650001385, 2.4299743769997804, 2.67046717500034, 2.862668802999906, 3.104374010000356, 3.32224695900004, 3.5381166859997393, 3.741620800000419, 4.010065631000543, 4.227599134000229, 5.809256223000375, 7.184767149999971, 8.550543121000374, 9.923031666000497, 11.329385642000489, 12.911365132000356, 14.307267012000011, 15.663889139000275, 17.065571911000006, 18.41535552300047, 19.819295285000408, 21.202764538999872, 22.709291078000206, 24.042428986000232, 25.732384420000017, 25.9312051349998, 26.183760038999935, 26.412906409999778, 26.61830315000043, 26.855693547000556, 27.050723016999655, 27.28677242699996, 27.52746980200027, 27.765533300000243, 27.970793123000476, 28.197342635000496, 29.677809333999903, 31.04888916300024, 32.42374823699993, 33.98980597300033, 35.40728008099995, 36.75439337300031, 38.18200040800002, 39.90860623499975, 40.13832715599983, 40.36971348000043, 40.58094771800006, 40.784019124000224, 40.990598760000466, 41.227546551999694, 41.53011763900031, 41.92065388799983, 42.16206820299976, 42.460215224999956, 42.67479388399988, 44.23093565199997, 45.70703041100023, 47.07414319700001, 48.44252813000003, 49.74495745600052], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26, 27, 28, 29]}}], 'exploration_sample': array([[ 9.20677822, 50.64405072, 26.13687265], [ 9.36875 , 39.375 , 18.75 ], [ 8.778125 , 63.4375 , 28.125 ], [ 9.959375 , 6.5625 , 84.375 ], @@ -8091,7 +11054,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.12810637 [17.6375 , 61.25 , 12.5 ], [18.228125 , 28.4375 , 78.125 ], [18.81875 , 4.375 , 68.75 ], - [19.409375 , 41.5625 , 34.375 ]]), 'exploration_results': array([0.64119885, 0.64211564, 0.64842422, 0.66164008, 0.68191739, + [19.409375 , 41.5625 , 34.375 ]]), 'exploration_results': array([0.64119816, 0.64211564, 0.64842422, 0.66164008, 0.68191739, 0.7044766 , 0.74714868, 0.84823283, 0.8626059 , 0.97951322, 0.98996599, 1.12431839, 1.18221304, 1.30406645, 1.43892243, 1.52011014, 1.78024917, 1.78131285, 2.0971452 , 2.24344059, diff --git a/content/tables/parameters.tex b/content/tables/parameters.tex index 581f604..9d52ab1 100644 --- a/content/tables/parameters.tex +++ b/content/tables/parameters.tex @@ -2,8 +2,8 @@ \toprule Name & criterion & CRRA & WealthShare & BeqFac & BeqShift \\ \midrule -Portfolio & 0.594000 & 8.810000 & & & \\ -WarmGlowPortfolio & 0.594000 & 8.765000 & & 43.773000 & 26.256000 \\ -WealthPortfolio & 0.397000 & 3.472000 & 0.531000 & & \\ +Portfolio & 0.642000 & 9.252000 & & & \\ +WarmGlowPortfolio & 0.641000 & 9.207000 & & 23.051000 & 45.643000 \\ +WealthPortfolio & 0.242000 & 5.336000 & 0.171000 & & \\ \bottomrule \end{tabular} diff --git a/src/msm_notebooks/FinAssets_Cov.ipynb b/src/msm_notebooks/FinAssets_Cov.ipynb index 4ab6ac0..5a6cf68 100644 --- a/src/msm_notebooks/FinAssets_Cov.ipynb +++ b/src/msm_notebooks/FinAssets_Cov.ipynb @@ -31,6 +31,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import warnings\n", "\n", "import numpy as np\n", diff --git a/src/msm_notebooks/MSM Full Bequest model.ipynb b/src/msm_notebooks/MSM Full Bequest model.ipynb index 781741c..5fa0764 100644 --- a/src/msm_notebooks/MSM Full Bequest model.ipynb +++ b/src/msm_notebooks/MSM Full Bequest model.ipynb @@ -26,6 +26,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "from copy import copy\n", "from pathlib import Path\n", "\n", diff --git a/src/msm_notebooks/MSM LCIM model.ipynb b/src/msm_notebooks/MSM LCIM model.ipynb index 2e4f901..6c464db 100644 --- a/src/msm_notebooks/MSM LCIM model.ipynb +++ b/src/msm_notebooks/MSM LCIM model.ipynb @@ -26,6 +26,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "from copy import copy\n", "from pathlib import Path\n", "\n", diff --git a/src/msm_notebooks/MSM TRP model.ipynb b/src/msm_notebooks/MSM TRP model.ipynb index cef6616..9d6bc3d 100644 --- a/src/msm_notebooks/MSM TRP model.ipynb +++ b/src/msm_notebooks/MSM TRP model.ipynb @@ -26,6 +26,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "from copy import copy\n", "from pathlib import Path\n", "\n", diff --git a/src/msm_notebooks/MSM Term Bequest model.ipynb b/src/msm_notebooks/MSM Term Bequest model.ipynb index 0e75c07..a8aab4a 100644 --- a/src/msm_notebooks/MSM Term Bequest model.ipynb +++ b/src/msm_notebooks/MSM Term Bequest model.ipynb @@ -26,6 +26,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "from copy import copy\n", "from pathlib import Path\n", "\n", diff --git a/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb b/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb index 87b9104..919822f 100644 --- a/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb +++ b/src/msm_notebooks/MSM Warm Glow Bequest model.ipynb @@ -26,6 +26,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "from copy import copy\n", "from pathlib import Path\n", "\n", diff --git a/src/msm_notebooks/NetWorth_Cov.ipynb b/src/msm_notebooks/NetWorth_Cov.ipynb index 566b0e2..0dd53ab 100644 --- a/src/msm_notebooks/NetWorth_Cov.ipynb +++ b/src/msm_notebooks/NetWorth_Cov.ipynb @@ -31,6 +31,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import warnings\n", "\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n", diff --git a/src/msm_notebooks/savres.ipynb b/src/msm_notebooks/savres.ipynb index ce88ef6..813b93e 100644 --- a/src/msm_notebooks/savres.ipynb +++ b/src/msm_notebooks/savres.ipynb @@ -6,6 +6,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from matplotlib.ticker import FuncFormatter\n", diff --git a/src/notebooks/IndShock.ipynb b/src/notebooks/IndShock.ipynb index 68804eb..be7bbf5 100644 --- a/src/notebooks/IndShock.ipynb +++ b/src/notebooks/IndShock.ipynb @@ -6,11 +6,14 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", "from estimark.agents import IndShkLifeCycleConsumerType\n", - "from estimark.parameters import init_calibration\n", - "from HARK.utilities import plot_funcs" + "from estimark.parameters import init_calibration" ] }, { diff --git a/src/notebooks/Model_Comparisons.ipynb b/src/notebooks/Model_Comparisons.ipynb index 3fb2521..8f0a67d 100644 --- a/src/notebooks/Model_Comparisons.ipynb +++ b/src/notebooks/Model_Comparisons.ipynb @@ -2,13 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", + "\n", "from estimark.agents import (\n", " BequestWarmGlowLifeCyclePortfolioType,\n", " PortfolioLifeCycleConsumerType,\n", @@ -25,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -45,7 +46,7 @@ "(9.252286005027539, 1.0)" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -58,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -69,16 +70,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(9.206778216146489, 1.0, 26.1368726540768, 50.64405071849033)" + "(9.206775856414323, 1.0, 23.05054873023735, 45.64298427855443)" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -110,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -119,7 +120,7 @@ "(5.335577372664163, 1.0, 0.1706005756625005, 0.0)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -133,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -193,12 +194,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -249,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { diff --git a/src/notebooks/Portfolio.ipynb b/src/notebooks/Portfolio.ipynb index ea0d86a..234bbe8 100644 --- a/src/notebooks/Portfolio.ipynb +++ b/src/notebooks/Portfolio.ipynb @@ -6,13 +6,16 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", "from estimark.agents import PortfolioLifeCycleConsumerType\n", "from estimark.parameters import init_calibration\n", - "from estimark.snp import snp_data, snp_data_full\n", - "from HARK.utilities import plot_funcs" + "from estimark.snp import snp_data, snp_data_full" ] }, { @@ -45,7 +48,7 @@ "source": [ "portfolio_agent = PortfolioLifeCycleConsumerType(**init_calibration)\n", "portfolio_agent.CRRA = float(res[\"CRRA\"])\n", - "portfolio_agent.update()\n", + "\n", "portfolio_agent.CRRA, portfolio_agent.DiscFac" ] }, @@ -55,6 +58,7 @@ "metadata": {}, "outputs": [], "source": [ + "portfolio_agent.update()\n", "portfolio_agent.solve()" ] }, diff --git a/src/notebooks/SCF_notebook.ipynb b/src/notebooks/SCF_notebook.ipynb index b38b5f2..e6f59eb 100644 --- a/src/notebooks/SCF_notebook.ipynb +++ b/src/notebooks/SCF_notebook.ipynb @@ -6,11 +6,14 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", + "from statsmodels.stats.weightstats import DescrStatsW\n", + "\n", "from estimark.estimation import get_weighted_moments\n", "from estimark.parameters import age_mapping\n", - "from estimark.scf import scf_data_full\n", - "from statsmodels.stats.weightstats import DescrStatsW" + "from estimark.scf import scf_data_full" ] }, { diff --git a/src/notebooks/WarmGlow.ipynb b/src/notebooks/WarmGlow.ipynb index 318ba89..3cb128b 100644 --- a/src/notebooks/WarmGlow.ipynb +++ b/src/notebooks/WarmGlow.ipynb @@ -14,12 +14,15 @@ } ], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", "from estimark.agents import BequestWarmGlowLifeCycleConsumerType\n", - "from estimark.calibration import parameters\n", - "from HARK.utilities import plot_funcs" + "from estimark.calibration import parameters" ] }, { diff --git a/src/notebooks/WarmGlowPortfolio.ipynb b/src/notebooks/WarmGlowPortfolio.ipynb index b3bc89d..90ad3da 100644 --- a/src/notebooks/WarmGlowPortfolio.ipynb +++ b/src/notebooks/WarmGlowPortfolio.ipynb @@ -6,12 +6,15 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", "from estimark.agents import BequestWarmGlowLifeCyclePortfolioType\n", "from estimark.parameters import init_calibration\n", - "from estimark.snp import snp_data, snp_data_full\n", - "from HARK.utilities import plot_funcs" + "from estimark.snp import snp_data, snp_data_full" ] }, { @@ -33,7 +36,7 @@ { "data": { "text/plain": [ - "(9.206778216146489, 26.1368726540768, 50.64405071849033)" + "(9.206775856414323, 23.05054873023735, 45.64298427855443)" ] }, "execution_count": 3, @@ -61,6 +64,7 @@ "metadata": {}, "outputs": [], "source": [ + "portfolio_agent.update()\n", "portfolio_agent.solve()" ] }, @@ -71,7 +75,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -91,7 +95,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -150,7 +154,7 @@ "outputs": [ { "data": { - "image/png": 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", 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snqej+/bty4ULF9i+fTsAp0+fZs+ePTmKkbzUqlWLyMhIFixYYNm2YMECfH19adWqlWVbWloa48aNs4zbyv6ci4+PL/DrIAqHjJER+VK3bl0iIyP55ZdfePvtt/nll19QFCVHj8B7773H2LFjefrpp5k8eTLe3t5otVpGjRqV56DU/PTGGI1G2rZty40bN3jjjTeIjIzExcWFS5cuMWTIkDyPW1DZx3jyySdznR/PVrNmzXwfr0GDBoSHhzNq1Ciio6MZMGDAPZ93zJgxd+0RqFixYr6f925++uknhgwZQo8ePXjttdfw8/NDp9Mxbdo0Tp8+nWv/7ML0Trf/hTtq1Ci6du3K0qVLWbNmDWPHjmXatGn89ddf1KlT564/i52dHYcOHXronykv+cltrfKT/YknnuCff/7htddeo3bt2ri6umIymejQoUOh/D8ojIz34+7uTtu2bWnbti16vZ558+bx77//0rx587uO8bnbvEJ3+/zo2rUrzs7OLFy4kMaNG7Nw4UK0Wq1lsPa99O3bl6lTpxIXF4ebmxvLli2jf//+2Nn996vypZde4vvvv2fUqFE0atQIDw8PNBoN/fr1K5bXQeQmhYzIt4EDBzJ27FgOHjzI/PnziYiIyDGg7rfffqNly5Z8++23OR4XHx+Pr6/vAz3noUOHOHHiBPPmzctx+fe6devy3N9kMnHmzBlLLwzAiRMnAO46CLVMmTK4ublhNBrvOqFdQfXv358pU6ZQpUqVXINps2X3HOn1+vs+b0hICCdPnsy1/fjx4/fN8ttvvxEWFsaSJUty/LLI7vV5UOHh4bz66qu8+uqrnDx5ktq1azNz5kx++umnPPd3dnamVatW/PXXX1y4cIHg4OB7Hj8kJIT169eTlJSUo1fm2LFjlvsLokyZMjg5OT1wOxZkMG1ISAgHDx7EZDLl6JV50Ow3b95kw4YNTJw4kXHjxlm25/Wz5Dcf5P1zHzt2DF9fX1xcXB7o2PlVr1495s2bx5UrV4D/enji4+Nz7Hdn79X9uLi40KVLFxYtWsRHH33EggULaNq0aa6LDfLSt29fJk6cyOLFi/H39ycxMZF+/frl2Oe3335j8ODBzJw507ItPT09V25RfOTUksi37N6XcePGsX///lzjM3Q6Xa6/zhYtWmQZ5/Egsv8KvP24iqLwf//3f3d9zGeffZZj388++wy9Xm8Zy5PXc/Tu3ZvFixfnOYfJtWvXCpz72WefZfz48Tk+7O7k5+dHixYt+Oqrrywf5nd73k6dOrFjxw527tyZ4/6ff/75vlnyasN///3X0vVeUKmpqblOB4SHh+Pm5kZGRsY9Hzt+/HgUReGpp54iOTk51/179uyxXALbqVMnjEZjjtcT4OOPP0aj0dCxY8cC5dbpdLRv356lS5dy/vx5y/ajR4+yZs2a+z4++xd7fn5hderUiZiYmBynKbKysvj0009xdXW1nBotSHbI3fsxa9asAh0nW2BgILVr12bevHk5fp7Dhw+zdu1aOnXq9EDHvVNqaupd32fZY5yyT29lj2/asmWLZR+j0cjXX39d4Oft27cvly9f5ptvvuHAgQP3Pa2UrUqVKtSoUYMFCxawYMECAgMDadasWY598vqc+/TTT612RurSQHpkRL6FhobSuHFj/vjjD4BchUyXLl2YNGkSQ4cOpXHjxhw6dIiff/75rvM65EdkZCTh4eGMGTOGS5cu4e7uzuLFi+96Xt7R0ZHVq1czePBgGjRowKpVq1ixYgVvv/02ZcqUuevzTJ8+nY0bN9KgQQOee+45qlatyo0bN9i7dy/r16/nxo0bBcodEhKSr7WcPv/8cx577DFq1KjBc889R1hYGFevXmX79u1cvHjRMgfP66+/zo8//kiHDh14+eWXLZdfZ//lfy9dunRhyZIl9OzZk86dOxMdHc2XX35J1apV8ywm7ufEiRO0bt2aJ554gqpVq2JnZ8fvv//O1atXc/31eqfGjRvz+eefM2zYMCIjI3PM7Ltp0yaWLVvGlClTAPMpgpYtW/LOO+9w9uxZatWqxdq1a/njjz8YNWpUjoG9+TVx4kRWr15N06ZNGTZsmKW4qFat2n3bsXbt2uh0OmbMmEFCQgIODg60atUqx1wo2f73v//x1VdfMWTIEPbs2UOFChX47bff+Pvvv5k1a1a+Bzxnc3d3t4xFMhgMlC1blrVr1xIdHV2g49zugw8+oGPHjjRq1IhnnnnGcvm1h4dHoa1DlpqaSuPGjWnYsCEdOnQgODiY+Ph4li5dytatW+nRo4flVGS1atVo2LAhb731Fjdu3MDb25tff/2VrKysAj9vp06dcHNzY8yYMZY/VPKrb9++jBs3DkdHR5555plc45y6dOnCjz/+iIeHB1WrVmX79u2sX7++UGY2Fw+omK+SEjbu888/VwDl0UcfzXVfenq68uqrryqBgYGKk5OT0qRJE2X79u25Lk3Nvixx0aJFuY6R1yWLUVFRSps2bRRXV1fF19dXee655yyX1n7//feW/QYPHqy4uLgop0+fVtq1a6c4Ozsr/v7+yvjx43NdFs4dl18riqJcvXpVGT58uBIcHKzo9XolICBAad26tfL111/ft12yL7++l7wuv1YURTl9+rQyaNAgJSAgQNHr9UrZsmWVLl26KL/99luO/Q4ePKg0b95ccXR0VMqWLatMnjxZ+fbbb+97+bXJZFLee+89JSQkRHFwcFDq1KmjLF++XBk8eLASEhJi2S/7UuC8Lqu+vb3i4uKU4cOHK5GRkYqLi4vi4eGhNGjQQFm4cOF92ynbnj17lAEDBihBQUGKXq9XvLy8lNatWyvz5s3L8VolJSUpr7zyimW/iIgI5YMPPlBMJlOufMOHD8/1PHdeYqsoirJ582albt26ir29vRIWFqZ8+eWXlkuM7/fYOXPmKGFhYZbLtbPfp3e2uaKY309Dhw5VfH19FXt7e6VGjRo53q+Kkv82VxRFuXjxotKzZ0/F09NT8fDwUPr06aNcvnw51375vfxaURRl/fr1SpMmTRQnJyfF3d1d6dq1qxIVFZVjn+y2uX36gvw+j8FgUObMmaP06NHD8v5zdnZW6tSpo3zwwQe5pgU4ffq00qZNG8XBwUHx9/dX3n77bWXdunV5Xn5drVq1e/5sAwcOVAClTZs2ed6f1+urKIpy8uRJBVAAZdu2bbnuv3nzpuV1dXV1Vdq3b68cO3Ys1/Hk8uvio1EUGxgJJ4QQQgiRBxkjI4QQQgibJYWMEEIIIWyWFDJCCCGEsFlSyAghhBDCZkkhI4QQQgibJYWMEEIIIWxWiZ8Qz2QycfnyZdzc3Ao0xbgQQggh1KMoCklJSQQFBeWamPB2Jb6QuXz58n3XdBFCCCGEdbpw4QLlypW76/0lvpDJngr8woULuLu7q5ym5DIYDKxdu5Z27dqh1+vVjlPqSPurR9peXdL+6irK9k9MTCQ4OPi+S3qU+EIm+3SSu7u7FDJFyGAw4OzsjLu7u3yYqEDaXz3S9uqS9ldXcbT//YaFyGBfIYQQQtgsKWSEEEIIYbOkkBFCCCGEzSrxY2SEEKK0MxqNGAwGtWMUCYPBgJ2dHenp6RiNRrXjlDoP0/56vR6dTvfQGaSQEUKIEkpRFGJiYoiPj1c7SpFRFIWAgAAuXLggc4Wp4GHb39PTk4CAgId67aSQEUKIEiq7iPHz88PZ2blE/qI3mUwkJyfj6up6z0nTRNF40PZXFIXU1FRiY2MBCAwMfOAMUsgIIUQJZDQaLUWMj4+P2nGKjMlkIjMzE0dHRylkVPAw7e/k5ARAbGwsfn5+D3yaSV51IYQogbLHxDg7O6ucRIi7y35/PswYLilkhBCiBCuJp5NEyVEY708pZIQQQghhs6SQEUIIUSrNnTsXT09PtWOIhySFjBBCCKsyZMgQNBoNL7zwQq77hg8fjkajYciQIcUf7A6bNm1Co9GU6MvbbYEUMkKIYpWamYXBaFI7hrBywcHB/Prrr6SlpVm2paenM3/+fMqXL//Qx1dzgkCj0YjJZJ3/B2xx4kQpZIQQhWLj8Vg+3XCSy/Fped5/PTmDd34/RPXxa6g2bg1dPt3Km4sP8uP2s+w7fxOTSSnmxMKaPfLIIwQHB7NkyRLLtiVLllC+fHnq1KmTY9/169fTrFkzPD098fHxoUuXLpw+fdpy/9mzZ9FoNCxYsIDmzZvj6OjIzz//nOs5r127Rr169ejZsycZGRmYTCamTZtGaGgoTk5O1KpVi99++81yzJYtWwLg5eV1z16i7FNYy5Yto2rVqjg4OHD+/HkyMjIYM2YMZcuWxcXFhQYNGrBp0ybL486dO0fXrl3x8vLCxcWFatWqsXLlSsv9mzdv5tFHH8XBwYHAwEDefPNNsrKyLPdXqFCBWbNm5chSu3ZtJkyYYPleo9Ewe/ZsunXrhouLC1OnTgXgzz//pH79+jg6OuLr60vPnj0tj7k9t5ubG23atClQ7sIm88gIIR6a0aQwcv4+kjKy+L8NJ+lWO4gXmodTyd+NjCwj8/45y6cbTpGUYf6QzTSaOHwpkcOXEi3HCPV14ekmFehdtxzO9vLRVBQURSHNoM40/k56XYGvUHn66af5/vvvGThwIADfffcdQ4cOzfFLEyA1NZVRo0ZRu3ZtkpOTGTduHD179mT//v055jZ58803mTlzJnXq1MHR0ZE1a9ZY7rtw4QJt27alYcOGfPvtt+h0OqZOncpPP/3El19+SUREBFu2bOHJJ5+kTJkyPPbYYyxevJjevXtz/Phx3N3dLfOi5CU1NZUZM2bwzTff4OPjg5+fHyNGjCAqKopff/2VoKAgfv/9dzp06MChQ4eIiIhg+PDhZGZmsmXLFlxcXIiKisLV1RWAS5cu0alTJ4YMGcIPP/zAsWPHeO6553B0dMxRqOTHhAkTmD59OrNmzcLOzo4VK1bQs2dP3nnnHX744QcyMzNzFCK35w4ICODXX3+lU6dO+cpdFOTTQgjx0E7GJpGUkYVGA1kmhSV7L7Fk7yVaRfpx+loy566nAlAtyJ13O1clyNORI5cTOXI5gSOXE9lz9ibRcSmM/eMIH649wcAG5RnUqAIBHo4q/2QlS5rBSNVxa+6/YxGImtS+wAXqk08+yVtvvcW5c+cA+Pvvv/n1119zFTLdunXD3d3dUrR89913lClThqioKKpXr27Zb9SoUfTq1SvX8xw/fpy2bdvSs2dPZs2ahUajISMjg/fee4/169fTqFEjAMLCwti2bRtfffUVzZs3x9vbGwA/P7/7Dho2GAx88cUX1KpVC4Dz58/z/fffc/78eYKCggAYM2YMq1ev5vvvv+e9997j/Pnz9O7dmxo1alieP9sXX3xBcHAwn332GRqNhsjISC5fvswbb7zBuHHjCjQ53YABAxg6dKjl+379+tGvXz8mTpxo2Xa33CaTiZdeeonNmzfnK3dRkEJGCPHQ9p+PB6BhqA9vdIzky02nWRMVw1/HzNOPl3Fz4LX2len9SDl0WvNf5SE+LnSqYZ6WPCUji9/2XOS7v6M5dz2VLzad5qstZ6gX4kXbqv60qeJPBV8XwNyrcOFGGvsvxhN1OZHqZd3pXCNQ5kspgcqUKUPnzp2ZO3cuiqLQuXNnfH19c+13+vRpPvjgA3bu3ElcXJxl/Mn58+dzFDL16tXL9di0tDSaNm3KgAEDcpyGOXXqFKmpqbRt2zbH/pmZmblObeWHvb09NWvWtHx/6NAhjEYjlSpVyrFfRkaGZSbmkSNH8uKLL7J27VratGlD7969Lcc4evQojRo1yvG+b9KkCcnJyVy8eLFA44jubJf9+/fz3HPP5bnvw+YuClLICCEe2oGL8QDUCvakdrAnXz5Vl9PXkvlx+zm8nO15tmkoLg53/7hxcbBjcOMKPNkwhPVHr/Lt1mh2nr3Bv9Hm25QVR6no50qQpxOHLsZzMzXngMR/GlxnYrdq6HUy7O9enPQ6oia1V+25H8TTTz/NiBEjAPj888/z3Kd///5UqFCBOXPmWHoJqlevTmZmZo79XFxccj3WwcGBNm3asHz5cl577TXKli0LQHJyMgArVqywbLv9MQXl5OSUo+hITk5Gp9OxZ8+eXFPzZ5+GefbZZ2nfvj0rVqxg7dq1TJs2jZkzZ/LSSy/l6zm1Wi2KknPsWV6Dee9sl3udIrsz9+1rLbm7uxdK7oKSQkYI8dD2X0gAoHawp2VbeBlXJnSrVqDj6LQa2lcLoH21AC7cSGXD0ausPxrLjjPXORWbzKlY8y8Xe52WKkHulPd2ZvnBy8z/9zxn41L4YuAjeDrbF9rPVdJoNBqbG3/UoUMHMjMz0Wg0tG+fuwi7fv06J0+eZM6cOTRv3hyAbdu25fv4Wq2WH3/8kQEDBtCyZUs2bdpEUFBQjkG52ce9k729+b1mNBZ83FGdOnUwGo3ExsbStGnTu+4XHBzMCy+8wAsvvMBbb73FnDlzeOmll6hSpQqLFy9GURRLgfT333/j5uZGuXLlAHOP1pUrVyzHSkxMJDo6+r7ZatasyYYNG3KcbrpbbpPJRGJiYo5Te/fKXRRs6x0thLA6qZlZHI8xD9q9vZB5WMHezgxpEsqQJqEkphvYcuIa8akGapT1IDLQDQc781+x3WsF8fKv+/jn9HV6fvEP3w6uR1iZ/wYWphuMJGdk4eta8L+ihfp0Oh1Hjx61/PtOXl5eeHt7M2fOHMqWLcv58+d58803C/wcP//8M/3796dVq1Zs2rSJgIAAxowZwyuvvILJZOKxxx4jISGBv//+G3d3dwYPHkxISAgajYbly5fTqVMnnJyc8j2otVKlSgwcOJBBgwZZBiBfu3aNDRs2ULNmTTp37syoUaPo2LEjlSpV4ubNm2zcuJEqVaoAMGzYMGbNmsVLL73EiBEjOH78OOPHj2f06NGWgqJVq1bMnTuXrl274unpybhx4/K1MOP48eNp3bo14eHh9OvXj6ysLFauXMkbb7yRK3etWrU4e/Ys//77L7Vq1bpv7qIghYwQ4qEcvpSISQF/d4ciG5zr7qinS82gPO9rU9Wf315szLPzdhMdl0KPz//m0VAfriSkcSUhnRsp5tMLE7pWZUiT0CLJJ4pW9imLvGi1Wr799lvefvttqlevTuXKlfnkk09o0aJFgZ7Dzs6OX375hb59+1qKmcmTJ1OmTBmmTZvGmTNn8PT05JFHHuHtt98GoGzZskycOJE333yToUOHMmjQIObOnZvv5/z++++ZMmUKr776KpcuXcLX15eGDRvSpUsXwNzTM3z4cC5evIi7uzsdOnTg448/tjz3ypUree2116hVqxbe3t4888wzvPvuu5bjv/XWW0RHR9OlSxc8PDyYPHlyvnpkWrRowaJFi5g8eTLTp0/H3d2dZs2a3TW3j48PDRs2pGvXrvfNXRQ0yp0n0EqYxMREPDw8SEhIuOd/BvFwDAYDK1eupFOnTuj1erXjlDpqtv+cLWeYuvIo7av589VTuQdTFpdrSRn878fd7Ls18PhObo52bH6tJd4uhXvqyVrf++np6URHRxMaGoqjY8m9+utupzZE8XjY9r/X+zS/v7+lR0YI8VD23zbQV01l3Bz45bmGLNt/mUyjiSBPRwLcnQjwcOTJb/4l6koin2w4WeBxO0II6yblqxDioWRfel27nKeqOQAc9TqeqB/Mkw1DaBXpT9Ugd7xd7Hm7k/n8/E87znE2LkXllEKIwiSFjBDigV1LyuBSfBoaDdQo56F2nLt6LMKX5pXKkGVSeH/NMbXjCCEKkRQyQogHdvDWaaWKZVxxc7Se8SF5ebtTFbQaWHkohj3nbqgdRwhRSKSQEUI8sP0X4gH1x8fkR+UAN/rUDQZg6oqjuSYKE0LYJilkhBAPLLuQKcz5Y4rS6HaVcNLr2Hs+nlWHY9SOI4QoBFLICCEeiKIoHLCxQsbf3ZHnmpkXsJux+hhpmeqsBC2EKDxSyAghHkh0XAqJ6Vk42GmpHOCmdpx8e75ZGL6uDpy7nkqrmZv4fd9FTCY5zSSErZJCRgjxQLIXiqxe1sOmFmt0cbDjswF1KOvpxJWEdF5ZcIAeX/zNzmgZACyELbKdTx8hhFU5cGuhyFpWMH9MQTUM82HDq815o0Mkrg52HLyYwBNfbWfE/L2kZmapHU+Iu9JoNCxdulTtGFZFChkhxAP574ol650/5l4c9TpebBHOptdaMLBBebQaWH7wCoO+3UliukHteAKIiYnhpZdeIiwsDAcHB4KDg+natSsbNmxQO1qRmzBhArVr1861/cqVK3Ts2LH4A1kxKWSEEAWWkWUk6rJ5xes6wV4qp3k4vq4OTO1Zg0UvNMLd0Y7d524yYM4Oy2KTQh1nz56lbt26/PXXX3zwwQccOnSI1atX07JlS4YPH652PNUEBATg4CArud9OChkhRIEdu5JEptGEl7OeYG8nteMUiroh3vzyv4b4uNhz+FIifb/aztXEdLVjlVrDhg1Do9Gwc+dOevfuTaVKlahWrRqjR49mx44dAJw/f54ePXpQrlw5PD09eeKJJ7h69arlGNm9Gj/++CMVKlTAw8ODfv36kZSUZNnnt99+o0aNGjg5OeHj40ObNm1ISTEvY9GiRQtGjRqVI1ePHj0YMmSI5fsKFSowZcoUBg0ahKurKyEhISxbtoxr167RvXt3XF1dqVmzJrt377Y8Zu7cuXh6erJ06VIiIiJwdHSkffv2XLhwwXL/xIkTOXDgABqNBo1GY1lV+85TS4cOHaJVq1aW/P/73/9ITk623D9kyBB69OjBhx9+SGBgID4+PgwfPhyDoeT0OqpayGzZsoWuXbsSFBSU53k/RVEYN24cgYGBODk50aZNG06ePKlOWCGExYHbForUaDTqhilE1YI8WPB8IwLcHTkZm8wTX23nwo1UtWMVHkWBzBR1bgWYgPDGjRusXr2a4cOH4+Likut+T09PTCYT3bt35+bNmyxfvpw1a9Zw5swZ+vbtm2Pf06dPs3TpUpYvX87y5cvZvHkz06dPB8ynafr378/TTz/N0aNH2bRpE7169SrwZIkff/wxTZo0Yd++fXTu3JmnnnqKQYMG8eSTT7J3717Cw8MZNGhQjuOmpqYydepUfvjhB/7++2/i4+Pp168fAH379uXVV1+lWrVqXLlyhStXruT6uQBSUlJo3749Xl5e7Nq1i0WLFrF+/XpGjBiRY7+NGzdy+vRpNm7cyLx585g7d66lMCoJVF39OiUlhVq1avH000/Tq1evXPe///77fPLJJ8ybN4/Q0FDGjh1L+/btiYqKKtHL0gthjWIT09kRfYPtp6+z/qj5r15bHOh7PxX9XFn0QiMGfLODc9dTGfDNDpaPaIqHs3UvwZAvhlR4L0id5377MtjnLkrycurUKRRFITIy8q77bNiwgUOHDnH69Gk8PDxwd3fnhx9+oFq1auzatYv69esDYDKZmDt3Lm5u5ikCnnrqKTZs2MDUqVO5cuUKWVlZ9OrVi5CQEABq1KhR4B+tU6dOPP/88wCMGzeO2bNnU79+ffr06QPAG2+8QaNGjbh69SoBAQEAGAwGPvvsMxo0aADAvHnzqFKlCjt37uTRRx/F1dUVOzs7y/55mT9/Punp6fzwww+Wgu+zzz6ja9euzJgxA39/fwC8vLz47LPP0Ol0REZG0rlzZzZs2MBzzz1X4J/VGqnaI9OxY0emTJlCz549c92nKAqzZs3i3XffpXv37tSsWZMffviBy5cvy4htIYrRrrM3aPfxZh59bwMjf9nHLzvPcy0pA0e9lrZV/dWOVySCvZ1Z9Hxjgr2duHAjjVcXHZAlDYpRftr66NGjBAcHExwcbNlWtWpVPD09OXr0qGVbhQoVLEUMQGBgILGxsQDUqlWL1q1bU6NGDfr06cOcOXO4efNmgfPWrFnT8u/s4uH2gih7W/bzAtjZ2VmKLYDIyMhc2e/n6NGj1KpVK0evVZMmTTCZTBw/ftyyrVq1auh0Osv3t7dBSaBqj8y9REdHExMTQ5s2bSzbPDw8aNCgAdu3b7d0wQkhis7RK4k8/f0ukjKy0GigSoA7jcJ9aBTmQ/1QbzycSkAvxV0EeDjyxYC69J79D+uPXuWbrdGWWYFtlt7Z3DOi1nPnU0REBBqNhmPHHn6lcr0+53tUo9FgMpkA0Ol0rFu3jn/++Ye1a9fy6aef8s477/Dvv/8SGhqKVqvNVVTlNbbk9ufIPtWa17bs5y1u92qDksBqC5mYGPM6KNmVbDZ/f3/LfXnJyMggIyPD8n1iovnKCoPBUKIGN1mb7LaVNlZHUbT/pfg0Bn+3k6SMLOqFePLFgNp4Odvn+bwlVaS/M+90qsz4P48yffUxagS5Ujck51Va1vreNxgMKIqCyWTK+UvLTqXB2YqS73Eynp6etGvXjs8//5wRI0bkGicTHx9P5cqVuXDhAufPn8fT0xNFUTh8+DDx8fFERkZiMpksRcjtP39e2xo1akSjRo149913CQ0NZcmSJbzyyiv4+vpy+fJly75Go5HDhw/TokWLXMe8szC4vd1v/5p9y8rKspxGAjh+/Ljl5zKZTOj1eoxGY54FR/YxKleuzNy5c0lKSrK00datW9FqtURERFja4M58ebXBg8o+Vl5tkB/ZGQ0GQ45eI8j//ymrLWQe1LRp05g4cWKu7WvXrsXZOf9/EYgHs27dOrUjlGqF1f4pBph1WEdsuoZAJ4XefnFs37S+UI5tazwUeMRHy97rWp7/YSev1zTimkdHlLW997PHVyQnJ5OZaXuXkk+fPp0OHTrw6KOP8tZbb1GtWjWysrLYtGkT3333HTt27KBq1aoMGDCAadOmkZWVxZgxY2jSpAmVKlUiMTGRjIwMjEaj5Q9agPT0dEwmE4mJiezevZvNmzfTqlUrfH192bNnD9euXaN8+fIkJiZaiptFixYRGhrKF198wc2bNzEYDJZjmkwm0tPTczwHQFpammVb9lVEKSkpJCYmkp6ejl6vZ8SIEUyfPh07Oztef/116tevT2RkJImJifj5+REdHc3ff/9NUFAQrq6ulsuus4/dtWtXJkyYwJNPPskbb7zB9evXGTlyJH379sXJyYnExEQMBgNZWVk58mVmZuba9rBuvxKsIDIzM0lLS2PLli1kZeWcjDI1NX8D7a22kMke4HT16lUCAwMt269evZrnJEHZ3nrrLUaPHm35PjExkeDgYNq1a4e7u3uR5S3tDAYD69ato23btrm6MUXRK8z2T8s0MmjubmLTEwj0cGTBc48S6FG6B9c3z8ii95c7OBOXyup4f7556hG0WvPpAmt976enp3PhwgVcXV1t8uKImjVrsmfPHt577z3GjRvHlStXKFOmDI888gizZ8/Gw8ODZcuWMXLkSDp37oxWq6V9+/Z88sknls96BwcHdDpdjs9+R0dHtFot7u7uBAYGsnPnTr766isSExMJCQnhww8/pHfv3oD5EvATJ04wbNgw7OzsGDVqFC1btkSv11uOqdVqcXR0zPX7xcnJybLN1dUVABcXF9zd3XF0dMTZ2Zk333yT559/nkuXLvHYY4/xzTffWB7z5JNPsnr1arp160Z8fDzffvut5bLv7GO7u7uzevVqXnnlFVq3bo2zszO9evVi5syZlufU6/XY2dnlyGdvb59r24NSFIWkpCTc3Nwe6ArG9PR0nJycaNasWa73aX4LLY1iJSPYNBoNv//+Oz169ADMjRMUFMSYMWN49dVXASxV6ty5c/M9RiYxMREPDw8SEhKkkClCBoOBlStX0qlTJ6v6MC8tCqv9k9INjPp1PxuOxeLhpOe3FxoR4W87C0IWpeMxSXT/fBvpBhP9Hw3mnc5VcXWws9r3fnp6OtHR0YSGhtpkIZNf2b0r7u7uaLW2MTXa3LlzGTVqFPHx8WpHeWgP2/73ep/m9/e3qq96cnIy+/fvZ//+/YB5gO/+/fs5f/48Go2GUaNGMWXKFJYtW8ahQ4cYNGgQQUFBlmJHCFE40jKNfL3lNM3e38iGY7E42Gn5dnA9KWJuUznAjak9zFei/LLzAm1mbmb14StyNZMQKlP11NLu3btp2bKl5fvsU0KDBw9m7ty5vP7666SkpPC///2P+Ph4HnvsMVavXl2i/7oQojhlZBlZsOsCn/11itgk8yD5MF8XpvSoTr0K3iqnsz6965bDx9WecX8c4fyNVF74aS8tKvnSLH/TowghioCqhUyLFi3u+deMRqNh0qRJTJo0qRhTCVE6JGdk0euLvzlx1TwQsaynEy+3iaBXnbLY6Wyji14NLSr7sfYVHz7feIovN59m04k4/tbqMAae45nHwi1jZ4S4myFDhuRY5kA8HPm0EqKU+mLjKU5cTcbbxZ7J3auxcUwLnqgXLEVMPjjqdbzarjKrXm5Gg1AvDCYNU1ceZ/D3O2V9JiGKmXxiCVEKXbiRyjfbogGY3qsGTzWqgL2dfBwUVEU/V34cWo/HQ4046rVsPRlH+1lbWHXoitrRLGQMj7BmhfH+lE8uIUqh6auPkZllonG4T4ldZqC4aDQamgYoLH2xEdXLuhOfauDFn/fy2qIDZGapN3tq9hVU+Z2LQwg1ZL8/H+aKP6udR0YIUTR2nb3BioNX0GpgbJeqJWr1ajWFl3FhyYtN+L8NJ5i96TSL9lwkrIwrL7YIVyWPTqfD09PTsqaOs7NziXytTSYTmZmZpKen28zl1yXJg7a/oiikpqYSGxuLp6dnrll9C0IKGSFKEZNJYdKfUQD0rV+eKoEyt1JhsrfT8lr7SEK8XXh98UG+2HSKfvWD8XKxv/+Di0D2xKIlaYHAOymKQlpaGk5OTiWyULN2D9v+np6e91zhOz+kkBGiFFmy7xKHLiXg6mDH6LaV1I5TYvWuW47v/o7mWEwSX2w6xTudq6qSQ6PREBgYiJ+fn9WtBVVYDAYDW7ZsoVmzZlY1IWFp8TDtr9frH6onJpsUMkKUEikZWby/2rya8IhWFSnj5qByopJLp9XwZsdIhny/i3n/nGNw4wqU81JvrTedTlcovzCskU6nIysrC0dHRylkVGAN7S8nFIUoJb7YZJ70rry3M0ObVFA7TonXvFIZGoX5kGk08dG6E2rHEaLEkkJGiFLgm61n+HzjaQDe6hiJg13J/Ovcmmg05l4ZgN/3XSLqcuGtNCyE+I8UMkKUYIqiMG3VUaasOArAM4+F0qH6ww2sE/lXK9iTzjUDURR4f80xteMIUSJJISNECZVlNPH6bwf5avMZAN7oEMm7navIlR3F7LV2lbHTath0/Br/nI5TO44QJY4UMkKUQOkGIy/8tJdFey6i1cCM3jV4sUW4FDEqqODrwoAG5QGYvuoYJpPMtCtEYZJCRogSRlEURv6yj/VHr2Jvp+XLJ+vSt355tWOVaiNbR+Bir+PgxQR+2H5W7ThClChSyAhRwvyw/Rxro65ir9Pyw9OP0q6ajIlRm6+rg2Xg7/TVxzgbl6JyIiFKDilkhChBjl5JYupK88DetzpF0jDMR+VEItvABiE0Dvch3WBizKIDGOUUkxCFQgoZIUqIDCOMWmheqLBNFT+GNK6gdiRxG61Ww4zeNXGx17H73E2+/zta7UhClAhSyAhRQiyO1nImLhV/dwfef7yWDOy1QsHezpblCj5Yc5zT15JVTiSE7ZNCRogSYPnBK/x7TYtGA7P61sFbpUUKxf31fzSYphG+ZGSZeHWhnGIS4mFJISOEjbtwI5Wxy8zjYoY1D6NRuIyLsWYajfkUk5uDHfsvxPP1ljNqRxLCpkkhI4SNm/jnEZIzsgh1UxjRIkztOCIfgjydGNvVfIpp1voTXLiRqnIiIWyXFDJC2LB/TsWx/mgsdloN/cON2Onkv7St6FO3HI3CfMjIMjFlRZTacYSwWfKpJ4SNMpoUyxpK/euXw99J5UCiQDQaDRO7V0On1bDmyFU2n7imdiQhbJIUMkLYqMV7LxJ1JRE3RztGtAxXO454AJX83SyXyU9cdoTMLJO6gYSwQVLICGGDUjKy+HDNcQBealVRrlKyYS+3icDX1YEzcSl8J3PLCFFgUsgIYYO+3nKG2KQMgr2dGCwT39k0d0e9ZfmCTzacJCYhXeVEQtgWKWSEsDExCel8teU0AG92qIKDnU7lROJh9apTlkfKe5KaaeS9W0tMCCHyRwoZIWzMh2uPk24wUTfEi041ZEHIkkCr1TCpe3U0Glh24DI7zlxXO5IQNkMKGSFsyL9nrrN470UA3u1cRZYhKEGql/VgwKPlAXj9t4MkpBpUTiSEbZBCRggbceFGKi/+vBdFgd6PlKNOeS+1I4lC9nr7SMp5OXH+RiqjF+7HJMsXCHFfUsgIYQOSM7J4dt5ubqRkUr2sO1N6VFc7kigCHs56vnyyLvZ2WjYci2X25tNqRxLC6kkhI4SVM5oURv26j+NXkyjj5sCcQfVwspcBviVV9bIeTO5eDYCZa4+z7WScyomEsG5SyAhh5T5ce5z1R2Oxt9Py9VN1CfSQKXxLur71y/NEvXKYFBj56z4ux6epHUkIqyWFjBBW7Pd9F5m9yXx64f3eNWVcTCkyqXt1qgW5cyMlk2E/75VZf4W4CylkhLBSF26k8ubiQwAMaxFOjzplVU4kipOjXsfsgXVxd7Rj/4V4vpTxMkLkSQoZIazU9NXHyMgy0SjMhzHtKqsdR6igvI8zk7qbB3Z/93c0KRlZKicSwvpIISOEFdpz7gYrDl5Bo4FxXaui1cp8MaVV11pBhPq6EJ9qYP6/59WOI4TVkUJGCCtjMilMWm6epr5vvWCqBLqrnEioSafV8GJz8+rmX289Q7rBqHIiIayLFDJCWJk/D17mwIV4XOx1jG5XSe04wgr0qFOWIA9HriVl8Nuei2rHEcKqSCEjhBVJyzQyY9UxAIa1rIifm6PKiYQ1sLfT8r9mYQB8ufk0BqNcwSRENilkhLAi3247w+WEdMp6OvHMY6FqxxFWpN+j5fF1tefizTT+PHBZ7ThCWA0pZISwErFJ6Xxxa86Y1ztUxlEvs/eK/zjqdTx9q7j9YtNpWYdJiFukkBHCSsxYdZzUTCO1gz3pVitI7TjCCj3VMAR3RztOxSazNipG7ThCWAUpZISwAnP/jmbxXvMgzrFdqqDRyOXWIjc3Rz1DGlcA4LONp1AU6ZURQgoZIVS2Puoqk5ZHAfBa+8rUDfFWOZGwZkOahOKk13H4UiIrD0mvjBBSyAihokMXE3jpl32YFOhXP5hhLcLVjiSsnLeLPc/duoJp7B+HiUvOUDmREOqSQkYIlVyKT+PpebtIMxhpGuHL5B7V5ZSSyJcRLSsSGeDGjZRM3v39sJxiEqWaFDJCqCAx3cDQ73dyLSmDyAA3Ph/4CHqd/HcU+WNvp2XmE7Ww02pYfSSGZXI5tijF5JNTCBW8ufggJ64m4+fmwHdD6uPuqFc7krAx1YI8eKlVBADj/jjC1cR0lRMJoQ4pZIQoZuuirrLyUAx2Wg3fDK5HkKeT2pGEjRrWMpwaZT1ISDPw9pJDcopJlEpSyAhRjFIyshj/x2EAnm0aRs1ynuoGEjZNr9PyYZ9a2Ou0bDgWK+swiVJJChkhitHMtSe4nJBOsLcTL7eOUDuOKAEqB7gxqq35vTTpzyhi5RSTKGWkkBGimBy6mMDcf6IBmNy9Ok72sgSBKBz/axpGrXIeJGVkMXnFUbXjCFGspJARohhkGU289ftBTAp0rRVEi8p+akcSJYidTsvUnjXQauDPA5fZevKa2pGEKDZSyAhRDOb+c5bDlxJxd7RjXJeqascRJVD1sh4MalQBMF/FlG4wqhtIiGIihYwQRexSfBofrTsBwFudqlDGzUHlRKKkerVdJfzcHIiOS+GrzWfUjiNEsZBCRogi9tHaE6RmGqkX4kXfesFqxxElmJujnrG3evw+33SKs3EpKicSouhZdSFjNBoZO3YsoaGhODk5ER4ezuTJk2WuBGEzzlxL5vd92ataV0WrlSUIRNHqUjOQphG+ZGaZGPuHLF8gSj6rLmRmzJjB7Nmz+eyzzzh69CgzZszg/fff59NPP1U7mhD58smGk5gUaFPFj1rBnmrHEaWARqNhUvfq2Ntp2XoyjhWHrqgdSYgiZdWFzD///EP37t3p3LkzFSpU4PHHH6ddu3bs3LlT7WhC3Nep2CT+uLUGzqg2lVROI0qTUF8Xy0rqU1cclYG/okSz6kKmcePGbNiwgRMnzAMlDxw4wLZt2+jYsaPKyYS4v1nrT6Io0L6aP9XLeqgdR5QyLzQPJ9DDkSsJ6fyy87zacYQoMnZqB7iXN998k8TERCIjI9HpdBiNRqZOncrAgQPv+piMjAwyMjIs3ycmJgJgMBgwGAxFnrm0ym5baWOz4zFJli79ES3CirxdpP3VY61trwNebB7KuGVH+XzjKXrXDiyRkzBaa/uXFkXZ/vk9pkax4pFgv/76K6+99hoffPAB1apVY//+/YwaNYqPPvqIwYMH5/mYCRMmMHHixFzb58+fj7Ozc1FHFgKA745rOXBDS21vE0Mrm9SOI0qpLBNM3a/jRoaG7iFGWgVZ7ce9ELmkpqYyYMAAEhIScHd3v+t+Vl3IBAcH8+abbzJ8+HDLtilTpvDTTz9x7NixPB+TV49McHAwcXFx92wI8XAMBgPr1q2jbdu26PV6teOo6uiVJLp9sR2NBpYPb0Qlf7cif05pf/VYe9sv3nuJN38/gpeznr9GN8XVwao74gvM2tu/pCvK9k9MTMTX1/e+hYxVv6NTU1PRanMO49HpdJhMd/8L18HBAQeH3BOO6fV6eZMXA2ln+GyTeSKyLjWDqFbOu1ifW9pfPdba9o/XK8/XW89yJi6F+bsuMbxlRbUjFQlrbf/SoijaP7/Hs+rBvl27dmXq1KmsWLGCs2fP8vvvv/PRRx/Rs2dPtaMJkaf9F+JZG3UVrQZZ3VpYBTudlpfbmN+LX285Q2K6jCURJYtVFzKffvopjz/+OMOGDaNKlSqMGTOG559/nsmTJ6sdTYhcTCaF8X8cBqBnnXJU9HNVOZEQZl1qBhHh50pCmoFvt0arHUeIQmXVhYybmxuzZs3i3LlzpKWlcfr0aaZMmYK9vb3a0YTIZcHuCxy4mICrgx1vdKysdhwhLHRaDa+0Nc9l9O22aG6mZKqcSIjCY9WFjBC2Ij41k/dXmwegj2oTgZ+bo8qJhMipQ7UAqgS6k5yRxZytsqCkKDmkkBGiEHy49jg3Uw1U8ndlcOMKascRIhetVsPoW70y8/45S3yq9MqIkkEKGSEe0uFLCfz8r3nm1Endq6PXyX8rYZ3aVPGjSqA7KZlG5v5zVu04QhQK+cQV4iGYTArj/jiMokC3WkE0DPNRO5IQd6XRaBhx6/Lr7/8+S5JcwSRKAClkhHgIi/deZO/5eFzsdbzTuYracYS4rw7VAwgv40JCmoGfdsgaTML2SSEjxAOKS85g+irzAN+RrSPwd5cBvsL66bQay6R432w9Q2pmlsqJhHg4UsgI8QBMJoXRCw9wPSWTyv5uDG0SqnYkIfKtW60ggr2duJ6SyS87L6gdR4iHIoWMEA9gztYzbDlxDUe9lk8H1MHeTv4rCdthp9MyrIW5V+brLadJNxhVTiTEgyvQp29KSgrjxo2jevXquLq64ubmRs2aNZk0aRKpqalFlVEIq7L3/E0+WHMcgPFdqxXLopBCFLZej5Ql0MORq4kZ/LbnotpxhHhg+S5kMjMzad68Oe+//z4RERG89NJLDB8+nNDQUKZOnUrr1q0xGGQEvCjZEtIMjPxlH1kmhc41A+lXP1jtSEI8EAc7Hc83CwNg9qbTGIx3X4xXCGuW79WvZ8+ezcWLFzlw4ACVK+ecfv3YsWO0aNGCL7/8kpdeeqnQQwphDRRF4a0lB7l4M41gbyem9aqBRqNRO5YQD6zfo+X5bONpLsWn8cf+yzxet5zakYQosHz3yCxZsoSxY8fmKmIAIiMjeeedd/jtt98KNZwQ1mT+zvOsPBSDnVbDp/0fwd2xcJesF6K4Oep1DG1SAYBfd8ql2MI25buQiYqKokWLFne9v2XLlkRFRRVGJiGszuX4NKauOArAGx0iqR3sqW4gIQrJ43XLodXA7nM3OX0tWe04QhRYvguZ+Ph4fHzuPmupj48PCQkJhRJKCGszeXkUqZlG6oV48cxjcqm1KDn83R1pXqkMgAz6FTYp34WMyWRCp9Pd/UBaLUajXMInSp5Nx2NZdTgGnVbD5B7V0WplXIwoWfrUMw9aX7L3IkaTonIaIQom34N9FUWhdevW2Nnl/ZCsLJkdUpQ86QYj45cdAWBI4wpUCXRXOZEQha91FT88nfVcTcxgy8lrtKzsp3YkIfIt34XM+PHj77tP7969HyqMENbmq81nOHc9FT83B0a1iVA7jhBFwsFOR4/aZZn7z1kW7b4ghYywKYVayAhRkpy7nsLnm04B8G6XqrjJVUqiBOtTrxxz/znL+qhYbqZk4uVir3YkIfLloedV37x5MytXruTmzZuFkUcIq6AoChOWHSEzy0STij50rRmodiQhilS1IA+qBrqTaTTxx/5LascRIt/yXcjMmDGDsWPHWr5XFIUOHTrQsmVLunTpQpUqVThy5EiRhBSiuK05cpWNx6+h12mY1L26THwnSoU+9cwT4i2Sq5eEDcl3IbNgwQKqV69u+f63335jy5YtbN26lbi4OOrVq8fEiROLJKQQxSk5I4uJf5qL8ueahhFexlXlREIUjx61y2Kv03LkciJHLst0GsI25LuQiY6OpmbNmpbvV65cyeOPP06TJk3w9vbm3XffZfv27UUSUojiNHPtca4kpBPs7cRLrWSAryg9vFzsaVPVPNB30W7plRG2Id+FTFZWFg4ODpbvt2/fTuPGjS3fBwUFERcXV7jphChmBy/GM++fswBM6VEDJ/u7z50kREnUp655Tpk/9l8iM0sWkhTWL9+FTHh4OFu2bAHg/PnznDhxgmbNmlnuv3jx4j1n/hXC2mUZTby15BAmBbrVCrLMdipEadI0whc/NwduphpYceiy2nGEuK98FzLDhw9nxIgRPPPMM3Ts2JFGjRpRtWpVy/1//fUXderUKZKQQhSHuf+c5cjlRNwd7Rjbper9HyBECWSn0zKoUQgAX2w8jUlm+hVWLt+FzHPPPccnn3zCjRs3aNasGYsXL85x/+XLl3n66acLPaAQxeHizVRmrj0BwNudqlDGzeE+jxCi5HqqUQXcHOw4GZvM2qirascR4p7yPSEewNNPP33XYuWLL74olEBCFDdFURj/xxHSDEbqV/DiiVvrzghRWnk46RnUOITPN57m842naF/NX6YgEFYr34XMwYMH89zu4eFB+fLl5U0ubNaaI1fZcCwWvU7Dez1ryKKQQgBPNwnl223RHLqUwNaTcTSTMWPCSuW7kKlduzYajQZFyXm+VKPR4OjoyKhRo5g0adI9V8gWwtpkGU28v/oYAP9rFkaEv5vKiYSwDj6uDvR/tDzf/32WzzaekkJGWK18FzLR0dF5bo+Pj2fPnj2MHTsWLy8vxowZU2jhhChqC3df5ExcCt4u9rzQPFztOEJYlf81C+OnHefYGX2DXWdvUL+Ct9qRhMgl34VMSEjIXbfXqlULd3d3Jk6cKIWMsBlpmUZmrTcP8B3RsqIsCinEHQI9nHi8bjl+2XmBz/46xbynH1U7khC5PPSikdnq1q17114bIazR9/9EE5uUQTkvJwY2LK92HCGs0gvNw9FqYPOJaxy6KMsWCOtTaIVMTEwMZcrIOVRhG+JTM5m96TQAo9tWwsFOxnYJkZcQHxe61QoC4ItNp1ROI0RuhVLIXLt2jbFjx9KyZcvCOJwQRW72ptMkpWcRGeBG99pl1Y4jhFV7sUVFAFYfieH0tWSV0wiRU77HyNSpUyfPS6wTEhK4ePEilStX5qeffirUcEIUhSsJacy9tZ7S6x0qo5PLrYW4p8oBbrSp4sf6o7F8uy2a93rWUDuSEBb5LmR69OiR53Z3d3cqV65M+/bt5dJrYRNmrTtJRpaJRyt407Kyn9pxhLAJzzYNY/3RWBbvucirbSvh4yqzXwvrkO9CZvz48UWZQ4hiEXU5kUV7LgDwRsdImchRiHxqEOpNzXIeHLyYwE87zvNymwi1IwkBFOJgXyGsXbrByKgF+zAp0KlGAHVDvNSOJITN0Gg0PNs0DIAftp8l3WBUOZEQZlLIiFJj+qpjnLiajK+rA5O7V1c7jhA2p1P1AMp6OnE9JZPf911SO44QgBQyopTYfOKaZYDvB31qyvl9IR6AnU7L0CYVAPhm6xlMJuXeDxCiGOSrkElMTCzqHEIUmevJGYxZdACAwY1CZICvEA+hb/1g3BzsOH0thY3HY9WOI0T+ChkvLy9iY81v2FatWhEfH1+UmYQoNIqi8NaSQ1xLyqCinytvdaqidiQhbJqbo54BDcwzYc/ZekblNELks5BxdXXl+vXrAGzatAmDwVCkoYQoLAt2XWBt1FX0Og2z+tbGUS9TBAjxsIY0qYCdVsOOMzdk2QKhunxdft2mTRtatmxJlSrmv2Z79uyJvb19nvv+9ddfhZdOiIdw/noqk5ZHAfBqu8pUL+uhciIhSoZADye61Axk6f7LfL31DJ/2r6N2JFGK5auQ+emnn5g3bx6nT59m8+bNVKtWDWdn56LOJsQDM5kUxvx2gNRMI4+GevPcrctGhRCF47lmYSzdf5nlBy/zYvNwqga5qx1JlFL5KmScnJx44YUXANi9ezczZszA09OzKHMJ8VDmbT/LzugbONvr+PDxWrIMgRCFrFqQB11rBfHngcu8t/IoPz7zqEwwKVRR4MuvN27caCliFEVBUeTyO2FdzlxLZsbqYwC81akK5X2k91CIovB6+8rY67RsOxXHphPX1I4jSqkHmkfmhx9+oEaNGjg5OeHk5ETNmjX58ccfCzubEAVmNCm89ttB0g0mmlT0YeCj5dWOJESJFeztzJBb88q8t+IoWUaTuoFEqVTgQuajjz7ixRdfpFOnTixcuJCFCxfSoUMHXnjhBT7++OOiyChEvn23LZo9527i6mDHjN410copJSGK1PAWFfF01nMyNplFey6qHUeUQvleNDLbp59+yuzZsxk0aJBlW7du3ahWrRoTJkzglVdeKdSAQuTXqdhkPlh7HIB3O1ehnJecUhKiqHk46xnZKoJJy6OYufYE3WoF4eJQ4F8tQjywAvfIXLlyhcaNG+fa3rhxY65cuVIooYQoKEVReGPxQTKzTDSrVIa+9YPVjiREqfFkwxBCfJyJS87gqy0ySZ4oXgUuZCpWrMjChQtzbV+wYAEREbKsu1DHsgOX2XPuJs72Oqb3qiFXTwhRjOzttLzZIRKAr7ecJiYhXeVEojQpcP/fxIkT6du3L1u2bKFJkyYA/P3332zYsCHPAkeIopaWaWT6KvNVSsNbViTI00nlREKUPh2qB1A3xIs9527ywZrjzHyiltqRRClR4B6Z3r178++//+Lr68vSpUtZunQpvr6+7Ny5k549exZFRiHu6estZ7iSkE5ZTyeeeSxU7ThClEoajYZ3O5tnf1+89yIHLsSrG0iUGg80Iqtu3br89NNPhZ1FiAK7kpDGl5tPA/BWp0hZS0kIFdUp70WvOmVZsu8SE/48wuIXGsuVg6LIPdA8MkJYiw9WHyfNYKReiBedawSqHUeIUu+NjpE42+vYdz6ePw5cUjuOKAWsvpC5dOkSTz75JD4+Pjg5OVGjRg12796tdixhBfadv8mSfeYPynFdq8oAXyGsgL+7IyNaVQRg+qpjpGRkqZxIlHRWXcjcvHmTJk2aoNfrWbVqFVFRUcycORMvLy+1owmVKYpiWdm69yPlqFnOU91AQgiLp5uEUt7bmauJGXy+8ZTacUQJZ9WzFs2YMYPg4GC+//57y7bQUBnMKeCP/ZfZdz4eJ72O1ztUVjuOEOI2jnod73auwv9+3MM3W6PpV7+8rHkmisxDFzKJiYn89ddfVK5cmSpVqhRGJotly5bRvn17+vTpw+bNmylbtizDhg3jueeeu+tjMjIyyMjIyJEPwGAwYDAYCjWf+E922xZHG5+9nsK7Sw8D8HyzULyddKX+tS3O9hc5SdvnrUWEN43Dvfnn9A0mLz/CFwNqF8nzSPurqyjbP7/H1CgFXL76iSeeoFmzZowYMYK0tDRq1arF2bNnURSFX3/9ld69ez9Q4Lw4OjoCMHr0aPr06cOuXbt4+eWX+fLLLxk8eHCej5kwYQITJ07MtX3+/Pk4O8tfBLYuwwgfH9JxJU1DqJvCiKpG7Kz6BKkQpdeVVHj/gA4TGoZVNVLZo0C/bkQpl5qayoABA0hISMDd3f2u+xW4kAkICGDNmjXUqlWL+fPnM378eA4cOMC8efP4+uuv2bdv30OHz2Zvb0+9evX4559/LNtGjhzJrl272L59e56PyatHJjg4mLi4uHs2hHg4BoOBdevW0bZtW/R6fZE8h6IovLLwECsOx1DG1Z6lwxrh5+ZQJM9la4qj/UXepO3vbdLyo/z47wUereDFz8/UL/TjS/urqyjbPzExEV9f3/sWMgU+tZSQkIC3tzcAq1evpnfv3jg7O9O5c2dee+21B0+ch8DAQKpWrZpjW5UqVVi8ePFdH+Pg4ICDQ+5fbnq9Xt7kxaAo2/mbrWdYcTgGO62GL56sS1lv1yJ5Hlsm73P1SNvnbVirCH7dfZGdZ29y8HIydUOK5mINaX91FUX75/d4Be6UDw4OZvv27aSkpLB69WratWsHmK8wyj4VVFiaNGnC8ePHc2w7ceIEISEhhfo8wvrtOHOdabeWIXi3cxXqV/BWOZEQIj8CPZzoWacsALM3nVY5jSiJClzIjBo1ioEDB1KuXDmCgoJo0aIFAFu2bKFGjRqFGu6VV15hx44dvPfee5w6dYr58+fz9ddfM3z48EJ9HmHdriamM2L+XowmhR61gxjcuILakYQQBfB883A0Glh/9CrHY5LUjiNKmAIXMsOGDWP79u189913bNu2Da3WfIiwsDCmTJlSqOHq16/P77//zi+//EL16tWZPHkys2bNYuDAgYX6PMK6TfzzCHHJmUQGuDGtV02Z+E4IGxNexpUO1QIA+Gqz9MqIwvVAl1/Xq1ePevXq5djWuXPnQgl0py5dutClS5ciObawfn+fimPloRh0Wg0f962Nk72spSSELXqxRTirDsfwx4HLvNK2EsHechWpKBz5KmRGjx6d7wN+9NFHDxxGiNsZjCbGLzsCwFMNQ6gSKFedCWGrapbz5LGKvmw7Fcc3W88wsXt1tSOJEiJfhcydl1Tv3buXrKwsKlc2z6h64sQJdDoddevWLfyEotSa989ZTsUm4+NizyttK6kdRwjxkIa1CGfbqTh+3XWBl1pH4Osq0yeIh5evQmbjxo2Wf3/00Ue4ubkxb948y5pHN2/eZOjQoTRt2rRoUopSJzYxnVnrTwLwRodIPJzkskohbF2jcB9qlfPgwMUE5v59ljHtZXkR8fAKPNh35syZTJs2LcfCjV5eXkyZMoWZM2cWajhRek1fdYzkjCxqBXvyeN1yascRQhQCjUbDiy3MK2PP236WpHRZVkA8vAIXMomJiVy7di3X9mvXrpGUJJfViYe36+wNluy7hEYDk7pVQ6uVq5SEKCnaVfUnvIwLSelZ/LTjvNpxRAlQ4EKmZ8+eDB06lCVLlnDx4kUuXrzI4sWLeeaZZ+jVq1dRZBSliNGkMP4P8wDffvWDqRXsqW4gIUSh0mo1DLvVK/PN1jOkZRpVTiRsXYELmS+//JKOHTsyYMAAQkJCCAkJYcCAAXTo0IEvvviiKDKKUmT+zvNEXUnE3dGOMe3k/LkQJVG32kEEeztxPSWTX3ZKr4x4OAUuZJydnfniiy+4fv06+/btY9++fdy4cYMvvvgCFxeXosgoSombKZnMXGtekuLVdpXxkSsahCiR9DotLzY398p8teU0GVnSKyMeXIELmWwuLi7UrFmTmjVrSgEjCsWHa48Tn2ogMsCNgQ3Kqx1HCFGEetctS4C7I1cTM1i855LacYQNK3Ahk5KSwtixY2ncuDEVK1YkLCwsx02IB3H4UgLzb3UxT+hWDTvdA9fYQggb4GCn4/nm5t8ZX2w6hcFoUjmRsFUFXqLg2WefZfPmzTz11FMEBgbKujfioSmKwsQ/j6Ao0KVmIA3DfNSOJIQoBv3ql+fzjae4eDONZfsv01umWhAPoMCFzKpVq1ixYgVNmjQpijyiFFp24DK7zt7ESa/jnc5V1I4jhCgmTvY6nnksjBmrj/H5plP0qFMWnUy3IAqowP33Xl5eeHt7F0UWUQqlZGTx3sqjAIxoVZFADyeVEwkhitOTDcvj4aTnzLUUVh2+onYcYYMKXMhMnjyZcePGkZqaWhR5RCnz6V+nuJqYQXlvZ555LFTtOEKIYubmqGdokwoAfPbXKUwmRd1AwuYU+NTSzJkzOX36NP7+/lSoUAG9PucaOHv37i20cKJki7qcyLfbzgAwrktVHPU6lRMJIdQwtHEo326N5lhMEvN3nufJhiFqRxI2pMCFTI8ePYoghiht0g1GXlmwH4NRoV1Vf1pX8VM7khBCJR7Oel5tV4kJf0YxY/Ux2lX1x8/dUe1YwkYUuJAZP358UeQQpcxH605w/GoSvq72TOtVQ65+E6KUe6pRBZbsu8TBiwlMWh7FZwMeUTuSsBEyWYcodjvOXGfOVvMppem9asoMvkIIdFoN7/WsgVYDyw9eYePxWLUjCRtR4ELGaDTy4Ycf8uijjxIQEIC3t3eOmxD3kphu4NWFB1AU6FsvmDZV/dWOJISwEtXLevB0E/Og/7FLD8uCkiJfClzITJw4kY8++oi+ffuSkJDA6NGj6dWrF1qtlgkTJhRBRFGSTFwWxaX4NIK9nRjbtaracYQQVuaVtpUI8nDk4s00/m/DSbXjCBtQ4ELm559/Zs6cObz66qvY2dnRv39/vvnmG8aNG8eOHTuKIqMoIVYfjmHx3otoNPDRE7VxdSjwEC0hRAnn4mDHxO7VAfhm6xmOxSSqnEhYuwIXMjExMdSoUQMAV1dXEhISAOjSpQsrVqwo3HSixIhNTOetJQcBeL5ZOPUryGlIIUTe2lb1p301f7JMCu/+fhhFkbllxN0VuJApV64cV66YZ18MDw9n7dq1AOzatQsHBxm0KXJTFIXXfjvIzVQDVQPdeaVthNqRhBBWbkK3ajjqtew+d5M1R66qHUdYsQIXMj179mTDhg0AvPTSS4wdO5aIiAgGDRrE008/XegBhe37ccc5Np+4hoOdlv/rVxsHO5n4Tghxb4EeTjz7mHl17PdXH5PVscVdFXiQwvTp0y3/7tu3L+XLl2f79u1ERETQtWvXQg0nbN+p2CSmrjCvpfRmx0gi/N1UTiSEsBXPNw9j/s7znIlLYcGuCzLjr8jTQ88j06hRI0aPHi1FjMglM8vEy7/uJyPLRNMIXwY3qqB2JCGEDXFz1DOyVUUAZq0/SUpGlsqJhDUqcCEzb968HIN6X3/9dTw9PWncuDHnzp0r1HDCts1af4IjlxPxdNbzYZ9aaLUye68QomAGNAghxMeZuOQMy0SaQtyuwIXMe++9h5OTEwDbt2/ns88+4/3338fX15dXXnml0AMK27Qz+gazN58GYHqvGvjLuilCiAdgb6fltfaVAfh6yxmuJWWonEhYmwIXMhcuXKBiRXNX39KlS3n88cf53//+x7Rp09i6dWuhBxS2JzHdwCsL9qMo0KduOTpUD1Q7khDChnWuEUitch6kZhr5RCbJE3cocCHj6urK9evXAVi7di1t27YFwNHRkbS0tMJNJ2zS7bP3ju9WTe04Qggbp9FoeLNjFQDz4N9rySonEtakwIVM27ZtefbZZ3n22Wc5ceIEnTp1AuDIkSNUqFChsPMJG7P6yFUW772IVgMfy+y9QohC0ijch1aRfhhNCpOWR8kkecKiwIXM559/TqNGjbh27RqLFy/Gx8cHgD179tC/f/9CDyhsR0ImjP0jCoAXmodTT2bvFUIUorc7VcHeTsum49f4bc9FteMIK1HgP5c9PT357LPPcm2fOHFioQQStklRFOaf0hKfZqB6WXdGtamkdiQhRAlT0c+VV9pUYsbqY0xaHkXTiDL4OMsEm6VdgQuZLVu23PP+Zs2aPXAYYbt+3nmBYwlaHOy0zOpbG3u7h56iSAghcnmuaSirj8Rw4EI8by45yNcDa6sdSaiswIVMixYtcm3TaP6bH8RoND5UIGF7zlxLZsaaEwC83r4SFf1k9l4hRNGw02mZ2acmnT7Zxqbj11i87zLOaocSqirwn803b97McYuNjWX16tXUr1/fsoCkKF3eW3mUdIOJyh4mnnw0WO04QogSrqKfG6Pbmk9fT115nHiZWqZUK3CPjIeHR65tbdu2xd7entGjR7Nnz55CCSZsw79nrrP+aCw6rYbeoSaZvVcIUSyeaxrG6sMx7L8Qz69ntPSXq5hKrUIbyODv78/x48cL63DCBiiKwnurjgHQt15Z/J1UDiSEKDV0Wg0f9qmFvZ2Wo/FaFu65pHYkoZIC98gcPHgwx/eKonDlyhWmT59O7dq1CyuXsAErD5kH3Dnb63ipZTg7t5xVO5IQohSp6OfK6DYVmb76BFNXHqNRxTKEl3FVO5YoZgUuZGrXro1Go8k1GVHDhg357rvvCi2YsG6ZWSY+WGPujflfszB8XR1UTiSEKI2GNgphyfZjnEiAl3/dx5IXm8hVk6VMgQuZ6OjoHN9rtVrKlCmDo6MsClia/LLzPGevp+Lr6sBzTcMAOT8thCh+Wq2GgeEmPj7qwOFLicxce5y3OlVRO5YoRgUuZEJCQooih7AhSekGy8JtL7eJwMXBDoPBoHIqIURp5ekA7/WoxrBf9vPVljM0q1SGJhV91Y4liskD9b9t3ryZrl27UrFiRSpWrEi3bt1k5etSZM6WM1xPySTM14V+9eVyayGE+tpW9WNAg/IAjF64n5spmSonEsWlwIXMTz/9RJs2bXB2dmbkyJGMHDkSJycnWrduzfz584sio7Ail+LTmLPVfHrx9Q6V0evkXLQQwjqM7VyV8DIuXE3M4I3FB2VhyVKiwL+Fpk6dyvvvv8+CBQsshcyCBQuYPn06kydPLoqMwkpkGU2M+nUfaQYj9UK8aF8tQO1IQghh4WSv4//61cFep2Vt1FXWHIlRO5IoBgUuZM6cOUPXrl1zbe/WrVuugcCiZPn0r1PsOnsTVwc7Zj5RK8fSFEIIYQ2ql/XgheZhAHy49gRGk/TKlHQFLmSCg4PZsGFDru3r168nOFjGS5RU/565zqd/mQf4Tu1ZnRAfF5UTCSFE3p5tFoans55Tscks2XtR7TiiiBX4qqVXX32VkSNHsn//fho3bgzA33//zdy5c/m///u/Qg8o1BefmsmoBfsxKdD7kXJ0r11W7UhCCHFX7o56hrUI572Vx5i1/iTdagfhYKdTO5YoIgUuZF588UUCAgKYOXMmCxcuBKBKlSosWLCA7t27F3pAoS5FUXhj8UGuJKQT6uvCxO7V1I4khBD3NahRBb7dFs2l+DR++fc8Q5qEqh1JFJECFzIAPXv2pGfPnoWdRVihn/89z5ojV9HrNHzSrw6uDg/0lhFCiGLlqNcxsnUE7/x+mM82nqJPvWBc5POrRHrga2czMzO5ePEi58+fz3ETJcf566lMWREFwBsdIqlRLvfK50IIYa2eqBdMiI8zccmZfP+3XIxSUhW4kDl58iRNmzbFycmJkJAQQkNDCQ0NpUKFCoSGStddSaEoCmP/OEy6wUSjMB+elm5ZIYSN0eu0jG5bCYCvtpwhPlUmySuJCtzPNmTIEOzs7Fi+fDmBgYFyCW4JtfJQDJtPXMNep2VKz+potfI6CyFsT9eaQczedJpjMUl8ufkMb3aMVDuSKGQFLmT279/Pnj17iIyUN0NJlZhuYOKfRwB4sUU44WVcVU4khBAPRqvVMKZdZZ79YTff/x1Nl5qBVC8rp8lLkgKfWqpatSpxcXFFkUVYiZlrjhOblEGorwsvtghXO44QQjyU1lX8aBXpR0aWied/3ENccobakUQhylchk5iYaLnNmDGD119/nU2bNnH9+vUc9yUmJhZ1XlHEDlyI54cd5wCY3L06jnqZe0EIYds0Gg0f961NqK8Ll+LTGPbzXgxGk9qxRCHJVyHj6emJl5cXXl5etG3blh07dtC6dWv8/Pws27P3KUrTp09Ho9EwatSoIn2e0irLaOLt3w+hKNCjdhCPRfiqHUkIIQqFh5OeOYPq4upgx87oG0z6M0rtSKKQ5GuMzMaNG4s6x33t2rWLr776ipo1a6odpcT6Yfs5jlxOxN3Rjnc6V1U7jhBCFKqKfm7M6lub537czY87zlE1yJ3+j5ZXO5Z4SPkqZJo3b86kSZMYM2YMzs7ORZ0pl+TkZAYOHMicOXOYMmVKsT9/aXD6WjLvrzkGwBsdIynj5qByIiGEKHxtqvozuk0lZq47wbg/DhPh50q9Ct5qxxIPId9XLU2cOJEXXnhBlUJm+PDhdO7cmTZt2ty3kMnIyCAj47+BXNnjdgwGAwaDoUhz2qrMLBMv/7KPdIOJxmHePF47sMBtlb2/tLE6pP3VI22vrgdp/+ebhnDkcgKrj1zlxZ/2sGx4I3xd5Y+3B1GU7//8HjPfhYyiqLMU+q+//srevXvZtWtXvvafNm0aEydOzLV97dq1qhRhtuDPc1oOX9bibKfQ3jOW1atXPfCx1q1bV4jJREFJ+6tH2l5dBW3/Vi6w30lHTHImg7/cyItVTMh0WQ+uKN7/qamp+dqvQPPIFPfkdxcuXODll19m3bp1ODo65usxb731FqNHj7Z8n5iYSHBwMO3atcPd3b2ootqsf6NvsGHHbgBmPF6bDtX8H+g4BoOBdevW0bZtW/R6fWFGFPkg7a8eaXt1PUz712iQTK8vd3AiAc44VWRkq4pFlLLkKsr3f36vhC5QIVOpUqX7FjM3btwoyCHvac+ePcTGxvLII49YthmNRrZs2cJnn31GRkYGOl3Oy4MdHBxwcMjdRajX6+VD5g4JaQZeX3wYRYE+dcvRtXa5hz6mtLO6pP3VI22vrgdp/yplvXivVw1eWXCAzzad4dEwX5pGlCmihCVbUbz/83u8AhUyEydOxMOj+GZEbN26NYcOHcqxbejQoURGRvLGG2/kKmJEwYxdepjLCemE+Dgzvls1teMIIUSx61mnHDujb/LLzvOM+nU/K0Y2JcAjf2cAhHUoUCHTr18//Pz8iipLLm5ublSvXj3HNhcXF3x8fHJtFwXzx/5LLDtwGZ3WPFGUqyxvL4QopcZ3rcqBC/FEXUlk5C/7mP9cA+x0BZ74Xqgk36+ULA5ZclxJSOPdpYcBeKlVRR4pX7QTGQohhDVz1Ov4YuAj5snyzt5gwp9HVLvARRSc1V+1dKdNmzapHcGmKYrC678dJCk9i1rBnoxoKYPbhBCigq8LHzxek2Hz9/LTjvPoNBomdKsmf8TbgHz3yJhMpmI9rSSKxk//nmfryTgc7LTM7FNLuk+FEOKWjjUCmdGrJhoNzNt+jgnLpGfGFsjAiFLkbFwK7604CsAbHSKp6OeqciIhhLAuT9QPBuCNJQeZt928gK70zFg3KWRKCaNJYcyiA6QZjDQK82FI4wpqRxJCCKskxYxtkfMKpcScrWfYfe4mrg52fNCnJlqZwlIIIe7qifrBOU4zTV99TO1I4i6kkCkFjsck8dHaEwCM61KVcl6yVIMQQtzPE/WDmd6rBgBfbT7D3L+jVU4k8iKFTAlnMJoYvXA/mUYTbar40afew8/eK4QQpUXf+uV5rX1lACYuj2LFwSsqJxJ3kkKmhPvsr1McuZyIp7Oe93rVkHO8QghRQMNahPNUwxAUBV5ZsJ8dZ66rHUncRgqZEuzwpQQ+33gKgEndq+PnJtNuCyFEQWluzSnToVoAmUYTz/2wm+MxSWrHErdIIVNCZWQZGb1wP1kmhU41AuhaM1DtSEIIYbN0Wg2z+tWmfgUvktKzGPzdTs5cS1Y7lkAKmRJr1vqTnLiajI+LPZO7V5dTSkII8ZAc9Tq+GVSfCD9XYhLT6fPldg5ejFc7VqknhUwJtPf8Tb7afBqAqT1r4OPqoHIiIYQoGTyc9fzyv4ZUL+vO9ZRM+n+9g20n49SOVapJIVPCpBuMjFl4AJMCPWoH0aF6gNqRhBCiRPF1deCX5xrSpKIPKZlGhs7dyZ8HLqsdq9SSQqaE+fSvk5yJS8HPzYEJ3aqpHUcIIUokN0c93w2pT+eagRiMCiN/3ce8f86qHatUkkKmBDl5NYmvt5wBYFL3ang626ucSAghSi4HOx2f9KvDoEbmS7PHLztiuVJUFB8pZEoIk0nh7d8PYTAqtKniR/tqckpJCCGKmk6rYWK3aoxsHQHAB2uOM3PtcVk1uxhJIVNCLNpzgV1nb+Kk18niZkIIUYw0Gg2j21bijQ6RAHz61yneW3lUipliIoVMCXA9OYNpq8wLmo1uW0nWUhJCCBW82CKcCV2rAjBnazTj/jiCySTFTFGTQqYEmLryKPGpBqoEujO0SQW14wghRKk1pEko03vVQKOBH3ecY8Qve7mZkql2rBJNChkb98+pOJbsvYRGA+/1rI6dTl5SIYRQU79Hy/PxE7XRaTWsPBRD2483s/KQLDZZVOS3ng1LNxh5d+lhAJ5sEEKd8l4qJxJCCAHQo05ZFr/YmEr+rsQlZzLs570M+3kP15Iy1I5W4kghY8M+2fDfnDGvdaisdhwhhBC3qR3syZ8vPcbIVhWxu613ZsVB6Z0pTFLI2KjDlxL46tacMZN7VMfdUa9yIiGEEHdysNMxul1l/hjRhKqB7sSnGhg+fy9v/HaQ1MwsteOVCFLI2KAso4k3Fh/EaFLoXCNQ5owRQggrVy3Igz9GNGFEy4poNLBg9wW6fLqNI5cT1I5m86SQsUFztkZz5HIiHk56WYZACCFshF6nZUz7yvz8bAP83R04cy2Fnp//w3fboskymtSOZ7OkkLExZ64l8/H6EwCM61KVMm6ysrUQQtiSxuG+rHq5GW2q+JNpNDFpeRRN39/IJxtOEpuYrnY8myOFjA0xmRTeXHyIzCwTzSqVodcjZdWOJIQQ4gF4u9gzZ1BdJnWvhpeznisJ6Xy07gSNp//FsJ/3sOPMdbUj2gwpZGzIzzvPs/PsDZztdbzXs7osQyCEEDZMo9EwqFEFtr/Vmo/71qJeiBdZJoWVh2Lo9/UOhv+8V3po8kEKGRtxOT6NGbeWIXi9fWVZhkAIIUoIR72OnnXK8duLjVn1clMGNCiPTqthxaErtP5oM/P/PS9LHdyDFDI2QFEUxi49THJGFo+U92RQowpqRxJCCFEEqgS6817PGiwb0YSa5TxISs/i7d8P8cRX2zlxNUnteLkcuZzIl0e1JKWrdym5FDI2YMWhK2w4Fotep2FG75potXJKSQghSrJqQR78PqwJ47pUxdlex+5zN2k/awvDf97L4UvWccn2nnM3eOr73RyN1/LR+pOq5ZBCxsrFp2YyYdkRAIa3rEiEv5vKiYQQQhQHnVbD04+Fsm50czpUC0BRzH/Ydvl0G0O+38nO6BuqZfvnVBxPfbuTpPQswt0URreJUC2LnWrPLPJl6oqjxCVnEuHnyostwtWOI4QQopiV9XTiy6fqciwmkdmbTvPngctsOn6NTcevEeHnSovKZWhR2Y96FbxwsNMVeZ4NR6/y4s97ycwy8VhFH7p7X8XNUb1yQgoZK7btZByL9lxEo4HpvWsUyxtUCCGEdYoMcOf/+tVhdNtKfLn5DIv3XORkbDInY5OZszUaZ3sdjcN9eLxuOdpWDUBXBMMQlh+8zKhf95NlUmhX1Z+P+tRgw9rVhf48BSGFjJVKyzTy9u+HAHiqYQh1Q7xVTiSEEMIahPi4MK1XDd7oUJmtJ+PYfOIam09c41pSBuuPxrL+aCzB3k4MaRzKE/XK4VYIa/FdSUhj9qbT/LTjHCYFutcO4sM+tcBkLISf6OFIIWOlZm04wfkbqQR6OPJae1nZWgghRE6ezvZ0rRVE11pBmEwKR2MSWXHwCvN3nufCjTQmL49i1roT9K5bjkbhPlQLcqesp1OB5iC7FJ/G7E2nWLjrIpm3llEY0KA8k7tXR6fVYJBCRuTlyOUEvtkaDcDk7tULpZoWQghRcmm1GqoFeVAtyIOXWkWwZN9FvtsWzelrKcz95yxz/zkLgKeznmpB7lQJcCesjCuhvi6E+rrg7+6AopgLl+i4FM5cS+bgpQT+PHAZg9E8h02DUG9ebhNBozAfq5qQVQoZK2M0Kby15JBlZes2Vf3VjiSEEMKGONnrGNgghP71y7P55DVWHrzCkcuJnLiaRHyqgb9PXefvUzmXQHC212E0KWRk5V68snG4DyNbR9AwzKe4foQCkULGyvyw/SwHLybg5mjH+K5V1Y4jhBDCRmm1GlpW9qNlZT8AMrKMnIhJ5vDlBE5cTeJsXArRcSlcuJlGaqb5FJFepyHEx4UwXxfCyrjSuoof9StY9xhNKWSsyKX4ND5YcxyANztG4ufuqHIiIYQQJYWDnY4a5TyoUc4jx/bMLBMXbqai02go5+WEnc62ppiTQsZKKIrC+D8Ok5pppF6IF/3rl1c7khBCiFLA3k5LeBlXtWM8MNsqu0qw1YdjWH/UvAzBe71qyDIEQgghRD5IIWMFEtMNjL+1DMGLzcOpJMsQCCGEEPkihYwVeH/1MWKTMgjzdWFYy4pqxxFCCCFshhQyKttz7iY//3segCk9q+Ool2UIhBBCiPySQkZFBqOJd34/hKJA70fK0TjcV+1IQgghhE2RQkZF326L5lhMEl7Oet7pXEXtOEIIIYTNkUJGJRdupDJr/QkA3ulcFW8Xe5UTCSGEELZHChkVKIrCu0sPk24w0TDMm96PlFU7khBCCGGTpJBRwYpDV9h84hr2Oi1Te9awqsW3hBBCCFsihUwxS0w3MPHPKACGtQy36dkUhRBCCLVJIVPMZq07ybVbc8a82CJc7ThCCCGETZNCphgdj0li3vazAEzoVg0HO5kzRgghhHgYUsgUE0VRmLDsCEaTQvtq/jSrVEbtSEIIIYTNk0KmmKw4dIXtZ67jYKfl3c5V1Y4jhBBClAhSyBSD1Mwspq44CsCLLcIJ9nZWOZEQQghRMkghUww+33iKKwnplPNy4oXmMsBXCCGEKCxWXchMmzaN+vXr4+bmhp+fHz169OD48eNqxyqQs3EpzNkSDcDYLlVlUUghhBCiEFl1IbN582aGDx/Ojh07WLduHQaDgXbt2pGSkqJ2tHybvDyKTKOJZpXK0K6qv9pxhBBCiBLFTu0A97J69eoc38+dOxc/Pz/27NlDs2bNVEqVf/+cjmPDsVjstBrGd60qM/gKIYQQhcyqC5k7JSQkAODt7X3XfTIyMsjIyLB8n5iYCIDBYMBgMBRtwNsoisL0VeYBvv3ql6O8p0OxPn9xy/7ZSvLPaM2k/dUjba8uaX91FWX75/eYGkVRlEJ/9iJgMpno1q0b8fHxbNu27a77TZgwgYkTJ+baPn/+fJydi+9qof3XNXx/Qoe9VmFsHSPusri1EEIIkW+pqakMGDCAhIQE3N3d77qfzRQyL774IqtWrWLbtm2UK1furvvl1SMTHBxMXFzcPRuiMGUZTXT+7B/OxKUyvEUYo1pXLJbnVZPBYGDdunW0bdsWvV6vdpxSR9pfPdL26pL2V1dRtn9iYiK+vr73LWRs4tTSiBEjWL58OVu2bLlnEQPg4OCAg4NDru16vb7Y3uS/7TvPmbhUvF3seaFFxVL1n6s421nkJu2vHml7dUn7q6so2j+/x7PqQkZRFF566SV+//13Nm3aRGhoqNqR7ist08is9ScAGN6yIm6O8h9LCCGEKCpWXcgMHz6c+fPn88cff+Dm5kZMTAwAHh4eODk5qZwub3P/OcvVxAzKejrxZMPyascRQgghSjSrnkdm9uzZJCQk0KJFCwIDAy23BQsWqB0tTwmpBmZvOgXA6LaVZHVrIYQQoohZdY+MjYxDtpi9+TSJ6VlU9nejR52yascRQgghSjyr7pGxJdeSMpj7j3kpgtfaV0anlcnvhBBCiKImhUwh+WrzadINJmoHe9K6ip/acYQQQohSQQqZQhCblM5P/54DYFSbCFmKQAghhCgmUsgUgi83nSHdYKJOeU+aVyqjdhwhhBCi1JBC5iFdTfyvN2Z020rSGyOEEEIUI6u+askWzN50mswsE/VCvHisoq/acYQQpZExC4wZYDSAyQimLPNNMQIa0NrduunMX+0cQGcP8oeXKAGkkHkIMQnpzN95HoBXpDdGiNJHUSAjCeeMWDSX9kJmAqTGQep1SE80FxKKyVxcKCZzcZGVDlmZ5q/GzFvFRxaYbhUhRgMY0iAzGQypkJli/qrR3VaQaAGN+fFZGbcKlgLSaMHO0XyzdwFXf3APAvey4B4IrgHg5AmOnuDoYf63k5e5CBLCikgh8xC+2HSKzCwTj1bwpnG4j9pxhBB3Y8wCFEBzqxdCYy4e0uMh7eZ/t/QEyEgy3zKTIeNWMZGVfuuWYS4y0uItBYvemElbgKgi/hkUk7nYyS+tnbn4gdt6Z+44niHVfEu7AQkX4FI+juvgAS6+4Opn/uroCQ7u4OAKDm633TzA0d18n5MnuPjdKsCEKFxSyDygy/Fp/LrzAgCj2sqVSkIUG5MRUq5BUgwkx5qLD0vvRar53ynXIOmKeZ+kK+YipQgZNXq0bn5oXHzB2Qecfc2/xLOLCY3mttM6jubTOnaOYGdv/vedp370zuZekuybnROg/HfKyGQ09wbZ2YPOwdxLYucAWv1tPTZ3UJRbp50MOYuyrHRzwZZ0xXxLvASJlyH5qrlt0xPMhVt6gjlDRoL5duN0wRrJzhG8QsE7DHzCbn2taL65BcppLvHApJB5QJ9vPEWm0UTDMG8ah8vYGCEKxJB+65dk/H+/LDMS/+sNyUgyn5pJj//vl2h6PKTEmXtCFFMhBdH8d8rEyetWz8KtHgV7V3Mvg97Z/EtY7/jfqRgnz1sFiw8Gew9WrttEp06drHv1ZY0GdHbmm/4B1qozmf57DVKu/XdLjzcXQre/dhlJ5mInPdH8uqYnmAuma0fNtzvpXczFjVcFcAsCtwBzceMWAJ7lwTPEnFuIPMg74wFF+Lni42LPK20qqR1FCOuQeAUu7bk1piPF/DUz1TxeJDnmVu/IrV6UrLSHey6NFlzKmMd1OHn913OR3ZPh4vvfL0LXAPNpEM1tvRSKYu61cPB4+NMdhgKc7rFlWi04e5tvZQr4uWc0mE9d3TgDN6Lh+mlzj87103DzrPn9EnPIfMvzue3MxYxPOHiH3ypugsGjHLgEml9PUWpJIfOAhjQJpd+j5XHUy8KQopQzmWD3t7BunPn0Tr5pzINIb7/lGGPhZh5/4eR5635P8y9R1wBzoaKV/3s2Q6c3n0ryDst9n9EAN8/B9ZMQf+G/U1xJV8ynuOLPm3tzbpzO83SWHuistUd3KfzWc4Sae3Z8K0FQHfMpPlGiSSHzEKSIEaVe/Hn4YzhEbzF/71vZfMWL/a1TMvbO5h4T1wBw8//vq7MP2LvJ4E9hLnJ8K5pveTGZIOnyf704N86YC56EC5BwEZKvYmfKvMtpKw34VYXg+hDcAAJqmntyHD2K/McSxUcKGSFEwSkK7PsJVr8FmUnmwahtJ0H9Z6U4EYVLqzWfQvIoB2HNc91tSEti87KfaVGrAnaJF8ynrm5Gw9XD5kI79oj5tmfufw9y9DCfnvIob+69yR587B1ufh7p7bMpUsgIIe7OkAan/4LTG/+7kiX7Zsw07xPcAHrMNo9fEKK42TmS4hiIEt4a7hxsnRQDF3bCxZ1wYRfEnTBfap6ecPcxOVr9rcHGAeYxWNkDj8tEgn9V8KwgxbqVkUJGCJFTRhKcXAtRy+DkOvNAzLzonaHFW9BouPwFK6yTWwBU7Wa+ZctINp+Wij9vvt2IvjUI+dbAY2PmrdNWF/I+pt4F/CLNhY1X6H9jcrwqmE+ZymXkxU4KGSFKO2MWXN4LZzbDmU3mv16ze1sA3MtBlS7mwZPZf6G6+pnHu9jZqxZbiAfi4Ap+Vcy3O5mM5nl0LFfYXTV/TbgAsUfh2nFzYX9pj/mW69ju/82N4xth/upRzjw43aWMeeyYFDqFTgoZIUqj+Atwap25xyV6q3mcy+28w6DKrb9kgx6RD19ROmh1ty7tLp/3/cYsc+9N7BGIO2nuwcm+JV4yz5lzea/5lhc7R3NB4xZgXg7CLejW14Bb0wc4mXs69c7mq/Zc/c0D5sU9SSEjRGmQngiXdpt7XE6ug9g75tN38oIKTc2DKUNbmMe7SPEiRE46O/McOnnNo2NINw8yvn7KXORcP2W+JcWYJxE0pJgvI7/Xaau8OHiYr/RzCzAv8+DsY+7hcfY2zyBt+f7WtlJ4mlcKGSFKGkUxX6p6cad5oOOFnbcKl9smDdNoodyjENEWKraGgFoygFGIh6F3vPspKzBPEJlyDZKvmS8nT7y1HETSFfMpLEParVvqf+t5ZaX9tyRE3Il8hNCYixnvcChT2ZylTOVb0yKULbH/x6WQEcLWZSShOb+TSjHL0C340XzuPu1G7v08Q6B8I3PxEt7K/IEnhCge2bNPe1XI3/6KYj5VlXT1v2In5dp/y3Sk3vjv3ylx5qUiUMwzaadeN/8hc7vsta58ws0DlH0rQ/Cj4BNh8wWOFDJC2BJjlvnqiou74eIu8y02CjvFRI6/A3UOEFTb/EEV3MDc++Lmr1JoIUSBaW6b+To/S0IYs8x/wCTFmGdJvnYcrh0zf71+6u5rXTl6/vc5EdoMytazucJGChkhrFVKnHmei6tHzLfYIxB7DIwZuXZV3MtxSVuWwPrd0IU0goAackWREKWJzu7W1YR+EFgz533GLEg4bx6ofP3WpeYxh829t+nx5ukWTq417+sWeGugf3co39AmxtxIISOE2lJv3JrL4rT5L6jsibqSruS9v94ZAmtBufqWW5aTL3tWrqTTo53QWfMKzEKI4qez+2+tq9tXgjAazJ81F/6F89vh1F/mz52dX5lvLn5Qbyg0edl8WsxKSSEjRHHITDEPwL1+6tbXW1c13DgDaTfv/jjvcPCvBv7VzbOK+lfLe2bR0rICsxCi8Oj0UPYR863hi5CVYZ7FO+oPOL4CUmJh8wzY+yO0mQA1+ljlaScpZIQoLEaD+bLKm+duXYJ5wnwZZtxJSLx478e6BZr/WvKpaD4tFFDTXLg4uBVPdiGEsHOAyh3Mt6xMOLYc1o83z4D8+/9g1xzoMAPK1VU7aQ5SyAiRHyaT+YqB7MslEy/f+nrlv+Il8SIoprsfw8nLfIWAz62Vfr3Db11BEGbV3bZCiFLIzh6q94LKnWDH57Blpvnigm9aQZ2nzIvEWsmVj1LIiNJNUcwLyCXF/HeJo2V68hhzwZJdtJiy7n88O0fzrKCWacor3ZqqPAJcfIr+5xFCiMKkd4Smr0KtAbBhEhyYD/t+hOOroMM0iOyhdkIpZEQJZEgzD6BNu3Hb1+vmf2fPsZAU81+BYkjN33E12ltrDQXemlY80DzbpkcweIWY54dw8bPKc8hCCPFQ3AOh52yoOxj+fNl8YcKS59CF/oSLUxdVo0khI6xTVoZ5Zsv0+P++ZiT9d8tMNk+7n3r9vwmhsguVrLSCP5+j563CxN+8GGL2V/cg84yY7kHmIkYn/2WEEKVY+Ybw/Fb45xPY8gHa6M201PwD5YGG/1Mlknwqi8JhMmJnTDP3dCiZ5l6OzFTz+iKZqeardgwp5q8ZyeZCJCPp1veJ5tM7t9/y20tyN1o7cPI2n8N18jaf1nH2+W+ba4D5Lwy3WzdZmE0IIfLHzh6ajYFqPTEtH40uehNZ7kHqxVHtmUXxURQwZppPuWSl3/E1w9yDkZWRc62PzJScX7Pvz35cdo9IRiKkJ6I3pNAZ4GBhBteAo7u5t8TJExzczVfxOLiBvav5q2WxNJ+cxYqDmyx6KIQQRcknHGP/Rfyz8GMaRLRXLYYUMtbAaDAXBpnZvRfJOXs0DGn/FRS3Lypm2ef2bWm3HpNuLlAM6ebi4/YFA4uQggaNvYt50jZ7Z9DfWl/E3tlcfOidwcHV/G97V/O/HdzMxUr2dNyOHrcKFw8ZbyKEENZMo+G6W6SqEaSQKWzpCeZFvpJjIDnWfKol9bp50rO0m7fGfNw092Zkn17JSi++fBot2DmZ5wvQO5mvsrnzq6UAcTYXHnpn88j12x9n72ruLXFwB0d3DFonVm/cRofO3dHby9T4QgghiocUMg/KZDLPyhpzwDzF85WD5q8psQ9+TJ39rZ6MO3s0nM3Fg97lVhFx66tlm+N/9+mdbhUe2cXJrQIk+6tOXzSnXAwGTFp7OZ0jhBCiWEkh86AWDYajy/K+z8HDvHCXW4D5ShcXX/NkaNk3R89bvRl3jPfQyRo5QgghREFIIfOgykTCyXXmtW8CaphXGw2oad7u4Kp2OiGEEKJUkELmQT02Cpq/IfOKCCGEECqS38IPStbGEUIIIVQn17YKIYQQwmZJISOEEEIImyWFjBBCCCFslhQyQgghhLBZUsgIIYQQwmZJISOEEEIImyWFjBBCCCFslhQyQgghhLBZUsgIIYQQwmZJISOEEEIImyWFjBBCCCFslhQyQgghhLBZUsgIIYQQwmaV+NWvFUUBIDExUeUkJZvBYCA1NZXExET0er3acUodaX/1SNurS9pfXUXZ/tm/t7N/j99NiS9kkpKSAAgODlY5iRBCCCEKKikpCQ8Pj7ver1HuV+rYOJPJxOXLl3Fzc0Oj0agdp8RKTEwkODiYCxcu4O7urnacUkfaXz3S9uqS9ldXUba/oigkJSURFBSEVnv3kTAlvkdGq9VSrlw5tWOUGu7u7vJhoiJpf/VI26tL2l9dRdX+9+qJySaDfYUQQghhs6SQEUIIIYTNkkJGFAoHBwfGjx+Pg4OD2lFKJWl/9Ujbq0vaX13W0P4lfrCvEEIIIUou6ZERQgghhM2SQkYIIYQQNksKGSGEEELYLClkhBBCCGGzpJARD2z69OloNBpGjRpl2Zaens7w4cPx8fHB1dWV3r17c/XqVfVCljCXLl3iySefxMfHBycnJ2rUqMHu3bst9yuKwrhx4wgMDMTJyYk2bdpw8uRJFROXHEajkbFjxxIaGoqTkxPh4eFMnjw5xzow0v6FZ8uWLXTt2pWgoCA0Gg1Lly7NcX9+2vrGjRsMHDgQd3d3PD09eeaZZ0hOTi7Gn8J23av9DQYDb7zxBjVq1MDFxYWgoCAGDRrE5cuXcxyjuNpfChnxQHbt2sVXX31FzZo1c2x/5ZVX+PPPP1m0aBGbN2/m8uXL9OrVS6WUJcvNmzdp0qQJer2eVatWERUVxcyZM/Hy8rLs8/777/PJJ5/w5Zdf8u+//+Li4kL79u1JT09XMXnJMGPGDGbPns1nn33G0aNHmTFjBu+//z6ffvqpZR9p/8KTkpJCrVq1+Pzzz/O8Pz9tPXDgQI4cOcK6detYvnw5W7Zs4X//+19x/Qg27V7tn5qayt69exk7dix79+5lyZIlHD9+nG7duuXYr9jaXxGigJKSkpSIiAhl3bp1SvPmzZWXX35ZURRFiY+PV/R6vbJo0SLLvkePHlUAZfv27SqlLTneeOMN5bHHHrvr/SaTSQkICFA++OADy7b4+HjFwcFB+eWXX4ojYonWuXNn5emnn86xrVevXsrAgQMVRZH2L0qA8vvvv1u+z09bR0VFKYCya9cuyz6rVq1SNBqNcunSpWLLXhLc2f552blzpwIo586dUxSleNtfemREgQ0fPpzOnTvTpk2bHNv37NmDwWDIsT0yMpLy5cuzffv24o5Z4ixbtox69erRp08f/Pz8qFOnDnPmzLHcHx0dTUxMTI729/DwoEGDBtL+haBx48Zs2LCBEydOAHDgwAG2bdtGx44dAWn/4pSftt6+fTuenp7Uq1fPsk+bNm3QarX8+++/xZ65pEtISECj0eDp6QkUb/uX+EUjReH69ddf2bt3L7t27cp1X0xMDPb29pY3cjZ/f39iYmKKKWHJdebMGWbPns3o0aN5++232bVrFyNHjsTe3p7Bgwdb2tjf3z/H46T9C8ebb75JYmIikZGR6HQ6jEYjU6dOZeDAgQDS/sUoP20dExODn59fjvvt7Ozw9vaW16OQpaen88Ybb9C/f3/LwpHF2f5SyIh8u3DhAi+//DLr1q3D0dFR7Tiljslkol69erz33nsA1KlTh8OHD/Pll18yePBgldOVfAsXLuTnn39m/vz5VKtWjf379zNq1CiCgoKk/UWpZTAYeOKJJ1AUhdmzZ6uSQU4tiXzbs2cPsbGxPPLII9jZ2WFnZ8fmzZv55JNPsLOzw9/fn8zMTOLj43M87urVqwQEBKgTugQJDAykatWqObZVqVKF8+fPA1ja+M6rxKT9C8drr73Gm2++Sb9+/ahRowZPPfUUr7zyCtOmTQOk/YtTfto6ICCA2NjYHPdnZWVx48YNeT0KSXYRc+7cOdatW2fpjYHibX8pZES+tW7dmkOHDrF//37LrV69egwcONDyb71ez4YNGyyPOX78OOfPn6dRo0YqJi8ZmjRpwvHjx3NsO3HiBCEhIQCEhoYSEBCQo/0TExP5999/pf0LQWpqKlptzo9MnU6HyWQCpP2LU37aulGjRsTHx7Nnzx7LPn/99Rcmk4kGDRoUe+aSJruIOXnyJOvXr8fHxyfH/cXa/oU6dFiUOrdftaQoivLCCy8o5cuXV/766y9l9+7dSqNGjZRGjRqpF7AE2blzp2JnZ6dMnTpVOXnypPLzzz8rzs7Oyk8//WTZZ/r06Yqnp6fyxx9/KAcPHlS6d++uhIaGKmlpaSomLxkGDx6slC1bVlm+fLkSHR2tLFmyRPH19VVef/11yz7S/oUnKSlJ2bdvn7Jv3z4FUD766CNl3759lqti8tPWHTp0UOrUqaP8+++/yrZt25SIiAilf//+av1INuVe7Z+Zmal069ZNKVeunLJ//37lypUrlltGRoblGMXV/lLIiIdyZyGTlpamDBs2TPHy8lKcnZ2Vnj17KleuXFEvYAnz559/KtWrV1ccHByUyMhI5euvv85xv8lkUsaOHav4+/srDg4OSuvWrZXjx4+rlLZkSUxMVF5++WWlfPnyiqOjoxIWFqa88847OT64pf0Lz8aNGxUg123w4MGKouSvra9fv670799fcXV1Vdzd3ZWhQ4cqSUlJKvw0tude7R8dHZ3nfYCyceNGyzGKq/01inLbtJRCCCGEEDZExsgIIYQQwmZJISOEEEIImyWFjBBCCCFslhQyQgghhLBZUsgIIYQQwmZJISOEEEIImyWFjBBCCCFslhQyQgghhLBZUsgIIazS9u3b0el0dO7cWe0oQggrJjP7CiGs0rPPPourqyvffvstx48fJygoSO1IQggrJD0yQgirk5yczIIFC3jxxRfp3Lkzc+fOzXH/smXLiIiIwNHRkZYtWzJv3jw0Gg3x8fGWfbZt20bTpk1xcnIiODiYkSNHkpKSUrw/iBCiyEkhI4SwOgsXLiQyMpLKlSvz5JNP8t1335HdeRwdHc3jjz9Ojx49OHDgAM8//zzvvPNOjsefPn2aDh060Lt3bw4ePMiCBQvYtm0bI0aMUOPHEUIUITm1JISwOk2aNOGJJ57g5ZdfJisri8DAQBYtWkSLFi148803WbFiBYcOHbLs/+677zJ16lRu3ryJp6cnzz77LDqdjq+++sqyz7Zt22jevDkpKSk4Ojqq8WMJIYqA9MgIIazK8ePH2blzJ/379+f/27lD0GTCOI7jvyAYFAzrJsF2oOCwrImrguG6E2yCGMbKkqZhE105EIswREyWgWAwuTANF2YwzGQQQaPo2+Q9VhbeV/e87/cDFx7+3PG/9uO5/z2S5PP5ZNu2HMc51ROJhOee6+trz3o6narVaikYDJ6u29tbHQ4HLRaL87wIgLPwXboBAPid4zja7/ee4d7j8Si/3696vf6tZ+x2OxUKBRWLxS+1cDj8x3oFcHkEGQA/xn6/V7vdVq1WUzqd9tQymYw6nY6i0agGg4GnNplMPOt4PC7XdRWJRP56zwAuixkZAD9Gv9+XbdtarVYKhUKe2v39vYbDoV5eXhSNRlUqlXR3d6f393eVy2Utl0ttNhuFQiHNZjMlk0nlcjnl83kFAgG5rqvX19dv7+oAMAMzMgB+DMdxlEqlvoQYScpms3p7e9N2u1W321Wv15NlWWo2m6e/lvx+vyTJsiyNRiN9fHzo5uZGsVhMj4+PnEUD/IPYkQFgvGq1qufnZ31+fl66FQBnxowMAOM0Gg0lEgldXV1pPB7r6emJM2KA/xRBBoBx5vO5KpWK1uu1wuGwyuWyHh4eLt0WgAvg0xIAADAWw74AAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFi/AFdDiSqMFIM+AAAAAElFTkSuQmCC", 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" ] @@ -177,7 +181,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/src/notebooks/WealthPortfolio.ipynb b/src/notebooks/WealthPortfolio.ipynb index f35515f..116d180 100644 --- a/src/notebooks/WealthPortfolio.ipynb +++ b/src/notebooks/WealthPortfolio.ipynb @@ -6,13 +6,16 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", + "from HARK.utilities import plot_funcs\n", + "\n", "from estimark.agents import WealthPortfolioLifeCycleConsumerType\n", "from estimark.parameters import init_calibration\n", - "from estimark.snp import snp_data, snp_data_full\n", - "from HARK.utilities import plot_funcs" + "from estimark.snp import snp_data, snp_data_full" ] }, { @@ -34,7 +37,7 @@ { "data": { "text/plain": [ - "(5.35399091577092, 1.0, 0.1710302407154898, 0.0)" + "(5.335577372664163, 1.0, 0.1706005756625005, 0.0)" ] }, "execution_count": 3, @@ -61,6 +64,7 @@ "metadata": {}, "outputs": [], "source": [ + "portfolio_agent.update()\n", "portfolio_agent.solve()" ] }, @@ -71,7 +75,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -91,7 +95,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -117,7 +121,6 @@ "# Set up the variables we want to keep track of.\n", "portfolio_agent.track_vars = [\"aNrm\", \"cNrm\", \"pLvl\", \"t_age\", \"mNrm\", \"Share\"]\n", "\n", - "portfolio_agent.LivPrb = [1.0] * portfolio_agent.T_cycle\n", "\n", "portfolio_agent.T_sim = portfolio_agent.T_cycle\n", "# Run the simulations\n", @@ -190,7 +193,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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Uv7d+mjVrZlLfucmeTejq6pqvmMx9Tw4aNMjk2/q974Psz9ZLL71kUm7gwIG4u7vnK6b80Ol0rFmzhm7dulGmTBnj9hIlStC3b1+2bt2aY2bl0KFDTX5vzZo1Q6fTmXST3P07unnzJteuXaNZs2akpqaaPeso+/3yxhtvoFbf+fc1ZMgQ3NzccnxGXVxcTMYJ2tnZERYW9sDfuUqlYvXq1bz//vt4enry+++/8+qrrxIcHExERMQjjblp3rw5VapUMdn29NNP4+PjY9LidePGDdauXUtERMR9jxcREcG+fftMhiIsWLAAe3t7unbtatxm7meluJPkppgbOnQoa9eu5Z9//jH2n96vL/uvv/5i7dq1bNq0iTNnznD06FHq1q1r1rnWr1/Pvn37uHr1KmPGjAEw/jGrWLFijv0qVaqUo0/YxsYmX103D3L+/Plcz1m5cmWTuPJ7LLVaTbly5Uy2BwQE4OHhka9jtW3bltWrV5OYmMh///3Hq6++yvnz5+nUqVOOQcWlS5c2eZyd6Nw9JiUhIYHXX38df39/HB0djYkpQFJSUo7zZz+X7fTp0yiKQvny5fH19TW5nThxwqyBzr6+vrRq1Yr58+ezePFidDpdnkn06dOnSUpKws/PL8d5b926ZTxvdp2WL18+x7ny07V64cIFBg4ciJeXl3EcTfPmzYGc9ePg4ICvr6/JNk9PzxxjgO7l5uYGGP4x54e578kHvQ/yqiNbW1uTJORRxcfHk5qammfser0+xzit/LyHjx07Rvfu3XF3d8fNzQ1fX19jwpHbe/h+8vo7Y2dnR5kyZXLUbalSpXJ03ebndw5gb2/PO++8w4kTJ4iJieH333+nYcOGxi6lh3XvZxQMfw979OjB0qVLjeP7Fi9ejFarfWBy06tXL9RqtTExUhSFRYsWGcdNZTPns2INZMxNMVe+fHlatWoFQKdOndBoNLz11lu0aNGCevXq5Sj/1FNPGWdLPcq5HpW9vb3JN6+ipCAWsHJycqJZs2Y0a9YMHx8fJk+ezL///suAAQOMZfIav6HcHisDhtaS7du387///Y9atWrh4uKCXq+nXbt26PX6HPve/e0MDIN6VSoV//77b67nc3FxMet19e3blyFDhhAbG0v79u3zHK+j1+vx8/Nj3rx5uT5/b5LxMHQ6Ha1btyYhIYGxY8dSqVIlnJ2duXz5MgMHDsxRP/kZL5Ob7Cm/R44coVu3bo8adg75eR8UVQ+KPTExkebNm+Pm5sZ7771H2bJlcXBwYP/+/YwdOzbX9/DjjC+/SpQoQe/evenRowdVq1Zl4cKFzJ07Fxsbmzz/XuT1JfPez2i23r1788033/Dvv//SrVs3Fi5cSKVKlahZs+Z9YytZsiTNmjVj4cKFvP322+zcuZMLFy7w8ccfm8RizmfFGkhyY2XeeecdvvvuO959911WrVr12M6bPQAvMjLSOIsqW2RkpPH5BzE3sQgODiYyMjLH9uzm7vyeN7usXq/n9OnTxm/ZYBggmpiYaNax7padZF65csWs/W7cuMH69euZPHkyEyZMMG4/ffp0vo9RtmxZFEUhNDSUChUqmHX+3HTv3p1hw4axc+dOkyb03M67bt06mjRpkucfc7jz+zl9+rRJK0R8fPwDv10fOXKEU6dO8fPPP5sMMr97RldBaNq0qbFr4u23335gklSQ78m7y58+fdrks6XVaomKinrgP7/8fqZ8fX1xcnLKM3a1Wn3fwcu52bRpE9evX2fx4sU89dRTxu0PMzsTTP/O3P1+yczMJCoqqsC+fOXF1taWGjVqcPr0aWP3rqenZ67dVOa0GoPhi2eJEiVYsGABTZs2ZcOGDbzzzjv52jciIoJXXnmFyMhIFixYgJOTE507dzY+/7g+K0VJ0fzqLB6ah4cHw4YNY/Xq1QW+evD91KtXDz8/P+bMmWMybfrff//lxIkTdOzYMV/HcXZ2NquJtEOHDuzevdtkVeaUlBS+/fZbQkJCcvRtP+hYQI4VkqdPnw7wwNewfv36XLdnj+XJrbn/frL/id77LdOcy04888wzaDQaJk+enOM4iqLkmFL+IC4uLsyePZtJkyaZ/PG817PPPotOp2PKlCk5nsvKyjL+M2jVqhW2trZ8+eWXJvHl5zXmVj+KovD555/n89Xkj5OTE2PHjuXEiROMHTs212/9v/32G7t37wYK9j0Jhs+Wr68vc+bMMZlBNnfu3HyN/che6+hBZTUaDW3atGHp0qXGWZdgSO6zF3C8u5sjP3L7HWVmZua6Bld+tGrVCjs7O7744guTY/7www8kJSXl++/Mg5w+fZoLFy7k2J6YmMiOHTvw9PQ0tj6WLVuWpKQkDh8+bCyXvbijOdRqNT179uSff/7h119/JSsr64FdUtl69OiBRqPh999/Z9GiRXTq1MlkjavH9VkpSqTlxgq9/vrrzJw5k48++og//vjjsZzT1taWjz/+mEGDBtG8eXP69OljnAoeEhLCyJEj83WcunXrsmDBAkaNGkX9+vVxcXG57z/Rt956yzg9ecSIEXh5efHzzz8TFRXFX3/9ZVbXV82aNRkwYADffvutsTl99+7d/Pzzz3Tr1o0WLVrcd/+uXbsSGhpK586dKVu2LCkpKaxbt45//vmH+vXr3/d15MbNzY2nnnqKTz75BK1WS2BgIGvWrDHrW2/ZsmV5//33GTduHNHR0XTr1g1XV1eioqL4+++/GTp0qNkrPt/dtZaX5s2bM2zYMKZOncrBgwdp06YNtra2nD59mkWLFvH555/Ts2dP41ozU6dOpVOnTnTo0IEDBw7w77//PrD7tFKlSpQtW5bRo0dz+fJl3Nzc+Ouvv/I1nsJc//vf/zh27BifffYZGzdupGfPngQEBBAbG8uSJUvYvXu3cdB4Qb4nwfDZev/99xk2bBhPP/00ERERREVF8dNPP+VrzE32mLoRI0bQtm1bNBpNnlOL33//fdauXUvTpk155ZVXsLGx4ZtvviEjIyNf68Pcq3Hjxnh6ejJgwABGjBiBSqXi119/feguN19fX8aNG8fkyZNp164dXbp0ITIykq+//pr69evnuuDpwzh06BB9+/alffv2NGvWDC8vLy5fvszPP/9MTEwMM2fONCYMvXv3ZuzYsXTv3p0RI0aQmprK7NmzqVChQq6D/u8nIiKCL7/8kokTJ1K9enWTFuT78fPzo0WLFkyfPp2bN2/mSIoe52elyHisc7OEWfIzFfzTTz/N9fmBAwcqGo1GOXPmjKIod6b3xcfHmx3Hg851twULFii1a9dW7O3tFS8vL6Vfv37KpUuXTMoMGDBAcXZ2znX/W7duKX379lU8PDwUwGSKJblMBVcURTl79qzSs2dPxcPDQ3FwcFDCwsKU5cuX5/oa7jcVXFEURavVKpMnT1ZCQ0MVW1tbJSgoSBk3bpzJlNW8/P7770rv3r2VsmXLKo6OjoqDg4NSpUoV5Z133lGSk5NzxJJbfXLP1NxLly4p3bt3Vzw8PBR3d3elV69eSkxMTI5yD/r9/vXXX0rTpk0VZ2dnxdnZWalUqZLy6quvKpGRkfd9TXdPBb+fe6eCZ/v222+VunXrKo6Ojoqrq6tSvXp1ZcyYMUpMTIyxjE6nUyZPnqyUKFFCcXR0VMLDw5WjR48qwcHBD5wKfvz4caVVq1aKi4uL4uPjowwZMkQ5dOhQjt91Xu85c6e9/vnnn0qbNm0ULy8vxcbGRilRooQSERGhbNq0yaRcft6T2a/n3mUbcnuvKoqifP3110poaKhib2+v1KtXT/nvv/+U5s2bP3AqeFZWlvLaa68pvr6+ikqlMnm9976PFEVR9u/fr7Rt21ZxcXFRnJyclBYtWijbt283KZPX+yK339G2bduUhg0bKo6OjkrJkiWVMWPGGKc+310uP1PBs3311VdKpUqVFFtbW8Xf3195+eWXlRs3bpiUad68uVK1atUc++bnPHFxccpHH32kNG/eXClRooRiY2OjeHp6Kk8//bTy559/5ii/Zs0apVq1aoqdnZ1SsWJF5bfffstzKnhuf8Oy6fV6JSgoSAGU999/P8fzeb03FEVRvvvuOwVQXF1dTZbjyJbfz4q1TAVXKUoxGLUmhBBCCJFPMuZGCCGEEFZFkhshhBBCWBVJboQQQghhVSya3Pz333907tyZkiVLolKpWLJkyQP32bRpE3Xq1MHe3p5y5coZr44qhBBCCAEWTm5SUlKoWbMms2bNylf5qKgoOnbsSIsWLTh48CBvvPEGL774IqtXry7kSIUQQghRXBSZ2VIqlYq///77vsubjx07lhUrVnD06FHjtt69e5OYmPhYV+MVQgghRNFVrBbx27FjR47ltdu2bXvfKwhnZGSYrJir1+tJSEjA29u7QK4hJIQQQojCpygKN2/epGTJkg9cDLNYJTexsbH4+/ubbPP39yc5OZm0tLRcr2EzdepUJk+e/LhCFEIIIUQhunjxIqVKlbpvmWKV3DyMcePGMWrUKOPjpKQkSpcuTVRUFK6urhaMzECr1bJx40ZatGiBra2tpcMpUqRucif1kjepm7xJ3eRN6iZvRalubt68SWhoaL7+dxer5CYgIIC4uDiTbXFxcbi5ueV55WF7e3vs7e1zbPfy8jL7InCFQavV4uTkhLe3t8XfOEWN1E3upF7yJnWTN6mbvEnd5K0o1U32+fMzpKRYrXPTqFGjHFdeXrt2LY0aNbJQREIIIYQoaiya3Ny6dYuDBw9y8OBBwDDV++DBg8ZLzY8bN47+/fsby7/00kucO3eOMWPGcPLkSb7++msWLlyY7ytOCyGEEML6WTS52bt3L7Vr16Z27doAjBo1itq1azNhwgQArly5Ykx0AEJDQ1mxYgVr166lZs2afPbZZ3z//fe0bdvWIvELIYQQouix6Jib8PBw7rfMTm6rD4eHh3PgwIFCjEoIIR4/nU6HVqsttONrtVpsbGxIT09Hp9MV2nmKI6mbvD3uurGzs3vgNO/8KFYDioUQwtooikJsbCyJiYmFfp6AgAAuXrwoa3zdQ+omb4+7btRqNaGhodjZ2T3ScSS5EUIIC8pObPz8/HByciq0fyB6vZ5bt27h4uJSIN+MrYnUTd4eZ93o9XpiYmK4cuUKpUuXfqTPgiQ3QghhITqdzpjYeHt7F+q59Ho9mZmZODg4yD/we0jd5O1x142vry8xMTFkZWU90tRz+S0KIYSFZI+xcXJysnAkQhQN2d1Rjzq+R5IbIYSwMBnnIYRBQX0WJLkRQgghhFWR5EYIIUSRdvLkSRo2bIiDgwO1atXK1z4DBw6kW7duxsfh4eG88cYbhRJftk2bNqFSqQp95pt4MEluhBBCmGXgwIGoVCpUKhV2dnaUK1eO9957j6ysrEc+7t0JSbaJEyfi7OxMZGRkjkvw5NfixYuZMmXKI8V36NAhunTpgp+fHw4ODoSEhBAREcHVq1cf6bii4MlsKSGEEGZr164dP/30ExkZGaxcuZJXX30VW1tbxo0bZ/axdDrdfcdanD17lo4dOxIcHPzQ8Xp5eT30vgDx8fG0bNmSTp06sXr1ajw8PIiOjmbZsmWkpKQ80rEfJDMz85HXfXnSSMuNEEIIs9nb2xMQEEBwcDAvv/wyrVq1YtmyZQDcuHGD/v374+npiZOTE+3bt+f06dPGfefOnYuHhwfLli2jSpUq2NvbM3jwYH7++WeWLl1qbBXK7ubZt28f7733HiqVikmTJgFw5MgRnn76aRwdHfH29mbo0KHcunUrz3jv7Za6O0YXFxd69uxpEuO9tm3bRlJSEt9//z21a9cmNDSUFi1aMGPGDEJDQ03K7tu3j3r16uHk5ETjxo2JjIw0Pnf27Fm6du2Kv78/Li4u1K9fn3Xr1pnsHxISwpQpU+jfvz9ubm4MHToUgK1bt9KsWTMcHR0JCgpixIgRhZ5YFVeS3AghRBGiKAqpmVmFckvL1OX53P0uhZMfjo6OZGZmAobupb1797Js2TJ27NiBoih06NDB5PISqampfPzxx3z//fccO3aML774gmeffZZ27dpx5coVrly5QuPGjbly5QpVq1blzTff5MqVK4wePZqUlBTatm2Lp6cne/bsYdGiRaxbt47hw4fnO967Y9y2bRsAnTp1yvMSGAEBAWRlZfH3338/sK7eeecdPvvsM/bu3YuNjQ2DBw82Pnfr1i06dOjA+vXrOXDgAO3ataNz584m11EEmDZtGjVr1uTAgQOMHz+es2fP0q5dO3r06MHhw4dZsGABW7duNes1P0mkW0oIIYqQNK2OKhNWP/bzHn+vLU525v9LUBSF9evXs3r1al577TVOnz5tTBgaN24MwLx58wgKCmLJkiX06tULMKzx8/XXX1OzZk3jsRwdHcnIyCAgIMC4LSAgABsbG1xcXIzbv/vuO9LT0/nll19wdnYG4KuvvqJz5858/PHH+Pv73zfme2PU6/V8++23VKtWzSTGuzVs2JC3336bvn378tJLLxEWFsbTTz9N//79c5zvgw8+oHnz5gC89dZbdOzYkfT0dBwcHKhZs6bJa54yZQp///03y5YtM0lUnn76ad58803j4xdffJF+/foZW5/Kly/PF198QfPmzZk9ezYODg73fc1PGmm5EUIIYbbly5fj4uKCg4MD7du3JyIigkmTJnHixAlsbGxo0KCBsay3tzcVK1bkxIkTxm12dnbUqFHjoc594sQJatasaUxsAJo0aYJerzfpArrf/vfG6OXllSPGe33wwQfExsYyZ84cqlatypw5c6hUqRJHjhwxKXf36ypRogSAcdDxrVu3GD16NJUrV8bDwwMXFxdOnDiRo+WmXr16Jo8PHTrE3LlzcXFxMd7atm2LXq8nKirqga/5SSMtN0IIUYQ42mo4/l7bAj+uXq/nZvJNXN1cc11G39FWY9bxWrRowezZs7Gzs6NkyZLY2Jj378TR0bFYLl7o7e1Nr1696NWrFx9++CG1a9dm2rRp/Pzzz8Yyd182IPs16vV6AEaPHs3atWuZNm0a5cqVw9HRkZ49exq79LLdnbiBISkaNmwYI0aMyBFT6dKlC+z1WQtJboQQoghRqVQP1T30IHq9niw7DU52NgVyjSBnZ2fKlSuXY3vlypXJyspi165dxm6p69evExkZSZUqVe57TDs7u3wtu1+5cmXmzp1LSkqKMQnYtm0barWaihUr5mv/e2NMSEjIV4z3xlu2bFmzBvVu27aNgQMH0r17d8CQtERHRz9wvzp16nD8+PFc61zkJN1SQgghCkz58uXp2rUrQ4YMYevWrRw6dIjnnnuOwMBAunbtet99Q0JCOHz4MJGRkVy7di3Pwb39+vXDwcGBAQMGcPToUTZu3Mhrr73G888//8DxNnnFOHTo0PvGuHz5cp577jmWL1/OqVOniIyMZNq0aaxcufKBr+vecy9evJiDBw9y6NAh+vbta2zVuZ+xY8eyfft2hg8fzsGDBzl9+jRLly6VAcV5kORGCCFEgfrpp5+oW7cunTp1olGjRiiKwsqVKx94lechQ4ZQsWJF6tWrh6+vr3EW072cnJxYvXo1CQkJ1K9fn549e9KyZUu++uqrh4qxSZMmgCGBySvGKlWq4OTkxJtvvkmtWrVo2LAhCxcu5Pvvv+f555/P93mnT5+Op6cnjRs3pnPnzrRt25Y6deo8cL8aNWqwefNmTp06RbNmzahduzYTJkygZMmS+T73k0SlPOr8v2ImOTkZd3d3kpKScHNzs3Q4aLVaVq5cSYcOHR7p8u7WSOomd1IveStudZOenk5UVBShoaGFPttFr9eTnJyMm5tbgXRLWROpm7w97rq532fCnP/f8lsUQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFbn8wr3OboTE8+BRGjyCwb0U2NhbOiohhBBC5JMkN/c69DscXnDXBhW4lrid7JSGDp+Co4fhqbQbYOsMNnaWiFQIIYQQuZDk5l4lakFaoqH1JvECaFPhZozhdmk3dJ11p+y/bxkSIdcS4BEE9q5g4wC2TmDrAB2ng+b2Kqmn18KNaLB1NCmjUtnhnhoN+izgdtmsTFBrDDchhBBCmEWSm3s1esVwA1AUSL1uSHRunDfcv7uV5uYVQLmT/Nyr8xd37h/4FY4vzVHEBggHtFn9wd7RsHH5G3BwHmjsbidDjoaf2UlTvz/ByctQ9tACuLDd8JwxaXI0dKVp7KBaD7B3MZSNPwXJl+88l32zuf3Txf9OMqYooFI9fD0KIaxWfHw8EyZMYMWKFcTFxeHp6UnNmjWZMGGC8TpNAL/99hvvv/8+0dHRBAQE8MILLzB+/HiTY0VHRxMaGmp87OXlRd26dfn444+pXbt2rufftGkTLVq0yLH9ypUrBAQEGB/PmjWLTz/9lNjYWGrWrMmXX35JWFiY8fn09HTefPNN/vjjDzIyMmjTpg2zZ8/O18U3RdEmyc39qFTg7GO4BdbN+Xz/pZByzdDCk3TR0MqjTTPcdJmmyUGp2x+o7Odv3xRtKum3ErGxcbxTVptq+KnLNNxIMj3v3S060VsMiVNeyre+k9zs/RF2zc677PC94FPecH/D+7B1hmnyo7E3JD829vDsL+Bb0VD2yJ+GFixjsnS7nOZ2EtVgKHiG5H1eIUSx0qNHDzIzM/n5558pU6YMcXFxrF+/nuvXrxvLREdH079/f8aMGcOwYcOIj4/n1KlTeR5z3bp1VK1alUuXLjFixAjat2/PyZMn8fDwyHOfyMhIk2sM+fn5Ge8vWLCAUaNGMWfOHBo0aMDMmTNp27YtkZGRxnIjR45kxYoVLFiwABsbG8aNG8czzzyT5wU7RfEhyc2jUKnAxddwK5VL8nO3xrlflj5Lq2XNypV0uDth6TYbOnxmSHKy0m8nTel3kic7lztlK3cG9yDIMk2a0GUYurfsnO+UdfEFvyqGhCnrduKkywCdFrIyDIlINl0GKDrDcbPScgau6O/cv34GTq/J+7VXe+ZOcnMjGlRqw/glIUSxk5iYyJYtW9i0aRPNmzcHIDg42KRFBEClUqFSqRg8eDChoaGEhobmKHM3b29vAgICCAgIYNq0aTRp0oRdu3bRtm3bPPfx8/PLM/mZPn06Q4YMYdCgQQDMmTOHFStW8OOPP/LWW2+RlJTEDz/8wPz583n66adJTk7mhx9+oGrVquzcuZOGDRuaWTOiKJHkpiiyvd0NhfeDy1Zoa7jlR7M3Dbf8aP4WNHz1TuuRLtOQAOm0hsTn7uSkYgdwC7xd7vbzdydQriXulP1vmqGlKbgJ1HgWqnS7M0BbCHFHZkrez6k0hi7qfJVV3/57clfZe6/ufPeXoAdwcXHBxcWFJUuW0LBhQ+ztc59NGhgYSL169Rg+fDjLli0z66rnjo6GeDMzM+9brlatWmRkZFCtWjUmTZpk7BLLzMxk3759jBs3zlhWrVbTqlUrduzYAcC+ffvQarW0atXKWKZSpUqULl2aHTt2SHJTzElyI3Jn73KnO+tBStQw3PIj7QaggvPbDLeV/4MK7aBmbyjXWmaeCZHtw5J5P1e+DfRbdOfxp+XudGffK7gpDFphfKj6oqZh/ODdJt3T9X0fNjY2zJ07lyFDhjBnzhzq1KlD8+bN6d27NzVq3Pk7MGTIEBRFoUyZMrRv356lS5cau5A6d+5McHAwX331VY7jJyYmMmXKFFxcXPJs6SlRogRz5syhXr16ZGRk8P333xMeHs6uXbuoU6cO165dQ6fT5Rg74+/vz8mTJwGIjY3Fzs4ODw8P9Hq9SZnY2Nh814commQRP/F49Z4HI49Cq0ngW9nQsnNiGfzRF37MZwuUEMKievToQUxMDMuWLaNdu3Zs2rSJOnXqMHfuXACOHz/O3LlzmTt3LrNnz6Z06dKEh4dz9epVAI4ePUqzZs1Mjtm4cWNcXFzw9PTk0KFDLFiwIM+BvRUrVmTYsGHUrVuXxo0b8+OPP9K4cWNmzJhRqK9bFB/SciMeP/dS0HQkNHkDYo/AkYWGQcnlW98pk5WBestnOKd7WSxMISzq7VxmYGZT3bNMxP/O3Kes6XdYZcQhVPd2Sz0EBwcHWrduTevWrRk/fjwvvvgiEydOZODAgRw+fBh7e3uqVKkCwI8//khERARNmjRhzJgx3Lx5ky5dupgcb8GCBVSpUgVvb+/7DiLOS1hYGFu3bgXAx8cHjUZDXFycSZm4uDjjbKqAgAAyMzNJTEw0GZR8dxlRfEnLjbAclcrQndXmfRh5DJq8fue502vQ/PcxrU6MRfNTG9j1jWFmmhBPCjvnvG+2DmaUdXxw2QJQpUoVUlIMY38CAwPJyMhg165dAGg0GubPn0/ZsmUZOnQo77zzjnFcTbagoCDKli37UIkNwMGDBylRwjC+z87Ojrp167J+/Xrj83q9nvXr19OoUSMA6tati62trUmZyMhILly4YCwjii9puRFFg1pj+kfWyRt92VZwdgPqmP0Qsx9WjYNyraBmhGEQ871/tIUQhe769ev06tWLwYMHU6NGDVxdXdm7dy+ffPIJXbt2BaBp06Y0btyYiIgIZs6cSfXq1Tl27BgxMTE4Ozszf/58hg0bhpOT00PFMHPmTEJDQ6latSrp6el8//33bNiwgTVr7szaHDVqFAMGDKBevXqEhYUxc+ZMUlJSjLOn3N3deeGFFxg1ahQeHh5oNBrefvttGjVqJIOJrYAkN6JoCm6Mrnd91i/9ndYlktEc+xNiDsDp1Ybbq7vvrLMjhHhsXFxcaNCgATNmzODs2bNotVqCgoIYMmQIb7/9NmCYBr5q1SomT57MqFGjuHz5MuXKlWPo0KH06tWLBg0a0K9fP/7666+HiiEzM5M333yTy5cv4+TkRI0aNVi3bp3Jwn4RERHGxQZjY2OpVasWq1atMhnHM2PGDNRqNb169TJZxE8UfypFURRLB/E4JScn4+7uTlJSkkk/q6VotVpWrlxJhw4dsLW1tXQ4RUqOuok/ZVgsMP6kYWBytn/fMsyyqhEB/lUtF/BjIu+ZvBW3uklPTycqKorQ0FCzpko/DL1eT3JyMm5ubqgLYMyNNZG6ydvjrpv7fSbM+f8tLTei+PCtAC1Nl24nPQn2/WRY7HDb5+BfzbB+TvVe4HafqbRCCCGslqSoonizcYQe30OlTqC2hbijsHYCTK8CP3eByFWWjlAIIcRjJsmNKN5s7AyXoOg9D0afgk4zoHQjQIGozXDtrmvZZN1eQVkIIYRVk+RGWA8nL6g3GAavgtcPQYt3oXrPO88fWwyfVYL/PgVdluXiFEIIUagkuRHWyTMEmv/PdNxN5L+Qes1wxfOf2hsu4imEEMLqSHIjnhw9voduc8DeDS7thtlN4dACS0clhBCigElyI54cGluo1Qde2gpBDSHzJvw9FP560TDrSgghhFWQ5EY8eTyDYeAKaPGO4Ro9RxbBhZ2WjkoIIUQBkXVuxJNJYwPNx0CZcDi7ESrIFcmFEMJaSMuNeLIFhUH42DuPk2NgfoQMNhZC5CkyMpKAgABu3rxZKMcPCQlh5syZhXJsS+rduzefffbZYzmXJDdC3G3l/+DUKhlsLMR9xMfH8/LLL1O6dGns7e0JCAigbdu2bNu2zaTcb7/9RqVKlXBwcCAkJIQpU6bkOFZ0dDQqlcp48/b2pk2bNhw4cCDP82/atMlkn+xbbGysSblZs2YREhKCg4MDDRo0YPfu3SbPp6en8+qrr+Lr60upUqXo2bMncXFxD3z948aN47XXXsPV1fWBZe9n7ty5uV4Ffc+ePQwdOvSRjm1J2b+fxMREk+3vvvsuH3zwAUlJhT/GUZIbIe7W9kMZbCzEA/To0YMDBw7w888/c+rUKZYtW0Z4eDjXr183lomOjqZ///5069aNEydOsHDhQkJDQ/M85rp167hy5QqrV6/m1q1btG/fPsc/x3tFRkZy5coV483Pz8/43IIFCxg1ahQTJ05k//791KxZk7Zt23L16lVjmZEjR/LPP/+wYMECli9fzpUrV3jmmWfue84LFy6wfPlyBg4cmGcZnU6HXq+/73Hux9fX975XTNdqi+dipNWqVaNs2bL89ttvhX8y5QmTlJSkAEpSUpKlQ1EURVEyMzOVJUuWKJmZmZYOpcixWN1kaRVl08eKMslTUSa6KcqMaopyfsfjjeE+5D2Tt+JWN2lpacrx48eVtLS0Qj+XTqdTbty4oeh0ukc6zo0bNxRA2bRp033LRUdHK2q1WomMjLxvuaioKAVQDhw4YNy2bds2BVBWrVqV6z4bN25UAOXGjRt5HjcsLEx59dVXjY91Op1SsmRJZerUqYqiKEpiYqJia2urLFq0yFg3x44dUwBlx468P++ffvqpUq9ePZNtP/30k+Lu7q4sXbpUqVy5sqLRaJSoqCglPT1defPNN5WSJUsqTk5OSlhYmLJx40aT13D3beLEiYqiKEpwcLAyY8YM4/EB5euvv1Y6d+6sODk5GcstWbJEqV27tmJvb6+EhoYqkyZNUrRarcl+c+bMUTp27Kg4OjoqlSpVUrZv366cPn1aad68ueLk5KQ0atRIOXPmjMnrufe4b731lpKRkWFy3O+++07p1q2b4ujoqJQrV05ZunSpoih3fp933wYMGGDcd/LkyUrTpk3zrN/7fSbM+f8tLTdC3Ct7sPHgVeARDIkXDIv+nVpt6cjEE0BRFFK1qYVyS8tKy/M5w/+sB3NxccHFxYUlS5aQkZGRZ7nAwEDq1avH8OHDSU9PN6sOHB0dAcjMzLxvuVq1alGiRAlat25t0iWWmZnJvn37aNWqlXGbWq2mVatW7NixA4B9+/ah1WpNylSqVInSpUsby+Rmy5Yt1KtXL8f21NRUPv74Y77//nuOHTuGn58fw4cPZ8eOHfzxxx8cPnyYXr160a5dO06fPk3jxo2ZOXMmbm5uxpan0aNH53neSZMm0b17d44cOcLgwYPZsmUL/fv35/XXX+f48eN88803zJ07lw8++MBkvylTptC/f38OHjxIpUqV6Nu3L8OGDWPcuHHs3bsXRVEYPny4yeu7+7izZ89m/vz5fPjhhybHnTx5Ms8++yyHDx+mQ4cO9OvXj4SEBIKCgvjrr7+AOy1rn3/+uXG/sLAwdu/efd/3TkGQ2VJC5CUozLAmzsr/QcwBCGlm6YjEEyAtK40G8xs89vPu6rsLJ9u8u0Ky2djYMHfuXIYMGcKcOXOoU6cOzZs3p3fv3tSoUcNYbsiQISiKQpkyZWjfvj1Lly7Fzc0NgM6dOxMcHMxXX32V4/iJiYlMmTIFFxcXwsLCco2hRIkSzJkzh3r16pGRkcH3339PeHg4u3btok6dOly7dg2dToe/v7/Jfv7+/pw8eRKA2NhY7Ozs8PDwMOlC8vf3zzF2527nz5/PNbnRarV8/fXX1KxZEzB0X/30009cuHCBkiUNK6WPHj2aVatW8dNPP/Hhhx/i7u6OSqUiICAgz/Nl69u3L4MGDTI+Hjx4MG+99RYDBgwAoEyZMkyZMoUxY8YwceJEY7lBgwbx7LPPAjB27FgaNWrE+PHjadvWMEP09ddfNznu5MmTTY4bEhLC22+/zeTJk5k0aZKx3MCBA+nTpw8AH374IV988QW7d++mXbt2eHl5AeDn55djTFHJkiXJzMwkNjaW4ODgB77uhyXJjRD34+AGz3wDaYlgd/sPv14PZzdA+Vb33VUIa9WjRw86duzIli1b2LlzJ//++y+ffPIJ33//PQMHDuT48ePMnTuXY8eOUblyZQYOHEh4eDirVq3Cz8+Po0eP8txzz5kcs3HjxqjValJSUihTpgwLFizIkZxkq1ixIhUrVjTZ9+zZs8yYMYNff/21UF97WloaDg4OObbb2dmZJHdHjhxBp9NRoUIFk3IZGRl4e3ubfd57E6pDhw6xbds2k5YanU5Heno6qampxjE7d8eUXZ/Vq1c32Zaenk5ycjJubm4PdVxnZ2fc3NxMxjPlJbtVLjU1Nd+v/WFIciNEfjh63Lm/40tYOwGq94KOn4GDu8XCEtbH0caRXX13Ffhx9Xo9N2/exNXVFbU654gERxtHs47n4OBA69atad26NePHj+fFF19k4sSJDBw4kMOHD2Nvb0+VKlUA+PHHH4mIiKBJkyaMGTOGmzdv0qVLF5PjLViwgCpVquDt7Z3rDKIHCQsLY+vWrQD4+Pig0WhyzHyKi4sztpIEBASQmZlJYmKisUXp3jK58fHx4caNGzm2Ozo6olKpjI9v3bqFRqNh3759aDQak7IuLi5mvz5nZ2eTx7du3WLy5Mm5DoC+O/mytbU13s+OL7dt2a1X9x5Xr9dz69YtXFxc8jxu9nHyM4g6ISEBMAyaLkyS3AhhLr3urpWNd0GP76B0Q0tHJayESqXKV/eQufR6PVk2WTjZOuWa3DyqKlWqsGTJEsAw3iYjI4Ndu3bRoEEDNBoN8+fPp0uXLgwdOpTp06cbv8FnCwoKomzZsg99/oMHD1KiRAnA0IpSt25d1q9fT7du3QDD61+/fr1xfEndunWxtbVl/fr1dO/eHTCMEblw4QKNGjXK8zy1a9fm+PHjD4yndu3a6HQ6rl69SrNmuXdp29nZodPpzHmZRnXq1CEyMpJy5co91P75Pa5erze26uT3fWNnZweQ62s7evQopUqVwsfHp+CCzoUkN0KYq9koCGlqmCaeeN4w2Pip/8FTYwyDkYWwYtevX6dXr14MHjyYGjVq4Orqyt69e/nkk0/o2rUrAE2bNqVx48ZEREQwc+ZMqlevzrFjx4iJicHZ2Zn58+czbNiw+053vp+ZM2cSGhpK1apVSU9P5/vvv2fDhg2sWbPGWGbUqFEMGDCAevXqERYWxsyZM0lJSTGOL3F3d+eFF15g1KhReHh4oNFoePvtt2nUqBENG+b9ZaVt27a8+OKL6HS6HC0yd6tQoQL9+vWjf//+fPbZZ9SuXZv4+HjWr19PjRo16NixIyEhIdy6dYv169dTs2ZNnJyc8l0nEyZMoFOnTpQuXZqePXuiVqs5dOgQR48e5f33389nTT74uAA7d+7k3LlzOQYr5yU4OBiVSsXy5cvp0KEDjo6OxtaqLVu20KZNm4eOL79ktpQQDyN7sHGN3qDoYfPHhiRHVjYWVs7FxYUGDRowY8YMnnrqKapVq8b48eMZMmSIcYCwSqVi1apV9OzZk1GjRlGlShXGjRvHCy+8wKlTp4iNjaVfv34PvRZMZmYmb775JtWrV6d58+YcOnSIdevW0bJlS2OZiIgIpk2bxoQJE6hVqxYHDx5k1apVJuN4ZsyYQadOnejVqxcdO3bE39+fxYsX3/fc7du3x8bGhnXr1j0wzp9++on+/fvz5ptvUrFiRbp168aePXsoXbo0YBgr9NJLLxEREYGvry+ffPJJvuugbdu2LF++nDVr1lC/fn0aNmzIjBkzHnmQ7r3Hbdy4MbNnzzbGnB+BgYHGgcn+/v7G1rL09HSWLFnCkCFDHinG/FAp+Z3/ZyWSk5Nxd3cnKSnJpJ/VUrRaLStXrqRDhw45+jCfdMWmbo78CctHgjYNhmyAEjUevM8jKDb1YgHFrW7S09OJiooiNDQ010GqBelhuheeFObWzaxZs1i2bBmrV1v/8hAF+b6ZPXs2f//9t0kL273u95kw5/+3tKEL8aiq94RS9eHyPtPERq8Ddd7N1kKI4mnYsGEkJiYaB2iL/LG1teXLL798LOeS5EaIguAZbLhlizkAf74A3b6WwcZCWBkbGxveeecdS4dR7Lz44ouP7VzSNilEYdjwPiScNYzD2fgh6LIsHZEQQjwxLJ7cPOiqrfeaOXMmFStWxNHRkaCgIEaOHGn20t5CFLqeP8lgYyGEsBCLJjf5uWrr3ebPn89bb73FxIkTOXHiBD/88AMLFizg7bfffsyRC/EA2Ssb9/gB7N3g0m6Y3RQOLbB0ZKIIesLmdQiRp4L6LFg0uZk+fTpDhgxh0KBBVKlShTlz5uDk5MSPP/6Ya/nt27fTpEkT+vbtS0hICG3atKFPnz4PbO0RwmKq9zRMGS/dCDJvwt9D5QKcwih7RldhL0UvRHGRfbHU+60hlB8WG1CcfdXWcePGGbfde9XWezVu3JjffvuN3bt3ExYWxrlz51i5ciXPP/98nufJyMgwufpocnIyYJgyqtVqC+jVPLzsGIpCLEWN1dSNS0no9zfqbTNRXd6LLiQcHuE1WU29FILiWDeurq7ExcWh1+txcnIyWcK/ICmKQmZmJmlpaYV2juJK6iZvj7Nu9Ho9V69excHBAUVRcnyOzflcWyy5yc9VW+/Vt29frl27RtOmTVEUhaysLF566aX7dktNnTqVyZMn59i+Zs2ah14dszCsXbvW0iEUWdZTN1XAtRL8uwoAjS6d4OubOOfbBlTmN6JaT70UvOJWN66urqSkpMj6M+KJp9VqiY+P5/DhwzmeM6eFs1hNBd+0aRMffvghX3/9NQ0aNODMmTO8/vrrTJkyhfHjx+e6z7hx4xg1apTxcXJyMkFBQbRp06bILOK3du1aWrduXSwWHXucrL1uNMtfR315PlX8bNG3nwb5/FZk7fXyKIpz3eh0OrKysgpt/E1WVhbbt2+ncePG2NgUqz/9hU7qJm+Ps25UKhW2trZ5JvnZPS/5YbHfYn6u2nqv8ePH8/zzzxvnylevXp2UlBSGDh3KO++8k2uF2NvbY29vn2O7ra1tkfrjV9TiKUqstm5CmsCh+WgO/IzG3hnafpjvBAesuF4KQHGsm8KOV6vVkpWVhYuLS7Grm8ImdZO3olQ35pzfYm2gd1+1NVv2VVvzuiJrampqjgQme9CRzDYQxU7tftDVcC0edn5tWBtHCCHEI7No+9uDrtrav39/AgMDmTp1KgCdO3dm+vTp1K5d29gtNX78eDp37vzII6uFsIjazxmuSbVyNGyZBraO8NRoS0clhBDFmkWTm4iICOLj45kwYQKxsbHUqlXL5KqtFy5cMGmpeffdd1GpVLz77rtcvnwZX19fOnfunO/LsAtRJIUNMSQ4a8fDhilg7woNhlk6KiGEKLYsPnJq+PDhxsuh32vTpk0mj21sbJg4cSITJ058DJEJ8Rg1GQHaVNj+JfhXs3Q0QghRrFk8uRFC3NZ8LNTqBx5Blo5ECCGKNVlUQYiiQqUyTWzijsGJ5ZaLRwghiilJboQoihLOwdyOsGiAXK5BCCHMJMmNEEWRRzCUbQn6LFjwPJzdaOmIhBCi2JDkRoiiSK2B7nOgUifQZcAffeF87tdcE0IIYUqSGyGKKo0t9PwRyrUyzKSa1wsu77N0VEIIUeRJciNEUWZjDxG/QUgzyLwJvz4D8ZGWjkoIIYo0SW6EKOpsHaHP71AqzLAGjltJS0ckhBBFmqxzI0RxYO8Kz/0JaltQyYX9hBDifqTlRojiwsEd7JyMD9V7f4DEixYMSAghiiZJboQohspcXY1m9Vj4pSvcjLN0OEIIUaRIciNEMRTjUQ/FPQgSzhoSnJTrlg5JCCGKDEluhCiG0u28yer3N7iWgPgT8Gs3SEu0dFhCCFEkSHIjRHHlGQL9l4GTD8Qehnk9IeOmpaMSQgiLk+RGiOLMtwL0XwoOHnBpD/zeB/Q6S0clhBAWJcmNEMVdQDV4fjHYu0OVroZLNwghxBNM1rkRwhoE1oURB8DZ29KRCCGExUnLjRDW4u7EJjUBNk6VLiohxBNJWm6EsDZ6HfzaHa4chORL0PlLUMv3GCHEk0P+4glhbdQaaDoSVGo48BusGguKYumohBDisZHkRghrVLUbdJsNqGD3t7BuoiQ4QognhiQ3Qlirmr2h0wzD/W2fw+ZPLBuPEEI8JpLcCGHN6g2CtlMN9zd9CLu/s2w8QgjxGMiAYiGsXaNXQJsK++ZC2actHY0QQhQ6SW6EeBI8NRrqvwiOHpaORAghCp10SwnxpLg7sTm9Fo7+ZbFQhBCiMEnLjRBPmpgDhmtQKXrQ2EPlTpaOSAghCpS03AjxpAmoCdV6gKKDPwfBmXWWjkgIIQqUJDdCPGnUaug6y3CRTV0m/NEPorZYOiohhCgwktwI8STS2MAz30OFdpCVDvMj4OJuS0clhBAFQpIbIZ5UNnbQ62coEw7aFPitJ1w/a+mohBDikUlyI8STzNYBes+H0o0MA4s9QywdkRBCPDKZLSXEk87OGZ77C2wc5erhQgirIH/JhBCGBCc7sdHr4PBCudCmEKLYkpYbIcQdigILnoPIlZB0CZqNsnREQghhNmm5EULcoVJBuZaG++vfg8h/LRuPEEI8BEluhBCm6r8I9V4AFPjrRYg7bumIhBDCLJLcCCFyav8xhDSDzFvwe29IuW7piIQQIt8kuRFC5KSxhWd/MUwNTzwPiwaATmvpqIQQIl/MTm7S0tJITU01Pj5//jwzZ85kzZo1BRqYEMLCnLygzx9g5wqX90HsEUtHJIQQ+WL2bKmuXbvyzDPP8NJLL5GYmEiDBg2wtbXl2rVrTJ8+nZdffrkw4hRCWIJfZXh2Ljj7Qomalo5GCCHyxeyWm/3799OsWTMA/vzzT/z9/Tl//jy//PILX3zxRYEHKISwsHKtTBMbvd5ysQghRD6Yndykpqbi6uoKwJo1a3jmmWdQq9U0bNiQ8+fPF3iAQogi5OIemN0YEs5ZOhIhhMiT2clNuXLlWLJkCRcvXmT16tW0adMGgKtXr+Lm5lbgAQohighFgXUTIf4EzO8N6cmWjkgIIXJldnIzYcIERo8eTUhICA0aNKBRo0aAoRWndu3aBR6gEKKIUKmgxw/gWgKuRRrWwNHrLB2VEELkYHZy07NnTy5cuMDevXtZtWqVcXvLli2ZMWNGgQYnhChi3EpA73lg4wCnV8P6yZaOSAghcniodW4CAgKoXbs26ruuIBwWFkalSpUKLDAhRBEVWBe6fGW4v+1zOPSHZeMRQoh7mD0VvHv37qhUqhzbVSoVDg4OlCtXjr59+1KxYsUCCVAIUQTV6AVXj8PW6bBsBPhWhJLSLS2EKBrMbrlxd3dnw4YN7N+/H5VKhUql4sCBA2zYsIGsrCwWLFhAzZo12bZtW2HEK4QoKp4eDxU7QPnW4F3e0tEIIYSR2S03AQEB9O3bl6+++srYLaXX63n99ddxdXXljz/+4KWXXmLs2LFs3bq1wAMWQhQRajX0/BE09ob7QghRRJj9F+mHH37gjTfeMBlvo1aree211/j2229RqVQMHz6co0ePFmigQogiyNbxTmKjKHBiueGnEEJYkNnJTVZWFidPnsyx/eTJk+h0hmmhDg4OuY7LEUJYsaWvwoJ+hnE4QghhQWZ3Sz3//PO88MILvP3229SvXx+APXv28OGHH9K/f38ANm/eTNWqVQs2UiFE0VaqHhycB+ungG9lqNTB0hEJIZ5QZic3M2bMwN/fn08++YS4uDgA/P39GTlyJGPHjgWgTZs2tGvXrmAjFUIUbfUGQ9xx2PMdLB4CL6wBf/mSI4R4/MxObjQaDe+88w7vvPMOycmG5dfvvexC6dKlCyY6IUTx0m6qYfXiqP/g994wZBM4e1s6KiHEE+aRpji4ubnJ9aSEEHdobKHXz+AZCokXYGF/yMq0dFRCiCeM2clNXFwczz//PCVLlsTGxgaNRmNyE0I84Zy8oM8fYOcKF3fCpT2WjkgI8YQxu1tq4MCBXLhwgfHjx1OiRAmZFSWEyMmvEvSaCzb2ENLE0tEIIZ4wZic3W7duZcuWLdSqVasQwhFCWI3yrUwfK4rhyuJCCFHIzO6WCgoKQpFFuoQQ5rh6Er5vCdfPWjoSIcQTwOzkZubMmbz11ltER0cXQjhCCKu0ehxc3ge/94H0JEtHI4SwcmZ3S0VERJCamkrZsmVxcnLC1tbW5PmEhIQCC04IYSW6zYZvWximif/1omHAsVomIAghCofZyc3MmTMLIQwhhFVzDYDe8+Cn9nB6DaybBG2mWDoqIYSVMju5GTBgQIEGMGvWLD799FNiY2OpWbMmX375JWFhYXmWT0xM5J133mHx4sUkJCQQHBzMzJkz6dBBlnoXokgLrAPdvoY/B8P2L8CvCtTqY+mohBBWKF/JTXJysnGxvuxVifNizqJ+CxYsYNSoUcyZM4cGDRowc+ZM2rZtS2RkJH5+fjnKZ2Zm0rp1a/z8/Pjzzz8JDAzk/PnzeHh45PucQggLqtbDcImGLdPgnxHgUwFK1bV0VEIIK5Ov5MbT05MrV67g5+eHh4dHrmvbKIqCSqUyXhk8P6ZPn86QIUMYNGgQAHPmzGHFihX8+OOPvPXWWznK//jjjyQkJLB9+3bjWJ+QkJB8n08IUQS0eAeunoCMZPAMsXQ0QggrlK/kZsOGDXh5eRnvF8TCfZmZmezbt49x48YZt6nValq1asWOHTty3WfZsmU0atSIV199laVLl+Lr60vfvn0ZO3ZsnqsjZ2RkkJGRYXyc3fKk1WrRarWP/DoeVXYMRSGWokbqJndWUS9dvgaNneFyDQX4OqyibgqJ1E3epG7yVpTqxpwYVIqFFq2JiYkhMDCQ7du306hRI+P2MWPGsHnzZnbt2pVjn0qVKhEdHU2/fv145ZVXOHPmDK+88gojRoxg4sSJuZ5n0qRJTJ48Ocf2+fPn4+TkVHAvSAjx0HyTjxLvWlUW+RNC5Ck1NZW+ffuSlJT0wCEwZg8oLl++PP369aNfv36UL1/+oYN8GHq9Hj8/P7799ls0Gg1169bl8uXLfPrpp3kmN+PGjWPUqFHGx8nJyQQFBdGmTZsicdFPrVbL2rVrad26dY5p9WYfS6cnXasnM0uHo50GJzuzf71FSkHWjTWxtnpRr5uA5uzX6Jq/jb7pqAfvcB/WVjcFSeomb1I3eStKdfOgMb93M/u/3yuvvML8+fOZMmUKderU4bnnniMiIoKAgACzjuPj44NGoyEuLs5ke1xcXJ7HKlGiBLa2tiZdUJUrVyY2NpbMzEzs7Oxy7GNvb4+9vX2O7ba2thb/Rd3tfvHo9AoXE1I5FXeT01dvGX7G3SIxNZP0LD3pWh0ZWXp0etNGOB8Xe4K9nSjt5USQl+FnsLcTwV5O+LraF5vrghW131VRYTX14lcRAM3mD9EEVIXKnR75kFZTN4VA6iZvUjd5Kwp1Y875zU5uRo4cyciRIzl16hTz5s1j1qxZjB49mhYtWvDcc8/Rv3//fB3Hzs6OunXrsn79erp16wYYWmbWr1/P8OHDc92nSZMmzJ8/H71ej1ptWFz51KlTlChRItfEpji6lZHF8ZhkjlxO4tjlJCLjbnLm6i0ysvRmH+varQyu3cpg3/kbOZ5zttMQ7O1MqK8zod7OhPg4E+rjTDlfF9yd5MMtHqO6Aw0zqHZ/A4uHwgtrIKCapaMSQhRjD91vUaFCBSZPnszkyZPZuXMnL7/8MoMGDcp3cgMwatQoBgwYQL169QgLC2PmzJmkpKQYZ0/179+fwMBApk6dCsDLL7/MV199xeuvv85rr73G6dOn+fDDDxkxYsTDvgyLytLp2Xv+BhtjVKxbdJhjV24SdS2F3EZB2duoKevrQgV/F8r7u1LB3xV/N3vsbTQ42KpxsNVgb2P4aadRczM9iwsJqffcUjh/PZWYxDRSMnUcv5LM8Ss5m/lCfZypWcqdWkEe1AzyoEpJN+xtZDVZUYjafmhYvfjcJsMlGoZuBGcfS0clhCimHmlQxu7du5k/fz4LFiwgOTmZXr16mbV/REQE8fHxTJgwgdjYWGrVqsWqVavw9/cH4MKFC8YWGjBctHP16tWMHDmSGjVqEBgYyOuvv87YsWMf5WU8VplZerafvcaqo7GsOR5HQkomoAFijWVKuDtQtaQ71QLdqFzCjQr+rpT2ckKjzn83kruTLdWd3Kleyj3XGC4kpBJ9LYWoaylEXU8x3r+SlG7Ydi2FJQdjALDVqKhSwo3apT2pF+JJ/RAv/N0cHrUqhLhDYwM9fzJcXDPhHCx4HvovBRvraJEVQjxeZic32d1Rv//+O1FRUTz99NN8/PHHPPPMM7i4uJgdwPDhw/Pshtq0aVOObY0aNWLnzp1mn8eS0rU6/jsVz6qjsaw7EUdyepbxOU8nW4IcMmhZuwI1SntSraQ7vq45xwgVJDsbNeX8XCjnl/P3dSMlk0OXEjl4MZFDFw0/b6RqOXQpiUOXkpi7PRqA0l5O1AvxJCzEi3ohXpT1dS42Y3hEEeXkBX0WGBKcC9sNrTgV2lg6KiFEMWR2clOpUiXq16/Pq6++Su/evY2tLCKnM1dv8fP2aBbvv0RK5p3FDX1d7Wlb1Z/21UpQp5Qra1avokN4GYsP1gLwdLYjvKIf4RUNK0QrisLFhDQOXLzBgQuJ7I5K4ERssrGra/H+y4Bh8HLH6gF0rR1I7aDcF3oU4oF8KxhacLLSJbERQjw0s5ObyMjIxz4FvDjR6xU2n4rnp+3R/Hcq3ri9pLsD7aqVoH31AOqU9jR2MRWFhZHuR6VSUdrbidLeTnStFQhAcrqWAxcS2RudwO6oBA5eTOTarQx+3nGen3ecJ/h22W61SlLG1/zWPPGEK9/K9LGiyPo3QgizPNQ6NyKnm+la/tp3iZ93nCfqWgpg+HvcqrI/AxuH0List9W0Zrg52NK8gi/NK/gCd8YRLT0Yw+pjsZy/nsoX60/zxfrT1CzlTrfagXSrFYins4yfEGZKugQLB0D7T+QaVEKIfDM7udHpdMyYMYOFCxdy4cIFMjMzTZ5PSEgosOCKi+1nrjHs133czDCMpXF1sKF3/SCebxhCaW/rXwXZzkZt7MpKzcxi7fE4/j5wmS2nrxnH6kxdeZL21QPoE1aaBqFeVpPoiUK28UO4vBd+j4AX14NnsKUjEkIUA2YnN5MnT+b777/nzTff5N133+Wdd94hOjqaJUuWMGHChMKIsUhLzczif38e5mZGFmV9nRnYJJRnagfibF+8Vwd+WE52NnStFUjXWoFcu5XB8kMxLNp3iWMxySw9GMPSgzGU8XWmb1hpnqlTCi9pzRH30/5juHII4o7C/Gdh8Gpw9LB0VEKIIk794CKm5s2bx3fffcebb76JjY0Nffr04fvvv2fChAnFbhZTQfh8/WkuJ6ZRytOR5a814/mGwU9sYnMvHxd7BjYJZflrTVk2vAl9woJwstNwLj6F91ecoOGH6xnx+wEiY29aOlRRVNm7Qt+F4FoC4k/CogGgK9rj1IQQlmd2chMbG0v16tUBcHFxISkpCYBOnTqxYsWKgo2uiDsZm8wPW6IAeK9rVRztZKG73KhUKmqU8mDqMzXY9XZLPuhejaol3cjU6Vl2KIb2n//HmD8PEZuUbulQRVHkHgh9F4Cts2F6+PKR5LrSpRBC3GZ2clOqVCmuXLkCQNmyZVmzZg0Ae/bsyfUaTtZKr1d49++jZOkV2lb15+lKMiU+P1wdbOnXIJgVI5rxz/CmtK8WgF6BhXsvET5tI9NWR3IzXb6Zi3uUqAk9fwSVGg78Cru+sXREQogizOzkpnv37qxfvx6A1157jfHjx1O+fHn69+/P4MGDCzzAourPfZfYe/4GTnYaJnauaulwiqXqpdyZ/Vxd/nq5MfWCPUnX6vlq4xmaf7qJn7dHk/kQ19MSVqxiO2j3MQRUhypdLB2NEKIIM3twyEcffWS8HxERQenSpdmxYwfly5enc+fOBRpcUZWQksmH/54AYGSrCpT0cLRwRMVb3WBPFr3UiLXH4/ho1UnOxacwcdkxftwaRStfFe2lC0JkazAU6g4AmyenlVgIYb5HHvnaqFEjGjVqVBCxFBsf/XuCxFQtlQJcGdgkxNLhWAWVSkWbqgE8XcmPBXsvMmPtac4npPJDgoYTP+9jcpdqlPd3tXSYoii4O7E5vtTQkuNVxnLxCCGKnHx3S506dYrdu3ebbFu/fj0tWrQgLCyMDz/8sMCDK4r2RCewcO8lAD7oXg1bjdk9e+I+bDRq+jUIZvP/wnm5eSg2KoXtZxNo9/kWJv9zjKQ0GY8jbjswDxb2h3nPQuqTt76WECJv+f7PPHbsWJYvX258HBUVRefOnbGzs6NRo0ZMnTqVmTNnFkaMRYZWp+edv48A0CcsiLrBXhaOyHo529swqlV5xtXS0bqyHzq9wk/bomkxbRPzd11Ap5euqide2afBrRRcP224inhW5oP3EUI8EfKd3Ozdu5f27dsbH8+bN48KFSqwevVqPv/8c2bOnMncuXMLI8Yi44etUZyKu4WXsx1j21WydDhPBB8H+LpvLX59IYxyfi4kpGTy9t9H6PLVVjafikeR8ThPLrcShinidq5wfiv8M0KmiAshADOSm2vXrlGqVCnj440bN5oMIA4PDyc6OrpAgytKLt1I5fN1pwF4u0NlPJxkZd3HqVl5X/59vRkTOlXB1cGGYzHJDPhxN+0/38Jf+y7JzKonVUA1eHYuqDRw6HfUWz+zdERCiCIg38mNl5eXcX0bvV7P3r17adiwofH5zMxMq/4WPWvjGdK0OhqEetGjTqClw3ki2WrUDG4ayqbR4QxuEoqTnYaTsTd5c9EhnvpkI9/+d1bWyHkSlWsFHacBoPnvI0olbLdwQEIIS8t3chMeHs6UKVO4ePEiM2fORK/XEx4ebnz++PHjhISEFEKIlqfV6Vl5JBaA11uWl4s+Wpi3iz0TOldhx1st+V/bivi62hObnM6HK0/SeOoGpq48QVyyrHb8RKk3GBq/BoBr+mULByOEsLR8TwX/4IMPaN26NcHBwWg0Gr744gucnZ2Nz//66688/fTThRKkpW07c42kNC0+LnY0KONt6XDEbe5OtrzaohwvNgtlyYHLfPvfOc7Gp/DNf+f4aVs0z9YvxUvNy1LK0/qvzC6AVu+RVbopJyIzCLV0LEIIi8p3chMSEsKJEyc4duwYvr6+lCxZ0uT5yZMnm4zJsSYrDhu649pXK4FGLa02RY29jYaI+qXpVTeIDSevMmfzWfaev8FvOy/wx+6LPFMnkFfCyxHi4/zgg4niS61GKdsSIlcaHmdlgDYVHD0tG5cQ4rEzaxE/GxsbatasmetzeW0v7jKz9Kw+ZuiS6lijhIWjEfejVqtoVcWflpX92Hkuga82nmbbmess3HuJP/ddokvNkgx/uhzl/GQxQKuXmgB/DTDMnuq/FGwdLB2REOIxkhXoHmDrmXiS07Pwc7Wnfoisa1McqFQqGpX1Zt6LDfnr5ca0qOiLXoElB2NoPeM/Rvx+gMuJaZYOUxSm1GsQdxwu7oSlr8oUcSGeMJLcPMDy211SHapLl1RxVDfYk58GhbH8taa0reqPosCyQzE8PW0T09dEkpqZZekQRWHwqQARv4DaBo7+CRufjBXUhRAGktzcR7pWx9pjcYB0SRV31QLd+eb5eix/rSkNQr3IyNLzxYYztJi2icX7L6GXFY+tT5lw6DTTcP+/TwyXaxBCPBHyldw888wzJCcnA/DLL7+QkZFRqEEVFVtOX+NmRhYBbg7ULS2DEq1BtUB3/hjakDnP1SHIy5G45AxGLTxE99nb2Xf+hqXDEwWtzvPQ7E3D/X9GwLnNlo1HCPFY5Cu5Wb58OSkpKQAMGjSIpKSkQg2qqFhxOAYwdEmppUvKaqhUKtpVK8Hakc0Z264SznYaDl1MpMfs7TIexxq1eBeqPgP6LEOCo5OFHoWwdvmaLVWpUiXGjRtHixYtUBSFhQsX4ubmlmvZ/v37F2iAlpKu1bH2uHRJWTMHWw0vh5elR91APlt9ioX7LrLsUAyrj8Uy7KkyvBReFic7syYUiqJIrYZusw3jb5qPAY2tpSMSQhSyfP3lnjNnDqNGjWLFihWoVCrefffdXFfpValUVpPcbIqMJyVTR6CHI3VKe1g6HFGI/Fwd+LhnDZ5vFMx7y4+zOyqBLzacYcHei4xtV4lutQKl5a64s3WAHt9ZOgohxGOSr26pxo0bs3PnTuLjDVdhPnXqFDdu3MhxS0hIKOx4H5sVR7JnSQXI5RaeENUC3VkwtCGz+90zHufrbew7bz3vbQGc3QhLh4NeLrgqhDUye7ZUVFQUvr6+hRFLkZGWqWP9iewuqZIPKC2siUqlon31O+NxXOxtOHQpiR6zdzB8/n4uJqRaOkTxqFKuwx/94MCvsH6ypaMRQhQCswcUBAcHk5iYyA8//MCJEycAqFKlCi+88ALu7u4FHqAlbIy8SmqmjlKejtQsZR2vSZjn7vE409ecYsHeiyw/fIU1x+N4oWkor4SXxdVBxm4US87e0Gk6/D0Mts0Er1CoO9DSUQkhCpDZLTd79+6lbNmyzJgxg4SEBBISEpgxYwZly5Zl//79hRHjY5d9LamONUpIl9QTzs/VgY961GD5a01pVMabzCw9szedpcW0TczfdQGdrI9TPNXsDc3fMtxfPgrObrBsPEKIAmV2cjNy5Ei6dOlCdHQ0ixcvZvHixURFRdGpUyfeeOONQgjx8UrJyGL9SUOXVKfq0iUlDKqWdGf+kAZ8178eoT7OXLuVydt/H6HjF1vYcjre0uGJhxH+FtSIAEUHCwcYLtcghLAKD9VyM3bsWGxs7vRo2djYMGbMGPbu3VugwVnChpNXSdfqKe3lRLXA3Ke7iyeTSqWidRV/Vr/xFBM6VcHd0ZaTsTd5/ofdvDB3D7FJ6ZYOUZhDpYIuX0JwE8hIhvnPwq2rlo5KCFEAzE5u3NzcuHDhQo7tFy9exNW1+F9tObtLqpN0SYk82NmoGdw0lM3/C2dwk1Bs1CrWn7xK25n/sexQjKXDE+awsYeI38C7HIQ+BQ4elo5ICFEAzE5uIiIieOGFF1iwYAEXL17k4sWL/PHHH7z44ov06dOnMGJ8bG5lZLEx0vDNTRbuEw/i4WTHhM5V+Pf1ZlQPdCcpTcuI3w/w2u8HSEzNtHR4Ir+cvOCFtdB1FtjYWToaIUQBMHu21LRp04yL9WVlGa6obGtry8svv8xHH31U4AE+TutPxJGRpSfUx5kqJaRLSuRPeX9XFr/SmK82nOGrjWf451AMu85d55OeNQiv6Gfp8ER+OHndua/XwalVUKmj5eIRQjwSs1tu7Ozs+Pzzz7lx4wYHDx7k4MGDxhlT9vb2hRHjY7NcuqTEQ7LVqBnZugKLX25MGV9nrt7MYOBPe3h3yRFSM7MsHZ7IL70eFjwPf/SFPd9bOhohxEMyO7nJ5uTkRPXq1alevTpOTk4FGZPFNC7rTY1S7nSoLl1S4uHUDPJgxWvNGNg4BIDfdl6gw+cyo6rYUKshsLbh/sr/wak1lo1HCPFQHjq5sUaDmoSybHhTKkuXlHgEjnYaJnWpyrwXG1DC3YHo66k8/8Nuhs/fT1yyzKgq8pqNhlrPgaKHPwdB7BFLRySEMJMkN0IUkiblfFgz8ikGNQlBrTJ0e7b8bDM/bI0iSyfXNCqyVCroNMMweyrzFsx7FpJlFpwQxYkkN0IUIlcHWyZ2rso/rzWldmkPbmVkMWX5cTp/tY19529YOjyRFxs7ePZX8KkIN2NgfgSkJ1s6KiFEPklyI8RjULWkO3+91Jipz1TH3dGWE1eS6TF7O2P+PMSF63IxziLJ0QP6LQQnH0i8ABq5lpgQxYXZU8EBTp8+zcaNG7l69Sp6vWnz+oQJEwokMCGsjVqtok9YadpU8efjVSdZuPcSC/de4s99l2hXLYAhzcpQu7SnpcMUd/MMgX6L4Px2sHU0bFMUWNgfyraA6r3AvvgvXiqEtTE7ufnuu+94+eWX8fHxISAgwGTKtEqlkuRGiAfwdrHnk541iagfxBfrz7D5VDwrj8Sy8kgs9YI9GfJUGVpV9kejluUIioTAOoZbtugtcGKZ4bZmPFTvCXUHQclaFgtRCGHK7OTm/fff54MPPmDs2LGFEY8QT4y6wV78PDiMyNibfL/lHEsOXmbv+Rvs/XUfId5OvNCsDBH1grCzkd7jIsW/GrT5APb9BNfPwL65hlvJ2oYkp1oPsHexdJRCPNHM/qt548YNevXqVRixCPFEqhjgyqe9arJt7NO82qIs7o62RF9PZfySo7SZsZm1x+NQFMXSYYpsTl7QeDgM3wsDlhuSGbUtxByAf0ZAzH5LRyjEE8/s5KZXr16sWSMLWwlR0PzcHPhf20psf+tpJnWugq+rPdHXUxnyy16e+2EXJ2Nltk6RolJBaDPo+SO8eRJavwflWkNIsztldn0LB36DTBk0LsTjZHa3VLly5Rg/fjw7d+6kevXq2NqaziAYMWJEgQUnxJPI2d6GgU1C6VkviK83nuH7rVFsO3OdDp9voU9YaV5rUcbSIYp7OftAk9cNt2zaNNj4AaQnwqq3oWYE1B0I/lUtFaUQTwyzk5tvv/0WFxcXNm/ezObNm02eU6lUktwIUUBc7G0Y064SfcJKM/XfE6w8Esu8XRdYejCGlgEqWmp1Ob5ciCJEr4OmbxjG49yIht3fGm6lwqDeIKja/c4MLCFEgTI7uYmKiiqMOIQQeQjycuLrfnXZde467y0/zrGYZJae17Bz5lZebVGOZ+sF4WCrsXSY4l72LtB0JDR+HaI2wd6fIHIlXNptuMVHQuvJlo5SCKv0SNMwFEWRgY5CPCYNynizbHhTPuxWBXc7hbjkDCYsPUb4p5v4ZUc06VqdpUMUuVGroezTEPErjDwOLScY1s+p/fydMpf3waEFoJVrjwlREB4qufnll1+oXr06jo6OODo6UqNGDX799deCjk0IcQ+NWkWvuqWYUFvHpE6VCHBzIDY5XZKc4sLVH5q9CSMOgk+5O9u3fQ5/D4XplQzjc+JPWSxEIayB2cnN9OnTefnll+nQoQMLFy5k4cKFtGvXjpdeeokZM2YURoxCiHvYqKFfg9JsHhPOlK5VJckpblT3LNBYoha4lYK0G7BzFsyqDz91gMOLICvDIiEKUZyZPebmyy+/ZPbs2fTv39+4rUuXLlStWpVJkyYxcuTIAg1QCJE3exsNzzcK4dn6QSzcc5FZG88ak5yvN57l1RZlebZ+EPY2MianSGs2yjDT6sw6w9ic06vh/DbDbW8jGLzK0hEKUayY3XJz5coVGjdunGN748aNuXLlSoEEJYQwT3aSc29LzvjbLTm/7ogmI0tacoo0tQYqtIW+f8AbRyF8HLgFQuUud8pkpsLRv6Q1R4gHMDu5KVeuHAsXLsyxfcGCBZQvX75AghJCPJy7k5z3bic5V5LuSnJ2npckpzhwD4Twt+D1w1Bv8J3tx/6GPwfDl3Xh5ErLxSdEEWd2t9TkyZOJiIjgv//+o0mTJgBs27aN9evX55r0CCEeP3sbDf0bhfBsvSAW7r3IrI1nDEnOkqN8sf40g5uE0rdBadwdZZ2cIk1jY7gZKeASAEkX4Y8+ULEjdPgE3EtZLEQhiiKzW2569OjBrl278PHxYcmSJSxZsgQfHx92795N9+7dCyNGIcRDcrA1JDmb/9eCyV0MLTnxNzP4eNVJmny0gQ9WHOdKUpqlwxT5Vfs5GHHAsH6O2gYiV8BXYbD9K9BlWTo6IYoMs1tuAOrWrctvv/1W0LEIIQqJg62GAY1D6BNWmn8OxfDNf2c5FXeL77ZE8dO2aLrWCmToU2WoGOBq6VDFg9g5QatJUCMClo+ECztgzTsQdwy6z7Z0dEIUCflKbpKTk3FzczPev5/sckKIosfORk2PuqV4pk4gmyLjmbP5LLuiEvhr/yX+2n+J1lX8eaNVeaqWdLd0qOJB/CrDwJVwcB6snwwNhlk6IiGKjHwlN56enly5cgU/Pz88PDxQ3btGA4bVilUqFTqdDFYUoqhTqVS0qORHi0p+HLhwg282n2P18VjWHo9j7fE42lcL4PVW5akUIF9WijS1Guo8D9V7ml6natsX4OIPNZ7NuaaOEE+AfCU3GzZswMvLC4CNGzcWakBCiMerdmlP5jxflzNXb/L5+jMsPxzDv0dj+fdoLB1rlOCNluUp7y/dVUXa3YlN/ClY/x7otXDwN+g4HXxkJqt4suQruWnevLnxfmhoKEFBQTlabxRF4eLFiwUbnRDisSnn58qXfWrz2tPl+HzdaVYcucKKw1dYeeQKXWqW5PWW5Snj62LpMMWDeIYYppH/9ylE/QezGxsGIDd8zdKRCfHYmD1bKjQ0lPj4+BzbExISCA0NfaggZs2aRUhICA4ODjRo0IDdu3fna78//vgDlUpFt27dHuq8QoicKvi7MqtfHf59vRntqgagKLD0YAytZ/zHu0uOEH9TFpAr0mzs4KnR8MoOKNsSdJmw+WNsvm2Gb/JRS0cnxGNhdnKTPbbmXrdu3cLBwcHsABYsWMCoUaOYOHEi+/fvp2bNmrRt25arV6/ed7/o6GhGjx5Ns2bNzD6nEOLBKpdwY87zdVkxoiktK/mh0yv8tvMCzT/dyIy1p7iVIVOPizSvMvDcX9DzJ3AJQHUjirCozyE1wdKRCVHo8j0VfNSoUYBhIOL48eNxcnIyPqfT6di1axe1atUyO4Dp06czZMgQBg0aBMCcOXNYsWIFP/74I2+99Vau++h0Ovr168fkyZPZsmULiYmJZp9XCJE/VUu688PA+uw6d52p/57k4MVEPl9/mnm7zjOiZXn6hJXGVmP29yTxOKhUUO0ZKNcS3br3OHEllUpOXneeVxQZcCysUr6TmwMHDgCGlpsjR45gZ2dnfM7Ozo6aNWsyevRos06emZnJvn37GDdunHGbWq2mVatW7NixI8/93nvvPfz8/HjhhRfYsmXLfc+RkZFBRsadZvTsqexarRatVmtWvIUhO4aiEEtRI3WTO0vVS50gNxYOqc+qY3FMX3eG6OupTFh6jB+2RPFS81DaVfXHxf6hls4qMPKeyYPGCe3TUzi3di1lb9eNKnoL6k0foGs/DfyrWThAy5L3Td6KUt2YE4NKURTFnIMPGjSIzz//vEDWs4mJiSEwMJDt27fTqFEj4/YxY8awefNmdu3alWOfrVu30rt3bw4ePIiPjw8DBw4kMTGRJUuW5HqOSZMmMXny5Bzb58+fb9L6JITIP50etl9VseqSmltawzd/W7VCdU+Fer4KldwVpDGnCFMUnjo1Gc/Uc+hRc8SvFWf8u2BjI1P/RdGVmppK3759SUpKemAOYvbXrJkzZ5KVlbOvPSEhARsbm0JdxO/mzZs8//zzfPfdd/j4+ORrn3Hjxhm71MDQchMUFESbNm2KxIKDWq2WtWvX0rp1a2xt5To/d5O6yV1RqZfOwDsZWczbdZHFBy5z7loq+6+r2H8dPJ1s6VQ9gC41S1CzlHuu4/QKQ1Gpm6IoR908VQf92ndRn1jKIv1ediWdYEyF5whv8KalQ33s5H2Tt6JUNw9aRPhuZic3vXv3pnPnzrzyyism2xcuXMiyZctYuTL/V6r18fFBo9EQFxdnsj0uLo6AgIAc5c+ePUt0dDSdO3c2btPr9QDY2NgQGRlJ2bJlTfaxt7fH3t4+x7FsbW0t/ou6W1GLpyiRusldUagXT1tbhreswKtPl+fI5SSWHIhh2aEYrt3K4NddF/l110UCPRwJr+hLi4p+NC7njZNd4XddFYW6KaqMdeNVGiJ+4daJf9i3421iNTDq7DzCzy1jXKsvKFmynqVDfezkfZO3olA35pzf7IbjXbt20aJFixzbw8PDc+1Guh87Ozvq1q3L+vXrjdv0ej3r16836abKVqlSJY4cOcLBgweNty5dutCiRQsOHjxIUFCQuS9HCFEAVCoVNUp5MKFzFXaOe5qfB4fRvXYgTnYaLiemMW/XBV78ZS+1Jq/l+R928f2Wc5yNv4WZveKiELhU7szi3pt50aUiNorCJuUm3dYPZe7RuWj1lh9nIcTDMPsrVEZGRq7dUlqtlrQ0868uPGrUKAYMGEC9evUICwtj5syZpKSkGGdP9e/fn8DAQKZOnYqDgwPVqpkOfPPw8ADIsV0IYRk2GjXNK/jSvIIvaZk6dp67zsbIq2yMvMrFhDS2nL7GltPXeH/FCUJ9nOlaqyTdagUS4uNs6dCfWI5OXrze4086nl3DlN0fsj/zOp/t+4xl55Yxoc4oapVqaukQhTCL2clNWFgY3377LV9++aXJ9jlz5lC3bl2zA4iIiCA+Pp4JEyYQGxtLrVq1WLVqFf7+/gBcuHABtVpGJgpRHDnaaYzXsFIUhXPXUth48iqbIuPZHZVA1LUUZq47zcx1p6ld2oPutQPpWL0E3i45u5JF4StXtg1zy7RmyZklTN83ndM3TvP8+pfpYV+Ske2+xd0j2NIhCpEvZic377//Pq1ateLQoUO0bNkSgPXr17Nnzx7WrFnzUEEMHz6c4cOH5/rcpk2b7rvv3LlzH+qcQojHS6VSUdbXhbK+LrzYrAy3MrJYezyWvw/EsPV0PAcuJHLgQiLv/XOc5hV86VSzBI3K+BDgbv7ioOLhqVQqupfvTnhQONPXDmdJwmH+yohh498dGR3SlU7Np6CSL5yiiDM7uWnSpAk7duzg008/ZeHChTg6OlKjRg1++OEHypeXi7MJIfLHxd6G7rVL0b12Ka7eTOefQ1dYcuAyRy4nsf7kVdafNKxSHurjTINQLxqW8aZhGW9Jdh4TTwdPpnSeR9eDc5myfzrnNPD2hWUs+WU174ZPIzQk3NIhCpGnh5q2UKtWLebNm1fQsQghnlB+rg680DSUF5qGcubqLZYevMymyHiOxSQRdS2FqGsp/LHHcGHeEG8nGpX1oUP1ABqV8cZGFtQpVPVqDeTPyr34efUrzLm+j93qDHpsGs4L7tV5sfNP2NtIsimKnkeak5menk5mZqbJtqKwdowQovgq5+fCm20q8mabiiSladkbncDOc9fZFZXA0ctJRF9PJfr6BX7ffQEvZzvaVg2gc40ShIV6Pfjg4qHY2jvzYpefaXtxBx9uGMlWVQpzko+yclkP3mnwDo0DG1s6RCFMmJ3cpKamMmbMGBYuXMj169dzPK/T6QokMCGEcHe0pWVlf1pWNkwwSE7XsicqgfUnr7LqaCwJKZn8vtuQ6Pi42NGmih8et1S01OosviaHNQoKasTXz29n7fapfHx5HRduXmDYumFUcA6kk0Mg7eu9RkBALUuHKYT5yc3//vc/Nm7cyOzZs3n++eeZNWsWly9f5ptvvuGjjz4qjBiFEAIAN4c7yc57Xaqy49x1Vhy+wqpjsVy7lcn83ZcADd99sIFaQR6EhXoRFupN3WBPi1/3ylqo1GraNH2HxpmvM+vgLBZELuBUymWmp1xmxqrnCFM50rFUC1rVfx1Xt0BLhyueUGZ/2v/55x9++eUXwsPDGTRoEM2aNaNcuXIEBwczb948+vXrVxhxCiGECRuNmmblfWlW3pcp3aqx/ex1/jl4mTVHLpGshT3RN9gTfYNZG8+iVkG1QHfqh3hRK8iDmqU8CPJyfGyXhbBGLnYujA0by0s1X2Lt1g9YfnED+1QZ7CKdXZf+5f2LKwm38aRT2a40rT8CWxu7Bx9UiAJidnKTkJBAmTJlAMP4moSEBACaNm3Kyy+/XLDRCSFEPtjeXjiwcagHTe3OU61hOPsvJrMrKoHd0de5mJDG4UtJHL6UZNzH08mWmkEe1CjlQa0gd2qU8sBH1tcxm7u9Oz1bfkJPICZmLyv3fsk/1w5wTgNrdImsOfUz7ueX0C6kHZ3KdKKmb01JKkWhMzu5KVOmDFFRUZQuXZpKlSqxcOFCwsLC+Oeff4yrBQshhKWoVBDs7US5AHeerW+4JMuVpDR2RyWwN/oGhy8lcvxKMjdStWyKjGdTZLxx39JeTtQp7UGdYE/qlPakUoCrzMYyQ8mS9Xixy8+8oNdz8vQ//HP4R/7NjONaRhILIhewIHIBpfQqOnpUoWPtl2Q6uSg0Zic3gwYN4tChQzRv3py33nqLzp0789VXX6HVapk+fXphxCiEEI+khLsjXWsF0rWWYQxIRpaOk1ducuhSIocuJnHoUiJn429xISGVCwmpLDkYA4CjrYYapdypFeSBi70NarUKtUqFWoXhp9pw31ajxs5Gjd09P+1t1FQq4Ya745M1uFmlVlO5YlcqV+zKm3odu2J3seLcCtZFreISmXyTfIxvNr9G1Y0aOgU0pF39N/DxqWTpsIUVMTu5GTlypPF+q1atOHnyJPv27aNcuXLUqFGjQIMTQojCYG+joWaQBzWDPOD2NXqT07UcvJDI/gs32H8hkQMXbnAzPYtdUQnsikp46HPZqFU0KutNmyr+tKriTwl3x4J5EcWERq2hccnGNC7ZmHfqjGTTni9ZcX4N2/Q3OabWcezqNqYt30ojlTOdar5Ii6r9cLJ1snTYopgzK7nRarW0a9eOOXPmGFcjDg4OJjhYrjcihCje3BxseaqCL09V8AVAr1c4G3+L/RducDwmmUydHr0e9IqCTlFQlNv39QpanZ7MLD2Z2T+z9GRk6bmZnsXlxDsXCx2/9Bg1S7nTpmoAbar4U87P5Ykaf+Lk5EOH5pPpwGSuXzvF6r2fs+LKdg6rs9hKKlsPfYHjse9oVboVnUo2Jaz009jYyiKBwnxmJTe2trYcPny4sGIRQogiQ61WUd7flfL+ro90nHPxt1h7PI41x+PYf+EGhy4lcehSEp+ujiTQw5Em5bxpUs6HRmW98XN9cv6Re/tUoG+7WfQFzp/fworjv7E87SIXb17kn3P/8M+5f/DRKbR3LUun6oOoXKGLXNNK5JvZ3VLPPfccP/zwg6xpI4QQ+VDG14VhzV0Y1rws8TczWH/CkOhsPX2Ny4lpLNx7iYV7LwFQ3s+FJuV8aFzWm3ohXng5PxnTp4ODm/FKcDNeVhQOXzvMP6f/ZnXkn1zTqPg19Ry/7hpPme0T6ORTiw51hxMYGGbpkEURZ3Zyk5WVxY8//si6deuoW7cuzs7OJs/LoGIhhMidr6s9vcNK0zusNKmZWeyJvsH2M9fYdvYax2KSOX31Fqev3mLu9mgAAj0cqVLSjWol3ala0o1qge74u9lbbVeWSqWipm9NavrWZGy9/7F9/zcsP7OEjdoEzmngixsH+GLdC9Sx9aRj3eG0DWmLu727pcMWRZDZyc3Ro0epU6cOAKdOnTJ5zlo/cEIIUdCc7GxoXsGX5rfH+NxIyWRX1HW2nbnO9rPXOBufwuXENC4nprH2eJxxPx8XO2qX9qRFRT/CK/pS0sM6Byjb2jnTvOEomjccxa2bV1i3eybLL21gt5LGfu0N9u+cwtTdU3kqoCGdHEvxVL3h2DtIoiMM8p3cnDt3jtDQUDZu3FiY8QghxBPJ09mOdtVK0K5aCQBupms5HpPM0ZhkjsUkcexyMqev3uTarUzWHo8zJjyVAlxpUcmPFhX9qFPawyrX5XFxLUG3lh/TDYiLO8yq2B0sv7COkwkn2RCzlQ2A6+nfae1QgkaBTQnxr01wUFMcneRiqk+qfCc35cuX58qVK/j5+QEQERHBF198gb+/f6EFJ4QQTypXB1salPGmQRlv47a0TB0nYpPZdvoaGyOvcuBiIidjb3Iy9iazN53FzcGGxmV9qBvsSZ1gD6qWdMfBVmPBV1Hw/P1rMMC/BgNqDuP0jdOs2D6VFXG7idWoWJwZy+KoPyHqT9gJATqFkIDahHhVIsQ9hBC1IyEupSgRUBu15slae+hJk+/kRlEUk8crV65k6tSpBR6QEEKI3DnaaahT2rB68msty5OQksmW0/FsPHmVzafiuZGqZdWxWFYdiwXAVqOiSkl36pT2oGagG4kZFn4BBay8Z3ne6PgjI3RZ7DvyC6tOLiQy7SrRZJKkVhGrUREbf5Cd8QdN9rNTFEorGkJtXAl2CiDIvSw3b0JyekO8beULuzWQy+QKIUQx5eVsZ1x5WadXOHQpkd1RCew/b1iI8NqtDA5dTOTQxcTbe9gw58xm6oV4US/Yk7rBXlQuUfwvMaHW2FC/1mDq1xps3HYj4Sznr+whys6e6ORoopOiOX95Bxd0qWSqVJxR6TmjT4JbSXArEoBpi1fiae9paOXJ0hOCHSHelQgpUZegkg2xtXfOKwRRxOQ7uVGpVDkGDMsAYiGEKBo0apWxVQcMre2XbqQZVlw+f4N9529wPCaJ2OQMlh++wvLDVwBwstNQK8iDusGeVAt0p3qgOyXcHYr933dPr7J4epWl1j3bdVmZxFzZQ9SVvZy/dpzo5PNEpV8jSp/BNQ3cyLjBjas3OJC9Q8IeOP0rGkUhUK8ixMaFYEc/Qqr3JdSjDCFuIfg4+hT7+rI2ZnVLDRw4EHt7w1Vz09PTeemll3JMBV+8eHHBRiiEEMJsKpWKIC8ngryc6ForEK1Wy9//rKRE1YYcvJTM3vM32H/7EhPbz15n+9nrxn29ne2oGuhOtZJuVA90p0aQB4FWMitLY2NHUFATgoKaGLdptVpWrlxJeOtwLqddNrTyHF9EVOI5orVJnCeLVLWKCxq4oNyC1Fuw633j/s6oCdarCLHzIMQ5kBDP8oQE1KaEXw3UGjtwuGshyPSboOhzD06lNi2bcQv0ujzKqsDBLX9lARzvmkmWkQL6rHyVzUpJQqu9zs3ky9jY5DJ+y8HNEAtAZirotHkf194VshdizEwDXWb+ymrTISuD5OSbeZe/R76TmwEDBpg8fu655/J9EiGEEJZnr4GGZbxoVtEwrkSvVzh99RZ7zydw8EIiRy4ncfrqLa6nZPLfqXj+O3XniullfJ0Jr+BHi0q+hIV6YZ/bP7pizsnWiSpOVajiXQXKdDBuV/R64uOPER2zi+j4I0SnJxDt5Ep0cjSXb10mRdFzXA3Hs65D0nVIOgzRf1nwlRS8yctnWDoEdGn3Sd7uke/k5qeffnqoYIQQQhRNarWKigGuVAxwpV8DwzUC07U6Tsbe5OjlJMMtJokTV25yLj6Fc/FR/LgtCkdbDU3KedO8oh9PlfchyNMJtdp6u2VUajV+/tXx86/OvWsja3VaLl74j6gre4m+fpLzty4SnXGDaCWDG1ZcJ0WdDCgWQghh5GBrGINTK8jDuC0pTcu2M9fYFHmVTZHxXL2ZwboTV1l34ioAdjZqgjwdKe3lROnbXWHB3s4EeztR1tcFjRX/k7fV2FImtCVlQlvmeE6rTTXcuXva+f26bR6pbBag5Fn0YctqM9JZvepf2rZth61tLimD2uZOt9SDjnt3Wb0u7+65PMomJyfj83KJvPe5iyQ3Qggh7svd0ZYO1UvQoXoJFEXh+JVkNkXGszkynv0XbpCZpedsfApn41Ny7Otib0OdYE/CQjypF+JFrSAPq1t7Jy+2tk45N6rNWF+nKJS1AbXaHltbR2xtH7BfIcdra3ufcUL3kORGCCFEvqlUKqqWdKdqSXdebVGOLJ2eK0npXEhINbldTEjlXHwKtzKyTMbv2GnUVC/lTr1gT/zdHHB1sMHVwRa32z9dHWxwc7TF08lWZiCJhybJjRBCiIdmo1EbZ2U1uec5nV7hZGwye6IS2BN9g93RCcTfzGDf7anp9+PuaEvlEq5ULuFGlRJuVC7hRnl/F6scyCwKniQ3QgghCoVGfaeVZ2CTUBRF4fz1VHZHJ3D4UiKJqVpupmdxMz37p+F+SqaOpDQtO88lsPNcgvF4NmoV5fxcCPF2poSHA4EejpQ03hzwcba36oHNIv8kuRFCCPFYqFQqQnycCfFx5tl6QXmWy8jScebqLU5cucnxmGROXEnm+JVkktK0xmtp5cZOo6aUlyNlfFwo6+tMGV9nyvi6UMbHGS9nO+nmKubuvQzU/UhyI4QQokixt9EYW3yoa9imKApXktI5GZvMpRtpXE5MIyYxnZjENGIS04hLTidTp789ZT2FdSdMj+nuaEuIjzPBXk4Ee9+ZzRXs5YSHQ/G+/ERxptcr3EzP4kZqJjdSM0lM1XI9JZO45HSuJqcTl5xB3M10riZnEHst4cEHvE2SGyGEEEWeSqUydkHlRqvTE5uUTvR1Q3ITdS2Fs/G3OBefQkxSGklp2nuus3WHo60aNxsNv8fuwd/dET9Xe3xd7fFzdcDX1R5/N3t8XR1wc7CR1h8zpGRkEZOYMxG9nJhG/M0MbqRmkpSmRZ/PBhm9TlpuhBBCPEFs7xrY3Ky8r8lz6VodUddSOH89hfPXU4m+nsqFBMP9mMQ00rR60rQq4qJuAHkPdHawVePn6oC/myHx8XOzx93RFhUqlNvru9zdc2KjVuHpbIe3sx1eznZ4u9jh5WzYpziv/ZOWqeNs/C3Oxt8iLjmd6ymZJNzKJCEl03D/9u1WRv6nbjvbafBwssPDyRYvZzv83Qz17O/mYKxzRzKpPDN/x5PkRgghhFVzsNVQ+faMq3tlZumJjk9m6dr/CK1Si4RULfE3M7h6M8P482pyOsnpWaRr9cap7o9CrTJ0kznZ2eBkp8HJ3gYnWw3O9hoc7WxwtFWjUatRqwyDstUqw02jNqwqbadRG242auxt1NjZaLCzMTy202RvU2OrubPNzsZwPLh9IWzurJGnQkWWXk+WXiEzS49WZ7ivzdKTlqllW5yKAytPcu56Gmev3uJyYlq+X6ubgw0lPRxzDP72d3PA08kOTydb3J1s8zULLjk5Od/nleRGCCHEE8vORk2ojzMV3BU61CyR50J16VodV+8a/xGXnM7Vmxkkp2u5uw3m7l6rzCw9CSlaElIyuJGq5fqtDJLTs9ArcCNVy43UB6xAXGRo4NwFky2eTraU83Mh0MMRL2f7261St1uobv/0dbXH1cGMxfoKkCQ3QgghxAM42Goo7e1Eae9cVh02g1an50ZKJjdStaRmZpGWqSMlU2dyP12rQ6dX0CsKer2CTlHQ6Q2DqrP0Clqdnswswy3j7vtZOrQ6xfhYq9OTkaUn83YZRbndeabcuUhC9jbN7RYhG40KW4369k2FRqWC9CQaVgmlQoAbZX1dKOfngpez3SPWaOGS5EYIIYR4TGw1avzcHPBzc7B0KPmi1WpZuXIlHdpXfPDlF4oQmf8mhBBCCKsiyY0QQgghrIokN0IIIYSwKpLcCCGEEMKqSHIjhBBCCKsiyY0QQgghrIokN0IIIYSwKpLcCCGEEMKqSHIjhBBCCKsiyY0QQgghrIokN0IIIYSwKpLcCCGEEMKqSHIjhBBCCKsiyY0QQgghrIokN0IIIYSwKpLcCCGEEMKqSHIjhBBCCKsiyY0QQgghrIokN0IIIYSwKpLcCCGEEMKqSHIjhBBCCKsiyY0QQgghrIokN0IIIYSwKpLcCCGEEMKqSHIjhBBCCKsiyY0QQgghrIokN0IIIYSwKkUiuZk1axYhISE4ODjQoEEDdu/enWfZ7777jmbNmuHp6YmnpyetWrW6b3khhBBCPFksntwsWLCAUaNGMXHiRPbv30/NmjVp27YtV69ezbX8pk2b6NOnDxs3bmTHjh0EBQXRpk0bLl++/JgjF0IIIURRZPHkZvr06QwZMoRBgwZRpUoV5syZg5OTEz/++GOu5efNm8crr7xCrVq1qFSpEt9//z16vZ7169c/5siFEEIIURTZWPLkmZmZ7Nu3j3Hjxhm3qdVqWrVqxY4dO/J1jNTUVLRaLV5eXrk+n5GRQUZGhvFxcnIyAFqtFq1W+wjRF4zsGIpCLEWN1E3upF7yJnWTN6mbvEnd5K0o1Y05MagURVEKMZb7iomJITAwkO3bt9OoUSPj9jFjxrB582Z27dr1wGO88sorrF69mmPHjuHg4JDj+UmTJjF58uQc2+fPn4+Tk9OjvQAhhBBCPBapqan07duXpKQk3Nzc7lvWoi03j+qjjz7ijz/+YNOmTbkmNgDjxo1j1KhRxsfJycnGcToPqpzHQavVsnbtWlq3bo2tra2lwylSpG5yJ/WSN6mbvEnd5E3qJm9FqW6ye17yw6LJjY+PDxqNhri4OJPtcXFxBAQE3HffadOm8dFHH7Fu3Tpq1KiRZzl7e3vs7e1zbLe1tbX4L+puRS2eokTqJndSL3mTusmb1E3epG7yVhTqxpzzW3RAsZ2dHXXr1jUZDJw9OPjubqp7ffLJJ0yZMoVVq1ZRr169xxGqEEIIIYoJi3dLjRo1igEDBlCvXj3CwsKYOXMmKSkpDBo0CID+/fsTGBjI1KlTAfj444+ZMGEC8+fPJyQkhNjYWABcXFxwcXGx2OsQQgghRNFg8eQmIiKC+Ph4JkyYQGxsLLVq1WLVqlX4+/sDcOHCBdTqOw1Ms2fPJjMzk549e5ocZ+LEiUyaNOlxhi6EEEKIIsjiyQ3A8OHDGT58eK7Pbdq0yeRxdHR04QckhBBCiGLL4ov4CSGEEEIUJEluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLkRQgghhFWR5EYIIYQQVkWSGyGEEEJYFUluhBBCCGFVJLn5f3v3HlN1/f8B/HkEzuEoCQ6T2wRBDUSFRBQP5qzJUseWlCk5LBRpVjhRnIlXcka4OVs2S7MwK2+AMzLvhMmGMREMkFmAyhKbiIVHLiq38/r98Z3n5wmovt/sfA6f83xsZ/O83++jr89rb+fT87lAREREqsJwQ0RERKrCcENERESqwnBDREREqsJwQ0RERKrCcENERESqwnBDREREqsJwQ0RERKrCcENERESqwnBDREREqsJwQ0RERKrCcENERESqwnBDREREqsJwQ0RERKrCcENERESqwnBDREREqsJwQ0RERKrCcENERESqwnBDREREqsJwQ0RERKpiE+Hmo48+wrBhw+Ds7IyIiAgUFxf/6fqcnBwEBQXB2dkZY8eOxfHjx61UKREREdk6xcNNVlYWUlJSkJaWhosXLyI0NBTTp09HQ0NDj+t/+OEHzJs3D4sWLcKPP/6ImJgYxMTEoLKy0sqVExERkS1SPNy8//77eP3117Fw4UIEBwdj586d6N+/P3bv3t3j+m3btmHGjBlYuXIlRo0ahU2bNiEsLAzbt2+3cuVERERkixQNN+3t7SgtLUVUVJR5rF+/foiKikJRUVGPnykqKrJYDwDTp0/vdT0RERHZF0cl//DffvsNXV1d8PDwsBj38PDAzz//3ONn6uvre1xfX1/f4/q2tja0tbWZ39+9excA0NjYiI6Ojn9S/mPR0dGBe/fu4ffff4eTk5PS5dgU9qZn7Evv2JvesTe9Y296Z0u9aW5uBgCIyF+uVTTcWENGRgY2btzYbdzf31+BaoiIiOifaG5uhqur65+uUTTcDB48GA4ODrh165bF+K1bt+Dp6dnjZzw9Pf+r9atXr0ZKSor5vclkQmNjI9zd3aHRaP7hEfxzTU1NGDp0KOrq6jBw4ECly7Ep7E3P2JfesTe9Y296x970zpZ6IyJobm6Gt7f3X65VNNxotVqMHz8e+fn5iImJAfCf8JGfn48lS5b0+BmDwYD8/HwsW7bMPJaXlweDwdDjep1OB51OZzHm5ub2OMp/rAYOHKj4xrFV7E3P2JfesTe9Y296x970zlZ681ff2Dyk+GmplJQUxMfHIzw8HBMnTsQHH3yA1tZWLFy4EADw2muvwcfHBxkZGQCA5ORkTJ06FVu3bkV0dDQOHjyIkpIS7Nq1S8nDICIiIhuheLiJjY3F7du3sWHDBtTX1+Ppp5/GyZMnzRcNX79+Hf36/f9NXZGRkdi/fz/WrVuHNWvWYOTIkcjNzcWYMWOUOgQiIiKyIYqHGwBYsmRJr6ehzp49221szpw5mDNnzr9clXXodDqkpaV1O3VG7E1v2JfesTe9Y296x970rq/2RiN/554qIiIioj5C8ScUExERET1ODDdERESkKgw3REREpCoMN0RERKQqDDdWsGPHDoSEhJgfgmQwGHDixAnz/IMHD5CUlAR3d3e4uLhg9uzZ3Z7CbC82b94MjUZj8ZBGe+3PO++8A41GY/EKCgoyz9trXwDg119/xfz58+Hu7g69Xo+xY8eipKTEPC8i2LBhA7y8vKDX6xEVFYWamhoFK7aeYcOGdds3Go0GSUlJAOx333R1dWH9+vXw9/eHXq/H8OHDsWnTJoufU2TP+6a5uRnLli2Dn58f9Ho9IiMjceHCBfN8n+uN0L/uyJEjcuzYMamurpaqqipZs2aNODk5SWVlpYiIvPHGGzJ06FDJz8+XkpISmTRpkkRGRipctfUVFxfLsGHDJCQkRJKTk83j9tqftLQ0GT16tNy8edP8un37tnneXvvS2Ngofn5+smDBAjl//rxcu3ZNTp06JVeuXDGv2bx5s7i6ukpubq6Ul5fLCy+8IP7+/nL//n0FK7eOhoYGiz2Tl5cnAOT7778XEfvdN+np6eLu7i5Hjx6V2tpaycnJERcXF9m2bZt5jT3vm7lz50pwcLAUFBRITU2NpKWlycCBA+XGjRsi0vd6w3CjkEGDBslnn30mRqNRnJycJCcnxzz3008/CQApKipSsELram5ulpEjR0peXp5MnTrVHG7suT9paWkSGhra45w992XVqlXyzDPP9DpvMpnE09NTtmzZYh4zGo2i0+nkwIED1ijRpiQnJ8vw4cPFZDLZ9b6Jjo6WhIQEi7GXXnpJ4uLiRMS+9829e/fEwcFBjh49ajEeFhYma9eu7ZO94WkpK+vq6sLBgwfR2toKg8GA0tJSdHR0ICoqyrwmKCgIvr6+KCoqUrBS60pKSkJ0dLRFHwDYfX9qamrg7e2NgIAAxMXF4fr16wDsuy9HjhxBeHg45syZgyFDhmDcuHH49NNPzfO1tbWor6+36I2rqysiIiJU35s/am9vx969e5GQkACNRmPX+yYyMhL5+fmorq4GAJSXl6OwsBAzZ84EYN/7prOzE11dXXB2drYY1+v1KCws7JO9sYknFNuDS5cuwWAw4MGDB3BxccHXX3+N4OBglJWVQavVdvthnh4eHqivr1emWCs7ePAgLl68aHF+96H6+nq77U9ERAT27NmDwMBA3Lx5Exs3bsSUKVNQWVlp1325du0aduzYgZSUFKxZswYXLlzA0qVLodVqER8fbz7+hz/C5SF76M0f5ebmwmg0YsGCBQDs++9TamoqmpqaEBQUBAcHB3R1dSE9PR1xcXEAYNf75oknnoDBYMCmTZswatQoeHh44MCBAygqKsKIESP6ZG8YbqwkMDAQZWVluHv3Lg4dOoT4+HgUFBQoXZbi6urqkJycjLy8vG7/a7B3D/9HCQAhISGIiIiAn58fsrOzodfrFaxMWSaTCeHh4XjvvfcAAOPGjUNlZSV27tyJ+Ph4hauzLZmZmZg5cya8vb2VLkVx2dnZ2LdvH/bv34/Ro0ejrKwMy5Ytg7e3N/cNgK+++goJCQnw8fGBg4MDwsLCMG/ePJSWlipd2v+Ep6WsRKvVYsSIERg/fjwyMjIQGhqKbdu2wdPTE+3t7TAajRbrb926BU9PT2WKtaLS0lI0NDQgLCwMjo6OcHR0REFBAT788EM4OjrCw8PDrvvzKDc3Nzz11FO4cuWKXe8bLy8vBAcHW4yNGjXKfMru4fH/8Q4ge+jNo3755Rd89913SExMNI/Z875ZuXIlUlNT8corr2Ds2LF49dVXsXz5cmRkZADgvhk+fDgKCgrQ0tKCuro6FBcXo6OjAwEBAX2yNww3CjGZTGhra8P48ePh5OSE/Px881xVVRWuX78Og8GgYIXWMW3aNFy6dAllZWXmV3h4OOLi4sy/tuf+PKqlpQVXr16Fl5eXXe+byZMno6qqymKsuroafn5+AAB/f394enpa9KapqQnnz59XfW8e9fnnn2PIkCGIjo42j9nzvrl37x769bP8J8/BwQEmkwkA981DAwYMgJeXF+7cuYNTp05h1qxZfbM3Sl/RbA9SU1OloKBAamtrpaKiQlJTU0Wj0cjp06dF5D+3Zvr6+sqZM2ekpKREDAaDGAwGhatWzqN3S4nYb39WrFghZ8+eldraWjl37pxERUXJ4MGDpaGhQUTsty/FxcXi6Ogo6enpUlNTI/v27ZP+/fvL3r17zWs2b94sbm5u8s0330hFRYXMmjXLpm9bfdy6urrE19dXVq1a1W3OXvdNfHy8+Pj4mG8FP3z4sAwePFjefvtt8xp73jcnT56UEydOyLVr1+T06dMSGhoqERER0t7eLiJ9rzcMN1aQkJAgfn5+otVq5cknn5Rp06aZg42IyP379+Wtt96SQYMGSf/+/eXFF1+UmzdvKlixsv4Ybuy1P7GxseLl5SVarVZ8fHwkNjbW4lku9toXEZFvv/1WxowZIzqdToKCgmTXrl0W8yaTSdavXy8eHh6i0+lk2rRpUlVVpVC11nfq1CkB0OMx2+u+aWpqkuTkZPH19RVnZ2cJCAiQtWvXSltbm3mNPe+brKwsCQgIEK1WK56enpKUlCRGo9E839d6oxF55PGMRERERH0cr7khIiIiVWG4ISIiIlVhuCEiIiJVYbghIiIiVWG4ISIiIlVhuCEiIiJVYbghIiIiVWG4ISIiIlVhuCGiPqGoqAgODg4WPyuJiKgnfEIxEfUJiYmJcHFxQWZmJqqqquDt7a10SURko/jNDRHZvJaWFmRlZeHNN99EdHQ09uzZYzF/5MgRjBw5Es7OznjuuefwxRdfQKPRwGg0mtcUFhZiypQp0Ov1GDp0KJYuXYrW1lbrHggRWQXDDRHZvOzsbAQFBSEwMBDz58/H7t278fBL59raWrz88suIiYlBeXk5Fi9ejLVr11p8/urVq5gxYwZmz56NiooKZGVlobCwEEuWLFHicIjoX8bTUkRk8yZPnoy5c+ciOTkZnZ2d8PLyQk5ODp599lmkpqbi2LFjuHTpknn9unXrkJ6ejjt37sDNzQ2JiYlwcHDAJ598Yl5TWFiIqVOnorW1Fc7OzkocFhH9S/jNDRH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" 0.594\n", - " 8.810\n", + " 0.642\n", + " 9.252\n", " \n", " \n", " \n", @@ -99,18 +101,18 @@ " \n", " 1\n", " WarmGlowPortfolio\n", - " 0.594\n", - " 8.765\n", + " 0.641\n", + " 9.207\n", " \n", - " 43.773\n", - " 26.256\n", + " 23.051\n", + " 45.643\n", " \n", " \n", " 2\n", " WealthPortfolio\n", - " 0.397\n", - " 3.472\n", - " 0.531\n", + " 0.242\n", + " 5.336\n", + " 0.171\n", " \n", " \n", " \n", @@ -120,9 +122,9 @@ ], "text/plain": [ " Name criterion CRRA WealthShare BeqFac BeqShift\n", - "0 Portfolio 0.594 8.810 \n", - "1 WarmGlowPortfolio 0.594 8.765 43.773 26.256\n", - "2 WealthPortfolio 0.397 3.472 0.531 " + "0 Portfolio 0.642 9.252 \n", + "1 WarmGlowPortfolio 0.641 9.207 23.051 45.643\n", + "2 WealthPortfolio 0.242 5.336 0.171 " ] }, "execution_count": 3, @@ -196,7 +198,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/src/notebooks/testing_notebook.ipynb b/src/notebooks/testing_notebook.ipynb index 1625916..2eb0ffe 100644 --- a/src/notebooks/testing_notebook.ipynb +++ b/src/notebooks/testing_notebook.ipynb @@ -18,6 +18,8 @@ } ], "source": [ + "from __future__ import annotations\n", + "\n", "from estimark.agents import IndShkLifeCycleConsumerType\n", "from estimark.parameters import (\n", " init_consumer_objects,\n", From c514183e36299d4e9cc09959be091cfd488b25fe Mon Sep 17 00:00:00 2001 From: Alan Lujan Date: Fri, 20 Sep 2024 16:14:22 -0400 Subject: [PATCH 5/7] pre-commit --- .github/workflows/deploy.yml | 4 +- content/figures/AllSMMcontour.svg | 28414 +++--- content/figures/AllSensitivity.svg | 2250 +- content/figures/IndShockSMMcontour.svg | 7728 +- content/figures/IndShockSensitivity.svg | 1306 +- content/figures/PortfolioSMMcontour.svg | 7728 +- content/figures/PortfolioSensitivity.svg | 1306 +- .../figures/WarmGlowPortfolioSMMcontour.svg | 8628 +- .../figures/WarmGlowPortfolioSensitivity.svg | 1306 +- content/figures/WarmGlowSMMcontour.svg | 8240 +- content/figures/WarmGlowSensitivity.svg | 1306 +- content/figures/WealthPortfolioSMMcontour.svg | 7140 +- .../figures/WealthPortfolioSensitivity.svg | 1306 +- content/paper/01-paper.md | 616 +- content/paper/README.md | 14 +- content/paper/main.bib | 2 +- content/paper/math.ipynb | 2 +- content/paper/math.tex | 2 +- .../011d25769b11310e562c3ce7d2e79c0e.tex | 2 +- .../0f925feca89d21db8c043d4286dc6933.tex | 2 +- .../39e6ec1d01074f6b7dbe2583aad7433c.txt | 2 +- .../43c2c5b4c54174baf304c138ed1f189c.tex | 2 +- .../4c26ad3e47dae878071b6eb0d2ef5c2b.txt | 2 +- .../84becedb1d1468d154062be42a69cdfa.txt | 2 +- .../8f4c54c2d54e757a9c23b97da31b253f.tex | 2 +- .../9699bf796da83a5db10fd2ca4ad9b758.tex | 2 +- .../a6255a8d7ac11cabe5829e143599f112.txt | 2 +- .../a9c3a14d33ff0dadb24311a59c17222b.txt | 2 +- .../afd4f5d1c192186cb948ee1a50c9c60c.txt | 2 +- .../c46244155f82f0a2188b900fb12670c3.svg | 2 +- .../d858573459fc3de88fae0a4f1006736d.txt | 2 +- .../da1a98a5ef37f6169d89399c1d3f37da.tex | 2 +- .../dacd59a235f501f74d6b767be019ee45.txt | 2 +- .../dc12638639751951782f917a44aa00b5.tex | 2 +- .../ee9fa939ae6f56514559fd582d5f7dc2.txt | 2 +- .../structural_estimation_pdf_tex/main.bib | 2 +- .../structural_estimation.tex | 8 +- .../grantmcdermott/clean/_extension.yml | 1 - 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- - - @@ -888,115 +888,115 @@ z - - - - - - - - - @@ -1015,66 +1015,66 @@ z - - - - - - - - @@ -1308,48 +1308,48 @@ z - - - - - diff --git a/content/paper/01-paper.md b/content/paper/01-paper.md index da0450c..cf23f04 100644 --- a/content/paper/01-paper.md +++ b/content/paper/01-paper.md @@ -1,5 +1,6 @@ --- -title: Structural Estimation of Life Cycle Models with Wealth in the Utility Function +title: + Structural Estimation of Life Cycle Models with Wealth in the Utility Function subject: Economics # subtitle: Evolve your markdown documents into structured data short_title: Structural Estimation @@ -17,192 +18,417 @@ exports: output: structural_estimation.pdf show_date: true abbreviations: - HAM: Heterogeneous Agent Models - HANK: Heterogeneous Agent New Keynesian - WUNK: Wealth in the Utility New Keynesian - SIM: Standard Incomplete Markets - LCIM: Life Cycle Incomplete Markets - WUFIM: Wealth in the Utility Function Incomplete Markets + HAM: Heterogeneous Agent Models + HANK: Heterogeneous Agent New Keynesian + WUNK: Wealth in the Utility New Keynesian + SIM: Standard Incomplete Markets + LCIM: Life Cycle Incomplete Markets + WUFIM: Wealth in the Utility Function Incomplete Markets --- +++ {"part": "abstract"} -Heterogeneous Agent Models (HAM) are a powerful tool for understanding the effects of monetary and fiscal policy on the economy. However, current state-of-the-art frameworks such as Heterogeneous Agent New Keynesian (HANK) models have limitations that hinder their ability to accurately replicate real-world economic phenomena. Specifically, HANK models struggle to account for the observed accumulation of wealth at the very top of the distribution and lack important life cycle properties such as time-varying preferences, mortality, and income risk. On the one hand, the inability to pin down wealth at the tail of the distribution has been a problem for HANK models precisely because it has implications for the transmission of monetary and fiscal policy. On the other hand, agents in HANK are generally conceived as perpetual youth with infinite horizons and without age-specific profiles of mortality and income risk. This is problematic as it ignores the effects of these policies on potentially more affected communities, such as young families with children or the low-wealth elderly. In this paper, I investigate the effects of both life cycle considerations as well as wealth in the utility on the structural estimation of HAMs. Structural estimation is the first step in evaluating the effect of monetary and fiscal policies in a HANK framework, and my hope is that this paper will lead to better models of the economy that can be used to inform policy. +Heterogeneous Agent Models (HAM) are a powerful tool for understanding the +effects of monetary and fiscal policy on the economy. However, current +state-of-the-art frameworks such as Heterogeneous Agent New Keynesian (HANK) +models have limitations that hinder their ability to accurately replicate +real-world economic phenomena. Specifically, HANK models struggle to account for +the observed accumulation of wealth at the very top of the distribution and lack +important life cycle properties such as time-varying preferences, mortality, and +income risk. On the one hand, the inability to pin down wealth at the tail of +the distribution has been a problem for HANK models precisely because it has +implications for the transmission of monetary and fiscal policy. On the other +hand, agents in HANK are generally conceived as perpetual youth with infinite +horizons and without age-specific profiles of mortality and income risk. This is +problematic as it ignores the effects of these policies on potentially more +affected communities, such as young families with children or the low-wealth +elderly. In this paper, I investigate the effects of both life cycle +considerations as well as wealth in the utility on the structural estimation of +HAMs. Structural estimation is the first step in evaluating the effect of +monetary and fiscal policies in a HANK framework, and my hope is that this paper +will lead to better models of the economy that can be used to inform policy. +++ +++ {"part": "acknowledgements"} -I would like to thank my advisor, Chris Carroll, for his guidance and support throughout this project. His expertise and mentorship have been invaluable in shaping my work. Additionally, I would like to extend my appreciation to the members of the [Econ-ARK] team for fostering a dynamic and collaborative community that has greatly enriched my experience. Their contributions and feedback have been instrumental in helping me achieve my goals. All remaining errors are my own. The figures in this paper were generated using the [Econ-ARK/HARK] toolkit. +I would like to thank my advisor, Chris Carroll, for his guidance and support +throughout this project. His expertise and mentorship have been invaluable in +shaping my work. Additionally, I would like to extend my appreciation to the +members of the [Econ-ARK] team for fostering a dynamic and collaborative +community that has greatly enriched my experience. Their contributions and +feedback have been instrumental in helping me achieve my goals. All remaining +errors are my own. The figures in this paper were generated using the +[Econ-ARK/HARK] toolkit. +++ # Introduction -Increased computational power has allowed economists to focus on more complex models of household consumption and saving. In particular, Heterogeneous Agent Models (HAM) have become a popular tool to analyze households' response to aggregate economic shocks in the face of uncertainty. Nevertheless, current state-of-the-art models have struggled to replicate the distribution of wealth at the very top of the distribution and the extent of wealth inequality. A new class of models, the Heterogeneous Agent New Keynesian (HANK) models, have taken the literature by storm and become the standard for analyzing the effects of monetary and fiscal policy[^hank]. Even HANK models, however, inherit the inability to capture the distribution of wealth from their predecessors. Moreover, they also lack important life cycle properties such as time-varying preferences, household composition, and mortality and income risk. These limitations make the workhorse HANK models ill-suited for analyzing the effects of economic shocks and policy on the spectrum of households in the economy, from young families with children to retirees, and in particular, on the most vulnerable households among these subgroups. In this paper, I investigate the effects of both life cycle considerations as well as wealth in the utility on the structural estimation of HAMs. Thorough this effort, we hope to contribute to the development of better models of the economy that can be used to inform policy. - -[^hank]: See [](doi:10.1257/aer.20160042), [](doi:10.3982/ECTA16409), [](doi:10.1093/jeea/jvaa028), and [](doi:10.3386/w26647), among others. - -[](doi:10.3386/w6549) demonstrates that the rich have higher lifetime savings rates[^savings] which can not be explained by models of consumption smoothing and precautionary savings alone. Instead, he argues that the simplest model that accounts for this is one with wealth in the utility function. This is because households either derive utility from the accumulation wealth itself or wealth provides a flow of services such as political power and social status. In either case, this pattern of higher savings can be modeled by putting wealth directly in the utility function. In his paper, he proposes the use of additively separable utility of wealth and consumption[^portfolios], which we will explore in this paper. Additionally, however, we will also explore the use of non-separable utility of consumption and wealth. With non-separability of utility, we obtain a model that allows for a marginal utility of consumption that is increasing in wealth even while it is decreasing in consumption. This dynamic complementarity between consumption and wealth is a key feature of our model that induces a strong savings motive for the rich. - -[^savings]: See also [](doi:10.1086/381475). -[^portfolios]: Also see [](doi:10.3386/w7826), which uses additively separable utility of wealth and consumption to explain the portfolio choices of the rich. - -Wealth Inequality has been a persistent problem for the HAM literature[^surveys]. Models with entrepreneurship, preference heterogeneity, habit formation, bequest motives, human capital, and large earnings risk have had varying degrees of success in replicating the distribution of wealth. However, these models have been unable to account for the fat tail in the distribution. Recent research highlights the importance of savings among the richest in the United States and its distributional effects. [](doi:10.3386/w26941) find that the rise in savings by the richest households in the U.S. over the last 40 years is strongly associated with dissaving by the non-rich and the government, in the form of debt, which might have implications for the rise in household debt and declining interest rates in the last few decades. Similarly, [](doi:10.3386/w30900) find that as non-rich households spend down their excess savings, the incomes of the rich rise, which in turns leads to an increase in their excess savings. This movement of savings across the distribution leads to a prolonged increase in aggregate demand and can dampen the effects of monetary policy. [](doi:10.1162/rest_a_00893) present a similar model within the HANK literature that includes wealth in the utility function. However, because this paper uses continuous time methods, it is unable to capture the life cycle properties of more realistic models. - -[^surveys]: See [](doi:10.1017/S1365100507070150), [](doi:10.3386/w21106), [](doi:10.1016/j.red.2017.06.002), for surveys on the topic. - -The purpose of this paper is to investigate the effects of wealth in the utility function as well as life-cycle properties on the structural estimation of HAMs. By using wealth in the utility function, we can better match the top of the wealth distribution and the motives for wealth accumulation. Also, by parameterizing a rich model of life cycle properties such as age-specific and household-size-adjusted preferences, and mortality and income risk, we can better understand the effects that economic shocks and policies have on young working families, workers saving toward retirement, and retirees. In particular, we can better understand the effects of monetary policy on the distribution of wealth and consumption across the life cycle. - -The remainder of the paper is organized as follows. Section 2 provides the baseline models and alternative specifications with wealth in the utility function. Section 3 describes the solution methods used to solve these models. Section 4 describes the quantitative strategy used to calibrate and estimate the models, followed by sensitivity analysis of the results. Finally, Section 5 contains closing remarks and future directions. +Increased computational power has allowed economists to focus on more complex +models of household consumption and saving. In particular, Heterogeneous Agent +Models (HAM) have become a popular tool to analyze households' response to +aggregate economic shocks in the face of uncertainty. Nevertheless, current +state-of-the-art models have struggled to replicate the distribution of wealth +at the very top of the distribution and the extent of wealth inequality. A new +class of models, the Heterogeneous Agent New Keynesian (HANK) models, have taken +the literature by storm and become the standard for analyzing the effects of +monetary and fiscal policy[^hank]. Even HANK models, however, inherit the +inability to capture the distribution of wealth from their predecessors. +Moreover, they also lack important life cycle properties such as time-varying +preferences, household composition, and mortality and income risk. These +limitations make the workhorse HANK models ill-suited for analyzing the effects +of economic shocks and policy on the spectrum of households in the economy, from +young families with children to retirees, and in particular, on the most +vulnerable households among these subgroups. In this paper, I investigate the +effects of both life cycle considerations as well as wealth in the utility on +the structural estimation of HAMs. Thorough this effort, we hope to contribute +to the development of better models of the economy that can be used to inform +policy. + +[^hank]: + See [](doi:10.1257/aer.20160042), [](doi:10.3982/ECTA16409), + [](doi:10.1093/jeea/jvaa028), and [](doi:10.3386/w26647), among others. + +[](doi:10.3386/w6549) demonstrates that the rich have higher lifetime savings +rates[^savings] which can not be explained by models of consumption smoothing +and precautionary savings alone. Instead, he argues that the simplest model that +accounts for this is one with wealth in the utility function. This is because +households either derive utility from the accumulation wealth itself or wealth +provides a flow of services such as political power and social status. In either +case, this pattern of higher savings can be modeled by putting wealth directly +in the utility function. In his paper, he proposes the use of additively +separable utility of wealth and consumption[^portfolios], which we will explore +in this paper. Additionally, however, we will also explore the use of +non-separable utility of consumption and wealth. With non-separability of +utility, we obtain a model that allows for a marginal utility of consumption +that is increasing in wealth even while it is decreasing in consumption. This +dynamic complementarity between consumption and wealth is a key feature of our +model that induces a strong savings motive for the rich. + +[^savings]: See also [](doi:10.1086/381475). + +[^portfolios]: + Also see [](doi:10.3386/w7826), which uses additively separable utility of + wealth and consumption to explain the portfolio choices of the rich. + +Wealth Inequality has been a persistent problem for the HAM +literature[^surveys]. Models with entrepreneurship, preference heterogeneity, +habit formation, bequest motives, human capital, and large earnings risk have +had varying degrees of success in replicating the distribution of wealth. +However, these models have been unable to account for the fat tail in the +distribution. Recent research highlights the importance of savings among the +richest in the United States and its distributional effects. +[](doi:10.3386/w26941) find that the rise in savings by the richest households +in the U.S. over the last 40 years is strongly associated with dissaving by the +non-rich and the government, in the form of debt, which might have implications +for the rise in household debt and declining interest rates in the last few +decades. Similarly, [](doi:10.3386/w30900) find that as non-rich households +spend down their excess savings, the incomes of the rich rise, which in turns +leads to an increase in their excess savings. This movement of savings across +the distribution leads to a prolonged increase in aggregate demand and can +dampen the effects of monetary policy. [](doi:10.1162/rest_a_00893) present a +similar model within the HANK literature that includes wealth in the utility +function. However, because this paper uses continuous time methods, it is unable +to capture the life cycle properties of more realistic models. + +[^surveys]: + See [](doi:10.1017/S1365100507070150), [](doi:10.3386/w21106), + [](doi:10.1016/j.red.2017.06.002), for surveys on the topic. + +The purpose of this paper is to investigate the effects of wealth in the utility +function as well as life-cycle properties on the structural estimation of HAMs. +By using wealth in the utility function, we can better match the top of the +wealth distribution and the motives for wealth accumulation. Also, by +parameterizing a rich model of life cycle properties such as age-specific and +household-size-adjusted preferences, and mortality and income risk, we can +better understand the effects that economic shocks and policies have on young +working families, workers saving toward retirement, and retirees. In particular, +we can better understand the effects of monetary policy on the distribution of +wealth and consumption across the life cycle. + +The remainder of the paper is organized as follows. Section 2 provides the +baseline models and alternative specifications with wealth in the utility +function. Section 3 describes the solution methods used to solve these models. +Section 4 describes the quantitative strategy used to calibrate and estimate the +models, followed by sensitivity analysis of the results. Finally, Section 5 +contains closing remarks and future directions. # Life Cycle Incomplete Markets Models -An important extension to the Standard Incomplete Markets (SIM) model is the Life Cycle Incomplete Markets (LCIM) model as in [](doi:10.1198/073500103288619007), [](doi:10.1111/1468-0262.00269), and [](doi:10.1111/1467-937X.00092), among others. The LCIM model is a natural extension to the SIM model that allows for age-specific profiles of preferences, mortality, and income risk. +An important extension to the Standard Incomplete Markets (SIM) model is the +Life Cycle Incomplete Markets (LCIM) model as in +[](doi:10.1198/073500103288619007), [](doi:10.1111/1468-0262.00269), and +[](doi:10.1111/1467-937X.00092), among others. The LCIM model is a natural +extension to the SIM model that allows for age-specific profiles of preferences, +mortality, and income risk. ## The Baseline Model -The agent's objective is to maximize present discounted utility from consumption over the life cycle with a terminal period of $T$: - -\begin{equation} - \vFunc_{t}(\pLvl_{t},\mLvl_{t}) = \max_{\{\cFunc\}_{t}^{T}} ~ \uFunc(\cLvl_{t})+\Ex_{t}\left[\sum_{n=1}^{T-t} {\beth}^{n} \Alive_{t}^{t+n}\hat{\DiscFac}_{t}^{t+n} \uFunc(\cLvl_{t+n}) \right] \label{eq:lifecyclemax} -\end{equation} - -where $\pLvl_{t}$ is the permanent income level, $\mLvl_{t}$ is total market resources, $\cLvl_{t}$ is consumption, and - -\begin{align} - \beth & : \text{time-invariant `pure' discount factor} - \\ \Alive _{t}^{t+n} & : \text{probability to }\Alive\text{ive until age $t+n$ given alive at age $t$} - \\ \hat{\DiscFac}_{t}^{t+n} & : \text{age-varying discount factor between ages $t$ and $t+n$.} +The agent's objective is to maximize present discounted utility from consumption +over the life cycle with a terminal period of $T$: + +\begin{equation} \vFunc*{t}(\pLvl*{t},\mLvl*{t}) = \max*{\{\cFunc\}_{t}^{T}} ~ +\uFunc(\cLvl_{t})+\Ex*{t}\left[\sum*{n=1}^{T-t} {\beth}^{n} +\Alive*{t}^{t+n}\hat{\DiscFac}*{t}^{t+n} \uFunc(\cLvl\_{t+n}) \right] +\label{eq:lifecyclemax} \end{equation} + +where $\pLvl_{t}$ is the permanent income level, $\mLvl_{t}$ is total market +resources, $\cLvl_{t}$ is consumption, and + +\begin{align} \beth & : \text{time-invariant `pure' discount factor} \\ \Alive +_{t}^{t+n} & : \text{probability to }\Alive\text{ive until age $t+n$ given alive +at age $t$} \\ \hat{\DiscFac}_{t}^{t+n} & : \text{age-varying discount factor +between ages $t$ and $t+n$.} \end{align} + +It will be convenient to work with the problem in permanent-income-normalized +form as in [](doi:10.3386/w10867), which allows us to reduce a 2 dimensional +problem of permanent income and wealth into a 1 dimensional problem of wealth +normalized by permanent income. The recursive Bellman equation can be expressed +as: + +\begin{align} {\vFunc}_{t}({m}_{t}) & = \max*{\cNrm*{t}} ~ +\uFunc(\cNrm*{t})+\beth\Alive*{t+1}\hat{\DiscFac}_{t+1} +\Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] \\ +& \text{s.t.} & \\ \aNrm*{t} & = {m}*{t}-\cNrm*{t} \\ {m}*{t+1} & = +\aNrm*{t}\underbrace{\left(\frac{\Rfree}{\PermShk*{t+1}\PermGroFac*{t+1}}\right)}*{\equiv +\RNrm*{t+1}} + ~\TranShkEmp*{t+1} \end{align} + +where $\cNrm$, $\aNrm$, and $\mNrm$ are consumption, assets, and market +resources normalized by permanent income, respectively, $\vFunc$ and $\uFunc$ +are now the normalized value and utility functions, and + +\begin{align} \PermShk*{t+1} & : \text{mean-one shock to permanent income} \\ +\PermGroFac*{t+1} & : \text{permanent income growth factor} \\ \TranShkEmp*{t+1} +& : \text{transitory shock to permanent income} \\ \RNrm*{t+1} & : +\text{permanent income growth normalized return factor} \end{align} + +with all other variables are defined as above. The transitory and permanent +shocks to income are defined as: + +\begin{align} \TranShkEmp*{s} = & \begin{cases} 0\phantom{/\pZero} & \text{with +probability $\pZero>0$} \\ \xi*{s}/\pZero & \text{with probability $(1-\pZero)$, +where +$\log \xi_{s}\thicksim \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2})$} +\end{cases} \\ \phantom{/\pZero} \\ & \text{and } \log \PermShk*{s} \thicksim +\mathcal{N}(-\sigma*{[\PermShk, t]}^{2}/2,\sigma\_{[\PermShk, t]}^{2}). \end{align} -It will be convenient to work with the problem in permanent-income-normalized form as in [](doi:10.3386/w10867), which allows us to reduce a 2 dimensional problem of permanent income and wealth into a 1 dimensional problem of wealth normalized by permanent income. The recursive Bellman equation can be expressed as: - -\begin{align} - {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} - \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} - \\ {m}_{t+1} & = \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv \RNrm_{t+1}} - + ~\TranShkEmp_{t+1} -\end{align} - -where $\cNrm$, $\aNrm$, and $\mNrm$ are consumption, assets, and market resources normalized by permanent income, respectively, $\vFunc$ and $\uFunc$ are now the normalized value and utility functions, and - -\begin{align} - \PermShk_{t+1} & : \text{mean-one shock to permanent income} - \\ \PermGroFac_{t+1} & : \text{permanent income growth factor} - \\ \TranShkEmp_{t+1} & : \text{transitory shock to permanent income} - \\ \RNrm_{t+1} & : \text{permanent income growth normalized return factor} -\end{align} - -with all other variables are defined as above. The transitory and permanent shocks to income are defined as: - -\begin{align} -\TranShkEmp_{s} = & - \begin{cases} - 0\phantom{/\pZero} & \text{with probability $\pZero>0$} - \\ \xi_{s}/\pZero & \text{with probability $(1-\pZero)$, where - $\log \xi_{s}\thicksim \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2})$} - \end{cases} - \\ \phantom{/\pZero} \\ & \text{and } \log \PermShk_{s} \thicksim \mathcal{N}(-\sigma_{[\PermShk, t]}^{2}/2,\sigma_{[\PermShk, t]}^{2}). - \end{align} - ## Wealth in the Utility Function -A simple extension to the Life Cycle Incomplete Markets (LCIM) model is to include wealth in the utility function. [](doi:10.3386/w6549) argues that models in which the only driver of wealth accumulation is consumption smoothing are not consistent with the saving behavior of the wealthiest households. Instead, they propose a model in which households derive utility from their level of wealth itself or they derive a flow of services from political power and social status, calling it the `Capitalist Spirit' model. In turn, we can add this feature to the LCIM model by adding a utility function with consumption and wealth. We call this the Wealth in the Utility Function Incomplete Markets (WUFIM) model. - -\begin{align} - {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t}, \aNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} - \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} - \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} -\end{align} - -**Separable Utility** [](doi:10.3386/w6549) presents extensive empirical and informal evidence for a LCIM model with wealth in the utility function. Specifically, the paper uses a utility that is separable in consumption and wealth: - -\begin{equation} - \uFunc(\cNrm_{t}, \aNrm_{t}) = \frac{\cNrm_{t}^{1-\CRRA}}{1-\CRRA} - + \kapShare_{t} \frac{(\aFunc_{t} - \underline\aNrm)^{1-\wealthShare}}{1-\wealthShare} -\end{equation} - -where $\kapShare$ is the relative weight of the utility of wealth and $\wealthShare$ is the relative risk aversion of wealth. - -**Non-separable Utility** A different model that we will explore is one in which the utility function is non-separable in consumption and wealth; i.e. consumption and wealth are complimentary goods. In the case of the LCIM model, this dynamic complementarity drives the accumulation of wealth not only for the sake of wealth itself, but also because it increases the marginal utility of consumption. - -\begin{equation} - \uFunc(\cNrm_{t}, \aNrm_{t}) = \frac{(\cNrm_{t}^{1-\wealthShare} (\aNrm_{t} - \underline\aNrm)^\wealthShare)^{1-\CRRA}}{(1-\CRRA)} -\end{equation} +A simple extension to the Life Cycle Incomplete Markets (LCIM) model is to +include wealth in the utility function. [](doi:10.3386/w6549) argues that models +in which the only driver of wealth accumulation is consumption smoothing are not +consistent with the saving behavior of the wealthiest households. Instead, they +propose a model in which households derive utility from their level of wealth +itself or they derive a flow of services from political power and social status, +calling it the `Capitalist Spirit' model. In turn, we can add this feature to +the LCIM model by adding a utility function with consumption and wealth. We call +this the Wealth in the Utility Function Incomplete Markets (WUFIM) model. + +\begin{align} {\vFunc}_{t}({m}_{t}) & = \max*{\cNrm*{t}} ~ \uFunc(\cNrm*{t}, +\aNrm*{t})+\beth\Alive*{t+1}\hat{\DiscFac}*{t+1} +\Ex*{t}[(\PermShk*{t+1}\PermGroFac*{t+1})^{1-\CRRA}{\vFunc}*{t+1}({m}_{t+1})] \\ +& \text{s.t.} & \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = +\aNrm_{t}\RNrm*{t+1}+ ~\TranShkEmp*{t+1} \end{align} + +**Separable Utility** [](doi:10.3386/w6549) presents extensive empirical and +informal evidence for a LCIM model with wealth in the utility function. +Specifically, the paper uses a utility that is separable in consumption and +wealth: + +\begin{equation} \uFunc(\cNrm*{t}, \aNrm*{t}) = +\frac{\cNrm*{t}^{1-\CRRA}}{1-\CRRA} + \kapShare*{t} \frac{(\aFunc\_{t} - +\underline\aNrm)^{1-\wealthShare}}{1-\wealthShare} \end{equation} + +where $\kapShare$ is the relative weight of the utility of wealth and +$\wealthShare$ is the relative risk aversion of wealth. + +**Non-separable Utility** A different model that we will explore is one in which +the utility function is non-separable in consumption and wealth; i.e. +consumption and wealth are complimentary goods. In the case of the LCIM model, +this dynamic complementarity drives the accumulation of wealth not only for the +sake of wealth itself, but also because it increases the marginal utility of +consumption. + +\begin{equation} \uFunc(\cNrm*{t}, \aNrm*{t}) = +\frac{(\cNrm*{t}^{1-\wealthShare} (\aNrm*{t} - +\underline\aNrm)^\wealthShare)^{1-\CRRA}}{(1-\CRRA)} \end{equation} # Solution Methods -For a brief departure, let's consider how we solve these problems generally. Define the post-decision value function as: +For a brief departure, let's consider how we solve these problems generally. +Define the post-decision value function as: -\begin{align} - \DiscFac_{t+1} \wFunc_{t}(\aNrm_{t}) & = \beth\Alive_{t+1}\hat{\DiscFac}_{t+1} - \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} - \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} +\begin{align} \DiscFac*{t+1} \wFunc*{t}(\aNrm*{t}) & = +\beth\Alive*{t+1}\hat{\DiscFac}_{t+1} +\Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] \\ +& \text{s.t.} \\ {m}_{t+1} & = \aNrm_{t}\RNrm*{t+1}+ ~\TranShkEmp*{t+1} \end{align} -For our purposes, it will be useful to simplify the notation a bit by dropping time subscripts. The recursive problem can then be written as: +For our purposes, it will be useful to simplify the notation a bit by dropping +time subscripts. The recursive problem can then be written as: -\begin{align} - \vFunc(\mRat) & = \max_{\cNrm} ~ \uFunc(\cNrm, \aNrm) + \DiscFac \wFunc(\aRat) - \\ & \text{s.t.} - \\ \aNrm & = \mRat-\cNrm -\end{align} +\begin{align} \vFunc(\mRat) & = \max\_{\cNrm} ~ \uFunc(\cNrm, \aNrm) + \DiscFac +\wFunc(\aRat) \\ & \text{s.t.} \\ \aNrm & = \mRat-\cNrm \end{align} ## Endogenous Grid Method, Abridged -In the standard incomplete markets (SIM) model, the utility function is simply $\uFunc(\cNrm)$ and the Euler equation is $\uFunc'(\cNrm) = \DiscFac \wFunc'(\aNrm)$, which is a necessary and sufficient condition for an internal solution of the optimal choice of consumption. If $\uFunc(\cNrm)$ is differentiable and its marginal utility is invertible, then the Euler equation can be inverted to obtain the optimal consumption function as $\cNrm(\aNrm) = \uFunc'^{-1}(\DiscFac \wFunc'(\aNrm))$. Using an _exogenous_ grid of post-decision savings $\aMat$, we can obtain an _endogenous_ grid of market resources $\mMat$ by using the budget constraint $\mNrm(\aMat) = \aMat + \cNrm(\aMat)$ such that this collection of grids satisfy the Euler equation. This is the endogenous grid method (EGM) of [](doi:10.1016/j.econlet.2005.09.013). - -In the presence of wealth in the utility function, the Euler equation is more complicated and may not be invertible in terms of optimal consumption. Consider the first order condition for an optimal combination of consumption and savings, denoted by $^*$: - -\begin{equation} - \uFunc_{c}'(\cNrm^*, \aNrm^*) - \uFunc_{a}'(\cNrm^*, \aNrm^*) = \DiscFac \wFunc'(\aNrm^*) -\end{equation} - -If the utility of consumption and wealth is additively separable, then the Euler equation can be written as $\uFunc_{c}'(\cNrm) = \uFunc_{a}'(\aNrm) + \DiscFac \wFunc'(\aNrm)$. This makes sense, as the agent will equalize the marginal utility of consumption with the marginal utility of wealth today plus the discounted marginal value of wealth tomorrow. In this case, the EGM is simple: we can invert the Euler equation to obtain the optimal consumption policy as $\cNrm(\aNrm) = \uFunc_{c}'^{-1}\big(\uFunc_{a}'(\aNrm) + \DiscFac \wFunc'(\aNrm)\big)$. We can proceed with EGM as usual, using the budget constraint to obtain the endogenous grid of market resources $\mNrm(\aMat) = \aMat + \cNrm(\aMat)$. +In the standard incomplete markets (SIM) model, the utility function is simply +$\uFunc(\cNrm)$ and the Euler equation is +$\uFunc'(\cNrm) = \DiscFac \wFunc'(\aNrm)$, which is a necessary and sufficient +condition for an internal solution of the optimal choice of consumption. If +$\uFunc(\cNrm)$ is differentiable and its marginal utility is invertible, then +the Euler equation can be inverted to obtain the optimal consumption function as +$\cNrm(\aNrm) = \uFunc'^{-1}(\DiscFac \wFunc'(\aNrm))$. Using an _exogenous_ +grid of post-decision savings $\aMat$, we can obtain an _endogenous_ grid of +market resources $\mMat$ by using the budget constraint +$\mNrm(\aMat) = \aMat + \cNrm(\aMat)$ such that this collection of grids satisfy +the Euler equation. This is the endogenous grid method (EGM) of +[](doi:10.1016/j.econlet.2005.09.013). + +In the presence of wealth in the utility function, the Euler equation is more +complicated and may not be invertible in terms of optimal consumption. Consider +the first order condition for an optimal combination of consumption and savings, +denoted by $^*$: + +\begin{equation} \uFunc*{c}'(\cNrm^*, \aNrm^*) - \uFunc*{a}'(\cNrm^_, \aNrm^_) = +\DiscFac \wFunc'(\aNrm^\*) \end{equation} + +If the utility of consumption and wealth is additively separable, then the Euler +equation can be written as +$\uFunc_{c}'(\cNrm) = \uFunc_{a}'(\aNrm) + \DiscFac \wFunc'(\aNrm)$. This makes +sense, as the agent will equalize the marginal utility of consumption with the +marginal utility of wealth today plus the discounted marginal value of wealth +tomorrow. In this case, the EGM is simple: we can invert the Euler equation to +obtain the optimal consumption policy as +$\cNrm(\aNrm) = \uFunc_{c}'^{-1}\big(\uFunc_{a}'(\aNrm) + \DiscFac \wFunc'(\aNrm)\big)$. +We can proceed with EGM as usual, using the budget constraint to obtain the +endogenous grid of market resources $\mNrm(\aMat) = \aMat + \cNrm(\aMat)$. ## Root Finding -When the utility of consumption and wealth is not additively separable, the Euler equation is not analytically invertible for the optimal consumption policy. The usual recourse is to use a root-finding algorithm to obtain the optimal consumption policy for each point on the grid of market resources, which turns out to be more efficient than grid search maximization. +When the utility of consumption and wealth is not additively separable, the +Euler equation is not analytically invertible for the optimal consumption +policy. The usual recourse is to use a root-finding algorithm to obtain the +optimal consumption policy for each point on the grid of market resources, which +turns out to be more efficient than grid search maximization. -Holding $\mNrm$ constant, we can define a function $f_{m}$ as the difference between the marginal utility of consumption and the marginal utility of wealth: +Holding $\mNrm$ constant, we can define a function $f_{m}$ as the difference +between the marginal utility of consumption and the marginal utility of wealth: -\begin{equation} - f_{m}(\cNrm) = \uFunc_{c}'(\cNrm, \mNrm - \cNrm) - \uFunc_{a}'(\cNrm, \mNrm - \cNrm) - \DiscFac \wFunc'(\mNrm - \cNrm) +\begin{equation} f*{m}(\cNrm) = \uFunc*{c}'(\cNrm, \mNrm - \cNrm) - +\uFunc\_{a}'(\cNrm, \mNrm - \cNrm) - \DiscFac \wFunc'(\mNrm - \cNrm) \end{equation} -The optimal consumption policy is the value of $\cNrm$ that satisfies $f_{m}(\cNrm) = 0$. We can use a root-finding algorithm to obtain the optimal consumption policy for each point on the grid of market resources. Although this is more efficient than grid search maximization, it is still computationally expensive. Unlike the single-step EGM, root finding requires a number of iterations to find the optimal consumption policy, which makes it relatively slower. Nevertheless, we can use clever tricks to speed up the process. One such trick used in this paper is to use the optimal consumption policy from the previous iteration as the initial guess for the next iteration. This is possible because the optimal consumption policy is a continuous function of the grid of market resources and the optional decision from one period to the next is not too different. This is the method used in the code for this paper. +The optimal consumption policy is the value of $\cNrm$ that satisfies +$f_{m}(\cNrm) = 0$. We can use a root-finding algorithm to obtain the optimal +consumption policy for each point on the grid of market resources. Although this +is more efficient than grid search maximization, it is still computationally +expensive. Unlike the single-step EGM, root finding requires a number of +iterations to find the optimal consumption policy, which makes it relatively +slower. Nevertheless, we can use clever tricks to speed up the process. One such +trick used in this paper is to use the optimal consumption policy from the +previous iteration as the initial guess for the next iteration. This is possible +because the optimal consumption policy is a continuous function of the grid of +market resources and the optional decision from one period to the next is not +too different. This is the method used in the code for this paper. # Quantitative Strategy -This section describes the quantitative strategy used for calibrating and estimating the Life Cycle Incomplete Markets model with and without Wealth in the Utility Function, following the works of [](doi:10.1198/073500103288619007), [](doi:10.1111/1467-937X.00092), [](doi:10.1111/1468-0262.00269), and [](doi:10.1016/j.jmoneco.2010.04.003), among others. The main objective is to find a set of parameters that can best match the empirical moments of some real-life data using simulation. +This section describes the quantitative strategy used for calibrating and +estimating the Life Cycle Incomplete Markets model with and without Wealth in +the Utility Function, following the works of [](doi:10.1198/073500103288619007), +[](doi:10.1111/1467-937X.00092), [](doi:10.1111/1468-0262.00269), and +[](doi:10.1016/j.jmoneco.2010.04.003), among others. The main objective is to +find a set of parameters that can best match the empirical moments of some +real-life data using simulation. ## Calibration -The calibration of the Life Cycle Incomplete Markets model necessitates a richness not present in the SIM model precisely because we are interested in the heterogeneity of agents across different stages of the life cycle, such as the early working period, parenthood, saving for retirement, and retirement. To calibrate this model, we need to identify important patterns in preferences, mortality, and income risk across the life cycle. The first and perhaps most important departure from SIM is that life is finite and agents don't life forever; moreover, the terminal age is not certain as the probability of staying alive decreases with age. In this model, households start their life cycle at age $t = 25$ and live with certainty until retirement at age $t = 65$. After retirement, the probability of staying alive decreases with age, and the terminal age is set to $t = 91$. During their early adulthood, their utility of consumption might need to be adjusted by the arrival and subsequent departure of children. This is handled by a `household-size-adjusted' discount factor that is greater than 1.0 in the presence of children. This is the rationale for parameters $\Alive_{t}$ and $\hat{\DiscFac}_{t}$ in the model, whose values we take from [](doi:10.1198/073500103288619007) directly. - -The unemployment probability is taken from [](doi:10.2307/2534582) to be $\pZero = 0.5$ which represents a long run equilibrium of 5\% unemployment in the United States. The remaining life cycle attributes for the distribution of shocks to income ($\PermGroFac_{t}, \ \sigma_{[\PermShk, t]}, \ \sigma_{[\xi, t]}$) are taken from [](doi:10.1016/j.jmoneco.2010.04.003). In their paper, they analyze the variability of labor earnings growth rates between the 80's and 90's and find evidence for the ``Great Moderation'', a decline in variability of earnings across all age groups. - -After careful calibration based on the Life Cycle Incomplete Markets literature, we can structurally estimate the remaining parameters $\beth$ and $\CRRA$ to match specific empirical moments of the wealth distribution. +The calibration of the Life Cycle Incomplete Markets model necessitates a +richness not present in the SIM model precisely because we are interested in the +heterogeneity of agents across different stages of the life cycle, such as the +early working period, parenthood, saving for retirement, and retirement. To +calibrate this model, we need to identify important patterns in preferences, +mortality, and income risk across the life cycle. The first and perhaps most +important departure from SIM is that life is finite and agents don't life +forever; moreover, the terminal age is not certain as the probability of staying +alive decreases with age. In this model, households start their life cycle at +age $t = 25$ and live with certainty until retirement at age $t = 65$. After +retirement, the probability of staying alive decreases with age, and the +terminal age is set to $t = 91$. During their early adulthood, their utility of +consumption might need to be adjusted by the arrival and subsequent departure of +children. This is handled by a `household-size-adjusted' discount factor that is +greater than 1.0 in the presence of children. This is the rationale for +parameters $\Alive_{t}$ and $\hat{\DiscFac}_{t}$ in the model, whose values we +take from [](doi:10.1198/073500103288619007) directly. + +The unemployment probability is taken from [](doi:10.2307/2534582) to be +$\pZero = 0.5$ which represents a long run equilibrium of 5\% unemployment in +the United States. The remaining life cycle attributes for the distribution of +shocks to income +($\PermGroFac_{t}, \ \sigma_{[\PermShk, t]}, \ \sigma_{[\xi, t]}$) are taken +from [](doi:10.1016/j.jmoneco.2010.04.003). In their paper, they analyze the +variability of labor earnings growth rates between the 80's and 90's and find +evidence for the ``Great Moderation'', a decline in variability of earnings +across all age groups. + +After careful calibration based on the Life Cycle Incomplete Markets literature, +we can structurally estimate the remaining parameters $\beth$ and $\CRRA$ to +match specific empirical moments of the wealth distribution. ## Estimation -Structural estimation consists of finding the set of parameters that, when used to solve and simulate the model, result in simulated moments that are as close as possible to the empirical moments observed in the data. For this exercise, we focus on matching the median of the wealth to permanent income ratio for 7 age groups starting from age 25-30 up to age 56-60. The data is aggregated from the waves of the Survey of Consumer Finances (SCF). Matching the median has been standard in the literature precisely because it has been so difficult to match the mean of the wealth distribution given the high degree of wealth inequality in the United States. The Wealth in the Utility Function models however are constructed to better match the dispersion of wealth accumulation, and in future work we will attempt to match the mean of the wealth distribution as well. - -Given an initial vector of parameters $\Theta_0 = \{\beth_0, \CRRA_0 \}$, the first step in the estimation procedure is to solve for the steady state of the model. As this is a life cycle exercise, the strategy is to start from the terminal period and work backwards to the initial period. This is known as backward induction. The terminal period is characterized by simple decisions over consumption and bequest, as the agent is certain to die and has no continuation value and thus no use for savings. Having constructed the terminal policy functions and their corresponding value and marginal value, we can solve for the optimal policies in the second to last period using the methods described in the previous section. We can then continue this process until we arrive at the initial period. In the end, and unlike in the SIM model, we have a complete set of policy functions for consumption and saving for every age of the life cycle. - -Having solved the steady state of the model for the given set of parameters, we can now use the optimal policy functions to generate simulated data of consumption and savings over the life cycle. We can then calculate the simulated moments of the wealth distribution at the 7 age groups. We can define the objective function as - -\begin{equation} - g(\Theta) = \sum_{\tau=1}^{7} \weight_{\tau} |\varsigma^{\tau} -\mathbf{s}^{\tau}(\Theta)| \label{eq:naivePowell} -\end{equation} - -where $\varsigma^{\tau}$ is the empirical moment of the wealth distribution at age $\tau$, $\mathbf{s}^{\tau}(\Theta)$ is the simulated moment of the wealth distribution at age $\tau$ for a given set of parameters $\Theta$, and $\weight_{\tau}$ is the population weight for a particular age group in our data. The goal is thus to minimize the objective function by choice of $\Theta$ such that $\hat{\Theta} = \arg \min_{\Theta} g(\Theta)$. To find $\hat{\Theta}$, we use the Nelder-Mead algorithm which uses a simplex method and does not require derivatives of the objective function. This consists of trying a significant number of guesses for $\Theta$, solving the model, and simulating moments which can be quite computationally intensive. Future work will focus on using more efficient methods such as those presented by [](doi:10.3982/ECTA17434), where the Jacobian (partial first derivatives) of the objective function is used to find the optimal parameters $\hat{\Theta}$ more efficiently and quickly. +Structural estimation consists of finding the set of parameters that, when used +to solve and simulate the model, result in simulated moments that are as close +as possible to the empirical moments observed in the data. For this exercise, we +focus on matching the median of the wealth to permanent income ratio for 7 age +groups starting from age 25-30 up to age 56-60. The data is aggregated from the +waves of the Survey of Consumer Finances (SCF). Matching the median has been +standard in the literature precisely because it has been so difficult to match +the mean of the wealth distribution given the high degree of wealth inequality +in the United States. The Wealth in the Utility Function models however are +constructed to better match the dispersion of wealth accumulation, and in future +work we will attempt to match the mean of the wealth distribution as well. + +Given an initial vector of parameters $\Theta_0 = \{\beth_0, \CRRA_0 \}$, the +first step in the estimation procedure is to solve for the steady state of the +model. As this is a life cycle exercise, the strategy is to start from the +terminal period and work backwards to the initial period. This is known as +backward induction. The terminal period is characterized by simple decisions +over consumption and bequest, as the agent is certain to die and has no +continuation value and thus no use for savings. Having constructed the terminal +policy functions and their corresponding value and marginal value, we can solve +for the optimal policies in the second to last period using the methods +described in the previous section. We can then continue this process until we +arrive at the initial period. In the end, and unlike in the SIM model, we have a +complete set of policy functions for consumption and saving for every age of the +life cycle. + +Having solved the steady state of the model for the given set of parameters, we +can now use the optimal policy functions to generate simulated data of +consumption and savings over the life cycle. We can then calculate the simulated +moments of the wealth distribution at the 7 age groups. We can define the +objective function as + +\begin{equation} g(\Theta) = \sum*{\tau=1}^{7} \weight*{\tau} |\varsigma^{\tau} +-\mathbf{s}^{\tau}(\Theta)| \label{eq:naivePowell} \end{equation} + +where $\varsigma^{\tau}$ is the empirical moment of the wealth distribution at +age $\tau$, $\mathbf{s}^{\tau}(\Theta)$ is the simulated moment of the wealth +distribution at age $\tau$ for a given set of parameters $\Theta$, and +$\weight_{\tau}$ is the population weight for a particular age group in our +data. The goal is thus to minimize the objective function by choice of $\Theta$ +such that $\hat{\Theta} = \arg \min_{\Theta} g(\Theta)$. To find $\hat{\Theta}$, +we use the Nelder-Mead algorithm which uses a simplex method and does not +require derivatives of the objective function. This consists of trying a +significant number of guesses for $\Theta$, solving the model, and simulating +moments which can be quite computationally intensive. Future work will focus on +using more efficient methods such as those presented by +[](doi:10.3982/ECTA17434), where the Jacobian (partial first derivatives) of the +objective function is used to find the optimal parameters $\hat{\Theta}$ more +efficiently and quickly. ```{list-table} LCIM Estimation Results :header-rows: 1 @@ -216,9 +442,17 @@ where $\varsigma^{\tau}$ is the empirical moment of the wealth distribution at a - (0.0266) ``` -**Results for LCIM model** We can see the estimated parameters for the LCIM model in {numref}`LCIMestimation`. The estimated values for $\beth$ and $\CRRA$ are 0.878 and 3.1516, respectively, with standard errors estimated via the bootstrap. Additionally, {numref}`fig:IndShockSMMcontour` shows a contour plot of the objective function for the structural estimation exercise where the red star represents the estimated parameters. The contour plot shows that the objective function has a relatively flat region around the estimated parameters that extends toward higher values of $\CRRA$ and lower values of $\beth$, showing the trade-offs between the estimation of these two parameters. - -```{figure} ../Figures/IndShockSMMcontour.* +**Results for LCIM model** We can see the estimated parameters for the LCIM +model in {numref}`LCIMestimation`. The estimated values for $\beth$ and $\CRRA$ +are 0.878 and 3.1516, respectively, with standard errors estimated via the +bootstrap. Additionally, {numref}`fig:IndShockSMMcontour` shows a contour plot +of the objective function for the structural estimation exercise where the red +star represents the estimated parameters. The contour plot shows that the +objective function has a relatively flat region around the estimated parameters +that extends toward higher values of $\CRRA$ and lower values of $\beth$, +showing the trade-offs between the estimation of these two parameters. + +```{figure} ../Figures/IndShockSMMcontour.* :name: fig:IndShockSMMcontour :alt: IndShockSMMcontour :align: center @@ -236,30 +470,55 @@ Contour plot of the objective function for the structural estimation of the Life * - LCIM w/ Portfolio Choice - 0.866 - 3.756 -* - +* - - (0.0011) - (0.0313) * - Separable WUFIM - 0.876 - 3.506 -* - +* - - (0.0012) - (0.0254) * - Separable WUFIM w/ Portfolio - 0.864 - 3.806 -* - +* - - (0.0012) - (0.0263) * - Non-Separable WUFIM - 0.601 - 5.032 -* - +* - - (0.0026) - (0.0634) ``` -**Results for WUFIM models** We can see the estimated parameters for our alternative specifications of the LCIM with Wealth in the Utility Function (WUFIM) in {numref}`WUFIMestimation`. The estimated values for $\beth$ and $\CRRA$ are 0.866 and 3.756, respectively, for the LCIM with portfolio choice, 0.876 and 3.506, respectively, for the separable WUFIM, 0.864 and 3.806, respectively, for the separable WUFIM with portfolio choice, and 0.601 and 5.032, respectively, for the non-separable WUFIM. The standard errors are estimated via the bootstrap. Additionally, {numref}`fig:AllSMMcontour` shows a contour plot of the objective function for the structural estimation exercise where the red star represents the estimated parameters. From these results, a clear pattern emerges which is worth discussion and further analysis. The estimated parameters for the Separable WUFIM model are not very different from those in LCIM model, which perhaps points at the inability of warm glow bequest models to resolve many of the issues of the SIM and LCIM model. The separable WUFIM model does not produce significant differences in the accumulation of wealth over the life cycle beyond a simple shifting out of the savings function. When we add a portfolio choice to either model, the pure discount factor $\beth$ becomes slightly lower and the coefficient of risk aversion $\CRRA$ increases by a few decimal points. This is because, as the portfolio choice model exposes agents to more risk, they become both more risk averse and more patient. Finally, the non-separable WUFIM model produces a much lower estimate of the pure discount factor $\beth$ and a much higher estimate of the coefficient of risk aversion $\CRRA$. This could be because of the dynamic complementarity between consumption and savings, which causes agents to save more in order to enjoy their consumption even more. This is an important result that requires further exploration. +**Results for WUFIM models** We can see the estimated parameters for our +alternative specifications of the LCIM with Wealth in the Utility Function +(WUFIM) in {numref}`WUFIMestimation`. The estimated values for $\beth$ and +$\CRRA$ are 0.866 and 3.756, respectively, for the LCIM with portfolio choice, +0.876 and 3.506, respectively, for the separable WUFIM, 0.864 and 3.806, +respectively, for the separable WUFIM with portfolio choice, and 0.601 and +5.032, respectively, for the non-separable WUFIM. The standard errors are +estimated via the bootstrap. Additionally, {numref}`fig:AllSMMcontour` shows a +contour plot of the objective function for the structural estimation exercise +where the red star represents the estimated parameters. From these results, a +clear pattern emerges which is worth discussion and further analysis. The +estimated parameters for the Separable WUFIM model are not very different from +those in LCIM model, which perhaps points at the inability of warm glow bequest +models to resolve many of the issues of the SIM and LCIM model. The separable +WUFIM model does not produce significant differences in the accumulation of +wealth over the life cycle beyond a simple shifting out of the savings function. +When we add a portfolio choice to either model, the pure discount factor $\beth$ +becomes slightly lower and the coefficient of risk aversion $\CRRA$ increases by +a few decimal points. This is because, as the portfolio choice model exposes +agents to more risk, they become both more risk averse and more patient. +Finally, the non-separable WUFIM model produces a much lower estimate of the +pure discount factor $\beth$ and a much higher estimate of the coefficient of +risk aversion $\CRRA$. This could be because of the dynamic complementarity +between consumption and savings, which causes agents to save more in order to +enjoy their consumption even more. This is an important result that requires +further exploration. ```{figure} ../Figures/AllSMMcontour.* :name: fig:AllSMMcontour @@ -271,9 +530,30 @@ Contour plot of the objective function for the structural estimation of the Life ## Sensitivity Analysis -**Results for LCIM model** For our sensitivity analysis, we use the methods introduced by [](doi:10.1093/qje/qjx023). {numref}`fig:IndShockSensitivity` shows the sensitivity of the pure discount factor $\beth$ and the coefficient of risk aversion $\CRRA$. As in [](doi:10.1093/qje/qjx023), the plots are inverses of each other, reflecting the trade-off between the two parameters in fitting lifetime consumption and wealth dynamics. Because the pure discount factor is a multiplicative adjustment on already calibrated life cycle discount factors, the sensitivity analysis has a different interpretation from the one in [](doi:10.1111/1468-0262.00269). In our analysis, the adjusted discount factor $\beth$ matters relatively more than $\CRRA$ up to age 40, indicating a potential overshot of the mortality risk and household-adjusted discount factors. For ages 40-50, the sensitivity of $\beth$ and $\CRRA$ is relatively low, indicating that the model is not very sensitive to the values of these parameters in this age range. Finally, from ages 50 and above, the coefficient of relative risk aversion $\CRRA$ matters relatively more than $\beth$. The differences between the sensitivity of this model and that in [](doi:10.1111/1468-0262.00269) are likely due to the fact that our model uses time-varying discount factors and applies the adjusted discount factor $\beth$ multiplicatively. Thus, it might be that the life cycle discount factors are imprecisely calibrated, which would explain the reversal of the sensitivity of $\beth$ and $\CRRA$ over the life cycle. MOre research is needed to understand this result. - -```{figure} ../Figures/IndShockSensitivity.* +**Results for LCIM model** For our sensitivity analysis, we use the methods +introduced by [](doi:10.1093/qje/qjx023). {numref}`fig:IndShockSensitivity` +shows the sensitivity of the pure discount factor $\beth$ and the coefficient of +risk aversion $\CRRA$. As in [](doi:10.1093/qje/qjx023), the plots are inverses +of each other, reflecting the trade-off between the two parameters in fitting +lifetime consumption and wealth dynamics. Because the pure discount factor is a +multiplicative adjustment on already calibrated life cycle discount factors, the +sensitivity analysis has a different interpretation from the one in +[](doi:10.1111/1468-0262.00269). In our analysis, the adjusted discount factor +$\beth$ matters relatively more than $\CRRA$ up to age 40, indicating a +potential overshot of the mortality risk and household-adjusted discount +factors. For ages 40-50, the sensitivity of $\beth$ and $\CRRA$ is relatively +low, indicating that the model is not very sensitive to the values of these +parameters in this age range. Finally, from ages 50 and above, the coefficient +of relative risk aversion $\CRRA$ matters relatively more than $\beth$. The +differences between the sensitivity of this model and that in +[](doi:10.1111/1468-0262.00269) are likely due to the fact that our model uses +time-varying discount factors and applies the adjusted discount factor $\beth$ +multiplicatively. Thus, it might be that the life cycle discount factors are +imprecisely calibrated, which would explain the reversal of the sensitivity of +$\beth$ and $\CRRA$ over the life cycle. MOre research is needed to understand +this result. + +```{figure} ../Figures/IndShockSensitivity.* :name: fig:IndShockSensitivity :alt: IndShockSensitivity :align: center @@ -281,7 +561,13 @@ Contour plot of the objective function for the structural estimation of the Life Sensitivity analysis of the structural estimation of the Life Cycle Incomplete Markets model. The red dot represents the estimated parameters. ``` -**Results for WUFIM models** For completeness, {numref}`fig:AllSensitivity` shows the sensitivity analysis for the alternative specifications of the LCIM model. The sensitivity of the Non-Separable WUFIM model appears to diminish in the beginning of the lifecycle, from ages 26-40, and then increases significantly from ages 41-60. This is likely due to the fact that the non-separable WUFIM model has a much higher estimate of the coefficient of relative risk aversion $\CRRA$ than the other models. +**Results for WUFIM models** For completeness, {numref}`fig:AllSensitivity` +shows the sensitivity analysis for the alternative specifications of the LCIM +model. The sensitivity of the Non-Separable WUFIM model appears to diminish in +the beginning of the lifecycle, from ages 26-40, and then increases +significantly from ages 41-60. This is likely due to the fact that the +non-separable WUFIM model has a much higher estimate of the coefficient of +relative risk aversion $\CRRA$ than the other models. ```{figure} ../Figures/AllSensitivity.* :name: fig:AllSensitivity @@ -293,9 +579,33 @@ Sensitivity analysis of the structural estimation of the Life Cycle Incomplete M # Conclusion -In this paper, I estimate a Life Cycle Incomplete Markets model with separable and non-separable wealth in the utility function (WUFIM) using the method of simulated moments (SMM) and data from the Survey of Consumer Finances (SCF). I then compare the estimated parameters to those of the standard Life Cycle Incomplete Markets model (LCIM), which is known to be unable to match the distribution of wealth. I find that the estimated parameters for the separable WUFIM model are not very different from those in the LCIM model, which perhaps points at the inability of warm glow and accidental bequest motives to resolve many of the issues of the SIM and LCIM models. The non-separable WUFIM model produces a much lower estimate of the pure discount factor $\beth$ and a significantly higher estimate of the coefficient of risk aversion $\CRRA$. Finally, I conduct sensitivity analysis of the estimated models using the Jacobian of the objective function and find that the sensitivity of the models has the reverse pattern from what we expected. This is because our LCIM and WUFIM models already account for time-varying discount factors due to mortality risk and household size. Thus, the sensitivity analysis is likely picking up on the imprecision of the calibration of the life cycle discount factors. - -Further work is needed to understand the implications of these results. First, I will use the mean wealth of each age group instead of the median wealth as the WUFIM models are intended to better match the distribution of wealth. Second, I will evaluate the result of the objective function to see if the WUFIM models are able to match the distribution of wealth better than the LCIM and SIM models. Third, I will use a numerical approximation to the Jacobian of the objective function which will allow for both faster estimation and more accurate sensitivity analysis. Finally, I will use the estimated models to conduct policy analysis and evaluate the welfare implications of macroeconomic shocks. +In this paper, I estimate a Life Cycle Incomplete Markets model with separable +and non-separable wealth in the utility function (WUFIM) using the method of +simulated moments (SMM) and data from the Survey of Consumer Finances (SCF). I +then compare the estimated parameters to those of the standard Life Cycle +Incomplete Markets model (LCIM), which is known to be unable to match the +distribution of wealth. I find that the estimated parameters for the separable +WUFIM model are not very different from those in the LCIM model, which perhaps +points at the inability of warm glow and accidental bequest motives to resolve +many of the issues of the SIM and LCIM models. The non-separable WUFIM model +produces a much lower estimate of the pure discount factor $\beth$ and a +significantly higher estimate of the coefficient of risk aversion $\CRRA$. +Finally, I conduct sensitivity analysis of the estimated models using the +Jacobian of the objective function and find that the sensitivity of the models +has the reverse pattern from what we expected. This is because our LCIM and +WUFIM models already account for time-varying discount factors due to mortality +risk and household size. Thus, the sensitivity analysis is likely picking up on +the imprecision of the calibration of the life cycle discount factors. + +Further work is needed to understand the implications of these results. First, I +will use the mean wealth of each age group instead of the median wealth as the +WUFIM models are intended to better match the distribution of wealth. Second, I +will evaluate the result of the objective function to see if the WUFIM models +are able to match the distribution of wealth better than the LCIM and SIM +models. Third, I will use a numerical approximation to the Jacobian of the +objective function which will allow for both faster estimation and more accurate +sensitivity analysis. Finally, I will use the estimated models to conduct policy +analysis and evaluate the welfare implications of macroeconomic shocks. [Econ-ARK]: https://econ-ark.org/ -[Econ-ARK/HARK]: https://github.com/econ-ark/HARK \ No newline at end of file +[Econ-ARK/HARK]: https://github.com/econ-ark/HARK diff --git a/content/paper/README.md b/content/paper/README.md index 9dab052..e42e41f 100644 --- a/content/paper/README.md +++ b/content/paper/README.md @@ -2,16 +2,22 @@ [![Made with MyST](https://img.shields.io/badge/made%20with-myst-orange)](https://myst.tools) -This repository contains the files used in the [quickstart guide](https://myst.tools/docs/mystjs/quickstart), and can be used to follow that guide, before trying MyST with your own content. +This repository contains the files used in the +[quickstart guide](https://myst.tools/docs/mystjs/quickstart), and can be used +to follow that guide, before trying MyST with your own content. -> **Note** This is **not** a good example of an actual myst project! The repositories purpose is to be a simple markdown + notebook repository that can be transformed throughout a tutorial. +> **Note** This is **not** a good example of an actual myst project! The +> repositories purpose is to be a simple markdown + notebook repository that can +> be transformed throughout a tutorial. -The goals of the [quickstart guide](https://myst.tools/docs/mystjs/quickstart) are: +The goals of the [quickstart guide](https://myst.tools/docs/mystjs/quickstart) +are: 1. Create a `myst` site, using the standard template 2. Improve the frontmatter, to add authors, affiliations and other metadata 3. Export the paper as a PDF, Word document, and LaTeX files -4. Integrate a Jupyter Notebook output into our paper, to improve reproducibility +4. Integrate a Jupyter Notebook output into our paper, to improve + reproducibility 5. Publish a website of with our work 🚀 ## Improving Frontmatter and MyST Site diff --git a/content/paper/main.bib b/content/paper/main.bib index 543b64a..9c2a969 100644 --- a/content/paper/main.bib +++ b/content/paper/main.bib @@ -247,4 +247,4 @@ @article{Sabelhaus_2010 author = {John Sabelhaus and Jae Song}, title = {The great moderation in micro labor earnings}, journal = {Journal of Monetary Economics} -} \ No newline at end of file +} diff --git a/content/paper/math.ipynb b/content/paper/math.ipynb index 316af62..eae210b 100644 --- a/content/paper/math.ipynb +++ b/content/paper/math.ipynb @@ -267,4 +267,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/content/paper/math.tex b/content/paper/math.tex index 630046d..a726f80 100644 --- a/content/paper/math.tex +++ b/content/paper/math.tex @@ -176,4 +176,4 @@ [](doi:10.3386/w26941) [](doi:10.1257/aer.20160042) [](doi:10.3386/w26647) -[](doi:10.1198/073500103288619007) \ No newline at end of file +[](doi:10.1198/073500103288619007) diff --git a/content/paper/structural_estimation_pdf_tex/files/011d25769b11310e562c3ce7d2e79c0e.tex b/content/paper/structural_estimation_pdf_tex/files/011d25769b11310e562c3ce7d2e79c0e.tex index b8e2115..966da3e 100644 --- a/content/paper/structural_estimation_pdf_tex/files/011d25769b11310e562c3ce7d2e79c0e.tex +++ b/content/paper/structural_estimation_pdf_tex/files/011d25769b11310e562c3ce7d2e79c0e.tex @@ -1 +1 @@ -$\displaystyle \frac{\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho} \left(1 - \delta\right)}{c}$ \ No newline at end of file +$\displaystyle \frac{\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho} \left(1 - \delta\right)}{c}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/0f925feca89d21db8c043d4286dc6933.tex b/content/paper/structural_estimation_pdf_tex/files/0f925feca89d21db8c043d4286dc6933.tex index 82dd3fe..2683f2f 100644 --- a/content/paper/structural_estimation_pdf_tex/files/0f925feca89d21db8c043d4286dc6933.tex +++ b/content/paper/structural_estimation_pdf_tex/files/0f925feca89d21db8c043d4286dc6933.tex @@ -1 +1 @@ -$\displaystyle \left(x^{- \rho}\right)^{- \frac{1}{\rho}}$ \ No newline at end of file +$\displaystyle \left(x^{- \rho}\right)^{- \frac{1}{\rho}}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/39e6ec1d01074f6b7dbe2583aad7433c.txt b/content/paper/structural_estimation_pdf_tex/files/39e6ec1d01074f6b7dbe2583aad7433c.txt index f7e6040..f56e4db 100644 --- a/content/paper/structural_estimation_pdf_tex/files/39e6ec1d01074f6b7dbe2583aad7433c.txt +++ b/content/paper/structural_estimation_pdf_tex/files/39e6ec1d01074f6b7dbe2583aad7433c.txt @@ -1 +1 @@ -(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c \ No newline at end of file +(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c diff --git a/content/paper/structural_estimation_pdf_tex/files/43c2c5b4c54174baf304c138ed1f189c.tex b/content/paper/structural_estimation_pdf_tex/files/43c2c5b4c54174baf304c138ed1f189c.tex index 7703e2b..ecdc506 100644 --- a/content/paper/structural_estimation_pdf_tex/files/43c2c5b4c54174baf304c138ed1f189c.tex +++ b/content/paper/structural_estimation_pdf_tex/files/43c2c5b4c54174baf304c138ed1f189c.tex @@ -1 +1 @@ -$\displaystyle \frac{\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho} \left(1 - \delta\right)}{c} - \frac{\delta \left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}}{a}$ \ No newline at end of file +$\displaystyle \frac{\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho} \left(1 - \delta\right)}{c} - \frac{\delta \left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}}{a}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/4c26ad3e47dae878071b6eb0d2ef5c2b.txt b/content/paper/structural_estimation_pdf_tex/files/4c26ad3e47dae878071b6eb0d2ef5c2b.txt index 5a52b24..5b9e174 100644 --- a/content/paper/structural_estimation_pdf_tex/files/4c26ad3e47dae878071b6eb0d2ef5c2b.txt +++ b/content/paper/structural_estimation_pdf_tex/files/4c26ad3e47dae878071b6eb0d2ef5c2b.txt @@ -1 +1 @@ -(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c - delta*(a**delta*c**(1 - delta))**(1 - rho)/a \ No newline at end of file +(a**delta*c**(1 - delta))**(1 - rho)*(1 - delta)/c - delta*(a**delta*c**(1 - delta))**(1 - rho)/a diff --git a/content/paper/structural_estimation_pdf_tex/files/84becedb1d1468d154062be42a69cdfa.txt b/content/paper/structural_estimation_pdf_tex/files/84becedb1d1468d154062be42a69cdfa.txt index abffecf..3e3f95c 100644 --- a/content/paper/structural_estimation_pdf_tex/files/84becedb1d1468d154062be42a69cdfa.txt +++ b/content/paper/structural_estimation_pdf_tex/files/84becedb1d1468d154062be42a69cdfa.txt @@ -1 +1 @@ -(a**delta*c**(1 - delta))**(1 - rho)/(1 - rho) \ No newline at end of file +(a**delta*c**(1 - delta))**(1 - rho)/(1 - rho) diff --git a/content/paper/structural_estimation_pdf_tex/files/8f4c54c2d54e757a9c23b97da31b253f.tex b/content/paper/structural_estimation_pdf_tex/files/8f4c54c2d54e757a9c23b97da31b253f.tex index 3795b2a..94d7e29 100644 --- a/content/paper/structural_estimation_pdf_tex/files/8f4c54c2d54e757a9c23b97da31b253f.tex +++ b/content/paper/structural_estimation_pdf_tex/files/8f4c54c2d54e757a9c23b97da31b253f.tex @@ -1 +1 @@ -$\displaystyle \frac{\delta \left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}}{a}$ \ No newline at end of file +$\displaystyle \frac{\delta \left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}}{a}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/9699bf796da83a5db10fd2ca4ad9b758.tex b/content/paper/structural_estimation_pdf_tex/files/9699bf796da83a5db10fd2ca4ad9b758.tex index 831fc38..4b9df7b 100644 --- a/content/paper/structural_estimation_pdf_tex/files/9699bf796da83a5db10fd2ca4ad9b758.tex +++ b/content/paper/structural_estimation_pdf_tex/files/9699bf796da83a5db10fd2ca4ad9b758.tex @@ -1 +1 @@ -$\displaystyle \left(a^{- \delta} \left(\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}\right)^{- \frac{1}{\rho - 1}}\right)^{- \frac{1}{\delta - 1}}$ \ No newline at end of file +$\displaystyle \left(a^{- \delta} \left(\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}\right)^{- \frac{1}{\rho - 1}}\right)^{- \frac{1}{\delta - 1}}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/a6255a8d7ac11cabe5829e143599f112.txt b/content/paper/structural_estimation_pdf_tex/files/a6255a8d7ac11cabe5829e143599f112.txt index f932db1..696027f 100644 --- a/content/paper/structural_estimation_pdf_tex/files/a6255a8d7ac11cabe5829e143599f112.txt +++ b/content/paper/structural_estimation_pdf_tex/files/a6255a8d7ac11cabe5829e143599f112.txt @@ -1 +1 @@ -
\ No newline at end of file +
diff --git a/content/paper/structural_estimation_pdf_tex/files/a9c3a14d33ff0dadb24311a59c17222b.txt b/content/paper/structural_estimation_pdf_tex/files/a9c3a14d33ff0dadb24311a59c17222b.txt index 1e6f4f0..a7fedd6 100644 --- a/content/paper/structural_estimation_pdf_tex/files/a9c3a14d33ff0dadb24311a59c17222b.txt +++ b/content/paper/structural_estimation_pdf_tex/files/a9c3a14d33ff0dadb24311a59c17222b.txt @@ -1 +1 @@ -(1/(a**delta*((a**delta*c**(1 - delta))**(1 - rho))**(1/(rho - 1))))**(-1/(delta - 1)) \ No newline at end of file +(1/(a**delta*((a**delta*c**(1 - delta))**(1 - rho))**(1/(rho - 1))))**(-1/(delta - 1)) diff --git a/content/paper/structural_estimation_pdf_tex/files/afd4f5d1c192186cb948ee1a50c9c60c.txt b/content/paper/structural_estimation_pdf_tex/files/afd4f5d1c192186cb948ee1a50c9c60c.txt index 581ae60..2399908 100644 --- a/content/paper/structural_estimation_pdf_tex/files/afd4f5d1c192186cb948ee1a50c9c60c.txt +++ b/content/paper/structural_estimation_pdf_tex/files/afd4f5d1c192186cb948ee1a50c9c60c.txt @@ -1 +1 @@ -(x**(-rho))**(-1/rho) \ No newline at end of file +(x**(-rho))**(-1/rho) diff --git a/content/paper/structural_estimation_pdf_tex/files/c46244155f82f0a2188b900fb12670c3.svg b/content/paper/structural_estimation_pdf_tex/files/c46244155f82f0a2188b900fb12670c3.svg index e35b2f1..f13cad5 100644 --- a/content/paper/structural_estimation_pdf_tex/files/c46244155f82f0a2188b900fb12670c3.svg +++ b/content/paper/structural_estimation_pdf_tex/files/c46244155f82f0a2188b900fb12670c3.svg @@ -1 +1 @@ -made with: mystmade withmyst \ No newline at end of file +made with: mystmade withmyst diff --git a/content/paper/structural_estimation_pdf_tex/files/d858573459fc3de88fae0a4f1006736d.txt b/content/paper/structural_estimation_pdf_tex/files/d858573459fc3de88fae0a4f1006736d.txt index a4d68f2..17b3755 100644 --- a/content/paper/structural_estimation_pdf_tex/files/d858573459fc3de88fae0a4f1006736d.txt +++ b/content/paper/structural_estimation_pdf_tex/files/d858573459fc3de88fae0a4f1006736d.txt @@ -1 +1 @@ -0.00025*((c**1.0 - 0.001*c**2.0)/(0.0158113883008419*c**0.5 - 1.58113883008419e-5*c**1.5))**2.0 \ No newline at end of file +0.00025*((c**1.0 - 0.001*c**2.0)/(0.0158113883008419*c**0.5 - 1.58113883008419e-5*c**1.5))**2.0 diff --git a/content/paper/structural_estimation_pdf_tex/files/da1a98a5ef37f6169d89399c1d3f37da.tex b/content/paper/structural_estimation_pdf_tex/files/da1a98a5ef37f6169d89399c1d3f37da.tex index 6eb425e..b8ed68e 100644 --- a/content/paper/structural_estimation_pdf_tex/files/da1a98a5ef37f6169d89399c1d3f37da.tex +++ b/content/paper/structural_estimation_pdf_tex/files/da1a98a5ef37f6169d89399c1d3f37da.tex @@ -1 +1 @@ -$\displaystyle 0.00025 \left(\frac{c^{1.0} - 0.001 c^{2.0}}{0.0158113883008419 c^{0.5} - 1.58113883008419 \cdot 10^{-5} c^{1.5}}\right)^{2.0}$ \ No newline at end of file +$\displaystyle 0.00025 \left(\frac{c^{1.0} - 0.001 c^{2.0}}{0.0158113883008419 c^{0.5} - 1.58113883008419 \cdot 10^{-5} c^{1.5}}\right)^{2.0}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/dacd59a235f501f74d6b767be019ee45.txt b/content/paper/structural_estimation_pdf_tex/files/dacd59a235f501f74d6b767be019ee45.txt index 9e5c7db..f2fe019 100644 --- a/content/paper/structural_estimation_pdf_tex/files/dacd59a235f501f74d6b767be019ee45.txt +++ b/content/paper/structural_estimation_pdf_tex/files/dacd59a235f501f74d6b767be019ee45.txt @@ -1 +1 @@ - \ No newline at end of file + diff --git a/content/paper/structural_estimation_pdf_tex/files/dc12638639751951782f917a44aa00b5.tex b/content/paper/structural_estimation_pdf_tex/files/dc12638639751951782f917a44aa00b5.tex index f28d82e..6f9cce2 100644 --- a/content/paper/structural_estimation_pdf_tex/files/dc12638639751951782f917a44aa00b5.tex +++ b/content/paper/structural_estimation_pdf_tex/files/dc12638639751951782f917a44aa00b5.tex @@ -1 +1 @@ -$\displaystyle \frac{\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}}{1 - \rho}$ \ No newline at end of file +$\displaystyle \frac{\left(a^{\delta} c^{1 - \delta}\right)^{1 - \rho}}{1 - \rho}$ diff --git a/content/paper/structural_estimation_pdf_tex/files/ee9fa939ae6f56514559fd582d5f7dc2.txt b/content/paper/structural_estimation_pdf_tex/files/ee9fa939ae6f56514559fd582d5f7dc2.txt index 1a9cb6c..73b3b87 100644 --- a/content/paper/structural_estimation_pdf_tex/files/ee9fa939ae6f56514559fd582d5f7dc2.txt +++ b/content/paper/structural_estimation_pdf_tex/files/ee9fa939ae6f56514559fd582d5f7dc2.txt @@ -1 +1 @@ -delta*(a**delta*c**(1 - delta))**(1 - rho)/a \ No newline at end of file +delta*(a**delta*c**(1 - delta))**(1 - rho)/a diff --git a/content/paper/structural_estimation_pdf_tex/main.bib b/content/paper/structural_estimation_pdf_tex/main.bib index 9df9925..855b210 100644 --- a/content/paper/structural_estimation_pdf_tex/main.bib +++ b/content/paper/structural_estimation_pdf_tex/main.bib @@ -501,4 +501,4 @@ @techreport{Auclert_2023 publisher = {National Bureau of Economic Research}, author = {Adrien Auclert and Matthew Rognlie and Ludwig Straub}, title = {The Trickling Up of Excess Savings} -} \ No newline at end of file +} diff --git a/content/paper/structural_estimation_pdf_tex/structural_estimation.tex b/content/paper/structural_estimation_pdf_tex/structural_estimation.tex index 6532292..e97ba67 100644 --- a/content/paper/structural_estimation_pdf_tex/structural_estimation.tex +++ b/content/paper/structural_estimation_pdf_tex/structural_estimation.tex @@ -141,8 +141,8 @@ \subsection{The Baseline Model}\label{The Baseline Model} \begin{align} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv \RNrm_{t+1}} + ~\TranShkEmp_{t+1} \end{align} @@ -181,8 +181,8 @@ \subsection{Wealth in the Utility Function}\label{Wealth in the Utility Function \begin{align} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t}, \aNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} \end{align} \end{equation} diff --git a/content/slides/_extensions/grantmcdermott/clean/_extension.yml b/content/slides/_extensions/grantmcdermott/clean/_extension.yml index 10b1e45..427d34a 100644 --- a/content/slides/_extensions/grantmcdermott/clean/_extension.yml +++ b/content/slides/_extensions/grantmcdermott/clean/_extension.yml @@ -10,4 +10,3 @@ contributes: side: left slide-number: true date-format: long - diff --git a/content/slides/_extensions/grantmcdermott/clean/clean.scss b/content/slides/_extensions/grantmcdermott/clean/clean.scss index 3382dc0..4ae4d7e 100644 --- a/content/slides/_extensions/grantmcdermott/clean/clean.scss +++ b/content/slides/_extensions/grantmcdermott/clean/clean.scss @@ -13,7 +13,7 @@ $right-arrow: "\2192"; // Unicode character for right arrow /* Note: This theme uses the Roboto font family, which it imports from Google Fonts to ensure consistent weighting in addition to availability. While - you can use a local installation of Roboto, this is generally not + you can use a local installation of Roboto, this is generally not recommended since the weighting will likely be wrong (probably too light). OTOH, importing from Google Fonts can cause some issues in certain secure environments due the external CDN (see: @@ -24,7 +24,7 @@ Note: This theme uses the Roboto font family, which it imports from Google preserving consistent font weights, you may also wish to remove "Roboto" from the choice set if the family is installed locally. */ -@import url('https://fonts.googleapis.com/css?family=Roboto:200,200i,300,300i,350,350i,400,400i&display=swap'); +@import url("https://fonts.googleapis.com/css?family=Roboto:200,200i,300,300i,350,350i,400,400i&display=swap"); $font-family-sans-serif: "Roboto", sans-serif !default; $presentation-heading-font: "Roboto", sans-serif !default; @@ -41,7 +41,6 @@ $body-color: $jet !default; $link-color: $accent !default; $selection-bg: #26351c !default; - /*-- scss:rules --*/ .reveal a { @@ -96,10 +95,8 @@ $selection-bg: #26351c !default; width: 100%; } } - } - .reveal h2 { // font-weight: 350; font-weight: lighter; @@ -193,7 +190,6 @@ $selection-bg: #26351c !default; pointer-events: auto; } - 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- @@ -41,8 +41,8 @@ z - @@ -53,142 +53,142 @@ L 0 3.5 - - - - - - - @@ -212,23 +212,23 @@ z - @@ -252,29 +252,29 @@ z - @@ -317,14 +317,14 @@ z - @@ -348,43 +348,43 @@ z - @@ -408,34 +408,34 @@ z - @@ -453,218 +453,218 @@ z - - - - - - - - - @@ -686,8 +686,8 @@ z - @@ -698,32 +698,32 @@ L -3.5 0 - - - @@ -873,174 +873,174 @@ z - - - - - - - - @@ -1064,31 +1064,31 @@ z - - @@ -1110,28 +1110,28 @@ z - - - - - @@ -1139,14 +1139,14 @@ L 579.04 14.382704 - - @@ -1167,8 +1167,8 @@ L 3.5 0 - @@ -1189,8 +1189,8 @@ L 579.04 231.656426 - @@ -1211,8 +1211,8 @@ L 579.04 186.154567 - @@ -1233,8 +1233,8 @@ L 579.04 140.652707 - @@ -1255,8 +1255,8 @@ L 579.04 95.150847 - @@ -1279,36 +1279,36 @@ L 579.04 49.648988 - - @@ -1335,31 +1335,31 @@ z - - @@ -1381,23 +1381,23 @@ z - - - - diff --git a/content/slides/libs/header-attrs-2.27/header-attrs.js b/content/slides/libs/header-attrs-2.27/header-attrs.js index dd57d92..65f8173 100644 --- a/content/slides/libs/header-attrs-2.27/header-attrs.js +++ b/content/slides/libs/header-attrs-2.27/header-attrs.js @@ -1,11 +1,13 @@ // Pandoc 2.9 adds attributes on both header and div. We remove the former (to // be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); +document.addEventListener("DOMContentLoaded", function (e) { + var hs = document.querySelectorAll( + "div.section[class*='level'] > :first-child", + ); var i, h, a; for (i = 0; i < hs.length; i++) { h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 a = h.attributes; while (a.length > 0) h.removeAttribute(a[0].name); } diff --git a/content/slides/libs/remark-css-0.0.1/default.css b/content/slides/libs/remark-css-0.0.1/default.css index d37bfd2..aef9a93 100644 --- a/content/slides/libs/remark-css-0.0.1/default.css +++ b/content/slides/libs/remark-css-0.0.1/default.css @@ -1,4 +1,5 @@ -a, a > code { +a, +a > code { color: rgb(249, 38, 114); text-decoration: none; } @@ -8,14 +9,18 @@ a, a > code { padding-right: 4em; font-size: 90%; } -.remark-code-line-highlighted { background-color: #ffff88; } +.remark-code-line-highlighted { + background-color: #ffff88; +} .inverse { background-color: #272822; color: #d6d6d6; text-shadow: 0 0 20px #333; } -.inverse h1, .inverse h2, .inverse h3 { +.inverse h1, +.inverse h2, +.inverse h3 { color: #f3f3f3; } /* Two-column layout */ @@ -25,7 +30,8 @@ a, a > code { height: 92%; float: left; } -.left-column h2:last-of-type, .left-column h3:last-child { +.left-column h2:last-of-type, +.left-column h3:last-child { color: #000; } .right-column { @@ -44,7 +50,9 @@ a, a > code { .pull-right + * { clear: both; } -img, video, iframe { +img, +video, +iframe { max-width: 100%; } blockquote { @@ -56,11 +64,22 @@ blockquote { border-top: 1px solid #666; border-bottom: 1px solid #666; } -.remark-slide table thead th { border-bottom: 1px solid #ddd; } -th, td { padding: 5px; } -.remark-slide thead, .remark-slide tfoot, .remark-slide tr:nth-child(even) { background: #eee } +.remark-slide table thead th { + border-bottom: 1px solid #ddd; +} +th, +td { + padding: 5px; +} +.remark-slide thead, +.remark-slide tfoot, +.remark-slide tr:nth-child(even) { + background: #eee; +} -@page { margin: 0; } +@page { + margin: 0; +} @media print { .remark-slide-scaler { width: 100% !important; diff --git a/content/slides/libs/remark-css-0.0.1/metropolis-fonts.css b/content/slides/libs/remark-css-0.0.1/metropolis-fonts.css index 5efb424..da598e0 100644 --- a/content/slides/libs/remark-css-0.0.1/metropolis-fonts.css +++ b/content/slides/libs/remark-css-0.0.1/metropolis-fonts.css @@ -2,10 +2,12 @@ @import url(https://cdn.rawgit.com/tonsky/FiraCode/1.204/distr/fira_code.css); body { - font-family: 'Fira Sans','Droid Serif', 'Palatino Linotype', 'Book Antiqua', Palatino, 'Microsoft YaHei', 'Songti SC', serif; + font-family: "Fira Sans", "Droid Serif", "Palatino Linotype", "Book Antiqua", + Palatino, "Microsoft YaHei", "Songti SC", serif; } -.remark-code, .remark-inline-code { - font-family: 'Fira Code', 'Lucida Console', Monaco, monospace; +.remark-code, +.remark-inline-code { + font-family: "Fira Code", "Lucida Console", Monaco, monospace; font-size: 80%; } diff --git a/content/slides/libs/remark-css-0.0.1/metropolis.css b/content/slides/libs/remark-css-0.0.1/metropolis.css index 59f0072..222605e 100644 --- a/content/slides/libs/remark-css-0.0.1/metropolis.css +++ b/content/slides/libs/remark-css-0.0.1/metropolis.css @@ -1,37 +1,39 @@ .remark-slide-content { - background-color: #FAFAFA; - border-top: 80px solid #23373B; + background-color: #fafafa; + border-top: 80px solid #23373b; font-size: 20px; font-weight: 300; line-height: 1.5; - padding: 1em 2em 1em 2em + padding: 1em 2em 1em 2em; } .title-slide .inverse .remark-slide-content { - background-color: #FAFAFA; + background-color: #fafafa; } .inverse { - background-color: #23373B; + background-color: #23373b; text-shadow: none; } /* Removes colored bar from top of the slide resulting in a clear slide */ -.clear{ - border-top: 0px solid #FAFAFA; +.clear { + border-top: 0px solid #fafafa; } h1 { font-weight: normal; margin-top: -95px; margin-left: -00px; - color: #FAFAFA; + color: #fafafa; } -h2, h3, h4 { +h2, +h3, +h4 { padding-top: -15px; padding-bottom: 00px; - color: #1A292C; + color: #1a292c; text-shadow: none; font-weight: 400; text-align: left; @@ -51,21 +53,24 @@ h2, h3, h4 { font-size: 30px; } -.left-column h2, .left-column h3, .left-column h4 { +.left-column h2, +.left-column h3, +.left-column h4 { color: #777; } -.left-column h2:last-of-type, .left-column h3:last-child { - color: #1A292C; +.left-column h2:last-of-type, +.left-column h3:last-child { + color: #1a292c; } .title-slide { - background-color: #FAFAFA; - border-top: 80px solid #FAFAFA; + background-color: #fafafa; + border-top: 80px solid #fafafa; } -.title-slide h1 { - color: #1A292C; +.title-slide h1 { + color: #1a292c; font-size: 40px; text-shadow: none; font-weight: 400; @@ -73,18 +78,18 @@ h2, h3, h4 { margin-left: 15px; padding-top: 80px; } -.title-slide h2 { +.title-slide h2 { margin-top: -25px; padding-bottom: -20px; - color: #1A292C; + color: #1a292c; text-shadow: none; font-weight: 300; font-size: 35px; text-align: left; margin-left: 15px; } -.title-slide h3 { - color: #1A292C; +.title-slide h3 { + color: #1a292c; text-shadow: none; font-weight: 300; font-size: 25px; @@ -93,16 +98,19 @@ h2, h3, h4 { margin-bottom: -30px; } -hr, .title-slide h2::after, .mline h1::after { - content: ''; +hr, +.title-slide h2::after, +.mline h1::after { + content: ""; display: block; border: none; - background-color: #EB811B; - color: #EB811B; + background-color: #eb811b; + color: #eb811b; height: 1px; } -hr, .mline h1::after { +hr, +.mline h1::after { margin: 1em 15px 0 15px; } @@ -121,7 +129,7 @@ hr, .mline h1::after { } .inverse .remark-slide-number { font-size: 13pt; - color: #FAFAFA; + color: #fafafa; opacity: 1; } diff --git a/content/slides/slides.aux b/content/slides/slides.aux index b804887..9a7e80a 100644 --- a/content/slides/slides.aux +++ b/content/slides/slides.aux @@ -1,4 +1,4 @@ -\relax +\relax \providecommand*\new@tpo@label[2]{} \providecommand\hyper@newdestlabel[2]{} \providecommand*\HyPL@Entry[1]{} diff --git a/content/slides/slides.html b/content/slides/slides.html index 554e28a..8b4fdb4 100644 --- a/content/slides/slides.html +++ b/content/slides/slides.html @@ -1,740 +1,585 @@ - - - - - - - - - - - - - - - Structural Estimation of Life Cycle Models with Wealth in the Utility Function - - - - - - - - - - - - - - - -
-
- -
-

Structural Estimation of Life Cycle Models with Wealth in the Utility Function

-

CEF 2024 – NTU, Singapore

- -
-
-
-Alan Lujan -
-
-alujan@jhu.edu -
-

- Johns Hopkins University
Econ-ARK -

-
-
- -

June 20, 2024

-
-
-

Why do people save?

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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-Reasons for Saving - -Can’t save - -Education - -Family - -Home - -Investment - -Liquidity/the future - -No particular reason - -Purchases - -Retirement -
-edcl_lbl - - - - - - - - - -
-Bachelors degree or higher - -2% - -9% - -4% - -4% - -2% - -34% - -1% - -7% - -38% -
-high school diploma or GED - -5% - -8% - -6% - -5% - -3% - -35% - -1% - -12% - -26% -
-no high school diploma/GED - -11% - -7% - -7% - -4% - -3% - -34% - -1% - -15% - -18% -
-some college or Assoc. degree - -4% - -9% - -5% - -5% - -3% - -36% - -1% - -11% - -28% -
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-Reasons for Saving - -Can’t save - -Education - -Family - -Home - -Investment - -Liquidity/the future - -No particular reason - -Purchases - -Retirement -
-edcl_lbl - - - - - - - - - -
-Bachelors degree or higher - -5% - -2% - -6% - -1% - -2% - -42% - -3% - -13% - -27% -
-high school diploma or GED - -7% - -1% - -8% - -0% - -2% - -42% - -3% - -14% - -22% -
-no high school diploma/GED - -14% - -1% - -5% - -1% - -2% - -40% - -2% - -17% - -19% -
-some college or Assoc. degree - -7% - -1% - -7% - -0% - -2% - -43% - -3% - -15% - -21% -
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*   reasons for saving: 1=cant save, 2=education, 3=family, 4=home,
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+      Structural Estimation of Life Cycle Models with Wealth in the Utility
+      Function
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+            Structural Estimation of Life Cycle Models with Wealth in the
+            Utility Function
+          

+          
CEF 2024 – NTU, Singapore

+
+          

+            

+              

+                Alan Lujan
+                
+                  
+              

+              

+                alujan@jhu.edu
+              

+              

+                Johns Hopkins University 

+                Econ-ARK
+              

+            

+          

+
+          
June 20, 2024

+        

+        

+          
Why do people save?

+          

+            
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Reasons for SavingCan’t saveEducationFamilyHomeInvestmentLiquidity/the futureNo particular reasonPurchasesRetirement
edcl_lbl
Bachelors degree or higher2%9%4%4%2%34%1%7%38%
high school diploma or GED5%8%6%5%3%35%1%12%26%
no high school diploma/GED11%7%7%4%3%34%1%15%18%
some college or Assoc. degree4%9%5%5%3%36%1%11%28%

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Reasons for SavingCan’t saveEducationFamilyHomeInvestmentLiquidity/the futureNo particular reasonPurchasesRetirement
edcl_lbl
Bachelors degree or higher5%2%6%1%2%42%3%13%27%
high school diploma or GED7%1%8%0%2%42%3%14%22%
no high school diploma/GED14%1%5%1%2%40%2%17%19%
some college or Assoc. degree7%1%7%0%2%43%3%15%21%

+              

+              

+                
*   reasons for saving: 1=cant save, 2=education, 3=family, 4=home,
     5=purchases, 6=retirement, 7=liquidity/the future, 8=investment,
     9=no particular reason;
 *   NOTE: multiple saving reasons may be reported: here choosing only
@@ -797,1096 +642,939 @@ 
Why do people save?

    91.  Wise/prudent thing to do; good discipline to save; habit
    92.  Liquidity; to have cash available/on hand
    -1.  Don't/can't save; "have no money"

-

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How do people save?

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-Figure 1: Median Net Worth by Age
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-Figure 2: Median Net Worth by Education
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-Figure 3: Median Normalized Net Worth
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Motivation and Research Quesitions

-
Motivation

-
    
-
  • Savings and wealth accumulation
    • 
-
  • Patterns of inequality
    • 
-
  • Life cycle / Retirement / Bequests
    • 
-

-
Research Questions

-
    
-
  • What are our models missing?
    • 
-
  • How do we fit the distribution of wealth for the rich and/or the old?
    • 
-
  • How important are life cycle properties?
    • 
-
  • How much does wealth in the utility function matter?
    • 
-

-
\[
-\newcommand{\DiscFac}{\beta}
-\newcommand{\cFunc}{\mathrm{c}}
-\newcommand{\uFunc}{\mathrm{u}}
-\newcommand{\vFunc}{\mathrm{v}}
-\newcommand{\Alive}{\mathcal{L}}
-\newcommand{\h}{h}
-\newcommand{\cLvl}{\mathbf{c}}
-\newcommand{\mLvl}{\mathbf{m}}
-\newcommand{\pLvl}{\mathbf{p}}
-\newcommand{\Ex}{\mathbb{E}}
-\newcommand{\CRRA}{\rho}
-\newcommand{\PermGroFac}{\pmb{\Phi}}
-\newcommand{\Rfree}{\mathsf{R}}
-\newcommand{\PermShk}{\mathbf{\Psi}}
-\newcommand{\TranShk}{\pmb{\xi}}
-\newcommand{\aNrm}{a}
-\newcommand{\cNrm}{c}
-\newcommand{\RNrm}{\mathcal{R}}
-\newcommand{\TranShkEmp}{\pmb{\theta}}
-\newcommand{\mNrm}{m}
-\newcommand{\pZero}{\wp}
-\newcommand{\aFunc}{\mathrm{a}}
-\newcommand{\kapShare}{\alpha}
-\newcommand{\wealth}{o}
-\newcommand{\kap}{k}
-\newcommand{\wealthShare}{\delta}
-\newcommand{\wFunc}{\mathrm{w}}
-\newcommand{\aRat}{a}
-\newcommand{\mRat}{m}
-\newcommand{\aMat}{[\mathrm{a}]}
-\newcommand{\mMat}{[\mathrm{m}]}
-\newcommand{\weight}{\omega}
-\]

-

-

-
Some Literature

-
    
-
  • Why do the rich save so much? - Carroll [1998]
    
-
      
-
    • the rich have higher lifetime savings rates
      • 
-
    • models of consumption smoothing and precautionary savings can not explain this
      • 
-
    • propose a model where wealth is in the utility function
      • 
-
    • households derive utility from wealth itself OR
      • 
-
    • wealth provides a flow of services such as political power or social status
      • 
-
    • 
-
  • Do the rich save more? - Dynan Skinner Zeldes [2004]
    
-
      
-
    • Yes! (savings rate increases by income)
      • 
-
    • 
-

-

-

-
The baseline Life Cycle Incomplete Markets model

-
The agent maximizes PDV of utility from consumption over life cycle with terminal period \(T\):

-
\[\begin{equation}
-\label{eq:lifecyclemax}
-\vFunc_{t}(\pLvl_{t},\mLvl_{t})  = \max_{\{\cFunc\}_{t}^{T}} ~ \uFunc(\cLvl_{t})+\Ex_{t}\left[\sum_{n=1}^{T-t} \DiscFac^n \Alive_{t}^{t+n} \uFunc(\cLvl_{t+n}) \right]
-\end{equation}\]

-
where \(\pLvl_{t}\) is permanent income level, \(\mLvl_{t}\) is total market resources, \(\cLvl_{t}\) is consumption, and

-
\[\begin{aligned}
-    \DiscFac & :  \text{time-invariant pure discount factor}
-    \\ \Alive _{t}^{t+n} & :  \text{probability to }\Alive\text{ive until age t+n given alive at age t}
-\end{aligned}\]

-

-

-
Recursive Bellman Equation

-
\[\begin{aligned}
-    {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t})+\DiscFac\Alive_{t+1}
-    \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})]
-    \\ & \text{s.t.} &
-    \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t}
-    \\ {m}_{t+1} & = \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv \RNrm_{t+1}} + \TranShkEmp_{t+1}
-\end{aligned}\]

-
where \(\vFunc(\cdot)\) and \(\uFunc(\cdot)\) are now the normalized value and utility functions, and

-
\[\begin{aligned}
-  \CRRA & : \text{constant relative risk aversion parameter} \\
-  \mNrm_{t} & : \text{normalized market resources} \\
-  \cNrm_{t} & : \text{normalized consumption} \\
-  \aNrm_{t} & : \text{normalized liquid assets after consumption} \\
-  \Rfree & : \text{risk free interest rate}
-    \\ \RNrm_{t+1} & :  \text{permanent income growth normalized return factor}
-\end{aligned}\]

-

-

-
Distribution of Shocks to Income

-
The transitory and permanent shocks to income are defined as:

-
\[\begin{aligned}
-  \PermShk_{t+1} & :  \text{mean-one shock to permanent income}
-    \\ \PermGroFac_{t+1} & :  \text{permanent income growth factor}
-    \\ \TranShkEmp_{t+1} & :  \text{mean-one transitory shock to permanent income}
-\end{aligned}\]

-
where

-
\[\begin{aligned}
-\TranShkEmp_{s}  = & \begin{cases} 0  & \text{with probability } \pZero>0  \\
-\xi_{s}/\pZero & \text{with probability } (1-\pZero)  \end{cases} \\
-\phantom{/\pZero} \\ & \text{with } \log \xi_{s}\thicksim \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2})
-\\ & \text{and }  \log \PermShk_{s}   \thicksim \mathcal{N}(-\sigma_{[\PermShk, t]}^{2}/2,\sigma_{[\PermShk, t]}^{2}).
-\end{aligned}\]

-

-

-
Wealth in the Utility Literature

-
Separable – Bequest Models

-
    
-
  • Wealth inequality and intergenerational links - De Nardi [2004]
    • 
-
  • To Leave or Not to Leave: The Distribution of Bequest Motives - Kopczuk, Lupton [2007]
    • 
-
  • Bequests and heterogeneity in Retirement Wealth - De Nardi, Yang [2014]
    • 
-

-
Non-separable – Money in the Utility

-
    
-
  • A monetary equilibrium model with transactions costs - Rotemberg [1984]
    • 
-
  • Money in the Utility Function: An Empirical Implementation - Poterba Rotemberg [1986]
    • 
-
  • A Novel Model to Measure Utility from Consumption and Wealth - Tzitzouris [2024]
    • 
-

-

-

-
The Wealth in the Utility Function Incomplete Markets model

-
\[\begin{aligned}
-    {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}}  \uFunc(\cNrm_{t}, \aNrm_{t})+\DiscFac\Alive_{t+1}
-    \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})]
-    \\ & \text{s.t.} &
-    \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t}
-    \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1}
-\end{aligned}\]

-
Separable Utility (as in Carroll [1998], De Nardi [2004])

-
\[\begin{equation}
-\uFunc(\cNrm_{t}, \aNrm_{t}) = \frac{\cNrm_{t}^{1-\CRRA}}{1-\CRRA} + \kapShare_{t} \frac{(\aFunc_{t} - \underline\aNrm)^{1-\wealthShare}}{1-\wealthShare}
-\end{equation}\]

-
Non-separable Utility (as in Tzitzouris [2024], Rotemberg Poterba [1986])

-
\[\begin{equation}
-\uFunc(\cNrm_{t}, \aNrm_{t}) = \frac{(\cNrm_{t}^{1-\wealthShare} (\aNrm_{t} - \underline\aNrm)^\wealthShare)^{1-\CRRA}}{(1-\CRRA)}
-\end{equation}\]

-

-

-
Parameterization and Calibration

-
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ParameterDescriptionValues
\(\PermGroFac_{t}\)Perm. income growth factorCaggetti [2003]
\(\sigma_{[\xi, t]}, \sigma_{[\PermShk, t]}\)Std. dev. of trans. and perm. shocksSabelhaus and Song [2010]
\(\pZero = 0.005\)Probability of zero incomeCarroll [1992]
\(\Alive_{t}\)Survival and discount factorsCaggetti [2003]
\(\Rfree = 1.03\)Risk free interest rateCaggetti [2003]

-

-

-
Estimation - Method of Simulated Moments

-
Steps of MSM estimation

-
    
-
  1. Obtain empirical data
    2. 
-
  2. Calculate empirical moments
    3. 
-
  3. Calculate covariance matrix
    4. 
-
  4. Define Heterogeneous Agent Model
    5. 
-
  5. Given a guess \(\theta_0\), simulate model and calculate moments
    6. 
-
  6. Estimate model parameters by minimizing criterion function
    7. 
-

-

-

-
Method of Simulated Moments

-
1. Empirical data

-
Survey of Consumer Finances (SCF)

-
    
-
  • every 3 years
    • 
-
  • cross-sectional survey of U.S. households
    • 
-
  • includes balance sheets, pensions, income, demographics
    • 
-
  • pooled data from 1998 to 2022
    • 
-

-
2. Calculate empirical moments

-
    
-
  • Bin by age groups of 5 years [25-30), [30-35), …
    • 
-
  • SCF is weighted survey, calculate weighted median networth
    • 
-
  • Remove households with no income
    • 
-

-

-

-
Method of Simulated Moments

-
3. Calculate covariance matrix

-
    
-
  • Via the bootstrap
    • 
-

-
4. Define Heterogeneous Agent model

-
    
-
  1. Life cycle Incomplete Markets model
    2. 
-
  2. Warm Glow Homothetic Separable Utility model
    3. 
-
  3. Warm Glow Non-Homothetic Separable Utility model
    4. 
-
  4. T.R.P. Non-Separable Utility model
    5. 
-

-

-

-
Method of Simulated Moments

-
5. Given \(\theta_0\), simulate moments

-
    
-
  • Solve model given parameters \(\theta_0\)
    • 
-
  • Simulate model
-
      
-
    • 1,000 agents
      • 
-
    • 1 full lifetime (25 to 100 years old)
      • 
-
    • Ignore mortality for simulation
      • 
-
    • Calculate median wealth for each age bracket
      • 
-
    • 
-

-
6. Estimate the Model Parameters

-
Objective function

-
\[\begin{equation}
-  \min_{\theta \in \Theta} \hat{g}(\theta)' \hat{W} \hat{g}(\theta) \qquad \qquad \hat{g}(\theta) = \hat{s} - s(\theta)
-\end{equation}\]

-

-

-
Results

-
-

-Figure 4: Simulated vs. Empirical Moments
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-CRRA
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-0
-
-3.038009
-
-0.227844
-
-2.591442
-
-3.484576
-
-1.474017e-40
-
-True
-
-***
-

-DiscFac
-
-0
-
-0.972248
-
-0.003222
-
-0.965933
-
-0.978563
-
-0.000000e+00
-
-True
-
-***
-

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-
Sensitivity

-
-

-Figure 5: Sensitivity from Andrews, Gentzkow & Shapiro [2017]
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More Results

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More Results

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-CRRA
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-
-0.227844
-
-2.591442
-
-3.484576
-
-1.474017e-40
-
-True
-
-***
-

-DiscFac
-
-0
-
-0.972248
-
-0.003222
-
-0.965933
-
-0.978563
-
-0.000000e+00
-
-True
-
-***
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-CRRA
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-0
-
-2.646309
-
-0.539211
-
-1.589474
-
-3.703144
-
-9.213221e-07
-
-True
-
-***
-

-DiscFac
-
-0
-
-0.950000
-
-0.002992
-
-0.944136
-
-0.955864
-
-0.000000e+00
-
-True
-
-***
-

-WealthShare
-
-0
-
-0.246470
-
-0.021571
-
-0.204191
-
-0.288749
-
-3.109380e-30
-
-True
-
-***
-

-WealthShift
-
-0
-
-3.592352
-
-1.406597
-
-0.835473
-
-6.349232
-
-1.065142e-02
-
-True
-
-**
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-CRRA
-
-0
-
-2.155238
-
-0.164354
-
-1.833111
-
-2.477365
-
-2.758510e-39
-
-True
-
-***
-

-DiscFac
-
-0
-
-0.970986
-
-0.002005
-
-0.967058
-
-0.974915
-
-0.000000e+00
-
-True
-
-***
-

-BeqFac
-
-0
-
-75.566673
-
-34.238945
-
-8.459575
-
-142.673772
-
-2.731136e-02
-
-True
-
-**
-

-BeqShift
-
-0
-
-2.056486
-
-1.255906
-
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-
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-
-1.015360e-01
-
-True
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-0
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-
-0.205016
-
-3.831775
-
-4.635424
-
-9.748335e-95
-
-True
-
-***
-

-DiscFac
-
-0
-
-0.923009
-
-0.008554
-
-0.906242
-
-0.939775
-
-0.000000e+00
-
-True
-
-***
-

-BeqCRRA
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-0
-
-2.427211
-
-2.614537
-
--2.697187
-
-7.551609
-
-3.532248e-01
-
-True
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-BeqFac
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-70.763872
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-698.885867
-
--1299.027256
-
-1440.555000
-
-9.193501e-01
-
-True
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-BeqShift
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-0
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-1.630113
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-17.348288
-
--32.371906
-
-35.632133
-
-9.251378e-01
-
-True
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More Results

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Conclusion and Future Work

-
Conclusion

-
    
-
  • Need wealth in the utility function to better capture distribution of wealth
    • 
-
  • Need life cycle structure to understand effect of policies on:
-
      
-
    • young parents with children and low income
      • 
-
    • working middle aged
      • 
-
    • retirees with low wealth
      • 
-
    • 
-
  • Separable WG Non-Homothetic has best fit, but not well identified
    • 
-

-
Future Work

-
    
-
  • Portfolio Choice: How well do these models match risky share of portfolio?
    • 
-
  • Alternative sources of data: PSID, CES, HRS
    • 
-
  • Correlation of income / mortality / health expense shocks
    • 
-

-

-

-

-
Powered by Econ-ARK

-

-

-

-
+              

+            

+
+ + +
+
+

How do people save?

+
+ +
+
+
+
+
+ +
+
+ Figure 1: Median Net Worth by Age +
+
+
+
+
+
+
+
+ +
+
+ Figure 2: Median Net Worth by Education +
+
+
+
+
+
+
+
+ +
+
+ Figure 3: Median Normalized Net Worth +
+
+
+
+
+

+
+
+
+
+
+

Motivation and Research Quesitions

+

Motivation

+
    +
  • Savings and wealth accumulation
    • +
  • Patterns of inequality
    • +
  • Life cycle / Retirement / Bequests
    • +
+

Research Questions

+
    +
  • What are our models missing?
    • +
  • + How do we fit the distribution of wealth for the rich and/or the + old? +
    • +
  • How important are life cycle properties?
    • +
  • How much does wealth in the utility function matter?
    • +
+

+ \[ \newcommand{\DiscFac}{\beta} \newcommand{\cFunc}{\mathrm{c}} + \newcommand{\uFunc}{\mathrm{u}} \newcommand{\vFunc}{\mathrm{v}} + \newcommand{\Alive}{\mathcal{L}} \newcommand{\h}{h} + \newcommand{\cLvl}{\mathbf{c}} \newcommand{\mLvl}{\mathbf{m}} + \newcommand{\pLvl}{\mathbf{p}} \newcommand{\Ex}{\mathbb{E}} + \newcommand{\CRRA}{\rho} \newcommand{\PermGroFac}{\pmb{\Phi}} + \newcommand{\Rfree}{\mathsf{R}} + \newcommand{\PermShk}{\mathbf{\Psi}} + \newcommand{\TranShk}{\pmb{\xi}} \newcommand{\aNrm}{a} + \newcommand{\cNrm}{c} \newcommand{\RNrm}{\mathcal{R}} + \newcommand{\TranShkEmp}{\pmb{\theta}} \newcommand{\mNrm}{m} + \newcommand{\pZero}{\wp} \newcommand{\aFunc}{\mathrm{a}} + \newcommand{\kapShare}{\alpha} \newcommand{\wealth}{o} + \newcommand{\kap}{k} \newcommand{\wealthShare}{\delta} + \newcommand{\wFunc}{\mathrm{w}} \newcommand{\aRat}{a} + \newcommand{\mRat}{m} \newcommand{\aMat}{[\mathrm{a}]} + \newcommand{\mMat}{[\mathrm{m}]} \newcommand{\weight}{\omega} + \] +

+
+
+

Some Literature

+
    +
  • +

    Why do the rich save so much? - Carroll [1998]

    +
      +
    • the rich have higher lifetime savings rates
      • +
    • + models of consumption smoothing and precautionary savings can + not explain this +
      • +
    • propose a model where wealth is in the utility function
      • +
    • households derive utility from wealth itself OR
      • +
    • + wealth provides a flow of services such as political power or + social status +
      • +
    +
    • +
  • +

    Do the rich save more? - Dynan Skinner Zeldes [2004]

    +
      +
    • Yes! (savings rate increases by income)
      • +
    +
    • +
+
+
+

The baseline Life Cycle Incomplete Markets model

+

+ The agent maximizes PDV of utility from consumption over life cycle + with terminal period \(T\): +

+

+ \[\begin{equation} \label{eq:lifecyclemax} + \vFunc_{t}(\pLvl_{t},\mLvl_{t}) = \max_{\{\cFunc\}_{t}^{T}} ~ + \uFunc(\cLvl_{t})+\Ex_{t}\left[\sum_{n=1}^{T-t} \DiscFac^n + \Alive_{t}^{t+n} \uFunc(\cLvl_{t+n}) \right] + \end{equation}\] +

+

+ where \(\pLvl_{t}\) is permanent + income level, \(\mLvl_{t}\) is + total market resources, + \(\cLvl_{t}\) is consumption, and +

+

+ \[\begin{aligned} \DiscFac & : \text{time-invariant pure + discount factor} \\ \Alive _{t}^{t+n} & : \text{probability to + }\Alive\text{ive until age t+n given alive at age t} + \end{aligned}\] +

+
+
+

Recursive Bellman Equation

+

+ \[\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} + ~ \uFunc(\cNrm_{t})+\DiscFac\Alive_{t+1} + \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] + \\ & \text{s.t.} & \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ {m}_{t+1} & = + \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv + \RNrm_{t+1}} + \TranShkEmp_{t+1} \end{aligned}\] +

+

+ where \(\vFunc(\cdot)\) and + \(\uFunc(\cdot)\) are now the + normalized value and utility functions, and +

+

+ \[\begin{aligned} \CRRA & : \text{constant relative risk + aversion parameter} \\ \mNrm_{t} & : \text{normalized market + resources} \\ \cNrm_{t} & : \text{normalized consumption} \\ + \aNrm_{t} & : \text{normalized liquid assets after + consumption} \\ \Rfree & : \text{risk free interest rate} \\ + \RNrm_{t+1} & : \text{permanent income growth normalized + return factor} \end{aligned}\] +

+
+
+

Distribution of Shocks to Income

+

The transitory and permanent shocks to income are defined as:

+

+ \[\begin{aligned} \PermShk_{t+1} & : \text{mean-one shock to + permanent income} \\ \PermGroFac_{t+1} & : \text{permanent + income growth factor} \\ \TranShkEmp_{t+1} & : \text{mean-one + transitory shock to permanent income} \end{aligned}\] +

+

where

+

+ \[\begin{aligned} \TranShkEmp_{s} = & \begin{cases} 0 & + \text{with probability } \pZero>0 \\ \xi_{s}/\pZero & + \text{with probability } (1-\pZero) \end{cases} \\ + \phantom{/\pZero} \\ & \text{with } \log \xi_{s}\thicksim + \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2}) \\ + & \text{and } \log \PermShk_{s} \thicksim + \mathcal{N}(-\sigma_{[\PermShk, t]}^{2}/2,\sigma_{[\PermShk, + t]}^{2}). \end{aligned}\] +

+
+
+

Wealth in the Utility Literature

+

Separable – Bequest Models

+
    +
  • + Wealth inequality and intergenerational links - De Nardi [2004] +
    • +
  • + To Leave or Not to Leave: The Distribution of Bequest Motives - + Kopczuk, Lupton [2007] +
    • +
  • + Bequests and heterogeneity in Retirement Wealth - De Nardi, Yang + [2014] +
    • +
+

+ Non-separable – Money in the Utility +

+
    +
  • +

    + A monetary equilibrium model with transactions costs - Rotemberg + [1984] +

    +
    • +
  • +

    + Money in the Utility Function: An Empirical Implementation - + Poterba Rotemberg [1986] +

    +
    • +
  • +

    + A Novel Model to Measure Utility from Consumption and Wealth - + Tzitzouris [2024] +

    +
    • +
+
+
+

The Wealth in the Utility Function Incomplete Markets model

+

+ \[\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} + \uFunc(\cNrm_{t}, \aNrm_{t})+\DiscFac\Alive_{t+1} + \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] + \\ & \text{s.t.} & \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} + \end{aligned}\] +

+

+ Separable Utility (as in Carroll [1998], De Nardi [2004]) +

+

+ \[\begin{equation} \uFunc(\cNrm_{t}, \aNrm_{t}) = + \frac{\cNrm_{t}^{1-\CRRA}}{1-\CRRA} + \kapShare_{t} + \frac{(\aFunc_{t} - + \underline\aNrm)^{1-\wealthShare}}{1-\wealthShare} + \end{equation}\] +

+

+ Non-separable Utility (as in Tzitzouris [2024], Rotemberg Poterba + [1986]) +

+

+ \[\begin{equation} \uFunc(\cNrm_{t}, \aNrm_{t}) = + \frac{(\cNrm_{t}^{1-\wealthShare} (\aNrm_{t} - + \underline\aNrm)^\wealthShare)^{1-\CRRA}}{(1-\CRRA)} + \end{equation}\] +

+
+
+

Parameterization and Calibration

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ParameterDescriptionValues
\(\PermGroFac_{t}\)Perm. income growth factorCaggetti [2003]
+ \(\sigma_{[\xi, t]}, \sigma_{[\PermShk, t]}\) + Std. dev. of trans. and perm. shocksSabelhaus and Song [2010]
\(\pZero = 0.005\)Probability of zero incomeCarroll [1992]
\(\Alive_{t}\)Survival and discount factorsCaggetti [2003]
\(\Rfree = 1.03\)Risk free interest rateCaggetti [2003]
+
+
+

Estimation - Method of Simulated Moments

+

Steps of MSM estimation

+
    +
  1. Obtain empirical data
    2. +
  2. Calculate empirical moments
    3. +
  3. Calculate covariance matrix
    4. +
  4. Define Heterogeneous Agent Model
    5. +
  5. + Given a guess \(\theta_0\), + simulate model and calculate moments +
    6. +
  6. Estimate model parameters by minimizing criterion function
    7. +
+
+
+

Method of Simulated Moments

+

1. Empirical data

+

+ Survey of Consumer Finances (SCF) +

+
    +
  • every 3 years
    • +
  • cross-sectional survey of U.S. households
    • +
  • includes balance sheets, pensions, income, demographics
    • +
  • pooled data from 1998 to 2022
    • +
+

+ 2. Calculate empirical moments +

+
    +
  • Bin by age groups of 5 years [25-30), [30-35), …
    • +
  • + SCF is weighted survey, calculate weighted median + networth +
    • +
  • Remove households with no income
    • +
+
+
+

Method of Simulated Moments

+

+ 3. Calculate covariance matrix +

+
    +
  • Via the bootstrap
    • +
+

+ 4. Define Heterogeneous Agent model +

+
    +
  1. Life cycle Incomplete Markets model
    2. +
  2. Warm Glow Homothetic Separable Utility model
    3. +
  3. Warm Glow Non-Homothetic Separable Utility model
    4. +
  4. T.R.P. Non-Separable Utility model
    5. +
+
+
+

Method of Simulated Moments

+

+ 5. Given \(\theta_0\), simulate + moments +

+
    +
  • + Solve model given parameters + \(\theta_0\) +
    • +
  • + Simulate model +
      +
    • 1,000 agents
      • +
    • 1 full lifetime (25 to 100 years old)
      • +
    • Ignore mortality for simulation
      • +
    • Calculate median wealth for each age bracket
      • +
    +
    • +
+

+ 6. Estimate the Model Parameters +

+

Objective function

+

+ \[\begin{equation} \min_{\theta \in \Theta} \hat{g}(\theta)' + \hat{W} \hat{g}(\theta) \qquad \qquad \hat{g}(\theta) = \hat{s} - + s(\theta) \end{equation}\] +

+
+
+

Results

+ + +

+ Figure 4: Simulated vs. Empirical Moments +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
valuestandard_errorci_lowerci_upperp_valuefreestars
CRRA03.0380090.2278442.5914423.4845761.474017e-40True***
DiscFac00.9722480.0032220.9659330.9785630.000000e+00True***
+
+
+

Sensitivity

+ + +

+ Figure 5: Sensitivity from Andrews, Gentzkow & Shapiro + [2017] +

+
+
+

More Results

+
+ +
+
+

+
+
+

+
+
+

+
+
+

+
+
+
+
+
+

More Results

+
+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
valuestandard_errorci_lowerci_upperp_valuefreestars
CRRA03.0380090.2278442.5914423.4845761.474017e-40True***
DiscFac00.9722480.0032220.9659330.9785630.000000e+00True***
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
valuestandard_errorci_lowerci_upperp_valuefreestars
CRRA02.6463090.5392111.5894743.7031449.213221e-07True***
DiscFac00.9500000.0029920.9441360.9558640.000000e+00True***
WealthShare00.2464700.0215710.2041910.2887493.109380e-30True***
WealthShift03.5923521.4065970.8354736.3492321.065142e-02True**
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
valuestandard_errorci_lowerci_upperp_valuefreestars
CRRA02.1552380.1643541.8331112.4773652.758510e-39True***
DiscFac00.9709860.0020050.9670580.9749150.000000e+00True***
BeqFac075.56667334.2389458.459575142.6737722.731136e-02True**
BeqShift02.0564861.255906-0.4050454.5180171.015360e-01True
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
valuestandard_errorci_lowerci_upperp_valuefreestars
CRRA04.2336000.2050163.8317754.6354249.748335e-95True***
DiscFac00.9230090.0085540.9062420.9397750.000000e+00True***
BeqCRRA02.4272112.614537-2.6971877.5516093.532248e-01True
BeqFac070.763872698.885867-1299.0272561440.5550009.193501e-01True
BeqShift01.63011317.348288-32.37190635.6321339.251378e-01True
+
+
+
+
+
+

More Results

+
+ +
+
+

+
+
+

+
+
+

+
+
+

+
+
+
+
+
+

Conclusion and Future Work

+

Conclusion

+
    +
  • + Need wealth in the utility function to better capture distribution + of wealth +
    • +
  • + Need life cycle structure to understand effect of policies on: +
      +
    • young parents with children and low income
      • +
    • working middle aged
      • +
    • retirees with low wealth
      • +
    +
    • +
  • + Separable WG Non-Homothetic has best fit, but not well identified +
    • +
+

Future Work

+
    +
  • + Portfolio Choice: How well do these models match risky share of + portfolio? +
    • +
  • Alternative sources of data: PSID, CES, HRS
    • +
  • Correlation of income / mortality / health expense shocks
    • +
+
+

+
+

Powered by Econ-ARK

+
+
+
+
- - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + - - - \ No newline at end of file + + diff --git a/content/slides/slides.qmd b/content/slides/slides.qmd index 5e96ac2..c9ff6ed 100644 --- a/content/slides/slides.qmd +++ b/content/slides/slides.qmd @@ -1,7 +1,7 @@ --- title: "Structural Estimation of Life Cycle Models with Wealth in the Utility Function" -subtitle: "CEF 2024 -- NTU, Singapore" -format: +subtitle: "CEF 2024 -- NTU, Singapore" +format: clean-revealjs: footer: "Powered by [Econ-ARK](https://econ-ark.org)" logo: econ-ark-logo.png @@ -14,8 +14,8 @@ author: email: alujan@jhu.edu affiliations: "Johns Hopkins University
Econ-ARK" date: "June 20, 2024" -editor: - markdown: +editor: + markdown: wrap: 72 --- @@ -165,8 +165,8 @@ $$\begin{aligned} $$\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t})+\DiscFac\Alive_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv \RNrm_{t+1}} + \TranShkEmp_{t+1} \end{aligned}$$ @@ -195,7 +195,7 @@ $$\begin{aligned} where $$\begin{aligned} -\TranShkEmp_{s} = & \begin{cases} 0 & \text{with probability } \pZero>0 \\ +\TranShkEmp_{s} = & \begin{cases} 0 & \text{with probability } \pZero>0 \\ \xi_{s}/\pZero & \text{with probability } (1-\pZero) \end{cases} \\ \phantom{/\pZero} \\ & \text{with } \log \xi_{s}\thicksim \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2}) \\ & \text{and } \log \PermShk_{s} \thicksim \mathcal{N}(-\sigma_{[\PermShk, t]}^{2}/2,\sigma_{[\PermShk, t]}^{2}). @@ -225,8 +225,8 @@ $$\begin{aligned} $$\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} \uFunc(\cNrm_{t}, \aNrm_{t})+\DiscFac\Alive_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} \end{aligned}$$ diff --git a/content/slides/slides.tex b/content/slides/slides.tex index d7f5a5d..fcd9072 100644 --- a/content/slides/slides.tex +++ b/content/slides/slides.tex @@ -20,7 +20,7 @@ \defaultfontfeatures[\rmfamily]{Ligatures=TeX,Scale=1} \fi \usepackage{lmodern} -\ifPDFTeX\else +\ifPDFTeX\else % xetex/luatex font selection \fi % Use upquote if available, for straight quotes in verbatim environments @@ -392,8 +392,8 @@ \subsection{Recursive Bellman \[\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv \RNrm_{t+1}} + \TranShkEmp_{t+1} \end{aligned}\] @@ -423,7 +423,7 @@ \subsection{Distribution of Shocks to where \[\begin{aligned} -\TranShkEmp_{s} = & \begin{cases} 0 & \text{with probability } \pZero>0 \\ +\TranShkEmp_{s} = & \begin{cases} 0 & \text{with probability } \pZero>0 \\ \xi_{s}/\pZero & \text{with probability } (1-\pZero) \text{, where } \log \xi_{s}\thicksim \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2}) \end{cases} \\ \phantom{/\pZero} \\ & \text{and } \log \PermShk_{s} \thicksim \mathcal{N}(-\sigma_{[\PermShk, t]}^{2}/2,\sigma_{[\PermShk, t]}^{2}). \end{aligned}\] @@ -436,8 +436,8 @@ \subsection{The WUFIM model}\label{the-wufim-model} \[\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} \uFunc(\cNrm_{t}, \aNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} \end{aligned}\] diff --git a/content/slides/slides_files/libs/clipboard/clipboard.min.js b/content/slides/slides_files/libs/clipboard/clipboard.min.js index 1103f81..9cde805 100644 --- a/content/slides/slides_files/libs/clipboard/clipboard.min.js +++ b/content/slides/slides_files/libs/clipboard/clipboard.min.js @@ -4,4 +4,559 @@ * * Licensed MIT © Zeno Rocha */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof 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(e.nodeName || "").toLowerCase() : null; + } + function d(e) { + return ((n(e) ? e.ownerDocument : e.document) || window.document) + .documentElement; + } + function h(e) { + return p(d(e)).left + u(e).scrollLeft; + } + function m(e) { + return t(e).getComputedStyle(e); + } + function v(e) { + var t = m(e), + n = t.overflow, + r = t.overflowX, + o = t.overflowY; + return /auto|scroll|overlay|hidden/.test(n + o + r); + } + function y(e, n, o) { + void 0 === o && (o = !1); + var i, + a, + f = r(n), + c = + r(n) && + (function (e) { + var t = e.getBoundingClientRect(), + n = s(t.width) / e.offsetWidth || 1, + r = s(t.height) / e.offsetHeight || 1; + return 1 !== n || 1 !== r; + })(n), + m = d(n), + y = p(e, c, o), + g = { scrollLeft: 0, scrollTop: 0 }, + b = { x: 0, y: 0 }; + return ( + (f || (!f && !o)) && + (("body" !== l(n) || v(m)) && + (g = + (i = n) !== t(i) && r(i) + ? { scrollLeft: (a = i).scrollLeft, scrollTop: a.scrollTop } + : u(i)), + r(n) + ? (((b = p(n, !0)).x += n.clientLeft), (b.y += n.clientTop)) + : m && (b.x = h(m))), + { + x: y.left + g.scrollLeft - b.x, + y: y.top + g.scrollTop - b.y, + width: y.width, + height: y.height, + } + ); + } + function g(e) { + var t = p(e), + n = e.offsetWidth, + r = e.offsetHeight; + return ( + Math.abs(t.width - n) <= 1 && (n = t.width), + Math.abs(t.height - r) <= 1 && (r = t.height), + { x: e.offsetLeft, y: e.offsetTop, width: n, height: r } + ); + } + function b(e) { + return "html" === l(e) + ? e + : e.assignedSlot || e.parentNode || (o(e) ? e.host : null) || d(e); + } + function x(e) { + return ["html", "body", "#document"].indexOf(l(e)) >= 0 + ? e.ownerDocument.body + : r(e) && v(e) + ? e + : x(b(e)); + } + function w(e, n) { + var r; + void 0 === n && (n = []); + var o = x(e), + i = o === (null == (r = e.ownerDocument) ? void 0 : r.body), + a = t(o), + s = i ? [a].concat(a.visualViewport || [], v(o) ? o : []) : o, + f = n.concat(s); + return i ? f : f.concat(w(b(s))); + } + function O(e) { + return ["table", "td", "th"].indexOf(l(e)) >= 0; + } + function j(e) { + return r(e) && "fixed" !== m(e).position ? e.offsetParent : null; + } + function E(e) { + for (var n = t(e), i = j(e); i && O(i) && "static" === m(i).position; ) + i = j(i); + return i && + ("html" === l(i) || ("body" === l(i) && "static" === m(i).position)) + ? n + : i || + (function (e) { + var t = /firefox/i.test(f()); + if (/Trident/i.test(f()) && r(e) && "fixed" === m(e).position) + return null; + var n = b(e); + for ( + o(n) && (n = n.host); + r(n) && ["html", "body"].indexOf(l(n)) < 0; + ) { + var i = m(n); + if ( + "none" !== i.transform || + "none" !== i.perspective || + "paint" === i.contain || + -1 !== ["transform", "perspective"].indexOf(i.willChange) || + (t && "filter" === i.willChange) || + (t && i.filter && "none" !== i.filter) + ) + return n; + n = n.parentNode; + } + return null; + })(e) || + n; + } + var D = "top", + A = "bottom", + L = "right", + P = "left", + M = "auto", + k = [D, A, L, P], + W = "start", + B = "end", + H = "viewport", + T = "popper", + R = k.reduce(function (e, t) { + return e.concat([t + "-" + W, t + "-" + B]); + }, []), + S = [].concat(k, [M]).reduce(function (e, t) { + return e.concat([t, t + "-" + W, t + "-" + B]); + }, []), + V = [ + "beforeRead", + "read", + "afterRead", + "beforeMain", + "main", + "afterMain", + "beforeWrite", + "write", + "afterWrite", + ]; + function q(e) { + var t = new Map(), + n = new Set(), + r = []; + function o(e) { + n.add(e.name), + [] + .concat(e.requires || [], e.requiresIfExists || []) + .forEach(function (e) { + if (!n.has(e)) { + var r = t.get(e); + r && o(r); + } + }), + r.push(e); + } + return ( + e.forEach(function (e) { + t.set(e.name, e); + }), + e.forEach(function (e) { + n.has(e.name) || o(e); + }), + r + ); + } + function C(e) { + return e.split("-")[0]; + } + function N(e, t) { + var n = t.getRootNode && t.getRootNode(); + if (e.contains(t)) return !0; + if (n && o(n)) { + var r = t; + do { + if (r && e.isSameNode(r)) return !0; + r = r.parentNode || r.host; + } while (r); + } + return !1; + } + function I(e) { + return Object.assign({}, e, { + left: e.x, + top: e.y, + right: e.x + e.width, + bottom: e.y + e.height, + }); + } + function _(e, r, o) { + return r === H + ? I( + (function (e, n) { + var r = t(e), + o = d(e), + i = r.visualViewport, + a = o.clientWidth, + s = o.clientHeight, + f = 0, + p = 0; + if (i) { + (a = i.width), (s = i.height); + var u = c(); + (u || (!u && "fixed" === n)) && + ((f = i.offsetLeft), (p = i.offsetTop)); + } + return { width: a, height: s, x: f + h(e), y: p }; + })(e, o), + ) + : n(r) + ? (function (e, t) { + var n = p(e, !1, "fixed" === t); + return ( + (n.top = n.top + e.clientTop), + (n.left = n.left + e.clientLeft), + (n.bottom = n.top + e.clientHeight), + (n.right = n.left + e.clientWidth), + (n.width = e.clientWidth), + (n.height = e.clientHeight), + (n.x = n.left), + (n.y = n.top), + n + ); + })(r, o) + : I( + (function (e) { + var t, + n = d(e), + r = u(e), + o = null == (t = e.ownerDocument) ? void 0 : t.body, + a = i( + n.scrollWidth, + n.clientWidth, + o ? o.scrollWidth : 0, + o ? o.clientWidth : 0, + ), + s = i( + n.scrollHeight, + n.clientHeight, + o ? o.scrollHeight : 0, + o ? o.clientHeight : 0, + ), + f = -r.scrollLeft + h(e), + c = -r.scrollTop; + return ( + "rtl" === m(o || n).direction && + (f += i(n.clientWidth, o ? o.clientWidth : 0) - a), + { width: a, height: s, x: f, y: c } + ); + })(d(e)), + ); + } + function F(e, t, o, s) { + var f = + "clippingParents" === t + ? (function (e) { + var t = w(b(e)), + o = + ["absolute", "fixed"].indexOf(m(e).position) >= 0 && r(e) + ? E(e) + : e; + return n(o) + ? t.filter(function (e) { + return n(e) && N(e, o) && "body" !== l(e); + }) + : []; + })(e) + : [].concat(t), + c = [].concat(f, [o]), + p = c[0], + u = c.reduce( + function (t, n) { + var r = _(e, n, s); + return ( + (t.top = i(r.top, t.top)), + (t.right = a(r.right, t.right)), + (t.bottom = a(r.bottom, t.bottom)), + (t.left = i(r.left, t.left)), + t + ); + }, + _(e, p, s), + ); + return ( + (u.width = u.right - u.left), + (u.height = u.bottom - u.top), + (u.x = u.left), + (u.y = u.top), + u + ); + } + function U(e) { + return e.split("-")[1]; + } + function z(e) { + return ["top", "bottom"].indexOf(e) >= 0 ? "x" : "y"; + } + function X(e) { + var t, + n = e.reference, + r = e.element, + o = e.placement, + i = o ? C(o) : null, + a = o ? U(o) : null, + s = n.x + n.width / 2 - r.width / 2, + f = n.y + n.height / 2 - r.height / 2; + switch (i) { + case D: + t = { x: s, y: n.y - r.height }; + break; + case A: + t = { x: s, y: n.y + n.height }; + break; + case L: + t = { x: n.x + n.width, y: f }; + break; + case P: + t = { x: n.x - r.width, y: f }; + break; + default: + t = { x: n.x, y: n.y }; + } + var c = i ? z(i) : null; + if (null != c) { + var p = "y" === c ? "height" : "width"; + switch (a) { + case W: + t[c] = t[c] - (n[p] / 2 - r[p] / 2); + break; + case B: + t[c] = t[c] + (n[p] / 2 - r[p] / 2); + } + } + return t; + } + function Y(e) { + return Object.assign({}, { top: 0, right: 0, bottom: 0, left: 0 }, e); + } + function G(e, t) { + return t.reduce(function (t, n) { + return (t[n] = e), t; + }, {}); + } + function J(e, t) { + void 0 === t && (t = {}); + var r = t, + o = r.placement, + i = void 0 === o ? e.placement : o, + a = r.strategy, + s = void 0 === a ? e.strategy : a, + f = r.boundary, + c = void 0 === f ? "clippingParents" : f, + u = r.rootBoundary, + l = void 0 === u ? H : u, + h = r.elementContext, + m = void 0 === h ? T : h, + v = r.altBoundary, + y = void 0 !== v && v, + g = r.padding, + b = void 0 === g ? 0 : g, + x = Y("number" != typeof b ? b : G(b, k)), + w = m === T ? "reference" : T, + O = e.rects.popper, + j = e.elements[y ? w : m], + E = F(n(j) ? j : j.contextElement || d(e.elements.popper), c, l, s), + P = p(e.elements.reference), + M = X({ reference: P, element: O, strategy: "absolute", placement: i }), + W = I(Object.assign({}, O, M)), + B = m === T ? W : P, + R = { + top: E.top - B.top + x.top, + bottom: B.bottom - E.bottom + x.bottom, + left: E.left - B.left + x.left, + right: B.right - E.right + x.right, + }, + S = e.modifiersData.offset; + if (m === T && S) { + var V = S[i]; + Object.keys(R).forEach(function (e) { + var t = [L, A].indexOf(e) >= 0 ? 1 : -1, + n = [D, A].indexOf(e) >= 0 ? "y" : "x"; + R[e] += V[n] * t; + }); + } + return R; + } + var K = { placement: "bottom", modifiers: [], strategy: "absolute" }; + function Q() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return !t.some(function (e) { + return !(e && "function" == typeof e.getBoundingClientRect); + }); + } + function Z(e) { + void 0 === e && (e = {}); + var t = e, + r = t.defaultModifiers, + o = void 0 === r ? [] : r, + i = t.defaultOptions, + a = void 0 === i ? K : i; + return function (e, t, r) { + void 0 === r && (r = a); + var i, + s, + f = { + placement: "bottom", + orderedModifiers: [], + options: Object.assign({}, K, a), + modifiersData: {}, + elements: { reference: e, popper: t }, + attributes: {}, + styles: {}, + }, + c = [], + p = !1, + u = { + state: f, + setOptions: function (r) { + var i = "function" == typeof r ? r(f.options) : r; + l(), + (f.options = Object.assign({}, a, f.options, i)), + (f.scrollParents = { + reference: n(e) + ? w(e) + : e.contextElement + ? w(e.contextElement) + : [], + popper: w(t), + }); + var s, + p, + d = (function (e) { + var t = q(e); + return V.reduce(function (e, n) { + return e.concat( + t.filter(function (e) { + return e.phase === n; + }), + ); + }, []); + })( + ((s = [].concat(o, f.options.modifiers)), + (p = s.reduce(function (e, t) { + var n = e[t.name]; + return ( + (e[t.name] = n + ? Object.assign({}, n, t, { + options: Object.assign({}, n.options, t.options), + data: Object.assign({}, n.data, t.data), + }) + : t), + e + ); + }, {})), + Object.keys(p).map(function (e) { + return p[e]; + })), + ); + return ( + (f.orderedModifiers = d.filter(function (e) { + return e.enabled; + })), + f.orderedModifiers.forEach(function (e) { + var t = e.name, + n = e.options, + r = void 0 === n ? {} : n, + o = e.effect; + if ("function" == typeof o) { + var i = o({ state: f, name: t, instance: u, options: r }), + a = function () {}; + c.push(i || a); + } + }), + u.update() + ); + }, + forceUpdate: function () { + if (!p) { + var e = f.elements, + t = e.reference, + n = e.popper; + if (Q(t, n)) { + (f.rects = { + reference: y(t, E(n), "fixed" === f.options.strategy), + popper: g(n), + }), + (f.reset = !1), + (f.placement = f.options.placement), + f.orderedModifiers.forEach(function (e) { + return (f.modifiersData[e.name] = Object.assign( + {}, + e.data, + )); + }); + for (var r = 0; r < f.orderedModifiers.length; r++) + if (!0 !== f.reset) { + var o = f.orderedModifiers[r], + i = o.fn, + a = o.options, + s = void 0 === a ? {} : a, + c = o.name; + "function" == typeof i && + (f = + i({ state: f, options: s, name: c, instance: u }) || f); + } else (f.reset = !1), (r = -1); + } + } + }, + update: + ((i = function () { + return new Promise(function (e) { + u.forceUpdate(), e(f); + }); + }), + function () { + return ( + s || + (s = new Promise(function (e) { + Promise.resolve().then(function () { + (s = void 0), e(i()); + }); + })), + s + ); + }), + destroy: function () { + l(), (p = !0); + }, + }; + if (!Q(e, t)) return u; + function l() { + c.forEach(function (e) { + return e(); + }), + (c = []); + } + return ( + u.setOptions(r).then(function (e) { + !p && r.onFirstUpdate && r.onFirstUpdate(e); + }), + u + ); + }; + } + var $ = { passive: !0 }; + var ee = { + name: "eventListeners", + enabled: !0, + phase: "write", + fn: function () {}, + effect: function (e) { + var n = e.state, + r = e.instance, + o = e.options, + i = o.scroll, + a = void 0 === i || i, + s = o.resize, + f = void 0 === s || s, + c = t(n.elements.popper), + p = [].concat(n.scrollParents.reference, n.scrollParents.popper); + return ( + a && + p.forEach(function (e) { + e.addEventListener("scroll", r.update, $); + }), + f && c.addEventListener("resize", r.update, $), + function () { + a && + p.forEach(function (e) { + e.removeEventListener("scroll", r.update, $); + }), + f && c.removeEventListener("resize", r.update, $); + } + ); + }, + data: {}, + }; + var te = { + name: "popperOffsets", + enabled: !0, + phase: "read", + fn: function (e) { + var t = e.state, + n = e.name; + t.modifiersData[n] = X({ + reference: t.rects.reference, + element: t.rects.popper, + strategy: "absolute", + placement: t.placement, + }); + }, + data: {}, + }, + ne = { top: "auto", right: "auto", bottom: "auto", left: "auto" }; + function re(e) { + var n, + r = e.popper, + o = e.popperRect, + i = e.placement, + a = e.variation, + f = e.offsets, + c = e.position, + p = e.gpuAcceleration, + u = e.adaptive, + l = e.roundOffsets, + h = e.isFixed, + v = f.x, + y = void 0 === v ? 0 : v, + g = f.y, + b = void 0 === g ? 0 : g, + x = "function" == typeof l ? l({ x: y, y: b }) : { x: y, y: b }; + (y = x.x), (b = x.y); + var w = f.hasOwnProperty("x"), + O = f.hasOwnProperty("y"), + j = P, + M = D, + k = window; + if (u) { + var W = E(r), + H = "clientHeight", + T = "clientWidth"; + if ( + (W === t(r) && + "static" !== m((W = d(r))).position && + "absolute" === c && + ((H = "scrollHeight"), (T = "scrollWidth")), + (W = W), + i === D || ((i === P || i === L) && a === B)) + ) + (M = A), + (b -= + (h && W === k && k.visualViewport + ? k.visualViewport.height + : W[H]) - o.height), + (b *= p ? 1 : -1); + if (i === P || ((i === D || i === A) && a === B)) + (j = L), + (y -= + (h && W === k && k.visualViewport ? k.visualViewport.width : W[T]) - + o.width), + (y *= p ? 1 : -1); + } + var R, + S = Object.assign({ position: c }, u && ne), + V = + !0 === l + ? (function (e, t) { + var n = e.x, + r = e.y, + o = t.devicePixelRatio || 1; + return { x: s(n * o) / o || 0, y: s(r * o) / o || 0 }; + })({ x: y, y: b }, t(r)) + : { x: y, y: b }; + return ( + (y = V.x), + (b = V.y), + p + ? Object.assign( + {}, + S, + (((R = {})[M] = O ? "0" : ""), + (R[j] = w ? "0" : ""), + (R.transform = + (k.devicePixelRatio || 1) <= 1 + ? "translate(" + y + "px, " + b + "px)" + : "translate3d(" + y + "px, " + b + "px, 0)"), + R), + ) + : Object.assign( + {}, + S, + (((n = {})[M] = O ? b + "px" : ""), + (n[j] = w ? y + "px" : ""), + (n.transform = ""), + n), + ) + ); + } + var oe = { + name: "computeStyles", + enabled: !0, + phase: "beforeWrite", + fn: function (e) { + var t = e.state, + n = e.options, + r = n.gpuAcceleration, + o = void 0 === r || r, + i = n.adaptive, + a = void 0 === i || i, + s = n.roundOffsets, + f = void 0 === s || s, + c = { + placement: C(t.placement), + variation: U(t.placement), + popper: t.elements.popper, + popperRect: t.rects.popper, + gpuAcceleration: o, + isFixed: "fixed" === t.options.strategy, + }; + null != t.modifiersData.popperOffsets && + (t.styles.popper = Object.assign( + {}, + t.styles.popper, + re( + Object.assign({}, c, { + offsets: t.modifiersData.popperOffsets, + position: t.options.strategy, + adaptive: a, + roundOffsets: f, + }), + ), + )), + null != t.modifiersData.arrow && + (t.styles.arrow = Object.assign( + {}, + t.styles.arrow, + re( + Object.assign({}, c, { + offsets: t.modifiersData.arrow, + position: "absolute", + adaptive: !1, + roundOffsets: f, + }), + ), + )), + (t.attributes.popper = Object.assign({}, t.attributes.popper, { + "data-popper-placement": t.placement, + })); + }, + data: {}, + }; + var ie = { + name: "applyStyles", + enabled: !0, + phase: "write", + fn: function (e) { + var t = e.state; + Object.keys(t.elements).forEach(function (e) { + var n = t.styles[e] || {}, + o = t.attributes[e] || {}, + i = t.elements[e]; + r(i) && + l(i) && + (Object.assign(i.style, n), + Object.keys(o).forEach(function (e) { + var t = o[e]; + !1 === t + ? i.removeAttribute(e) + : i.setAttribute(e, !0 === t ? "" : t); + })); + }); + }, + effect: function (e) { + var t = e.state, + n = { + popper: { + position: t.options.strategy, + left: "0", + top: "0", + margin: "0", + }, + arrow: { position: "absolute" }, + reference: {}, + }; + return ( + Object.assign(t.elements.popper.style, n.popper), + (t.styles = n), + t.elements.arrow && Object.assign(t.elements.arrow.style, n.arrow), + function () { + Object.keys(t.elements).forEach(function (e) { + var o = t.elements[e], + i = t.attributes[e] || {}, + a = Object.keys( + t.styles.hasOwnProperty(e) ? t.styles[e] : n[e], + ).reduce(function (e, t) { + return (e[t] = ""), e; + }, {}); + r(o) && + l(o) && + (Object.assign(o.style, a), + Object.keys(i).forEach(function (e) { + o.removeAttribute(e); + })); + }); + } + ); + }, + requires: ["computeStyles"], + }; + var ae = { + name: "offset", + enabled: !0, + phase: "main", + requires: ["popperOffsets"], + fn: function (e) { + var t = e.state, + n = e.options, + r = e.name, + o = n.offset, + i = void 0 === o ? [0, 0] : o, + a = S.reduce(function (e, n) { + return ( + (e[n] = (function (e, t, n) { + var r = C(e), + o = [P, D].indexOf(r) >= 0 ? -1 : 1, + i = + "function" == typeof n + ? n(Object.assign({}, t, { placement: e })) + : n, + a = i[0], + s = i[1]; + return ( + (a = a || 0), + (s = (s || 0) * o), + [P, L].indexOf(r) >= 0 ? { x: s, y: a } : { x: a, y: s } + ); + })(n, t.rects, i)), + e + ); + }, {}), + s = a[t.placement], + f = s.x, + c = s.y; + null != t.modifiersData.popperOffsets && + ((t.modifiersData.popperOffsets.x += f), + (t.modifiersData.popperOffsets.y += c)), + (t.modifiersData[r] = a); + }, + }, + se = { left: "right", right: "left", bottom: "top", top: "bottom" }; + function fe(e) { + return e.replace(/left|right|bottom|top/g, function (e) { + return se[e]; + }); + } + var ce = { start: "end", end: "start" }; + function pe(e) { + return e.replace(/start|end/g, function (e) { + return ce[e]; + }); + } + function ue(e, t) { + void 0 === t && (t = {}); + var n = t, + r = n.placement, + o = n.boundary, + i = n.rootBoundary, + a = n.padding, + s = n.flipVariations, + f = n.allowedAutoPlacements, + c = void 0 === f ? S : f, + p = U(r), + u = p + ? s + ? R + : R.filter(function (e) { + return U(e) === p; + }) + : k, + l = u.filter(function (e) { + return c.indexOf(e) >= 0; + }); + 0 === l.length && (l = u); + var d = l.reduce(function (t, n) { + return ( + (t[n] = J(e, { + placement: n, + boundary: o, + rootBoundary: i, + padding: a, + })[C(n)]), + t + ); + }, {}); + return Object.keys(d).sort(function (e, t) { + return d[e] - d[t]; + }); + } + var le = { + name: "flip", + enabled: !0, + phase: "main", + fn: function (e) { + var t = e.state, + n = e.options, + r = e.name; + if (!t.modifiersData[r]._skip) { + for ( + var o = n.mainAxis, + i = void 0 === o || o, + a = n.altAxis, + s = void 0 === a || a, + f = n.fallbackPlacements, + c = n.padding, + p = n.boundary, + u = n.rootBoundary, + l = n.altBoundary, + d = n.flipVariations, + h = void 0 === d || d, + m = n.allowedAutoPlacements, + v = t.options.placement, + y = C(v), + g = + f || + (y === v || !h + ? [fe(v)] + : (function (e) { + if (C(e) === M) return []; + var t = fe(e); + return [pe(e), t, pe(t)]; + })(v)), + b = [v].concat(g).reduce(function (e, n) { + return e.concat( + C(n) === M + ? ue(t, { + placement: n, + boundary: p, + rootBoundary: u, + padding: c, + flipVariations: h, + allowedAutoPlacements: m, + }) + : n, + ); + }, []), + x = t.rects.reference, + w = t.rects.popper, + O = new Map(), + j = !0, + E = b[0], + k = 0; + k < b.length; + k++ + ) { + var B = b[k], + H = C(B), + T = U(B) === W, + R = [D, A].indexOf(H) >= 0, + S = R ? "width" : "height", + V = J(t, { + placement: B, + boundary: p, + rootBoundary: u, + altBoundary: l, + padding: c, + }), + q = R ? (T ? L : P) : T ? A : D; + x[S] > w[S] && (q = fe(q)); + var N = fe(q), + I = []; + if ( + (i && I.push(V[H] <= 0), + s && I.push(V[q] <= 0, V[N] <= 0), + I.every(function (e) { + return e; + })) + ) { + (E = B), (j = !1); + break; + } + O.set(B, I); + } + if (j) + for ( + var _ = function (e) { + var t = b.find(function (t) { + var n = O.get(t); + if (n) + return n.slice(0, e).every(function (e) { + return e; + }); + }); + if (t) return (E = t), "break"; + }, + F = h ? 3 : 1; + F > 0; + F-- + ) { + if ("break" === _(F)) break; + } + t.placement !== E && + ((t.modifiersData[r]._skip = !0), (t.placement = E), (t.reset = !0)); + } + }, + requiresIfExists: ["offset"], + data: { _skip: !1 }, + }; + function de(e, t, n) { + return i(e, a(t, n)); + } + var he = { + name: "preventOverflow", + enabled: !0, + phase: "main", + fn: function (e) { + var t = e.state, + n = e.options, + r = e.name, + o = n.mainAxis, + s = void 0 === o || o, + f = n.altAxis, + c = void 0 !== f && f, + p = n.boundary, + u = n.rootBoundary, + l = n.altBoundary, + d = n.padding, + h = n.tether, + m = void 0 === h || h, + v = n.tetherOffset, + y = void 0 === v ? 0 : v, + b = J(t, { boundary: p, rootBoundary: u, padding: d, altBoundary: l }), + x = C(t.placement), + w = U(t.placement), + O = !w, + j = z(x), + M = "x" === j ? "y" : "x", + k = t.modifiersData.popperOffsets, + B = t.rects.reference, + H = t.rects.popper, + T = + "function" == typeof y + ? y(Object.assign({}, t.rects, { placement: t.placement })) + : y, + R = + "number" == typeof T + ? { mainAxis: T, altAxis: T } + : Object.assign({ mainAxis: 0, altAxis: 0 }, T), + S = t.modifiersData.offset ? t.modifiersData.offset[t.placement] : null, + V = { x: 0, y: 0 }; + if (k) { + if (s) { + var q, + N = "y" === j ? D : P, + I = "y" === j ? A : L, + _ = "y" === j ? "height" : "width", + F = k[j], + X = F + b[N], + Y = F - b[I], + G = m ? -H[_] / 2 : 0, + K = w === W ? B[_] : H[_], + Q = w === W ? -H[_] : -B[_], + Z = t.elements.arrow, + $ = m && Z ? g(Z) : { width: 0, height: 0 }, + ee = t.modifiersData["arrow#persistent"] + ? t.modifiersData["arrow#persistent"].padding + : { top: 0, right: 0, bottom: 0, left: 0 }, + te = ee[N], + ne = ee[I], + re = de(0, B[_], $[_]), + oe = O + ? B[_] / 2 - G - re - te - R.mainAxis + : K - re - te - R.mainAxis, + ie = O + ? -B[_] / 2 + G + re + ne + R.mainAxis + : Q + re + ne + R.mainAxis, + ae = t.elements.arrow && E(t.elements.arrow), + se = ae ? ("y" === j ? ae.clientTop || 0 : ae.clientLeft || 0) : 0, + fe = null != (q = null == S ? void 0 : S[j]) ? q : 0, + ce = F + ie - fe, + pe = de(m ? a(X, F + oe - fe - se) : X, F, m ? i(Y, ce) : Y); + (k[j] = pe), (V[j] = pe - F); + } + if (c) { + var ue, + le = "x" === j ? D : P, + he = "x" === j ? A : L, + me = k[M], + ve = "y" === M ? "height" : "width", + ye = me + b[le], + ge = me - b[he], + be = -1 !== [D, P].indexOf(x), + xe = null != (ue = null == S ? void 0 : S[M]) ? ue : 0, + we = be ? ye : me - B[ve] - H[ve] - xe + R.altAxis, + Oe = be ? me + B[ve] + H[ve] - xe - R.altAxis : ge, + je = + m && be + ? (function (e, t, n) { + var r = de(e, t, n); + return r > n ? n : r; + })(we, me, Oe) + : de(m ? we : ye, me, m ? Oe : ge); + (k[M] = je), (V[M] = je - me); + } + t.modifiersData[r] = V; + } + }, + requiresIfExists: ["offset"], + }; + var me = { + name: "arrow", + enabled: !0, + phase: "main", + fn: function (e) { + var t, + n = e.state, + r = e.name, + o = e.options, + i = n.elements.arrow, + a = n.modifiersData.popperOffsets, + s = C(n.placement), + f = z(s), + c = [P, L].indexOf(s) >= 0 ? "height" : "width"; + if (i && a) { + var p = (function (e, t) { + return Y( + "number" != + typeof (e = + "function" == typeof e + ? e(Object.assign({}, t.rects, { placement: t.placement })) + : e) + ? e + : G(e, k), + ); + })(o.padding, n), + u = g(i), + l = "y" === f ? D : P, + d = "y" === f ? A : L, + h = + n.rects.reference[c] + + n.rects.reference[f] - + a[f] - + n.rects.popper[c], + m = a[f] - n.rects.reference[f], + v = E(i), + y = v ? ("y" === f ? v.clientHeight || 0 : v.clientWidth || 0) : 0, + b = h / 2 - m / 2, + x = p[l], + w = y - u[c] - p[d], + O = y / 2 - u[c] / 2 + b, + j = de(x, O, w), + M = f; + n.modifiersData[r] = (((t = {})[M] = j), (t.centerOffset = j - O), t); + } + }, + effect: function (e) { + var t = e.state, + n = e.options.element, + r = void 0 === n ? "[data-popper-arrow]" : n; + null != r && + ("string" != typeof r || (r = t.elements.popper.querySelector(r))) && + N(t.elements.popper, r) && + (t.elements.arrow = r); + }, + requires: ["popperOffsets"], + requiresIfExists: ["preventOverflow"], + }; + function ve(e, t, n) { + return ( + void 0 === n && (n = { x: 0, y: 0 }), + { + top: e.top - t.height - n.y, + right: e.right - t.width + n.x, + bottom: e.bottom - t.height + n.y, + left: e.left - t.width - n.x, + } + ); + } + function ye(e) { + return [D, L, A, P].some(function (t) { + return e[t] >= 0; + }); + } + var ge = { + name: "hide", + enabled: !0, + phase: "main", + requiresIfExists: ["preventOverflow"], + fn: function (e) { + var t = e.state, + n = e.name, + r = t.rects.reference, + o = t.rects.popper, + i = t.modifiersData.preventOverflow, + a = J(t, { elementContext: "reference" }), + s = J(t, { altBoundary: !0 }), + f = ve(a, r), + c = ve(s, o, i), + p = ye(f), + u = ye(c); + (t.modifiersData[n] = { + referenceClippingOffsets: f, + popperEscapeOffsets: c, + isReferenceHidden: p, + hasPopperEscaped: u, + }), + (t.attributes.popper = Object.assign({}, t.attributes.popper, { + "data-popper-reference-hidden": p, + "data-popper-escaped": u, + })); + }, + }, + be = Z({ defaultModifiers: [ee, te, oe, ie] }), + xe = [ee, te, oe, ie, ae, le, he, me, ge], + we = Z({ defaultModifiers: xe }); + (e.applyStyles = ie), + (e.arrow = me), + (e.computeStyles = oe), + (e.createPopper = we), + (e.createPopperLite = be), + (e.defaultModifiers = xe), + (e.detectOverflow = J), + (e.eventListeners = ee), + (e.flip = le), + (e.hide = ge), + (e.offset = ae), + (e.popperGenerator = Z), + (e.popperOffsets = te), + (e.preventOverflow = he), + Object.defineProperty(e, "__esModule", { value: !0 }); +}); diff --git a/content/slides/slides_files/libs/quarto-html/quarto-html.min.css b/content/slides/slides_files/libs/quarto-html/quarto-html.min.css index 8b13789..e69de29 100644 --- a/content/slides/slides_files/libs/quarto-html/quarto-html.min.css +++ b/content/slides/slides_files/libs/quarto-html/quarto-html.min.css @@ -1 +0,0 @@ - diff --git a/content/slides/slides_files/libs/quarto-html/quarto-syntax-highlighting.css b/content/slides/slides_files/libs/quarto-html/quarto-syntax-highlighting.css index b30ce57..5e099e1 100644 --- a/content/slides/slides_files/libs/quarto-html/quarto-syntax-highlighting.css +++ b/content/slides/slides_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -1,60 +1,61 @@ /* quarto syntax highlight colors */ :root { - --quarto-hl-ot-color: #003B4F; + --quarto-hl-ot-color: #003b4f; --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; + --quarto-hl-ss-color: #20794d; + --quarto-hl-an-color: #5e5e5e; + --quarto-hl-fu-color: #4758ab; + --quarto-hl-st-color: #20794d; + --quarto-hl-cf-color: #003b4f; + --quarto-hl-op-color: #5e5e5e; + --quarto-hl-er-color: #ad0000; + --quarto-hl-bn-color: #ad0000; + --quarto-hl-al-color: #ad0000; --quarto-hl-va-color: #111111; --quarto-hl-bu-color: inherit; --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-pp-color: #ad0000; + --quarto-hl-in-color: #5e5e5e; + --quarto-hl-vs-color: #20794d; + --quarto-hl-wa-color: #5e5e5e; + --quarto-hl-do-color: #5e5e5e; + --quarto-hl-im-color: #00769e; + --quarto-hl-ch-color: #20794d; + --quarto-hl-dt-color: #ad0000; + --quarto-hl-fl-color: #ad0000; + --quarto-hl-co-color: #5e5e5e; + --quarto-hl-cv-color: #5e5e5e; --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; + --quarto-hl-sc-color: #5e5e5e; + --quarto-hl-dv-color: #ad0000; + --quarto-hl-kw-color: #003b4f; } /* other quarto variables */ :root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, + "Liberation Mono", "Courier New", monospace; } pre > code.sourceCode > span { - color: #003B4F; + color: #003b4f; } code span { - color: #003B4F; + color: #003b4f; } code.sourceCode > span { - color: #003B4F; + color: #003b4f; } div.sourceCode, div.sourceCode pre.sourceCode { - color: #003B4F; + color: #003b4f; } code span.ot { - color: #003B4F; + color: #003b4f; font-style: inherit; } @@ -64,48 +65,48 @@ code span.at { } code span.ss { - color: #20794D; + color: #20794d; font-style: inherit; } code span.an { - color: #5E5E5E; + color: #5e5e5e; font-style: inherit; } code span.fu { - color: #4758AB; + color: #4758ab; font-style: inherit; } code span.st { - color: #20794D; + color: #20794d; font-style: inherit; } code span.cf { - color: #003B4F; + color: #003b4f; font-weight: bold; font-style: inherit; } code span.op { - color: #5E5E5E; + color: #5e5e5e; font-style: inherit; } code span.er { - color: #AD0000; + color: #ad0000; font-style: inherit; } code span.bn { - color: #AD0000; + color: #ad0000; font-style: inherit; } code span.al { - color: #AD0000; + color: #ad0000; font-style: inherit; } @@ -123,57 +124,57 @@ code span.ex { } code span.pp { - color: #AD0000; + color: #ad0000; font-style: inherit; } code span.in { - color: #5E5E5E; + color: #5e5e5e; font-style: inherit; } code span.vs { - color: #20794D; + color: #20794d; font-style: inherit; } code span.wa { - color: #5E5E5E; + color: #5e5e5e; font-style: italic; } code span.do { - color: #5E5E5E; + color: #5e5e5e; font-style: italic; } code span.im { - color: #00769E; + color: #00769e; font-style: inherit; } code span.ch { - color: #20794D; + color: #20794d; font-style: inherit; } code span.dt { - color: #AD0000; + color: #ad0000; font-style: inherit; } code span.fl { - color: #AD0000; + color: #ad0000; font-style: inherit; } code span.co { - color: #5E5E5E; + color: #5e5e5e; font-style: inherit; } code span.cv { - color: #5E5E5E; + color: #5e5e5e; font-style: italic; } @@ -183,17 +184,17 @@ code span.cn { } code span.sc { - color: #5E5E5E; + color: #5e5e5e; font-style: inherit; } code span.dv { - color: #AD0000; + color: #ad0000; font-style: inherit; } code span.kw { - color: #003B4F; + color: #003b4f; font-weight: bold; font-style: inherit; } diff --git a/content/slides/slides_files/libs/quarto-html/tabby.min.js b/content/slides/slides_files/libs/quarto-html/tabby.min.js index 4f44c7d..f53173d 100644 --- a/content/slides/slides_files/libs/quarto-html/tabby.min.js +++ b/content/slides/slides_files/libs/quarto-html/tabby.min.js @@ -12,8 +12,8 @@ typeof global !== "undefined" ? global : typeof window !== "undefined" - ? window - : this, + ? window + : this, function (window) { "use strict"; @@ -77,10 +77,10 @@ var getKeyboardFocusableElements = function (element) { return [ ...element.querySelectorAll( - 'a[href], button, input, textarea, select, details,[tabindex]:not([tabindex="-1"])' + 'a[href], button, input, textarea, select, details,[tabindex]:not([tabindex="-1"])', ), ].filter( - (el) => !el.hasAttribute("disabled") && !el.getAttribute("aria-hidden") + (el) => !el.hasAttribute("disabled") && !el.getAttribute("aria-hidden"), ); }; @@ -291,7 +291,7 @@ document.documentElement.removeEventListener( "click", clickHandler, - true + true, ); tabWrapper.removeEventListener("keydown", keyHandler, true); @@ -348,7 +348,7 @@ var tab = id; if (typeof id === "string") { tab = document.querySelector( - selector + ' [role="tab"][href*="' + id + '"]' + selector + ' [role="tab"][href*="' + id + '"]', ); } @@ -414,5 +414,5 @@ // return Constructor; - } + }, ); diff --git a/content/slides/slides_files/libs/quarto-html/tippy.css b/content/slides/slides_files/libs/quarto-html/tippy.css index e6ae635..116782a 100644 --- a/content/slides/slides_files/libs/quarto-html/tippy.css +++ b/content/slides/slides_files/libs/quarto-html/tippy.css @@ -1 +1,74 @@ -.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1} \ No newline at end of file +.tippy-box[data-animation="fade"][data-state="hidden"] { + opacity: 0; +} +[data-tippy-root] { + max-width: calc(100vw - 10px); +} +.tippy-box { + position: relative; + background-color: #333; + color: #fff; + border-radius: 4px; + font-size: 14px; + line-height: 1.4; + white-space: normal; + outline: 0; + transition-property: transform, visibility, opacity; +} +.tippy-box[data-placement^="top"] > .tippy-arrow { + bottom: 0; +} +.tippy-box[data-placement^="top"] > .tippy-arrow:before { + bottom: -7px; + left: 0; + border-width: 8px 8px 0; + border-top-color: initial; + transform-origin: center top; +} +.tippy-box[data-placement^="bottom"] > .tippy-arrow { + top: 0; +} +.tippy-box[data-placement^="bottom"] > .tippy-arrow:before { + top: -7px; + left: 0; + border-width: 0 8px 8px; + border-bottom-color: initial; + transform-origin: center bottom; +} +.tippy-box[data-placement^="left"] > .tippy-arrow { + right: 0; +} +.tippy-box[data-placement^="left"] > .tippy-arrow:before { + border-width: 8px 0 8px 8px; + border-left-color: initial; + right: -7px; + transform-origin: center left; +} +.tippy-box[data-placement^="right"] > .tippy-arrow { + left: 0; +} +.tippy-box[data-placement^="right"] > .tippy-arrow:before { + left: -7px; + border-width: 8px 8px 8px 0; + border-right-color: initial; + transform-origin: center right; +} +.tippy-box[data-inertia][data-state="visible"] { + transition-timing-function: cubic-bezier(0.54, 1.5, 0.38, 1.11); +} +.tippy-arrow { + width: 16px; + height: 16px; + color: #333; +} +.tippy-arrow:before { + content: ""; + position: absolute; + border-color: transparent; + border-style: solid; +} +.tippy-content { + position: relative; + padding: 5px 9px; + z-index: 1; +} diff --git a/content/slides/slides_files/libs/quarto-html/tippy.umd.min.js b/content/slides/slides_files/libs/quarto-html/tippy.umd.min.js index ca292be..42f818f 100644 --- a/content/slides/slides_files/libs/quarto-html/tippy.umd.min.js +++ b/content/slides/slides_files/libs/quarto-html/tippy.umd.min.js @@ -1,2 +1,1496 @@ 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(module.exports = t(require("@popperjs/core"))) + : "function" == typeof define && define.amd + ? define(["@popperjs/core"], t) + : ((e = e || self).tippy = t(e.Popper)); +})(this, function (e) { + "use strict"; + var t = { passive: !0, capture: !0 }, + n = function () { + return document.body; + }; + function r(e, t, n) { + if (Array.isArray(e)) { + var r = e[t]; + return null == r ? (Array.isArray(n) ? n[t] : n) : r; + } + return e; + } + function o(e, t) { + var n = {}.toString.call(e); + return 0 === n.indexOf("[object") && n.indexOf(t + "]") > -1; + } + function i(e, t) { + return "function" == typeof e ? e.apply(void 0, t) : e; + } + function a(e, t) { + return 0 === t + ? e + : function (r) { + clearTimeout(n), + (n = setTimeout(function () { + e(r); + }, t)); + }; + var n; + } + function s(e, t) { + var n = Object.assign({}, e); + return ( + t.forEach(function (e) { + delete n[e]; + }), + n + ); + } + function u(e) { + return [].concat(e); + } + function c(e, t) { + -1 === e.indexOf(t) && e.push(t); + } + function p(e) { + return e.split("-")[0]; + } + function f(e) { + return [].slice.call(e); + } + function l(e) { + return Object.keys(e).reduce(function (t, n) { + return void 0 !== e[n] && (t[n] = e[n]), t; + }, {}); + } + function d() { + return document.createElement("div"); + } + function v(e) { + return ["Element", "Fragment"].some(function (t) { + return o(e, t); + }); + } + function m(e) { + return o(e, "MouseEvent"); + } + function g(e) { + return !(!e || !e._tippy || e._tippy.reference !== e); + } + function h(e) { + return v(e) + ? [e] + : (function (e) { + return o(e, "NodeList"); + })(e) + ? f(e) + : Array.isArray(e) + ? e + : f(document.querySelectorAll(e)); + } + function b(e, t) { + e.forEach(function (e) { + e && (e.style.transitionDuration = t + "ms"); + }); + } + function y(e, t) { + e.forEach(function (e) { + e && e.setAttribute("data-state", t); + }); + } + function w(e) { + var t, + n = u(e)[0]; + return null != n && null != (t = n.ownerDocument) && t.body + ? n.ownerDocument + : document; + } + function E(e, t, n) { + var r = t + "EventListener"; + ["transitionend", "webkitTransitionEnd"].forEach(function (t) { + e[r](t, n); + }); + } + function O(e, t) { + for (var n = t; n; ) { + var r; + if (e.contains(n)) return !0; + n = + null == n.getRootNode || null == (r = n.getRootNode()) + ? void 0 + : r.host; + } + return !1; + } + var x = { isTouch: !1 }, + C = 0; + function T() { + x.isTouch || + ((x.isTouch = !0), + window.performance && document.addEventListener("mousemove", A)); + } + function A() { + var e = performance.now(); + e - C < 20 && + ((x.isTouch = !1), document.removeEventListener("mousemove", A)), + (C = e); + } + function L() { + var e = document.activeElement; + if (g(e)) { + var t = e._tippy; + e.blur && !t.state.isVisible && e.blur(); + } + } + var D = + !!("undefined" != typeof window && "undefined" != typeof document) && + !!window.msCrypto, + R = Object.assign( + { + appendTo: n, + aria: { content: "auto", expanded: "auto" }, + delay: 0, + duration: [300, 250], + getReferenceClientRect: null, + hideOnClick: !0, + ignoreAttributes: !1, + interactive: !1, + interactiveBorder: 2, + interactiveDebounce: 0, + moveTransition: "", + offset: [0, 10], + onAfterUpdate: function () {}, + onBeforeUpdate: function () {}, + onCreate: function () {}, + onDestroy: function () {}, + onHidden: function () {}, + onHide: function () {}, + onMount: function () {}, + onShow: function () {}, + onShown: function () {}, + onTrigger: function () {}, + onUntrigger: function () {}, + onClickOutside: function () {}, + placement: "top", + plugins: [], + popperOptions: {}, + render: null, + showOnCreate: !1, + touch: !0, + trigger: "mouseenter focus", + triggerTarget: null, + }, + { animateFill: !1, followCursor: !1, inlinePositioning: !1, sticky: !1 }, + { + allowHTML: !1, + animation: "fade", + arrow: !0, + content: "", + inertia: !1, + maxWidth: 350, + role: "tooltip", + theme: "", + zIndex: 9999, + }, + ), + k = Object.keys(R); + function P(e) { + var t = (e.plugins || []).reduce(function (t, n) { + var r, + o = n.name, + i = n.defaultValue; + o && (t[o] = void 0 !== e[o] ? e[o] : null != (r = R[o]) ? r : i); + return t; + }, {}); + return Object.assign({}, e, t); + } + function j(e, t) { + var n = Object.assign( + {}, + t, + { content: i(t.content, [e]) }, + t.ignoreAttributes + ? {} + : (function (e, t) { + return ( + t ? Object.keys(P(Object.assign({}, R, { plugins: t }))) : k + ).reduce(function (t, n) { + var r = (e.getAttribute("data-tippy-" + n) || "").trim(); + if (!r) return t; + if ("content" === n) t[n] = r; + else + try { + t[n] = JSON.parse(r); + } catch (e) { + t[n] = r; + } + return t; + }, {}); + })(e, t.plugins), + ); + return ( + (n.aria = Object.assign({}, R.aria, n.aria)), + (n.aria = { + expanded: "auto" === n.aria.expanded ? t.interactive : n.aria.expanded, + content: + "auto" === n.aria.content + ? t.interactive + ? null + : "describedby" + : n.aria.content, + }), + n + ); + } + function M(e, t) { + e.innerHTML = t; + } + function V(e) { + var t = d(); + return ( + !0 === e + ? (t.className = "tippy-arrow") + : ((t.className = "tippy-svg-arrow"), + v(e) ? t.appendChild(e) : M(t, e)), + t + ); + } + function I(e, t) { + v(t.content) + ? (M(e, ""), e.appendChild(t.content)) + : "function" != typeof t.content && + (t.allowHTML ? M(e, t.content) : (e.textContent = t.content)); + } + function S(e) { + var t = e.firstElementChild, + n = f(t.children); + return { + box: t, + content: n.find(function (e) { + return e.classList.contains("tippy-content"); + }), + arrow: n.find(function (e) { + return ( + e.classList.contains("tippy-arrow") || + e.classList.contains("tippy-svg-arrow") + ); + }), + backdrop: n.find(function (e) { + return e.classList.contains("tippy-backdrop"); + }), + }; + } + function N(e) { + var t = d(), + n = d(); + (n.className = "tippy-box"), + n.setAttribute("data-state", "hidden"), + n.setAttribute("tabindex", "-1"); + var r = d(); + function o(n, r) { + var o = S(t), + i = o.box, + a = o.content, + s = o.arrow; + r.theme + ? i.setAttribute("data-theme", r.theme) + : i.removeAttribute("data-theme"), + "string" == typeof r.animation + ? i.setAttribute("data-animation", r.animation) + : i.removeAttribute("data-animation"), + r.inertia + ? i.setAttribute("data-inertia", "") + : i.removeAttribute("data-inertia"), + (i.style.maxWidth = + "number" == typeof r.maxWidth ? r.maxWidth + "px" : r.maxWidth), + r.role ? i.setAttribute("role", r.role) : i.removeAttribute("role"), + (n.content === r.content && n.allowHTML === r.allowHTML) || + I(a, e.props), + r.arrow + ? s + ? n.arrow !== r.arrow && + (i.removeChild(s), i.appendChild(V(r.arrow))) + : i.appendChild(V(r.arrow)) + : s && i.removeChild(s); + } + return ( + (r.className = "tippy-content"), + r.setAttribute("data-state", "hidden"), + I(r, e.props), + t.appendChild(n), + n.appendChild(r), + o(e.props, e.props), + { popper: t, onUpdate: o } + ); + } + N.$$tippy = !0; + var B = 1, + H = [], + U = []; + function _(o, s) { + var v, + g, + h, + C, + T, + A, + L, + k, + M = j(o, Object.assign({}, R, P(l(s)))), + V = !1, + I = !1, + N = !1, + _ = !1, + F = [], + W = a(we, M.interactiveDebounce), + X = B++, + Y = (k = M.plugins).filter(function (e, t) { + return k.indexOf(e) === t; + }), + $ = { + id: X, + reference: o, + popper: d(), + popperInstance: null, + props: M, + state: { + isEnabled: !0, + isVisible: !1, + isDestroyed: !1, + isMounted: !1, + isShown: !1, + }, + plugins: Y, + clearDelayTimeouts: function () { + clearTimeout(v), clearTimeout(g), cancelAnimationFrame(h); + }, + setProps: function (e) { + if ($.state.isDestroyed) return; + ae("onBeforeUpdate", [$, e]), be(); + var t = $.props, + n = j(o, Object.assign({}, t, l(e), { ignoreAttributes: !0 })); + ($.props = n), + he(), + t.interactiveDebounce !== n.interactiveDebounce && + (ce(), (W = a(we, n.interactiveDebounce))); + t.triggerTarget && !n.triggerTarget + ? u(t.triggerTarget).forEach(function (e) { + e.removeAttribute("aria-expanded"); + }) + : n.triggerTarget && o.removeAttribute("aria-expanded"); + ue(), ie(), J && J(t, n); + $.popperInstance && + (Ce(), + Ae().forEach(function (e) { + requestAnimationFrame(e._tippy.popperInstance.forceUpdate); + })); + ae("onAfterUpdate", [$, e]); + }, + setContent: function (e) { + $.setProps({ content: e }); + }, + show: function () { + var e = $.state.isVisible, + t = $.state.isDestroyed, + o = !$.state.isEnabled, + a = x.isTouch && !$.props.touch, + s = r($.props.duration, 0, R.duration); + if (e || t || o || a) return; + if (te().hasAttribute("disabled")) return; + if ((ae("onShow", [$], !1), !1 === $.props.onShow($))) return; + ($.state.isVisible = !0), ee() && (z.style.visibility = "visible"); + ie(), de(), $.state.isMounted || (z.style.transition = "none"); + if (ee()) { + var u = re(), + p = u.box, + f = u.content; + b([p, f], 0); + } + (A = function () { + var e; + if ($.state.isVisible && !_) { + if ( + ((_ = !0), + z.offsetHeight, + (z.style.transition = $.props.moveTransition), + ee() && $.props.animation) + ) { + var t = re(), + n = t.box, + r = t.content; + b([n, r], s), y([n, r], "visible"); + } + se(), + ue(), + c(U, $), + null == (e = $.popperInstance) || e.forceUpdate(), + ae("onMount", [$]), + $.props.animation && + ee() && + (function (e, t) { + me(e, t); + })(s, function () { + ($.state.isShown = !0), ae("onShown", [$]); + }); + } + }), + (function () { + var e, + t = $.props.appendTo, + r = te(); + e = + ($.props.interactive && t === n) || "parent" === t + ? r.parentNode + : i(t, [r]); + e.contains(z) || e.appendChild(z); + ($.state.isMounted = !0), Ce(); + })(); + }, + hide: function () { + var e = !$.state.isVisible, + t = $.state.isDestroyed, + n = !$.state.isEnabled, + o = r($.props.duration, 1, R.duration); + if (e || t || n) return; + if ((ae("onHide", [$], !1), !1 === $.props.onHide($))) return; + ($.state.isVisible = !1), + ($.state.isShown = !1), + (_ = !1), + (V = !1), + ee() && (z.style.visibility = "hidden"); + if ((ce(), ve(), ie(!0), ee())) { + var i = re(), + a = i.box, + s = i.content; + $.props.animation && (b([a, s], o), y([a, s], "hidden")); + } + se(), + ue(), + $.props.animation + ? ee() && + (function (e, t) { + me(e, function () { + !$.state.isVisible && + z.parentNode && + z.parentNode.contains(z) && + t(); + }); + })(o, $.unmount) + : $.unmount(); + }, + hideWithInteractivity: function (e) { + ne().addEventListener("mousemove", W), c(H, W), W(e); + }, + enable: function () { + $.state.isEnabled = !0; + }, + disable: function () { + $.hide(), ($.state.isEnabled = !1); + }, + unmount: function () { + $.state.isVisible && $.hide(); + if (!$.state.isMounted) return; + Te(), + Ae().forEach(function (e) { + e._tippy.unmount(); + }), + z.parentNode && z.parentNode.removeChild(z); + (U = U.filter(function (e) { + return e !== $; + })), + ($.state.isMounted = !1), + ae("onHidden", [$]); + }, + destroy: function () { + if ($.state.isDestroyed) return; + $.clearDelayTimeouts(), + $.unmount(), + be(), + delete o._tippy, + ($.state.isDestroyed = !0), + ae("onDestroy", [$]); + }, + }; + if (!M.render) return $; + var q = M.render($), + z = q.popper, + J = q.onUpdate; + z.setAttribute("data-tippy-root", ""), + (z.id = "tippy-" + $.id), + ($.popper = z), + (o._tippy = $), + (z._tippy = $); + var G = Y.map(function (e) { + return e.fn($); + }), + K = o.hasAttribute("aria-expanded"); + return ( + he(), + ue(), + ie(), + ae("onCreate", [$]), + M.showOnCreate && Le(), + z.addEventListener("mouseenter", function () { + $.props.interactive && $.state.isVisible && $.clearDelayTimeouts(); + }), + z.addEventListener("mouseleave", function () { + $.props.interactive && + $.props.trigger.indexOf("mouseenter") >= 0 && + ne().addEventListener("mousemove", W); + }), + $ + ); + function Q() { + var e = $.props.touch; + return Array.isArray(e) ? e : [e, 0]; + } + function Z() { + return "hold" === Q()[0]; + } + function ee() { + var e; + return !(null == (e = $.props.render) || !e.$$tippy); + } + function te() { + return L || o; + } + function ne() { + var e = te().parentNode; + return e ? w(e) : document; + } + function re() { + return S(z); + } + function oe(e) { + return ($.state.isMounted && !$.state.isVisible) || + x.isTouch || + (C && "focus" === C.type) + ? 0 + : r($.props.delay, e ? 0 : 1, R.delay); + } + function ie(e) { + void 0 === e && (e = !1), + (z.style.pointerEvents = $.props.interactive && !e ? "" : "none"), + (z.style.zIndex = "" + $.props.zIndex); + } + function ae(e, t, n) { + var r; + (void 0 === n && (n = !0), + G.forEach(function (n) { + n[e] && n[e].apply(n, t); + }), + n) && (r = $.props)[e].apply(r, t); + } + function se() { + var e = $.props.aria; + if (e.content) { + var t = "aria-" + e.content, + n = z.id; + u($.props.triggerTarget || o).forEach(function (e) { + var r = e.getAttribute(t); + if ($.state.isVisible) e.setAttribute(t, r ? r + " " + n : n); + else { + var o = r && r.replace(n, "").trim(); + o ? e.setAttribute(t, o) : e.removeAttribute(t); + } + }); + } + } + function ue() { + !K && + $.props.aria.expanded && + u($.props.triggerTarget || o).forEach(function (e) { + $.props.interactive + ? e.setAttribute( + "aria-expanded", + $.state.isVisible && e === te() ? "true" : "false", + ) + : e.removeAttribute("aria-expanded"); + }); + } + function ce() { + ne().removeEventListener("mousemove", W), + (H = H.filter(function (e) { + return e !== W; + })); + } + function pe(e) { + if (!x.isTouch || (!N && "mousedown" !== e.type)) { + var t = (e.composedPath && e.composedPath()[0]) || e.target; + if (!$.props.interactive || !O(z, t)) { + if ( + u($.props.triggerTarget || o).some(function (e) { + return O(e, t); + }) + ) { + if (x.isTouch) return; + if ($.state.isVisible && $.props.trigger.indexOf("click") >= 0) + return; + } else ae("onClickOutside", [$, e]); + !0 === $.props.hideOnClick && + ($.clearDelayTimeouts(), + $.hide(), + (I = !0), + setTimeout(function () { + I = !1; + }), + $.state.isMounted || ve()); + } + } + } + function fe() { + N = !0; + } + function le() { + N = !1; + } + function de() { + var e = ne(); + e.addEventListener("mousedown", pe, !0), + e.addEventListener("touchend", pe, t), + e.addEventListener("touchstart", le, t), + e.addEventListener("touchmove", fe, t); + } + function ve() { + var e = ne(); + e.removeEventListener("mousedown", pe, !0), + e.removeEventListener("touchend", pe, t), + e.removeEventListener("touchstart", le, t), + e.removeEventListener("touchmove", fe, t); + } + function me(e, t) { + var n = re().box; + function r(e) { + e.target === n && (E(n, "remove", r), t()); + } + if (0 === e) return t(); + E(n, "remove", T), E(n, "add", r), (T = r); + } + function ge(e, t, n) { + void 0 === n && (n = !1), + u($.props.triggerTarget || o).forEach(function (r) { + r.addEventListener(e, t, n), + F.push({ node: r, eventType: e, handler: t, options: n }); + }); + } + function he() { + var e; + Z() && + (ge("touchstart", ye, { passive: !0 }), + ge("touchend", Ee, { passive: !0 })), + ((e = $.props.trigger), e.split(/\s+/).filter(Boolean)).forEach( + function (e) { + if ("manual" !== e) + switch ((ge(e, ye), e)) { + case "mouseenter": + ge("mouseleave", Ee); + break; + case "focus": + ge(D ? "focusout" : "blur", Oe); + break; + case "focusin": + ge("focusout", Oe); + } + }, + ); + } + function be() { + F.forEach(function (e) { + var t = e.node, + n = e.eventType, + r = e.handler, + o = e.options; + t.removeEventListener(n, r, o); + }), + (F = []); + } + function ye(e) { + var t, + n = !1; + if ($.state.isEnabled && !xe(e) && !I) { + var r = "focus" === (null == (t = C) ? void 0 : t.type); + (C = e), + (L = e.currentTarget), + ue(), + !$.state.isVisible && + m(e) && + H.forEach(function (t) { + return t(e); + }), + "click" === e.type && + ($.props.trigger.indexOf("mouseenter") < 0 || V) && + !1 !== $.props.hideOnClick && + $.state.isVisible + ? (n = !0) + : Le(e), + "click" === e.type && (V = !n), + n && !r && De(e); + } + } + function we(e) { + var t = e.target, + n = te().contains(t) || z.contains(t); + ("mousemove" === e.type && n) || + ((function (e, t) { + var n = t.clientX, + r = t.clientY; + return e.every(function (e) { + var t = e.popperRect, + o = e.popperState, + i = e.props.interactiveBorder, + a = p(o.placement), + s = o.modifiersData.offset; + if (!s) return !0; + var u = "bottom" === a ? s.top.y : 0, + c = "top" === a ? s.bottom.y : 0, + f = "right" === a ? s.left.x : 0, + l = "left" === a ? s.right.x : 0, + d = t.top - r + u > i, + v = r - t.bottom - c > i, + m = t.left - n + f > i, + g = n - t.right - l > i; + return d || v || m || g; + }); + })( + Ae() + .concat(z) + .map(function (e) { + var t, + n = null == (t = e._tippy.popperInstance) ? void 0 : t.state; + return n + ? { + popperRect: e.getBoundingClientRect(), + popperState: n, + props: M, + } + : null; + }) + .filter(Boolean), + e, + ) && + (ce(), De(e))); + } + function Ee(e) { + xe(e) || + ($.props.trigger.indexOf("click") >= 0 && V) || + ($.props.interactive ? $.hideWithInteractivity(e) : De(e)); + } + function Oe(e) { + ($.props.trigger.indexOf("focusin") < 0 && e.target !== te()) || + ($.props.interactive && + e.relatedTarget && + z.contains(e.relatedTarget)) || + De(e); + } + function xe(e) { + return !!x.isTouch && Z() !== e.type.indexOf("touch") >= 0; + } + function Ce() { + Te(); + var t = $.props, + n = t.popperOptions, + r = t.placement, + i = t.offset, + a = t.getReferenceClientRect, + s = t.moveTransition, + u = ee() ? S(z).arrow : null, + c = a + ? { + getBoundingClientRect: a, + contextElement: a.contextElement || te(), + } + : o, + p = [ + { name: "offset", options: { offset: i } }, + { + name: "preventOverflow", + options: { padding: { top: 2, bottom: 2, left: 5, right: 5 } }, + }, + { name: "flip", options: { padding: 5 } }, + { name: "computeStyles", options: { adaptive: !s } }, + { + name: "$$tippy", + enabled: !0, + phase: "beforeWrite", + requires: ["computeStyles"], + fn: function (e) { + var t = e.state; + if (ee()) { + var n = re().box; + ["placement", "reference-hidden", "escaped"].forEach( + function (e) { + "placement" === e + ? n.setAttribute("data-placement", t.placement) + : t.attributes.popper["data-popper-" + e] + ? n.setAttribute("data-" + e, "") + : n.removeAttribute("data-" + e); + }, + ), + (t.attributes.popper = {}); + } + }, + }, + ]; + ee() && + u && + p.push({ name: "arrow", options: { element: u, padding: 3 } }), + p.push.apply(p, (null == n ? void 0 : n.modifiers) || []), + ($.popperInstance = e.createPopper( + c, + z, + Object.assign({}, n, { + placement: r, + onFirstUpdate: A, + modifiers: p, + }), + )); + } + function Te() { + $.popperInstance && + ($.popperInstance.destroy(), ($.popperInstance = null)); + } + function Ae() { + return f(z.querySelectorAll("[data-tippy-root]")); + } + function Le(e) { + $.clearDelayTimeouts(), e && ae("onTrigger", [$, e]), de(); + var t = oe(!0), + n = Q(), + r = n[0], + o = n[1]; + x.isTouch && "hold" === r && o && (t = o), + t + ? (v = setTimeout(function () { + $.show(); + }, t)) + : $.show(); + } + function De(e) { + if ( + ($.clearDelayTimeouts(), ae("onUntrigger", [$, e]), $.state.isVisible) + ) { + if ( + !( + $.props.trigger.indexOf("mouseenter") >= 0 && + $.props.trigger.indexOf("click") >= 0 && + ["mouseleave", "mousemove"].indexOf(e.type) >= 0 && + V + ) + ) { + var t = oe(!1); + t + ? (g = setTimeout(function () { + $.state.isVisible && $.hide(); + }, t)) + : (h = requestAnimationFrame(function () { + $.hide(); + })); + } + } else ve(); + } + } + function F(e, n) { + void 0 === n && (n = {}); + var r = R.plugins.concat(n.plugins || []); + document.addEventListener("touchstart", T, t), + window.addEventListener("blur", L); + var o = Object.assign({}, n, { plugins: r }), + i = h(e).reduce(function (e, t) { + var n = t && _(t, o); + return n && e.push(n), e; + }, []); + return v(e) ? i[0] : i; + } + (F.defaultProps = R), + (F.setDefaultProps = function (e) { + Object.keys(e).forEach(function (t) { + R[t] = e[t]; + }); + }), + (F.currentInput = x); + var W = Object.assign({}, e.applyStyles, { + effect: function (e) { + var t = e.state, + n = { + popper: { + position: t.options.strategy, + left: "0", + top: "0", + margin: "0", + }, + arrow: { position: "absolute" }, + reference: {}, + }; + Object.assign(t.elements.popper.style, n.popper), + (t.styles = n), + t.elements.arrow && Object.assign(t.elements.arrow.style, n.arrow); + }, + }), + X = { mouseover: "mouseenter", focusin: "focus", click: "click" }; + var Y = { + name: "animateFill", + defaultValue: !1, + fn: function (e) { + var t; + if (null == (t = e.props.render) || !t.$$tippy) return {}; + var n = S(e.popper), + r = n.box, + o = n.content, + i = e.props.animateFill + ? (function () { + var e = d(); + return (e.className = "tippy-backdrop"), y([e], "hidden"), e; + })() + : null; + return { + onCreate: function () { + i && + (r.insertBefore(i, r.firstElementChild), + r.setAttribute("data-animatefill", ""), + (r.style.overflow = "hidden"), + e.setProps({ arrow: !1, animation: "shift-away" })); + }, + onMount: function () { + if (i) { + var e = r.style.transitionDuration, + t = Number(e.replace("ms", "")); + (o.style.transitionDelay = Math.round(t / 10) + "ms"), + (i.style.transitionDuration = e), + y([i], "visible"); + } + }, + onShow: function () { + i && (i.style.transitionDuration = "0ms"); + }, + onHide: function () { + i && y([i], "hidden"); + }, + }; + }, + }; + var $ = { clientX: 0, clientY: 0 }, + q = []; + function z(e) { + var t = e.clientX, + n = e.clientY; + $ = { clientX: t, clientY: n }; + } + var J = { + name: "followCursor", + defaultValue: !1, + fn: function (e) { + var t = e.reference, + n = w(e.props.triggerTarget || t), + r = !1, + o = !1, + i = !0, + a = e.props; + function s() { + return "initial" === e.props.followCursor && e.state.isVisible; + } + function u() { + n.addEventListener("mousemove", f); + } + function c() { + n.removeEventListener("mousemove", f); + } + function p() { + (r = !0), e.setProps({ getReferenceClientRect: null }), (r = !1); + } + function f(n) { + var r = !n.target || t.contains(n.target), + o = e.props.followCursor, + i = n.clientX, + a = n.clientY, + s = t.getBoundingClientRect(), + u = i - s.left, + c = a - s.top; + (!r && e.props.interactive) || + e.setProps({ + getReferenceClientRect: function () { + var e = t.getBoundingClientRect(), + n = i, + r = a; + "initial" === o && ((n = e.left + u), (r = e.top + c)); + var s = "horizontal" === o ? e.top : r, + p = "vertical" === o ? e.right : n, + f = "horizontal" === o ? e.bottom : r, + l = "vertical" === o ? e.left : n; + return { + width: p - l, + height: f - s, + top: s, + right: p, + bottom: f, + left: l, + }; + }, + }); + } + function l() { + e.props.followCursor && + (q.push({ instance: e, doc: n }), + (function (e) { + e.addEventListener("mousemove", z); + })(n)); + } + function d() { + 0 === + (q = q.filter(function (t) { + return t.instance !== e; + })).filter(function (e) { + return e.doc === n; + }).length && + (function (e) { + e.removeEventListener("mousemove", z); + })(n); + } + return { + onCreate: l, + onDestroy: d, + onBeforeUpdate: function () { + a = e.props; + }, + onAfterUpdate: function (t, n) { + var i = n.followCursor; + r || + (void 0 !== i && + a.followCursor !== i && + (d(), + i ? (l(), !e.state.isMounted || o || s() || u()) : (c(), p()))); + }, + onMount: function () { + e.props.followCursor && !o && (i && (f($), (i = !1)), s() || u()); + }, + onTrigger: function (e, t) { + m(t) && ($ = { clientX: t.clientX, clientY: t.clientY }), + (o = "focus" === t.type); + }, + onHidden: function () { + e.props.followCursor && (p(), c(), (i = !0)); + }, + }; + }, + }; + var G = { + name: "inlinePositioning", + defaultValue: !1, + fn: function (e) { + var t, + n = e.reference; + var r = -1, + o = !1, + i = [], + a = { + name: "tippyInlinePositioning", + enabled: !0, + phase: "afterWrite", + fn: function (o) { + var a = o.state; + e.props.inlinePositioning && + (-1 !== i.indexOf(a.placement) && (i = []), + t !== a.placement && + -1 === i.indexOf(a.placement) && + (i.push(a.placement), + e.setProps({ + getReferenceClientRect: function () { + return (function (e) { + return (function (e, t, n, r) { + if (n.length < 2 || null === e) return t; + if (2 === n.length && r >= 0 && n[0].left > n[1].right) + return n[r] || t; + switch (e) { + case "top": + case "bottom": + var o = n[0], + i = n[n.length - 1], + a = "top" === e, + s = o.top, + u = i.bottom, + c = a ? o.left : i.left, + p = a ? o.right : i.right; + return { + top: s, + bottom: u, + left: c, + right: p, + width: p - c, + height: u - s, + }; + case "left": + case "right": + var f = Math.min.apply( + Math, + n.map(function (e) { + return e.left; + }), + ), + l = Math.max.apply( + Math, + n.map(function (e) { + return e.right; + }), + ), + d = n.filter(function (t) { + return "left" === e + ? t.left === f + : t.right === l; + }), + v = d[0].top, + m = d[d.length - 1].bottom; + return { + top: v, + bottom: m, + left: f, + right: l, + width: l - f, + height: m - v, + }; + default: + return t; + } + })( + p(e), + n.getBoundingClientRect(), + f(n.getClientRects()), + r, + ); + })(a.placement); + }, + })), + (t = a.placement)); + }, + }; + function s() { + var t; + o || + ((t = (function (e, t) { + var n; + return { + popperOptions: Object.assign({}, e.popperOptions, { + modifiers: [].concat( + ( + (null == (n = e.popperOptions) ? void 0 : n.modifiers) || [] + ).filter(function (e) { + return e.name !== t.name; + }), + [t], + ), + }), + }; + })(e.props, a)), + (o = !0), + e.setProps(t), + (o = !1)); + } + return { + onCreate: s, + onAfterUpdate: s, + onTrigger: function (t, n) { + if (m(n)) { + var o = f(e.reference.getClientRects()), + i = o.find(function (e) { + return ( + e.left - 2 <= n.clientX && + e.right + 2 >= n.clientX && + e.top - 2 <= n.clientY && + e.bottom + 2 >= n.clientY + ); + }), + a = o.indexOf(i); + r = a > -1 ? a : r; + } + }, + onHidden: function () { + r = -1; + }, + }; + }, + }; + var K = { + name: "sticky", + defaultValue: !1, + fn: function (e) { + var t = e.reference, + n = e.popper; + function r(t) { + return !0 === e.props.sticky || e.props.sticky === t; + } + var o = null, + i = null; + function a() { + var s = r("reference") + ? (e.popperInstance + ? e.popperInstance.state.elements.reference + : t + ).getBoundingClientRect() + : null, + u = r("popper") ? n.getBoundingClientRect() : null; + ((s && Q(o, s)) || (u && Q(i, u))) && + e.popperInstance && + e.popperInstance.update(), + (o = s), + (i = u), + e.state.isMounted && requestAnimationFrame(a); + } + return { + onMount: function () { + e.props.sticky && a(); + }, + }; + }, + }; + function Q(e, t) { + return ( + !e || + !t || + e.top !== t.top || + e.right !== t.right || + e.bottom !== t.bottom || + e.left !== t.left + ); + } + return ( + F.setDefaultProps({ plugins: [Y, J, G, K], render: N }), + (F.createSingleton = function (e, t) { + var n; + void 0 === t && (t = {}); + var r, + o = e, + i = [], + a = [], + c = t.overrides, + p = [], + f = !1; + function l() { + a = o + .map(function (e) { + return u(e.props.triggerTarget || e.reference); + }) + .reduce(function (e, t) { + return e.concat(t); + }, []); + } + function v() { + i = o.map(function (e) { + return e.reference; + }); + } + function m(e) { + o.forEach(function (t) { + e ? t.enable() : t.disable(); + }); + } + function g(e) { + return o.map(function (t) { + var n = t.setProps; + return ( + (t.setProps = function (o) { + n(o), t.reference === r && e.setProps(o); + }), + function () { + t.setProps = n; + } + ); + }); + } + function h(e, t) { + var n = a.indexOf(t); + if (t !== r) { + r = t; + var s = (c || []).concat("content").reduce(function (e, t) { + return (e[t] = o[n].props[t]), e; + }, {}); + e.setProps( + Object.assign({}, s, { + getReferenceClientRect: + "function" == typeof s.getReferenceClientRect + ? s.getReferenceClientRect + : function () { + var e; + return null == (e = i[n]) + ? void 0 + : e.getBoundingClientRect(); + }, + }), + ); + } + } + m(!1), v(), l(); + var b = { + fn: function () { + return { + onDestroy: function () { + m(!0); + }, + onHidden: function () { + r = null; + }, + onClickOutside: function (e) { + e.props.showOnCreate && !f && ((f = !0), (r = null)); + }, + onShow: function (e) { + e.props.showOnCreate && !f && ((f = !0), h(e, i[0])); + }, + onTrigger: function (e, t) { + h(e, t.currentTarget); + }, + }; + }, + }, + y = F( + d(), + Object.assign({}, s(t, ["overrides"]), { + plugins: [b].concat(t.plugins || []), + triggerTarget: a, + popperOptions: Object.assign({}, t.popperOptions, { + modifiers: [].concat( + (null == (n = t.popperOptions) ? void 0 : n.modifiers) || [], + [W], + ), + }), + }), + ), + w = y.show; + (y.show = function (e) { + if ((w(), !r && null == e)) return h(y, i[0]); + if (!r || null != e) { + if ("number" == typeof e) return i[e] && h(y, i[e]); + if (o.indexOf(e) >= 0) { + var t = e.reference; + return h(y, t); + } + return i.indexOf(e) >= 0 ? h(y, e) : void 0; + } + }), + (y.showNext = function () { + var e = i[0]; + if (!r) return y.show(0); + var t = i.indexOf(r); + y.show(i[t + 1] || e); + }), + (y.showPrevious = function () { + var e = i[i.length - 1]; + if (!r) return y.show(e); + var t = i.indexOf(r), + n = i[t - 1] || e; + y.show(n); + }); + var E = y.setProps; + return ( + (y.setProps = function (e) { + (c = e.overrides || c), E(e); + }), + (y.setInstances = function (e) { + m(!0), + p.forEach(function (e) { + return e(); + }), + (o = e), + m(!1), + v(), + l(), + (p = g(y)), + y.setProps({ triggerTarget: a }); + }), + (p = g(y)), + y + ); + }), + (F.delegate = function (e, n) { + var r = [], + o = [], + i = !1, + a = n.target, + c = s(n, ["target"]), + p = Object.assign({}, c, { trigger: "manual", touch: !1 }), + f = Object.assign({ touch: R.touch }, c, { showOnCreate: !0 }), + l = F(e, p); + function d(e) { + if (e.target && !i) { + var t = e.target.closest(a); + if (t) { + var r = + t.getAttribute("data-tippy-trigger") || n.trigger || R.trigger; + if ( + !t._tippy && + !( + ("touchstart" === e.type && "boolean" == typeof f.touch) || + ("touchstart" !== e.type && r.indexOf(X[e.type]) < 0) + ) + ) { + var s = F(t, f); + s && (o = o.concat(s)); + } + } + } + } + function v(e, t, n, o) { + void 0 === o && (o = !1), + e.addEventListener(t, n, o), + r.push({ node: e, eventType: t, handler: n, options: o }); + } + return ( + u(l).forEach(function (e) { + var n = e.destroy, + a = e.enable, + s = e.disable; + (e.destroy = function (e) { + void 0 === e && (e = !0), + e && + o.forEach(function (e) { + e.destroy(); + }), + (o = []), + r.forEach(function (e) { + var t = e.node, + n = e.eventType, + r = e.handler, + o = e.options; + t.removeEventListener(n, r, o); + }), + (r = []), + n(); + }), + (e.enable = function () { + a(), + o.forEach(function (e) { + return e.enable(); + }), + (i = !1); + }), + (e.disable = function () { + s(), + o.forEach(function (e) { + return e.disable(); + }), + (i = !0); + }), + (function (e) { + var n = e.reference; + v(n, "touchstart", d, t), + v(n, "mouseover", d), + v(n, "focusin", d), + v(n, "click", d); + })(e); + }), + l + ); + }), + (F.hideAll = function (e) { + var t = void 0 === e ? {} : e, + n = t.exclude, + r = t.duration; + U.forEach(function (e) { + var t = !1; + if ((n && (t = g(n) ? e.reference === n : e.popper === n.popper), !t)) { + var o = e.props.duration; + e.setProps({ duration: r }), + e.hide(), + e.state.isDestroyed || e.setProps({ duration: o }); + } + }); + }), + (F.roundArrow = + ''), + F + ); +}); diff --git a/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.esm.js b/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.esm.js index 20f35d7..ea14c3e 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.esm.js +++ b/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.esm.js @@ -1,5 +1,30063 @@ -function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as 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((e.target = void 0), { value: void 0, done: !0 }) + : "keys" == n + ? { value: a, done: !1 } + : "values" == n + ? { value: t[a], done: !1 } + : { value: [a, t[a]], done: !1 }; + }, + "values", + ); +(el.Arguments = el.Array), Js("keys"), Js("values"), Js("entries"); +var ol = { exports: {} }, + sl = !E(function () { + return Object.isExtensible(Object.preventExtensions({})); + }), + ll = He, + cl = T, + _l = Z, + dl = b.f, + ul = sl, + ml = te("meta"), + pl = 0, + gl = + Object.isExtensible || + function () { + return !0; + }, + El = function (e) { + dl(e, ml, { value: { objectID: "O" + ++pl, weakData: {} } }); + }, + Sl = (ol.exports = { + REQUIRED: !1, + fastKey: function (e, t) { + if (!cl(e)) + return "symbol" == typeof e + ? e + : ("string" == typeof e ? 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(bl(e), m ? p(e[0], e[1], g) : p(e[0], e[1])) + : m + ? p(e, g) + : p(e); + }; + if (u) a = e; + else { + if ("function" != typeof (r = Nl(e))) + throw TypeError("Target is not iterable"); + if (Tl(r)) { + for (i = 0, o = fl(e.length); o > i; i++) + if ((s = E(e[i])) && s instanceof vl) return s; + return new vl(!1); + } + a = r.call(e); + } + for (l = a.next; !(c = l.call(a)).done; ) { + try { + s = E(c.value); + } catch (e) { + throw (Rl(a), e); + } + if ("object" == typeof s && s && s instanceof vl) return s; + } + return new vl(!1); + }, + hl = function (e, t, n) { + if (!(e instanceof t)) + throw TypeError("Incorrect " + (n ? n + " " : "") + "invocation"); + return e; + }, + yl = Qn, + Il = p, + Al = Gn, + Dl = Ie.exports, + Ml = ol.exports, + Ll = Ol, + wl = hl, + xl = T, + Pl = E, + kl = Fo, + Ul = os, + Fl = va, + Bl = function (e, t, n) { + var a = -1 !== e.indexOf("Map"), + r = -1 !== e.indexOf("Weak"), + i = a ? "set" : "add", + o = Il[e], + s = o && o.prototype, + l = o, + c = {}, + _ = function (e) { + var t = s[e]; + Dl( + s, + e, + "add" == e + ? function (e) { + return t.call(this, 0 === e ? 0 : e), this; + } + : "delete" == e + ? function (e) { + return !(r && !xl(e)) && t.call(this, 0 === e ? 0 : e); + } + : "get" == e + ? function (e) { + return r && !xl(e) ? void 0 : t.call(this, 0 === e ? 0 : e); + } + : "has" == e + ? function (e) { + return !(r && !xl(e)) && t.call(this, 0 === e ? 0 : e); + } + : function (e, n) { + return t.call(this, 0 === e ? 0 : e, n), this; + }, + ); + }; + if ( + Al( + e, + "function" != typeof o || + !( + r || + (s.forEach && + !Pl(function () { + new o().entries().next(); + })) + ), + ) + ) + (l = n.getConstructor(t, e, a, i)), (Ml.REQUIRED = !0); + else if (Al(e, !0)) { + var d = new l(), + u = d[i](r ? {} : -0, 1) != d, + m = Pl(function () { + d.has(1); + }), + p = kl(function (e) { + new o(e); + }), + g = + !r && + Pl(function () { + for (var e = new o(), t = 5; t--; ) e[i](t, t); + return !e.has(-0); + }); + p || + (((l = t(function (t, n) { + wl(t, l, e); + var r = Fl(new o(), t, l); + return null != n && Ll(n, r[i], { that: r, AS_ENTRIES: a }), r; + })).prototype = s), + (s.constructor = l)), + (m || g) && (_("delete"), _("has"), a && _("get")), + (g || u) && _(i), + r && s.clear && delete s.clear; + } + return ( + (c[e] = l), + yl({ global: !0, forced: l != o }, c), + Ul(l, e), + r || n.setStrong(l, e, a), + l + ); + }, + Gl = Ie.exports, + Yl = oe, + Hl = b, + Vl = S, + ql = Oe("species"), + zl = function (e) { + var t = Yl(e), + n = Hl.f; + Vl && + t && + !t[ql] && + n(t, ql, { + configurable: !0, + get: function () { + return this; + }, + }); + }, + Wl = b.f, + $l = Va, + Ql = function (e, t, n) { + for (var a in t) Gl(e, a, t[a], n); + return e; + }, + Kl = qi, + jl = hl, + Xl = Ol, + Zl = Os, + Jl = zl, + ec = S, + tc = ol.exports.fastKey, + nc = nt.set, + ac = nt.getterFor, + rc = { + getConstructor: function (e, t, n, a) { + var r = e(function (e, i) { + jl(e, r, t), + nc(e, { + type: t, + index: $l(null), + first: void 0, + last: void 0, + size: 0, + }), + ec || (e.size = 0), + null != i && Xl(i, e[a], { that: e, AS_ENTRIES: n }); + }), + i = ac(t), + o = function (e, t, n) { + var a, + r, + o = i(e), + l = s(e, t); + return ( + l + ? (l.value = n) + : ((o.last = l = + { + index: (r = tc(t, !0)), + key: t, + value: n, + previous: (a = o.last), + next: void 0, + removed: !1, + }), + o.first || (o.first = l), + a && (a.next = l), + ec ? o.size++ : e.size++, + "F" !== r && (o.index[r] = l)), + e + ); + }, + s = function (e, t) { + var n, + a = i(e), + r = tc(t); + if ("F" !== r) return a.index[r]; + for (n = a.first; n; n = n.next) if (n.key == t) return n; + }; + return ( + Ql(r.prototype, { + clear: function () { + for (var e = i(this), t = e.index, n = e.first; n; ) + (n.removed = !0), + n.previous && (n.previous = n.previous.next = void 0), + delete t[n.index], + (n = n.next); + (e.first = e.last = void 0), ec ? (e.size = 0) : (this.size = 0); + }, + delete: function (e) { + var t = this, + n = i(t), + a = s(t, e); + if (a) { + var r = a.next, + o = a.previous; + delete n.index[a.index], + (a.removed = !0), + o && (o.next = r), + r && (r.previous = o), + n.first == a && (n.first = r), + n.last == a && (n.last = o), + ec ? n.size-- : t.size--; + } + return !!a; + }, + forEach: function (e) { + for ( + var t, + n = i(this), + a = Kl(e, arguments.length > 1 ? arguments[1] : void 0, 3); + (t = t ? t.next : n.first); + + ) + for (a(t.value, t.key, this); t && t.removed; ) t = t.previous; + }, + has: function (e) { + return !!s(this, e); + }, + }), + Ql( + r.prototype, + n + ? { + get: function (e) { + var t = s(this, e); + return t && t.value; + }, + set: function (e, t) { + return o(this, 0 === e ? 0 : e, t); + }, + } + : { + add: function (e) { + return o(this, (e = 0 === e ? 0 : e), e); + }, + }, + ), + ec && + Wl(r.prototype, "size", { + get: function () { + return i(this).size; + }, + }), + r + ); + }, + setStrong: function (e, t, n) { + var a = t + " Iterator", + r = ac(t), + i = ac(a); + Zl( + e, + t, + function (e, t) { + nc(this, { type: a, target: e, state: r(e), kind: t, last: void 0 }); + }, + function () { + for (var e = i(this), t = e.kind, n = e.last; n && n.removed; ) + n = n.previous; + return e.target && (e.last = n = n ? n.next : e.state.first) + ? "keys" == t + ? { value: n.key, done: !1 } + : "values" == t + ? { value: n.value, done: !1 } + : { value: [n.key, n.value], done: !1 } + : ((e.target = void 0), { value: void 0, done: !0 }); + }, + n ? "entries" : "values", + !n, + !0, + ), + Jl(t); + }, + }; +Bl( + "Map", + function (e) { + return function () { + return e(this, arguments.length ? arguments[0] : void 0); + }; + }, + rc, +); +var ic = p, + oc = Hi, + sc = il, + lc = F, + cc = Oe, + _c = cc("iterator"), + dc = cc("toStringTag"), + uc = sc.values; +for (var mc in oc) { + var pc = ic[mc], + gc = pc && pc.prototype; + if (gc) { + if (gc[_c] !== uc) + try { + lc(gc, _c, uc); + } catch (Am) { + gc[_c] = uc; + } + if ((gc[dc] || lc(gc, dc, mc), oc[mc])) + for (var Ec in sc) + if (gc[Ec] !== sc[Ec]) + try { + lc(gc, Ec, sc[Ec]); + } catch (Am) { + gc[Ec] = sc[Ec]; + } + } +} +Bl( + "Set", + function (e) { + return function () { + return e(this, arguments.length ? arguments[0] : void 0); + }; + }, + rc, +); +var Sc = Qn, + bc = sl, + Tc = E, + fc = T, + Cc = ol.exports.onFreeze, + Nc = Object.freeze; +Sc( + { + target: "Object", + stat: !0, + forced: Tc(function () { + Nc(1); + }), + sham: !bc, + }, + { + freeze: function (e) { + return Nc && fc(e) ? Nc(Cc(e)) : e; + }, + }, +); +var Rc = {}, + vc = Vt, + Oc = Zt.f, + hc = {}.toString, + yc = + "object" == typeof window && window && Object.getOwnPropertyNames + ? Object.getOwnPropertyNames(window) + : []; +Rc.f = function (e) { + return yc && "[object Window]" == hc.call(e) + ? (function (e) { + try { + return Oc(e); + } catch (e) { + return yc.slice(); + } + })(e) + : Oc(vc(e)); +}; +var Ic = Qn, + Ac = E, + Dc = Rc.f; +Ic( + { + target: "Object", + stat: !0, + forced: Ac(function () { + return !Object.getOwnPropertyNames(1); + }), + }, + { getOwnPropertyNames: Dc }, +); +var Mc = Qn, + Lc = E, + wc = T, + xc = Object.isFrozen; +Mc( + { + target: "Object", + stat: !0, + forced: Lc(function () { + xc(1); + }), + }, + { + isFrozen: function (e) { + return !wc(e) || (!!xc && xc(e)); + }, + }, +); +var Pc = Qn, + kc = E, + Uc = jn, + Fc = T, + Bc = K, + Gc = rn, + Yc = ea, + Hc = Qi, + Vc = ra, + qc = de, + zc = Oe("isConcatSpreadable"), + Wc = + qc >= 51 || + !kc(function () { + var e = []; + return (e[zc] = !1), e.concat()[0] !== e; + }), + $c = Vc("concat"), + Qc = function (e) { + if (!Fc(e)) return !1; + var t = e[zc]; + return void 0 !== t ? !!t : Uc(e); + }; +Pc( + { target: "Array", proto: !0, forced: !Wc || !$c }, + { + concat: function (e) { + var t, + n, + a, + r, + i, + o = Bc(this), + s = Hc(o, 0), + l = 0; + for (t = -1, a = arguments.length; t < a; t++) + if (Qc((i = -1 === t ? o : arguments[t]))) { + if (l + (r = Gc(i.length)) > 9007199254740991) + throw TypeError("Maximum allowed index exceeded"); + for (n = 0; n < r; n++, l++) n in i && Yc(s, l, i[n]); + } else { + if (l >= 9007199254740991) + throw TypeError("Maximum allowed index exceeded"); + Yc(s, l++, i); + } + return (s.length = l), s; + }, + }, +); +var Kc = S, + jc = p, + Xc = Gn, + Zc = va, + Jc = b.f, + e_ = Zt.f, + t_ = _i, + n_ = vt, + a_ = br, + r_ = Ie.exports, + i_ = E, + o_ = nt.enforce, + s_ = zl, + l_ = Oe("match"), + c_ = jc.RegExp, + __ = c_.prototype, + d_ = /a/g, + u_ = /a/g, + m_ = new c_(d_) !== d_, + p_ = a_.UNSUPPORTED_Y; +if ( + Kc && + Xc( + "RegExp", + !m_ || + p_ || + i_(function () { + return ( + (u_[l_] = !1), c_(d_) != d_ || c_(u_) == u_ || "/a/i" != c_(d_, "i") + ); + }), + ) +) { + for ( + var g_ = function (e, t) { + var n, + a = this instanceof g_, + r = t_(e), + i = void 0 === t; + if (!a && r && e.constructor === g_ && i) return e; + m_ + ? r && !i && (e = e.source) + : e instanceof g_ && (i && (t = n_.call(e)), (e = e.source)), + p_ && (n = !!t && t.indexOf("y") > -1) && (t = t.replace(/y/g, "")); + var o = Zc(m_ ? new c_(e, t) : c_(e, t), a ? this : __, g_); + p_ && n && (o_(o).sticky = !0); + return o; + }, + E_ = function (e) { + (e in g_) || + Jc(g_, e, { + configurable: !0, + get: function () { + return c_[e]; + }, + set: function (t) { + c_[e] = t; + }, + }); + }, + S_ = e_(c_), + b_ = 0; + S_.length > b_; + + ) + E_(S_[b_++]); + (__.constructor = g_), (g_.prototype = __), r_(jc, "RegExp", g_); +} +s_("RegExp"); +var T_ = S, + f_ = E, + C_ = ya, + N_ = Rn, + R_ = xt, + v_ = K, + O_ = Gt, + h_ = Object.assign, + y_ = Object.defineProperty, + I_ = + !h_ || + f_(function () { + if ( + T_ && + 1 !== + h_( + { b: 1 }, + h_( + y_({}, "a", { + enumerable: !0, + get: function () { + y_(this, "b", { value: 3, enumerable: !1 }); + }, + }), + { b: 2 }, + ), + ).b + ) + return !0; + var e = {}, + t = {}, + n = Symbol(), + a = "abcdefghijklmnopqrst"; + return ( + (e[n] = 7), + a.split("").forEach(function (e) { + t[e] = e; + }), + 7 != h_({}, e)[n] || C_(h_({}, t)).join("") != a + ); + }) + ? function (e, t) { + for ( + var n = v_(e), a = arguments.length, r = 1, i = N_.f, o = R_.f; + a > r; + + ) + for ( + var s, + l = O_(arguments[r++]), + c = i ? C_(l).concat(i(l)) : C_(l), + _ = c.length, + d = 0; + _ > d; + + ) + (s = c[d++]), (T_ && !o.call(l, s)) || (n[s] = l[s]); + return n; + } + : h_; +Qn( + { target: "Object", stat: !0, forced: Object.assign !== I_ }, + { assign: I_ }, +); +var A_ = K, + D_ = ya; +Qn( + { + target: "Object", + stat: !0, + forced: E(function () { + D_(1); + }), + }, + { + keys: function (e) { + return D_(A_(e)); + }, + }, +); +var M_ = pn.includes, + L_ = Xs; +Qn( + { target: "Array", proto: !0 }, + { + includes: function (e) { + return M_(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +), + L_("includes"); +var w_ = Qn, + x_ = no.findIndex, + P_ = Xs, + k_ = !0; +"findIndex" in [] && + Array(1).findIndex(function () { + k_ = !1; + }), + w_( + { target: "Array", proto: !0, forced: k_ }, + { + findIndex: function (e) { + return x_(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ), + P_("findIndex"); +var U_ = _i, + F_ = function (e) { + if (U_(e)) throw TypeError("The method doesn't accept regular expressions"); + return e; + }, + B_ = Oe("match"), + G_ = function (e) { + var t = /./; + try { + "/./"[e](t); + } catch (n) { + try { + return (t[B_] = !1), "/./"[e](t); + } catch (e) {} + } + return !1; + }, + Y_ = F_, + H_ = $; +Qn( + { target: "String", proto: !0, forced: !G_("includes") }, + { + includes: function (e) { + return !!~String(H_(this)).indexOf( + Y_(e), + arguments.length > 1 ? arguments[1] : void 0, + ); + }, + }, +); +var V_ = {}, + q_ = Oe; +V_.f = q_; +var z_ = ne, + W_ = Z, + $_ = V_, + Q_ = b.f, + K_ = Qn, + j_ = p, + X_ = oe, + Z_ = S, + J_ = pe, + ed = ge, + td = E, + nd = Z, + ad = jn, + rd = T, + id = y, + od = K, + sd = Vt, + ld = A, + cd = P, + _d = Va, + dd = ya, + ud = Zt, + md = Rc, + pd = Rn, + gd = wt, + Ed = b, + Sd = xt, + bd = F, + Td = Ie.exports, + fd = g.exports, + Cd = He, + Nd = te, + Rd = Oe, + vd = V_, + Od = function (e) { + var t = z_.Symbol || (z_.Symbol = {}); + W_(t, e) || Q_(t, e, { value: $_.f(e) }); + }, + hd = os, + yd = nt, + Id = no.forEach, + Ad = Ye("hidden"), + Dd = Rd("toPrimitive"), + Md = yd.set, + Ld = yd.getterFor("Symbol"), + wd = Object.prototype, + xd = j_.Symbol, + Pd = X_("JSON", "stringify"), + kd = gd.f, + Ud = Ed.f, + Fd = md.f, + Bd = Sd.f, + Gd = fd("symbols"), + Yd = fd("op-symbols"), + Hd = fd("string-to-symbol-registry"), + Vd = fd("symbol-to-string-registry"), + qd = fd("wks"), + zd = j_.QObject, + Wd = !zd || !zd.prototype || !zd.prototype.findChild, + $d = + Z_ && + td(function () { + return ( + 7 != + _d( + Ud({}, "a", { + get: function () { + return Ud(this, "a", { value: 7 }).a; + }, + }), + ).a + ); + }) + ? function (e, t, n) { + var a = kd(wd, t); + a && delete wd[t], Ud(e, t, n), a && e !== wd && Ud(wd, t, a); + } + : Ud, + Qd = function (e, t) { + var n = (Gd[e] = _d(xd.prototype)); + return ( + Md(n, { type: "Symbol", tag: e, description: t }), + Z_ || (n.description = t), + n + ); + }, + Kd = ed + ? function (e) { + return "symbol" == typeof e; + } + : function (e) { + return Object(e) instanceof xd; + }, + jd = function (e, t, n) { + e === wd && jd(Yd, t, n), id(e); + var a = ld(t, !0); + return ( + id(n), + nd(Gd, a) + ? (n.enumerable + ? (nd(e, Ad) && e[Ad][a] && (e[Ad][a] = !1), + (n = _d(n, { enumerable: cd(0, !1) }))) + : (nd(e, Ad) || Ud(e, Ad, cd(1, {})), (e[Ad][a] = !0)), + $d(e, a, n)) + : Ud(e, a, n) + ); + }, + Xd = function (e, t) { + id(e); + var n = sd(t), + a = dd(n).concat(tu(n)); + return ( + Id(a, function (t) { + (Z_ && !Zd.call(n, t)) || jd(e, t, n[t]); + }), + e + ); + }, + Zd = function (e) { + var t = ld(e, !0), + n = Bd.call(this, t); + return ( + !(this === wd && nd(Gd, t) && !nd(Yd, t)) && + (!(n || !nd(this, t) || !nd(Gd, t) || (nd(this, Ad) && this[Ad][t])) || n) + ); + }, + Jd = function (e, t) { + var n = sd(e), + a = ld(t, !0); + if (n !== wd || !nd(Gd, a) || nd(Yd, a)) { + var r = kd(n, a); + return ( + !r || !nd(Gd, a) || (nd(n, Ad) && n[Ad][a]) || (r.enumerable = !0), r + ); + } + }, + eu = function (e) { + var t = Fd(sd(e)), + n = []; + return ( + Id(t, function (e) { + nd(Gd, e) || nd(Cd, e) || n.push(e); + }), + n + ); + }, + tu = function (e) { + var t = e === wd, + n = Fd(t ? Yd : sd(e)), + a = []; + return ( + Id(n, function (e) { + !nd(Gd, e) || (t && !nd(wd, e)) || a.push(Gd[e]); + }), + a + ); + }; +(J_ || + (Td( + (xd = function () { + if (this instanceof xd) throw TypeError("Symbol is not a constructor"); + var e = + arguments.length && void 0 !== arguments[0] + ? String(arguments[0]) + : void 0, + t = Nd(e), + n = function (e) { + this === wd && n.call(Yd, e), + nd(this, Ad) && nd(this[Ad], t) && (this[Ad][t] = !1), + $d(this, t, cd(1, e)); + }; + return Z_ && Wd && $d(wd, t, { configurable: !0, set: n }), Qd(t, e); + }).prototype, + "toString", + function () { + return Ld(this).tag; + }, + ), + Td(xd, "withoutSetter", function (e) { + return Qd(Nd(e), e); + }), + (Sd.f = Zd), + (Ed.f = jd), + (gd.f = Jd), + (ud.f = md.f = eu), + (pd.f = tu), + (vd.f = function (e) { + return Qd(Rd(e), e); + }), + Z_ && + (Ud(xd.prototype, "description", { + configurable: !0, + get: function () { + return Ld(this).description; + }, + }), + Td(wd, "propertyIsEnumerable", Zd, { unsafe: !0 }))), +K_({ global: !0, wrap: !0, forced: !J_, sham: !J_ }, { Symbol: xd }), +Id(dd(qd), function (e) { + Od(e); +}), +K_( + { target: "Symbol", stat: !0, forced: !J_ }, + { + for: function (e) { + var t = String(e); + if (nd(Hd, t)) return Hd[t]; + var n = xd(t); + return (Hd[t] = n), (Vd[n] = t), n; + }, + keyFor: function (e) { + if (!Kd(e)) throw TypeError(e + " is not a symbol"); + if (nd(Vd, e)) return Vd[e]; + }, + useSetter: function () { + Wd = !0; + }, + useSimple: function () { + Wd = !1; + }, + }, +), +K_( + { target: "Object", stat: !0, forced: !J_, sham: !Z_ }, + { + create: function (e, t) { + return void 0 === t ? _d(e) : Xd(_d(e), t); + }, + defineProperty: jd, + defineProperties: Xd, + getOwnPropertyDescriptor: Jd, + }, +), +K_( + { target: "Object", stat: !0, forced: !J_ }, + { getOwnPropertyNames: eu, getOwnPropertySymbols: tu }, +), +K_( + { + target: "Object", + stat: !0, + forced: td(function () { + pd.f(1); + }), + }, + { + getOwnPropertySymbols: function (e) { + return pd.f(od(e)); + }, + }, +), +Pd) && + K_( + { + target: "JSON", + stat: !0, + forced: + !J_ || + td(function () { + var e = xd(); + return ( + "[null]" != Pd([e]) || "{}" != Pd({ a: e }) || "{}" != Pd(Object(e)) + ); + }), + }, + { + stringify: function (e, t, n) { + for (var a, r = [e], i = 1; arguments.length > i; ) + r.push(arguments[i++]); + if (((a = t), (rd(t) || void 0 !== e) && !Kd(e))) + return ( + ad(t) || + (t = function (e, t) { + if ( + ("function" == typeof a && (t = a.call(this, e, t)), !Kd(t)) + ) + return t; + }), + (r[1] = t), + Pd.apply(null, r) + ); + }, + }, + ); +xd.prototype[Dd] || bd(xd.prototype, Dd, xd.prototype.valueOf), + hd(xd, "Symbol"), + (Cd[Ad] = !0); +var nu = Qn, + au = S, + ru = p, + iu = Z, + ou = T, + su = b.f, + lu = Ln, + cu = ru.Symbol; +if ( + au && + "function" == typeof cu && + (!("description" in cu.prototype) || void 0 !== cu().description) +) { + var _u = {}, + du = function () { + var e = + arguments.length < 1 || void 0 === arguments[0] + ? void 0 + : String(arguments[0]), + t = this instanceof du ? new cu(e) : void 0 === e ? cu() : cu(e); + return "" === e && (_u[t] = !0), t; + }; + lu(du, cu); + var uu = (du.prototype = cu.prototype); + uu.constructor = du; + var mu = uu.toString, + pu = "Symbol(test)" == String(cu("test")), + gu = /^Symbol\((.*)\)[^)]+$/; + su(uu, "description", { + configurable: !0, + get: function () { + var e = ou(this) ? this.valueOf() : this, + t = mu.call(e); + if (iu(_u, e)) return ""; + var n = pu ? t.slice(7, -1) : t.replace(gu, "$1"); + return "" === n ? void 0 : n; + }, + }), + nu({ global: !0, forced: !0 }, { Symbol: du }); +} +var Eu = Qn, + Su = no.find, + bu = Xs, + Tu = !0; +"find" in [] && + Array(1).find(function () { + Tu = !1; + }), + Eu( + { target: "Array", proto: !0, forced: Tu }, + { + find: function (e) { + return Su(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ), + bu("find"); +var fu, + Cu = Qn, + Nu = wt.f, + Ru = rn, + vu = F_, + Ou = $, + hu = G_, + yu = "".startsWith, + Iu = Math.min, + Au = hu("startsWith"); +Cu( + { + target: "String", + proto: !0, + forced: + !!( + Au || ((fu = Nu(String.prototype, "startsWith")), !fu || fu.writable) + ) && !Au, + }, + { + startsWith: function (e) { + var t = String(Ou(this)); + vu(e); + var n = Ru(Iu(arguments.length > 1 ? arguments[1] : void 0, t.length)), + a = String(e); + return yu ? yu.call(t, a, n) : t.slice(n, n + a.length) === a; + }, + }, +); +var Du = no.filter; +Qn( + { target: "Array", proto: !0, forced: !ra("filter") }, + { + filter: function (e) { + return Du(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +); +var Mu = S, + Lu = b.f, + wu = Function.prototype, + xu = wu.toString, + Pu = /^\s*function ([^ (]*)/; +function ku(t) { + return ( + t instanceof Map + ? (t.clear = + t.delete = + t.set = + function () { + throw new Error("map is read-only"); + }) + : t instanceof Set && + (t.add = + t.clear = + t.delete = + function () { + throw new Error("set is read-only"); + }), + Object.freeze(t), + Object.getOwnPropertyNames(t).forEach(function (n) { + var a = t[n]; + "object" != e(a) || Object.isFrozen(a) || ku(a); + }), + t + ); +} +Mu && + !("name" in wu) && + Lu(wu, "name", { + configurable: !0, + get: function () { + try { + return xu.call(this).match(Pu)[1]; + } catch (e) { + return ""; + } + }, + }); +var Uu = ku, + Fu = ku; +Uu.default = Fu; +var Bu = (function () { + function e(n) { + t(this, e), + void 0 === n.data && (n.data = {}), + (this.data = n.data), + (this.isMatchIgnored = !1); + } + return ( + a(e, [ + { + key: "ignoreMatch", + value: function () { + this.isMatchIgnored = !0; + }, + }, + ]), + e + ); +})(); +function Gu(e) { + return e + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/'/g, "'"); +} +function Yu(e) { + var t = Object.create(null); + for (var n in e) t[n] = e[n]; + for ( + var a = arguments.length, r = new Array(a > 1 ? a - 1 : 0), i = 1; + i < a; + i++ + ) + r[i - 1] = arguments[i]; + return ( + r.forEach(function (e) { + for (var n in e) t[n] = e[n]; + }), + t + ); +} +var Hu = function (e) { + return !!e.kind; + }, + Vu = (function () { + function e(n, a) { + t(this, e), + (this.buffer = ""), + (this.classPrefix = a.classPrefix), + n.walk(this); + } + return ( + a(e, [ + { + key: "addText", + value: function (e) { + this.buffer += Gu(e); + }, + }, + { + key: "openNode", + value: function (e) { + if (Hu(e)) { + var t = e.kind; + e.sublanguage || (t = "".concat(this.classPrefix).concat(t)), + this.span(t); + } + }, + }, + { + key: "closeNode", + value: function (e) { + Hu(e) && (this.buffer += ""); + }, + }, + { + key: "value", + value: function () { + return this.buffer; + }, + }, + { + key: "span", + value: function (e) { + this.buffer += ''); + }, + }, + ]), + e + ); + })(), + qu = (function () { + function e() { + t(this, e), + (this.rootNode = { children: [] }), + (this.stack = [this.rootNode]); + } + return ( + a( + e, + [ + { + key: "top", + get: function () { + return this.stack[this.stack.length - 1]; + }, + }, + { + key: "root", + get: function () { + return this.rootNode; + }, + }, + { + key: "add", + value: function (e) { + this.top.children.push(e); + }, + }, + { + key: "openNode", + value: function (e) { + var t = { kind: e, children: [] }; + this.add(t), this.stack.push(t); + }, + }, + { + key: "closeNode", + value: function () { + if (this.stack.length > 1) return this.stack.pop(); + }, + }, + { + key: "closeAllNodes", + value: function () { + for (; this.closeNode(); ); + }, + }, + { + key: "toJSON", + value: function () { + return JSON.stringify(this.rootNode, null, 4); + }, + }, + { + key: "walk", + value: function (e) { + return this.constructor._walk(e, this.rootNode); + }, + }, + ], + [ + { + key: "_walk", + value: function (e, t) { + var n = this; + return ( + "string" == typeof t + ? e.addText(t) + : t.children && + (e.openNode(t), + t.children.forEach(function (t) { + return n._walk(e, t); + }), + e.closeNode(t)), + e + ); + }, + }, + { + key: "_collapse", + value: function (t) { + "string" != typeof t && + t.children && + (t.children.every(function (e) { + return "string" == typeof e; + }) + ? (t.children = [t.children.join("")]) + : t.children.forEach(function (t) { + e._collapse(t); + })); + }, + }, + ], + ), + e + ); + })(), + zu = (function (e) { + !(function (e, t) { + if ("function" != typeof t && null !== t) + throw new TypeError( + "Super expression must either be null or a function", + ); + (e.prototype = Object.create(t && t.prototype, { + constructor: { value: e, writable: !0, configurable: !0 }, + })), + t && i(e, t); + })(r, qu); + var n = s(r); + function r(e) { + var a; + return t(this, r), ((a = n.call(this)).options = e), a; + } + return ( + a(r, [ + { + key: "addKeyword", + value: function (e, t) { + "" !== e && (this.openNode(t), this.addText(e), this.closeNode()); + }, + }, + { + key: "addText", + value: function (e) { + "" !== e && this.add(e); + }, + }, + { + key: "addSublanguage", + value: function (e, t) { + var n = e.root; + (n.kind = t), (n.sublanguage = !0), this.add(n); + }, + }, + { + key: "toHTML", + value: function () { + return new Vu(this, this.options).value(); + }, + }, + { + key: "finalize", + value: function () { + return !0; + }, + }, + ]), + r + ); + })(); +function Wu(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function $u() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Wu(e); + }) + .join(""); + return a; +} +function Qu() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return Wu(e); + }) + .join("|") + + ")"; + return a; +} +var Ku = /\[(?:[^\\\]]|\\.)*\]|\(\??|\\([1-9][0-9]*)|\\./; +var ju = + "(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)", + Xu = { begin: "\\\\[\\s\\S]", relevance: 0 }, + Zu = { + className: "string", + begin: "'", + end: "'", + illegal: "\\n", + contains: [Xu], + }, + Ju = { + className: "string", + begin: '"', + end: '"', + illegal: "\\n", + contains: [Xu], + }, + em = { + begin: + /\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/, + }, + tm = function (e, t) { + var n = arguments.length > 2 && void 0 !== arguments[2] ? arguments[2] : {}, + a = Yu({ className: "comment", begin: e, end: t, contains: [] }, n); + return ( + a.contains.push(em), + a.contains.push({ + className: "doctag", + begin: "(?:TODO|FIXME|NOTE|BUG|OPTIMIZE|HACK|XXX):", + relevance: 0, + }), + a + ); + }, + nm = tm("//", "$"), + am = tm("/\\*", "\\*/"), + rm = tm("#", "$"), + im = { className: "number", begin: "\\b\\d+(\\.\\d+)?", relevance: 0 }, + om = { className: "number", begin: ju, relevance: 0 }, + sm = { className: "number", begin: "\\b(0b[01]+)", relevance: 0 }, + lm = { + className: "number", + begin: + "\\b\\d+(\\.\\d+)?(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?", + relevance: 0, + }, + cm = { + begin: /(?=\/[^/\n]*\/)/, + contains: [ + { + className: "regexp", + begin: /\//, + end: /\/[gimuy]*/, + illegal: /\n/, + contains: [ + Xu, + { begin: /\[/, end: /\]/, relevance: 0, contains: [Xu] }, + ], + }, + ], + }, + _m = { className: "title", begin: "[a-zA-Z]\\w*", relevance: 0 }, + dm = { className: "title", begin: "[a-zA-Z_]\\w*", relevance: 0 }, + um = { begin: "\\.\\s*[a-zA-Z_]\\w*", relevance: 0 }, + mm = Object.freeze({ + __proto__: null, + MATCH_NOTHING_RE: /\b\B/, + IDENT_RE: "[a-zA-Z]\\w*", + UNDERSCORE_IDENT_RE: "[a-zA-Z_]\\w*", + NUMBER_RE: "\\b\\d+(\\.\\d+)?", + C_NUMBER_RE: ju, + BINARY_NUMBER_RE: "\\b(0b[01]+)", + RE_STARTERS_RE: + "!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~", + SHEBANG: function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : {}, + t = /^#![ ]*\//; + return ( + e.binary && (e.begin = $u(t, /.*\b/, e.binary, /\b.*/)), + Yu( + { + className: "meta", + begin: t, + end: /$/, + relevance: 0, + "on:begin": function (e, t) { + 0 !== e.index && t.ignoreMatch(); + }, + }, + e, + ) + ); + }, + BACKSLASH_ESCAPE: Xu, + APOS_STRING_MODE: Zu, + QUOTE_STRING_MODE: Ju, + PHRASAL_WORDS_MODE: em, + COMMENT: tm, + C_LINE_COMMENT_MODE: nm, + C_BLOCK_COMMENT_MODE: am, + HASH_COMMENT_MODE: rm, + NUMBER_MODE: im, + C_NUMBER_MODE: om, + BINARY_NUMBER_MODE: sm, + CSS_NUMBER_MODE: lm, + REGEXP_MODE: cm, + TITLE_MODE: _m, + UNDERSCORE_TITLE_MODE: dm, + METHOD_GUARD: um, + END_SAME_AS_BEGIN: function (e) { + return Object.assign(e, { + "on:begin": function (e, t) { + t.data._beginMatch = e[1]; + }, + "on:end": function (e, t) { + t.data._beginMatch !== e[1] && t.ignoreMatch(); + }, + }); + }, + }); +function pm(e, t) { + "." === e.input[e.index - 1] && t.ignoreMatch(); +} +function gm(e, t) { + t && + e.beginKeywords && + ((e.begin = + "\\b(" + e.beginKeywords.split(" ").join("|") + ")(?!\\.)(?=\\b|\\s)"), + (e.__beforeBegin = pm), + (e.keywords = e.keywords || e.beginKeywords), + delete e.beginKeywords, + void 0 === e.relevance && (e.relevance = 0)); +} +function Em(e, t) { + Array.isArray(e.illegal) && (e.illegal = Qu.apply(void 0, c(e.illegal))); +} +function Sm(e, t) { + if (e.match) { + if (e.begin || e.end) + throw new Error("begin & end are not supported with match"); + (e.begin = e.match), delete e.match; + } +} +function bm(e, t) { + void 0 === e.relevance && (e.relevance = 1); +} +var Tm = [ + "of", + "and", + "for", + "in", + "not", + "or", + "if", + "then", + "parent", + "list", + "value", +]; +function fm(e, t) { + var n = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "keyword", + a = {}; + return ( + "string" == typeof e + ? r(n, e.split(" ")) + : Array.isArray(e) + ? r(n, e) + : Object.keys(e).forEach(function (n) { + Object.assign(a, fm(e[n], t, n)); + }), + a + ); + function r(e, n) { + t && + (n = n.map(function (e) { + return e.toLowerCase(); + })), + n.forEach(function (t) { + var n = t.split("|"); + a[n[0]] = [e, Cm(n[0], n[1])]; + }); + } +} +function Cm(e, t) { + return t + ? Number(t) + : (function (e) { + return Tm.includes(e.toLowerCase()); + })(e) + ? 0 + : 1; +} +function Nm(n, r) { + function i(e, t) { + return new RegExp( + Wu(e), + "m" + (n.case_insensitive ? "i" : "") + (t ? "g" : ""), + ); + } + r.plugins; + var o = (function () { + function e() { + t(this, e), + (this.matchIndexes = {}), + (this.regexes = []), + (this.matchAt = 1), + (this.position = 0); + } + return ( + a(e, [ + { + key: "addRule", + value: function (e, t) { + (t.position = this.position++), + (this.matchIndexes[this.matchAt] = t), + this.regexes.push([t, e]), + (this.matchAt += + (function (e) { + return new RegExp(e.toString() + "|").exec("").length - 1; + })(e) + 1); + }, + }, + { + key: "compile", + value: function () { + 0 === this.regexes.length && + (this.exec = function () { + return null; + }); + var e = this.regexes.map(function (e) { + return e[1]; + }); + (this.matcherRe = i( + (function (e) { + var t = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : "|", + n = 0; + return e + .map(function (e) { + for ( + var t = (n += 1), a = Wu(e), r = ""; + a.length > 0; + + ) { + var i = Ku.exec(a); + if (!i) { + r += a; + break; + } + (r += a.substring(0, i.index)), + (a = a.substring(i.index + i[0].length)), + "\\" === i[0][0] && i[1] + ? (r += "\\" + String(Number(i[1]) + t)) + : ((r += i[0]), "(" === i[0] && n++); + } + return r; + }) + .map(function (e) { + return "(".concat(e, ")"); + }) + .join(t); + })(e), + !0, + )), + (this.lastIndex = 0); + }, + }, + { + key: "exec", + value: function (e) { + this.matcherRe.lastIndex = this.lastIndex; + var t = this.matcherRe.exec(e); + if (!t) return null; + var n = t.findIndex(function (e, t) { + return t > 0 && void 0 !== e; + }), + a = this.matchIndexes[n]; + return t.splice(0, n), Object.assign(t, a); + }, + }, + ]), + e + ); + })(), + s = (function () { + function e() { + t(this, e), + (this.rules = []), + (this.multiRegexes = []), + (this.count = 0), + (this.lastIndex = 0), + (this.regexIndex = 0); + } + return ( + a(e, [ + { + key: "getMatcher", + value: function (e) { + if (this.multiRegexes[e]) return this.multiRegexes[e]; + var t = new o(); + return ( + this.rules.slice(e).forEach(function (e) { + var n = l(e, 2), + a = n[0], + r = n[1]; + return t.addRule(a, r); + }), + t.compile(), + (this.multiRegexes[e] = t), + t + ); + }, + }, + { + key: "resumingScanAtSamePosition", + value: function () { + return 0 !== this.regexIndex; + }, + }, + { + key: "considerAll", + value: function () { + this.regexIndex = 0; + }, + }, + { + key: "addRule", + value: function (e, t) { + this.rules.push([e, t]), "begin" === t.type && this.count++; + }, + }, + { + key: "exec", + value: function (e) { + var t = this.getMatcher(this.regexIndex); + t.lastIndex = this.lastIndex; + var n = t.exec(e); + if (this.resumingScanAtSamePosition()) + if (n && n.index === this.lastIndex); + else { + var a = this.getMatcher(0); + (a.lastIndex = this.lastIndex + 1), (n = a.exec(e)); + } + return ( + n && + ((this.regexIndex += n.position + 1), + this.regexIndex === this.count && this.considerAll()), + n + ); + }, + }, + ]), + e + ); + })(); + if ( + (n.compilerExtensions || (n.compilerExtensions = []), + n.contains && n.contains.includes("self")) + ) + throw new Error( + "ERR: contains `self` is not supported at the top-level of a language. See documentation.", + ); + return ( + (n.classNameAliases = Yu(n.classNameAliases || {})), + (function t(a, r) { + var o, + l = a; + if (a.isCompiled) return l; + [Sm].forEach(function (e) { + return e(a, r); + }), + n.compilerExtensions.forEach(function (e) { + return e(a, r); + }), + (a.__beforeBegin = null), + [gm, Em, bm].forEach(function (e) { + return e(a, r); + }), + (a.isCompiled = !0); + var _ = null; + if ( + ("object" === e(a.keywords) && + ((_ = a.keywords.$pattern), delete a.keywords.$pattern), + a.keywords && (a.keywords = fm(a.keywords, n.case_insensitive)), + a.lexemes && _) + ) + throw new Error( + "ERR: Prefer `keywords.$pattern` to `mode.lexemes`, BOTH are not allowed. (see mode reference) ", + ); + return ( + (_ = _ || a.lexemes || /\w+/), + (l.keywordPatternRe = i(_, !0)), + r && + (a.begin || (a.begin = /\B|\b/), + (l.beginRe = i(a.begin)), + a.endSameAsBegin && (a.end = a.begin), + a.end || a.endsWithParent || (a.end = /\B|\b/), + a.end && (l.endRe = i(a.end)), + (l.terminatorEnd = Wu(a.end) || ""), + a.endsWithParent && + r.terminatorEnd && + (l.terminatorEnd += (a.end ? "|" : "") + r.terminatorEnd)), + a.illegal && (l.illegalRe = i(a.illegal)), + a.contains || (a.contains = []), + (a.contains = (o = []).concat.apply( + o, + c( + a.contains.map(function (e) { + return (function (e) { + e.variants && + !e.cachedVariants && + (e.cachedVariants = e.variants.map(function (t) { + return Yu(e, { variants: null }, t); + })); + if (e.cachedVariants) return e.cachedVariants; + if (Rm(e)) + return Yu(e, { starts: e.starts ? Yu(e.starts) : null }); + if (Object.isFrozen(e)) return Yu(e); + return e; + })("self" === e ? a : e); + }), + ), + )), + a.contains.forEach(function (e) { + t(e, l); + }), + a.starts && t(a.starts, r), + (l.matcher = (function (e) { + var t = new s(); + return ( + e.contains.forEach(function (e) { + return t.addRule(e.begin, { rule: e, type: "begin" }); + }), + e.terminatorEnd && t.addRule(e.terminatorEnd, { type: "end" }), + e.illegal && t.addRule(e.illegal, { type: "illegal" }), + t + ); + })(l)), + l + ); + })(n) + ); +} +function Rm(e) { + return !!e && (e.endsWithParent || Rm(e.starts)); +} +function vm(e) { + var t = { + props: ["language", "code", "autodetect"], + data: function () { + return { detectedLanguage: "", unknownLanguage: !1 }; + }, + computed: { + className: function () { + return this.unknownLanguage ? "" : "hljs " + this.detectedLanguage; + }, + highlighted: function () { + if (!this.autoDetect && !e.getLanguage(this.language)) + return ( + console.warn( + 'The language "'.concat( + this.language, + '" you specified could not be found.', + ), + ), + (this.unknownLanguage = !0), + Gu(this.code) + ); + var t = {}; + return ( + this.autoDetect + ? 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коннедели лог лог10 максимальноеколичествосубконто названиеинтерфейса названиенабораправ назначитьвид назначитьсчет найтиссылки началопериодаби началостандартногоинтервала начгода начквартала начмесяца начнедели номерднягода номерднянедели номернеделигода обработкаожидания основнойжурналрасчетов основнойплансчетов основнойязык очиститьокносообщений периодстр получитьвремята получитьдатута получитьдокументта получитьзначенияотбора получитьпозициюта получитьпустоезначение получитьта префиксавтонумерации пропись пустоезначение разм разобратьпозициюдокумента рассчитатьрегистрына рассчитатьрегистрыпо симв создатьобъект статусвозврата стрколичествострок сформироватьпозициюдокумента счетпокоду текущеевремя типзначения типзначениястр установитьтана установитьтапо фиксшаблон шаблон acos asin atan base64значение base64строка cos exp log log10 pow sin sqrt tan xmlзначение xmlстрока xmlтип xmlтипзнч активноеокно безопасныйрежим безопасныйрежимразделенияданных булево ввестидату ввестизначение ввестистроку ввестичисло возможностьчтенияxml вопрос восстановитьзначение врег выгрузитьжурналрегистрации выполнитьобработкуоповещения выполнитьпроверкуправдоступа вычислить год данныеформывзначение дата день деньгода деньнедели добавитьмесяц заблокироватьданныедляредактирования заблокироватьработупользователя завершитьработусистемы загрузитьвнешнююкомпоненту закрытьсправку записатьjson записатьxml записатьдатуjson записьжурналарегистрации заполнитьзначениясвойств запроситьразрешениепользователя запуститьприложение запуститьсистему зафиксироватьтранзакцию значениевданныеформы значениевстрокувнутр значениевфайл значениезаполнено значениеизстрокивнутр значениеизфайла изxmlтипа импортмоделиxdto имякомпьютера имяпользователя инициализироватьпредопределенныеданные информацияобошибке каталогбиблиотекимобильногоустройства каталогвременныхфайлов каталогдокументов каталогпрограммы кодироватьстроку кодлокализацииинформационнойбазы кодсимвола командасистемы конецгода конецдня конецквартала 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сохранитьнастройкипользователя сред стрдлина стрзаканчиваетсяна стрзаменить стрнайти стрначинаетсяс строка строкасоединенияинформационнойбазы стрполучитьстроку стрразделить стрсоединить стрсравнить стрчисловхождений стрчислострок стршаблон текущаядата текущаядатасеанса текущаяуниверсальнаядата текущаяуниверсальнаядатавмиллисекундах текущийвариантинтерфейсаклиентскогоприложения текущийвариантосновногошрифтаклиентскогоприложения текущийкодлокализации текущийрежимзапуска текущийязык текущийязыксистемы тип типзнч транзакцияактивна трег удалитьданныеинформационнойбазы удалитьизвременногохранилища удалитьобъекты удалитьфайлы универсальноевремя установитьбезопасныйрежим установитьбезопасныйрежимразделенияданных установитьблокировкусеансов установитьвнешнююкомпоненту установитьвремязавершенияспящегосеанса установитьвремязасыпанияпассивногосеанса установитьвремяожиданияблокировкиданных установитьзаголовокклиентскогоприложения установитьзаголовоксистемы установитьиспользованиежурналарегистрации 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документы доставляемыеуведомления журналыдокументов задачи информацияобинтернетсоединении использованиерабочейдаты историяработыпользователя константы критерииотбора метаданные обработки отображениерекламы отправкадоставляемыхуведомлений отчеты панельзадачос параметрзапуска параметрысеанса перечисления планывидоврасчета планывидовхарактеристик планыобмена планысчетов полнотекстовыйпоиск пользователиинформационнойбазы последовательности проверкавстроенныхпокупок рабочаядата расширенияконфигурации регистрыбухгалтерии регистрынакопления регистрырасчета регистрысведений регламентныезадания сериализаторxdto справочники средствагеопозиционирования средствакриптографии средствамультимедиа средстваотображениярекламы средствапочты средствателефонии фабрикаxdto файловыепотоки фоновыезадания хранилищанастроек хранилищевариантовотчетов хранилищенастроекданныхформ хранилищеобщихнастроек хранилищепользовательскихнастроекдинамическихсписков хранилищепользовательскихнастроекотчетов хранилищесистемныхнастроек ", + class: + "webцвета windowsцвета windowsшрифты библиотекакартинок рамкистиля символы цветастиля шрифтыстиля автоматическоесохранениеданныхформывнастройках автонумерациявформе автораздвижениесерий анимациядиаграммы вариантвыравниванияэлементовизаголовков вариантуправлениявысотойтаблицы вертикальнаяпрокруткаформы вертикальноеположение вертикальноеположениеэлемента видгруппыформы виддекорацииформы виддополненияэлементаформы видизмененияданных видкнопкиформы видпереключателя видподписейкдиаграмме видполяформы видфлажка влияниеразмеранапузырекдиаграммы горизонтальноеположение горизонтальноеположениеэлемента группировкаколонок группировкаподчиненныхэлементовформы группыиэлементы действиеперетаскивания дополнительныйрежимотображения допустимыедействияперетаскивания интервалмеждуэлементамиформы использованиевывода использованиеполосыпрокрутки используемоезначениеточкибиржевойдиаграммы историявыборапривводе источникзначенийоситочекдиаграммы 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("string" == typeof e ? e : e.source) : null; +} +function Fm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Um(e); + }) + .join(""); + return a; +} +var Bm = function (e) { + var t = { + ruleDeclaration: /^[a-zA-Z][a-zA-Z0-9-]*/, + unexpectedChars: /[!@#$^&',?+~`|:]/, + }, + n = e.COMMENT(/;/, /$/), + a = { className: "attribute", begin: Fm(t.ruleDeclaration, /(?=\s*=)/) }; + return { + name: "Augmented Backus-Naur Form", + illegal: t.unexpectedChars, + keywords: [ + "ALPHA", + "BIT", + "CHAR", + "CR", + "CRLF", + "CTL", + "DIGIT", + "DQUOTE", + "HEXDIG", + "HTAB", + "LF", + "LWSP", + "OCTET", + "SP", + "VCHAR", + "WSP", + ], + contains: [ + a, + n, + { className: "symbol", begin: /%b[0-1]+(-[0-1]+|(\.[0-1]+)+){0,1}/ }, + { className: "symbol", begin: /%d[0-9]+(-[0-9]+|(\.[0-9]+)+){0,1}/ }, + { + className: "symbol", + begin: /%x[0-9A-F]+(-[0-9A-F]+|(\.[0-9A-F]+)+){0,1}/, + }, + { className: "symbol", begin: /%[si]/ }, + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + ], + }; +}; +function Gm(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Ym() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Gm(e); + }) + .join(""); + return a; +} +function Hm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return Gm(e); + }) + .join("|") + + ")"; + return a; +} +var Vm = function (e) { + var t = [ + "GET", + "POST", + "HEAD", + "PUT", + "DELETE", + "CONNECT", + "OPTIONS", + "PATCH", + "TRACE", + ]; + return { + name: "Apache Access Log", + contains: [ + { + className: "number", + begin: /^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}(:\d{1,5})?\b/, + relevance: 5, + }, + { className: "number", begin: /\b\d+\b/, relevance: 0 }, + { + className: "string", + begin: Ym(/"/, Hm.apply(void 0, t)), + end: /"/, + keywords: t, + illegal: /\n/, + relevance: 5, + contains: [{ begin: /HTTP\/[12]\.\d'/, relevance: 5 }], + }, + { + className: "string", + begin: /\[\d[^\]\n]{8,}\]/, + illegal: /\n/, + relevance: 1, + }, + { + className: "string", + begin: /\[/, + end: /\]/, + illegal: /\n/, + relevance: 0, + }, + { + className: "string", + begin: /"Mozilla\/\d\.\d \(/, + end: /"/, + illegal: /\n/, + relevance: 3, + }, + { + className: "string", + begin: /"/, + end: /"/, + illegal: /\n/, + relevance: 0, + }, + ], + }; +}; +function qm(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function zm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return qm(e); + }) + .join(""); + return a; +} +var Wm = function (e) { + var t = { + className: "rest_arg", + begin: /[.]{3}/, + end: /[a-zA-Z_$][a-zA-Z0-9_$]*/, + relevance: 10, + }; + return { + name: "ActionScript", + aliases: ["as"], + keywords: { + keyword: + "as break case catch class const continue default delete do dynamic each else extends final finally for function get if implements import in include instanceof interface internal is namespace native new override package private protected public return set static super switch this throw try typeof use var void while with", + literal: "true false null undefined", + }, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.C_NUMBER_MODE, + { + className: "class", + beginKeywords: "package", + end: /\{/, + contains: [e.TITLE_MODE], + }, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + contains: [{ beginKeywords: "extends implements" }, e.TITLE_MODE], + }, + { + className: "meta", + beginKeywords: "import include", + end: /;/, + keywords: { "meta-keyword": "import include" }, + }, + { + className: "function", + beginKeywords: "function", + end: /[{;]/, + excludeEnd: !0, + illegal: /\S/, + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: /\(/, + end: /\)/, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + ], + }, + { begin: zm(/:\s*/, /([*]|[a-zA-Z_$][a-zA-Z0-9_$]*)/) }, + ], + }, + e.METHOD_GUARD, + ], + illegal: /#/, + }; +}; +var $m = function (e) { + var t = "[A-Za-z](_?[A-Za-z0-9.])*", + n = "[]\\{\\}%#'\"", + a = e.COMMENT("--", "$"), + r = { + begin: "\\s+:\\s+", + end: "\\s*(:=|;|\\)|=>|$)", + illegal: n, + contains: [ + { beginKeywords: "loop for declare others", endsParent: !0 }, + { + className: "keyword", + beginKeywords: + "not null constant access function procedure in out aliased exception", + }, + { className: "type", begin: t, endsParent: !0, relevance: 0 }, + ], + }; + return { + name: "Ada", + case_insensitive: !0, + keywords: { + keyword: + "abort else new return abs elsif not reverse abstract end accept entry select access exception of separate aliased exit or some all others subtype and for out synchronized array function overriding at tagged generic package task begin goto pragma terminate body private then if procedure type case in protected constant interface is raise use declare range delay limited record when delta loop rem while digits renames with do mod requeue xor", + literal: "True False", + }, + contains: [ + a, + { + className: "string", + begin: /"/, + end: /"/, + contains: [{ begin: /""/, relevance: 0 }], + }, + { className: "string", begin: /'.'/ }, + { + className: "number", + begin: + "\\b(\\d(_|\\d)*#\\w+(\\.\\w+)?#([eE][-+]?\\d(_|\\d)*)?|\\d(_|\\d)*(\\.\\d(_|\\d)*)?([eE][-+]?\\d(_|\\d)*)?)", + relevance: 0, + }, + { className: "symbol", begin: "'" + t }, + { + className: "title", + begin: "(\\bwith\\s+)?(\\bprivate\\s+)?\\bpackage\\s+(\\bbody\\s+)?", + end: "(is|$)", + keywords: "package body", + excludeBegin: !0, + excludeEnd: !0, + illegal: n, + }, + { + begin: "(\\b(with|overriding)\\s+)?\\b(function|procedure)\\s+", + end: "(\\bis|\\bwith|\\brenames|\\)\\s*;)", + keywords: "overriding function procedure with is renames return", + returnBegin: !0, + contains: [ + a, + { + className: "title", + begin: "(\\bwith\\s+)?\\b(function|procedure)\\s+", + end: "(\\(|\\s+|$)", + excludeBegin: !0, + excludeEnd: !0, + illegal: n, + }, + r, + { + className: "type", + begin: "\\breturn\\s+", + end: "(\\s+|;|$)", + keywords: "return", + excludeBegin: !0, + excludeEnd: !0, + endsParent: !0, + illegal: n, + }, + ], + }, + { + className: "type", + begin: "\\b(sub)?type\\s+", + end: "\\s+", + keywords: "type", + excludeBegin: !0, + illegal: n, + }, + r, + ], + }; +}; +var Qm = function (e) { + var t = { + className: "built_in", + begin: + "\\b(void|bool|int|int8|int16|int32|int64|uint|uint8|uint16|uint32|uint64|string|ref|array|double|float|auto|dictionary)", + }, + n = { className: "symbol", begin: "[a-zA-Z0-9_]+@" }, + a = { className: "keyword", begin: "<", end: ">", contains: [t, n] }; + return ( + (t.contains = [a]), + (n.contains = [a]), + { + name: "AngelScript", + aliases: ["asc"], + keywords: + "for in|0 break continue while do|0 return if else case switch namespace is cast or and xor not get|0 in inout|10 out override set|0 private public const default|0 final shared external mixin|10 enum typedef funcdef this super import from interface abstract|0 try catch protected explicit property", + illegal: "(^using\\s+[A-Za-z0-9_\\.]+;$|\\bfunction\\s*[^\\(])", + contains: [ + { + className: "string", + begin: "'", + end: "'", + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + { className: "string", begin: '"""', end: '"""' }, + { + className: "string", + begin: '"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "string", begin: "^\\s*\\[", end: "\\]" }, + { + beginKeywords: "interface namespace", + end: /\{/, + illegal: "[;.\\-]", + contains: [{ className: "symbol", begin: "[a-zA-Z0-9_]+" }], + }, + { + beginKeywords: "class", + end: /\{/, + illegal: "[;.\\-]", + contains: [ + { + className: "symbol", + begin: "[a-zA-Z0-9_]+", + contains: [ + { + begin: "[:,]\\s*", + contains: [{ className: "symbol", begin: "[a-zA-Z0-9_]+" }], + }, + ], + }, + ], + }, + t, + n, + { className: "literal", begin: "\\b(null|true|false)" }, + { + className: "number", + relevance: 0, + begin: + "(-?)(\\b0[xXbBoOdD][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?f?|\\.\\d+f?)([eE][-+]?\\d+f?)?)", + }, + ], + } + ); +}; +var Km = function (e) { + var t = { + className: "number", + begin: /\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}(:\d{1,5})?/, + }; + return { + name: "Apache config", + aliases: ["apacheconf"], + case_insensitive: !0, + contains: [ + e.HASH_COMMENT_MODE, + { + className: "section", + begin: /<\/?/, + end: />/, + contains: [ + t, + { className: "number", begin: /:\d{1,5}/ }, + e.inherit(e.QUOTE_STRING_MODE, { relevance: 0 }), + ], + }, + { + className: "attribute", + begin: /\w+/, + relevance: 0, + keywords: { + nomarkup: + "order deny allow setenv rewriterule rewriteengine rewritecond documentroot sethandler errordocument loadmodule options header listen serverroot servername", + }, + starts: { + end: /$/, + relevance: 0, + keywords: { literal: "on off all deny allow" }, + contains: [ + { className: "meta", begin: /\s\[/, end: /\]$/ }, + { + className: "variable", + begin: /[\$%]\{/, + end: /\}/, + contains: ["self", { className: "number", begin: /[$%]\d+/ }], + }, + t, + { className: "number", begin: /\d+/ }, + e.QUOTE_STRING_MODE, + ], + }, + }, + ], + illegal: /\S/, + }; +}; +function jm(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Xm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return jm(e); + }) + .join(""); + return a; +} +function Zm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return jm(e); + }) + .join("|") + + ")"; + return a; +} +var Jm = function (e) { + var t = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + n = { + className: "params", + begin: /\(/, + end: /\)/, + contains: ["self", e.C_NUMBER_MODE, t], + }, + a = e.COMMENT(/--/, /$/), + r = [ + a, + e.COMMENT(/\(\*/, /\*\)/, { contains: ["self", a] }), + e.HASH_COMMENT_MODE, + ]; + return { + name: "AppleScript", + aliases: ["osascript"], + keywords: { + keyword: + "about above after against and around as at back before beginning behind below beneath beside between but by considering contain contains continue copy div does eighth else end equal equals error every exit fifth first for fourth from front get given global if ignoring in into is it its last local me middle mod my ninth not of on onto or over prop property put ref reference repeat returning script second set seventh since sixth some tell tenth that the|0 then third through thru timeout times to transaction try until where while whose with without", + literal: + "AppleScript false linefeed return pi quote result space tab true", + built_in: + "alias application boolean class constant date file integer list number real record string text activate beep count delay launch log offset read round run say summarize write character characters contents day frontmost id item length month name paragraph paragraphs rest reverse running time version weekday word words year", + }, + contains: [ + t, + e.C_NUMBER_MODE, + { + className: "built_in", + begin: Xm( + /\b/, + Zm.apply(void 0, [ + /clipboard info/, + /the clipboard/, + /info for/, + /list (disks|folder)/, + /mount volume/, + /path to/, + /(close|open for) access/, + /(get|set) eof/, + /current date/, + /do shell script/, + /get volume settings/, + /random number/, + /set volume/, + /system attribute/, + /system info/, + /time to GMT/, + /(load|run|store) script/, + /scripting components/, + /ASCII (character|number)/, + /localized string/, + /choose (application|color|file|file name|folder|from list|remote application|URL)/, + /display (alert|dialog)/, + ]), + /\b/, + ), + }, + { className: "built_in", begin: /^\s*return\b/ }, + { + className: "literal", + begin: /\b(text item delimiters|current application|missing value)\b/, + }, + { + className: "keyword", + begin: Xm( + /\b/, + Zm.apply(void 0, [ + /apart from/, + /aside from/, + /instead of/, + /out of/, + /greater than/, + /isn't|(doesn't|does not) (equal|come before|come after|contain)/, + /(greater|less) than( or equal)?/, + /(starts?|ends|begins?) with/, + /contained by/, + /comes (before|after)/, + /a (ref|reference)/, + /POSIX (file|path)/, + /(date|time) string/, + /quoted form/, + ]), + /\b/, + ), + }, + { + beginKeywords: "on", + illegal: /[${=;\n]/, + contains: [e.UNDERSCORE_TITLE_MODE, n], + }, + ].concat(r), + illegal: /\/\/|->|=>|\[\[/, + }; +}; +var ep = function (e) { + var t = "[A-Za-z_][0-9A-Za-z_]*", + n = { + keyword: "if for while var new function do return void else break", + literal: + "BackSlash DoubleQuote false ForwardSlash Infinity NaN NewLine null PI SingleQuote Tab TextFormatting true undefined", + built_in: + "Abs Acos Angle Attachments Area AreaGeodetic Asin Atan Atan2 Average Bearing Boolean Buffer BufferGeodetic Ceil Centroid Clip Console Constrain Contains Cos Count Crosses Cut Date DateAdd DateDiff Day Decode DefaultValue Dictionary Difference Disjoint Distance DistanceGeodetic Distinct DomainCode DomainName Equals Exp Extent Feature FeatureSet FeatureSetByAssociation FeatureSetById FeatureSetByPortalItem FeatureSetByRelationshipName FeatureSetByTitle FeatureSetByUrl Filter First Floor Geometry GroupBy Guid HasKey Hour IIf IndexOf Intersection Intersects IsEmpty IsNan IsSelfIntersecting Length LengthGeodetic Log Max Mean Millisecond Min Minute Month MultiPartToSinglePart Multipoint NextSequenceValue Now Number OrderBy Overlaps Point Polygon Polyline Portal Pow Random Relate Reverse RingIsClockWise Round Second SetGeometry Sin Sort Sqrt Stdev Sum SymmetricDifference Tan Text Timestamp Today ToLocal Top Touches ToUTC TrackCurrentTime TrackGeometryWindow TrackIndex TrackStartTime TrackWindow TypeOf Union UrlEncode Variance Weekday When Within Year ", + }, + a = { + className: "number", + variants: [ + { begin: "\\b(0[bB][01]+)" }, + { begin: "\\b(0[oO][0-7]+)" }, + { begin: e.C_NUMBER_RE }, + ], + relevance: 0, + }, + r = { + className: "subst", + begin: "\\$\\{", + end: "\\}", + keywords: n, + contains: [], + }, + i = { + className: "string", + begin: "`", + end: "`", + contains: [e.BACKSLASH_ESCAPE, r], + }; + r.contains = [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE, i, a, e.REGEXP_MODE]; + var o = r.contains.concat([e.C_BLOCK_COMMENT_MODE, e.C_LINE_COMMENT_MODE]); + return { + name: "ArcGIS Arcade", + keywords: n, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + i, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "symbol", + begin: + "\\$[datastore|feature|layer|map|measure|sourcefeature|sourcelayer|targetfeature|targetlayer|value|view]+", + }, + a, + { + begin: /[{,]\s*/, + relevance: 0, + contains: [ + { + begin: t + "\\s*:", + returnBegin: !0, + relevance: 0, + contains: [{ className: "attr", begin: t, relevance: 0 }], + }, + ], + }, + { + begin: "(" + e.RE_STARTERS_RE + "|\\b(return)\\b)\\s*", + keywords: "return", + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.REGEXP_MODE, + { + className: "function", + begin: "(\\(.*?\\)|" + t + ")\\s*=>", + returnBegin: !0, + end: "\\s*=>", + contains: [ + { + className: "params", + variants: [ + { begin: t }, + { begin: /\(\s*\)/ }, + { + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: n, + contains: o, + }, + ], + }, + ], + }, + ], + relevance: 0, + }, + { + className: "function", + beginKeywords: "function", + end: /\{/, + excludeEnd: !0, + contains: [ + e.inherit(e.TITLE_MODE, { begin: t }), + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + contains: o, + }, + ], + illegal: /\[|%/, + }, + { begin: /\$[(.]/ }, + ], + illegal: /#(?!!)/, + }; +}; +function tp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function np(e) { + return ap("(", e, ")?"); +} +function ap() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return tp(e); + }) + .join(""); + return a; +} +var rp = function (e) { + var t = "boolean byte word String", + n = + "KeyboardController MouseController SoftwareSerial EthernetServer EthernetClient LiquidCrystal RobotControl GSMVoiceCall EthernetUDP EsploraTFT HttpClient RobotMotor WiFiClient GSMScanner FileSystem Scheduler GSMServer YunClient YunServer IPAddress GSMClient GSMModem Keyboard Ethernet Console GSMBand Esplora Stepper Process WiFiUDP GSM_SMS Mailbox USBHost Firmata PImage Client Server GSMPIN FileIO Bridge Serial EEPROM Stream Mouse Audio Servo File Task GPRS WiFi Wire TFT GSM SPI SD ", + a = + "setup loop runShellCommandAsynchronously analogWriteResolution retrieveCallingNumber printFirmwareVersion analogReadResolution sendDigitalPortPair noListenOnLocalhost readJoystickButton setFirmwareVersion readJoystickSwitch scrollDisplayRight getVoiceCallStatus scrollDisplayLeft writeMicroseconds delayMicroseconds beginTransmission getSignalStrength runAsynchronously getAsynchronously listenOnLocalhost getCurrentCarrier readAccelerometer messageAvailable sendDigitalPorts lineFollowConfig countryNameWrite runShellCommand readStringUntil rewindDirectory readTemperature setClockDivider readLightSensor endTransmission analogReference detachInterrupt countryNameRead attachInterrupt encryptionType readBytesUntil robotNameWrite readMicrophone robotNameRead cityNameWrite userNameWrite readJoystickY readJoystickX mouseReleased openNextFile scanNetworks noInterrupts digitalWrite beginSpeaker mousePressed isActionDone mouseDragged displayLogos noAutoscroll addParameter remoteNumber getModifiers keyboardRead userNameRead waitContinue processInput parseCommand printVersion readNetworks writeMessage blinkVersion 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(e) { + var t, + n = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + a = "decltype\\(auto\\)", + r = "[a-zA-Z_]\\w*::", + i = + "(decltype\\(auto\\)|" + + np(r) + + "[a-zA-Z_]\\w*" + + np("<[^<>]+>") + + ")", + o = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + s = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + c = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(s, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + n, + e.C_BLOCK_COMMENT_MODE, + ], + }, + _ = { className: "title", begin: np(r) + e.IDENT_RE, relevance: 0 }, + d = np(r) + e.IDENT_RE + "\\s*\\(", + u = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: "_Bool _Complex _Imaginary", + _relevance_hints: [ + "asin", + "atan2", + "atan", + "calloc", + "ceil", + "cosh", + "cos", + "exit", + "exp", + "fabs", + "floor", + "fmod", + "fprintf", + "fputs", + "free", + "frexp", + "auto_ptr", + "deque", + "list", + "queue", + "stack", + "vector", + "map", + "set", + "pair", + "bitset", + "multiset", + "multimap", + "unordered_set", + "fscanf", + "future", + "isalnum", + "isalpha", + "iscntrl", + "isdigit", + "isgraph", + "islower", + "isprint", + "ispunct", + "isspace", + "isupper", + "isxdigit", + "tolower", + "toupper", + "labs", + "ldexp", + "log10", + "log", + "malloc", + "realloc", + "memchr", + "memcmp", + "memcpy", + "memset", + "modf", + "pow", + "printf", + "putchar", + "puts", + "scanf", + "sinh", + "sin", + "snprintf", + "sprintf", + "sqrt", + "sscanf", + "strcat", + "strchr", + "strcmp", + "strcpy", + "strcspn", + "strlen", + "strncat", + "strncmp", + "strncpy", + "strpbrk", + "strrchr", + "strspn", + "strstr", + "tanh", + "tan", + "unordered_map", + "unordered_multiset", + "unordered_multimap", + "priority_queue", + "make_pair", + "array", + "shared_ptr", + "abort", + "terminate", + "abs", + "acos", + "vfprintf", + "vprintf", + "vsprintf", + "endl", + "initializer_list", + "unique_ptr", + "complex", + "imaginary", + "std", + "string", + "wstring", + "cin", + "cout", + "cerr", + "clog", + "stdin", + "stdout", + "stderr", + "stringstream", + "istringstream", + "ostringstream", + ], + literal: "true false nullptr NULL", + }, + m = { + className: "function.dispatch", + relevance: 0, + keywords: u, + begin: ap( + /\b/, + /(?!decltype)/, + /(?!if)/, + /(?!for)/, + /(?!while)/, + e.IDENT_RE, + ((t = /\s*\(/), ap("(?=", t, ")")), + ), + }, + p = [m, c, o, n, e.C_BLOCK_COMMENT_MODE, l, s], + g = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: u, + contains: p.concat([ + { + begin: /\(/, + end: /\)/, + keywords: u, + contains: p.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + E = { + className: "function", + begin: "(" + i + "[\\*&\\s]+)+" + d, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: u, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: a, keywords: u, relevance: 0 }, + { begin: d, returnBegin: !0, contains: [_], relevance: 0 }, + { begin: /::/, relevance: 0 }, + { begin: /:/, endsWithParent: !0, contains: [s, l] }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: [ + n, + e.C_BLOCK_COMMENT_MODE, + s, + l, + o, + { + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: ["self", n, e.C_BLOCK_COMMENT_MODE, s, l, o], + }, + ], + }, + o, + n, + e.C_BLOCK_COMMENT_MODE, + c, + ], + }; + return { + name: "C++", + aliases: ["cc", "c++", "h++", "hpp", "hh", "hxx", "cxx"], + keywords: u, + illegal: "", + keywords: u, + contains: ["self", o], + }, + { begin: e.IDENT_RE + "::", keywords: u }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: c, strings: s, keywords: u }, + }; + })(e), + o = i.keywords; + return ( + (o.keyword += " " + t), + (o.literal += " " + r), + (o.built_in += " " + n), + (o._ += " " + a), + (i.name = "Arduino"), + (i.aliases = ["ino"]), + (i.supersetOf = "cpp"), + i + ); +}; +var ip = function (e) { + var t = { + variants: [ + e.COMMENT("^[ \\t]*(?=#)", "$", { relevance: 0, excludeBegin: !0 }), + e.COMMENT("[;@]", "$", { relevance: 0 }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; + return { + name: "ARM Assembly", + case_insensitive: !0, + aliases: ["arm"], + keywords: { + $pattern: "\\.?" + e.IDENT_RE, + meta: ".2byte .4byte .align .ascii .asciz .balign .byte .code .data .else .end .endif .endm .endr .equ .err .exitm .extern .global .hword .if .ifdef .ifndef .include .irp .long .macro .rept .req .section .set .skip .space .text .word .arm .thumb .code16 .code32 .force_thumb .thumb_func .ltorg ALIAS ALIGN ARM AREA ASSERT ATTR CN CODE CODE16 CODE32 COMMON CP DATA DCB DCD DCDU DCDO DCFD DCFDU DCI DCQ DCQU DCW DCWU DN ELIF ELSE END ENDFUNC ENDIF ENDP ENTRY EQU EXPORT EXPORTAS EXTERN FIELD FILL FUNCTION GBLA GBLL GBLS GET GLOBAL IF IMPORT INCBIN INCLUDE INFO KEEP LCLA LCLL LCLS LTORG MACRO MAP MEND MEXIT NOFP OPT PRESERVE8 PROC QN READONLY RELOC REQUIRE REQUIRE8 RLIST FN ROUT SETA SETL SETS SN SPACE SUBT THUMB THUMBX TTL WHILE WEND ", + built_in: + "r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 pc lr sp ip sl sb fp a1 a2 a3 a4 v1 v2 v3 v4 v5 v6 v7 v8 f0 f1 f2 f3 f4 f5 f6 f7 p0 p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 c0 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 cpsr_c cpsr_x cpsr_s cpsr_f cpsr_cx cpsr_cxs cpsr_xs cpsr_xsf cpsr_sf cpsr_cxsf spsr_c spsr_x spsr_s spsr_f spsr_cx spsr_cxs spsr_xs spsr_xsf spsr_sf spsr_cxsf s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 d13 d14 d15 d16 d17 d18 d19 d20 d21 d22 d23 d24 d25 d26 d27 d28 d29 d30 d31 {PC} {VAR} {TRUE} {FALSE} {OPT} {CONFIG} {ENDIAN} {CODESIZE} {CPU} {FPU} {ARCHITECTURE} {PCSTOREOFFSET} {ARMASM_VERSION} {INTER} {ROPI} {RWPI} {SWST} {NOSWST} . @", + }, + contains: [ + { + className: "keyword", + begin: + "\\b(adc|(qd?|sh?|u[qh]?)?add(8|16)?|usada?8|(q|sh?|u[qh]?)?(as|sa)x|and|adrl?|sbc|rs[bc]|asr|b[lx]?|blx|bxj|cbn?z|tb[bh]|bic|bfc|bfi|[su]bfx|bkpt|cdp2?|clz|clrex|cmp|cmn|cpsi[ed]|cps|setend|dbg|dmb|dsb|eor|isb|it[te]{0,3}|lsl|lsr|ror|rrx|ldm(([id][ab])|f[ds])?|ldr((s|ex)?[bhd])?|movt?|mvn|mra|mar|mul|[us]mull|smul[bwt][bt]|smu[as]d|smmul|smmla|mla|umlaal|smlal?([wbt][bt]|d)|mls|smlsl?[ds]|smc|svc|sev|mia([bt]{2}|ph)?|mrr?c2?|mcrr2?|mrs|msr|orr|orn|pkh(tb|bt)|rbit|rev(16|sh)?|sel|[su]sat(16)?|nop|pop|push|rfe([id][ab])?|stm([id][ab])?|str(ex)?[bhd]?|(qd?)?sub|(sh?|q|u[qh]?)?sub(8|16)|[su]xt(a?h|a?b(16)?)|srs([id][ab])?|swpb?|swi|smi|tst|teq|wfe|wfi|yield)(eq|ne|cs|cc|mi|pl|vs|vc|hi|ls|ge|lt|gt|le|al|hs|lo)?[sptrx]?(?=\\s)", + }, + t, + e.QUOTE_STRING_MODE, + { className: "string", begin: "'", end: "[^\\\\]'", relevance: 0 }, + { + className: "title", + begin: "\\|", + end: "\\|", + illegal: "\\n", + relevance: 0, + }, + { + className: "number", + variants: [ + { begin: "[#$=]?0x[0-9a-f]+" }, + { begin: "[#$=]?0b[01]+" }, + { begin: "[#$=]\\d+" }, + { begin: "\\b\\d+" }, + ], + relevance: 0, + }, + { + className: "symbol", + variants: [ + { begin: "^[ \\t]*[a-z_\\.\\$][a-z0-9_\\.\\$]+:" }, + { begin: "^[a-z_\\.\\$][a-z0-9_\\.\\$]+" }, + { begin: "[=#]\\w+" }, + ], + relevance: 0, + }, + ], + }; +}; +function op(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function sp(e) { + return lp("(?=", e, ")"); +} +function lp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return op(e); + }) + .join(""); + return a; +} +function cp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return op(e); + }) + .join("|") + + ")"; + return a; +} +var _p = function (e) { + var t = lp(/[A-Z_]/, lp("(", /[A-Z0-9_.-]*:/, ")?"), /[A-Z0-9_.-]*/), + n = { className: "symbol", begin: /&[a-z]+;|&#[0-9]+;|&#x[a-f0-9]+;/ }, + a = { + begin: /\s/, + contains: [ + { + className: "meta-keyword", + begin: /#?[a-z_][a-z1-9_-]+/, + illegal: /\n/, + }, + ], + }, + r = e.inherit(a, { begin: /\(/, end: /\)/ }), + i = e.inherit(e.APOS_STRING_MODE, { className: "meta-string" }), + o = e.inherit(e.QUOTE_STRING_MODE, { className: "meta-string" }), + s = { + endsWithParent: !0, + illegal: /`]+/ }, + ], + }, + ], + }, + ], + }; + return { + name: "HTML, XML", + aliases: [ + "html", + "xhtml", + "rss", + "atom", + "xjb", + "xsd", + "xsl", + "plist", + "wsf", + "svg", + ], + case_insensitive: !0, + contains: [ + { + className: "meta", + begin: //, + relevance: 10, + contains: [ + a, + o, + i, + r, + { + begin: /\[/, + end: /\]/, + contains: [ + { + className: "meta", + begin: //, + contains: [a, r, o, i], + }, + ], + }, + ], + }, + e.COMMENT(//, { relevance: 10 }), + { begin: //, relevance: 10 }, + n, + { className: "meta", begin: /<\?xml/, end: /\?>/, relevance: 10 }, + { + className: "tag", + begin: /)/, + end: />/, + keywords: { name: "style" }, + contains: [s], + starts: { + end: /<\/style>/, + returnEnd: !0, + subLanguage: ["css", "xml"], + }, + }, + { + className: "tag", + begin: /)/, + end: />/, + keywords: { name: "script" }, + contains: [s], + starts: { + end: /<\/script>/, + returnEnd: !0, + subLanguage: ["javascript", "handlebars", "xml"], + }, + }, + { className: "tag", begin: /<>|<\/>/ }, + { + className: "tag", + begin: lp(//, />/, /\s/)))), + end: /\/?>/, + contains: [{ className: "name", begin: t, relevance: 0, starts: s }], + }, + { + className: "tag", + begin: lp(/<\//, sp(lp(t, />/))), + contains: [ + { className: "name", begin: t, relevance: 0 }, + { begin: />/, relevance: 0, endsParent: !0 }, + ], + }, + ], + }; +}; +function dp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function up() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return dp(e); + }) + .join(""); + return a; +} +var mp = function (e) { + var t = [ + { className: "strong", begin: /\*{2}([^\n]+?)\*{2}/ }, + { + className: "strong", + begin: up( + /\*\*/, + /((\*(?!\*)|\\[^\n]|[^*\n\\])+\n)+/, + /(\*(?!\*)|\\[^\n]|[^*\n\\])*/, + /\*\*/, + ), + relevance: 0, + }, + { className: "strong", begin: /\B\*(\S|\S[^\n]*?\S)\*(?!\w)/ }, + { className: "strong", begin: /\*[^\s]([^\n]+\n)+([^\n]+)\*/ }, + ], + n = [ + { className: "emphasis", begin: /_{2}([^\n]+?)_{2}/ }, + { + className: "emphasis", + begin: up( + /__/, + /((_(?!_)|\\[^\n]|[^_\n\\])+\n)+/, + /(_(?!_)|\\[^\n]|[^_\n\\])*/, + /__/, + ), + relevance: 0, + }, + { className: "emphasis", begin: /\b_(\S|\S[^\n]*?\S)_(?!\w)/ }, + { className: "emphasis", begin: /_[^\s]([^\n]+\n)+([^\n]+)_/ }, + { + className: "emphasis", + begin: "\\B'(?!['\\s])", + end: "(\\n{2}|')", + contains: [{ begin: "\\\\'\\w", relevance: 0 }], + relevance: 0, + }, + ]; + return { + name: "AsciiDoc", + aliases: ["adoc"], + contains: [ + e.COMMENT("^/{4,}\\n", "\\n/{4,}$", { relevance: 10 }), + e.COMMENT("^//", "$", { relevance: 0 }), + { className: "title", begin: "^\\.\\w.*$" }, + { begin: "^[=\\*]{4,}\\n", end: "\\n^[=\\*]{4,}$", relevance: 10 }, + { + className: "section", + relevance: 10, + variants: [ + { begin: "^(={1,6})[ \t].+?([ \t]\\1)?$" }, + { begin: "^[^\\[\\]\\n]+?\\n[=\\-~\\^\\+]{2,}$" }, + ], + }, + { + className: "meta", + begin: "^:.+?:", + end: "\\s", + excludeEnd: !0, + relevance: 10, + }, + { className: "meta", begin: "^\\[.+?\\]$", relevance: 0 }, + { + className: "quote", + begin: "^_{4,}\\n", + end: "\\n_{4,}$", + relevance: 10, + }, + { + className: "code", + begin: "^[\\-\\.]{4,}\\n", + end: "\\n[\\-\\.]{4,}$", + relevance: 10, + }, + { + begin: "^\\+{4,}\\n", + end: "\\n\\+{4,}$", + contains: [{ begin: "<", end: ">", subLanguage: "xml", relevance: 0 }], + relevance: 10, + }, + { className: "bullet", begin: "^(\\*+|-+|\\.+|[^\\n]+?::)\\s+" }, + { + className: "symbol", + begin: "^(NOTE|TIP|IMPORTANT|WARNING|CAUTION):\\s+", + relevance: 10, + }, + ].concat( + [ + { begin: /\\[*_`]/ }, + { begin: /\\\\\*{2}[^\n]*?\*{2}/ }, + { begin: /\\\\_{2}[^\n]*_{2}/ }, + { begin: /\\\\`{2}[^\n]*`{2}/ }, + { begin: /[:;}][*_`](?![*_`])/ }, + ], + t, + n, + [ + { + className: "string", + variants: [{ begin: "``.+?''" }, { begin: "`.+?'" }], + }, + { className: "code", begin: /`{2}/, end: /(\n{2}|`{2})/ }, + { className: "code", begin: "(`.+?`|\\+.+?\\+)", relevance: 0 }, + { className: "code", begin: "^[ \\t]", end: "$", relevance: 0 }, + { begin: "^'{3,}[ \\t]*$", relevance: 10 }, + { + begin: "(link:)?(http|https|ftp|file|irc|image:?):\\S+?\\[[^[]*?\\]", + returnBegin: !0, + contains: [ + { begin: "(link|image:?):", relevance: 0 }, + { className: "link", begin: "\\w", end: "[^\\[]+", relevance: 0 }, + { + className: "string", + begin: "\\[", + end: "\\]", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + ], + relevance: 10, + }, + ], + ), + }; +}; +function pp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function gp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return pp(e); + }) + .join(""); + return a; +} +var Ep = function (e) { + var t = + "false synchronized int abstract float private char boolean static null if const for true while long throw strictfp finally protected import native final return void enum else extends implements break transient new catch instanceof byte super volatile case assert short package default double public try this switch continue throws privileged aspectOf adviceexecution proceed cflowbelow cflow initialization preinitialization staticinitialization withincode target within execution getWithinTypeName handler thisJoinPoint thisJoinPointStaticPart thisEnclosingJoinPointStaticPart declare parents warning error soft precedence thisAspectInstance", + n = "get set args call"; + return { + name: "AspectJ", + keywords: t, + illegal: /<\/|#/, + contains: [ + e.COMMENT(/\/\*\*/, /\*\//, { + relevance: 0, + contains: [ + { begin: /\w+@/, relevance: 0 }, + { className: "doctag", begin: /@[A-Za-z]+/ }, + ], + }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { + className: "class", + beginKeywords: "aspect", + end: /[{;=]/, + excludeEnd: !0, + illegal: /[:;"\[\]]/, + contains: [ + { + beginKeywords: + "extends implements pertypewithin perthis pertarget percflowbelow percflow issingleton", + }, + e.UNDERSCORE_TITLE_MODE, + { + begin: /\([^\)]*/, + end: /[)]+/, + keywords: t + " " + n, + excludeEnd: !1, + }, + ], + }, + { + className: "class", + beginKeywords: "class interface", + end: /[{;=]/, + excludeEnd: !0, + relevance: 0, + keywords: "class interface", + illegal: /[:"\[\]]/, + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { + beginKeywords: "pointcut after before around throwing returning", + end: /[)]/, + excludeEnd: !1, + illegal: /["\[\]]/, + contains: [ + { + begin: gp(e.UNDERSCORE_IDENT_RE, /\s*\(/), + returnBegin: !0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + ], + }, + { + begin: /[:]/, + returnBegin: !0, + end: /[{;]/, + relevance: 0, + excludeEnd: !1, + keywords: t, + illegal: /["\[\]]/, + contains: [ + { + begin: gp(e.UNDERSCORE_IDENT_RE, /\s*\(/), + keywords: t + " " + n, + relevance: 0, + }, + e.QUOTE_STRING_MODE, + ], + }, + { beginKeywords: "new throw", relevance: 0 }, + { + className: "function", + begin: /\w+ +\w+(\.\w+)?\s*\([^\)]*\)\s*((throws)[\w\s,]+)?[\{;]/, + returnBegin: !0, + end: /[{;=]/, + keywords: t, + excludeEnd: !0, + contains: [ + { + begin: gp(e.UNDERSCORE_IDENT_RE, /\s*\(/), + returnBegin: !0, + relevance: 0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "params", + begin: /\(/, + end: /\)/, + relevance: 0, + keywords: t, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_NUMBER_MODE, + { className: "meta", begin: /@[A-Za-z]+/ }, + ], + }; +}; +var Sp = function (e) { + var t = { begin: "`[\\s\\S]" }; + return { + name: "AutoHotkey", + case_insensitive: !0, + aliases: ["ahk"], + keywords: { + keyword: + "Break Continue Critical Exit ExitApp Gosub Goto New OnExit Pause return SetBatchLines SetTimer Suspend Thread Throw Until ahk_id ahk_class ahk_pid ahk_exe ahk_group", + literal: "true false NOT AND OR", + built_in: "ComSpec Clipboard ClipboardAll ErrorLevel", + }, + contains: [ + t, + e.inherit(e.QUOTE_STRING_MODE, { contains: [t] }), + e.COMMENT(";", "$", { relevance: 0 }), + e.C_BLOCK_COMMENT_MODE, + { className: "number", begin: e.NUMBER_RE, relevance: 0 }, + { className: "variable", begin: "%[a-zA-Z0-9#_$@]+%" }, + { className: "built_in", begin: "^\\s*\\w+\\s*(,|%)" }, + { + className: "title", + variants: [ + { begin: '^[^\\n";]+::(?!=)' }, + { begin: '^[^\\n";]+:(?!=)', relevance: 0 }, + ], + }, + { className: "meta", begin: "^\\s*#\\w+", end: "$", relevance: 0 }, + { className: "built_in", begin: "A_[a-zA-Z0-9]+" }, + { begin: ",\\s*," }, + ], + }; +}; +var bp = function (e) { + var t = { + variants: [ + e.COMMENT(";", "$", { relevance: 0 }), + e.COMMENT("#cs", "#ce"), + e.COMMENT("#comments-start", "#comments-end"), + ], + }, + n = { begin: "\\$[A-z0-9_]+" }, + a = { + className: "string", + variants: [ + { begin: /"/, end: /"/, contains: [{ begin: /""/, relevance: 0 }] }, + { begin: /'/, end: /'/, contains: [{ begin: /''/, relevance: 0 }] }, + ], + }, + r = { variants: [e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE] }; + return { + name: "AutoIt", + case_insensitive: !0, + illegal: /\/\*/, + keywords: { + keyword: + "ByRef Case Const ContinueCase ContinueLoop Dim Do Else ElseIf EndFunc EndIf EndSelect EndSwitch EndWith Enum Exit ExitLoop For Func Global If In Local Next ReDim Return Select Static Step Switch Then To Until Volatile WEnd While With", + built_in: + "Abs ACos AdlibRegister AdlibUnRegister Asc AscW ASin Assign ATan AutoItSetOption AutoItWinGetTitle AutoItWinSetTitle Beep Binary BinaryLen BinaryMid BinaryToString BitAND BitNOT BitOR BitRotate BitShift BitXOR BlockInput Break Call CDTray Ceiling Chr ChrW ClipGet ClipPut ConsoleRead ConsoleWrite ConsoleWriteError ControlClick ControlCommand ControlDisable ControlEnable ControlFocus ControlGetFocus ControlGetHandle ControlGetPos ControlGetText ControlHide ControlListView ControlMove ControlSend ControlSetText ControlShow ControlTreeView Cos Dec DirCopy DirCreate DirGetSize DirMove DirRemove DllCall DllCallAddress DllCallbackFree DllCallbackGetPtr DllCallbackRegister DllClose DllOpen DllStructCreate DllStructGetData DllStructGetPtr DllStructGetSize DllStructSetData DriveGetDrive DriveGetFileSystem DriveGetLabel DriveGetSerial DriveGetType DriveMapAdd DriveMapDel DriveMapGet DriveSetLabel DriveSpaceFree DriveSpaceTotal DriveStatus EnvGet EnvSet EnvUpdate Eval Execute Exp FileChangeDir FileClose FileCopy FileCreateNTFSLink FileCreateShortcut FileDelete FileExists FileFindFirstFile FileFindNextFile FileFlush FileGetAttrib FileGetEncoding FileGetLongName FileGetPos FileGetShortcut FileGetShortName FileGetSize FileGetTime FileGetVersion FileInstall FileMove FileOpen FileOpenDialog FileRead FileReadLine FileReadToArray FileRecycle FileRecycleEmpty FileSaveDialog FileSelectFolder FileSetAttrib FileSetEnd FileSetPos FileSetTime FileWrite FileWriteLine Floor FtpSetProxy FuncName GUICreate GUICtrlCreateAvi GUICtrlCreateButton GUICtrlCreateCheckbox GUICtrlCreateCombo GUICtrlCreateContextMenu GUICtrlCreateDate GUICtrlCreateDummy GUICtrlCreateEdit GUICtrlCreateGraphic GUICtrlCreateGroup GUICtrlCreateIcon GUICtrlCreateInput GUICtrlCreateLabel GUICtrlCreateList GUICtrlCreateListView GUICtrlCreateListViewItem GUICtrlCreateMenu GUICtrlCreateMenuItem GUICtrlCreateMonthCal GUICtrlCreateObj GUICtrlCreatePic GUICtrlCreateProgress GUICtrlCreateRadio GUICtrlCreateSlider GUICtrlCreateTab GUICtrlCreateTabItem GUICtrlCreateTreeView GUICtrlCreateTreeViewItem GUICtrlCreateUpdown GUICtrlDelete GUICtrlGetHandle GUICtrlGetState GUICtrlRead GUICtrlRecvMsg GUICtrlRegisterListViewSort GUICtrlSendMsg GUICtrlSendToDummy GUICtrlSetBkColor GUICtrlSetColor GUICtrlSetCursor GUICtrlSetData GUICtrlSetDefBkColor GUICtrlSetDefColor GUICtrlSetFont GUICtrlSetGraphic GUICtrlSetImage GUICtrlSetLimit GUICtrlSetOnEvent GUICtrlSetPos GUICtrlSetResizing GUICtrlSetState GUICtrlSetStyle GUICtrlSetTip GUIDelete GUIGetCursorInfo GUIGetMsg GUIGetStyle GUIRegisterMsg GUISetAccelerators GUISetBkColor GUISetCoord GUISetCursor GUISetFont GUISetHelp GUISetIcon GUISetOnEvent GUISetState GUISetStyle GUIStartGroup GUISwitch Hex HotKeySet HttpSetProxy HttpSetUserAgent HWnd InetClose InetGet InetGetInfo InetGetSize InetRead IniDelete IniRead IniReadSection IniReadSectionNames IniRenameSection IniWrite IniWriteSection InputBox Int IsAdmin IsArray IsBinary IsBool IsDeclared IsDllStruct IsFloat IsFunc IsHWnd IsInt IsKeyword IsNumber IsObj IsPtr IsString Log MemGetStats Mod MouseClick MouseClickDrag MouseDown MouseGetCursor MouseGetPos MouseMove MouseUp MouseWheel MsgBox Number ObjCreate ObjCreateInterface ObjEvent ObjGet ObjName OnAutoItExitRegister OnAutoItExitUnRegister Ping PixelChecksum PixelGetColor PixelSearch ProcessClose ProcessExists ProcessGetStats ProcessList ProcessSetPriority ProcessWait ProcessWaitClose ProgressOff ProgressOn ProgressSet Ptr Random RegDelete RegEnumKey RegEnumVal RegRead RegWrite Round Run RunAs RunAsWait RunWait Send SendKeepActive SetError SetExtended ShellExecute ShellExecuteWait Shutdown Sin Sleep SoundPlay SoundSetWaveVolume SplashImageOn SplashOff SplashTextOn Sqrt SRandom StatusbarGetText StderrRead StdinWrite StdioClose StdoutRead String StringAddCR StringCompare StringFormat StringFromASCIIArray StringInStr StringIsAlNum StringIsAlpha StringIsASCII StringIsDigit StringIsFloat StringIsInt StringIsLower StringIsSpace StringIsUpper StringIsXDigit StringLeft StringLen StringLower StringMid StringRegExp StringRegExpReplace StringReplace StringReverse StringRight StringSplit StringStripCR StringStripWS StringToASCIIArray StringToBinary StringTrimLeft StringTrimRight StringUpper Tan TCPAccept TCPCloseSocket TCPConnect TCPListen TCPNameToIP TCPRecv TCPSend TCPShutdown, UDPShutdown TCPStartup, UDPStartup TimerDiff TimerInit ToolTip TrayCreateItem TrayCreateMenu TrayGetMsg TrayItemDelete TrayItemGetHandle TrayItemGetState TrayItemGetText TrayItemSetOnEvent TrayItemSetState TrayItemSetText TraySetClick TraySetIcon TraySetOnEvent TraySetPauseIcon TraySetState TraySetToolTip TrayTip UBound UDPBind UDPCloseSocket UDPOpen UDPRecv UDPSend VarGetType WinActivate WinActive WinClose WinExists WinFlash WinGetCaretPos WinGetClassList WinGetClientSize WinGetHandle WinGetPos WinGetProcess WinGetState WinGetText WinGetTitle WinKill WinList WinMenuSelectItem WinMinimizeAll WinMinimizeAllUndo WinMove WinSetOnTop WinSetState WinSetTitle WinSetTrans WinWait WinWaitActive WinWaitClose WinWaitNotActive", + literal: "True False And Null Not Or Default", + }, + contains: [ + t, + n, + a, + r, + { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": [ + "EndRegion", + "forcedef", + "forceref", + "ignorefunc", + "include", + "include-once", + "NoTrayIcon", + "OnAutoItStartRegister", + "pragma", + "Region", + "RequireAdmin", + "Tidy_Off", + "Tidy_On", + "Tidy_Parameters", + ], + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + { + beginKeywords: "include", + keywords: { "meta-keyword": "include" }, + end: "$", + contains: [ + a, + { + className: "meta-string", + variants: [ + { begin: "<", end: ">" }, + { + begin: /"/, + end: /"/, + contains: [{ begin: /""/, relevance: 0 }], + }, + { + begin: /'/, + end: /'/, + contains: [{ begin: /''/, relevance: 0 }], + }, + ], + }, + ], + }, + a, + t, + ], + }, + { className: "symbol", begin: "@[A-z0-9_]+" }, + { + className: "function", + beginKeywords: "Func", + end: "$", + illegal: "\\$|\\[|%", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { + className: "params", + begin: "\\(", + end: "\\)", + contains: [n, a, r], + }, + ], + }, + ], + }; +}; +var Tp = function (e) { + return { + name: "AVR Assembly", + case_insensitive: !0, + keywords: { + $pattern: "\\.?" + e.IDENT_RE, + keyword: + "adc add adiw and andi asr bclr bld brbc brbs brcc brcs break breq brge brhc brhs brid brie brlo brlt brmi brne brpl brsh brtc brts brvc brvs bset bst call cbi cbr clc clh cli cln clr cls clt clv clz com cp cpc cpi cpse dec eicall eijmp elpm eor fmul fmuls fmulsu icall ijmp in inc jmp ld ldd ldi lds lpm lsl lsr mov movw mul muls mulsu neg nop or ori out pop push rcall ret reti rjmp rol ror sbc sbr sbrc sbrs sec seh sbi sbci sbic sbis sbiw sei sen ser ses set sev sez sleep spm st std sts sub subi swap tst wdr", + built_in: + "r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 x|0 xh xl y|0 yh yl z|0 zh zl ucsr1c udr1 ucsr1a ucsr1b ubrr1l ubrr1h ucsr0c ubrr0h tccr3c tccr3a tccr3b tcnt3h tcnt3l ocr3ah ocr3al ocr3bh ocr3bl ocr3ch ocr3cl icr3h icr3l etimsk etifr tccr1c ocr1ch ocr1cl twcr twdr twar twsr twbr osccal xmcra xmcrb eicra spmcsr spmcr portg ddrg ping portf ddrf sreg sph spl xdiv rampz eicrb eimsk gimsk gicr eifr gifr timsk tifr mcucr mcucsr tccr0 tcnt0 ocr0 assr tccr1a tccr1b tcnt1h tcnt1l ocr1ah ocr1al ocr1bh ocr1bl icr1h icr1l tccr2 tcnt2 ocr2 ocdr wdtcr sfior eearh eearl eedr eecr porta ddra pina portb ddrb pinb portc ddrc pinc portd ddrd pind spdr spsr spcr udr0 ucsr0a ucsr0b ubrr0l acsr admux adcsr adch adcl porte ddre pine pinf", + meta: ".byte .cseg .db .def .device .dseg .dw .endmacro .equ .eseg .exit .include .list .listmac .macro .nolist .org .set", + }, + contains: [ + e.C_BLOCK_COMMENT_MODE, + e.COMMENT(";", "$", { relevance: 0 }), + e.C_NUMBER_MODE, + e.BINARY_NUMBER_MODE, + { className: "number", begin: "\\b(\\$[a-zA-Z0-9]+|0o[0-7]+)" }, + e.QUOTE_STRING_MODE, + { + className: "string", + begin: "'", + end: "[^\\\\]'", + illegal: "[^\\\\][^']", + }, + { className: "symbol", begin: "^[A-Za-z0-9_.$]+:" }, + { className: "meta", begin: "#", end: "$" }, + { className: "subst", begin: "@[0-9]+" }, + ], + }; +}; +var fp = function (e) { + return { + name: "Awk", + keywords: { + keyword: + "BEGIN END if else while do for in break continue delete next nextfile function func exit|10", + }, + contains: [ + { + className: "variable", + variants: [{ begin: /\$[\w\d#@][\w\d_]*/ }, { begin: /\$\{(.*?)\}/ }], + }, + { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { begin: /(u|b)?r?'''/, end: /'''/, relevance: 10 }, + { begin: /(u|b)?r?"""/, end: /"""/, relevance: 10 }, + { begin: /(u|r|ur)'/, end: /'/, relevance: 10 }, + { begin: /(u|r|ur)"/, end: /"/, relevance: 10 }, + { begin: /(b|br)'/, end: /'/ }, + { begin: /(b|br)"/, end: /"/ }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + }, + e.REGEXP_MODE, + e.HASH_COMMENT_MODE, + e.NUMBER_MODE, + ], + }; +}; +var Cp = function (e) { + return { + name: "X++", + aliases: ["x++"], + keywords: { + keyword: [ + "abstract", + "as", + "asc", + "avg", + "break", + "breakpoint", + "by", + "byref", + "case", + "catch", + "changecompany", + "class", + "client", + "client", + "common", + "const", + "continue", + "count", + "crosscompany", + "delegate", + "delete_from", + "desc", + "display", + "div", + "do", + "edit", + "else", + "eventhandler", + "exists", + "extends", + "final", + "finally", + "firstfast", + "firstonly", + "firstonly1", + "firstonly10", + "firstonly100", + "firstonly1000", + "flush", + "for", + "forceliterals", + "forcenestedloop", + "forceplaceholders", + "forceselectorder", + "forupdate", + "from", + "generateonly", + "group", + "hint", + "if", + "implements", + "in", + "index", + "insert_recordset", + "interface", + "internal", + "is", + "join", + "like", + "maxof", + "minof", + "mod", + "namespace", + "new", + "next", + "nofetch", + "notexists", + "optimisticlock", + "order", + "outer", + "pessimisticlock", + "print", + "private", + "protected", + "public", + "readonly", + "repeatableread", + "retry", + "return", + "reverse", + "select", + "server", + "setting", + "static", + "sum", + "super", + "switch", + "this", + "throw", + "try", + "ttsabort", + "ttsbegin", + "ttscommit", + "unchecked", + "update_recordset", + "using", + "validtimestate", + "void", + "where", + "while", + ], + built_in: [ + "anytype", + "boolean", + "byte", + "char", + "container", + "date", + "double", + "enum", + "guid", + "int", + "int64", + "long", + "real", + "short", + "str", + "utcdatetime", + "var", + ], + literal: ["default", "false", "null", "true"], + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + { className: "meta", begin: "#", end: "$" }, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + illegal: ":", + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + ], + }; +}; +function Np(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Rp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Np(e); + }) + .join(""); + return a; +} +var vp = function (e) { + var t = {}, + n = { + begin: /\$\{/, + end: /\}/, + contains: ["self", { begin: /:-/, contains: [t] }], + }; + Object.assign(t, { + className: "variable", + variants: [{ begin: Rp(/\$[\w\d#@][\w\d_]*/, "(?![\\w\\d])(?![$])") }, n], + }); + var a = { + className: "subst", + begin: /\$\(/, + end: /\)/, + contains: [e.BACKSLASH_ESCAPE], + }, + r = { + begin: /<<-?\s*(?=\w+)/, + starts: { + contains: [ + e.END_SAME_AS_BEGIN({ + begin: /(\w+)/, + end: /(\w+)/, + className: "string", + }), + ], + }, + }, + i = { + className: "string", + begin: /"/, + end: /"/, + contains: [e.BACKSLASH_ESCAPE, t, a], + }; + a.contains.push(i); + var o = { + begin: /\$\(\(/, + end: /\)\)/, + contains: [ + { begin: /\d+#[0-9a-f]+/, className: "number" }, + e.NUMBER_MODE, + t, + ], + }, + s = e.SHEBANG({ + binary: "(".concat( + [ + "fish", + "bash", + "zsh", + "sh", + "csh", + "ksh", + "tcsh", + "dash", + "scsh", + ].join("|"), + ")", + ), + relevance: 10, + }), + l = { + className: "function", + begin: /\w[\w\d_]*\s*\(\s*\)\s*\{/, + returnBegin: !0, + contains: [e.inherit(e.TITLE_MODE, { begin: /\w[\w\d_]*/ })], + relevance: 0, + }; + return { + name: "Bash", + aliases: ["sh", "zsh"], + keywords: { + $pattern: /\b[a-z._-]+\b/, + keyword: "if then else elif fi for while in do done case esac function", + literal: "true false", + built_in: + "break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp", + }, + contains: [ + s, + e.SHEBANG(), + l, + o, + e.HASH_COMMENT_MODE, + r, + i, + { className: "", begin: /\\"/ }, + { className: "string", begin: /'/, end: /'/ }, + t, + ], + }; +}; +var Op = function (e) { + return { + name: "BASIC", + case_insensitive: !0, + illegal: "^.", + keywords: { + $pattern: "[a-zA-Z][a-zA-Z0-9_$%!#]*", + keyword: + "ABS ASC AND ATN AUTO|0 BEEP BLOAD|10 BSAVE|10 CALL CALLS CDBL CHAIN CHDIR CHR$|10 CINT CIRCLE CLEAR CLOSE CLS COLOR COM COMMON CONT COS CSNG CSRLIN CVD CVI CVS DATA DATE$ DEFDBL DEFINT DEFSNG DEFSTR DEF|0 SEG USR DELETE DIM DRAW EDIT END ENVIRON ENVIRON$ EOF EQV ERASE ERDEV ERDEV$ ERL ERR ERROR EXP FIELD FILES FIX FOR|0 FRE GET GOSUB|10 GOTO HEX$ IF THEN ELSE|0 INKEY$ INP INPUT INPUT# INPUT$ INSTR IMP INT IOCTL IOCTL$ KEY ON OFF LIST KILL LEFT$ LEN LET LINE LLIST LOAD LOC LOCATE LOF LOG LPRINT USING LSET MERGE MID$ MKDIR MKD$ MKI$ MKS$ MOD NAME NEW NEXT NOISE NOT OCT$ ON OR PEN PLAY STRIG OPEN OPTION BASE OUT PAINT PALETTE PCOPY PEEK PMAP POINT POKE POS PRINT PRINT] PSET PRESET PUT RANDOMIZE READ REM RENUM RESET|0 RESTORE RESUME RETURN|0 RIGHT$ RMDIR RND RSET RUN SAVE SCREEN SGN SHELL SIN SOUND SPACE$ SPC SQR STEP STICK STOP STR$ STRING$ SWAP SYSTEM TAB TAN TIME$ TIMER TROFF TRON TO USR VAL VARPTR VARPTR$ VIEW WAIT WHILE WEND WIDTH WINDOW WRITE XOR", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.COMMENT("REM", "$", { relevance: 10 }), + e.COMMENT("'", "$", { relevance: 0 }), + { className: "symbol", begin: "^[0-9]+ ", relevance: 10 }, + { + className: "number", + begin: "\\b\\d+(\\.\\d+)?([edED]\\d+)?[#!]?", + relevance: 0, + }, + { className: "number", begin: "(&[hH][0-9a-fA-F]{1,4})" }, + { className: "number", begin: "(&[oO][0-7]{1,6})" }, + ], + }; +}; +var hp = function (e) { + return { + name: "Backus–Naur Form", + contains: [ + { className: "attribute", begin: // }, + { + begin: /::=/, + end: /$/, + contains: [ + { begin: // }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + }, + ], + }; +}; +var yp = function (e) { + var t = { className: "literal", begin: /[+-]/, relevance: 0 }; + return { + name: "Brainfuck", + aliases: ["bf"], + contains: [ + e.COMMENT("[^\\[\\]\\.,\\+\\-<> \r\n]", "[\\[\\]\\.,\\+\\-<> \r\n]", { + returnEnd: !0, + relevance: 0, + }), + { className: "title", begin: "[\\[\\]]", relevance: 0 }, + { className: "string", begin: "[\\.,]", relevance: 0 }, + { begin: /(?:\+\+|--)/, contains: [t] }, + t, + ], + }; +}; +function Ip(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Ap(e) { + return Dp("(", e, ")?"); +} +function Dp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Ip(e); + }) + .join(""); + return a; +} +var Mp = function (e) { + var t, + n, + a = (function (e) { + var t, + n = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + a = "decltype\\(auto\\)", + r = "[a-zA-Z_]\\w*::", + i = + "(decltype\\(auto\\)|" + + Ap(r) + + "[a-zA-Z_]\\w*" + + Ap("<[^<>]+>") + + ")", + o = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + s = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + c = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(s, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + n, + e.C_BLOCK_COMMENT_MODE, + ], + }, + _ = { className: "title", begin: Ap(r) + e.IDENT_RE, relevance: 0 }, + d = Ap(r) + e.IDENT_RE + "\\s*\\(", + u = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: "_Bool _Complex _Imaginary", + _relevance_hints: [ + "asin", + "atan2", + "atan", + "calloc", + "ceil", + "cosh", + "cos", + "exit", + "exp", + "fabs", + "floor", + "fmod", + "fprintf", + "fputs", + "free", + "frexp", + "auto_ptr", + "deque", + "list", + "queue", + "stack", + "vector", + "map", + "set", + "pair", + "bitset", + "multiset", + "multimap", + "unordered_set", + "fscanf", + "future", + "isalnum", + "isalpha", + "iscntrl", + "isdigit", + "isgraph", + "islower", + "isprint", + "ispunct", + "isspace", + "isupper", + "isxdigit", + "tolower", + "toupper", + "labs", + "ldexp", + "log10", + "log", + "malloc", + "realloc", + "memchr", + "memcmp", + "memcpy", + "memset", + "modf", + "pow", + "printf", + "putchar", + "puts", + "scanf", + "sinh", + "sin", + "snprintf", + "sprintf", + "sqrt", + "sscanf", + "strcat", + "strchr", + "strcmp", + "strcpy", + "strcspn", + "strlen", + "strncat", + "strncmp", + "strncpy", + "strpbrk", + "strrchr", + "strspn", + "strstr", + "tanh", + "tan", + "unordered_map", + "unordered_multiset", + "unordered_multimap", + "priority_queue", + "make_pair", + "array", + "shared_ptr", + "abort", + "terminate", + "abs", + "acos", + "vfprintf", + "vprintf", + "vsprintf", + "endl", + "initializer_list", + "unique_ptr", + "complex", + "imaginary", + "std", + "string", + "wstring", + "cin", + "cout", + "cerr", + "clog", + "stdin", + "stdout", + "stderr", + "stringstream", + "istringstream", + "ostringstream", + ], + literal: "true false nullptr NULL", + }, + m = { + className: "function.dispatch", + relevance: 0, + keywords: u, + begin: Dp( + /\b/, + /(?!decltype)/, + /(?!if)/, + /(?!for)/, + /(?!while)/, + e.IDENT_RE, + ((t = /\s*\(/), Dp("(?=", t, ")")), + ), + }, + p = [m, c, o, n, e.C_BLOCK_COMMENT_MODE, l, s], + g = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: u, + contains: p.concat([ + { + begin: /\(/, + end: /\)/, + keywords: u, + contains: p.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + E = { + className: "function", + begin: "(" + i + "[\\*&\\s]+)+" + d, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: u, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: a, keywords: u, relevance: 0 }, + { begin: d, returnBegin: !0, contains: [_], relevance: 0 }, + { begin: /::/, relevance: 0 }, + { begin: /:/, endsWithParent: !0, contains: [s, l] }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: [ + n, + e.C_BLOCK_COMMENT_MODE, + s, + l, + o, + { + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: ["self", n, e.C_BLOCK_COMMENT_MODE, s, l, o], + }, + ], + }, + o, + n, + e.C_BLOCK_COMMENT_MODE, + c, + ], + }; + return { + name: "C++", + aliases: ["cc", "c++", "h++", "hpp", "hh", "hxx", "cxx"], + keywords: u, + illegal: "", + keywords: u, + contains: ["self", o], + }, + { begin: e.IDENT_RE + "::", keywords: u }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: c, strings: s, keywords: u }, + }; + })(e); + return ( + (a.disableAutodetect = !0), + (a.aliases = []), + e.getLanguage("c") || (t = a.aliases).push.apply(t, ["c", "h"]), + e.getLanguage("cpp") || + (n = a.aliases).push.apply(n, [ + "cc", + "c++", + "h++", + "hpp", + "hh", + "hxx", + "cxx", + ]), + a + ); +}; +function Lp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function wp(e) { + return (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return t + .map(function (e) { + return Lp(e); + }) + .join(""); + })("(", e, ")?"); +} +var xp = function (e) { + var t = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + n = "decltype\\(auto\\)", + a = "[a-zA-Z_]\\w*::", + r = "(decltype\\(auto\\)|" + wp(a) + "[a-zA-Z_]\\w*" + wp("<[^<>]+>") + ")", + i = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + o = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + s = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + l = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(o, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + t, + e.C_BLOCK_COMMENT_MODE, + ], + }, + c = { className: "title", begin: wp(a) + e.IDENT_RE, relevance: 0 }, + _ = wp(a) + e.IDENT_RE + "\\s*\\(", + d = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: + "std string wstring cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set pair bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap priority_queue make_pair array shared_ptr abort terminate abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf future isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr _Bool complex _Complex imaginary _Imaginary", + literal: "true false nullptr NULL", + }, + u = [l, i, t, e.C_BLOCK_COMMENT_MODE, s, o], + m = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: d, + contains: u.concat([ + { + begin: /\(/, + end: /\)/, + keywords: d, + contains: u.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + p = { + className: "function", + begin: "(" + r + "[\\*&\\s]+)+" + _, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: d, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: n, keywords: d, relevance: 0 }, + { begin: _, returnBegin: !0, contains: [c], relevance: 0 }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: d, + relevance: 0, + contains: [ + t, + e.C_BLOCK_COMMENT_MODE, + o, + s, + i, + { + begin: /\(/, + end: /\)/, + keywords: d, + relevance: 0, + contains: ["self", t, e.C_BLOCK_COMMENT_MODE, o, s, i], + }, + ], + }, + i, + t, + e.C_BLOCK_COMMENT_MODE, + l, + ], + }; + return { + name: "C", + aliases: ["h"], + keywords: d, + disableAutodetect: !0, + illegal: "", + keywords: d, + contains: ["self", i], + }, + { begin: e.IDENT_RE + "::", keywords: d }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: l, strings: o, keywords: d }, + }; +}; +var Pp = function (e) { + var t = + "div mod in and or not xor asserterror begin case do downto else end exit for if of repeat then to until while with var", + n = [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/\{/, /\}/, { relevance: 0 }), + e.COMMENT(/\(\*/, /\*\)/, { relevance: 10 }), + ], + a = { + className: "string", + begin: /'/, + end: /'/, + contains: [{ begin: /''/ }], + }, + r = { className: "string", begin: /(#\d+)+/ }, + i = { + className: "function", + beginKeywords: "procedure", + end: /[:;]/, + keywords: "procedure|10", + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: t, + contains: [a, r], + }, + ].concat(n), + }, + o = { + className: "class", + begin: + "OBJECT (Table|Form|Report|Dataport|Codeunit|XMLport|MenuSuite|Page|Query) (\\d+) ([^\\r\\n]+)", + returnBegin: !0, + contains: [e.TITLE_MODE, i], + }; + return { + name: "C/AL", + case_insensitive: !0, + keywords: { keyword: t, literal: "false true" }, + illegal: /\/\*/, + contains: [ + a, + r, + { className: "number", begin: "\\b\\d+(\\.\\d+)?(DT|D|T)", relevance: 0 }, + { className: "string", begin: '"', end: '"' }, + e.NUMBER_MODE, + o, + i, + ], + }; +}; +var kp = function (e) { + return { + name: "Cap’n Proto", + aliases: ["capnp"], + keywords: { + keyword: + "struct enum interface union group import using const annotation extends in of on as with from fixed", + built_in: + "Void Bool Int8 Int16 Int32 Int64 UInt8 UInt16 UInt32 UInt64 Float32 Float64 Text Data AnyPointer AnyStruct Capability List", + literal: "true false", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + e.HASH_COMMENT_MODE, + { className: "meta", begin: /@0x[\w\d]{16};/, illegal: /\n/ }, + { className: "symbol", begin: /@\d+\b/ }, + { + className: "class", + beginKeywords: "struct enum", + end: /\{/, + illegal: /\n/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, excludeEnd: !0 }, + }), + ], + }, + { + className: "class", + beginKeywords: "interface", + end: /\{/, + illegal: /\n/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, excludeEnd: !0 }, + }), + ], + }, + ], + }; +}; +var Up = function (e) { + var t = + "assembly module package import alias class interface object given value assign void function new of extends satisfies abstracts in out return break continue throw assert dynamic if else switch case for while try catch finally then let this outer super is exists nonempty", + n = { + className: "subst", + excludeBegin: !0, + excludeEnd: !0, + begin: /``/, + end: /``/, + keywords: t, + relevance: 10, + }, + a = [ + { className: "string", begin: '"""', end: '"""', relevance: 10 }, + { className: "string", begin: '"', end: '"', contains: [n] }, + { className: "string", begin: "'", end: "'" }, + { + className: "number", + begin: + "#[0-9a-fA-F_]+|\\$[01_]+|[0-9_]+(?:\\.[0-9_](?:[eE][+-]?\\d+)?)?[kMGTPmunpf]?", + relevance: 0, + }, + ]; + return ( + (n.contains = a), + { + name: "Ceylon", + keywords: { + keyword: + t + + " shared abstract formal default actual variable late native deprecated final sealed annotation suppressWarnings small", + meta: "doc by license see throws tagged", + }, + illegal: "\\$[^01]|#[^0-9a-fA-F]", + contains: [ + e.C_LINE_COMMENT_MODE, + e.COMMENT("/\\*", "\\*/", { contains: ["self"] }), + { className: "meta", begin: '@[a-z]\\w*(?::"[^"]*")?' }, + ].concat(a), + } + ); +}; +var Fp = function (e) { + return { + name: "Clean", + aliases: ["icl", "dcl"], + keywords: { + keyword: + "if let in with where case of class instance otherwise implementation definition system module from import qualified as special code inline foreign export ccall stdcall generic derive infix infixl infixr", + built_in: "Int Real Char Bool", + literal: "True False", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + { begin: "->|<-[|:]?|#!?|>>=|\\{\\||\\|\\}|:==|=:|<>" }, + ], + }; +}; +var Bp = function (e) { + var t = "a-zA-Z_\\-!.?+*=<>&#'", + n = "[" + t + "][" + t + "0-9/;:]*", + a = + "def defonce defprotocol defstruct defmulti defmethod defn- defn defmacro deftype defrecord", + r = { + $pattern: n, + "builtin-name": + a + + " cond apply if-not if-let if not not= =|0 <|0 >|0 <=|0 >=|0 ==|0 +|0 /|0 *|0 -|0 rem quot neg? pos? delay? symbol? keyword? true? false? integer? empty? coll? list? set? ifn? fn? associative? sequential? sorted? counted? reversible? number? decimal? class? distinct? isa? float? rational? reduced? ratio? odd? even? char? seq? vector? string? map? nil? contains? zero? instance? not-every? not-any? libspec? -> ->> .. . inc compare do dotimes mapcat take remove take-while drop letfn drop-last take-last drop-while while intern condp case reduced cycle split-at split-with repeat replicate iterate range merge zipmap declare line-seq sort comparator sort-by dorun doall nthnext nthrest partition eval doseq await await-for let agent atom send send-off release-pending-sends add-watch mapv filterv remove-watch agent-error restart-agent set-error-handler error-handler set-error-mode! error-mode shutdown-agents quote var fn loop recur throw try monitor-enter monitor-exit macroexpand macroexpand-1 for dosync and or when when-not when-let comp juxt partial sequence memoize constantly complement identity assert peek pop doto proxy first rest cons cast coll last butlast sigs reify second ffirst fnext nfirst nnext meta with-meta ns in-ns create-ns import refer keys select-keys vals key val rseq name namespace promise into transient persistent! conj! assoc! dissoc! pop! disj! use class type num float double short byte boolean bigint biginteger bigdec print-method print-dup throw-if printf format load compile get-in update-in pr pr-on newline flush read slurp read-line subvec with-open memfn time re-find re-groups rand-int rand mod locking assert-valid-fdecl alias resolve ref deref refset swap! reset! set-validator! compare-and-set! alter-meta! reset-meta! commute get-validator alter ref-set ref-history-count ref-min-history ref-max-history ensure sync io! new next conj set! to-array future future-call into-array aset gen-class reduce map filter find empty hash-map hash-set sorted-map sorted-map-by sorted-set sorted-set-by vec vector seq flatten reverse assoc dissoc list disj get union difference intersection extend extend-type extend-protocol int nth delay count concat chunk chunk-buffer chunk-append chunk-first chunk-rest max min dec unchecked-inc-int unchecked-inc unchecked-dec-inc unchecked-dec unchecked-negate unchecked-add-int unchecked-add unchecked-subtract-int unchecked-subtract chunk-next chunk-cons chunked-seq? prn vary-meta lazy-seq spread list* str find-keyword keyword symbol gensym force rationalize", + }, + i = { begin: n, relevance: 0 }, + o = { className: "number", begin: "[-+]?\\d+(\\.\\d+)?", relevance: 0 }, + s = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + l = e.COMMENT(";", "$", { relevance: 0 }), + c = { className: "literal", begin: /\b(true|false|nil)\b/ }, + _ = { begin: "[\\[\\{]", end: "[\\]\\}]" }, + d = { className: "comment", begin: "\\^" + n }, + u = e.COMMENT("\\^\\{", "\\}"), + m = { className: "symbol", begin: "[:]{1,2}" + n }, + p = { begin: "\\(", end: "\\)" }, + g = { endsWithParent: !0, relevance: 0 }, + E = { keywords: r, className: "name", begin: n, relevance: 0, starts: g }, + S = [p, s, d, u, l, m, _, o, c, i], + b = { + beginKeywords: a, + lexemes: n, + end: '(\\[|#|\\d|"|:|\\{|\\)|\\(|$)', + contains: [ + { + className: "title", + begin: n, + relevance: 0, + excludeEnd: !0, + endsParent: !0, + }, + ].concat(S), + }; + return ( + (p.contains = [e.COMMENT("comment", ""), b, E, g]), + (g.contains = S), + (_.contains = S), + (u.contains = [_]), + { + name: "Clojure", + aliases: ["clj"], + illegal: /\S/, + contains: [p, s, d, u, l, m, _, o, c], + } + ); +}; +var Gp = function (e) { + return { + name: "Clojure REPL", + contains: [ + { + className: "meta", + begin: /^([\w.-]+|\s*#_)?=>/, + starts: { end: /$/, subLanguage: "clojure" }, + }, + ], + }; +}; +var Yp = function (e) { + return { + name: "CMake", + aliases: ["cmake.in"], + case_insensitive: !0, + keywords: { + keyword: + "break cmake_host_system_information cmake_minimum_required cmake_parse_arguments cmake_policy configure_file continue elseif else endforeach endfunction endif endmacro endwhile execute_process file find_file find_library find_package find_path find_program foreach function get_cmake_property get_directory_property get_filename_component get_property if include include_guard list macro mark_as_advanced math message option return separate_arguments set_directory_properties set_property set site_name string unset variable_watch while add_compile_definitions add_compile_options add_custom_command add_custom_target add_definitions add_dependencies add_executable add_library add_link_options add_subdirectory add_test aux_source_directory build_command create_test_sourcelist define_property enable_language enable_testing export fltk_wrap_ui get_source_file_property get_target_property get_test_property include_directories include_external_msproject include_regular_expression install link_directories link_libraries load_cache project qt_wrap_cpp qt_wrap_ui remove_definitions set_source_files_properties set_target_properties set_tests_properties source_group target_compile_definitions target_compile_features target_compile_options target_include_directories target_link_directories target_link_libraries target_link_options target_sources try_compile try_run ctest_build ctest_configure ctest_coverage ctest_empty_binary_directory ctest_memcheck ctest_read_custom_files ctest_run_script ctest_sleep ctest_start ctest_submit ctest_test ctest_update ctest_upload build_name exec_program export_library_dependencies install_files install_programs install_targets load_command make_directory output_required_files remove subdir_depends subdirs use_mangled_mesa utility_source variable_requires write_file qt5_use_modules qt5_use_package qt5_wrap_cpp on off true false and or not command policy target test exists is_newer_than is_directory is_symlink is_absolute matches less greater equal less_equal greater_equal strless strgreater strequal strless_equal strgreater_equal version_less version_greater version_equal version_less_equal version_greater_equal in_list defined", + }, + contains: [ + { className: "variable", begin: /\$\{/, end: /\}/ }, + e.HASH_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + ], + }; + }, + Hp = [ + "as", + "in", + "of", + "if", + "for", + "while", + "finally", + "var", + "new", + "function", + "do", + "return", + "void", + "else", + "break", + "catch", + "instanceof", + "with", + "throw", + "case", + "default", + "try", + "switch", + "continue", + "typeof", + "delete", + "let", + "yield", + "const", + "class", + "debugger", + "async", + "await", + "static", + "import", + "from", + "export", + "extends", + ], + Vp = ["true", "false", "null", "undefined", "NaN", "Infinity"], + qp = [].concat( + [ + "setInterval", + "setTimeout", + "clearInterval", + "clearTimeout", + "require", + "exports", + "eval", + "isFinite", + "isNaN", + "parseFloat", + "parseInt", + "decodeURI", + "decodeURIComponent", + "encodeURI", + "encodeURIComponent", + "escape", + "unescape", + ], + [ + "arguments", + "this", + "super", + "console", + "window", + "document", + "localStorage", + "module", + "global", + ], + [ + "Intl", + "DataView", + "Number", + "Math", + "Date", + "String", + "RegExp", + "Object", + "Function", + "Boolean", + "Error", + "Symbol", + "Set", + "Map", + "WeakSet", + "WeakMap", + "Proxy", + "Reflect", + "JSON", + "Promise", + "Float64Array", + "Int16Array", + "Int32Array", + "Int8Array", + "Uint16Array", + "Uint32Array", + "Float32Array", + "Array", + "Uint8Array", + "Uint8ClampedArray", + "ArrayBuffer", + "BigInt64Array", + "BigUint64Array", + "BigInt", + ], + [ + "EvalError", + "InternalError", + "RangeError", + "ReferenceError", + "SyntaxError", + "TypeError", + "URIError", + ], + ); +var zp = function (e) { + var t, + n = { + keyword: Hp.concat([ + "then", + "unless", + "until", + "loop", + "by", + "when", + "and", + "or", + "is", + "isnt", + "not", + ]).filter( + ((t = ["var", "const", "let", "function", "static"]), + function (e) { + return !t.includes(e); + }), + ), + literal: Vp.concat(["yes", "no", "on", "off"]), + built_in: qp.concat(["npm", "print"]), + }, + a = "[A-Za-z$_][0-9A-Za-z$_]*", + r = { className: "subst", begin: /#\{/, end: /\}/, keywords: n }, + i = [ + e.BINARY_NUMBER_MODE, + e.inherit(e.C_NUMBER_MODE, { starts: { end: "(\\s*/)?", relevance: 0 } }), + { + className: "string", + variants: [ + { begin: /'''/, end: /'''/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /'/, end: /'/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /"""/, end: /"""/, contains: [e.BACKSLASH_ESCAPE, r] }, + { begin: /"/, end: /"/, contains: [e.BACKSLASH_ESCAPE, r] }, + ], + }, + { + className: "regexp", + variants: [ + { begin: "///", end: "///", contains: [r, e.HASH_COMMENT_MODE] }, + { begin: "//[gim]{0,3}(?=\\W)", relevance: 0 }, + { begin: /\/(?![ *]).*?(?![\\]).\/[gim]{0,3}(?=\W)/ }, + ], + }, + { begin: "@" + a }, + { + subLanguage: "javascript", + excludeBegin: !0, + excludeEnd: !0, + variants: [ + { begin: "```", end: "```" }, + { begin: "`", end: "`" }, + ], + }, + ]; + r.contains = i; + var o = e.inherit(e.TITLE_MODE, { begin: a }), + s = "(\\(.*\\)\\s*)?\\B[-=]>", + l = { + className: "params", + begin: "\\([^\\(]", + returnBegin: !0, + contains: [ + { begin: /\(/, end: /\)/, keywords: n, contains: ["self"].concat(i) }, + ], + }; + return { + name: "CoffeeScript", + aliases: ["coffee", "cson", "iced"], + keywords: n, + illegal: /\/\*/, + contains: i.concat([ + e.COMMENT("###", "###"), + e.HASH_COMMENT_MODE, + { + className: "function", + begin: "^\\s*" + a + "\\s*=\\s*" + s, + end: "[-=]>", + returnBegin: !0, + contains: [o, l], + }, + { + begin: /[:\(,=]\s*/, + relevance: 0, + contains: [ + { + className: "function", + begin: s, + end: "[-=]>", + returnBegin: !0, + contains: [l], + }, + ], + }, + { + className: "class", + beginKeywords: "class", + end: "$", + illegal: /[:="\[\]]/, + contains: [ + { + beginKeywords: "extends", + endsWithParent: !0, + illegal: /[:="\[\]]/, + contains: [o], + }, + o, + ], + }, + { + begin: a + ":", + end: ":", + returnBegin: !0, + returnEnd: !0, + relevance: 0, + }, + ]), + }; +}; +var Wp = function (e) { + return { + name: "Coq", + keywords: { + keyword: + "_|0 as at cofix else end exists exists2 fix for forall fun if IF in let match mod Prop return Set then Type using where with Abort About Add Admit Admitted All Arguments Assumptions Axiom Back BackTo Backtrack Bind Blacklist Canonical Cd Check Class Classes Close Coercion Coercions CoFixpoint CoInductive Collection Combined Compute Conjecture Conjectures Constant constr Constraint Constructors Context Corollary CreateHintDb Cut Declare Defined Definition Delimit Dependencies Dependent Derive Drop eauto End Equality Eval Example Existential Existentials Existing Export exporting Extern Extract Extraction Fact Field Fields File Fixpoint Focus for From Function Functional Generalizable Global Goal Grab Grammar Graph Guarded Heap Hint HintDb Hints Hypotheses Hypothesis ident Identity If Immediate Implicit Import Include Inductive Infix Info Initial Inline Inspect Instance Instances Intro Intros Inversion Inversion_clear Language Left Lemma Let Libraries Library Load LoadPath Local Locate Ltac ML Mode Module Modules Monomorphic Morphism Next NoInline Notation Obligation Obligations Opaque Open Optimize Options Parameter Parameters Parametric Path Paths pattern Polymorphic Preterm Print Printing Program Projections Proof Proposition Pwd Qed Quit Rec Record Recursive Redirect Relation Remark Remove Require Reserved Reset Resolve Restart Rewrite Right Ring Rings Save Scheme Scope Scopes Script Search SearchAbout SearchHead SearchPattern SearchRewrite Section Separate Set Setoid Show Solve Sorted Step Strategies Strategy Structure SubClass Table Tables Tactic Term Test Theorem Time Timeout Transparent Type Typeclasses Types Undelimit Undo Unfocus Unfocused Unfold Universe Universes Unset Unshelve using Variable Variables Variant Verbose Visibility where with", + built_in: + "abstract absurd admit after apply as assert assumption at auto autorewrite autounfold before bottom btauto by case case_eq cbn cbv change classical_left classical_right clear clearbody cofix compare compute congruence constr_eq constructor contradict contradiction cut cutrewrite cycle decide decompose dependent destruct destruction dintuition discriminate discrR do double dtauto eapply eassumption eauto ecase econstructor edestruct ediscriminate eelim eexact eexists einduction einjection eleft elim elimtype enough equality erewrite eright esimplify_eq esplit evar exact exactly_once exfalso exists f_equal fail field field_simplify field_simplify_eq first firstorder fix fold fourier functional generalize generalizing gfail give_up has_evar hnf idtac in induction injection instantiate intro intro_pattern intros intuition inversion inversion_clear is_evar is_var lapply lazy left lia lra move native_compute nia nsatz omega once pattern pose progress proof psatz quote record red refine reflexivity remember rename repeat replace revert revgoals rewrite rewrite_strat right ring ring_simplify rtauto set setoid_reflexivity setoid_replace setoid_rewrite setoid_symmetry setoid_transitivity shelve shelve_unifiable simpl simple simplify_eq solve specialize split split_Rabs split_Rmult stepl stepr subst sum swap symmetry tactic tauto time timeout top transitivity trivial try tryif unfold unify until using vm_compute with", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.COMMENT("\\(\\*", "\\*\\)"), + e.C_NUMBER_MODE, + { className: "type", excludeBegin: !0, begin: "\\|\\s*", end: "\\w+" }, + { begin: /[-=]>/ }, + ], + }; +}; +var $p = function (e) { + return { + name: "Caché Object Script", + case_insensitive: !0, + aliases: ["cls"], + keywords: + "property parameter class classmethod clientmethod extends as break catch close continue do d|0 else elseif for goto halt hang h|0 if job j|0 kill k|0 lock l|0 merge new open quit q|0 read r|0 return set s|0 tcommit throw trollback try tstart use view while write w|0 xecute x|0 zkill znspace zn ztrap zwrite zw zzdump zzwrite print zbreak zinsert zload zprint zremove zsave zzprint mv mvcall mvcrt mvdim mvprint zquit zsync ascii", + contains: [ + { + className: "number", + begin: "\\b(\\d+(\\.\\d*)?|\\.\\d+)", + relevance: 0, + }, + { + className: "string", + variants: [ + { begin: '"', end: '"', contains: [{ begin: '""', relevance: 0 }] }, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "comment", begin: /;/, end: "$", relevance: 0 }, + { className: "built_in", begin: /(?:\$\$?|\.\.)\^?[a-zA-Z]+/ }, + { className: "built_in", begin: /\$\$\$[a-zA-Z]+/ }, + { className: "built_in", begin: /%[a-z]+(?:\.[a-z]+)*/ }, + { className: "symbol", begin: /\^%?[a-zA-Z][\w]*/ }, + { className: "keyword", begin: /##class|##super|#define|#dim/ }, + { + begin: /&sql\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + subLanguage: "sql", + }, + { + begin: /&(js|jscript|javascript)/, + excludeBegin: !0, + excludeEnd: !0, + subLanguage: "javascript", + }, + { begin: /&html<\s*\s*>/, subLanguage: "xml" }, + ], + }; +}; +function Qp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Kp(e) { + return jp("(", e, ")?"); +} +function jp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Qp(e); + }) + .join(""); + return a; +} +var Xp = function (e) { + var t, + n = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + a = "decltype\\(auto\\)", + r = "[a-zA-Z_]\\w*::", + i = "(decltype\\(auto\\)|" + Kp(r) + "[a-zA-Z_]\\w*" + Kp("<[^<>]+>") + ")", + o = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + s = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + c = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(s, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + n, + e.C_BLOCK_COMMENT_MODE, + ], + }, + _ = { className: "title", begin: Kp(r) + e.IDENT_RE, relevance: 0 }, + d = Kp(r) + e.IDENT_RE + "\\s*\\(", + u = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: "_Bool _Complex _Imaginary", + _relevance_hints: [ + "asin", + "atan2", + "atan", + "calloc", + "ceil", + "cosh", + "cos", + "exit", + "exp", + "fabs", + "floor", + "fmod", + "fprintf", + "fputs", + "free", + "frexp", + "auto_ptr", + "deque", + "list", + "queue", + "stack", + "vector", + "map", + "set", + "pair", + "bitset", + "multiset", + "multimap", + "unordered_set", + "fscanf", + "future", + "isalnum", + "isalpha", + "iscntrl", + "isdigit", + "isgraph", + "islower", + "isprint", + "ispunct", + "isspace", + "isupper", + "isxdigit", + "tolower", + "toupper", + "labs", + "ldexp", + "log10", + "log", + "malloc", + "realloc", + "memchr", + "memcmp", + "memcpy", + "memset", + "modf", + "pow", + "printf", + "putchar", + "puts", + "scanf", + "sinh", + "sin", + "snprintf", + "sprintf", + "sqrt", + "sscanf", + "strcat", + "strchr", + "strcmp", + "strcpy", + "strcspn", + "strlen", + "strncat", + "strncmp", + "strncpy", + "strpbrk", + "strrchr", + "strspn", + "strstr", + "tanh", + "tan", + "unordered_map", + "unordered_multiset", + "unordered_multimap", + "priority_queue", + "make_pair", + "array", + "shared_ptr", + "abort", + "terminate", + "abs", + "acos", + "vfprintf", + "vprintf", + "vsprintf", + "endl", + "initializer_list", + "unique_ptr", + "complex", + "imaginary", + "std", + "string", + "wstring", + "cin", + "cout", + "cerr", + "clog", + "stdin", + "stdout", + "stderr", + "stringstream", + "istringstream", + "ostringstream", + ], + literal: "true false nullptr NULL", + }, + m = { + className: "function.dispatch", + relevance: 0, + keywords: u, + begin: jp( + /\b/, + /(?!decltype)/, + /(?!if)/, + /(?!for)/, + /(?!while)/, + e.IDENT_RE, + ((t = /\s*\(/), jp("(?=", t, ")")), + ), + }, + p = [m, c, o, n, e.C_BLOCK_COMMENT_MODE, l, s], + g = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: u, + contains: p.concat([ + { + begin: /\(/, + end: /\)/, + keywords: u, + contains: p.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + E = { + className: "function", + begin: "(" + i + "[\\*&\\s]+)+" + d, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: u, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: a, keywords: u, relevance: 0 }, + { begin: d, returnBegin: !0, contains: [_], relevance: 0 }, + { begin: /::/, relevance: 0 }, + { begin: /:/, endsWithParent: !0, contains: [s, l] }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: [ + n, + e.C_BLOCK_COMMENT_MODE, + s, + l, + o, + { + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: ["self", n, e.C_BLOCK_COMMENT_MODE, s, l, o], + }, + ], + }, + o, + n, + e.C_BLOCK_COMMENT_MODE, + c, + ], + }; + return { + name: "C++", + aliases: ["cc", "c++", "h++", "hpp", "hh", "hxx", "cxx"], + keywords: u, + illegal: "", + keywords: u, + contains: ["self", o], + }, + { begin: e.IDENT_RE + "::", keywords: u }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: c, strings: s, keywords: u }, + }; +}; +var Zp = function (e) { + var t = + "group clone ms master location colocation order fencing_topology rsc_ticket acl_target acl_group user role tag xml"; + return { + name: "crmsh", + aliases: ["crm", "pcmk"], + case_insensitive: !0, + keywords: { + keyword: + "params meta operations op rule attributes utilization read write deny defined not_defined in_range date spec in ref reference attribute type xpath version and or lt gt tag lte gte eq ne \\ number string", + literal: + "Master Started Slave Stopped start promote demote stop monitor true false", + }, + contains: [ + e.HASH_COMMENT_MODE, + { + beginKeywords: "node", + starts: { + end: "\\s*([\\w_-]+:)?", + starts: { className: "title", end: "\\s*[\\$\\w_][\\w_-]*" }, + }, + }, + { + beginKeywords: "primitive rsc_template", + starts: { + className: "title", + end: "\\s*[\\$\\w_][\\w_-]*", + starts: { end: "\\s*@?[\\w_][\\w_\\.:-]*" }, + }, + }, + { + begin: "\\b(" + t.split(" ").join("|") + ")\\s+", + keywords: t, + starts: { className: "title", end: "[\\$\\w_][\\w_-]*" }, + }, + { + beginKeywords: "property rsc_defaults op_defaults", + starts: { className: "title", end: "\\s*([\\w_-]+:)?" }, + }, + e.QUOTE_STRING_MODE, + { + className: "meta", + begin: "(ocf|systemd|service|lsb):[\\w_:-]+", + relevance: 0, + }, + { + className: "number", + begin: "\\b\\d+(\\.\\d+)?(ms|s|h|m)?", + relevance: 0, + }, + { className: "literal", begin: "[-]?(infinity|inf)", relevance: 0 }, + { className: "attr", begin: /([A-Za-z$_#][\w_-]+)=/, relevance: 0 }, + { className: "tag", begin: "", relevance: 0 }, + ], + }; +}; +var Jp = function (e) { + var t = "(_?[ui](8|16|32|64|128))?", + n = + "[a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|[=!]~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~|]|//|//=|&[-+*]=?|&\\*\\*|\\[\\][=?]?", + a = "[A-Za-z_]\\w*(::\\w+)*(\\?|!)?", + r = { + $pattern: "[a-zA-Z_]\\w*[!?=]?", + keyword: + "abstract alias annotation as as? asm begin break case class def do else elsif end ensure enum extend for fun if include instance_sizeof is_a? lib macro module next nil? of out pointerof private protected rescue responds_to? return require select self sizeof struct super then type typeof union uninitialized unless until verbatim when while with yield __DIR__ __END_LINE__ __FILE__ __LINE__", + literal: "false nil true", + }, + i = { className: "subst", begin: /#\{/, end: /\}/, keywords: r }, + o = { + className: "template-variable", + variants: [ + { begin: "\\{\\{", end: "\\}\\}" }, + { begin: "\\{%", end: "%\\}" }, + ], + keywords: r, + }; + function s(e, t) { + var n = [{ begin: e, end: t }]; + return (n[0].contains = n), n; + } + var l = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, i], + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /`/, end: /`/ }, + { begin: "%[Qwi]?\\(", end: "\\)", contains: s("\\(", "\\)") }, + { begin: "%[Qwi]?\\[", end: "\\]", contains: s("\\[", "\\]") }, + { begin: "%[Qwi]?\\{", end: /\}/, contains: s(/\{/, /\}/) }, + { begin: "%[Qwi]?<", end: ">", contains: s("<", ">") }, + { begin: "%[Qwi]?\\|", end: "\\|" }, + { begin: /<<-\w+$/, end: /^\s*\w+$/ }, + ], + relevance: 0, + }, + c = { + className: "string", + variants: [ + { begin: "%q\\(", end: "\\)", contains: s("\\(", "\\)") }, + { begin: "%q\\[", end: "\\]", contains: s("\\[", "\\]") }, + { begin: "%q\\{", end: /\}/, contains: s(/\{/, /\}/) }, + { begin: "%q<", end: ">", contains: s("<", ">") }, + { begin: "%q\\|", end: "\\|" }, + { begin: /<<-'\w+'$/, end: /^\s*\w+$/ }, + ], + relevance: 0, + }, + _ = { + begin: + "(?!%\\})(" + + e.RE_STARTERS_RE + + "|\\n|\\b(case|if|select|unless|until|when|while)\\b)\\s*", + keywords: "case if select unless until when while", + contains: [ + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, i], + variants: [ + { begin: "//[a-z]*", relevance: 0 }, + { begin: "/(?!\\/)", end: "/[a-z]*" }, + ], + }, + ], + relevance: 0, + }, + d = [ + o, + l, + c, + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, i], + variants: [ + { begin: "%r\\(", end: "\\)", contains: s("\\(", "\\)") }, + { begin: "%r\\[", end: "\\]", contains: s("\\[", "\\]") }, + { begin: "%r\\{", end: /\}/, contains: s(/\{/, /\}/) }, + { begin: "%r<", end: ">", contains: s("<", ">") }, + { begin: "%r\\|", end: "\\|" }, + ], + relevance: 0, + }, + _, + { + className: "meta", + begin: "@\\[", + end: "\\]", + contains: [ + e.inherit(e.QUOTE_STRING_MODE, { className: "meta-string" }), + ], + }, + e.HASH_COMMENT_MODE, + { + className: "class", + beginKeywords: "class module struct", + end: "$|;", + illegal: /=/, + contains: [ + e.HASH_COMMENT_MODE, + e.inherit(e.TITLE_MODE, { begin: a }), + { begin: "<" }, + ], + }, + { + className: "class", + beginKeywords: "lib enum union", + end: "$|;", + illegal: /=/, + contains: [e.HASH_COMMENT_MODE, e.inherit(e.TITLE_MODE, { begin: a })], + }, + { + beginKeywords: "annotation", + end: "$|;", + illegal: /=/, + contains: [e.HASH_COMMENT_MODE, e.inherit(e.TITLE_MODE, { begin: a })], + relevance: 2, + }, + { + className: "function", + beginKeywords: "def", + end: /\B\b/, + contains: [e.inherit(e.TITLE_MODE, { begin: n, endsParent: !0 })], + }, + { + className: "function", + beginKeywords: "fun macro", + end: /\B\b/, + contains: [e.inherit(e.TITLE_MODE, { begin: n, endsParent: !0 })], + relevance: 2, + }, + { + className: "symbol", + begin: e.UNDERSCORE_IDENT_RE + "(!|\\?)?:", + relevance: 0, + }, + { + className: "symbol", + begin: ":", + contains: [l, { begin: n }], + relevance: 0, + }, + { + className: "number", + variants: [ + { begin: "\\b0b([01_]+)" + t }, + { begin: "\\b0o([0-7_]+)" + t }, + { begin: "\\b0x([A-Fa-f0-9_]+)" + t }, + { + begin: + "\\b([1-9][0-9_]*[0-9]|[0-9])(\\.[0-9][0-9_]*)?([eE]_?[-+]?[0-9_]*)?(_?f(32|64))?(?!_)", + }, + { begin: "\\b([1-9][0-9_]*|0)" + t }, + ], + relevance: 0, + }, + ]; + return ( + (i.contains = d), + (o.contains = d.slice(1)), + { name: "Crystal", aliases: ["cr"], keywords: r, contains: d } + ); +}; +var eg = function (e) { + var t = { + keyword: [ + "abstract", + "as", + "base", + "break", + "case", + "class", + "const", + "continue", + "do", + "else", + "event", + "explicit", + "extern", + "finally", + "fixed", + "for", + "foreach", + "goto", + "if", + "implicit", + "in", + "interface", + "internal", + "is", + "lock", + "namespace", + "new", + "operator", + "out", + "override", + "params", + "private", + "protected", + "public", + "readonly", + "record", + "ref", + "return", + "sealed", + "sizeof", + "stackalloc", + "static", + "struct", + "switch", + "this", + "throw", + "try", + "typeof", + "unchecked", + "unsafe", + "using", + "virtual", + "void", + "volatile", + "while", + ].concat([ + "add", + "alias", + "and", + "ascending", + "async", + "await", + "by", + "descending", + "equals", + "from", + "get", + "global", + "group", + "init", + "into", + "join", + "let", + "nameof", + "not", + "notnull", + "on", + "or", + "orderby", + "partial", + "remove", + "select", + "set", + "unmanaged", + "value|0", + "var", + "when", + "where", + "with", + "yield", + ]), + built_in: [ + "bool", + "byte", + "char", + "decimal", + "delegate", + "double", + "dynamic", + "enum", + "float", + "int", + "long", + "nint", + "nuint", + "object", + "sbyte", + "short", + "string", + "ulong", + "uint", + "ushort", + ], + literal: ["default", "false", "null", "true"], + }, + n = e.inherit(e.TITLE_MODE, { begin: "[a-zA-Z](\\.?\\w)*" }), + a = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + r = { + className: "string", + begin: '@"', + end: '"', + contains: [{ begin: '""' }], + }, + i = e.inherit(r, { illegal: /\n/ }), + o = { className: "subst", begin: /\{/, end: /\}/, keywords: t }, + s = e.inherit(o, { illegal: /\n/ }), + l = { + className: "string", + begin: /\$"/, + end: '"', + illegal: /\n/, + contains: [{ begin: /\{\{/ }, { begin: /\}\}/ }, e.BACKSLASH_ESCAPE, s], + }, + c = { + className: "string", + begin: /\$@"/, + end: '"', + contains: [{ begin: /\{\{/ }, { begin: /\}\}/ }, { begin: '""' }, o], + }, + _ = e.inherit(c, { + illegal: /\n/, + contains: [{ begin: /\{\{/ }, { begin: /\}\}/ }, { begin: '""' }, s], + }); + (o.contains = [ + c, + l, + r, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + a, + e.C_BLOCK_COMMENT_MODE, + ]), + (s.contains = [ + _, + l, + i, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + a, + e.inherit(e.C_BLOCK_COMMENT_MODE, { illegal: /\n/ }), + ]); + var d = { variants: [c, l, r, e.APOS_STRING_MODE, e.QUOTE_STRING_MODE] }, + u = { begin: "<", end: ">", contains: [{ beginKeywords: "in out" }, n] }, + m = + e.IDENT_RE + + "(<" + + e.IDENT_RE + + "(\\s*,\\s*" + + e.IDENT_RE + + ")*>)?(\\[\\])?", + p = { begin: "@" + e.IDENT_RE, relevance: 0 }; + return { + name: "C#", + aliases: ["cs", "c#"], + keywords: t, + illegal: /::/, + contains: [ + e.COMMENT("///", "$", { + returnBegin: !0, + contains: [ + { + className: "doctag", + variants: [ + { begin: "///", relevance: 0 }, + { begin: "\x3c!--|--\x3e" }, + { begin: "" }, + ], + }, + ], + }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line region endregion pragma checksum", + }, + }, + d, + a, + { + beginKeywords: "class interface", + relevance: 0, + end: /[{;=]/, + illegal: /[^\s:,]/, + contains: [ + { beginKeywords: "where class" }, + n, + u, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + { + beginKeywords: "namespace", + relevance: 0, + end: /[{;=]/, + illegal: /[^\s:]/, + contains: [n, e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + }, + { + beginKeywords: "record", + relevance: 0, + end: /[{;=]/, + illegal: /[^\s:]/, + contains: [n, u, e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + }, + { + className: "meta", + begin: "^\\s*\\[", + excludeBegin: !0, + end: "\\]", + excludeEnd: !0, + contains: [{ className: "meta-string", begin: /"/, end: /"/ }], + }, + { beginKeywords: "new return throw await else", relevance: 0 }, + { + className: "function", + begin: "(" + m + "\\s+)+" + e.IDENT_RE + "\\s*(<.+>\\s*)?\\(", + returnBegin: !0, + end: /\s*[{;=]/, + excludeEnd: !0, + keywords: t, + contains: [ + { + beginKeywords: [ + "public", + "private", + "protected", + "static", + "internal", + "protected", + "abstract", + "async", + "extern", + "override", + "unsafe", + "virtual", + "new", + "sealed", + "partial", + ].join(" "), + relevance: 0, + }, + { + begin: e.IDENT_RE + "\\s*(<.+>\\s*)?\\(", + returnBegin: !0, + contains: [e.TITLE_MODE, u], + relevance: 0, + }, + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: t, + relevance: 0, + contains: [d, a, e.C_BLOCK_COMMENT_MODE], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + p, + ], + }; +}; +var tg = function (e) { + return { + name: "CSP", + case_insensitive: !1, + keywords: { + $pattern: "[a-zA-Z][a-zA-Z0-9_-]*", + keyword: + "base-uri child-src connect-src default-src font-src form-action frame-ancestors frame-src img-src media-src object-src plugin-types report-uri sandbox script-src style-src", + }, + contains: [ + { className: "string", begin: "'", end: "'" }, + { className: "attribute", begin: "^Content", end: ":", excludeEnd: !0 }, + ], + }; + }, + ng = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + ag = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + rg = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + ig = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + og = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(); +function sg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function lg(e) { + return (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return t + .map(function (e) { + return sg(e); + }) + .join(""); + })("(?=", e, ")"); +} +var cg = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE]; + return { + name: "CSS", + case_insensitive: !0, + illegal: /[=|'\$]/, + keywords: { keyframePosition: "from to" }, + classNameAliases: { keyframePosition: "selector-tag" }, + contains: [ + e.C_BLOCK_COMMENT_MODE, + { begin: /-(webkit|moz|ms|o)-(?=[a-z])/ }, + e.CSS_NUMBER_MODE, + { className: "selector-id", begin: /#[A-Za-z0-9_-]+/, relevance: 0 }, + { + className: "selector-class", + begin: "\\.[a-zA-Z-][a-zA-Z0-9_-]*", + relevance: 0, + }, + t.ATTRIBUTE_SELECTOR_MODE, + { + className: "selector-pseudo", + variants: [ + { begin: ":(" + rg.join("|") + ")" }, + { begin: "::(" + ig.join("|") + ")" }, + ], + }, + { className: "attribute", begin: "\\b(" + og.join("|") + ")\\b" }, + { + begin: ":", + end: "[;}]", + contains: [t.HEXCOLOR, t.IMPORTANT, e.CSS_NUMBER_MODE].concat(n, [ + { + begin: /(url|data-uri)\(/, + end: /\)/, + relevance: 0, + keywords: { built_in: "url data-uri" }, + contains: [ + { + className: "string", + begin: /[^)]/, + endsWithParent: !0, + excludeEnd: !0, + }, + ], + }, + { className: "built_in", begin: /[\w-]+(?=\()/ }, + ]), + }, + { + begin: lg(/@/), + end: "[{;]", + relevance: 0, + illegal: /:/, + contains: [ + { className: "keyword", begin: /@-?\w[\w]*(-\w+)*/ }, + { + begin: /\s/, + endsWithParent: !0, + excludeEnd: !0, + relevance: 0, + keywords: { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: ag.join(" "), + }, + contains: [ + { begin: /[a-z-]+(?=:)/, className: "attribute" }, + ].concat(n, [e.CSS_NUMBER_MODE]), + }, + ], + }, + { className: "selector-tag", begin: "\\b(" + ng.join("|") + ")\\b" }, + ], + }; +}; +var _g = function (e) { + var t = { + $pattern: e.UNDERSCORE_IDENT_RE, + keyword: + "abstract alias align asm assert auto body break byte case cast catch class const continue debug default delete deprecated do else enum export extern final finally for foreach foreach_reverse|10 goto if immutable import in inout int interface invariant is lazy macro mixin module new nothrow out override package pragma private protected public pure ref return scope shared static struct super switch synchronized template this throw try typedef typeid typeof union unittest version void volatile while with __FILE__ __LINE__ __gshared|10 __thread __traits __DATE__ __EOF__ __TIME__ __TIMESTAMP__ __VENDOR__ __VERSION__", + built_in: + "bool cdouble cent cfloat char creal dchar delegate double dstring float function idouble ifloat ireal long real short string ubyte ucent uint ulong ushort wchar wstring", + literal: "false null true", + }, + n = + "((0|[1-9][\\d_]*)|0[bB][01_]+|0[xX]([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*))", + a = + "\\\\(['\"\\?\\\\abfnrtv]|u[\\dA-Fa-f]{4}|[0-7]{1,3}|x[\\dA-Fa-f]{2}|U[\\dA-Fa-f]{8})|&[a-zA-Z\\d]{2,};", + r = { + className: "number", + begin: "\\b" + n + "(L|u|U|Lu|LU|uL|UL)?", + relevance: 0, + }, + i = { + className: "number", + begin: + "\\b(((0[xX](([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*)\\.([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*)|\\.?([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*))[pP][+-]?(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d))|((0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d)(\\.\\d*|([eE][+-]?(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d)))|\\d+\\.(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d)|\\.(0|[1-9][\\d_]*)([eE][+-]?(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d))?))([fF]|L|i|[fF]i|Li)?|" + + n + + "(i|[fF]i|Li))", + relevance: 0, + }, + o = { + className: "string", + begin: "'(" + a + "|.)", + end: "'", + illegal: ".", + }, + s = { + className: "string", + begin: '"', + contains: [{ begin: a, relevance: 0 }], + end: '"[cwd]?', + }, + l = e.COMMENT("\\/\\+", "\\+\\/", { contains: ["self"], relevance: 10 }); + return { + name: "D", + keywords: t, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + l, + { + className: "string", + begin: 'x"[\\da-fA-F\\s\\n\\r]*"[cwd]?', + relevance: 10, + }, + s, + { className: "string", begin: '[rq]"', end: '"[cwd]?', relevance: 5 }, + { className: "string", begin: "`", end: "`[cwd]?" }, + { className: "string", begin: 'q"\\{', end: '\\}"' }, + i, + r, + o, + { className: "meta", begin: "^#!", end: "$", relevance: 5 }, + { className: "meta", begin: "#(line)", end: "$", relevance: 5 }, + { className: "keyword", begin: "@[a-zA-Z_][a-zA-Z_\\d]*" }, + ], + }; +}; +function dg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function ug() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return dg(e); + }) + .join(""); + return a; +} +var mg = function (e) { + var t = { + begin: /<\/?[A-Za-z_]/, + end: ">", + subLanguage: "xml", + relevance: 0, + }, + n = { + variants: [ + { begin: /\[.+?\]\[.*?\]/, relevance: 0 }, + { + begin: + /\[.+?\]\(((data|javascript|mailto):|(?:http|ftp)s?:\/\/).*?\)/, + relevance: 2, + }, + { + begin: ug(/\[.+?\]\(/, /[A-Za-z][A-Za-z0-9+.-]*/, /:\/\/.*?\)/), + relevance: 2, + }, + { begin: /\[.+?\]\([./?&#].*?\)/, relevance: 1 }, + { begin: /\[.+?\]\(.*?\)/, relevance: 0 }, + ], + returnBegin: !0, + contains: [ + { + className: "string", + relevance: 0, + begin: "\\[", + end: "\\]", + excludeBegin: !0, + returnEnd: !0, + }, + { + className: "link", + relevance: 0, + begin: "\\]\\(", + end: "\\)", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "symbol", + relevance: 0, + begin: "\\]\\[", + end: "\\]", + excludeBegin: !0, + excludeEnd: !0, + }, + ], + }, + a = { + className: "strong", + contains: [], + variants: [ + { begin: /_{2}/, end: /_{2}/ }, + { begin: /\*{2}/, end: /\*{2}/ }, + ], + }, + r = { + className: "emphasis", + contains: [], + variants: [ + { begin: /\*(?!\*)/, end: /\*/ }, + { begin: /_(?!_)/, end: /_/, relevance: 0 }, + ], + }; + a.contains.push(r), r.contains.push(a); + var i = [t, n]; + return ( + (a.contains = a.contains.concat(i)), + (r.contains = r.contains.concat(i)), + { + name: "Markdown", + aliases: ["md", "mkdown", "mkd"], + contains: [ + { + className: "section", + variants: [ + { begin: "^#{1,6}", end: "$", contains: (i = i.concat(a, r)) }, + { + begin: "(?=^.+?\\n[=-]{2,}$)", + contains: [ + { begin: "^[=-]*$" }, + { begin: "^", end: "\\n", contains: i }, + ], + }, + ], + }, + t, + { + className: "bullet", + begin: "^[ \t]*([*+-]|(\\d+\\.))(?=\\s+)", + end: "\\s+", + excludeEnd: !0, + }, + a, + r, + { className: "quote", begin: "^>\\s+", contains: i, end: "$" }, + { + className: "code", + variants: [ + { begin: "(`{3,})[^`](.|\\n)*?\\1`*[ ]*" }, + { begin: "(~{3,})[^~](.|\\n)*?\\1~*[ ]*" }, + { begin: "```", end: "```+[ ]*$" }, + { begin: "~~~", end: "~~~+[ ]*$" }, + { begin: "`.+?`" }, + { + begin: "(?=^( {4}|\\t))", + contains: [{ begin: "^( {4}|\\t)", end: "(\\n)$" }], + relevance: 0, + }, + ], + }, + { begin: "^[-\\*]{3,}", end: "$" }, + n, + { + begin: /^\[[^\n]+\]:/, + returnBegin: !0, + contains: [ + { + className: "symbol", + begin: /\[/, + end: /\]/, + excludeBegin: !0, + excludeEnd: !0, + }, + { className: "link", begin: /:\s*/, end: /$/, excludeBegin: !0 }, + ], + }, + ], + } + ); +}; +var pg = function (e) { + var t = { className: "subst", variants: [{ begin: "\\$[A-Za-z0-9_]+" }] }, + n = { + className: "subst", + variants: [{ begin: /\$\{/, end: /\}/ }], + keywords: "true false null this is new super", + }, + a = { + className: "string", + variants: [ + { begin: "r'''", end: "'''" }, + { begin: 'r"""', end: '"""' }, + { begin: "r'", end: "'", illegal: "\\n" }, + { begin: 'r"', end: '"', illegal: "\\n" }, + { begin: "'''", end: "'''", contains: [e.BACKSLASH_ESCAPE, t, n] }, + { begin: '"""', end: '"""', contains: [e.BACKSLASH_ESCAPE, t, n] }, + { + begin: "'", + end: "'", + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, t, n], + }, + { + begin: '"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, t, n], + }, + ], + }; + n.contains = [e.C_NUMBER_MODE, a]; + var r = [ + "Comparable", + "DateTime", + "Duration", + "Function", + "Iterable", + "Iterator", + "List", + "Map", + "Match", + "Object", + "Pattern", + "RegExp", + "Set", + "Stopwatch", + "String", + "StringBuffer", + "StringSink", + "Symbol", + "Type", + "Uri", + "bool", + "double", + "int", + "num", + "Element", + "ElementList", + ], + i = r.map(function (e) { + return "".concat(e, "?"); + }); + return { + name: "Dart", + keywords: { + keyword: + "abstract as assert async await break case catch class const continue covariant default deferred do dynamic else enum export extends extension external factory false final finally for Function get hide if implements import in inferface is late library mixin new null on operator part required rethrow return set show static super switch sync this throw true try typedef var void while with yield", + built_in: r + .concat(i) + .concat([ + "Never", + "Null", + "dynamic", + "print", + "document", + "querySelector", + "querySelectorAll", + "window", + ]), + $pattern: /[A-Za-z][A-Za-z0-9_]*\??/, + }, + contains: [ + a, + e.COMMENT(/\/\*\*(?!\/)/, /\*\//, { + subLanguage: "markdown", + relevance: 0, + }), + e.COMMENT(/\/{3,} ?/, /$/, { + contains: [ + { subLanguage: "markdown", begin: ".", end: "$", relevance: 0 }, + ], + }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + e.C_NUMBER_MODE, + { className: "meta", begin: "@[A-Za-z]+" }, + { begin: "=>" }, + ], + }; +}; +var gg = function (e) { + var t = + "exports register file shl array record property for mod while set ally label uses raise not stored class safecall var interface or private static exit index inherited to else stdcall override shr asm far resourcestring finalization packed virtual out and protected library do xorwrite goto near function end div overload object unit begin string on inline repeat until destructor write message program with read initialization except default nil if case cdecl in downto threadvar of try pascal const external constructor type public then implementation finally published procedure absolute reintroduce operator as is abstract alias assembler bitpacked break continue cppdecl cvar enumerator experimental platform deprecated unimplemented dynamic export far16 forward generic helper implements interrupt iochecks local name nodefault noreturn nostackframe oldfpccall otherwise saveregisters softfloat specialize strict unaligned varargs ", + n = [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/\{/, /\}/, { relevance: 0 }), + e.COMMENT(/\(\*/, /\*\)/, { relevance: 10 }), + ], + a = { + className: "meta", + variants: [ + { begin: /\{\$/, end: /\}/ }, + { begin: /\(\*\$/, end: /\*\)/ }, + ], + }, + r = { + className: "string", + begin: /'/, + end: /'/, + contains: [{ begin: /''/ }], + }, + i = { className: "string", begin: /(#\d+)+/ }, + o = { + begin: e.IDENT_RE + "\\s*=\\s*class\\s*\\(", + returnBegin: !0, + contains: [e.TITLE_MODE], + }, + s = { + className: "function", + beginKeywords: "function constructor destructor procedure", + end: /[:;]/, + keywords: "function constructor|10 destructor|10 procedure|10", + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: t, + contains: [r, i, a].concat(n), + }, + a, + ].concat(n), + }; + return { + name: "Delphi", + aliases: [ + "dpr", + "dfm", + "pas", + "pascal", + "freepascal", + "lazarus", + "lpr", + "lfm", + ], + case_insensitive: !0, + keywords: t, + illegal: /"|\$[G-Zg-z]|\/\*|<\/|\|/, + contains: [ + r, + i, + e.NUMBER_MODE, + { + className: "number", + relevance: 0, + variants: [ + { begin: "\\$[0-9A-Fa-f]+" }, + { begin: "&[0-7]+" }, + { begin: "%[01]+" }, + ], + }, + o, + s, + a, + ].concat(n), + }; +}; +var Eg = function (e) { + return { + name: "Diff", + aliases: ["patch"], + contains: [ + { + className: "meta", + relevance: 10, + variants: [ + { begin: /^@@ +-\d+,\d+ +\+\d+,\d+ +@@/ }, + { begin: /^\*\*\* +\d+,\d+ +\*\*\*\*$/ }, + { begin: /^--- +\d+,\d+ +----$/ }, + ], + }, + { + className: "comment", + variants: [ + { begin: /Index: /, end: /$/ }, + { begin: /^index/, end: /$/ }, + { begin: /={3,}/, end: /$/ }, + { begin: /^-{3}/, end: /$/ }, + { begin: /^\*{3} /, end: /$/ }, + { begin: /^\+{3}/, end: /$/ }, + { begin: /^\*{15}$/ }, + { begin: /^diff --git/, end: /$/ }, + ], + }, + { className: "addition", begin: /^\+/, end: /$/ }, + { className: "deletion", begin: /^-/, end: /$/ }, + { className: "addition", begin: /^!/, end: /$/ }, + ], + }; +}; +var Sg = function (e) { + var t = { + begin: /\|[A-Za-z]+:?/, + keywords: { + name: "truncatewords removetags linebreaksbr yesno get_digit timesince random striptags filesizeformat escape linebreaks length_is ljust rjust cut urlize fix_ampersands title floatformat capfirst pprint divisibleby add make_list unordered_list urlencode timeuntil urlizetrunc wordcount stringformat linenumbers slice date dictsort dictsortreversed default_if_none pluralize lower join center default truncatewords_html upper length phone2numeric wordwrap time addslashes slugify first escapejs force_escape iriencode last safe safeseq truncatechars localize unlocalize localtime utc timezone", + }, + contains: [e.QUOTE_STRING_MODE, e.APOS_STRING_MODE], + }; + return { + name: "Django", + aliases: ["jinja"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + e.COMMENT(/\{%\s*comment\s*%\}/, /\{%\s*endcomment\s*%\}/), + e.COMMENT(/\{#/, /#\}/), + { + className: "template-tag", + begin: /\{%/, + end: /%\}/, + contains: [ + { + className: "name", + begin: /\w+/, + keywords: { + name: "comment endcomment load templatetag ifchanged endifchanged if endif firstof for endfor ifnotequal endifnotequal widthratio extends include spaceless endspaceless regroup ifequal endifequal ssi now with cycle url filter endfilter debug block endblock else autoescape endautoescape csrf_token empty elif endwith static trans blocktrans endblocktrans get_static_prefix get_media_prefix plural get_current_language language get_available_languages get_current_language_bidi get_language_info get_language_info_list localize endlocalize localtime endlocaltime timezone endtimezone get_current_timezone verbatim", + }, + starts: { + endsWithParent: !0, + keywords: "in by as", + contains: [t], + relevance: 0, + }, + }, + ], + }, + { + className: "template-variable", + begin: /\{\{/, + end: /\}\}/, + contains: [t], + }, + ], + }; +}; +var bg = function (e) { + return { + name: "DNS Zone", + aliases: ["bind", "zone"], + keywords: { + keyword: + "IN A AAAA AFSDB APL CAA CDNSKEY CDS CERT CNAME DHCID DLV DNAME DNSKEY DS HIP IPSECKEY KEY KX LOC MX NAPTR NS NSEC NSEC3 NSEC3PARAM PTR RRSIG RP SIG SOA SRV SSHFP TA TKEY TLSA TSIG TXT", + }, + contains: [ + e.COMMENT(";", "$", { relevance: 0 }), + { className: "meta", begin: /^\$(TTL|GENERATE|INCLUDE|ORIGIN)\b/ }, + { + className: "number", + begin: + "((([0-9A-Fa-f]{1,4}:){7}([0-9A-Fa-f]{1,4}|:))|(([0-9A-Fa-f]{1,4}:){6}(:[0-9A-Fa-f]{1,4}|((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){5}(((:[0-9A-Fa-f]{1,4}){1,2})|:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){4}(((:[0-9A-Fa-f]{1,4}){1,3})|((:[0-9A-Fa-f]{1,4})?:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){3}(((:[0-9A-Fa-f]{1,4}){1,4})|((:[0-9A-Fa-f]{1,4}){0,2}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){2}(((:[0-9A-Fa-f]{1,4}){1,5})|((:[0-9A-Fa-f]{1,4}){0,3}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){1}(((:[0-9A-Fa-f]{1,4}){1,6})|((:[0-9A-Fa-f]{1,4}){0,4}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(:(((:[0-9A-Fa-f]{1,4}){1,7})|((:[0-9A-Fa-f]{1,4}){0,5}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:)))\\b", + }, + { + className: "number", + begin: + "((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]).){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\b", + }, + e.inherit(e.NUMBER_MODE, { begin: /\b\d+[dhwm]?/ }), + ], + }; +}; +var Tg = function (e) { + return { + name: "Dockerfile", + aliases: ["docker"], + case_insensitive: !0, + keywords: "from maintainer expose env arg user onbuild stopsignal", + contains: [ + e.HASH_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + { + beginKeywords: + "run cmd entrypoint volume add copy workdir label healthcheck shell", + starts: { end: /[^\\]$/, subLanguage: "bash" }, + }, + ], + illegal: "", illegal: "\\n" }, + ], + }, + t, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + r = { className: "variable", begin: /&[a-z\d_]*\b/ }, + i = { className: "meta-keyword", begin: "/[a-z][a-z\\d-]*/" }, + o = { className: "symbol", begin: "^\\s*[a-zA-Z_][a-zA-Z\\d_]*:" }, + s = { className: "params", begin: "<", end: ">", contains: [n, r] }, + l = { + className: "class", + begin: /[a-zA-Z_][a-zA-Z\d_@]*\s\{/, + end: /[{;=]/, + returnBegin: !0, + excludeEnd: !0, + }; + return { + name: "Device Tree", + keywords: "", + contains: [ + { + className: "class", + begin: "/\\s*\\{", + end: /\};/, + relevance: 10, + contains: [ + r, + i, + o, + l, + s, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + t, + ], + }, + r, + i, + o, + l, + s, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + t, + a, + { begin: e.IDENT_RE + "::", keywords: "" }, + ], + }; +}; +var Rg = function (e) { + return { + name: "Dust", + aliases: ["dst"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + { + className: "template-tag", + begin: /\{[#\/]/, + end: /\}/, + illegal: /;/, + contains: [ + { + className: "name", + begin: /[a-zA-Z\.-]+/, + starts: { + endsWithParent: !0, + relevance: 0, + contains: [e.QUOTE_STRING_MODE], + }, + }, + ], + }, + { + className: "template-variable", + begin: /\{/, + end: /\}/, + illegal: /;/, + keywords: "if eq ne lt lte gt gte select default math sep", + }, + ], + }; +}; +var vg = function (e) { + var t = e.COMMENT(/\(\*/, /\*\)/); + return { + name: "Extended Backus-Naur Form", + illegal: /\S/, + contains: [ + t, + { className: "attribute", begin: /^[ ]*[a-zA-Z]+([\s_-]+[a-zA-Z]+)*/ }, + { + begin: /=/, + end: /[.;]/, + contains: [ + t, + { className: "meta", begin: /\?.*\?/ }, + { + className: "string", + variants: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: "`", end: "`" }, + ], + }, + ], + }, + ], + }; +}; +var Og = function (e) { + var t = "[a-zA-Z_][a-zA-Z0-9_.]*(!|\\?)?", + n = { + $pattern: t, + keyword: + "and false then defined module in return redo retry end for true self when next until do begin unless nil break not case cond alias while ensure or include use alias fn quote require import with|0", + }, + a = { className: "subst", begin: /#\{/, end: /\}/, keywords: n }, + r = { + className: "number", + begin: + "(\\b0o[0-7_]+)|(\\b0b[01_]+)|(\\b0x[0-9a-fA-F_]+)|(-?\\b[1-9][0-9_]*(\\.[0-9_]+([eE][-+]?[0-9]+)?)?)", + relevance: 0, + }, + i = { + className: "string", + begin: "~[a-z](?=[/|([{<\"'])", + contains: [ + { + endsParent: !0, + contains: [ + { + contains: [e.BACKSLASH_ESCAPE, a], + variants: [ + { begin: /"/, end: /"/ }, + { begin: /'/, end: /'/ }, + { begin: /\//, end: /\// }, + { begin: /\|/, end: /\|/ }, + { begin: /\(/, end: /\)/ }, + { begin: /\[/, end: /\]/ }, + { begin: /\{/, end: /\}/ }, + { begin: // }, + ], + }, + ], + }, + ], + }, + o = { + className: "string", + begin: "~[A-Z](?=[/|([{<\"'])", + contains: [ + { begin: /"/, end: /"/ }, + { begin: /'/, end: /'/ }, + { begin: /\//, end: /\// }, + { begin: /\|/, end: /\|/ }, + { begin: /\(/, end: /\)/ }, + { begin: /\[/, end: /\]/ }, + { begin: /\{/, end: /\}/ }, + { begin: // }, + ], + }, + s = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, a], + variants: [ + { begin: /"""/, end: /"""/ }, + { begin: /'''/, end: /'''/ }, + { begin: /~S"""/, end: /"""/, contains: [] }, + { begin: /~S"/, end: /"/, contains: [] }, + { begin: /~S'''/, end: /'''/, contains: [] }, + { begin: /~S'/, end: /'/, contains: [] }, + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + ], + }, + l = { + className: "function", + beginKeywords: "def defp defmacro", + end: /\B\b/, + contains: [e.inherit(e.TITLE_MODE, { begin: t, endsParent: !0 })], + }, + c = e.inherit(l, { + className: "class", + beginKeywords: "defimpl defmodule defprotocol defrecord", + end: /\bdo\b|$|;/, + }), + _ = [ + s, + o, + i, + e.HASH_COMMENT_MODE, + c, + l, + { begin: "::" }, + { + className: "symbol", + begin: ":(?![\\s:])", + contains: [ + s, + { + begin: + "[a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?", + }, + ], + relevance: 0, + }, + { className: "symbol", begin: t + ":(?!:)", relevance: 0 }, + r, + { className: "variable", begin: "(\\$\\W)|((\\$|@@?)(\\w+))" }, + { begin: "->" }, + { + begin: "(" + e.RE_STARTERS_RE + ")\\s*", + contains: [ + e.HASH_COMMENT_MODE, + { begin: /\/: (?=\d+\s*[,\]])/, relevance: 0, contains: [r] }, + { + className: "regexp", + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, a], + variants: [ + { begin: "/", end: "/[a-z]*" }, + { begin: "%r\\[", end: "\\][a-z]*" }, + ], + }, + ], + relevance: 0, + }, + ]; + return (a.contains = _), { name: "Elixir", keywords: n, contains: _ }; +}; +var hg = function (e) { + var t = { + variants: [ + e.COMMENT("--", "$"), + e.COMMENT(/\{-/, /-\}/, { contains: ["self"] }), + ], + }, + n = { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + a = { + begin: "\\(", + end: "\\)", + illegal: '"', + contains: [ + { className: "type", begin: "\\b[A-Z][\\w]*(\\((\\.\\.|,|\\w+)\\))?" }, + t, + ], + }; + return { + name: "Elm", + keywords: + "let in if then else case of where module import exposing type alias as infix infixl infixr port effect command subscription", + contains: [ + { + beginKeywords: "port effect module", + end: "exposing", + keywords: "port effect module where command subscription exposing", + contains: [a, t], + illegal: "\\W\\.|;", + }, + { + begin: "import", + end: "$", + keywords: "import as exposing", + contains: [a, t], + illegal: "\\W\\.|;", + }, + { + begin: "type", + end: "$", + keywords: "type alias", + contains: [n, a, { begin: /\{/, end: /\}/, contains: a.contains }, t], + }, + { + beginKeywords: "infix infixl infixr", + end: "$", + contains: [e.C_NUMBER_MODE, t], + }, + { begin: "port", end: "$", keywords: "port", contains: [t] }, + { className: "string", begin: "'\\\\?.", end: "'", illegal: "." }, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + n, + e.inherit(e.TITLE_MODE, { begin: "^[_a-z][\\w']*" }), + t, + { begin: "->|<-" }, + ], + illegal: /;/, + }; +}; +function yg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Ig() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return yg(e); + }) + .join(""); + return a; +} +var Ag = function (e) { + var t, + n = + "([a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?)", + a = { + keyword: + "and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor __FILE__", + built_in: "proc lambda", + literal: "true false nil", + }, + r = { className: "doctag", begin: "@[A-Za-z]+" }, + i = { begin: "#<", end: ">" }, + o = [ + e.COMMENT("#", "$", { contains: [r] }), + e.COMMENT("^=begin", "^=end", { contains: [r], relevance: 10 }), + e.COMMENT("^__END__", "\\n$"), + ], + s = { className: "subst", begin: /#\{/, end: /\}/, keywords: a }, + l = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, s], + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /`/, end: /`/ }, + { begin: /%[qQwWx]?\(/, end: /\)/ }, + { begin: /%[qQwWx]?\[/, end: /\]/ }, + { begin: /%[qQwWx]?\{/, end: /\}/ }, + { begin: /%[qQwWx]?/ }, + { begin: /%[qQwWx]?\//, end: /\// }, + { begin: /%[qQwWx]?%/, end: /%/ }, + { begin: /%[qQwWx]?-/, end: /-/ }, + { begin: /%[qQwWx]?\|/, end: /\|/ }, + { begin: /\B\?(\\\d{1,3})/ }, + { begin: /\B\?(\\x[A-Fa-f0-9]{1,2})/ }, + { begin: /\B\?(\\u\{?[A-Fa-f0-9]{1,6}\}?)/ }, + { begin: /\B\?(\\M-\\C-|\\M-\\c|\\c\\M-|\\M-|\\C-\\M-)[\x20-\x7e]/ }, + { begin: /\B\?\\(c|C-)[\x20-\x7e]/ }, + { begin: /\B\?\\?\S/ }, + { + begin: /<<[-~]?'?(\w+)\n(?:[^\n]*\n)*?\s*\1\b/, + returnBegin: !0, + contains: [ + { begin: /<<[-~]?'?/ }, + e.END_SAME_AS_BEGIN({ + begin: /(\w+)/, + end: /(\w+)/, + contains: [e.BACKSLASH_ESCAPE, s], + }), + ], + }, + ], + }, + c = "[0-9](_?[0-9])*", + _ = { + className: "number", + relevance: 0, + variants: [ + { + begin: "\\b(" + .concat("[1-9](_?[0-9])*|0", ")(\\.(") + .concat(c, "))?([eE][+-]?(") + .concat(c, ")|r)?i?\\b"), + }, + { begin: "\\b0[dD][0-9](_?[0-9])*r?i?\\b" }, + { begin: "\\b0[bB][0-1](_?[0-1])*r?i?\\b" }, + { begin: "\\b0[oO][0-7](_?[0-7])*r?i?\\b" }, + { begin: "\\b0[xX][0-9a-fA-F](_?[0-9a-fA-F])*r?i?\\b" }, + { begin: "\\b0(_?[0-7])+r?i?\\b" }, + ], + }, + d = { + className: "params", + begin: "\\(", + end: "\\)", + endsParent: !0, + keywords: a, + }, + u = [ + l, + { + className: "class", + beginKeywords: "class module", + end: "$|;", + illegal: /=/, + contains: [ + e.inherit(e.TITLE_MODE, { begin: "[A-Za-z_]\\w*(::\\w+)*(\\?|!)?" }), + { + begin: "<\\s*", + contains: [ + { begin: "(" + e.IDENT_RE + "::)?" + e.IDENT_RE, relevance: 0 }, + ], + }, + ].concat(o), + }, + { + className: "function", + begin: Ig(/def\s+/, ((t = n + "\\s*(\\(|;|$)"), Ig("(?=", t, ")"))), + relevance: 0, + keywords: "def", + end: "$|;", + contains: [e.inherit(e.TITLE_MODE, { begin: n }), d].concat(o), + }, + { begin: e.IDENT_RE + "::" }, + { + className: "symbol", + begin: e.UNDERSCORE_IDENT_RE + "(!|\\?)?:", + relevance: 0, + }, + { + className: "symbol", + begin: ":(?!\\s)", + contains: [l, { begin: n }], + relevance: 0, + }, + _, + { + className: "variable", + begin: "(\\$\\W)|((\\$|@@?)(\\w+))(?=[^@$?])(?![A-Za-z])(?![@$?'])", + }, + { + className: "params", + begin: /\|/, + end: /\|/, + relevance: 0, + keywords: a, + }, + { + begin: "(" + e.RE_STARTERS_RE + "|unless)\\s*", + keywords: "unless", + contains: [ + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, s], + illegal: /\n/, + variants: [ + { begin: "/", end: "/[a-z]*" }, + { begin: /%r\{/, end: /\}[a-z]*/ }, + { begin: "%r\\(", end: "\\)[a-z]*" }, + { begin: "%r!", end: "![a-z]*" }, + { begin: "%r\\[", end: "\\][a-z]*" }, + ], + }, + ].concat(i, o), + relevance: 0, + }, + ].concat(i, o); + (s.contains = u), (d.contains = u); + var m = [ + { begin: /^\s*=>/, starts: { end: "$", contains: u } }, + { + className: "meta", + begin: + "^([>?]>|[\\w#]+\\(\\w+\\):\\d+:\\d+>|(\\w+-)?\\d+\\.\\d+\\.\\d+(p\\d+)?[^\\d][^>]+>)(?=[ ])", + starts: { end: "$", contains: u }, + }, + ]; + return ( + o.unshift(i), + { + name: "Ruby", + aliases: ["rb", "gemspec", "podspec", "thor", "irb"], + keywords: a, + illegal: /\/\*/, + contains: [e.SHEBANG({ binary: "ruby" })].concat(m).concat(o).concat(u), + } + ); +}; +var Dg = function (e) { + return { + name: "ERB", + subLanguage: "xml", + contains: [ + e.COMMENT("<%#", "%>"), + { + begin: "<%[%=-]?", + end: "[%-]?%>", + subLanguage: "ruby", + excludeBegin: !0, + excludeEnd: !0, + }, + ], + }; +}; +function Mg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Lg() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Mg(e); + }) + .join(""); + return a; +} +var wg = function (e) { + return { + name: "Erlang REPL", + keywords: { + built_in: "spawn spawn_link self", + keyword: + "after and andalso|10 band begin bnot bor bsl bsr bxor case catch cond div end fun if let not of or orelse|10 query receive rem try when xor", + }, + contains: [ + { className: "meta", begin: "^[0-9]+> ", relevance: 10 }, + e.COMMENT("%", "$"), + { + className: "number", + begin: + "\\b(\\d+(_\\d+)*#[a-fA-F0-9]+(_[a-fA-F0-9]+)*|\\d+(_\\d+)*(\\.\\d+(_\\d+)*)?([eE][-+]?\\d+)?)", + relevance: 0, + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: Lg(/\?(::)?/, /([A-Z]\w*)/, /((::)[A-Z]\w*)*/) }, + { begin: "->" }, + { begin: "ok" }, + { begin: "!" }, + { + begin: + "(\\b[a-z'][a-zA-Z0-9_']*:[a-z'][a-zA-Z0-9_']*)|(\\b[a-z'][a-zA-Z0-9_']*)", + relevance: 0, + }, + { begin: "[A-Z][a-zA-Z0-9_']*", relevance: 0 }, + ], + }; +}; +var xg = function (e) { + var t = "[a-z'][a-zA-Z0-9_']*", + n = "(" + t + ":" + t + "|" + t + ")", + a = { + keyword: + "after and andalso|10 band begin bnot bor bsl bzr bxor case catch cond div end fun if let not of orelse|10 query receive rem try when xor", + literal: "false true", + }, + r = e.COMMENT("%", "$"), + i = { + className: "number", + begin: + "\\b(\\d+(_\\d+)*#[a-fA-F0-9]+(_[a-fA-F0-9]+)*|\\d+(_\\d+)*(\\.\\d+(_\\d+)*)?([eE][-+]?\\d+)?)", + relevance: 0, + }, + o = { begin: "fun\\s+" + t + "/\\d+" }, + s = { + begin: n + "\\(", + end: "\\)", + returnBegin: !0, + relevance: 0, + contains: [ + { begin: n, relevance: 0 }, + { + begin: "\\(", + end: "\\)", + endsWithParent: !0, + returnEnd: !0, + relevance: 0, + }, + ], + }, + l = { begin: /\{/, end: /\}/, relevance: 0 }, + c = { begin: "\\b_([A-Z][A-Za-z0-9_]*)?", relevance: 0 }, + _ = { begin: "[A-Z][a-zA-Z0-9_]*", relevance: 0 }, + d = { + begin: "#" + e.UNDERSCORE_IDENT_RE, + relevance: 0, + returnBegin: !0, + contains: [ + { begin: "#" + e.UNDERSCORE_IDENT_RE, relevance: 0 }, + { begin: /\{/, end: /\}/, relevance: 0 }, + ], + }, + u = { beginKeywords: "fun receive if try case", end: "end", keywords: a }; + u.contains = [ + r, + o, + e.inherit(e.APOS_STRING_MODE, { className: "" }), + u, + s, + e.QUOTE_STRING_MODE, + i, + l, + c, + _, + d, + ]; + var m = [r, o, u, s, e.QUOTE_STRING_MODE, i, l, c, _, d]; + (s.contains[1].contains = m), (l.contains = m), (d.contains[1].contains = m); + var p = { className: "params", begin: "\\(", end: "\\)", contains: m }; + return { + name: "Erlang", + aliases: ["erl"], + keywords: a, + illegal: "(", + returnBegin: !0, + illegal: "\\(|#|//|/\\*|\\\\|:|;", + contains: [p, e.inherit(e.TITLE_MODE, { begin: t })], + starts: { end: ";|\\.", keywords: a, contains: m }, + }, + r, + { + begin: "^-", + end: "\\.", + relevance: 0, + excludeEnd: !0, + returnBegin: !0, + keywords: { + $pattern: "-" + e.IDENT_RE, + keyword: [ + "-module", + "-record", + "-undef", + "-export", + "-ifdef", + "-ifndef", + "-author", + "-copyright", + "-doc", + "-vsn", + "-import", + "-include", + "-include_lib", + "-compile", + "-define", + "-else", + "-endif", + "-file", + "-behaviour", + "-behavior", + "-spec", + ] + .map(function (e) { + return "".concat(e, "|1.5"); + }) + .join(" "), + }, + contains: [p], + }, + i, + e.QUOTE_STRING_MODE, + d, + c, + _, + l, + { begin: /\.$/ }, + ], + }; +}; +var Pg = function (e) { + return { + name: "Excel formulae", + aliases: ["xlsx", "xls"], + case_insensitive: !0, + keywords: { + $pattern: /[a-zA-Z][\w\.]*/, + built_in: + "ABS ACCRINT ACCRINTM ACOS ACOSH ACOT ACOTH AGGREGATE ADDRESS AMORDEGRC AMORLINC AND ARABIC AREAS ASC ASIN ASINH ATAN ATAN2 ATANH AVEDEV AVERAGE AVERAGEA AVERAGEIF AVERAGEIFS BAHTTEXT BASE BESSELI BESSELJ BESSELK BESSELY BETADIST BETA.DIST BETAINV BETA.INV BIN2DEC BIN2HEX BIN2OCT BINOMDIST BINOM.DIST BINOM.DIST.RANGE BINOM.INV BITAND BITLSHIFT BITOR BITRSHIFT BITXOR CALL CEILING CEILING.MATH CEILING.PRECISE CELL CHAR CHIDIST CHIINV CHITEST CHISQ.DIST CHISQ.DIST.RT CHISQ.INV CHISQ.INV.RT CHISQ.TEST CHOOSE CLEAN CODE COLUMN COLUMNS COMBIN COMBINA COMPLEX CONCAT CONCATENATE CONFIDENCE CONFIDENCE.NORM CONFIDENCE.T CONVERT CORREL COS COSH COT COTH COUNT COUNTA COUNTBLANK COUNTIF COUNTIFS COUPDAYBS COUPDAYS COUPDAYSNC COUPNCD COUPNUM COUPPCD COVAR COVARIANCE.P COVARIANCE.S CRITBINOM CSC CSCH CUBEKPIMEMBER CUBEMEMBER CUBEMEMBERPROPERTY CUBERANKEDMEMBER CUBESET CUBESETCOUNT CUBEVALUE CUMIPMT CUMPRINC DATE DATEDIF DATEVALUE DAVERAGE DAY DAYS DAYS360 DB DBCS DCOUNT DCOUNTA DDB DEC2BIN DEC2HEX DEC2OCT DECIMAL DEGREES DELTA DEVSQ DGET DISC DMAX DMIN DOLLAR DOLLARDE DOLLARFR DPRODUCT DSTDEV DSTDEVP DSUM DURATION DVAR DVARP EDATE EFFECT ENCODEURL EOMONTH ERF ERF.PRECISE ERFC ERFC.PRECISE ERROR.TYPE EUROCONVERT EVEN EXACT EXP EXPON.DIST EXPONDIST FACT FACTDOUBLE FALSE|0 F.DIST FDIST F.DIST.RT FILTERXML FIND FINDB F.INV F.INV.RT FINV FISHER FISHERINV FIXED FLOOR FLOOR.MATH FLOOR.PRECISE FORECAST FORECAST.ETS FORECAST.ETS.CONFINT FORECAST.ETS.SEASONALITY FORECAST.ETS.STAT FORECAST.LINEAR FORMULATEXT FREQUENCY F.TEST FTEST FV FVSCHEDULE GAMMA GAMMA.DIST GAMMADIST GAMMA.INV GAMMAINV GAMMALN GAMMALN.PRECISE GAUSS GCD GEOMEAN GESTEP GETPIVOTDATA GROWTH HARMEAN HEX2BIN HEX2DEC HEX2OCT HLOOKUP HOUR HYPERLINK HYPGEOM.DIST HYPGEOMDIST IF IFERROR IFNA IFS IMABS IMAGINARY IMARGUMENT IMCONJUGATE IMCOS IMCOSH IMCOT IMCSC IMCSCH IMDIV IMEXP IMLN IMLOG10 IMLOG2 IMPOWER IMPRODUCT IMREAL IMSEC IMSECH IMSIN IMSINH IMSQRT IMSUB IMSUM IMTAN INDEX INDIRECT INFO INT INTERCEPT INTRATE IPMT IRR ISBLANK ISERR ISERROR ISEVEN ISFORMULA ISLOGICAL ISNA ISNONTEXT ISNUMBER ISODD ISREF ISTEXT ISO.CEILING ISOWEEKNUM ISPMT JIS KURT LARGE LCM LEFT LEFTB LEN LENB LINEST LN LOG LOG10 LOGEST LOGINV LOGNORM.DIST LOGNORMDIST LOGNORM.INV LOOKUP LOWER MATCH MAX MAXA MAXIFS MDETERM MDURATION MEDIAN MID MIDBs MIN MINIFS MINA MINUTE MINVERSE MIRR MMULT MOD MODE MODE.MULT MODE.SNGL MONTH MROUND MULTINOMIAL MUNIT N NA NEGBINOM.DIST NEGBINOMDIST NETWORKDAYS NETWORKDAYS.INTL NOMINAL NORM.DIST NORMDIST NORMINV NORM.INV NORM.S.DIST NORMSDIST NORM.S.INV NORMSINV NOT NOW NPER NPV NUMBERVALUE OCT2BIN OCT2DEC OCT2HEX ODD ODDFPRICE ODDFYIELD ODDLPRICE ODDLYIELD OFFSET OR PDURATION PEARSON PERCENTILE.EXC PERCENTILE.INC PERCENTILE PERCENTRANK.EXC PERCENTRANK.INC PERCENTRANK PERMUT PERMUTATIONA PHI PHONETIC PI PMT POISSON.DIST POISSON POWER PPMT PRICE PRICEDISC PRICEMAT PROB PRODUCT PROPER PV QUARTILE QUARTILE.EXC QUARTILE.INC QUOTIENT RADIANS RAND RANDBETWEEN RANK.AVG RANK.EQ RANK RATE RECEIVED REGISTER.ID REPLACE REPLACEB REPT RIGHT RIGHTB ROMAN ROUND ROUNDDOWN ROUNDUP ROW ROWS RRI RSQ RTD SEARCH SEARCHB SEC SECH SECOND SERIESSUM SHEET SHEETS SIGN SIN SINH SKEW SKEW.P SLN SLOPE SMALL SQL.REQUEST SQRT SQRTPI STANDARDIZE STDEV STDEV.P STDEV.S STDEVA STDEVP STDEVPA STEYX SUBSTITUTE SUBTOTAL SUM SUMIF SUMIFS SUMPRODUCT SUMSQ SUMX2MY2 SUMX2PY2 SUMXMY2 SWITCH SYD T TAN TANH TBILLEQ TBILLPRICE TBILLYIELD T.DIST T.DIST.2T T.DIST.RT TDIST TEXT TEXTJOIN TIME TIMEVALUE T.INV T.INV.2T TINV TODAY TRANSPOSE TREND TRIM TRIMMEAN TRUE|0 TRUNC T.TEST TTEST TYPE UNICHAR UNICODE UPPER VALUE VAR VAR.P VAR.S VARA VARP VARPA VDB VLOOKUP WEBSERVICE WEEKDAY WEEKNUM WEIBULL WEIBULL.DIST WORKDAY WORKDAY.INTL XIRR XNPV XOR YEAR YEARFRAC YIELD YIELDDISC YIELDMAT Z.TEST ZTEST", + }, + contains: [ + { begin: /^=/, end: /[^=]/, returnEnd: !0, illegal: /=/, relevance: 10 }, + { + className: "symbol", + begin: /\b[A-Z]{1,2}\d+\b/, + end: /[^\d]/, + excludeEnd: !0, + relevance: 0, + }, + { + className: "symbol", + begin: /[A-Z]{0,2}\d*:[A-Z]{0,2}\d*/, + relevance: 0, + }, + e.BACKSLASH_ESCAPE, + e.QUOTE_STRING_MODE, + { className: "number", begin: e.NUMBER_RE + "(%)?", relevance: 0 }, + e.COMMENT(/\bN\(/, /\)/, { + excludeBegin: !0, + excludeEnd: !0, + illegal: /\n/, + }), + ], + }; +}; +var kg = function (e) { + return { + name: "FIX", + contains: [ + { + begin: /[^\u2401\u0001]+/, + end: /[\u2401\u0001]/, + excludeEnd: !0, + returnBegin: !0, + returnEnd: !1, + contains: [ + { + begin: /([^\u2401\u0001=]+)/, + end: /=([^\u2401\u0001=]+)/, + returnEnd: !0, + returnBegin: !1, + className: "attr", + }, + { + begin: /=/, + end: /([\u2401\u0001])/, + excludeEnd: !0, + excludeBegin: !0, + className: "string", + }, + ], + }, + ], + case_insensitive: !0, + }; +}; +var Ug = function (e) { + var t = { + className: "function", + beginKeywords: "def", + end: /[:={\[(\n;]/, + excludeEnd: !0, + contains: [ + { + className: "title", + relevance: 0, + begin: + /[^0-9\n\t "'(),.`{}\[\]:;][^\n\t "'(),.`{}\[\]:;]+|[^0-9\n\t "'(),.`{}\[\]:;=]/, + }, + ], + }; + return { + name: "Flix", + keywords: { + literal: "true false", + keyword: + "case class def else enum if impl import in lat rel index let match namespace switch type yield with", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "string", begin: /'(.|\\[xXuU][a-zA-Z0-9]+)'/ }, + { className: "string", variants: [{ begin: '"', end: '"' }] }, + t, + e.C_NUMBER_MODE, + ], + }; +}; +function Fg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Bg() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Fg(e); + }) + .join(""); + return a; +} +var Gg = function (e) { + var t = { + variants: [ + e.COMMENT("!", "$", { relevance: 0 }), + e.COMMENT("^C[ ]", "$", { relevance: 0 }), + e.COMMENT("^C$", "$", { relevance: 0 }), + ], + }, + n = /(_[a-z_\d]+)?/, + a = /([de][+-]?\d+)?/, + r = { + className: "number", + variants: [ + { begin: Bg(/\b\d+/, /\.(\d*)/, a, n) }, + { begin: Bg(/\b\d+/, a, n) }, + { begin: Bg(/\.\d+/, a, n) }, + ], + relevance: 0, + }, + i = { + className: "function", + beginKeywords: "subroutine function program", + illegal: "[${=\\n]", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }; + return { + name: "Fortran", + case_insensitive: !0, + aliases: ["f90", "f95"], + keywords: { + literal: ".False. .True.", + keyword: + "kind do concurrent local shared while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then block endblock endassociate public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure impure integer real character complex logical codimension dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data", + built_in: + "alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_of acosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image sync change team co_broadcast co_max co_min co_sum co_reduce", + }, + illegal: /\/\*/, + contains: [ + { + className: "string", + relevance: 0, + variants: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + i, + { begin: /^C\s*=(?!=)/, relevance: 0 }, + t, + r, + ], + }; +}; +var Yg = function (e) { + var t = { + begin: "<", + end: ">", + contains: [e.inherit(e.TITLE_MODE, { begin: /'[a-zA-Z0-9_]+/ })], + }; + return { + name: "F#", + aliases: ["fs"], + keywords: + "abstract and as assert base begin class default delegate do done downcast downto elif else end exception extern false finally for fun function global if in inherit inline interface internal lazy let match member module mutable namespace new null of open or override private public rec return sig static struct then to true try type upcast use val void when while with yield", + illegal: /\/\*/, + contains: [ + { className: "keyword", begin: /\b(yield|return|let|do)!/ }, + { + className: "string", + begin: '@"', + end: '"', + contains: [{ begin: '""' }], + }, + { className: "string", begin: '"""', end: '"""' }, + e.COMMENT("\\(\\*(\\s)", "\\*\\)", { contains: ["self"] }), + { + className: "class", + beginKeywords: "type", + end: "\\(|=|$", + excludeEnd: !0, + contains: [e.UNDERSCORE_TITLE_MODE, t], + }, + { className: "meta", begin: "\\[<", end: ">\\]", relevance: 10 }, + { + className: "symbol", + begin: "\\B('[A-Za-z])\\b", + contains: [e.BACKSLASH_ESCAPE], + }, + e.C_LINE_COMMENT_MODE, + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + e.C_NUMBER_MODE, + ], + }; +}; +function Hg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Vg() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Hg(e); + }) + .join(""); + return a; +} +var qg = function (e) { + var t, + n = { + keyword: + "abort acronym acronyms alias all and assign binary card diag display else eq file files for free ge gt if integer le loop lt maximizing minimizing model models ne negative no not option options or ord positive prod put putpage puttl repeat sameas semicont semiint smax smin solve sos1 sos2 sum system table then until using while xor yes", + literal: "eps inf na", + built_in: + "abs arccos arcsin arctan arctan2 Beta betaReg binomial ceil centropy cos cosh cvPower div div0 eDist entropy errorf execSeed exp fact floor frac gamma gammaReg log logBeta logGamma log10 log2 mapVal max min mod ncpCM ncpF ncpVUpow ncpVUsin normal pi poly power randBinomial randLinear randTriangle round rPower sigmoid sign signPower sin sinh slexp sllog10 slrec sqexp sqlog10 sqr sqrec sqrt tan tanh trunc uniform uniformInt vcPower bool_and bool_eqv bool_imp bool_not bool_or bool_xor ifThen rel_eq rel_ge rel_gt rel_le rel_lt rel_ne gday gdow ghour gleap gmillisec gminute gmonth gsecond gyear jdate jnow jstart jtime errorLevel execError gamsRelease gamsVersion handleCollect handleDelete handleStatus handleSubmit heapFree heapLimit heapSize jobHandle jobKill jobStatus jobTerminate licenseLevel licenseStatus maxExecError sleep timeClose timeComp timeElapsed timeExec timeStart", + }, + a = { + className: "symbol", + variants: [{ begin: /=[lgenxc]=/ }, { begin: /\$/ }], + }, + r = { + className: "comment", + variants: [ + { begin: "'", end: "'" }, + { begin: '"', end: '"' }, + ], + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + i = { + begin: "/", + end: "/", + keywords: n, + contains: [ + r, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + e.C_NUMBER_MODE, + ], + }, + o = /[a-z0-9&#*=?@\\><:,()$[\]_.{}!+%^-]+/, + s = { + begin: /[a-z][a-z0-9_]*(\([a-z0-9_, ]*\))?[ \t]+/, + excludeBegin: !0, + end: "$", + endsWithParent: !0, + contains: [ + r, + i, + { + className: "comment", + begin: Vg(o, ((t = Vg(/[ ]+/, o)), Vg("(", t, ")*"))), + relevance: 0, + }, + ], + }; + return { + name: "GAMS", + aliases: ["gms"], + case_insensitive: !0, + keywords: n, + contains: [ + e.COMMENT(/^\$ontext/, /^\$offtext/), + { + className: "meta", + begin: "^\\$[a-z0-9]+", + end: "$", + returnBegin: !0, + contains: [{ className: "meta-keyword", begin: "^\\$[a-z0-9]+" }], + }, + e.COMMENT("^\\*", "$"), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + beginKeywords: + "set sets parameter parameters variable variables scalar scalars equation equations", + end: ";", + contains: [ + e.COMMENT("^\\*", "$"), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + i, + s, + ], + }, + { + beginKeywords: "table", + end: ";", + returnBegin: !0, + contains: [ + { beginKeywords: "table", end: "$", contains: [s] }, + e.COMMENT("^\\*", "$"), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + e.C_NUMBER_MODE, + ], + }, + { + className: "function", + begin: /^[a-z][a-z0-9_,\-+' ()$]+\.{2}/, + returnBegin: !0, + contains: [ + { className: "title", begin: /^[a-z0-9_]+/ }, + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + }, + a, + ], + }, + e.C_NUMBER_MODE, + a, + ], + }; +}; +var zg = function (e) { + var t = { + keyword: + "bool break call callexe checkinterrupt clear clearg closeall cls comlog compile continue create debug declare delete disable dlibrary dllcall do dos ed edit else elseif enable end endfor endif endp endo errorlog errorlogat expr external fn for format goto gosub graph if keyword let lib library line load loadarray loadexe loadf loadk loadm loadp loads loadx local locate loopnextindex lprint lpwidth lshow matrix msym ndpclex new open output outwidth plot plotsym pop prcsn print printdos proc push retp return rndcon rndmod rndmult rndseed run save saveall screen scroll setarray show sparse stop string struct system trace trap threadfor threadendfor threadbegin threadjoin threadstat threadend until use while winprint ne ge le gt lt and xor or not eq eqv", + built_in: + "abs acf aconcat aeye amax amean AmericanBinomCall AmericanBinomCall_Greeks AmericanBinomCall_ImpVol AmericanBinomPut AmericanBinomPut_Greeks AmericanBinomPut_ImpVol AmericanBSCall AmericanBSCall_Greeks AmericanBSCall_ImpVol AmericanBSPut AmericanBSPut_Greeks AmericanBSPut_ImpVol amin amult annotationGetDefaults annotationSetBkd annotationSetFont annotationSetLineColor annotationSetLineStyle annotationSetLineThickness annualTradingDays arccos arcsin areshape arrayalloc arrayindex arrayinit arraytomat asciiload asclabel astd astds asum atan atan2 atranspose axmargin balance band bandchol bandcholsol bandltsol bandrv bandsolpd bar base10 begwind besselj bessely beta box boxcox cdfBeta cdfBetaInv cdfBinomial cdfBinomialInv cdfBvn cdfBvn2 cdfBvn2e cdfCauchy cdfCauchyInv cdfChic cdfChii cdfChinc cdfChincInv cdfExp cdfExpInv cdfFc cdfFnc cdfFncInv cdfGam cdfGenPareto cdfHyperGeo cdfLaplace cdfLaplaceInv cdfLogistic cdfLogisticInv cdfmControlCreate cdfMvn cdfMvn2e cdfMvnce cdfMvne cdfMvt2e cdfMvtce cdfMvte cdfN cdfN2 cdfNc cdfNegBinomial cdfNegBinomialInv cdfNi cdfPoisson cdfPoissonInv cdfRayleigh cdfRayleighInv cdfTc cdfTci cdfTnc cdfTvn cdfWeibull cdfWeibullInv cdir ceil ChangeDir chdir chiBarSquare chol choldn cholsol cholup chrs close code cols colsf combinate combinated complex con cond conj cons ConScore contour conv convertsatostr convertstrtosa corrm corrms corrvc corrx corrxs cos cosh counts countwts crossprd crout croutp csrcol csrlin csvReadM csvReadSA cumprodc cumsumc curve cvtos datacreate datacreatecomplex datalist dataload dataloop dataopen datasave date datestr datestring datestrymd dayinyr dayofweek dbAddDatabase dbClose dbCommit dbCreateQuery dbExecQuery dbGetConnectOptions dbGetDatabaseName dbGetDriverName dbGetDrivers dbGetHostName dbGetLastErrorNum dbGetLastErrorText dbGetNumericalPrecPolicy dbGetPassword dbGetPort dbGetTableHeaders dbGetTables dbGetUserName dbHasFeature dbIsDriverAvailable dbIsOpen dbIsOpenError dbOpen dbQueryBindValue dbQueryClear dbQueryCols dbQueryExecPrepared dbQueryFetchAllM dbQueryFetchAllSA dbQueryFetchOneM dbQueryFetchOneSA dbQueryFinish dbQueryGetBoundValue dbQueryGetBoundValues dbQueryGetField dbQueryGetLastErrorNum dbQueryGetLastErrorText dbQueryGetLastInsertID dbQueryGetLastQuery dbQueryGetPosition dbQueryIsActive dbQueryIsForwardOnly dbQueryIsNull dbQueryIsSelect dbQueryIsValid dbQueryPrepare dbQueryRows dbQuerySeek dbQuerySeekFirst dbQuerySeekLast dbQuerySeekNext dbQuerySeekPrevious dbQuerySetForwardOnly dbRemoveDatabase dbRollback dbSetConnectOptions dbSetDatabaseName dbSetHostName dbSetNumericalPrecPolicy dbSetPort dbSetUserName dbTransaction DeleteFile delif delrows denseToSp denseToSpRE denToZero design det detl dfft dffti diag diagrv digamma doswin DOSWinCloseall DOSWinOpen dotfeq dotfeqmt dotfge dotfgemt dotfgt dotfgtmt dotfle dotflemt dotflt dotfltmt dotfne dotfnemt draw drop dsCreate dstat dstatmt dstatmtControlCreate dtdate dtday dttime dttodtv dttostr dttoutc dtvnormal dtvtodt dtvtoutc dummy dummybr dummydn eig eigh eighv eigv elapsedTradingDays endwind envget eof eqSolve eqSolvemt eqSolvemtControlCreate eqSolvemtOutCreate eqSolveset erf erfc erfccplx erfcplx error etdays ethsec etstr EuropeanBinomCall EuropeanBinomCall_Greeks EuropeanBinomCall_ImpVol EuropeanBinomPut EuropeanBinomPut_Greeks EuropeanBinomPut_ImpVol EuropeanBSCall EuropeanBSCall_Greeks EuropeanBSCall_ImpVol EuropeanBSPut EuropeanBSPut_Greeks EuropeanBSPut_ImpVol exctsmpl exec execbg exp extern eye fcheckerr fclearerr feq feqmt fflush fft ffti fftm fftmi fftn fge fgemt fgets fgetsa fgetsat fgetst fgt fgtmt fileinfo filesa fle flemt floor flt fltmt fmod fne fnemt fonts fopen formatcv formatnv fputs fputst fseek fstrerror ftell ftocv ftos ftostrC gamma gammacplx gammaii gausset gdaAppend gdaCreate gdaDStat gdaDStatMat gdaGetIndex gdaGetName gdaGetNames gdaGetOrders gdaGetType gdaGetTypes gdaGetVarInfo gdaIsCplx gdaLoad gdaPack gdaRead gdaReadByIndex gdaReadSome gdaReadSparse gdaReadStruct gdaReportVarInfo gdaSave gdaUpdate gdaUpdateAndPack gdaVars gdaWrite gdaWrite32 gdaWriteSome getarray getdims getf getGAUSShome getmatrix getmatrix4D getname getnamef getNextTradingDay getNextWeekDay getnr getorders getpath getPreviousTradingDay getPreviousWeekDay getRow getscalar3D getscalar4D getTrRow getwind glm gradcplx gradMT gradMTm gradMTT gradMTTm gradp graphprt graphset hasimag header headermt hess hessMT hessMTg hessMTgw hessMTm hessMTmw hessMTT hessMTTg hessMTTgw hessMTTm hessMTw hessp hist histf histp hsec imag indcv indexcat 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nextnevn nextwind ntos null null1 numCombinations ols olsmt olsmtControlCreate olsqr olsqr2 olsqrmt ones optn optnevn orth outtyp pacf packedToSp packr parse pause pdfCauchy pdfChi pdfExp pdfGenPareto pdfHyperGeo pdfLaplace pdfLogistic pdfn pdfPoisson pdfRayleigh pdfWeibull pi pinv pinvmt plotAddArrow plotAddBar plotAddBox plotAddHist plotAddHistF plotAddHistP plotAddPolar plotAddScatter plotAddShape plotAddTextbox plotAddTS plotAddXY plotArea plotBar plotBox plotClearLayout plotContour plotCustomLayout plotGetDefaults plotHist plotHistF plotHistP plotLayout plotLogLog plotLogX plotLogY plotOpenWindow plotPolar plotSave plotScatter plotSetAxesPen plotSetBar plotSetBarFill plotSetBarStacked plotSetBkdColor plotSetFill plotSetGrid plotSetLegend plotSetLineColor plotSetLineStyle plotSetLineSymbol plotSetLineThickness plotSetNewWindow plotSetTitle plotSetWhichYAxis plotSetXAxisShow plotSetXLabel plotSetXRange plotSetXTicInterval plotSetXTicLabel plotSetYAxisShow plotSetYLabel plotSetYRange 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submat subscat substute subvec sumc sumr surface svd svd1 svd2 svdcusv svds svdusv sysstate tab tan tanh tempname time timedt timestr timeutc title tkf2eps tkf2ps tocart todaydt toeplitz token topolar trapchk trigamma trimr trunc type typecv typef union unionsa uniqindx uniqindxsa unique uniquesa upmat upmat1 upper utctodt utctodtv utrisol vals varCovMS varCovXS varget vargetl varmall varmares varput varputl vartypef vcm vcms vcx vcxs vec vech vecr vector vget view viewxyz vlist vnamecv volume vput vread vtypecv wait waitc walkindex where window writer xlabel xlsGetSheetCount xlsGetSheetSize xlsGetSheetTypes xlsMakeRange xlsReadM xlsReadSA xlsWrite xlsWriteM xlsWriteSA xpnd xtics xy xyz ylabel ytics zeros zeta zlabel ztics cdfEmpirical dot h5create h5open h5read h5readAttribute h5write h5writeAttribute ldl plotAddErrorBar plotAddSurface plotCDFEmpirical plotSetColormap plotSetContourLabels plotSetLegendFont plotSetTextInterpreter plotSetXTicCount plotSetYTicCount plotSetZLevels powerm strjoin sylvester strtrim", + literal: + "DB_AFTER_LAST_ROW DB_ALL_TABLES DB_BATCH_OPERATIONS DB_BEFORE_FIRST_ROW DB_BLOB DB_EVENT_NOTIFICATIONS DB_FINISH_QUERY DB_HIGH_PRECISION DB_LAST_INSERT_ID DB_LOW_PRECISION_DOUBLE DB_LOW_PRECISION_INT32 DB_LOW_PRECISION_INT64 DB_LOW_PRECISION_NUMBERS DB_MULTIPLE_RESULT_SETS DB_NAMED_PLACEHOLDERS DB_POSITIONAL_PLACEHOLDERS DB_PREPARED_QUERIES DB_QUERY_SIZE DB_SIMPLE_LOCKING DB_SYSTEM_TABLES DB_TABLES DB_TRANSACTIONS DB_UNICODE DB_VIEWS __STDIN __STDOUT __STDERR __FILE_DIR", + }, + n = e.COMMENT("@", "@"), + a = { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": + "define definecs|10 undef ifdef ifndef iflight ifdllcall ifmac ifos2win ifunix else endif lineson linesoff srcfile srcline", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + { + beginKeywords: "include", + end: "$", + keywords: { "meta-keyword": "include" }, + contains: [ + { className: "meta-string", begin: '"', end: '"', illegal: "\\n" }, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + ], + }, + r = { + begin: /\bstruct\s+/, + end: /\s/, + keywords: "struct", + contains: [ + { className: "type", begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + ], + }, + i = [ + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + endsWithParent: !0, + relevance: 0, + contains: [ + { className: "literal", begin: /\.\.\./ }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + r, + ], + }, + ], + o = { className: "title", begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + s = function (t, a, r) { + var s = e.inherit( + { + className: "function", + beginKeywords: t, + end: a, + excludeEnd: !0, + contains: [].concat(i), + }, + r || {}, + ); + return ( + s.contains.push(o), + s.contains.push(e.C_NUMBER_MODE), + s.contains.push(e.C_BLOCK_COMMENT_MODE), + s.contains.push(n), + s + ); + }, + l = { + className: "built_in", + begin: "\\b(" + t.built_in.split(" ").join("|") + ")\\b", + }, + c = { + className: "string", + begin: '"', + end: '"', + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + _ = { + begin: e.UNDERSCORE_IDENT_RE + "\\s*\\(", + returnBegin: !0, + keywords: t, + relevance: 0, + contains: [ + { beginKeywords: t.keyword }, + l, + { className: "built_in", begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + ], + }, + d = { + begin: /\(/, + end: /\)/, + relevance: 0, + keywords: { built_in: t.built_in, literal: t.literal }, + contains: [e.C_NUMBER_MODE, e.C_BLOCK_COMMENT_MODE, n, l, _, c, "self"], + }; + return ( + _.contains.push(d), + { + name: "GAUSS", + aliases: ["gss"], + case_insensitive: !0, + keywords: t, + illegal: /(\{[%#]|[%#]\}| <- )/, + contains: [ + e.C_NUMBER_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + c, + a, + { + className: "keyword", + begin: + /\bexternal (matrix|string|array|sparse matrix|struct|proc|keyword|fn)/, + }, + s("proc keyword", ";"), + s("fn", "="), + { + beginKeywords: "for threadfor", + end: /;/, + relevance: 0, + contains: [e.C_BLOCK_COMMENT_MODE, n, d], + }, + { + variants: [ + { begin: e.UNDERSCORE_IDENT_RE + "\\." + e.UNDERSCORE_IDENT_RE }, + { begin: e.UNDERSCORE_IDENT_RE + "\\s*=" }, + ], + relevance: 0, + }, + _, + r, + ], + } + ); +}; +var Wg = function (e) { + var t = { + $pattern: "[A-Z_][A-Z0-9_.]*", + keyword: + "IF DO WHILE ENDWHILE CALL ENDIF SUB ENDSUB GOTO REPEAT ENDREPEAT EQ LT GT NE GE LE OR XOR", + }, + n = e.inherit(e.C_NUMBER_MODE, { + begin: "([-+]?((\\.\\d+)|(\\d+)(\\.\\d*)?))|" + e.C_NUMBER_RE, + }), + a = [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT(/\(/, /\)/), + n, + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { className: "name", begin: "([G])([0-9]+\\.?[0-9]?)" }, + { className: "name", begin: "([M])([0-9]+\\.?[0-9]?)" }, + { className: "attr", begin: "(VC|VS|#)", end: "(\\d+)" }, + { className: "attr", begin: "(VZOFX|VZOFY|VZOFZ)" }, + { + className: "built_in", + begin: "(ATAN|ABS|ACOS|ASIN|SIN|COS|EXP|FIX|FUP|ROUND|LN|TAN)(\\[)", + contains: [n], + end: "\\]", + }, + { + className: "symbol", + variants: [{ begin: "N", end: "\\d+", illegal: "\\W" }], + }, + ]; + return { + name: "G-code (ISO 6983)", + aliases: ["nc"], + case_insensitive: !0, + keywords: t, + contains: [ + { className: "meta", begin: "%" }, + { className: "meta", begin: "([O])([0-9]+)" }, + ].concat(a), + }; +}; +var $g = function (e) { + return { + name: "Gherkin", + aliases: ["feature"], + keywords: + "Feature Background Ability Business Need Scenario Scenarios Scenario Outline Scenario Template Examples Given And Then But When", + contains: [ + { className: "symbol", begin: "\\*", relevance: 0 }, + { className: "meta", begin: "@[^@\\s]+" }, + { + begin: "\\|", + end: "\\|\\w*$", + contains: [{ className: "string", begin: "[^|]+" }], + }, + { className: "variable", begin: "<", end: ">" }, + e.HASH_COMMENT_MODE, + { className: "string", begin: '"""', end: '"""' }, + e.QUOTE_STRING_MODE, + ], + }; +}; +var Qg = function (e) { + return { + name: "GLSL", + keywords: { + keyword: + "break continue discard do else for if return while switch case default attribute binding buffer ccw centroid centroid varying coherent column_major const cw depth_any depth_greater depth_less depth_unchanged early_fragment_tests equal_spacing flat fractional_even_spacing fractional_odd_spacing highp in index inout invariant invocations isolines layout line_strip lines lines_adjacency local_size_x local_size_y local_size_z location lowp max_vertices mediump noperspective offset origin_upper_left out packed patch pixel_center_integer point_mode points precise precision quads r11f_g11f_b10f r16 r16_snorm r16f r16i r16ui r32f r32i r32ui r8 r8_snorm r8i r8ui readonly restrict rg16 rg16_snorm rg16f rg16i rg16ui rg32f rg32i rg32ui rg8 rg8_snorm rg8i rg8ui rgb10_a2 rgb10_a2ui rgba16 rgba16_snorm rgba16f rgba16i rgba16ui rgba32f rgba32i rgba32ui rgba8 rgba8_snorm rgba8i rgba8ui row_major sample shared smooth std140 std430 stream triangle_strip triangles triangles_adjacency uniform varying vertices volatile writeonly", + type: "atomic_uint bool bvec2 bvec3 bvec4 dmat2 dmat2x2 dmat2x3 dmat2x4 dmat3 dmat3x2 dmat3x3 dmat3x4 dmat4 dmat4x2 dmat4x3 dmat4x4 double dvec2 dvec3 dvec4 float iimage1D iimage1DArray iimage2D iimage2DArray iimage2DMS iimage2DMSArray iimage2DRect iimage3D iimageBuffer iimageCube iimageCubeArray image1D image1DArray image2D image2DArray image2DMS image2DMSArray image2DRect image3D imageBuffer imageCube imageCubeArray int isampler1D isampler1DArray isampler2D isampler2DArray isampler2DMS isampler2DMSArray isampler2DRect isampler3D isamplerBuffer isamplerCube isamplerCubeArray ivec2 ivec3 ivec4 mat2 mat2x2 mat2x3 mat2x4 mat3 mat3x2 mat3x3 mat3x4 mat4 mat4x2 mat4x3 mat4x4 sampler1D sampler1DArray sampler1DArrayShadow sampler1DShadow sampler2D sampler2DArray sampler2DArrayShadow sampler2DMS sampler2DMSArray sampler2DRect sampler2DRectShadow sampler2DShadow sampler3D samplerBuffer samplerCube samplerCubeArray samplerCubeArrayShadow samplerCubeShadow image1D uimage1DArray uimage2D uimage2DArray uimage2DMS uimage2DMSArray uimage2DRect uimage3D uimageBuffer uimageCube uimageCubeArray uint usampler1D usampler1DArray usampler2D usampler2DArray usampler2DMS usampler2DMSArray usampler2DRect usampler3D samplerBuffer usamplerCube usamplerCubeArray uvec2 uvec3 uvec4 vec2 vec3 vec4 void", + built_in: + "gl_MaxAtomicCounterBindings gl_MaxAtomicCounterBufferSize gl_MaxClipDistances gl_MaxClipPlanes gl_MaxCombinedAtomicCounterBuffers gl_MaxCombinedAtomicCounters gl_MaxCombinedImageUniforms gl_MaxCombinedImageUnitsAndFragmentOutputs gl_MaxCombinedTextureImageUnits gl_MaxComputeAtomicCounterBuffers gl_MaxComputeAtomicCounters gl_MaxComputeImageUniforms gl_MaxComputeTextureImageUnits gl_MaxComputeUniformComponents gl_MaxComputeWorkGroupCount gl_MaxComputeWorkGroupSize gl_MaxDrawBuffers gl_MaxFragmentAtomicCounterBuffers gl_MaxFragmentAtomicCounters gl_MaxFragmentImageUniforms gl_MaxFragmentInputComponents gl_MaxFragmentInputVectors gl_MaxFragmentUniformComponents gl_MaxFragmentUniformVectors gl_MaxGeometryAtomicCounterBuffers gl_MaxGeometryAtomicCounters gl_MaxGeometryImageUniforms gl_MaxGeometryInputComponents gl_MaxGeometryOutputComponents gl_MaxGeometryOutputVertices gl_MaxGeometryTextureImageUnits gl_MaxGeometryTotalOutputComponents gl_MaxGeometryUniformComponents gl_MaxGeometryVaryingComponents gl_MaxImageSamples gl_MaxImageUnits gl_MaxLights gl_MaxPatchVertices gl_MaxProgramTexelOffset gl_MaxTessControlAtomicCounterBuffers gl_MaxTessControlAtomicCounters gl_MaxTessControlImageUniforms gl_MaxTessControlInputComponents gl_MaxTessControlOutputComponents gl_MaxTessControlTextureImageUnits gl_MaxTessControlTotalOutputComponents gl_MaxTessControlUniformComponents gl_MaxTessEvaluationAtomicCounterBuffers gl_MaxTessEvaluationAtomicCounters gl_MaxTessEvaluationImageUniforms gl_MaxTessEvaluationInputComponents gl_MaxTessEvaluationOutputComponents gl_MaxTessEvaluationTextureImageUnits gl_MaxTessEvaluationUniformComponents gl_MaxTessGenLevel gl_MaxTessPatchComponents gl_MaxTextureCoords gl_MaxTextureImageUnits gl_MaxTextureUnits gl_MaxVaryingComponents gl_MaxVaryingFloats gl_MaxVaryingVectors gl_MaxVertexAtomicCounterBuffers gl_MaxVertexAtomicCounters gl_MaxVertexAttribs gl_MaxVertexImageUniforms gl_MaxVertexOutputComponents gl_MaxVertexOutputVectors gl_MaxVertexTextureImageUnits gl_MaxVertexUniformComponents gl_MaxVertexUniformVectors gl_MaxViewports gl_MinProgramTexelOffset gl_BackColor gl_BackLightModelProduct gl_BackLightProduct gl_BackMaterial gl_BackSecondaryColor gl_ClipDistance gl_ClipPlane gl_ClipVertex gl_Color gl_DepthRange gl_EyePlaneQ gl_EyePlaneR gl_EyePlaneS gl_EyePlaneT gl_Fog gl_FogCoord gl_FogFragCoord gl_FragColor gl_FragCoord gl_FragData gl_FragDepth gl_FrontColor gl_FrontFacing gl_FrontLightModelProduct gl_FrontLightProduct gl_FrontMaterial gl_FrontSecondaryColor gl_GlobalInvocationID gl_InstanceID gl_InvocationID gl_Layer gl_LightModel gl_LightSource gl_LocalInvocationID gl_LocalInvocationIndex gl_ModelViewMatrix gl_ModelViewMatrixInverse gl_ModelViewMatrixInverseTranspose gl_ModelViewMatrixTranspose gl_ModelViewProjectionMatrix gl_ModelViewProjectionMatrixInverse gl_ModelViewProjectionMatrixInverseTranspose gl_ModelViewProjectionMatrixTranspose gl_MultiTexCoord0 gl_MultiTexCoord1 gl_MultiTexCoord2 gl_MultiTexCoord3 gl_MultiTexCoord4 gl_MultiTexCoord5 gl_MultiTexCoord6 gl_MultiTexCoord7 gl_Normal gl_NormalMatrix gl_NormalScale gl_NumSamples gl_NumWorkGroups gl_ObjectPlaneQ gl_ObjectPlaneR gl_ObjectPlaneS gl_ObjectPlaneT gl_PatchVerticesIn gl_Point gl_PointCoord gl_PointSize gl_Position gl_PrimitiveID gl_PrimitiveIDIn gl_ProjectionMatrix gl_ProjectionMatrixInverse gl_ProjectionMatrixInverseTranspose gl_ProjectionMatrixTranspose gl_SampleID gl_SampleMask gl_SampleMaskIn gl_SamplePosition gl_SecondaryColor gl_TessCoord gl_TessLevelInner gl_TessLevelOuter gl_TexCoord gl_TextureEnvColor gl_TextureMatrix gl_TextureMatrixInverse gl_TextureMatrixInverseTranspose gl_TextureMatrixTranspose gl_Vertex gl_VertexID gl_ViewportIndex gl_WorkGroupID gl_WorkGroupSize gl_in gl_out EmitStreamVertex EmitVertex EndPrimitive EndStreamPrimitive abs acos acosh all any asin asinh atan atanh atomicAdd atomicAnd atomicCompSwap atomicCounter atomicCounterDecrement atomicCounterIncrement atomicExchange atomicMax atomicMin atomicOr atomicXor barrier bitCount bitfieldExtract bitfieldInsert bitfieldReverse ceil clamp cos cosh cross dFdx dFdy degrees determinant distance dot equal exp exp2 faceforward findLSB findMSB floatBitsToInt floatBitsToUint floor fma fract frexp ftransform fwidth greaterThan greaterThanEqual groupMemoryBarrier imageAtomicAdd imageAtomicAnd imageAtomicCompSwap imageAtomicExchange imageAtomicMax imageAtomicMin imageAtomicOr imageAtomicXor imageLoad imageSize imageStore imulExtended intBitsToFloat interpolateAtCentroid interpolateAtOffset interpolateAtSample inverse inversesqrt isinf isnan ldexp length lessThan lessThanEqual log log2 matrixCompMult max memoryBarrier memoryBarrierAtomicCounter memoryBarrierBuffer memoryBarrierImage memoryBarrierShared min mix mod modf noise1 noise2 noise3 noise4 normalize not notEqual outerProduct packDouble2x32 packHalf2x16 packSnorm2x16 packSnorm4x8 packUnorm2x16 packUnorm4x8 pow radians reflect refract round roundEven shadow1D shadow1DLod shadow1DProj shadow1DProjLod shadow2D shadow2DLod shadow2DProj shadow2DProjLod sign sin sinh smoothstep sqrt step tan tanh texelFetch texelFetchOffset texture texture1D texture1DLod texture1DProj texture1DProjLod texture2D texture2DLod texture2DProj texture2DProjLod texture3D texture3DLod texture3DProj texture3DProjLod textureCube textureCubeLod textureGather textureGatherOffset textureGatherOffsets textureGrad textureGradOffset textureLod textureLodOffset textureOffset textureProj textureProjGrad 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ds_grid_multiply_region ds_grid_set_disk ds_grid_add_disk ds_grid_multiply_disk ds_grid_set_grid_region ds_grid_add_grid_region ds_grid_multiply_grid_region ds_grid_get ds_grid_get_sum ds_grid_get_max ds_grid_get_min ds_grid_get_mean ds_grid_get_disk_sum ds_grid_get_disk_min ds_grid_get_disk_max ds_grid_get_disk_mean ds_grid_value_exists ds_grid_value_x ds_grid_value_y ds_grid_value_disk_exists ds_grid_value_disk_x ds_grid_value_disk_y ds_grid_shuffle ds_grid_write ds_grid_read ds_grid_sort ds_grid_set ds_grid_get effect_create_below effect_create_above effect_clear part_type_create part_type_destroy part_type_exists part_type_clear part_type_shape part_type_sprite part_type_size part_type_scale part_type_orientation part_type_life part_type_step part_type_death part_type_speed part_type_direction part_type_gravity part_type_colour1 part_type_colour2 part_type_colour3 part_type_colour_mix part_type_colour_rgb part_type_colour_hsv part_type_color1 part_type_color2 part_type_color3 part_type_color_mix part_type_color_rgb part_type_color_hsv part_type_alpha1 part_type_alpha2 part_type_alpha3 part_type_blend part_system_create part_system_create_layer part_system_destroy part_system_exists part_system_clear part_system_draw_order part_system_depth part_system_position part_system_automatic_update part_system_automatic_draw part_system_update part_system_drawit part_system_get_layer part_system_layer part_particles_create part_particles_create_colour part_particles_create_color part_particles_clear part_particles_count part_emitter_create part_emitter_destroy part_emitter_destroy_all part_emitter_exists part_emitter_clear part_emitter_region part_emitter_burst part_emitter_stream external_call external_define external_free window_handle window_device matrix_get matrix_set matrix_build_identity matrix_build matrix_build_lookat matrix_build_projection_ortho matrix_build_projection_perspective matrix_build_projection_perspective_fov matrix_multiply matrix_transform_vertex matrix_stack_push matrix_stack_pop matrix_stack_multiply matrix_stack_set matrix_stack_clear matrix_stack_top matrix_stack_is_empty browser_input_capture os_get_config os_get_info os_get_language os_get_region os_lock_orientation display_get_dpi_x display_get_dpi_y display_set_gui_size display_set_gui_maximise display_set_gui_maximize device_mouse_dbclick_enable display_set_timing_method display_get_timing_method display_set_sleep_margin display_get_sleep_margin virtual_key_add virtual_key_hide virtual_key_delete virtual_key_show draw_enable_drawevent draw_enable_swf_aa draw_set_swf_aa_level draw_get_swf_aa_level draw_texture_flush draw_flush gpu_set_blendenable gpu_set_ztestenable gpu_set_zfunc gpu_set_zwriteenable gpu_set_lightingenable gpu_set_fog gpu_set_cullmode gpu_set_blendmode gpu_set_blendmode_ext gpu_set_blendmode_ext_sepalpha gpu_set_colorwriteenable gpu_set_colourwriteenable gpu_set_alphatestenable gpu_set_alphatestref gpu_set_alphatestfunc gpu_set_texfilter gpu_set_texfilter_ext gpu_set_texrepeat gpu_set_texrepeat_ext gpu_set_tex_filter gpu_set_tex_filter_ext gpu_set_tex_repeat gpu_set_tex_repeat_ext gpu_set_tex_mip_filter gpu_set_tex_mip_filter_ext gpu_set_tex_mip_bias gpu_set_tex_mip_bias_ext gpu_set_tex_min_mip gpu_set_tex_min_mip_ext gpu_set_tex_max_mip gpu_set_tex_max_mip_ext gpu_set_tex_max_aniso gpu_set_tex_max_aniso_ext gpu_set_tex_mip_enable gpu_set_tex_mip_enable_ext gpu_get_blendenable gpu_get_ztestenable gpu_get_zfunc gpu_get_zwriteenable gpu_get_lightingenable gpu_get_fog gpu_get_cullmode gpu_get_blendmode gpu_get_blendmode_ext gpu_get_blendmode_ext_sepalpha gpu_get_blendmode_src gpu_get_blendmode_dest gpu_get_blendmode_srcalpha gpu_get_blendmode_destalpha gpu_get_colorwriteenable gpu_get_colourwriteenable gpu_get_alphatestenable gpu_get_alphatestref gpu_get_alphatestfunc gpu_get_texfilter gpu_get_texfilter_ext gpu_get_texrepeat gpu_get_texrepeat_ext gpu_get_tex_filter gpu_get_tex_filter_ext gpu_get_tex_repeat gpu_get_tex_repeat_ext gpu_get_tex_mip_filter gpu_get_tex_mip_filter_ext gpu_get_tex_mip_bias gpu_get_tex_mip_bias_ext gpu_get_tex_min_mip gpu_get_tex_min_mip_ext gpu_get_tex_max_mip gpu_get_tex_max_mip_ext gpu_get_tex_max_aniso gpu_get_tex_max_aniso_ext gpu_get_tex_mip_enable gpu_get_tex_mip_enable_ext gpu_push_state gpu_pop_state gpu_get_state gpu_set_state draw_light_define_ambient draw_light_define_direction draw_light_define_point draw_light_enable draw_set_lighting draw_light_get_ambient draw_light_get draw_get_lighting shop_leave_rating url_get_domain url_open url_open_ext url_open_full get_timer achievement_login achievement_logout achievement_post achievement_increment achievement_post_score achievement_available achievement_show_achievements achievement_show_leaderboards achievement_load_friends achievement_load_leaderboard achievement_send_challenge achievement_load_progress achievement_reset achievement_login_status achievement_get_pic achievement_show_challenge_notifications achievement_get_challenges achievement_event achievement_show achievement_get_info cloud_file_save cloud_string_save cloud_synchronise ads_enable ads_disable ads_setup ads_engagement_launch ads_engagement_available ads_engagement_active ads_event ads_event_preload ads_set_reward_callback ads_get_display_height ads_get_display_width ads_move ads_interstitial_available ads_interstitial_display device_get_tilt_x device_get_tilt_y device_get_tilt_z device_is_keypad_open device_mouse_check_button device_mouse_check_button_pressed device_mouse_check_button_released device_mouse_x device_mouse_y device_mouse_raw_x device_mouse_raw_y device_mouse_x_to_gui device_mouse_y_to_gui iap_activate iap_status iap_enumerate_products iap_restore_all iap_acquire iap_consume iap_product_details iap_purchase_details facebook_init facebook_login facebook_status facebook_graph_request facebook_dialog facebook_logout facebook_launch_offerwall facebook_post_message facebook_send_invite facebook_user_id facebook_accesstoken facebook_check_permission facebook_request_read_permissions facebook_request_publish_permissions gamepad_is_supported gamepad_get_device_count gamepad_is_connected gamepad_get_description gamepad_get_button_threshold gamepad_set_button_threshold gamepad_get_axis_deadzone gamepad_set_axis_deadzone gamepad_button_count gamepad_button_check gamepad_button_check_pressed gamepad_button_check_released gamepad_button_value gamepad_axis_count gamepad_axis_value gamepad_set_vibration gamepad_set_colour gamepad_set_color os_is_paused window_has_focus code_is_compiled http_get http_get_file http_post_string http_request json_encode json_decode zip_unzip load_csv base64_encode base64_decode md5_string_unicode md5_string_utf8 md5_file os_is_network_connected sha1_string_unicode sha1_string_utf8 sha1_file os_powersave_enable analytics_event analytics_event_ext win8_livetile_tile_notification win8_livetile_tile_clear win8_livetile_badge_notification win8_livetile_badge_clear win8_livetile_queue_enable win8_secondarytile_pin win8_secondarytile_badge_notification win8_secondarytile_delete win8_livetile_notification_begin win8_livetile_notification_secondary_begin win8_livetile_notification_expiry win8_livetile_notification_tag win8_livetile_notification_text_add win8_livetile_notification_image_add win8_livetile_notification_end win8_appbar_enable win8_appbar_add_element win8_appbar_remove_element win8_settingscharm_add_entry win8_settingscharm_add_html_entry win8_settingscharm_add_xaml_entry win8_settingscharm_set_xaml_property win8_settingscharm_get_xaml_property win8_settingscharm_remove_entry win8_share_image win8_share_screenshot win8_share_file win8_share_url win8_share_text win8_search_enable win8_search_disable win8_search_add_suggestions win8_device_touchscreen_available win8_license_initialize_sandbox win8_license_trial_version winphone_license_trial_version winphone_tile_title winphone_tile_count winphone_tile_back_title winphone_tile_back_content winphone_tile_back_content_wide winphone_tile_front_image winphone_tile_front_image_small winphone_tile_front_image_wide winphone_tile_back_image winphone_tile_back_image_wide winphone_tile_background_colour winphone_tile_background_color winphone_tile_icon_image winphone_tile_small_icon_image winphone_tile_wide_content winphone_tile_cycle_images winphone_tile_small_background_image physics_world_create physics_world_gravity physics_world_update_speed physics_world_update_iterations physics_world_draw_debug physics_pause_enable physics_fixture_create physics_fixture_set_kinematic physics_fixture_set_density physics_fixture_set_awake physics_fixture_set_restitution physics_fixture_set_friction physics_fixture_set_collision_group physics_fixture_set_sensor physics_fixture_set_linear_damping physics_fixture_set_angular_damping physics_fixture_set_circle_shape physics_fixture_set_box_shape physics_fixture_set_edge_shape physics_fixture_set_polygon_shape physics_fixture_set_chain_shape physics_fixture_add_point physics_fixture_bind physics_fixture_bind_ext physics_fixture_delete physics_apply_force physics_apply_impulse physics_apply_angular_impulse physics_apply_local_force physics_apply_local_impulse physics_apply_torque physics_mass_properties physics_draw_debug physics_test_overlap physics_remove_fixture physics_set_friction physics_set_density physics_set_restitution physics_get_friction physics_get_density physics_get_restitution physics_joint_distance_create physics_joint_rope_create physics_joint_revolute_create physics_joint_prismatic_create physics_joint_pulley_create physics_joint_wheel_create physics_joint_weld_create physics_joint_friction_create physics_joint_gear_create physics_joint_enable_motor physics_joint_get_value physics_joint_set_value physics_joint_delete physics_particle_create physics_particle_delete physics_particle_delete_region_circle physics_particle_delete_region_box physics_particle_delete_region_poly physics_particle_set_flags physics_particle_set_category_flags physics_particle_draw physics_particle_draw_ext physics_particle_count physics_particle_get_data physics_particle_get_data_particle physics_particle_group_begin physics_particle_group_circle physics_particle_group_box physics_particle_group_polygon physics_particle_group_add_point physics_particle_group_end physics_particle_group_join physics_particle_group_delete physics_particle_group_count physics_particle_group_get_data physics_particle_group_get_mass physics_particle_group_get_inertia physics_particle_group_get_centre_x physics_particle_group_get_centre_y physics_particle_group_get_vel_x physics_particle_group_get_vel_y physics_particle_group_get_ang_vel physics_particle_group_get_x physics_particle_group_get_y physics_particle_group_get_angle physics_particle_set_group_flags physics_particle_get_group_flags physics_particle_get_max_count physics_particle_get_radius physics_particle_get_density physics_particle_get_damping physics_particle_get_gravity_scale physics_particle_set_max_count physics_particle_set_radius physics_particle_set_density physics_particle_set_damping physics_particle_set_gravity_scale network_create_socket network_create_socket_ext network_create_server network_create_server_raw network_connect network_connect_raw network_send_packet network_send_raw network_send_broadcast network_send_udp network_send_udp_raw network_set_timeout network_set_config network_resolve network_destroy buffer_create buffer_write buffer_read buffer_seek buffer_get_surface buffer_set_surface buffer_delete buffer_exists buffer_get_type buffer_get_alignment buffer_poke buffer_peek buffer_save buffer_save_ext buffer_load buffer_load_ext buffer_load_partial buffer_copy buffer_fill buffer_get_size buffer_tell buffer_resize buffer_md5 buffer_sha1 buffer_base64_encode buffer_base64_decode buffer_base64_decode_ext buffer_sizeof buffer_get_address buffer_create_from_vertex_buffer buffer_create_from_vertex_buffer_ext buffer_copy_from_vertex_buffer buffer_async_group_begin buffer_async_group_option buffer_async_group_end buffer_load_async buffer_save_async gml_release_mode gml_pragma steam_activate_overlay steam_is_overlay_enabled steam_is_overlay_activated steam_get_persona_name steam_initialised steam_is_cloud_enabled_for_app steam_is_cloud_enabled_for_account steam_file_persisted steam_get_quota_total steam_get_quota_free steam_file_write steam_file_write_file steam_file_read steam_file_delete steam_file_exists steam_file_size steam_file_share steam_is_screenshot_requested steam_send_screenshot steam_is_user_logged_on steam_get_user_steam_id steam_user_owns_dlc steam_user_installed_dlc steam_set_achievement steam_get_achievement steam_clear_achievement steam_set_stat_int steam_set_stat_float steam_set_stat_avg_rate steam_get_stat_int steam_get_stat_float steam_get_stat_avg_rate steam_reset_all_stats steam_reset_all_stats_achievements steam_stats_ready steam_create_leaderboard steam_upload_score steam_upload_score_ext steam_download_scores_around_user steam_download_scores steam_download_friends_scores steam_upload_score_buffer steam_upload_score_buffer_ext steam_current_game_language steam_available_languages steam_activate_overlay_browser steam_activate_overlay_user steam_activate_overlay_store steam_get_user_persona_name steam_get_app_id steam_get_user_account_id steam_ugc_download steam_ugc_create_item steam_ugc_start_item_update steam_ugc_set_item_title steam_ugc_set_item_description steam_ugc_set_item_visibility steam_ugc_set_item_tags steam_ugc_set_item_content steam_ugc_set_item_preview steam_ugc_submit_item_update steam_ugc_get_item_update_progress steam_ugc_subscribe_item steam_ugc_unsubscribe_item steam_ugc_num_subscribed_items steam_ugc_get_subscribed_items steam_ugc_get_item_install_info steam_ugc_get_item_update_info steam_ugc_request_item_details steam_ugc_create_query_user steam_ugc_create_query_user_ex steam_ugc_create_query_all steam_ugc_create_query_all_ex steam_ugc_query_set_cloud_filename_filter steam_ugc_query_set_match_any_tag steam_ugc_query_set_search_text steam_ugc_query_set_ranked_by_trend_days steam_ugc_query_add_required_tag steam_ugc_query_add_excluded_tag steam_ugc_query_set_return_long_description steam_ugc_query_set_return_total_only steam_ugc_query_set_allow_cached_response steam_ugc_send_query shader_set shader_get_name shader_reset shader_current shader_is_compiled shader_get_sampler_index shader_get_uniform shader_set_uniform_i shader_set_uniform_i_array shader_set_uniform_f shader_set_uniform_f_array shader_set_uniform_matrix shader_set_uniform_matrix_array shader_enable_corner_id texture_set_stage texture_get_texel_width texture_get_texel_height shaders_are_supported vertex_format_begin vertex_format_end vertex_format_delete vertex_format_add_position vertex_format_add_position_3d vertex_format_add_colour vertex_format_add_color vertex_format_add_normal vertex_format_add_texcoord vertex_format_add_textcoord vertex_format_add_custom vertex_create_buffer vertex_create_buffer_ext vertex_delete_buffer vertex_begin vertex_end vertex_position vertex_position_3d vertex_colour vertex_color vertex_argb vertex_texcoord vertex_normal vertex_float1 vertex_float2 vertex_float3 vertex_float4 vertex_ubyte4 vertex_submit vertex_freeze vertex_get_number vertex_get_buffer_size vertex_create_buffer_from_buffer vertex_create_buffer_from_buffer_ext push_local_notification push_get_first_local_notification push_get_next_local_notification push_cancel_local_notification skeleton_animation_set skeleton_animation_get skeleton_animation_mix skeleton_animation_set_ext skeleton_animation_get_ext skeleton_animation_get_duration skeleton_animation_get_frames skeleton_animation_clear skeleton_skin_set skeleton_skin_get skeleton_attachment_set skeleton_attachment_get skeleton_attachment_create skeleton_collision_draw_set skeleton_bone_data_get skeleton_bone_data_set skeleton_bone_state_get skeleton_bone_state_set skeleton_get_minmax skeleton_get_num_bounds skeleton_get_bounds skeleton_animation_get_frame skeleton_animation_set_frame draw_skeleton draw_skeleton_time draw_skeleton_instance draw_skeleton_collision skeleton_animation_list skeleton_skin_list skeleton_slot_data layer_get_id layer_get_id_at_depth layer_get_depth layer_create layer_destroy layer_destroy_instances layer_add_instance layer_has_instance layer_set_visible layer_get_visible layer_exists layer_x layer_y layer_get_x layer_get_y layer_hspeed layer_vspeed layer_get_hspeed layer_get_vspeed layer_script_begin layer_script_end layer_shader layer_get_script_begin layer_get_script_end layer_get_shader layer_set_target_room layer_get_target_room layer_reset_target_room layer_get_all layer_get_all_elements layer_get_name layer_depth layer_get_element_layer layer_get_element_type layer_element_move layer_force_draw_depth layer_is_draw_depth_forced layer_get_forced_depth layer_background_get_id layer_background_exists layer_background_create layer_background_destroy layer_background_visible layer_background_change layer_background_sprite layer_background_htiled layer_background_vtiled layer_background_stretch layer_background_yscale layer_background_xscale layer_background_blend layer_background_alpha layer_background_index layer_background_speed layer_background_get_visible layer_background_get_sprite layer_background_get_htiled layer_background_get_vtiled layer_background_get_stretch layer_background_get_yscale layer_background_get_xscale layer_background_get_blend layer_background_get_alpha layer_background_get_index layer_background_get_speed layer_sprite_get_id layer_sprite_exists layer_sprite_create layer_sprite_destroy layer_sprite_change layer_sprite_index layer_sprite_speed layer_sprite_xscale layer_sprite_yscale layer_sprite_angle layer_sprite_blend layer_sprite_alpha layer_sprite_x layer_sprite_y layer_sprite_get_sprite layer_sprite_get_index layer_sprite_get_speed layer_sprite_get_xscale layer_sprite_get_yscale layer_sprite_get_angle layer_sprite_get_blend layer_sprite_get_alpha layer_sprite_get_x layer_sprite_get_y layer_tilemap_get_id layer_tilemap_exists layer_tilemap_create layer_tilemap_destroy tilemap_tileset tilemap_x tilemap_y tilemap_set tilemap_set_at_pixel tilemap_get_tileset tilemap_get_tile_width tilemap_get_tile_height tilemap_get_width tilemap_get_height tilemap_get_x tilemap_get_y tilemap_get tilemap_get_at_pixel tilemap_get_cell_x_at_pixel tilemap_get_cell_y_at_pixel tilemap_clear draw_tilemap draw_tile tilemap_set_global_mask tilemap_get_global_mask tilemap_set_mask tilemap_get_mask tilemap_get_frame tile_set_empty tile_set_index tile_set_flip tile_set_mirror tile_set_rotate tile_get_empty tile_get_index tile_get_flip tile_get_mirror tile_get_rotate layer_tile_exists layer_tile_create layer_tile_destroy layer_tile_change layer_tile_xscale layer_tile_yscale layer_tile_blend layer_tile_alpha layer_tile_x layer_tile_y layer_tile_region layer_tile_visible layer_tile_get_sprite layer_tile_get_xscale layer_tile_get_yscale layer_tile_get_blend layer_tile_get_alpha layer_tile_get_x layer_tile_get_y layer_tile_get_region layer_tile_get_visible layer_instance_get_instance instance_activate_layer instance_deactivate_layer camera_create camera_create_view camera_destroy camera_apply camera_get_active camera_get_default camera_set_default camera_set_view_mat camera_set_proj_mat camera_set_update_script camera_set_begin_script camera_set_end_script camera_set_view_pos camera_set_view_size camera_set_view_speed camera_set_view_border camera_set_view_angle camera_set_view_target camera_get_view_mat camera_get_proj_mat camera_get_update_script camera_get_begin_script camera_get_end_script camera_get_view_x camera_get_view_y camera_get_view_width camera_get_view_height camera_get_view_speed_x camera_get_view_speed_y camera_get_view_border_x camera_get_view_border_y camera_get_view_angle camera_get_view_target view_get_camera view_get_visible view_get_xport view_get_yport view_get_wport view_get_hport view_get_surface_id view_set_camera view_set_visible view_set_xport view_set_yport view_set_wport view_set_hport view_set_surface_id gesture_drag_time gesture_drag_distance gesture_flick_speed gesture_double_tap_time gesture_double_tap_distance gesture_pinch_distance gesture_pinch_angle_towards gesture_pinch_angle_away gesture_rotate_time gesture_rotate_angle gesture_tap_count gesture_get_drag_time gesture_get_drag_distance gesture_get_flick_speed gesture_get_double_tap_time gesture_get_double_tap_distance gesture_get_pinch_distance gesture_get_pinch_angle_towards gesture_get_pinch_angle_away gesture_get_rotate_time gesture_get_rotate_angle gesture_get_tap_count keyboard_virtual_show keyboard_virtual_hide keyboard_virtual_status keyboard_virtual_height", + literal: + "self other all noone global local undefined pointer_invalid pointer_null path_action_stop path_action_restart path_action_continue path_action_reverse true false pi GM_build_date GM_version GM_runtime_version timezone_local timezone_utc gamespeed_fps gamespeed_microseconds ev_create ev_destroy ev_step ev_alarm ev_keyboard ev_mouse ev_collision ev_other ev_draw ev_draw_begin ev_draw_end ev_draw_pre ev_draw_post ev_keypress ev_keyrelease ev_trigger ev_left_button ev_right_button ev_middle_button ev_no_button ev_left_press ev_right_press ev_middle_press ev_left_release ev_right_release ev_middle_release ev_mouse_enter ev_mouse_leave ev_mouse_wheel_up ev_mouse_wheel_down ev_global_left_button ev_global_right_button ev_global_middle_button ev_global_left_press ev_global_right_press ev_global_middle_press ev_global_left_release ev_global_right_release ev_global_middle_release ev_joystick1_left ev_joystick1_right ev_joystick1_up ev_joystick1_down ev_joystick1_button1 ev_joystick1_button2 ev_joystick1_button3 ev_joystick1_button4 ev_joystick1_button5 ev_joystick1_button6 ev_joystick1_button7 ev_joystick1_button8 ev_joystick2_left ev_joystick2_right ev_joystick2_up ev_joystick2_down ev_joystick2_button1 ev_joystick2_button2 ev_joystick2_button3 ev_joystick2_button4 ev_joystick2_button5 ev_joystick2_button6 ev_joystick2_button7 ev_joystick2_button8 ev_outside ev_boundary ev_game_start ev_game_end ev_room_start ev_room_end ev_no_more_lives ev_animation_end ev_end_of_path ev_no_more_health ev_close_button ev_user0 ev_user1 ev_user2 ev_user3 ev_user4 ev_user5 ev_user6 ev_user7 ev_user8 ev_user9 ev_user10 ev_user11 ev_user12 ev_user13 ev_user14 ev_user15 ev_step_normal ev_step_begin ev_step_end ev_gui ev_gui_begin ev_gui_end ev_cleanup ev_gesture ev_gesture_tap ev_gesture_double_tap ev_gesture_drag_start ev_gesture_dragging ev_gesture_drag_end ev_gesture_flick ev_gesture_pinch_start ev_gesture_pinch_in ev_gesture_pinch_out ev_gesture_pinch_end ev_gesture_rotate_start ev_gesture_rotating ev_gesture_rotate_end ev_global_gesture_tap ev_global_gesture_double_tap ev_global_gesture_drag_start ev_global_gesture_dragging ev_global_gesture_drag_end ev_global_gesture_flick ev_global_gesture_pinch_start ev_global_gesture_pinch_in ev_global_gesture_pinch_out ev_global_gesture_pinch_end ev_global_gesture_rotate_start ev_global_gesture_rotating ev_global_gesture_rotate_end vk_nokey vk_anykey vk_enter vk_return vk_shift vk_control vk_alt vk_escape vk_space vk_backspace vk_tab vk_pause vk_printscreen vk_left vk_right vk_up vk_down vk_home vk_end vk_delete vk_insert vk_pageup vk_pagedown vk_f1 vk_f2 vk_f3 vk_f4 vk_f5 vk_f6 vk_f7 vk_f8 vk_f9 vk_f10 vk_f11 vk_f12 vk_numpad0 vk_numpad1 vk_numpad2 vk_numpad3 vk_numpad4 vk_numpad5 vk_numpad6 vk_numpad7 vk_numpad8 vk_numpad9 vk_divide vk_multiply vk_subtract vk_add vk_decimal vk_lshift vk_lcontrol vk_lalt vk_rshift vk_rcontrol vk_ralt mb_any mb_none mb_left mb_right mb_middle c_aqua c_black c_blue c_dkgray c_fuchsia c_gray c_green c_lime c_ltgray c_maroon c_navy c_olive c_purple c_red c_silver c_teal c_white c_yellow c_orange fa_left fa_center fa_right fa_top fa_middle fa_bottom pr_pointlist pr_linelist pr_linestrip pr_trianglelist pr_trianglestrip pr_trianglefan bm_complex bm_normal bm_add bm_max bm_subtract bm_zero bm_one bm_src_colour bm_inv_src_colour bm_src_color bm_inv_src_color bm_src_alpha bm_inv_src_alpha bm_dest_alpha bm_inv_dest_alpha bm_dest_colour bm_inv_dest_colour bm_dest_color bm_inv_dest_color bm_src_alpha_sat tf_point tf_linear tf_anisotropic mip_off mip_on mip_markedonly audio_falloff_none audio_falloff_inverse_distance audio_falloff_inverse_distance_clamped audio_falloff_linear_distance audio_falloff_linear_distance_clamped audio_falloff_exponent_distance audio_falloff_exponent_distance_clamped audio_old_system audio_new_system audio_mono audio_stereo audio_3d cr_default cr_none cr_arrow cr_cross cr_beam cr_size_nesw cr_size_ns cr_size_nwse cr_size_we cr_uparrow cr_hourglass cr_drag cr_appstart 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GB2312_CHARSET CHINESEBIG5_CHARSET JOHAB_CHARSET HEBREW_CHARSET ARABIC_CHARSET GREEK_CHARSET TURKISH_CHARSET VIETNAMESE_CHARSET THAI_CHARSET MAC_CHARSET BALTIC_CHARSET OEM_CHARSET gp_face1 gp_face2 gp_face3 gp_face4 gp_shoulderl gp_shoulderr gp_shoulderlb gp_shoulderrb gp_select gp_start gp_stickl gp_stickr gp_padu gp_padd gp_padl gp_padr gp_axislh gp_axislv gp_axisrh gp_axisrv ov_friends ov_community ov_players ov_settings ov_gamegroup ov_achievements lb_sort_none lb_sort_ascending lb_sort_descending lb_disp_none lb_disp_numeric lb_disp_time_sec lb_disp_time_ms ugc_result_success ugc_filetype_community ugc_filetype_microtrans ugc_visibility_public ugc_visibility_friends_only ugc_visibility_private ugc_query_RankedByVote ugc_query_RankedByPublicationDate ugc_query_AcceptedForGameRankedByAcceptanceDate ugc_query_RankedByTrend ugc_query_FavoritedByFriendsRankedByPublicationDate ugc_query_CreatedByFriendsRankedByPublicationDate ugc_query_RankedByNumTimesReported 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"argument_relative argument argument0 argument1 argument2 argument3 argument4 argument5 argument6 argument7 argument8 argument9 argument10 argument11 argument12 argument13 argument14 argument15 argument_count x|0 y|0 xprevious yprevious xstart ystart hspeed vspeed direction speed friction gravity gravity_direction path_index path_position path_positionprevious path_speed path_scale path_orientation path_endaction object_index id solid persistent mask_index instance_count instance_id room_speed fps fps_real current_time current_year current_month current_day current_weekday current_hour current_minute current_second alarm timeline_index timeline_position timeline_speed timeline_running timeline_loop room room_first room_last room_width room_height room_caption room_persistent score lives health show_score show_lives show_health caption_score caption_lives caption_health event_type event_number event_object event_action application_surface gamemaker_pro gamemaker_registered gamemaker_version error_occurred error_last debug_mode keyboard_key keyboard_lastkey keyboard_lastchar keyboard_string mouse_x mouse_y mouse_button mouse_lastbutton cursor_sprite visible sprite_index sprite_width sprite_height sprite_xoffset sprite_yoffset image_number image_index image_speed depth image_xscale image_yscale image_angle image_alpha image_blend bbox_left bbox_right bbox_top bbox_bottom layer background_colour background_showcolour background_color background_showcolor view_enabled view_current view_visible view_xview view_yview view_wview view_hview view_xport view_yport view_wport view_hport view_angle view_hborder view_vborder view_hspeed view_vspeed view_object view_surface_id view_camera game_id game_display_name game_project_name game_save_id working_directory temp_directory program_directory browser_width browser_height os_type os_device os_browser os_version display_aa async_load delta_time webgl_enabled event_data iap_data phy_rotation phy_position_x phy_position_y phy_angular_velocity phy_linear_velocity_x phy_linear_velocity_y phy_speed_x phy_speed_y phy_speed phy_angular_damping phy_linear_damping phy_bullet phy_fixed_rotation phy_active phy_mass phy_inertia phy_com_x phy_com_y phy_dynamic phy_kinematic phy_sleeping phy_collision_points phy_collision_x phy_collision_y phy_col_normal_x phy_col_normal_y phy_position_xprevious phy_position_yprevious", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + ], + }; +}; +var jg = function (e) { + var t = { + keyword: + "break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune", + literal: "true false iota nil", + built_in: + "append cap close complex copy imag len make new panic print println real recover delete", + }; + return { + name: "Go", + aliases: ["golang"], + keywords: t, + illegal: " 1 && void 0 !== arguments[1] ? arguments[1] : {}; + return (t.variants = e), t; +} +var nE = function (e) { + var t = "[A-Za-z0-9_$]+", + n = tE([ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT("/\\*\\*", "\\*/", { + relevance: 0, + contains: [ + { begin: /\w+@/, relevance: 0 }, + { className: "doctag", begin: "@[A-Za-z]+" }, + ], + }), + ]), + a = { + className: "regexp", + begin: /~?\/[^\/\n]+\//, + contains: [e.BACKSLASH_ESCAPE], + }, + r = tE([e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE]), + i = tE( + [ + { begin: /"""/, end: /"""/ }, + { begin: /'''/, end: /'''/ }, + { begin: "\\$/", end: "/\\$", relevance: 10 }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + { className: "string" }, + ); + return { + name: "Groovy", + keywords: { + built_in: "this super", + literal: "true false null", + keyword: + "byte short char int long boolean float double void def as in assert trait abstract static volatile transient public private protected synchronized final class interface enum if else for while switch case break default continue throw throws try catch finally implements extends new import package return instanceof", + }, + contains: [ + e.SHEBANG({ binary: "groovy", relevance: 10 }), + n, + i, + a, + r, + { + className: "class", + beginKeywords: "class interface trait enum", + end: /\{/, + illegal: ":", + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { className: "meta", begin: "@[A-Za-z]+", relevance: 0 }, + { className: "attr", begin: t + "[ \t]*:", relevance: 0 }, + { begin: /\?/, end: /:/, relevance: 0, contains: [n, i, a, r, "self"] }, + { + className: "symbol", + begin: "^[ \t]*" + eE(t + ":"), + excludeBegin: !0, + end: t + ":", + relevance: 0, + }, + ], + illegal: /#|<\//, + }; +}; +var aE = function (e) { + return { + name: "HAML", + case_insensitive: !0, + contains: [ + { + className: "meta", + begin: "^!!!( (5|1\\.1|Strict|Frameset|Basic|Mobile|RDFa|XML\\b.*))?$", + relevance: 10, + }, + e.COMMENT("^\\s*(!=#|=#|-#|/).*$", !1, { relevance: 0 }), + { + begin: "^\\s*(-|=|!=)(?!#)", + starts: { end: "\\n", subLanguage: "ruby" }, + }, + { + className: "tag", + begin: "^\\s*%", + contains: [ + { className: "selector-tag", begin: "\\w+" }, + { className: "selector-id", begin: "#[\\w-]+" }, + { className: "selector-class", begin: "\\.[\\w-]+" }, + { + begin: /\{\s*/, + end: /\s*\}/, + contains: [ + { + begin: ":\\w+\\s*=>", + end: ",\\s+", + returnBegin: !0, + endsWithParent: !0, + contains: [ + { className: "attr", begin: ":\\w+" }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: "\\w+", relevance: 0 }, + ], + }, + ], + }, + { + begin: "\\(\\s*", + end: "\\s*\\)", + excludeEnd: !0, + contains: [ + { + begin: "\\w+\\s*=", + end: "\\s+", + returnBegin: !0, + endsWithParent: !0, + contains: [ + { className: "attr", begin: "\\w+", relevance: 0 }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: "\\w+", relevance: 0 }, + ], + }, + ], + }, + ], + }, + { begin: "^\\s*[=~]\\s*" }, + { begin: /#\{/, starts: { end: /\}/, subLanguage: "ruby" } }, + ], + }; +}; +function rE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function iE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return rE(e); + }) + .join(""); + return a; +} +var oE = function (e) { + var t = { + "builtin-name": [ + "action", + "bindattr", + "collection", + "component", + "concat", + "debugger", + "each", + "each-in", + "get", + "hash", + "if", + "in", + "input", + "link-to", + "loc", + "log", + "lookup", + "mut", + "outlet", + "partial", + "query-params", + "render", + "template", + "textarea", + "unbound", + "unless", + "view", + "with", + "yield", + ], + }, + n = /\[\]|\[[^\]]+\]/, + a = /[^\s!"#%&'()*+,.\/;<=>@\[\\\]^`{|}~]+/, + r = (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return ( + "(" + + t + .map(function (e) { + return rE(e); + }) + .join("|") + + ")" + ); + })(/""|"[^"]+"/, /''|'[^']+'/, n, a), + i = iE( + iE("(", /\.|\.\/|\//, ")?"), + r, + (function (e) { + return iE("(", e, ")*"); + })(iE(/(\.|\/)/, r)), + ), + o = iE("(", n, "|", a, ")(?==)"), + s = { begin: i, lexemes: /[\w.\/]+/ }, + l = e.inherit(s, { + keywords: { literal: ["true", "false", "undefined", "null"] }, + }), + c = { begin: /\(/, end: /\)/ }, + _ = { + className: "attr", + begin: o, + relevance: 0, + starts: { + begin: /=/, + end: /=/, + starts: { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + l, + c, + ], + }, + }, + }, + d = { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + begin: /as\s+\|/, + keywords: { keyword: "as" }, + end: /\|/, + contains: [{ begin: /\w+/ }], + }, + _, + l, + c, + ], + returnEnd: !0, + }, + u = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\)/ }), + }); + c.contains = [u]; + var m = e.inherit(s, { + keywords: t, + className: "name", + starts: e.inherit(d, { end: /\}\}/ }), + }), + p = e.inherit(s, { keywords: t, className: "name" }), + g = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\}\}/ }), + }); + return { + name: "Handlebars", + aliases: ["hbs", "html.hbs", "html.handlebars", "htmlbars"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + { begin: /\\\{\{/, skip: !0 }, + { begin: /\\\\(?=\{\{)/, skip: !0 }, + e.COMMENT(/\{\{!--/, /--\}\}/), + e.COMMENT(/\{\{!/, /\}\}/), + { + className: "template-tag", + begin: /\{\{\{\{(?!\/)/, + end: /\}\}\}\}/, + contains: [m], + starts: { end: /\{\{\{\{\//, returnEnd: !0, subLanguage: "xml" }, + }, + { + className: "template-tag", + begin: /\{\{\{\{\//, + end: /\}\}\}\}/, + contains: [p], + }, + { className: "template-tag", begin: /\{\{#/, end: /\}\}/, contains: [m] }, + { + className: "template-tag", + begin: /\{\{(?=else\}\})/, + end: /\}\}/, + keywords: "else", + }, + { + className: "template-tag", + begin: /\{\{(?=else if)/, + end: /\}\}/, + keywords: "else if", + }, + { + className: "template-tag", + begin: /\{\{\//, + end: /\}\}/, + contains: [p], + }, + { + className: "template-variable", + begin: /\{\{\{/, + end: /\}\}\}/, + contains: [g], + }, + { + className: "template-variable", + begin: /\{\{/, + end: /\}\}/, + contains: [g], + }, + ], + }; +}; +var sE = function (e) { + var t = { + variants: [ + e.COMMENT("--", "$"), + e.COMMENT(/\{-/, /-\}/, { contains: ["self"] }), + ], + }, + n = { className: "meta", begin: /\{-#/, end: /#-\}/ }, + a = { className: "meta", begin: "^#", end: "$" }, + r = { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + i = { + begin: "\\(", + end: "\\)", + illegal: '"', + contains: [ + n, + a, + { className: "type", begin: "\\b[A-Z][\\w]*(\\((\\.\\.|,|\\w+)\\))?" }, + e.inherit(e.TITLE_MODE, { begin: "[_a-z][\\w']*" }), + t, + ], + }; + return { + name: "Haskell", + aliases: ["hs"], + keywords: + "let in if then else case of where do module import hiding qualified type data newtype deriving class instance as default infix infixl infixr foreign export ccall stdcall cplusplus jvm dotnet safe unsafe family forall mdo proc rec", + contains: [ + { + beginKeywords: "module", + end: "where", + keywords: "module where", + contains: [i, t], + illegal: "\\W\\.|;", + }, + { + begin: "\\bimport\\b", + end: "$", + keywords: "import qualified as hiding", + contains: [i, t], + illegal: "\\W\\.|;", + }, + { + className: "class", + begin: "^(\\s*)?(class|instance)\\b", + end: "where", + keywords: "class family instance where", + contains: [r, i, t], + }, + { + className: "class", + begin: "\\b(data|(new)?type)\\b", + end: "$", + keywords: "data family type newtype deriving", + contains: [ + n, + r, + i, + { begin: /\{/, end: /\}/, contains: i.contains }, + t, + ], + }, + { beginKeywords: "default", end: "$", contains: [r, i, t] }, + { + beginKeywords: "infix infixl infixr", + end: "$", + contains: [e.C_NUMBER_MODE, t], + }, + { + begin: "\\bforeign\\b", + end: "$", + keywords: + "foreign import export ccall stdcall cplusplus jvm dotnet safe unsafe", + contains: [r, e.QUOTE_STRING_MODE, t], + }, + { className: "meta", begin: "#!\\/usr\\/bin\\/env runhaskell", end: "$" }, + n, + a, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + r, + e.inherit(e.TITLE_MODE, { begin: "^[_a-z][\\w']*" }), + t, + { begin: "->|<-" }, + ], + }; +}; +var lE = function (e) { + return { + name: "Haxe", + aliases: ["hx"], + keywords: { + keyword: + "break case cast catch continue default do dynamic else enum extern for function here if import in inline never new override package private get set public return static super switch this throw trace try typedef untyped using var while Int Float String Bool Dynamic Void Array ", + built_in: "trace this", + literal: "true false null _", + }, + contains: [ + { + className: "string", + begin: "'", + end: "'", + contains: [ + e.BACKSLASH_ESCAPE, + { className: "subst", begin: "\\$\\{", end: "\\}" }, + { className: "subst", begin: "\\$", end: /\W\}/ }, + ], + }, + e.QUOTE_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.C_NUMBER_MODE, + { className: "meta", begin: "@:", end: "$" }, + { + className: "meta", + begin: "#", + end: "$", + keywords: { "meta-keyword": "if else elseif end error" }, + }, + { + className: "type", + begin: ":[ \t]*", + end: "[^A-Za-z0-9_ \t\\->]", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + { + className: "type", + begin: ":[ \t]*", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "type", + begin: "new *", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "class", + beginKeywords: "enum", + end: "\\{", + contains: [e.TITLE_MODE], + }, + { + className: "class", + beginKeywords: "abstract", + end: "[\\{$]", + contains: [ + { + className: "type", + begin: "\\(", + end: "\\)", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "type", + begin: "from +", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "type", + begin: "to +", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + e.TITLE_MODE, + ], + keywords: { keyword: "abstract from to" }, + }, + { + className: "class", + begin: "\\b(class|interface) +", + end: "[\\{$]", + excludeEnd: !0, + keywords: "class interface", + contains: [ + { + className: "keyword", + begin: "\\b(extends|implements) +", + keywords: "extends implements", + contains: [{ className: "type", begin: e.IDENT_RE, relevance: 0 }], + }, + e.TITLE_MODE, + ], + }, + { + className: "function", + beginKeywords: "function", + end: "\\(", + excludeEnd: !0, + illegal: "\\S", + contains: [e.TITLE_MODE], + }, + ], + illegal: /<\//, + }; +}; +var cE = function (e) { + return { + name: "HSP", + case_insensitive: !0, + keywords: { + $pattern: /[\w._]+/, + keyword: + "goto gosub return break repeat loop continue wait await dim sdim foreach dimtype dup dupptr end stop newmod delmod mref run exgoto on mcall assert logmes newlab resume yield onexit onerror onkey onclick oncmd exist delete mkdir chdir dirlist bload bsave bcopy memfile if else poke wpoke lpoke getstr chdpm memexpand memcpy memset notesel noteadd notedel noteload notesave randomize noteunsel noteget split strrep setease button chgdisp exec dialog mmload mmplay mmstop mci pset pget syscolor mes print title pos circle cls font sysfont objsize picload color palcolor palette redraw width gsel gcopy gzoom gmode bmpsave hsvcolor getkey listbox chkbox combox input mesbox buffer screen bgscr mouse objsel groll line clrobj boxf objprm objmode stick grect grotate gsquare gradf objimage objskip objenable celload celdiv celput newcom querycom delcom cnvstow comres axobj winobj sendmsg comevent comevarg sarrayconv callfunc cnvwtos comevdisp libptr system hspstat hspver stat cnt err strsize looplev sublev iparam wparam lparam refstr refdval int rnd strlen length length2 length3 length4 vartype gettime peek wpeek lpeek varptr varuse noteinfo instr abs limit getease str strmid strf getpath strtrim sin cos tan atan sqrt double absf expf logf limitf powf geteasef mousex mousey mousew hwnd hinstance hdc ginfo objinfo dirinfo sysinfo thismod __hspver__ __hsp30__ __date__ __time__ __line__ __file__ _debug __hspdef__ and or xor not screen_normal screen_palette screen_hide screen_fixedsize screen_tool screen_frame gmode_gdi gmode_mem gmode_rgb0 gmode_alpha gmode_rgb0alpha gmode_add gmode_sub gmode_pixela ginfo_mx ginfo_my ginfo_act ginfo_sel ginfo_wx1 ginfo_wy1 ginfo_wx2 ginfo_wy2 ginfo_vx ginfo_vy ginfo_sizex ginfo_sizey ginfo_winx ginfo_winy ginfo_mesx ginfo_mesy ginfo_r ginfo_g ginfo_b ginfo_paluse ginfo_dispx ginfo_dispy ginfo_cx ginfo_cy ginfo_intid ginfo_newid ginfo_sx ginfo_sy objinfo_mode objinfo_bmscr objinfo_hwnd notemax notesize dir_cur dir_exe dir_win dir_sys dir_cmdline dir_desktop dir_mydoc dir_tv font_normal font_bold font_italic font_underline font_strikeout font_antialias objmode_normal objmode_guifont objmode_usefont gsquare_grad msgothic msmincho do until while wend for next _break _continue switch case default swbreak swend ddim ldim alloc m_pi rad2deg deg2rad ease_linear ease_quad_in ease_quad_out ease_quad_inout ease_cubic_in ease_cubic_out ease_cubic_inout ease_quartic_in ease_quartic_out ease_quartic_inout ease_bounce_in ease_bounce_out ease_bounce_inout ease_shake_in ease_shake_out ease_shake_inout ease_loop", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + className: "string", + begin: /\{"/, + end: /"\}/, + contains: [e.BACKSLASH_ESCAPE], + }, + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": + "addion cfunc cmd cmpopt comfunc const defcfunc deffunc define else endif enum epack func global if ifdef ifndef include modcfunc modfunc modinit modterm module pack packopt regcmd runtime undef usecom uselib", + }, + contains: [ + e.inherit(e.QUOTE_STRING_MODE, { className: "meta-string" }), + e.NUMBER_MODE, + e.C_NUMBER_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + { className: "symbol", begin: "^\\*(\\w+|@)" }, + e.NUMBER_MODE, + e.C_NUMBER_MODE, + ], + }; +}; +function _E(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function dE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return _E(e); + }) + .join(""); + return a; +} +function uE(e) { + var t = { + "builtin-name": [ + "action", + "bindattr", + "collection", + "component", + "concat", + "debugger", + "each", + "each-in", + "get", + "hash", + "if", + "in", + "input", + "link-to", + "loc", + "log", + "lookup", + "mut", + "outlet", + "partial", + "query-params", + "render", + "template", + "textarea", + "unbound", + "unless", + "view", + "with", + "yield", + ], + }, + n = /\[\]|\[[^\]]+\]/, + a = /[^\s!"#%&'()*+,.\/;<=>@\[\\\]^`{|}~]+/, + r = (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return ( + "(" + + t + .map(function (e) { + return _E(e); + }) + .join("|") + + ")" + ); + })(/""|"[^"]+"/, /''|'[^']+'/, n, a), + i = dE( + dE("(", /\.|\.\/|\//, ")?"), + r, + (function (e) { + return dE("(", e, ")*"); + })(dE(/(\.|\/)/, r)), + ), + o = dE("(", n, "|", a, ")(?==)"), + s = { begin: i, lexemes: /[\w.\/]+/ }, + l = e.inherit(s, { + keywords: { literal: ["true", "false", "undefined", "null"] }, + }), + c = { begin: /\(/, end: /\)/ }, + _ = { + className: "attr", + begin: o, + relevance: 0, + starts: { + begin: /=/, + end: /=/, + starts: { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + l, + c, + ], + }, + }, + }, + d = { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + begin: /as\s+\|/, + keywords: { keyword: "as" }, + end: /\|/, + contains: [{ begin: /\w+/ }], + }, + _, + l, + c, + ], + returnEnd: !0, + }, + u = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\)/ }), + }); + c.contains = [u]; + var m = e.inherit(s, { + keywords: t, + className: "name", + starts: e.inherit(d, { end: /\}\}/ }), + }), + p = e.inherit(s, { keywords: t, className: "name" }), + g = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\}\}/ }), + }); + return { + name: "Handlebars", + aliases: ["hbs", "html.hbs", "html.handlebars", "htmlbars"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + { begin: /\\\{\{/, skip: !0 }, + { begin: /\\\\(?=\{\{)/, skip: !0 }, + e.COMMENT(/\{\{!--/, /--\}\}/), + e.COMMENT(/\{\{!/, /\}\}/), + { + className: "template-tag", + begin: /\{\{\{\{(?!\/)/, + end: /\}\}\}\}/, + contains: [m], + starts: { end: /\{\{\{\{\//, returnEnd: !0, subLanguage: "xml" }, + }, + { + className: "template-tag", + begin: /\{\{\{\{\//, + end: /\}\}\}\}/, + contains: [p], + }, + { className: "template-tag", begin: /\{\{#/, end: /\}\}/, contains: [m] }, + { + className: "template-tag", + begin: /\{\{(?=else\}\})/, + end: /\}\}/, + keywords: "else", + }, + { + className: "template-tag", + begin: /\{\{(?=else if)/, + end: /\}\}/, + keywords: "else if", + }, + { + className: "template-tag", + begin: /\{\{\//, + end: /\}\}/, + contains: [p], + }, + { + className: "template-variable", + begin: /\{\{\{/, + end: /\}\}\}/, + contains: [g], + }, + { + className: "template-variable", + begin: /\{\{/, + end: /\}\}/, + contains: [g], + }, + ], + }; +} +var mE = function (e) { + var t = uE(e); + return ( + (t.name = "HTMLbars"), + e.getLanguage("handlebars") && (t.disableAutodetect = !0), + t + ); +}; +function pE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function gE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return pE(e); + }) + .join(""); + return a; +} +var EE = function (e) { + var t = "HTTP/(2|1\\.[01])", + n = { + className: "attribute", + begin: gE("^", /[A-Za-z][A-Za-z0-9-]*/, "(?=\\:\\s)"), + starts: { + contains: [ + { + className: "punctuation", + begin: /: /, + relevance: 0, + starts: { end: "$", relevance: 0 }, + }, + ], + }, + }, + a = [ + n, + { begin: "\\n\\n", starts: { subLanguage: [], endsWithParent: !0 } }, + ]; + return { + name: "HTTP", + aliases: ["https"], + illegal: /\S/, + contains: [ + { + begin: "^(?=" + t + " \\d{3})", + end: /$/, + contains: [ + { className: "meta", begin: t }, + { className: "number", begin: "\\b\\d{3}\\b" }, + ], + starts: { end: /\b\B/, illegal: /\S/, contains: a }, + }, + { + begin: "(?=^[A-Z]+ (.*?) " + t + "$)", + end: /$/, + contains: [ + { + className: "string", + begin: " ", + end: " ", + excludeBegin: !0, + excludeEnd: !0, + }, + { className: "meta", begin: t }, + { className: "keyword", begin: "[A-Z]+" }, + ], + starts: { end: /\b\B/, illegal: /\S/, contains: a }, + }, + e.inherit(n, { relevance: 0 }), + ], + }; +}; +var SE = function (e) { + var t = "a-zA-Z_\\-!.?+*=<>&#'", + n = "[" + t + "][" + t + "0-9/;:]*", + a = { + $pattern: n, + "builtin-name": + "!= % %= & &= * ** **= *= *map + += , --build-class-- --import-- -= . / // //= /= < << <<= <= = > >= >> >>= @ @= ^ ^= abs accumulate all and any ap-compose ap-dotimes ap-each ap-each-while ap-filter ap-first ap-if ap-last ap-map ap-map-when ap-pipe ap-reduce ap-reject apply as-> ascii assert assoc bin break butlast callable calling-module-name car case cdr chain chr coll? combinations compile compress cond cons cons? continue count curry cut cycle dec def default-method defclass defmacro defmacro-alias defmacro/g! defmain defmethod defmulti defn defn-alias defnc defnr defreader defseq del delattr delete-route dict-comp dir disassemble dispatch-reader-macro distinct divmod do doto drop drop-last drop-while empty? end-sequence eval eval-and-compile eval-when-compile even? every? except exec filter first flatten float? fn fnc fnr for for* format fraction genexpr gensym get getattr global globals group-by hasattr hash hex id identity if if* if-not if-python2 import in inc input instance? integer integer-char? integer? interleave interpose is is-coll is-cons is-empty is-even is-every is-float is-instance is-integer is-integer-char is-iterable is-iterator is-keyword is-neg is-none is-not is-numeric is-odd is-pos is-string is-symbol is-zero isinstance islice issubclass iter iterable? iterate iterator? keyword keyword? lambda last len let lif lif-not list* list-comp locals loop macro-error macroexpand macroexpand-1 macroexpand-all map max merge-with method-decorator min multi-decorator multicombinations name neg? next none? nonlocal not not-in not? nth numeric? oct odd? open or ord partition permutations pos? post-route postwalk pow prewalk print product profile/calls profile/cpu put-route quasiquote quote raise range read read-str recursive-replace reduce remove repeat repeatedly repr require rest round route route-with-methods rwm second seq set-comp setattr setv some sorted string string? sum switch symbol? take take-nth take-while tee try unless unquote unquote-splicing vars walk when while with with* with-decorator with-gensyms xi xor yield yield-from zero? zip zip-longest | |= ~", + }, + r = { begin: n, relevance: 0 }, + i = { className: "number", begin: "[-+]?\\d+(\\.\\d+)?", relevance: 0 }, + o = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + s = e.COMMENT(";", "$", { relevance: 0 }), + l = { className: "literal", begin: /\b([Tt]rue|[Ff]alse|nil|None)\b/ }, + c = { begin: "[\\[\\{]", end: "[\\]\\}]" }, + _ = { className: "comment", begin: "\\^" + n }, + d = e.COMMENT("\\^\\{", "\\}"), + u = { className: "symbol", begin: "[:]{1,2}" + n }, + m = { begin: "\\(", end: "\\)" }, + p = { endsWithParent: !0, relevance: 0 }, + g = { className: "name", relevance: 0, keywords: a, begin: n, starts: p }, + E = [m, o, _, d, s, u, c, i, l, r]; + return ( + (m.contains = [e.COMMENT("comment", ""), g, p]), + (p.contains = E), + (c.contains = E), + { + name: "Hy", + aliases: ["hylang"], + illegal: /\S/, + contains: [e.SHEBANG(), m, o, _, d, s, u, c, i, l], + } + ); +}; +var bE = function (e) { + return { + name: "Inform 7", + aliases: ["i7"], + case_insensitive: !0, + keywords: { + keyword: + "thing room person man woman animal container supporter backdrop door scenery open closed locked inside gender is are say understand kind of rule", + }, + contains: [ + { + className: "string", + begin: '"', + end: '"', + relevance: 0, + contains: [{ className: "subst", begin: "\\[", end: "\\]" }], + }, + { + className: "section", + begin: /^(Volume|Book|Part|Chapter|Section|Table)\b/, + end: "$", + }, + { + begin: + /^(Check|Carry out|Report|Instead of|To|Rule|When|Before|After)\b/, + end: ":", + contains: [{ begin: "\\(This", end: "\\)" }], + }, + { className: "comment", begin: "\\[", end: "\\]", contains: ["self"] }, + ], + }; +}; +function TE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function fE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return TE(e); + }) + .join(""); + return a; +} +var CE = function (e) { + var t = { + className: "number", + relevance: 0, + variants: [{ begin: /([+-]+)?[\d]+_[\d_]+/ }, { begin: e.NUMBER_RE }], + }, + n = e.COMMENT(); + n.variants = [ + { begin: /;/, end: /$/ }, + { begin: /#/, end: /$/ }, + ]; + var a = { + className: "variable", + variants: [{ begin: /\$[\w\d"][\w\d_]*/ }, { begin: /\$\{(.*?)\}/ }], + }, + r = { className: "literal", begin: /\bon|off|true|false|yes|no\b/ }, + i = { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { begin: "'''", end: "'''", relevance: 10 }, + { begin: '"""', end: '"""', relevance: 10 }, + { begin: '"', end: '"' }, + { begin: "'", end: "'" }, + ], + }, + o = { + begin: /\[/, + end: /\]/, + contains: [n, r, a, i, t, "self"], + relevance: 0, + }, + s = (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return ( + "(" + + t + .map(function (e) { + return TE(e); + }) + .join("|") + + ")" + ); + })(/[A-Za-z0-9_-]+/, /"(\\"|[^"])*"/, /'[^']*'/); + return { + name: "TOML, also INI", + aliases: ["toml"], + case_insensitive: !0, + illegal: /\S/, + contains: [ + n, + { className: "section", begin: /\[+/, end: /\]+/ }, + { + begin: fE(s, "(\\s*\\.\\s*", s, ")*", fE("(?=", /\s*=\s*[^#\s]/, ")")), + className: "attr", + starts: { end: /$/, contains: [n, o, r, a, i, t] }, + }, + ], + }; +}; +function NE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function RE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return NE(e); + }) + .join(""); + return a; +} +var vE = function (e) { + var t = /(_[a-z_\d]+)?/, + n = /([de][+-]?\d+)?/, + a = { + className: "number", + variants: [ + { begin: RE(/\b\d+/, /\.(\d*)/, n, t) }, + { begin: RE(/\b\d+/, n, t) }, + { begin: RE(/\.\d+/, n, t) }, + ], + relevance: 0, + }; + return { + name: "IRPF90", + case_insensitive: !0, + keywords: { + literal: ".False. .True.", + keyword: + "kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data begin_provider &begin_provider end_provider begin_shell end_shell begin_template end_template subst assert touch soft_touch provide no_dep free irp_if irp_else irp_endif irp_write irp_read", + built_in: + "alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_of acosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image IRP_ALIGN irp_here", + }, + illegal: /\/\*/, + contains: [ + e.inherit(e.APOS_STRING_MODE, { className: "string", relevance: 0 }), + e.inherit(e.QUOTE_STRING_MODE, { className: "string", relevance: 0 }), + { + className: "function", + beginKeywords: "subroutine function program", + illegal: "[${=\\n]", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }, + e.COMMENT("!", "$", { relevance: 0 }), + e.COMMENT("begin_doc", "end_doc", { relevance: 10 }), + a, + ], + }; +}; +var OE = function (e) { + var t = "[A-Za-zА-Яа-яёЁ_!][A-Za-zА-Яа-яёЁ_0-9]*", + n = { className: "number", begin: e.NUMBER_RE, relevance: 0 }, + a = { + className: "string", + variants: [ + { begin: '"', end: '"' }, + { begin: "'", end: "'" }, + ], + }, + r = { + className: "doctag", + begin: "\\b(?:TODO|DONE|BEGIN|END|STUB|CHG|FIXME|NOTE|BUG|XXX)\\b", + relevance: 0, + }, + i = { + variants: [ + { + className: "comment", + begin: "//", + end: "$", + relevance: 0, + contains: [e.PHRASAL_WORDS_MODE, r], + }, + { + className: "comment", + begin: "/\\*", + end: "\\*/", + relevance: 0, + contains: [e.PHRASAL_WORDS_MODE, r], + }, + ], + }, + o = { + $pattern: t, + keyword: + "and и else иначе endexcept endfinally endforeach конецвсе endif конецесли endwhile конецпока except exitfor finally foreach все if если in в not не or или try while пока ", + built_in: + "SYSRES_CONST_ACCES_RIGHT_TYPE_EDIT SYSRES_CONST_ACCES_RIGHT_TYPE_FULL SYSRES_CONST_ACCES_RIGHT_TYPE_VIEW SYSRES_CONST_ACCESS_MODE_REQUISITE_CODE SYSRES_CONST_ACCESS_NO_ACCESS_VIEW SYSRES_CONST_ACCESS_NO_ACCESS_VIEW_CODE SYSRES_CONST_ACCESS_RIGHTS_ADD_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_ADD_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_CHANGE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_CHANGE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_DELETE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_DELETE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_EXECUTE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_EXECUTE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW SYSRES_CONST_ACCESS_RIGHTS_VIEW_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_TYPE_CHANGE SYSRES_CONST_ACCESS_TYPE_CHANGE_CODE SYSRES_CONST_ACCESS_TYPE_EXISTS SYSRES_CONST_ACCESS_TYPE_EXISTS_CODE SYSRES_CONST_ACCESS_TYPE_FULL SYSRES_CONST_ACCESS_TYPE_FULL_CODE SYSRES_CONST_ACCESS_TYPE_VIEW SYSRES_CONST_ACCESS_TYPE_VIEW_CODE SYSRES_CONST_ACTION_TYPE_ABORT SYSRES_CONST_ACTION_TYPE_ACCEPT SYSRES_CONST_ACTION_TYPE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ADD_ATTACHMENT SYSRES_CONST_ACTION_TYPE_CHANGE_CARD SYSRES_CONST_ACTION_TYPE_CHANGE_KIND SYSRES_CONST_ACTION_TYPE_CHANGE_STORAGE SYSRES_CONST_ACTION_TYPE_CONTINUE SYSRES_CONST_ACTION_TYPE_COPY SYSRES_CONST_ACTION_TYPE_CREATE SYSRES_CONST_ACTION_TYPE_CREATE_VERSION SYSRES_CONST_ACTION_TYPE_DELETE SYSRES_CONST_ACTION_TYPE_DELETE_ATTACHMENT SYSRES_CONST_ACTION_TYPE_DELETE_VERSION SYSRES_CONST_ACTION_TYPE_DISABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE_AND_PASSWORD SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_PASSWORD SYSRES_CONST_ACTION_TYPE_EXPORT_WITH_LOCK SYSRES_CONST_ACTION_TYPE_EXPORT_WITHOUT_LOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITH_UNLOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITHOUT_UNLOCK SYSRES_CONST_ACTION_TYPE_LIFE_CYCLE_STAGE SYSRES_CONST_ACTION_TYPE_LOCK SYSRES_CONST_ACTION_TYPE_LOCK_FOR_SERVER SYSRES_CONST_ACTION_TYPE_LOCK_MODIFY SYSRES_CONST_ACTION_TYPE_MARK_AS_READED SYSRES_CONST_ACTION_TYPE_MARK_AS_UNREADED SYSRES_CONST_ACTION_TYPE_MODIFY SYSRES_CONST_ACTION_TYPE_MODIFY_CARD SYSRES_CONST_ACTION_TYPE_MOVE_TO_ARCHIVE SYSRES_CONST_ACTION_TYPE_OFF_ENCRYPTION SYSRES_CONST_ACTION_TYPE_PASSWORD_CHANGE SYSRES_CONST_ACTION_TYPE_PERFORM SYSRES_CONST_ACTION_TYPE_RECOVER_FROM_LOCAL_COPY SYSRES_CONST_ACTION_TYPE_RESTART SYSRES_CONST_ACTION_TYPE_RESTORE_FROM_ARCHIVE SYSRES_CONST_ACTION_TYPE_REVISION SYSRES_CONST_ACTION_TYPE_SEND_BY_MAIL SYSRES_CONST_ACTION_TYPE_SIGN SYSRES_CONST_ACTION_TYPE_START SYSRES_CONST_ACTION_TYPE_UNLOCK SYSRES_CONST_ACTION_TYPE_UNLOCK_FROM_SERVER SYSRES_CONST_ACTION_TYPE_VERSION_STATE SYSRES_CONST_ACTION_TYPE_VERSION_VISIBILITY SYSRES_CONST_ACTION_TYPE_VIEW SYSRES_CONST_ACTION_TYPE_VIEW_SHADOW_COPY SYSRES_CONST_ACTION_TYPE_WORKFLOW_DESCRIPTION_MODIFY SYSRES_CONST_ACTION_TYPE_WRITE_HISTORY SYSRES_CONST_ACTIVE_VERSION_STATE_PICK_VALUE SYSRES_CONST_ADD_REFERENCE_MODE_NAME SYSRES_CONST_ADDITION_REQUISITE_CODE SYSRES_CONST_ADDITIONAL_PARAMS_REQUISITE_CODE SYSRES_CONST_ADITIONAL_JOB_END_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_READ_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_START_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_STATE_REQUISITE_NAME SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC SYSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_CODE SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONENT_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_DATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CONST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENCE_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SYSRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FOLDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSRES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_CONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_KIND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CONST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYSRES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONST_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_REQUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONST_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYSRES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 SYSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TYPE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRING_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_REFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_TEXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR SYSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAITING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE SYSRES_CONST_YES_VALUE CR FALSE nil NO_VALUE NULL TAB TRUE YES_VALUE ADMINISTRATORS_GROUP_NAME CUSTOMIZERS_GROUP_NAME DEVELOPERS_GROUP_NAME SERVICE_USERS_GROUP_NAME DECISION_BLOCK_FIRST_OPERAND_PROPERTY DECISION_BLOCK_NAME_PROPERTY DECISION_BLOCK_OPERATION_PROPERTY DECISION_BLOCK_RESULT_TYPE_PROPERTY DECISION_BLOCK_SECOND_OPERAND_PROPERTY ANY_FILE_EXTENTION COMPRESSED_DOCUMENT_EXTENSION EXTENDED_DOCUMENT_EXTENSION SHORT_COMPRESSED_DOCUMENT_EXTENSION SHORT_EXTENDED_DOCUMENT_EXTENSION JOB_BLOCK_ABORT_DEADLINE_PROPERTY JOB_BLOCK_AFTER_FINISH_EVENT JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT JOB_BLOCK_ATTACHMENT_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT JOB_BLOCK_BEFORE_START_EVENT JOB_BLOCK_CREATED_JOBS_PROPERTY JOB_BLOCK_DEADLINE_PROPERTY JOB_BLOCK_EXECUTION_RESULTS_PROPERTY JOB_BLOCK_IS_PARALLEL_PROPERTY JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_CODE smHidden smMaximized smMinimized smNormal wmNo wmYes COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY NOTICE_BLOCK_BEFORE_START_EVENT NOTICE_BLOCK_CREATED_NOTICES_PROPERTY NOTICE_BLOCK_DEADLINE_PROPERTY NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY NOTICE_BLOCK_NAME_PROPERTY NOTICE_BLOCK_NOTICE_TEXT_PROPERTY NOTICE_BLOCK_PERFORMER_PROPERTY NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY NOTICE_BLOCK_SUBJECT_PROPERTY dseAfterCancel dseAfterClose dseAfterDelete dseAfterDeleteOutOfTransaction dseAfterInsert dseAfterOpen dseAfterScroll dseAfterUpdate dseAfterUpdateOutOfTransaction dseBeforeCancel dseBeforeClose dseBeforeDelete dseBeforeDetailUpdate dseBeforeInsert dseBeforeOpen dseBeforeUpdate dseOnAnyRequisiteChange dseOnCloseRecord dseOnDeleteError dseOnOpenRecord dseOnPrepareUpdate dseOnUpdateError dseOnUpdateRatifiedRecord dseOnValidDelete dseOnValidUpdate reOnChange reOnChangeValues SELECTION_BEGIN_ROUTE_EVENT SELECTION_END_ROUTE_EVENT CURRENT_PERIOD_IS_REQUIRED PREVIOUS_CARD_TYPE_NAME SHOW_RECORD_PROPERTIES_FORM ACCESS_RIGHTS_SETTING_DIALOG_CODE ADMINISTRATOR_USER_CODE ANALYTIC_REPORT_TYPE asrtHideLocal asrtHideRemote CALCULATED_ROLE_TYPE_CODE COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE DCTS_TEST_PROTOCOLS_FOLDER_PATH E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER E_EDOC_VERSION_ALREDY_SIGNED E_EDOC_VERSION_ALREDY_SIGNED_BY_USER EDOC_TYPES_CODE_REQUISITE_FIELD_NAME EDOCUMENTS_ALIAS_NAME FILES_FOLDER_PATH FILTER_OPERANDS_DELIMITER FILTER_OPERATIONS_DELIMITER FORMCARD_NAME FORMLIST_NAME GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE INTEGRATED_REPORT_TYPE IS_BUILDER_APPLICATION_ROLE IS_BUILDER_APPLICATION_ROLE2 IS_BUILDER_USERS ISBSYSDEV LOG_FOLDER_PATH mbCancel mbNo mbNoToAll mbOK mbYes mbYesToAll MEMORY_DATASET_DESRIPTIONS_FILENAME mrNo mrNoToAll mrYes mrYesToAll MULTIPLE_SELECT_DIALOG_CODE NONOPERATING_RECORD_FLAG_FEMININE NONOPERATING_RECORD_FLAG_MASCULINE OPERATING_RECORD_FLAG_FEMININE OPERATING_RECORD_FLAG_MASCULINE PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE PROGRAM_INITIATED_LOOKUP_ACTION ratDelete ratEdit ratInsert REPORT_TYPE REQUIRED_PICK_VALUES_VARIABLE rmCard rmList SBRTE_PROGID_DEV SBRTE_PROGID_RELEASE STATIC_ROLE_TYPE_CODE SUPPRESS_EMPTY_TEMPLATE_CREATION SYSTEM_USER_CODE UPDATE_DIALOG_DATASET USED_IN_OBJECT_HINT_PARAM USER_INITIATED_LOOKUP_ACTION USER_NAME_FORMAT USER_SELECTION_RESTRICTIONS WORKFLOW_TEST_PROTOCOLS_FOLDER_PATH ELS_SUBTYPE_CONTROL_NAME ELS_FOLDER_KIND_CONTROL_NAME REPEAT_PROCESS_CURRENT_OBJECT_EXCEPTION_NAME PRIVILEGE_COMPONENT_FULL_ACCESS PRIVILEGE_DEVELOPMENT_EXPORT PRIVILEGE_DEVELOPMENT_IMPORT PRIVILEGE_DOCUMENT_DELETE PRIVILEGE_ESD PRIVILEGE_FOLDER_DELETE PRIVILEGE_MANAGE_ACCESS_RIGHTS PRIVILEGE_MANAGE_REPLICATION PRIVILEGE_MANAGE_SESSION_SERVER PRIVILEGE_OBJECT_FULL_ACCESS PRIVILEGE_OBJECT_VIEW PRIVILEGE_RESERVE_LICENSE PRIVILEGE_SYSTEM_CUSTOMIZE PRIVILEGE_SYSTEM_DEVELOP PRIVILEGE_SYSTEM_INSTALL PRIVILEGE_TASK_DELETE PRIVILEGE_USER_PLUGIN_SETTINGS_CUSTOMIZE PRIVILEGES_PSEUDOREFERENCE_CODE ACCESS_TYPES_PSEUDOREFERENCE_CODE ALL_AVAILABLE_COMPONENTS_PSEUDOREFERENCE_CODE ALL_AVAILABLE_PRIVILEGES_PSEUDOREFERENCE_CODE ALL_REPLICATE_COMPONENTS_PSEUDOREFERENCE_CODE AVAILABLE_DEVELOPERS_COMPONENTS_PSEUDOREFERENCE_CODE COMPONENTS_PSEUDOREFERENCE_CODE FILTRATER_SETTINGS_CONFLICTS_PSEUDOREFERENCE_CODE GROUPS_PSEUDOREFERENCE_CODE RECEIVE_PROTOCOL_PSEUDOREFERENCE_CODE REFERENCE_REQUISITE_PSEUDOREFERENCE_CODE REFERENCE_REQUISITES_PSEUDOREFERENCE_CODE REFTYPES_PSEUDOREFERENCE_CODE REPLICATION_SEANCES_DIARY_PSEUDOREFERENCE_CODE SEND_PROTOCOL_PSEUDOREFERENCE_CODE SUBSTITUTES_PSEUDOREFERENCE_CODE SYSTEM_SETTINGS_PSEUDOREFERENCE_CODE UNITS_PSEUDOREFERENCE_CODE USERS_PSEUDOREFERENCE_CODE VIEWERS_PSEUDOREFERENCE_CODE CERTIFICATE_TYPE_ENCRYPT CERTIFICATE_TYPE_SIGN CERTIFICATE_TYPE_SIGN_AND_ENCRYPT STORAGE_TYPE_FILE STORAGE_TYPE_NAS_CIFS STORAGE_TYPE_SAPERION STORAGE_TYPE_SQL_SERVER COMPTYPE2_REQUISITE_DOCUMENTS_VALUE COMPTYPE2_REQUISITE_TASKS_VALUE COMPTYPE2_REQUISITE_FOLDERS_VALUE COMPTYPE2_REQUISITE_REFERENCES_VALUE SYSREQ_CODE SYSREQ_COMPTYPE2 SYSREQ_CONST_AVAILABLE_FOR_WEB SYSREQ_CONST_COMMON_CODE SYSREQ_CONST_COMMON_VALUE SYSREQ_CONST_FIRM_CODE SYSREQ_CONST_FIRM_STATUS SYSREQ_CONST_FIRM_VALUE SYSREQ_CONST_SERVER_STATUS SYSREQ_CONTENTS SYSREQ_DATE_OPEN SYSREQ_DATE_CLOSE SYSREQ_DESCRIPTION SYSREQ_DESCRIPTION_LOCALIZE_ID SYSREQ_DOUBLE SYSREQ_EDOC_ACCESS_TYPE SYSREQ_EDOC_AUTHOR SYSREQ_EDOC_CREATED SYSREQ_EDOC_DELEGATE_RIGHTS_REQUISITE_CODE SYSREQ_EDOC_EDITOR SYSREQ_EDOC_ENCODE_TYPE SYSREQ_EDOC_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_EXPORT_DATE SYSREQ_EDOC_EXPORTER SYSREQ_EDOC_KIND SYSREQ_EDOC_LIFE_STAGE_NAME SYSREQ_EDOC_LOCKED_FOR_SERVER_CODE SYSREQ_EDOC_MODIFIED SYSREQ_EDOC_NAME SYSREQ_EDOC_NOTE SYSREQ_EDOC_QUALIFIED_ID SYSREQ_EDOC_SESSION_KEY SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_SIGNATURE_TYPE SYSREQ_EDOC_SIGNED SYSREQ_EDOC_STORAGE SYSREQ_EDOC_STORAGES_ARCHIVE_STORAGE SYSREQ_EDOC_STORAGES_CHECK_RIGHTS SYSREQ_EDOC_STORAGES_COMPUTER_NAME SYSREQ_EDOC_STORAGES_EDIT_IN_STORAGE SYSREQ_EDOC_STORAGES_EXECUTIVE_STORAGE SYSREQ_EDOC_STORAGES_FUNCTION SYSREQ_EDOC_STORAGES_INITIALIZED SYSREQ_EDOC_STORAGES_LOCAL_PATH SYSREQ_EDOC_STORAGES_SAPERION_DATABASE_NAME SYSREQ_EDOC_STORAGES_SEARCH_BY_TEXT SYSREQ_EDOC_STORAGES_SERVER_NAME SYSREQ_EDOC_STORAGES_SHARED_SOURCE_NAME SYSREQ_EDOC_STORAGES_TYPE SYSREQ_EDOC_TEXT_MODIFIED SYSREQ_EDOC_TYPE_ACT_CODE SYSREQ_EDOC_TYPE_ACT_DESCRIPTION SYSREQ_EDOC_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_EDOC_TYPE_ACT_SECTION SYSREQ_EDOC_TYPE_ADD_PARAMS SYSREQ_EDOC_TYPE_COMMENT SYSREQ_EDOC_TYPE_EVENT_TEXT SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_EDOC_TYPE_NAME_LOCALIZE_ID SYSREQ_EDOC_TYPE_NUMERATION_METHOD SYSREQ_EDOC_TYPE_PSEUDO_REQUISITE_CODE SYSREQ_EDOC_TYPE_REQ_CODE SYSREQ_EDOC_TYPE_REQ_DESCRIPTION SYSREQ_EDOC_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_REQ_IS_LEADING SYSREQ_EDOC_TYPE_REQ_IS_REQUIRED SYSREQ_EDOC_TYPE_REQ_NUMBER SYSREQ_EDOC_TYPE_REQ_ON_CHANGE SYSREQ_EDOC_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_EDOC_TYPE_REQ_ON_SELECT SYSREQ_EDOC_TYPE_REQ_ON_SELECT_KIND SYSREQ_EDOC_TYPE_REQ_SECTION SYSREQ_EDOC_TYPE_VIEW_CARD SYSREQ_EDOC_TYPE_VIEW_CODE SYSREQ_EDOC_TYPE_VIEW_COMMENT SYSREQ_EDOC_TYPE_VIEW_IS_MAIN SYSREQ_EDOC_TYPE_VIEW_NAME SYSREQ_EDOC_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_EDOC_VERSION_AUTHOR SYSREQ_EDOC_VERSION_CRC SYSREQ_EDOC_VERSION_DATA SYSREQ_EDOC_VERSION_EDITOR SYSREQ_EDOC_VERSION_EXPORT_DATE SYSREQ_EDOC_VERSION_EXPORTER SYSREQ_EDOC_VERSION_HIDDEN SYSREQ_EDOC_VERSION_LIFE_STAGE SYSREQ_EDOC_VERSION_MODIFIED SYSREQ_EDOC_VERSION_NOTE SYSREQ_EDOC_VERSION_SIGNATURE_TYPE SYSREQ_EDOC_VERSION_SIGNED SYSREQ_EDOC_VERSION_SIZE SYSREQ_EDOC_VERSION_SOURCE SYSREQ_EDOC_VERSION_TEXT_MODIFIED SYSREQ_EDOCKIND_DEFAULT_VERSION_STATE_CODE SYSREQ_FOLDER_KIND SYSREQ_FUNC_CATEGORY SYSREQ_FUNC_COMMENT SYSREQ_FUNC_GROUP SYSREQ_FUNC_GROUP_COMMENT SYSREQ_FUNC_GROUP_NUMBER SYSREQ_FUNC_HELP SYSREQ_FUNC_PARAM_DEF_VALUE SYSREQ_FUNC_PARAM_IDENT SYSREQ_FUNC_PARAM_NUMBER SYSREQ_FUNC_PARAM_TYPE SYSREQ_FUNC_TEXT SYSREQ_GROUP_CATEGORY SYSREQ_ID SYSREQ_LAST_UPDATE SYSREQ_LEADER_REFERENCE SYSREQ_LINE_NUMBER SYSREQ_MAIN_RECORD_ID SYSREQ_NAME SYSREQ_NAME_LOCALIZE_ID SYSREQ_NOTE SYSREQ_ORIGINAL_RECORD SYSREQ_OUR_FIRM SYSREQ_PROFILING_SETTINGS_BATCH_LOGING SYSREQ_PROFILING_SETTINGS_BATCH_SIZE SYSREQ_PROFILING_SETTINGS_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_SQL_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_START_LOGGED SYSREQ_RECORD_STATUS SYSREQ_REF_REQ_FIELD_NAME SYSREQ_REF_REQ_FORMAT SYSREQ_REF_REQ_GENERATED SYSREQ_REF_REQ_LENGTH SYSREQ_REF_REQ_PRECISION SYSREQ_REF_REQ_REFERENCE SYSREQ_REF_REQ_SECTION SYSREQ_REF_REQ_STORED SYSREQ_REF_REQ_TOKENS SYSREQ_REF_REQ_TYPE SYSREQ_REF_REQ_VIEW SYSREQ_REF_TYPE_ACT_CODE SYSREQ_REF_TYPE_ACT_DESCRIPTION SYSREQ_REF_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_ACT_ON_EXECUTE SYSREQ_REF_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_REF_TYPE_ACT_SECTION SYSREQ_REF_TYPE_ADD_PARAMS SYSREQ_REF_TYPE_COMMENT SYSREQ_REF_TYPE_COMMON_SETTINGS SYSREQ_REF_TYPE_DISPLAY_REQUISITE_NAME SYSREQ_REF_TYPE_EVENT_TEXT SYSREQ_REF_TYPE_MAIN_LEADING_REF SYSREQ_REF_TYPE_NAME_IN_SINGULAR SYSREQ_REF_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_REF_TYPE_NAME_LOCALIZE_ID SYSREQ_REF_TYPE_NUMERATION_METHOD SYSREQ_REF_TYPE_REQ_CODE SYSREQ_REF_TYPE_REQ_DESCRIPTION SYSREQ_REF_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_REQ_IS_CONTROL SYSREQ_REF_TYPE_REQ_IS_FILTER SYSREQ_REF_TYPE_REQ_IS_LEADING SYSREQ_REF_TYPE_REQ_IS_REQUIRED SYSREQ_REF_TYPE_REQ_NUMBER SYSREQ_REF_TYPE_REQ_ON_CHANGE SYSREQ_REF_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_REF_TYPE_REQ_ON_SELECT SYSREQ_REF_TYPE_REQ_ON_SELECT_KIND SYSREQ_REF_TYPE_REQ_SECTION SYSREQ_REF_TYPE_VIEW_CARD SYSREQ_REF_TYPE_VIEW_CODE SYSREQ_REF_TYPE_VIEW_COMMENT SYSREQ_REF_TYPE_VIEW_IS_MAIN SYSREQ_REF_TYPE_VIEW_NAME SYSREQ_REF_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_REFERENCE_TYPE_ID SYSREQ_STATE SYSREQ_STATЕ SYSREQ_SYSTEM_SETTINGS_VALUE SYSREQ_TYPE SYSREQ_UNIT SYSREQ_UNIT_ID SYSREQ_USER_GROUPS_GROUP_FULL_NAME SYSREQ_USER_GROUPS_GROUP_NAME SYSREQ_USER_GROUPS_GROUP_SERVER_NAME SYSREQ_USERS_ACCESS_RIGHTS SYSREQ_USERS_AUTHENTICATION SYSREQ_USERS_CATEGORY SYSREQ_USERS_COMPONENT SYSREQ_USERS_COMPONENT_USER_IS_PUBLIC SYSREQ_USERS_DOMAIN SYSREQ_USERS_FULL_USER_NAME SYSREQ_USERS_GROUP SYSREQ_USERS_IS_MAIN_SERVER SYSREQ_USERS_LOGIN SYSREQ_USERS_REFERENCE_USER_IS_PUBLIC SYSREQ_USERS_STATUS SYSREQ_USERS_USER_CERTIFICATE SYSREQ_USERS_USER_CERTIFICATE_INFO SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_NAME SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_VERSION SYSREQ_USERS_USER_CERTIFICATE_STATE SYSREQ_USERS_USER_CERTIFICATE_SUBJECT_NAME SYSREQ_USERS_USER_CERTIFICATE_THUMBPRINT SYSREQ_USERS_USER_DEFAULT_CERTIFICATE SYSREQ_USERS_USER_DESCRIPTION SYSREQ_USERS_USER_GLOBAL_NAME SYSREQ_USERS_USER_LOGIN SYSREQ_USERS_USER_MAIN_SERVER SYSREQ_USERS_USER_TYPE SYSREQ_WORK_RULES_FOLDER_ID RESULT_VAR_NAME RESULT_VAR_NAME_ENG AUTO_NUMERATION_RULE_ID CANT_CHANGE_ID_REQUISITE_RULE_ID CANT_CHANGE_OURFIRM_REQUISITE_RULE_ID CHECK_CHANGING_REFERENCE_RECORD_USE_RULE_ID CHECK_CODE_REQUISITE_RULE_ID CHECK_DELETING_REFERENCE_RECORD_USE_RULE_ID CHECK_FILTRATER_CHANGES_RULE_ID CHECK_RECORD_INTERVAL_RULE_ID CHECK_REFERENCE_INTERVAL_RULE_ID CHECK_REQUIRED_DATA_FULLNESS_RULE_ID CHECK_REQUIRED_REQUISITES_FULLNESS_RULE_ID MAKE_RECORD_UNRATIFIED_RULE_ID RESTORE_AUTO_NUMERATION_RULE_ID SET_FIRM_CONTEXT_FROM_RECORD_RULE_ID SET_FIRST_RECORD_IN_LIST_FORM_RULE_ID SET_IDSPS_VALUE_RULE_ID SET_NEXT_CODE_VALUE_RULE_ID SET_OURFIRM_BOUNDS_RULE_ID SET_OURFIRM_REQUISITE_RULE_ID SCRIPT_BLOCK_AFTER_FINISH_EVENT SCRIPT_BLOCK_BEFORE_START_EVENT SCRIPT_BLOCK_EXECUTION_RESULTS_PROPERTY SCRIPT_BLOCK_NAME_PROPERTY SCRIPT_BLOCK_SCRIPT_PROPERTY SUBTASK_BLOCK_ABORT_DEADLINE_PROPERTY SUBTASK_BLOCK_AFTER_FINISH_EVENT SUBTASK_BLOCK_ASSIGN_PARAMS_EVENT SUBTASK_BLOCK_ATTACHMENTS_PROPERTY SUBTASK_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY SUBTASK_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY SUBTASK_BLOCK_BEFORE_START_EVENT SUBTASK_BLOCK_CREATED_TASK_PROPERTY SUBTASK_BLOCK_CREATION_EVENT SUBTASK_BLOCK_DEADLINE_PROPERTY SUBTASK_BLOCK_IMPORTANCE_PROPERTY SUBTASK_BLOCK_INITIATOR_PROPERTY SUBTASK_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY SUBTASK_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY SUBTASK_BLOCK_JOBS_TYPE_PROPERTY SUBTASK_BLOCK_NAME_PROPERTY SUBTASK_BLOCK_PARALLEL_ROUTE_PROPERTY SUBTASK_BLOCK_PERFORMERS_PROPERTY SUBTASK_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY SUBTASK_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY SUBTASK_BLOCK_REQUIRE_SIGN_PROPERTY SUBTASK_BLOCK_STANDARD_ROUTE_PROPERTY SUBTASK_BLOCK_START_EVENT SUBTASK_BLOCK_STEP_CONTROL_PROPERTY SUBTASK_BLOCK_SUBJECT_PROPERTY SUBTASK_BLOCK_TASK_CONTROL_PROPERTY SUBTASK_BLOCK_TEXT_PROPERTY SUBTASK_BLOCK_UNLOCK_ATTACHMENTS_ON_STOP_PROPERTY SUBTASK_BLOCK_USE_STANDARD_ROUTE_PROPERTY SUBTASK_BLOCK_WAIT_FOR_TASK_COMPLETE_PROPERTY SYSCOMP_CONTROL_JOBS SYSCOMP_FOLDERS SYSCOMP_JOBS SYSCOMP_NOTICES SYSCOMP_TASKS SYSDLG_CREATE_EDOCUMENT SYSDLG_CREATE_EDOCUMENT_VERSION SYSDLG_CURRENT_PERIOD SYSDLG_EDIT_FUNCTION_HELP SYSDLG_EDOCUMENT_KINDS_FOR_TEMPLATE SYSDLG_EXPORT_MULTIPLE_EDOCUMENTS SYSDLG_EXPORT_SINGLE_EDOCUMENT SYSDLG_IMPORT_EDOCUMENT SYSDLG_MULTIPLE_SELECT SYSDLG_SETUP_ACCESS_RIGHTS SYSDLG_SETUP_DEFAULT_RIGHTS SYSDLG_SETUP_FILTER_CONDITION SYSDLG_SETUP_SIGN_RIGHTS SYSDLG_SETUP_TASK_OBSERVERS SYSDLG_SETUP_TASK_ROUTE SYSDLG_SETUP_USERS_LIST SYSDLG_SIGN_EDOCUMENT SYSDLG_SIGN_MULTIPLE_EDOCUMENTS SYSREF_ACCESS_RIGHTS_TYPES SYSREF_ADMINISTRATION_HISTORY SYSREF_ALL_AVAILABLE_COMPONENTS SYSREF_ALL_AVAILABLE_PRIVILEGES SYSREF_ALL_REPLICATING_COMPONENTS SYSREF_AVAILABLE_DEVELOPERS_COMPONENTS SYSREF_CALENDAR_EVENTS SYSREF_COMPONENT_TOKEN_HISTORY SYSREF_COMPONENT_TOKENS SYSREF_COMPONENTS SYSREF_CONSTANTS SYSREF_DATA_RECEIVE_PROTOCOL SYSREF_DATA_SEND_PROTOCOL SYSREF_DIALOGS SYSREF_DIALOGS_REQUISITES SYSREF_EDITORS SYSREF_EDOC_CARDS SYSREF_EDOC_TYPES SYSREF_EDOCUMENT_CARD_REQUISITES SYSREF_EDOCUMENT_CARD_TYPES SYSREF_EDOCUMENT_CARD_TYPES_REFERENCE SYSREF_EDOCUMENT_CARDS SYSREF_EDOCUMENT_HISTORY SYSREF_EDOCUMENT_KINDS SYSREF_EDOCUMENT_REQUISITES SYSREF_EDOCUMENT_SIGNATURES SYSREF_EDOCUMENT_TEMPLATES SYSREF_EDOCUMENT_TEXT_STORAGES SYSREF_EDOCUMENT_VIEWS SYSREF_FILTERER_SETUP_CONFLICTS SYSREF_FILTRATER_SETTING_CONFLICTS SYSREF_FOLDER_HISTORY SYSREF_FOLDERS SYSREF_FUNCTION_GROUPS SYSREF_FUNCTION_PARAMS SYSREF_FUNCTIONS SYSREF_JOB_HISTORY SYSREF_LINKS SYSREF_LOCALIZATION_DICTIONARY SYSREF_LOCALIZATION_LANGUAGES SYSREF_MODULES SYSREF_PRIVILEGES SYSREF_RECORD_HISTORY SYSREF_REFERENCE_REQUISITES SYSREF_REFERENCE_TYPE_VIEWS SYSREF_REFERENCE_TYPES SYSREF_REFERENCES SYSREF_REFERENCES_REQUISITES SYSREF_REMOTE_SERVERS SYSREF_REPLICATION_SESSIONS_LOG SYSREF_REPLICATION_SESSIONS_PROTOCOL SYSREF_REPORTS SYSREF_ROLES SYSREF_ROUTE_BLOCK_GROUPS SYSREF_ROUTE_BLOCKS SYSREF_SCRIPTS SYSREF_SEARCHES SYSREF_SERVER_EVENTS SYSREF_SERVER_EVENTS_HISTORY SYSREF_STANDARD_ROUTE_GROUPS SYSREF_STANDARD_ROUTES SYSREF_STATUSES SYSREF_SYSTEM_SETTINGS SYSREF_TASK_HISTORY SYSREF_TASK_KIND_GROUPS SYSREF_TASK_KINDS SYSREF_TASK_RIGHTS SYSREF_TASK_SIGNATURES SYSREF_TASKS SYSREF_UNITS SYSREF_USER_GROUPS SYSREF_USER_GROUPS_REFERENCE SYSREF_USER_SUBSTITUTION SYSREF_USERS SYSREF_USERS_REFERENCE SYSREF_VIEWERS SYSREF_WORKING_TIME_CALENDARS ACCESS_RIGHTS_TABLE_NAME EDMS_ACCESS_TABLE_NAME EDOC_TYPES_TABLE_NAME TEST_DEV_DB_NAME TEST_DEV_SYSTEM_CODE TEST_EDMS_DB_NAME TEST_EDMS_MAIN_CODE TEST_EDMS_MAIN_DB_NAME TEST_EDMS_SECOND_CODE TEST_EDMS_SECOND_DB_NAME TEST_EDMS_SYSTEM_CODE TEST_ISB5_MAIN_CODE TEST_ISB5_SECOND_CODE TEST_SQL_SERVER_2005_NAME TEST_SQL_SERVER_NAME ATTENTION_CAPTION cbsCommandLinks cbsDefault CONFIRMATION_CAPTION ERROR_CAPTION INFORMATION_CAPTION mrCancel mrOk EDOC_VERSION_ACTIVE_STAGE_CODE EDOC_VERSION_DESIGN_STAGE_CODE EDOC_VERSION_OBSOLETE_STAGE_CODE cpDataEnciphermentEnabled cpDigitalSignatureEnabled cpID cpIssuer cpPluginVersion cpSerial cpSubjectName cpSubjSimpleName cpValidFromDate cpValidToDate ISBL_SYNTAX NO_SYNTAX XML_SYNTAX WAIT_BLOCK_AFTER_FINISH_EVENT WAIT_BLOCK_BEFORE_START_EVENT WAIT_BLOCK_DEADLINE_PROPERTY WAIT_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY WAIT_BLOCK_NAME_PROPERTY WAIT_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY SYSRES_COMMON SYSRES_CONST SYSRES_MBFUNC SYSRES_SBDATA SYSRES_SBGUI SYSRES_SBINTF SYSRES_SBREFDSC SYSRES_SQLERRORS SYSRES_SYSCOMP atUser atGroup atRole aemEnabledAlways aemDisabledAlways aemEnabledOnBrowse aemEnabledOnEdit aemDisabledOnBrowseEmpty apBegin apEnd alLeft alRight asmNever asmNoButCustomize asmAsLastTime asmYesButCustomize asmAlways cirCommon cirRevoked ctSignature ctEncode ctSignatureEncode clbUnchecked clbChecked clbGrayed ceISB ceAlways ceNever ctDocument ctReference ctScript ctUnknown ctReport ctDialog ctFunction ctFolder ctEDocument ctTask ctJob ctNotice ctControlJob cfInternal cfDisplay ciUnspecified ciWrite ciRead ckFolder ckEDocument ckTask ckJob ckComponentToken ckAny ckReference ckScript ckReport ckDialog ctISBLEditor ctBevel ctButton ctCheckListBox ctComboBox ctComboEdit ctGrid ctDBCheckBox ctDBComboBox ctDBEdit ctDBEllipsis ctDBMemo ctDBNavigator ctDBRadioGroup ctDBStatusLabel ctEdit ctGroupBox ctInplaceHint ctMemo ctPanel ctListBox ctRadioButton ctRichEdit ctTabSheet ctWebBrowser ctImage ctHyperLink ctLabel ctDBMultiEllipsis ctRibbon ctRichView ctInnerPanel ctPanelGroup ctBitButton cctDate cctInteger cctNumeric cctPick cctReference cctString cctText cltInternal cltPrimary cltGUI dseBeforeOpen dseAfterOpen dseBeforeClose dseAfterClose dseOnValidDelete dseBeforeDelete dseAfterDelete dseAfterDeleteOutOfTransaction dseOnDeleteError dseBeforeInsert dseAfterInsert dseOnValidUpdate dseBeforeUpdate dseOnUpdateRatifiedRecord dseAfterUpdate dseAfterUpdateOutOfTransaction dseOnUpdateError dseAfterScroll dseOnOpenRecord dseOnCloseRecord dseBeforeCancel dseAfterCancel dseOnUpdateDeadlockError dseBeforeDetailUpdate dseOnPrepareUpdate dseOnAnyRequisiteChange dssEdit dssInsert dssBrowse dssInActive dftDate dftShortDate dftDateTime dftTimeStamp dotDays dotHours dotMinutes dotSeconds dtkndLocal dtkndUTC arNone arView arEdit arFull ddaView ddaEdit emLock emEdit emSign emExportWithLock emImportWithUnlock emChangeVersionNote emOpenForModify emChangeLifeStage emDelete emCreateVersion emImport emUnlockExportedWithLock emStart emAbort emReInit emMarkAsReaded emMarkAsUnreaded emPerform emAccept emResume emChangeRights emEditRoute emEditObserver emRecoveryFromLocalCopy emChangeWorkAccessType emChangeEncodeTypeToCertificate emChangeEncodeTypeToPassword emChangeEncodeTypeToNone emChangeEncodeTypeToCertificatePassword emChangeStandardRoute emGetText emOpenForView emMoveToStorage emCreateObject emChangeVersionHidden emDeleteVersion emChangeLifeCycleStage emApprovingSign emExport emContinue emLockFromEdit emUnLockForEdit emLockForServer emUnlockFromServer emDelegateAccessRights emReEncode ecotFile ecotProcess eaGet eaCopy eaCreate eaCreateStandardRoute edltAll edltNothing edltQuery essmText essmCard esvtLast esvtLastActive esvtSpecified edsfExecutive edsfArchive edstSQLServer edstFile edvstNone edvstEDocumentVersionCopy edvstFile edvstTemplate edvstScannedFile vsDefault vsDesign vsActive vsObsolete etNone etCertificate etPassword etCertificatePassword ecException ecWarning ecInformation estAll estApprovingOnly evtLast evtLastActive evtQuery fdtString fdtNumeric fdtInteger fdtDate fdtText fdtUnknown fdtWideString fdtLargeInteger ftInbox ftOutbox ftFavorites ftCommonFolder ftUserFolder ftComponents ftQuickLaunch ftShortcuts ftSearch grhAuto grhX1 grhX2 grhX3 hltText hltRTF hltHTML iffBMP iffJPEG iffMultiPageTIFF iffSinglePageTIFF iffTIFF iffPNG im8bGrayscale im24bRGB im1bMonochrome itBMP itJPEG itWMF itPNG ikhInformation ikhWarning ikhError ikhNoIcon icUnknown icScript icFunction icIntegratedReport icAnalyticReport icDataSetEventHandler icActionHandler icFormEventHandler icLookUpEventHandler icRequisiteChangeEventHandler icBeforeSearchEventHandler icRoleCalculation icSelectRouteEventHandler icBlockPropertyCalculation icBlockQueryParamsEventHandler icChangeSearchResultEventHandler icBlockEventHandler icSubTaskInitEventHandler icEDocDataSetEventHandler icEDocLookUpEventHandler icEDocActionHandler icEDocFormEventHandler icEDocRequisiteChangeEventHandler icStructuredConversionRule icStructuredConversionEventBefore icStructuredConversionEventAfter icWizardEventHandler icWizardFinishEventHandler icWizardStepEventHandler icWizardStepFinishEventHandler icWizardActionEnableEventHandler icWizardActionExecuteEventHandler icCreateJobsHandler icCreateNoticesHandler icBeforeLookUpEventHandler icAfterLookUpEventHandler icTaskAbortEventHandler icWorkflowBlockActionHandler icDialogDataSetEventHandler icDialogActionHandler icDialogLookUpEventHandler icDialogRequisiteChangeEventHandler icDialogFormEventHandler icDialogValidCloseEventHandler icBlockFormEventHandler icTaskFormEventHandler icReferenceMethod icEDocMethod icDialogMethod icProcessMessageHandler isShow isHide isByUserSettings jkJob jkNotice jkControlJob jtInner jtLeft jtRight jtFull jtCross lbpAbove lbpBelow lbpLeft lbpRight eltPerConnection eltPerUser sfcUndefined sfcBlack sfcGreen sfcRed sfcBlue sfcOrange sfcLilac sfsItalic sfsStrikeout sfsNormal ldctStandardRoute ldctWizard ldctScript ldctFunction ldctRouteBlock ldctIntegratedReport ldctAnalyticReport ldctReferenceType ldctEDocumentType ldctDialog ldctServerEvents mrcrtNone mrcrtUser mrcrtMaximal mrcrtCustom vtEqual vtGreaterOrEqual vtLessOrEqual vtRange rdYesterday rdToday rdTomorrow rdThisWeek rdThisMonth rdThisYear rdNextMonth rdNextWeek rdLastWeek rdLastMonth rdWindow rdFile rdPrinter rdtString rdtNumeric rdtInteger rdtDate rdtReference rdtAccount rdtText rdtPick rdtUnknown rdtLargeInteger rdtDocument reOnChange reOnChangeValues ttGlobal ttLocal ttUser ttSystem ssmBrowse ssmSelect ssmMultiSelect ssmBrowseModal smSelect smLike smCard stNone stAuthenticating stApproving sctString sctStream sstAnsiSort sstNaturalSort svtEqual svtContain soatString soatNumeric soatInteger soatDatetime soatReferenceRecord soatText soatPick soatBoolean soatEDocument soatAccount soatIntegerCollection soatNumericCollection soatStringCollection soatPickCollection soatDatetimeCollection soatBooleanCollection soatReferenceRecordCollection soatEDocumentCollection soatAccountCollection soatContents soatUnknown tarAbortByUser tarAbortByWorkflowException tvtAllWords tvtExactPhrase tvtAnyWord usNone usCompleted usRedSquare usBlueSquare usYellowSquare usGreenSquare usOrangeSquare usPurpleSquare usFollowUp utUnknown utUser utDeveloper utAdministrator utSystemDeveloper utDisconnected btAnd btDetailAnd btOr btNotOr btOnly vmView vmSelect vmNavigation vsmSingle vsmMultiple vsmMultipleCheck vsmNoSelection wfatPrevious wfatNext wfatCancel wfatFinish wfepUndefined wfepText3 wfepText6 wfepText9 wfepSpinEdit wfepDropDown wfepRadioGroup wfepFlag wfepText12 wfepText15 wfepText18 wfepText21 wfepText24 wfepText27 wfepText30 wfepRadioGroupColumn1 wfepRadioGroupColumn2 wfepRadioGroupColumn3 wfetQueryParameter wfetText wfetDelimiter wfetLabel wptString wptInteger wptNumeric wptBoolean wptDateTime wptPick wptText wptUser wptUserList wptEDocumentInfo wptEDocumentInfoList wptReferenceRecordInfo wptReferenceRecordInfoList wptFolderInfo wptTaskInfo wptContents wptFileName wptDate wsrComplete wsrGoNext wsrGoPrevious wsrCustom wsrCancel wsrGoFinal wstForm wstEDocument wstTaskCard wstReferenceRecordCard wstFinal waAll waPerformers waManual wsbStart wsbFinish wsbNotice wsbStep wsbDecision wsbWait wsbMonitor wsbScript wsbConnector wsbSubTask wsbLifeCycleStage wsbPause wdtInteger wdtFloat wdtString wdtPick wdtDateTime wdtBoolean wdtTask wdtJob wdtFolder wdtEDocument wdtReferenceRecord wdtUser wdtGroup wdtRole wdtIntegerCollection wdtFloatCollection wdtStringCollection wdtPickCollection wdtDateTimeCollection wdtBooleanCollection wdtTaskCollection wdtJobCollection wdtFolderCollection wdtEDocumentCollection wdtReferenceRecordCollection wdtUserCollection wdtGroupCollection wdtRoleCollection wdtContents wdtUserList wdtSearchDescription wdtDeadLine wdtPickSet wdtAccountCollection wiLow wiNormal wiHigh wrtSoft wrtHard wsInit wsRunning wsDone wsControlled wsAborted wsContinued wtmFull wtmFromCurrent wtmOnlyCurrent ", + class: + "AltState Application CallType ComponentTokens CreatedJobs CreatedNotices ControlState DialogResult Dialogs EDocuments EDocumentVersionSource Folders GlobalIDs Job Jobs InputValue LookUpReference LookUpRequisiteNames LookUpSearch Object ParentComponent Processes References Requisite ReportName Reports Result Scripts Searches SelectedAttachments SelectedItems SelectMode Sender ServerEvents ServiceFactory ShiftState SubTask SystemDialogs Tasks Wizard Wizards Work ВызовСпособ ИмяОтчета РеквЗнач ", + literal: "null true false nil ", + }, + s = { + begin: "\\.\\s*" + e.UNDERSCORE_IDENT_RE, + keywords: o, + relevance: 0, + }, + l = { + className: "type", + begin: + ":[ \\t]*(" + + "IApplication IAccessRights IAccountRepository IAccountSelectionRestrictions IAction IActionList IAdministrationHistoryDescription IAnchors IApplication IArchiveInfo IAttachment IAttachmentList ICheckListBox ICheckPointedList IColumn IComponent IComponentDescription IComponentToken IComponentTokenFactory IComponentTokenInfo ICompRecordInfo IConnection IContents IControl IControlJob IControlJobInfo IControlList ICrypto ICrypto2 ICustomJob ICustomJobInfo ICustomListBox ICustomObjectWizardStep ICustomWork ICustomWorkInfo IDataSet IDataSetAccessInfo IDataSigner IDateCriterion IDateRequisite IDateRequisiteDescription IDateValue IDeaAccessRights IDeaObjectInfo IDevelopmentComponentLock IDialog IDialogFactory IDialogPickRequisiteItems IDialogsFactory IDICSFactory IDocRequisite IDocumentInfo IDualListDialog IECertificate IECertificateInfo IECertificates IEditControl IEditorForm IEdmsExplorer IEdmsObject IEdmsObjectDescription IEdmsObjectFactory IEdmsObjectInfo IEDocument IEDocumentAccessRights IEDocumentDescription IEDocumentEditor IEDocumentFactory IEDocumentInfo IEDocumentStorage IEDocumentVersion IEDocumentVersionListDialog IEDocumentVersionSource IEDocumentWizardStep IEDocVerSignature IEDocVersionState IEnabledMode IEncodeProvider IEncrypter IEvent IEventList IException IExternalEvents IExternalHandler IFactory IField IFileDialog IFolder IFolderDescription IFolderDialog IFolderFactory IFolderInfo IForEach IForm IFormTitle IFormWizardStep IGlobalIDFactory IGlobalIDInfo IGrid IHasher IHistoryDescription IHyperLinkControl IImageButton IImageControl IInnerPanel IInplaceHint IIntegerCriterion IIntegerList IIntegerRequisite IIntegerValue IISBLEditorForm IJob IJobDescription IJobFactory IJobForm IJobInfo ILabelControl ILargeIntegerCriterion ILargeIntegerRequisite ILargeIntegerValue ILicenseInfo ILifeCycleStage IList IListBox ILocalIDInfo ILocalization ILock IMemoryDataSet IMessagingFactory IMetadataRepository INotice INoticeInfo INumericCriterion INumericRequisite INumericValue IObject IObjectDescription IObjectImporter IObjectInfo IObserver IPanelGroup IPickCriterion IPickProperty IPickRequisite IPickRequisiteDescription IPickRequisiteItem IPickRequisiteItems IPickValue IPrivilege IPrivilegeList IProcess IProcessFactory IProcessMessage IProgress IProperty IPropertyChangeEvent IQuery IReference IReferenceCriterion IReferenceEnabledMode IReferenceFactory IReferenceHistoryDescription IReferenceInfo IReferenceRecordCardWizardStep IReferenceRequisiteDescription IReferencesFactory IReferenceValue IRefRequisite IReport IReportFactory IRequisite IRequisiteDescription IRequisiteDescriptionList IRequisiteFactory IRichEdit IRouteStep IRule IRuleList ISchemeBlock IScript IScriptFactory ISearchCriteria ISearchCriterion ISearchDescription ISearchFactory ISearchFolderInfo ISearchForObjectDescription ISearchResultRestrictions ISecuredContext ISelectDialog IServerEvent IServerEventFactory IServiceDialog IServiceFactory ISignature ISignProvider ISignProvider2 ISignProvider3 ISimpleCriterion IStringCriterion IStringList IStringRequisite IStringRequisiteDescription IStringValue ISystemDialogsFactory ISystemInfo ITabSheet ITask ITaskAbortReasonInfo ITaskCardWizardStep ITaskDescription ITaskFactory ITaskInfo ITaskRoute ITextCriterion ITextRequisite ITextValue ITreeListSelectDialog IUser IUserList IValue IView IWebBrowserControl IWizard IWizardAction IWizardFactory IWizardFormElement IWizardParam IWizardPickParam IWizardReferenceParam IWizardStep IWorkAccessRights IWorkDescription IWorkflowAskableParam IWorkflowAskableParams IWorkflowBlock IWorkflowBlockResult IWorkflowEnabledMode IWorkflowParam IWorkflowPickParam IWorkflowReferenceParam IWorkState IWorkTreeCustomNode IWorkTreeJobNode IWorkTreeTaskNode IXMLEditorForm SBCrypto " + .trim() + .replace(/\s/g, "|") + + ")", + end: "[ \\t]*=", + excludeEnd: !0, + }, + c = { + className: "variable", + keywords: o, + begin: t, + relevance: 0, + contains: [l, s], + }, + _ = "[A-Za-zА-Яа-яёЁ_][A-Za-zА-Яа-яёЁ_0-9]*\\("; + return { + name: "ISBL", + case_insensitive: !0, + keywords: o, + illegal: "\\$|\\?|%|,|;$|~|#|@|)?\\s+)+" + + e.UNDERSCORE_IDENT_RE + + "\\s*\\(", + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: n, + contains: [ + { + begin: e.UNDERSCORE_IDENT_RE + "\\s*\\(", + returnBegin: !0, + relevance: 0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: n, + relevance: 0, + contains: [ + a, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + r, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + r, + a, + ], + }; + }, + DE = [ + "as", + "in", + "of", + "if", + "for", + "while", + "finally", + "var", + "new", + "function", + "do", + "return", + "void", + "else", + "break", + "catch", + "instanceof", + "with", + "throw", + "case", + "default", + "try", + "switch", + "continue", + "typeof", + "delete", + "let", + "yield", + "const", + "class", + "debugger", + "async", + "await", + "static", + "import", + "from", + "export", + "extends", + ], + ME = ["true", "false", "null", "undefined", "NaN", "Infinity"], + LE = [].concat( + [ + "setInterval", + "setTimeout", + "clearInterval", + "clearTimeout", + "require", + "exports", + "eval", + "isFinite", + "isNaN", + "parseFloat", + "parseInt", + "decodeURI", + "decodeURIComponent", + "encodeURI", + "encodeURIComponent", + "escape", + "unescape", + ], + [ + "arguments", + "this", + "super", + "console", + "window", + "document", + "localStorage", + "module", + "global", + ], + [ + "Intl", + "DataView", + "Number", + "Math", + "Date", + "String", + "RegExp", + "Object", + "Function", + "Boolean", + "Error", + "Symbol", + "Set", + "Map", + "WeakSet", + "WeakMap", + "Proxy", + "Reflect", + "JSON", + "Promise", + "Float64Array", + "Int16Array", + "Int32Array", + "Int8Array", + "Uint16Array", + "Uint32Array", + "Float32Array", + "Array", + "Uint8Array", + "Uint8ClampedArray", + "ArrayBuffer", + "BigInt64Array", + "BigUint64Array", + "BigInt", + ], + [ + "EvalError", + "InternalError", + "RangeError", + "ReferenceError", + "SyntaxError", + "TypeError", + "URIError", + ], + ); +function wE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function xE(e) { + return PE("(?=", e, ")"); +} +function PE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return wE(e); + }) + .join(""); + return a; +} +var kE = function (e) { + var t = "[A-Za-z$_][0-9A-Za-z$_]*", + n = "<>", + a = "", + r = { + begin: /<[A-Za-z0-9\\._:-]+/, + end: /\/[A-Za-z0-9\\._:-]+>|\/>/, + isTrulyOpeningTag: function (e, t) { + var n = e[0].length + e.index, + a = e.input[n]; + "<" !== a + ? ">" === a && + ((function (e, t) { + var n = t.after, + a = "", + returnBegin: !0, + end: "\\s*=>", + contains: [ + { + className: "params", + variants: [ + { begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + { className: null, begin: /\(\s*\)/, skip: !0 }, + { + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: i, + contains: S, + }, + ], + }, + ], + }, + { begin: /,/, relevance: 0 }, + { className: "", begin: /\s/, end: /\s*/, skip: !0 }, + { + variants: [ + { begin: n, end: a }, + { begin: r.begin, "on:begin": r.isTrulyOpeningTag, end: r.end }, + ], + subLanguage: "xml", + contains: [ + { begin: r.begin, end: r.end, skip: !0, contains: ["self"] }, + ], + }, + ], + relevance: 0, + }, + { + className: "function", + beginKeywords: "function", + end: /[{;]/, + excludeEnd: !0, + keywords: i, + contains: ["self", e.inherit(e.TITLE_MODE, { begin: t }), b], + illegal: /%/, + }, + { beginKeywords: "while if switch catch for" }, + { + className: "function", + begin: + e.UNDERSCORE_IDENT_RE + + "\\([^()]*(\\([^()]*(\\([^()]*\\)[^()]*)*\\)[^()]*)*\\)\\s*\\{", + returnBegin: !0, + contains: [b, e.inherit(e.TITLE_MODE, { begin: t })], + }, + { variants: [{ begin: "\\." + t }, { begin: "\\$" + t }], relevance: 0 }, + { + className: "class", + beginKeywords: "class", + end: /[{;=]/, + excludeEnd: !0, + illegal: /[:"[\]]/, + contains: [{ beginKeywords: "extends" }, e.UNDERSCORE_TITLE_MODE], + }, + { + begin: /\b(?=constructor)/, + end: /[{;]/, + excludeEnd: !0, + contains: [e.inherit(e.TITLE_MODE, { begin: t }), "self", b], + }, + { + begin: "(get|set)\\s+(?=" + t + "\\()", + end: /\{/, + keywords: "get set", + contains: [e.inherit(e.TITLE_MODE, { begin: t }), { begin: /\(\)/ }, b], + }, + { begin: /\$[(.]/ }, + ], + }; +}; +var UE = function (e) { + var t = { + className: "params", + begin: /\(/, + end: /\)/, + contains: [ + { + begin: /[\w-]+ *=/, + returnBegin: !0, + relevance: 0, + contains: [{ className: "attr", begin: /[\w-]+/ }], + }, + ], + relevance: 0, + }; + return { + name: "JBoss CLI", + aliases: ["wildfly-cli"], + keywords: { + $pattern: "[a-z-]+", + keyword: + "alias batch cd clear command connect connection-factory connection-info data-source deploy deployment-info deployment-overlay echo echo-dmr help history if jdbc-driver-info jms-queue|20 jms-topic|20 ls patch pwd quit read-attribute read-operation reload rollout-plan run-batch set shutdown try unalias undeploy unset version xa-data-source", + literal: "true false", + }, + contains: [ + e.HASH_COMMENT_MODE, + e.QUOTE_STRING_MODE, + { className: "params", begin: /--[\w\-=\/]+/ }, + { className: "function", begin: /:[\w\-.]+/, relevance: 0 }, + { className: "string", begin: /\B([\/.])[\w\-.\/=]+/ }, + t, + ], + }; +}; +var FE = function (e) { + var t = { literal: "true false null" }, + n = [e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + a = [e.QUOTE_STRING_MODE, e.C_NUMBER_MODE], + r = { + end: ",", + endsWithParent: !0, + excludeEnd: !0, + contains: a, + keywords: t, + }, + i = { + begin: /\{/, + end: /\}/, + contains: [ + { + className: "attr", + begin: /"/, + end: /"/, + contains: [e.BACKSLASH_ESCAPE], + illegal: "\\n", + }, + e.inherit(r, { begin: /:/ }), + ].concat(n), + illegal: "\\S", + }, + o = { begin: "\\[", end: "\\]", contains: [e.inherit(r)], illegal: "\\S" }; + return ( + a.push(i, o), + n.forEach(function (e) { + a.push(e); + }), + { name: "JSON", contains: a, keywords: t, illegal: "\\S" } + ); +}; +var BE = function (e) { + var t = "[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*", + n = { + $pattern: t, + keyword: [ + "baremodule", + "begin", + "break", + "catch", + "ccall", + "const", + "continue", + "do", + "else", + "elseif", + "end", + "export", + "false", + "finally", + "for", + "function", + "global", + "if", + "import", + "in", + "isa", + "let", + "local", + "macro", + "module", + "quote", + "return", + "true", + "try", + "using", + "where", + "while", + ], + literal: [ + "ARGS", + "C_NULL", + "DEPOT_PATH", + "ENDIAN_BOM", + "ENV", + "Inf", + "Inf16", + "Inf32", + "Inf64", + "InsertionSort", + "LOAD_PATH", + "MergeSort", + "NaN", + "NaN16", + "NaN32", + "NaN64", + "PROGRAM_FILE", + "QuickSort", + "RoundDown", + "RoundFromZero", + "RoundNearest", + "RoundNearestTiesAway", + "RoundNearestTiesUp", + "RoundToZero", + "RoundUp", + "VERSION|0", + "devnull", + "false", + "im", + "missing", + "nothing", + "pi", + "stderr", + "stdin", + "stdout", + "true", + "undef", + "π", + "ℯ", + ], + built_in: [ + "AbstractArray", + "AbstractChannel", + "AbstractChar", + "AbstractDict", + "AbstractDisplay", + "AbstractFloat", + "AbstractIrrational", + "AbstractMatrix", + "AbstractRange", + "AbstractSet", + "AbstractString", + "AbstractUnitRange", + "AbstractVecOrMat", + "AbstractVector", + "Any", + "ArgumentError", + "Array", + "AssertionError", + "BigFloat", + "BigInt", + "BitArray", + "BitMatrix", + "BitSet", + "BitVector", + "Bool", + "BoundsError", + "CapturedException", + "CartesianIndex", + "CartesianIndices", + "Cchar", + "Cdouble", + "Cfloat", + "Channel", + "Char", + "Cint", + "Cintmax_t", + "Clong", + "Clonglong", + "Cmd", + "Colon", + "Complex", + "ComplexF16", + "ComplexF32", + "ComplexF64", + "CompositeException", + "Condition", + "Cptrdiff_t", + "Cshort", + "Csize_t", + "Cssize_t", + "Cstring", + "Cuchar", + "Cuint", + "Cuintmax_t", + "Culong", + "Culonglong", + "Cushort", + "Cvoid", + "Cwchar_t", + "Cwstring", + "DataType", + "DenseArray", + "DenseMatrix", + "DenseVecOrMat", + "DenseVector", + "Dict", + "DimensionMismatch", + "Dims", + "DivideError", + "DomainError", + "EOFError", + "Enum", + "ErrorException", + "Exception", + "ExponentialBackOff", + "Expr", + "Float16", + "Float32", + "Float64", + "Function", + "GlobalRef", + "HTML", + "IO", + "IOBuffer", + "IOContext", + "IOStream", + "IdDict", + "IndexCartesian", + "IndexLinear", + "IndexStyle", + "InexactError", + "InitError", + "Int", + "Int128", + "Int16", + "Int32", + "Int64", + "Int8", + "Integer", + "InterruptException", + "InvalidStateException", + "Irrational", + "KeyError", + "LinRange", + "LineNumberNode", + "LinearIndices", + "LoadError", + "MIME", + "Matrix", + "Method", + "MethodError", + "Missing", + "MissingException", + "Module", + "NTuple", + "NamedTuple", + "Nothing", + "Number", + "OrdinalRange", + "OutOfMemoryError", + "OverflowError", + "Pair", + "PartialQuickSort", + "PermutedDimsArray", + "Pipe", + "ProcessFailedException", + "Ptr", + "QuoteNode", + "Rational", + "RawFD", + "ReadOnlyMemoryError", + "Real", + "ReentrantLock", + "Ref", + "Regex", + "RegexMatch", + "RoundingMode", + "SegmentationFault", + "Set", + "Signed", + "Some", + "StackOverflowError", + "StepRange", + "StepRangeLen", + "StridedArray", + "StridedMatrix", + "StridedVecOrMat", + "StridedVector", + "String", + "StringIndexError", + "SubArray", + "SubString", + "SubstitutionString", + "Symbol", + "SystemError", + "Task", + "TaskFailedException", + "Text", + "TextDisplay", + "Timer", + "Tuple", + "Type", + "TypeError", + "TypeVar", + "UInt", + "UInt128", + "UInt16", + "UInt32", + "UInt64", + "UInt8", + "UndefInitializer", + "UndefKeywordError", + "UndefRefError", + "UndefVarError", + "Union", + "UnionAll", + "UnitRange", + "Unsigned", + "Val", + "Vararg", + "VecElement", + "VecOrMat", + "Vector", + "VersionNumber", + "WeakKeyDict", + "WeakRef", + ], + }, + a = { keywords: n, illegal: /<\// }, + r = { className: "subst", begin: /\$\(/, end: /\)/, keywords: n }, + i = { className: "variable", begin: "\\$" + t }, + o = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, r, i], + variants: [ + { begin: /\w*"""/, end: /"""\w*/, relevance: 10 }, + { begin: /\w*"/, end: /"\w*/ }, + ], + }, + s = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, r, i], + begin: "`", + end: "`", + }, + l = { className: "meta", begin: "@" + t }; + return ( + (a.name = "Julia"), + (a.contains = [ + { + className: "number", + begin: + /(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/, + relevance: 0, + }, + { className: "string", begin: /'(.|\\[xXuU][a-zA-Z0-9]+)'/ }, + o, + s, + l, + { + className: "comment", + variants: [ + { begin: "#=", end: "=#", relevance: 10 }, + { begin: "#", end: "$" }, + ], + }, + e.HASH_COMMENT_MODE, + { + className: "keyword", + begin: "\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b", + }, + { begin: /<:/ }, + ]), + (r.contains = a.contains), + a + ); +}; +var GE = function (e) { + return { + name: "Julia REPL", + contains: [ + { + className: "meta", + begin: /^julia>/, + relevance: 10, + starts: { end: /^(?![ ]{6})/, subLanguage: "julia" }, + aliases: ["jldoctest"], + }, + ], + }; + }, + YE = "\\.(".concat("[0-9](_*[0-9])*", ")"), + HE = "[0-9a-fA-F](_*[0-9a-fA-F])*", + VE = { + className: "number", + variants: [ + { + begin: + "(\\b(" + .concat("[0-9](_*[0-9])*", ")((") + .concat(YE, ")|\\.)?|(") + .concat(YE, "))") + + "[eE][+-]?(".concat("[0-9](_*[0-9])*", ")[fFdD]?\\b"), + }, + { + begin: "\\b(" + .concat("[0-9](_*[0-9])*", ")((") + .concat(YE, ")[fFdD]?\\b|\\.([fFdD]\\b)?)"), + }, + { begin: "(".concat(YE, ")[fFdD]?\\b") }, + { begin: "\\b(".concat("[0-9](_*[0-9])*", ")[fFdD]\\b") }, + { + begin: + "\\b0[xX]((" + .concat(HE, ")\\.?|(") + .concat(HE, ")?\\.(") + .concat(HE, "))") + + "[pP][+-]?(".concat("[0-9](_*[0-9])*", ")[fFdD]?\\b"), + }, + { begin: "\\b(0|[1-9](_*[0-9])*)[lL]?\\b" }, + { begin: "\\b0[xX](".concat(HE, ")[lL]?\\b") }, + { begin: "\\b0(_*[0-7])*[lL]?\\b" }, + { begin: "\\b0[bB][01](_*[01])*[lL]?\\b" }, + ], + relevance: 0, + }; +var qE = function (e) { + var t = { + keyword: + "abstract as val var vararg get set class object open private protected public noinline crossinline dynamic final enum if else do while for when throw try catch finally import package is in fun override companion reified inline lateinit init interface annotation data sealed internal infix operator out by constructor super tailrec where const inner suspend typealias external expect actual", + built_in: + "Byte Short Char Int Long Boolean Float Double Void Unit Nothing", + literal: "true false null", + }, + n = { className: "symbol", begin: e.UNDERSCORE_IDENT_RE + "@" }, + a = { + className: "subst", + begin: /\$\{/, + end: /\}/, + contains: [e.C_NUMBER_MODE], + }, + r = { className: "variable", begin: "\\$" + e.UNDERSCORE_IDENT_RE }, + i = { + className: "string", + variants: [ + { begin: '"""', end: '"""(?=[^"])', contains: [r, a] }, + { begin: "'", end: "'", illegal: /\n/, contains: [e.BACKSLASH_ESCAPE] }, + { + begin: '"', + end: '"', + illegal: /\n/, + contains: [e.BACKSLASH_ESCAPE, r, a], + }, + ], + }; + a.contains.push(i); + var o = { + className: "meta", + begin: + "@(?:file|property|field|get|set|receiver|param|setparam|delegate)\\s*:(?:\\s*" + + e.UNDERSCORE_IDENT_RE + + ")?", + }, + s = { + className: "meta", + begin: "@" + e.UNDERSCORE_IDENT_RE, + contains: [ + { + begin: /\(/, + end: /\)/, + contains: [e.inherit(i, { className: "meta-string" })], + }, + ], + }, + l = VE, + c = e.COMMENT("/\\*", "\\*/", { contains: [e.C_BLOCK_COMMENT_MODE] }), + _ = { + variants: [ + { className: "type", begin: e.UNDERSCORE_IDENT_RE }, + { begin: /\(/, end: /\)/, contains: [] }, + ], + }, + d = _; + return ( + (d.variants[1].contains = [_]), + (_.variants[1].contains = [d]), + { + name: "Kotlin", + aliases: ["kt", "kts"], + keywords: t, + contains: [ + e.COMMENT("/\\*\\*", "\\*/", { + relevance: 0, + contains: [{ className: "doctag", begin: "@[A-Za-z]+" }], + }), + e.C_LINE_COMMENT_MODE, + c, + { + className: "keyword", + begin: /\b(break|continue|return|this)\b/, + starts: { contains: [{ className: "symbol", begin: /@\w+/ }] }, + }, + n, + o, + s, + { + className: "function", + beginKeywords: "fun", + end: "[(]|$", + returnBegin: !0, + excludeEnd: !0, + keywords: t, + relevance: 5, + contains: [ + { + begin: e.UNDERSCORE_IDENT_RE + "\\s*\\(", + returnBegin: !0, + relevance: 0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "type", + begin: //, + keywords: "reified", + relevance: 0, + }, + { + className: "params", + begin: /\(/, + end: /\)/, + endsParent: !0, + keywords: t, + relevance: 0, + contains: [ + { + begin: /:/, + end: /[=,\/]/, + endsWithParent: !0, + contains: [_, e.C_LINE_COMMENT_MODE, c], + relevance: 0, + }, + e.C_LINE_COMMENT_MODE, + c, + o, + s, + i, + e.C_NUMBER_MODE, + ], + }, + c, + ], + }, + { + className: "class", + beginKeywords: "class interface trait", + end: /[:\{(]|$/, + excludeEnd: !0, + illegal: "extends implements", + contains: [ + { beginKeywords: "public protected internal private constructor" }, + e.UNDERSCORE_TITLE_MODE, + { + className: "type", + begin: //, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + { + className: "type", + begin: /[,:]\s*/, + end: /[<\(,]|$/, + excludeBegin: !0, + returnEnd: !0, + }, + o, + s, + ], + }, + i, + { + className: "meta", + begin: "^#!/usr/bin/env", + end: "$", + illegal: "\n", + }, + l, + ], + } + ); +}; +var zE = function (e) { + var t = "[a-zA-Z_][\\w.]*", + n = "<\\?(lasso(script)?|=)", + a = "\\]|\\?>", + r = { + $pattern: "[a-zA-Z_][\\w.]*|&[lg]t;", + literal: + "true false none minimal full all void and or not bw nbw ew new cn ncn lt lte gt gte eq neq rx nrx ft", + built_in: + "array date decimal duration integer map pair string tag xml null boolean bytes keyword list locale queue set stack staticarray local var variable global data self inherited currentcapture givenblock", + keyword: + "cache database_names database_schemanames database_tablenames define_tag define_type email_batch encode_set html_comment handle handle_error header if inline iterate ljax_target link link_currentaction link_currentgroup link_currentrecord link_detail link_firstgroup link_firstrecord link_lastgroup link_lastrecord link_nextgroup link_nextrecord link_prevgroup link_prevrecord log loop namespace_using output_none portal private protect records referer referrer repeating resultset rows search_args search_arguments select sort_args sort_arguments thread_atomic value_list while abort case else fail_if fail_ifnot fail if_empty if_false if_null if_true loop_abort loop_continue loop_count params params_up return return_value run_children soap_definetag soap_lastrequest soap_lastresponse tag_name ascending average by define descending do equals frozen group handle_failure import in into join let match max min on order parent protected provide public require returnhome skip split_thread sum take thread to trait type where with yield yieldhome", + }, + i = e.COMMENT("\x3c!--", "--\x3e", { relevance: 0 }), + o = { + className: "meta", + begin: "\\[noprocess\\]", + starts: { end: "\\[/noprocess\\]", returnEnd: !0, contains: [i] }, + }, + s = { className: "meta", begin: "\\[/noprocess|" + n }, + l = { className: "symbol", begin: "'[a-zA-Z_][\\w.]*'" }, + c = [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.inherit(e.C_NUMBER_MODE, { + begin: e.C_NUMBER_RE + "|(-?infinity|NaN)\\b", + }), + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { className: "string", begin: "`", end: "`" }, + { + variants: [ + { begin: "[#$][a-zA-Z_][\\w.]*" }, + { begin: "#", end: "\\d+", illegal: "\\W" }, + ], + }, + { className: "type", begin: "::\\s*", end: t, illegal: "\\W" }, + { + className: "params", + variants: [ + { begin: "-(?!infinity)[a-zA-Z_][\\w.]*", relevance: 0 }, + { begin: "(\\.\\.\\.)" }, + ], + }, + { begin: /(->|\.)\s*/, relevance: 0, contains: [l] }, + { + className: "class", + beginKeywords: "define", + returnEnd: !0, + end: "\\(|=>", + contains: [ + e.inherit(e.TITLE_MODE, { + begin: "[a-zA-Z_][\\w.]*(=(?!>))?|[-+*/%](?!>)", + }), + ], + }, + ]; + return { + name: "Lasso", + aliases: ["ls", "lassoscript"], + case_insensitive: !0, + keywords: r, + contains: [ + { + className: "meta", + begin: a, + relevance: 0, + starts: { end: "\\[|" + n, returnEnd: !0, relevance: 0, contains: [i] }, + }, + o, + s, + { + className: "meta", + begin: "\\[no_square_brackets", + starts: { + end: "\\[/no_square_brackets\\]", + keywords: r, + contains: [ + { + className: "meta", + begin: a, + relevance: 0, + starts: { + end: "\\[noprocess\\]|" + n, + returnEnd: !0, + contains: [i], + }, + }, + o, + s, + ].concat(c), + }, + }, + { className: "meta", begin: "\\[", relevance: 0 }, + { className: "meta", begin: "^#!", end: "lasso9$", relevance: 10 }, + ].concat(c), + }; +}; +function WE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function $E() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return WE(e); + }) + .join("|") + + ")"; + return a; +} +var QE = function (e) { + var t, + n = [ + { begin: /\^{6}[0-9a-f]{6}/ }, + { begin: /\^{5}[0-9a-f]{5}/ }, + { begin: /\^{4}[0-9a-f]{4}/ }, + { begin: /\^{3}[0-9a-f]{3}/ }, + { begin: /\^{2}[0-9a-f]{2}/ }, + { begin: /\^{2}[\u0000-\u007f]/ }, + ], + a = [ + { + className: "keyword", + begin: /\\/, + relevance: 0, + contains: [ + { + endsParent: !0, + begin: $E.apply( + void 0, + c( + [ + "(?:NeedsTeXFormat|RequirePackage|GetIdInfo)", + "Provides(?:Expl)?(?:Package|Class|File)", + "(?:DeclareOption|ProcessOptions)", + "(?:documentclass|usepackage|input|include)", + "makeat(?:letter|other)", + "ExplSyntax(?:On|Off)", + "(?:new|renew|provide)?command", + "(?:re)newenvironment", + "(?:New|Renew|Provide|Declare)(?:Expandable)?DocumentCommand", + "(?:New|Renew|Provide|Declare)DocumentEnvironment", + "(?:(?:e|g|x)?def|let)", + "(?:begin|end)", + "(?:part|chapter|(?:sub){0,2}section|(?:sub)?paragraph)", + "caption", + "(?:label|(?:eq|page|name)?ref|(?:paren|foot|super)?cite)", + "(?:alpha|beta|[Gg]amma|[Dd]elta|(?:var)?epsilon|zeta|eta|[Tt]heta|vartheta)", + "(?:iota|(?:var)?kappa|[Ll]ambda|mu|nu|[Xx]i|[Pp]i|varpi|(?:var)rho)", + "(?:[Ss]igma|varsigma|tau|[Uu]psilon|[Pp]hi|varphi|chi|[Pp]si|[Oo]mega)", + "(?:frac|sum|prod|lim|infty|times|sqrt|leq|geq|left|right|middle|[bB]igg?)", + "(?:[lr]angle|q?quad|[lcvdi]?dots|d?dot|hat|tilde|bar)", + ].map(function (e) { + return e + "(?![a-zA-Z@:_])"; + }), + ), + ), + }, + { + endsParent: !0, + begin: new RegExp( + [ + "(?:__)?[a-zA-Z]{2,}_[a-zA-Z](?:_?[a-zA-Z])+:[a-zA-Z]*", + "[lgc]__?[a-zA-Z](?:_?[a-zA-Z])*_[a-zA-Z]{2,}", + "[qs]__?[a-zA-Z](?:_?[a-zA-Z])+", + "use(?:_i)?:[a-zA-Z]*", + "(?:else|fi|or):", + "(?:if|cs|exp):w", + "(?:hbox|vbox):n", + "::[a-zA-Z]_unbraced", + "::[a-zA-Z:]", + ] + .map(function (e) { + return e + "(?![a-zA-Z:_])"; + }) + .join("|"), + ), + }, + { endsParent: !0, variants: n }, + { + endsParent: !0, + relevance: 0, + variants: [{ begin: /[a-zA-Z@]+/ }, { begin: /[^a-zA-Z@]?/ }], + }, + ], + }, + { className: "params", relevance: 0, begin: /#+\d?/ }, + { variants: n }, + { className: "built_in", relevance: 0, begin: /[$&^_]/ }, + { className: "meta", begin: "% !TeX", end: "$", relevance: 10 }, + e.COMMENT("%", "$", { relevance: 0 }), + ], + r = { begin: /\{/, end: /\}/, relevance: 0, contains: ["self"].concat(a) }, + i = e.inherit(r, { relevance: 0, endsParent: !0, contains: [r].concat(a) }), + o = { + begin: /\[/, + end: /\]/, + endsParent: !0, + relevance: 0, + contains: [r].concat(a), + }, + s = { begin: /\s+/, relevance: 0 }, + l = [i], + _ = [o], + d = function (e, t) { + return { + contains: [s], + starts: { relevance: 0, contains: e, starts: t }, + }; + }, + u = function (e, t) { + return { + begin: "\\\\" + e + "(?![a-zA-Z@:_])", + keywords: { $pattern: /\\[a-zA-Z]+/, keyword: "\\" + e }, + relevance: 0, + contains: [s], + starts: t, + }; + }, + m = function (t, n) { + return e.inherit( + { + begin: "\\\\begin(?=[ \t]*(\\r?\\n[ \t]*)?\\{" + t + "\\})", + keywords: { $pattern: /\\[a-zA-Z]+/, keyword: "\\begin" }, + relevance: 0, + }, + d(l, n), + ); + }, + p = function () { + var t = + arguments.length > 0 && void 0 !== arguments[0] + ? arguments[0] + : "string"; + return e.END_SAME_AS_BEGIN({ + className: t, + begin: /(.|\r?\n)/, + end: /(.|\r?\n)/, + excludeBegin: !0, + excludeEnd: !0, + endsParent: !0, + }); + }, + g = function (e) { + return { className: "string", end: "(?=\\\\end\\{" + e + "\\})" }; + }, + E = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] + ? arguments[0] + : "string"; + return { + relevance: 0, + begin: /\{/, + starts: { + endsParent: !0, + contains: [ + { + className: e, + end: /(?=\})/, + endsParent: !0, + contains: [ + { begin: /\{/, end: /\}/, relevance: 0, contains: ["self"] }, + ], + }, + ], + }, + }; + }, + S = [].concat( + c( + ["verb", "lstinline"].map(function (e) { + return u(e, { contains: [p()] }); + }), + ), + [ + u("mint", d(l, { contains: [p()] })), + u("mintinline", d(l, { contains: [E(), p()] })), + u("url", { contains: [E("link"), E("link")] }), + u("hyperref", { contains: [E("link")] }), + u("href", d(_, { contains: [E("link")] })), + ], + c( + (t = []).concat.apply( + t, + c( + ["", "\\*"].map(function (e) { + return [ + m("verbatim" + e, g("verbatim" + e)), + m("filecontents" + e, d(l, g("filecontents" + e))), + ].concat( + c( + ["", "B", "L"].map(function (t) { + return m(t + "Verbatim" + e, d(_, g(t + "Verbatim" + e))); + }), + ), + ); + }), + ), + ), + ), + [m("minted", d(_, d(l, g("minted"))))], + ); + return { name: "LaTeX", aliases: ["tex"], contains: [].concat(c(S), a) }; +}; +var KE = function (e) { + return { + name: "LDIF", + contains: [ + { + className: "attribute", + begin: "^dn", + end: ": ", + excludeEnd: !0, + starts: { end: "$", relevance: 0 }, + relevance: 10, + }, + { + className: "attribute", + begin: "^\\w", + end: ": ", + excludeEnd: !0, + starts: { end: "$", relevance: 0 }, + }, + { className: "literal", begin: "^-", end: "$" }, + e.HASH_COMMENT_MODE, + ], + }; +}; +var jE = function (e) { + return { + name: "Leaf", + contains: [ + { + className: "function", + begin: "#+[A-Za-z_0-9]*\\(", + end: / \{/, + returnBegin: !0, + excludeEnd: !0, + contains: [ + { className: "keyword", begin: "#+" }, + { className: "title", begin: "[A-Za-z_][A-Za-z_0-9]*" }, + { + className: "params", + begin: "\\(", + end: "\\)", + endsParent: !0, + contains: [ + { className: "string", begin: '"', end: '"' }, + { className: "variable", begin: "[A-Za-z_][A-Za-z_0-9]*" }, + ], + }, + ], + }, + ], + }; + }, + XE = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + ZE = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + JE = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + eS = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + tS = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(), + nS = JE.concat(eS); +var aS = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = nS, + a = "([\\w-]+|@\\{[\\w-]+\\})", + r = [], + i = [], + o = function (e) { + return { className: "string", begin: "~?" + e + ".*?" + e }; + }, + s = function (e, t, n) { + return { className: e, begin: t, relevance: n }; + }, + l = { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: ZE.join(" "), + }, + c = { begin: "\\(", end: "\\)", contains: i, keywords: l, relevance: 0 }; + i.push( + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + o("'"), + o('"'), + e.CSS_NUMBER_MODE, + { + begin: "(url|data-uri)\\(", + starts: { className: "string", end: "[\\)\\n]", excludeEnd: !0 }, + }, + t.HEXCOLOR, + c, + s("variable", "@@?[\\w-]+", 10), + s("variable", "@\\{[\\w-]+\\}"), + s("built_in", "~?`[^`]*?`"), + { + className: "attribute", + begin: "[\\w-]+\\s*:", + end: ":", + returnBegin: !0, + excludeEnd: !0, + }, + t.IMPORTANT, + ); + var _ = i.concat({ begin: /\{/, end: /\}/, contains: r }), + d = { + beginKeywords: "when", + endsWithParent: !0, + contains: [{ beginKeywords: "and not" }].concat(i), + }, + u = { + begin: a + "\\s*:", + returnBegin: !0, + end: /[;}]/, + relevance: 0, + contains: [ + { begin: /-(webkit|moz|ms|o)-/ }, + { + className: "attribute", + begin: "\\b(" + tS.join("|") + ")\\b", + end: /(?=:)/, + starts: { + endsWithParent: !0, + illegal: "[<=$]", + relevance: 0, + contains: i, + }, + }, + ], + }, + m = { + className: "keyword", + begin: + "@(import|media|charset|font-face|(-[a-z]+-)?keyframes|supports|document|namespace|page|viewport|host)\\b", + starts: { + end: "[;{}]", + keywords: l, + returnEnd: !0, + contains: i, + relevance: 0, + }, + }, + p = { + className: "variable", + variants: [ + { begin: "@[\\w-]+\\s*:", relevance: 15 }, + { begin: "@[\\w-]+" }, + ], + starts: { end: "[;}]", returnEnd: !0, contains: _ }, + }, + g = { + variants: [ + { begin: "[\\.#:&\\[>]", end: "[;{}]" }, + { begin: a, end: /\{/ }, + ], + returnBegin: !0, + returnEnd: !0, + illegal: "[<='$\"]", + relevance: 0, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + d, + s("keyword", "all\\b"), + s("variable", "@\\{[\\w-]+\\}"), + { begin: "\\b(" + XE.join("|") + ")\\b", className: "selector-tag" }, + s("selector-tag", a + "%?", 0), + s("selector-id", "#" + a), + s("selector-class", "\\." + a, 0), + s("selector-tag", "&", 0), + t.ATTRIBUTE_SELECTOR_MODE, + { className: "selector-pseudo", begin: ":(" + JE.join("|") + ")" }, + { className: "selector-pseudo", begin: "::(" + eS.join("|") + ")" }, + { begin: "\\(", end: "\\)", contains: _ }, + { begin: "!important" }, + ], + }, + E = { + begin: "[\\w-]+:(:)?" + "(".concat(n.join("|"), ")"), + returnBegin: !0, + contains: [g], + }; + return ( + r.push(e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE, m, p, E, u, g), + { name: "Less", case_insensitive: !0, illegal: "[=>'/<($\"]", contains: r } + ); +}; +var rS = function (e) { + var t = "[a-zA-Z_\\-+\\*\\/<=>&#][a-zA-Z0-9_\\-+*\\/<=>&#!]*", + n = "\\|[^]*?\\|", + a = "(-|\\+)?\\d+(\\.\\d+|\\/\\d+)?((d|e|f|l|s|D|E|F|L|S)(\\+|-)?\\d+)?", + r = { className: "literal", begin: "\\b(t{1}|nil)\\b" }, + i = { + className: "number", + variants: [ + { begin: a, relevance: 0 }, + { begin: "#(b|B)[0-1]+(/[0-1]+)?" }, + { begin: "#(o|O)[0-7]+(/[0-7]+)?" }, + { begin: "#(x|X)[0-9a-fA-F]+(/[0-9a-fA-F]+)?" }, + { begin: "#(c|C)\\(" + a + " +" + a, end: "\\)" }, + ], + }, + o = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + s = e.COMMENT(";", "$", { relevance: 0 }), + l = { begin: "\\*", end: "\\*" }, + c = { className: "symbol", begin: "[:&]" + t }, + _ = { begin: t, relevance: 0 }, + d = { begin: n }, + u = { + contains: [ + i, + o, + l, + c, + { begin: "\\(", end: "\\)", contains: ["self", r, o, i, _] }, + _, + ], + variants: [ + { begin: "['`]\\(", end: "\\)" }, + { begin: "\\(quote ", end: "\\)", keywords: { name: "quote" } }, + { begin: "'" + n }, + ], + }, + m = { + variants: [{ begin: "'" + t }, { begin: "#'" + t + "(::" + t + ")*" }], + }, + p = { begin: "\\(\\s*", end: "\\)" }, + g = { endsWithParent: !0, relevance: 0 }; + return ( + (p.contains = [ + { + className: "name", + variants: [{ begin: t, relevance: 0 }, { begin: n }], + }, + g, + ]), + (g.contains = [u, m, p, r, i, o, s, l, c, d, _]), + { + name: "Lisp", + illegal: /\S/, + contains: [i, e.SHEBANG(), r, o, s, u, m, p, _], + } + ); +}; +var iS = function (e) { + var t = { + className: "variable", + variants: [ + { begin: "\\b([gtps][A-Z]{1}[a-zA-Z0-9]*)(\\[.+\\])?(?:\\s*?)" }, + { begin: "\\$_[A-Z]+" }, + ], + relevance: 0, + }, + n = [ + e.C_BLOCK_COMMENT_MODE, + e.HASH_COMMENT_MODE, + e.COMMENT("--", "$"), + e.COMMENT("[^:]//", "$"), + ], + a = e.inherit(e.TITLE_MODE, { + variants: [ + { begin: "\\b_*rig[A-Z][A-Za-z0-9_\\-]*" }, + { begin: "\\b_[a-z0-9\\-]+" }, + ], + }), + r = e.inherit(e.TITLE_MODE, { begin: "\\b([A-Za-z0-9_\\-]+)\\b" }); + return { + name: "LiveCode", + case_insensitive: !1, + keywords: { + keyword: + "$_COOKIE $_FILES $_GET $_GET_BINARY $_GET_RAW $_POST $_POST_BINARY $_POST_RAW $_SESSION $_SERVER codepoint codepoints segment segments codeunit codeunits sentence sentences trueWord trueWords paragraph after byte bytes english the until http forever descending using line real8 with seventh for stdout finally element word words fourth before black ninth sixth characters chars stderr uInt1 uInt1s uInt2 uInt2s stdin string lines relative rel any fifth items from middle mid at else of catch then third it file milliseconds seconds second secs sec int1 int1s int4 int4s internet int2 int2s normal text item last long detailed effective uInt4 uInt4s repeat end repeat URL in try into switch to words https token binfile each tenth as ticks tick system real4 by dateItems without char character ascending eighth whole dateTime numeric short first ftp integer abbreviated abbr abbrev private case while if div mod wrap and or bitAnd bitNot bitOr bitXor among not in a an within contains ends with begins the keys of keys", + literal: + "SIX TEN FORMFEED NINE ZERO NONE SPACE FOUR FALSE COLON CRLF PI COMMA ENDOFFILE EOF EIGHT FIVE QUOTE EMPTY ONE TRUE RETURN CR LINEFEED RIGHT BACKSLASH NULL SEVEN TAB THREE TWO six ten formfeed nine zero none space four false colon crlf pi comma endoffile eof eight five quote empty one true return cr linefeed right backslash null seven tab three two RIVERSION RISTATE FILE_READ_MODE FILE_WRITE_MODE FILE_WRITE_MODE DIR_WRITE_MODE FILE_READ_UMASK FILE_WRITE_UMASK DIR_READ_UMASK DIR_WRITE_UMASK", + built_in: + "put abs acos aliasReference annuity arrayDecode arrayEncode asin atan atan2 average avg avgDev base64Decode base64Encode baseConvert binaryDecode binaryEncode byteOffset byteToNum cachedURL cachedURLs charToNum cipherNames codepointOffset codepointProperty codepointToNum codeunitOffset commandNames compound compress constantNames cos date dateFormat decompress difference directories diskSpace DNSServers exp exp1 exp2 exp10 extents files flushEvents folders format functionNames geometricMean global globals hasMemory harmonicMean hostAddress hostAddressToName hostName hostNameToAddress isNumber ISOToMac itemOffset keys len length libURLErrorData libUrlFormData libURLftpCommand libURLLastHTTPHeaders libURLLastRHHeaders libUrlMultipartFormAddPart libUrlMultipartFormData libURLVersion lineOffset ln ln1 localNames log log2 log10 longFilePath lower macToISO matchChunk matchText matrixMultiply max md5Digest median merge messageAuthenticationCode messageDigest millisec millisecs millisecond milliseconds min monthNames nativeCharToNum normalizeText num number numToByte numToChar numToCodepoint numToNativeChar offset open openfiles openProcesses openProcessIDs openSockets paragraphOffset paramCount param params peerAddress pendingMessages platform popStdDev populationStandardDeviation populationVariance popVariance processID random randomBytes replaceText result revCreateXMLTree revCreateXMLTreeFromFile revCurrentRecord revCurrentRecordIsFirst revCurrentRecordIsLast revDatabaseColumnCount revDatabaseColumnIsNull revDatabaseColumnLengths revDatabaseColumnNames revDatabaseColumnNamed revDatabaseColumnNumbered revDatabaseColumnTypes revDatabaseConnectResult revDatabaseCursors revDatabaseID revDatabaseTableNames revDatabaseType revDataFromQuery revdb_closeCursor revdb_columnbynumber revdb_columncount revdb_columnisnull revdb_columnlengths revdb_columnnames revdb_columntypes revdb_commit revdb_connect revdb_connections revdb_connectionerr revdb_currentrecord revdb_cursorconnection revdb_cursorerr revdb_cursors revdb_dbtype revdb_disconnect revdb_execute revdb_iseof revdb_isbof revdb_movefirst revdb_movelast revdb_movenext revdb_moveprev revdb_query revdb_querylist revdb_recordcount revdb_rollback revdb_tablenames revGetDatabaseDriverPath revNumberOfRecords revOpenDatabase revOpenDatabases revQueryDatabase revQueryDatabaseBlob revQueryResult revQueryIsAtStart revQueryIsAtEnd revUnixFromMacPath revXMLAttribute revXMLAttributes revXMLAttributeValues revXMLChildContents revXMLChildNames revXMLCreateTreeFromFileWithNamespaces revXMLCreateTreeWithNamespaces revXMLDataFromXPathQuery revXMLEvaluateXPath revXMLFirstChild revXMLMatchingNode revXMLNextSibling revXMLNodeContents revXMLNumberOfChildren revXMLParent revXMLPreviousSibling revXMLRootNode revXMLRPC_CreateRequest revXMLRPC_Documents revXMLRPC_Error revXMLRPC_GetHost revXMLRPC_GetMethod revXMLRPC_GetParam revXMLText revXMLRPC_Execute revXMLRPC_GetParamCount revXMLRPC_GetParamNode revXMLRPC_GetParamType revXMLRPC_GetPath revXMLRPC_GetPort revXMLRPC_GetProtocol revXMLRPC_GetRequest revXMLRPC_GetResponse revXMLRPC_GetSocket revXMLTree revXMLTrees revXMLValidateDTD revZipDescribeItem revZipEnumerateItems revZipOpenArchives round sampVariance sec secs seconds sentenceOffset sha1Digest shell shortFilePath sin specialFolderPath sqrt standardDeviation statRound stdDev sum sysError systemVersion tan tempName textDecode textEncode tick ticks time to tokenOffset toLower toUpper transpose truewordOffset trunc uniDecode uniEncode upper URLDecode URLEncode URLStatus uuid value variableNames variance version waitDepth weekdayNames wordOffset xsltApplyStylesheet xsltApplyStylesheetFromFile xsltLoadStylesheet xsltLoadStylesheetFromFile add breakpoint cancel clear local variable file word line folder directory URL close socket process combine constant convert create new alias folder directory decrypt delete variable word line folder directory URL dispatch divide do encrypt filter get include intersect kill libURLDownloadToFile libURLFollowHttpRedirects libURLftpUpload libURLftpUploadFile libURLresetAll libUrlSetAuthCallback libURLSetDriver libURLSetCustomHTTPHeaders libUrlSetExpect100 libURLSetFTPListCommand libURLSetFTPMode libURLSetFTPStopTime libURLSetStatusCallback load extension loadedExtensions multiply socket prepare process post seek rel relative read from process rename replace require resetAll resolve revAddXMLNode revAppendXML revCloseCursor revCloseDatabase revCommitDatabase revCopyFile revCopyFolder revCopyXMLNode revDeleteFolder revDeleteXMLNode revDeleteAllXMLTrees revDeleteXMLTree revExecuteSQL revGoURL revInsertXMLNode revMoveFolder revMoveToFirstRecord revMoveToLastRecord revMoveToNextRecord revMoveToPreviousRecord revMoveToRecord revMoveXMLNode revPutIntoXMLNode revRollBackDatabase revSetDatabaseDriverPath revSetXMLAttribute revXMLRPC_AddParam revXMLRPC_DeleteAllDocuments revXMLAddDTD revXMLRPC_Free revXMLRPC_FreeAll revXMLRPC_DeleteDocument revXMLRPC_DeleteParam revXMLRPC_SetHost revXMLRPC_SetMethod revXMLRPC_SetPort revXMLRPC_SetProtocol revXMLRPC_SetSocket revZipAddItemWithData revZipAddItemWithFile revZipAddUncompressedItemWithData revZipAddUncompressedItemWithFile revZipCancel revZipCloseArchive revZipDeleteItem revZipExtractItemToFile revZipExtractItemToVariable revZipSetProgressCallback revZipRenameItem revZipReplaceItemWithData revZipReplaceItemWithFile revZipOpenArchive send set sort split start stop subtract symmetric union unload vectorDotProduct wait write", + }, + contains: [ + t, + { className: "keyword", begin: "\\bend\\sif\\b" }, + { + className: "function", + beginKeywords: "function", + end: "$", + contains: [ + t, + r, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.BINARY_NUMBER_MODE, + e.C_NUMBER_MODE, + a, + ], + }, + { + className: "function", + begin: "\\bend\\s+", + end: "$", + keywords: "end", + contains: [r, a], + relevance: 0, + }, + { + beginKeywords: "command on", + end: "$", + contains: [ + t, + r, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.BINARY_NUMBER_MODE, + e.C_NUMBER_MODE, + a, + ], + }, + { + className: "meta", + variants: [ + { begin: "<\\?(rev|lc|livecode)", relevance: 10 }, + { begin: "<\\?" }, + { begin: "\\?>" }, + ], + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.BINARY_NUMBER_MODE, + e.C_NUMBER_MODE, + a, + ].concat(n), + illegal: ";$|^\\[|^=|&|\\{", + }; + }, + oS = [ + "as", + "in", + "of", + "if", + "for", + "while", + "finally", + "var", + "new", + "function", + "do", + "return", + "void", + "else", + "break", + "catch", + "instanceof", + "with", + "throw", + "case", + "default", + "try", + "switch", + "continue", + "typeof", + "delete", + "let", + "yield", + "const", + "class", + "debugger", + "async", + "await", + "static", + "import", + "from", + "export", + "extends", + ], + sS = ["true", "false", "null", "undefined", "NaN", "Infinity"], + lS = [].concat( + [ + "setInterval", + "setTimeout", + "clearInterval", + "clearTimeout", + "require", + "exports", + "eval", + "isFinite", + "isNaN", + "parseFloat", + "parseInt", + "decodeURI", + "decodeURIComponent", + "encodeURI", + "encodeURIComponent", + "escape", + "unescape", + ], + [ + "arguments", + "this", + "super", + "console", + "window", + "document", + "localStorage", + "module", + "global", + ], + [ + "Intl", + "DataView", + "Number", + "Math", + "Date", + "String", + "RegExp", + "Object", + "Function", + "Boolean", + "Error", + "Symbol", + "Set", + "Map", + "WeakSet", + "WeakMap", + "Proxy", + "Reflect", + "JSON", + "Promise", + "Float64Array", + "Int16Array", + "Int32Array", + "Int8Array", + "Uint16Array", + "Uint32Array", + "Float32Array", + "Array", + "Uint8Array", + "Uint8ClampedArray", + "ArrayBuffer", + "BigInt64Array", + "BigUint64Array", + "BigInt", + ], + [ + "EvalError", + "InternalError", + "RangeError", + "ReferenceError", + "SyntaxError", + "TypeError", + "URIError", + ], + ); +var cS = function (e) { + var t = { + keyword: oS.concat([ + "then", + "unless", + "until", + "loop", + "of", + "by", + "when", + "and", + "or", + "is", + "isnt", + "not", + "it", + "that", + "otherwise", + "from", + "to", + "til", + "fallthrough", + "case", + "enum", + "native", + "list", + "map", + "__hasProp", + "__extends", + "__slice", + "__bind", + "__indexOf", + ]), + literal: sS.concat(["yes", "no", "on", "off", "it", "that", "void"]), + built_in: lS.concat(["npm", "print"]), + }, + n = "[A-Za-z$_](?:-[0-9A-Za-z$_]|[0-9A-Za-z$_])*", + a = e.inherit(e.TITLE_MODE, { begin: n }), + r = { className: "subst", begin: /#\{/, end: /\}/, keywords: t }, + i = { + className: "subst", + begin: /#[A-Za-z$_]/, + end: /(?:-[0-9A-Za-z$_]|[0-9A-Za-z$_])*/, + keywords: t, + }, + o = [ + e.BINARY_NUMBER_MODE, + { + className: "number", + begin: + "(\\b0[xX][a-fA-F0-9_]+)|(\\b\\d(\\d|_\\d)*(\\.(\\d(\\d|_\\d)*)?)?(_*[eE]([-+]\\d(_\\d|\\d)*)?)?[_a-z]*)", + relevance: 0, + starts: { end: "(\\s*/)?", relevance: 0 }, + }, + { + className: "string", + variants: [ + { begin: /'''/, end: /'''/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /'/, end: /'/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /"""/, end: /"""/, contains: [e.BACKSLASH_ESCAPE, r, i] }, + { begin: /"/, end: /"/, contains: [e.BACKSLASH_ESCAPE, r, i] }, + { begin: /\\/, end: /(\s|$)/, excludeEnd: !0 }, + ], + }, + { + className: "regexp", + variants: [ + { begin: "//", end: "//[gim]*", contains: [r, e.HASH_COMMENT_MODE] }, + { begin: /\/(?![ *])(\\.|[^\\\n])*?\/[gim]*(?=\W)/ }, + ], + }, + { begin: "@" + n }, + { + begin: "``", + end: "``", + excludeBegin: !0, + excludeEnd: !0, + subLanguage: "javascript", + }, + ]; + r.contains = o; + var s = { + className: "params", + begin: "\\(", + returnBegin: !0, + contains: [ + { begin: /\(/, end: /\)/, keywords: t, contains: ["self"].concat(o) }, + ], + }; + return { + name: "LiveScript", + aliases: ["ls"], + keywords: t, + illegal: /\/\*/, + contains: o.concat([ + e.COMMENT("\\/\\*", "\\*\\/"), + e.HASH_COMMENT_MODE, + { begin: "(#=>|=>|\\|>>|-?->|!->)" }, + { + className: "function", + contains: [a, s], + returnBegin: !0, + variants: [ + { + begin: "(" + n + "\\s*(?:=|:=)\\s*)?(\\(.*\\)\\s*)?\\B->\\*?", + end: "->\\*?", + }, + { + begin: + "(" + n + "\\s*(?:=|:=)\\s*)?!?(\\(.*\\)\\s*)?\\B[-~]{1,2}>\\*?", + end: "[-~]{1,2}>\\*?", + }, + { + begin: + "(" + n + "\\s*(?:=|:=)\\s*)?(\\(.*\\)\\s*)?\\B!?[-~]{1,2}>\\*?", + end: "!?[-~]{1,2}>\\*?", + }, + ], + }, + { + className: "class", + beginKeywords: "class", + end: "$", + illegal: /[:="\[\]]/, + contains: [ + { + beginKeywords: "extends", + endsWithParent: !0, + illegal: /[:="\[\]]/, + contains: [a], + }, + a, + ], + }, + { + begin: n + ":", + end: ":", + returnBegin: !0, + returnEnd: !0, + relevance: 0, + }, + ]), + }; +}; +function _S(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function dS() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return _S(e); + }) + .join(""); + return a; +} +var uS = function (e) { + var t = /([-a-zA-Z$._][\w$.-]*)/, + n = { + className: "variable", + variants: [{ begin: dS(/%/, t) }, { begin: /%\d+/ }, { begin: /#\d+/ }], + }, + a = { + className: "title", + variants: [ + { begin: dS(/@/, t) }, + { begin: /@\d+/ }, + { begin: dS(/!/, t) }, + { begin: dS(/!\d+/, t) }, + { begin: /!\d+/ }, + ], + }; + return { + name: "LLVM IR", + keywords: + "begin end true false declare define global constant private linker_private internal available_externally linkonce linkonce_odr weak weak_odr appending dllimport dllexport common default hidden protected extern_weak external thread_local zeroinitializer undef null to tail target triple datalayout volatile nuw nsw nnan ninf nsz arcp fast exact inbounds align addrspace section alias module asm sideeffect gc dbg linker_private_weak attributes blockaddress initialexec localdynamic localexec prefix unnamed_addr ccc fastcc coldcc x86_stdcallcc x86_fastcallcc arm_apcscc arm_aapcscc arm_aapcs_vfpcc ptx_device ptx_kernel intel_ocl_bicc msp430_intrcc spir_func spir_kernel x86_64_sysvcc x86_64_win64cc x86_thiscallcc cc c signext zeroext inreg sret nounwind noreturn noalias nocapture byval nest readnone readonly inlinehint noinline alwaysinline optsize ssp sspreq noredzone noimplicitfloat naked builtin cold nobuiltin noduplicate nonlazybind optnone returns_twice sanitize_address sanitize_memory sanitize_thread sspstrong uwtable returned type opaque eq ne slt sgt sle sge ult ugt ule uge oeq one olt ogt ole oge ord uno ueq une x acq_rel acquire alignstack atomic catch cleanup filter inteldialect max min monotonic nand personality release seq_cst singlethread umax umin unordered xchg add fadd sub fsub mul fmul udiv sdiv fdiv urem srem frem shl lshr ashr and or xor icmp fcmp phi call trunc zext sext fptrunc fpext uitofp sitofp fptoui fptosi inttoptr ptrtoint bitcast addrspacecast select va_arg ret br switch invoke unwind unreachable indirectbr landingpad resume malloc alloca free load store getelementptr extractelement insertelement shufflevector getresult extractvalue insertvalue atomicrmw cmpxchg fence argmemonly double", + contains: [ + { className: "type", begin: /\bi\d+(?=\s|\b)/ }, + e.COMMENT(/;\s*$/, null, { relevance: 0 }), + e.COMMENT(/;/, /$/), + e.QUOTE_STRING_MODE, + { className: "string", variants: [{ begin: /"/, end: /[^\\]"/ }] }, + a, + { className: "punctuation", relevance: 0, begin: /,/ }, + { className: "operator", relevance: 0, begin: /=/ }, + n, + { + className: "symbol", + variants: [{ begin: /^\s*[a-z]+:/ }], + relevance: 0, + }, + { + className: "number", + variants: [ + { begin: /0[xX][a-fA-F0-9]+/ }, + { begin: /-?\d+(?:[.]\d+)?(?:[eE][-+]?\d+(?:[.]\d+)?)?/ }, + ], + relevance: 0, + }, + ], + }; +}; +var mS = function (e) { + var t = { + className: "string", + begin: '"', + end: '"', + contains: [{ className: "subst", begin: /\\[tn"\\]/ }], + }, + n = { className: "number", relevance: 0, begin: e.C_NUMBER_RE }; + return { + name: "LSL (Linden Scripting Language)", + illegal: ":", + contains: [ + t, + { + className: "comment", + variants: [e.COMMENT("//", "$"), e.COMMENT("/\\*", "\\*/")], + relevance: 0, + }, + n, + { + className: "section", + variants: [ + { begin: "\\b(state|default)\\b" }, + { + begin: + "\\b(state_(entry|exit)|touch(_(start|end))?|(land_)?collision(_(start|end))?|timer|listen|(no_)?sensor|control|(not_)?at_(rot_)?target|money|email|experience_permissions(_denied)?|run_time_permissions|changed|attach|dataserver|moving_(start|end)|link_message|(on|object)_rez|remote_data|http_re(sponse|quest)|path_update|transaction_result)\\b", + }, + ], + }, + { + className: "built_in", + begin: + "\\b(ll(AgentInExperience|(Create|DataSize|Delete|KeyCount|Keys|Read|Update)KeyValue|GetExperience(Details|ErrorMessage)|ReturnObjectsBy(ID|Owner)|Json(2List|[GS]etValue|ValueType)|Sin|Cos|Tan|Atan2|Sqrt|Pow|Abs|Fabs|Frand|Floor|Ceil|Round|Vec(Mag|Norm|Dist)|Rot(Between|2(Euler|Fwd|Left|Up))|(Euler|Axes)2Rot|Whisper|(Region|Owner)?Say|Shout|Listen(Control|Remove)?|Sensor(Repeat|Remove)?|Detected(Name|Key|Owner|Type|Pos|Vel|Grab|Rot|Group|LinkNumber)|Die|Ground|Wind|([GS]et)(AnimationOverride|MemoryLimit|PrimMediaParams|ParcelMusicURL|Object(Desc|Name)|PhysicsMaterial|Status|Scale|Color|Alpha|Texture|Pos|Rot|Force|Torque)|ResetAnimationOverride|(Scale|Offset|Rotate)Texture|(Rot)?Target(Remove)?|(Stop)?MoveToTarget|Apply(Rotational)?Impulse|Set(KeyframedMotion|ContentType|RegionPos|(Angular)?Velocity|Buoyancy|HoverHeight|ForceAndTorque|TimerEvent|ScriptState|Damage|TextureAnim|Sound(Queueing|Radius)|Vehicle(Type|(Float|Vector|Rotation)Param)|(Touch|Sit)?Text|Camera(Eye|At)Offset|PrimitiveParams|ClickAction|Link(Alpha|Color|PrimitiveParams(Fast)?|Texture(Anim)?|Camera|Media)|RemoteScriptAccessPin|PayPrice|LocalRot)|ScaleByFactor|Get((Max|Min)ScaleFactor|ClosestNavPoint|StaticPath|SimStats|Env|PrimitiveParams|Link(PrimitiveParams|Number(OfSides)?|Key|Name|Media)|HTTPHeader|FreeURLs|Object(Details|PermMask|PrimCount)|Parcel(MaxPrims|Details|Prim(Count|Owners))|Attached(List)?|(SPMax|Free|Used)Memory|Region(Name|TimeDilation|FPS|Corner|AgentCount)|Root(Position|Rotation)|UnixTime|(Parcel|Region)Flags|(Wall|GMT)clock|SimulatorHostname|BoundingBox|GeometricCenter|Creator|NumberOf(Prims|NotecardLines|Sides)|Animation(List)?|(Camera|Local)(Pos|Rot)|Vel|Accel|Omega|Time(stamp|OfDay)|(Object|CenterOf)?Mass|MassMKS|Energy|Owner|(Owner)?Key|SunDirection|Texture(Offset|Scale|Rot)|Inventory(Number|Name|Key|Type|Creator|PermMask)|Permissions(Key)?|StartParameter|List(Length|EntryType)|Date|Agent(Size|Info|Language|List)|LandOwnerAt|NotecardLine|Script(Name|State))|(Get|Reset|GetAndReset)Time|PlaySound(Slave)?|LoopSound(Master|Slave)?|(Trigger|Stop|Preload)Sound|((Get|Delete)Sub|Insert)String|To(Upper|Lower)|Give(InventoryList|Money)|RezObject|(Stop)?LookAt|Sleep|CollisionFilter|(Take|Release)Controls|DetachFromAvatar|AttachToAvatar(Temp)?|InstantMessage|(GetNext)?Email|StopHover|MinEventDelay|RotLookAt|String(Length|Trim)|(Start|Stop)Animation|TargetOmega|Request(Experience)?Permissions|(Create|Break)Link|BreakAllLinks|(Give|Remove)Inventory|Water|PassTouches|Request(Agent|Inventory)Data|TeleportAgent(Home|GlobalCoords)?|ModifyLand|CollisionSound|ResetScript|MessageLinked|PushObject|PassCollisions|AxisAngle2Rot|Rot2(Axis|Angle)|A(cos|sin)|AngleBetween|AllowInventoryDrop|SubStringIndex|List2(CSV|Integer|Json|Float|String|Key|Vector|Rot|List(Strided)?)|DeleteSubList|List(Statistics|Sort|Randomize|(Insert|Find|Replace)List)|EdgeOfWorld|AdjustSoundVolume|Key2Name|TriggerSoundLimited|EjectFromLand|(CSV|ParseString)2List|OverMyLand|SameGroup|UnSit|Ground(Slope|Normal|Contour)|GroundRepel|(Set|Remove)VehicleFlags|SitOnLink|(AvatarOn)?(Link)?SitTarget|Script(Danger|Profiler)|Dialog|VolumeDetect|ResetOtherScript|RemoteLoadScriptPin|(Open|Close)RemoteDataChannel|SendRemoteData|RemoteDataReply|(Integer|String)ToBase64|XorBase64|Log(10)?|Base64To(String|Integer)|ParseStringKeepNulls|RezAtRoot|RequestSimulatorData|ForceMouselook|(Load|Release|(E|Une)scape)URL|ParcelMedia(CommandList|Query)|ModPow|MapDestination|(RemoveFrom|AddTo|Reset)Land(Pass|Ban)List|(Set|Clear)CameraParams|HTTP(Request|Response)|TextBox|DetectedTouch(UV|Face|Pos|(N|Bin)ormal|ST)|(MD5|SHA1|DumpList2)String|Request(Secure)?URL|Clear(Prim|Link)Media|(Link)?ParticleSystem|(Get|Request)(Username|DisplayName)|RegionSayTo|CastRay|GenerateKey|TransferLindenDollars|ManageEstateAccess|(Create|Delete)Character|ExecCharacterCmd|Evade|FleeFrom|NavigateTo|PatrolPoints|Pursue|UpdateCharacter|WanderWithin))\\b", + }, + { + className: "literal", + variants: [ + { begin: "\\b(PI|TWO_PI|PI_BY_TWO|DEG_TO_RAD|RAD_TO_DEG|SQRT2)\\b" }, + { + begin: + "\\b(XP_ERROR_(EXPERIENCES_DISABLED|EXPERIENCE_(DISABLED|SUSPENDED)|INVALID_(EXPERIENCE|PARAMETERS)|KEY_NOT_FOUND|MATURITY_EXCEEDED|NONE|NOT_(FOUND|PERMITTED(_LAND)?)|NO_EXPERIENCE|QUOTA_EXCEEDED|RETRY_UPDATE|STORAGE_EXCEPTION|STORE_DISABLED|THROTTLED|UNKNOWN_ERROR)|JSON_APPEND|STATUS_(PHYSICS|ROTATE_[XYZ]|PHANTOM|SANDBOX|BLOCK_GRAB(_OBJECT)?|(DIE|RETURN)_AT_EDGE|CAST_SHADOWS|OK|MALFORMED_PARAMS|TYPE_MISMATCH|BOUNDS_ERROR|NOT_(FOUND|SUPPORTED)|INTERNAL_ERROR|WHITELIST_FAILED)|AGENT(_(BY_(LEGACY_|USER)NAME|FLYING|ATTACHMENTS|SCRIPTED|MOUSELOOK|SITTING|ON_OBJECT|AWAY|WALKING|IN_AIR|TYPING|CROUCHING|BUSY|ALWAYS_RUN|AUTOPILOT|LIST_(PARCEL(_OWNER)?|REGION)))?|CAMERA_(PITCH|DISTANCE|BEHINDNESS_(ANGLE|LAG)|(FOCUS|POSITION)(_(THRESHOLD|LOCKED|LAG))?|FOCUS_OFFSET|ACTIVE)|ANIM_ON|LOOP|REVERSE|PING_PONG|SMOOTH|ROTATE|SCALE|ALL_SIDES|LINK_(ROOT|SET|ALL_(OTHERS|CHILDREN)|THIS)|ACTIVE|PASS(IVE|_(ALWAYS|IF_NOT_HANDLED|NEVER))|SCRIPTED|CONTROL_(FWD|BACK|(ROT_)?(LEFT|RIGHT)|UP|DOWN|(ML_)?LBUTTON)|PERMISSION_(RETURN_OBJECTS|DEBIT|OVERRIDE_ANIMATIONS|SILENT_ESTATE_MANAGEMENT|TAKE_CONTROLS|TRIGGER_ANIMATION|ATTACH|CHANGE_LINKS|(CONTROL|TRACK)_CAMERA|TELEPORT)|INVENTORY_(TEXTURE|SOUND|OBJECT|SCRIPT|LANDMARK|CLOTHING|NOTECARD|BODYPART|ANIMATION|GESTURE|ALL|NONE)|CHANGED_(INVENTORY|COLOR|SHAPE|SCALE|TEXTURE|LINK|ALLOWED_DROP|OWNER|REGION(_START)?|TELEPORT|MEDIA)|OBJECT_(CLICK_ACTION|HOVER_HEIGHT|LAST_OWNER_ID|(PHYSICS|SERVER|STREAMING)_COST|UNKNOWN_DETAIL|CHARACTER_TIME|PHANTOM|PHYSICS|TEMP_(ATTACHED|ON_REZ)|NAME|DESC|POS|PRIM_(COUNT|EQUIVALENCE)|RETURN_(PARCEL(_OWNER)?|REGION)|REZZER_KEY|ROO?T|VELOCITY|OMEGA|OWNER|GROUP(_TAG)?|CREATOR|ATTACHED_(POINT|SLOTS_AVAILABLE)|RENDER_WEIGHT|(BODY_SHAPE|PATHFINDING)_TYPE|(RUNNING|TOTAL)_SCRIPT_COUNT|TOTAL_INVENTORY_COUNT|SCRIPT_(MEMORY|TIME))|TYPE_(INTEGER|FLOAT|STRING|KEY|VECTOR|ROTATION|INVALID)|(DEBUG|PUBLIC)_CHANNEL|ATTACH_(AVATAR_CENTER|CHEST|HEAD|BACK|PELVIS|MOUTH|CHIN|NECK|NOSE|BELLY|[LR](SHOULDER|HAND|FOOT|EAR|EYE|[UL](ARM|LEG)|HIP)|(LEFT|RIGHT)_PEC|HUD_(CENTER_[12]|TOP_(RIGHT|CENTER|LEFT)|BOTTOM(_(RIGHT|LEFT))?)|[LR]HAND_RING1|TAIL_(BASE|TIP)|[LR]WING|FACE_(JAW|[LR]EAR|[LR]EYE|TOUNGE)|GROIN|HIND_[LR]FOOT)|LAND_(LEVEL|RAISE|LOWER|SMOOTH|NOISE|REVERT)|DATA_(ONLINE|NAME|BORN|SIM_(POS|STATUS|RATING)|PAYINFO)|PAYMENT_INFO_(ON_FILE|USED)|REMOTE_DATA_(CHANNEL|REQUEST|REPLY)|PSYS_(PART_(BF_(ZERO|ONE(_MINUS_(DEST_COLOR|SOURCE_(ALPHA|COLOR)))?|DEST_COLOR|SOURCE_(ALPHA|COLOR))|BLEND_FUNC_(DEST|SOURCE)|FLAGS|(START|END)_(COLOR|ALPHA|SCALE|GLOW)|MAX_AGE|(RIBBON|WIND|INTERP_(COLOR|SCALE)|BOUNCE|FOLLOW_(SRC|VELOCITY)|TARGET_(POS|LINEAR)|EMISSIVE)_MASK)|SRC_(MAX_AGE|PATTERN|ANGLE_(BEGIN|END)|BURST_(RATE|PART_COUNT|RADIUS|SPEED_(MIN|MAX))|ACCEL|TEXTURE|TARGET_KEY|OMEGA|PATTERN_(DROP|EXPLODE|ANGLE(_CONE(_EMPTY)?)?)))|VEHICLE_(REFERENCE_FRAME|TYPE_(NONE|SLED|CAR|BOAT|AIRPLANE|BALLOON)|(LINEAR|ANGULAR)_(FRICTION_TIMESCALE|MOTOR_DIRECTION)|LINEAR_MOTOR_OFFSET|HOVER_(HEIGHT|EFFICIENCY|TIMESCALE)|BUOYANCY|(LINEAR|ANGULAR)_(DEFLECTION_(EFFICIENCY|TIMESCALE)|MOTOR_(DECAY_)?TIMESCALE)|VERTICAL_ATTRACTION_(EFFICIENCY|TIMESCALE)|BANKING_(EFFICIENCY|MIX|TIMESCALE)|FLAG_(NO_DEFLECTION_UP|LIMIT_(ROLL_ONLY|MOTOR_UP)|HOVER_((WATER|TERRAIN|UP)_ONLY|GLOBAL_HEIGHT)|MOUSELOOK_(STEER|BANK)|CAMERA_DECOUPLED))|PRIM_(ALLOW_UNSIT|ALPHA_MODE(_(BLEND|EMISSIVE|MASK|NONE))?|NORMAL|SPECULAR|TYPE(_(BOX|CYLINDER|PRISM|SPHERE|TORUS|TUBE|RING|SCULPT))?|HOLE_(DEFAULT|CIRCLE|SQUARE|TRIANGLE)|MATERIAL(_(STONE|METAL|GLASS|WOOD|FLESH|PLASTIC|RUBBER))?|SHINY_(NONE|LOW|MEDIUM|HIGH)|BUMP_(NONE|BRIGHT|DARK|WOOD|BARK|BRICKS|CHECKER|CONCRETE|TILE|STONE|DISKS|GRAVEL|BLOBS|SIDING|LARGETILE|STUCCO|SUCTION|WEAVE)|TEXGEN_(DEFAULT|PLANAR)|SCRIPTED_SIT_ONLY|SCULPT_(TYPE_(SPHERE|TORUS|PLANE|CYLINDER|MASK)|FLAG_(MIRROR|INVERT))|PHYSICS(_(SHAPE_(CONVEX|NONE|PRIM|TYPE)))?|(POS|ROT)_LOCAL|SLICE|TEXT|FLEXIBLE|POINT_LIGHT|TEMP_ON_REZ|PHANTOM|POSITION|SIT_TARGET|SIZE|ROTATION|TEXTURE|NAME|OMEGA|DESC|LINK_TARGET|COLOR|BUMP_SHINY|FULLBRIGHT|TEXGEN|GLOW|MEDIA_(ALT_IMAGE_ENABLE|CONTROLS|(CURRENT|HOME)_URL|AUTO_(LOOP|PLAY|SCALE|ZOOM)|FIRST_CLICK_INTERACT|(WIDTH|HEIGHT)_PIXELS|WHITELIST(_ENABLE)?|PERMS_(INTERACT|CONTROL)|PARAM_MAX|CONTROLS_(STANDARD|MINI)|PERM_(NONE|OWNER|GROUP|ANYONE)|MAX_(URL_LENGTH|WHITELIST_(SIZE|COUNT)|(WIDTH|HEIGHT)_PIXELS)))|MASK_(BASE|OWNER|GROUP|EVERYONE|NEXT)|PERM_(TRANSFER|MODIFY|COPY|MOVE|ALL)|PARCEL_(MEDIA_COMMAND_(STOP|PAUSE|PLAY|LOOP|TEXTURE|URL|TIME|AGENT|UNLOAD|AUTO_ALIGN|TYPE|SIZE|DESC|LOOP_SET)|FLAG_(ALLOW_(FLY|(GROUP_)?SCRIPTS|LANDMARK|TERRAFORM|DAMAGE|CREATE_(GROUP_)?OBJECTS)|USE_(ACCESS_(GROUP|LIST)|BAN_LIST|LAND_PASS_LIST)|LOCAL_SOUND_ONLY|RESTRICT_PUSHOBJECT|ALLOW_(GROUP|ALL)_OBJECT_ENTRY)|COUNT_(TOTAL|OWNER|GROUP|OTHER|SELECTED|TEMP)|DETAILS_(NAME|DESC|OWNER|GROUP|AREA|ID|SEE_AVATARS))|LIST_STAT_(MAX|MIN|MEAN|MEDIAN|STD_DEV|SUM(_SQUARES)?|NUM_COUNT|GEOMETRIC_MEAN|RANGE)|PAY_(HIDE|DEFAULT)|REGION_FLAG_(ALLOW_DAMAGE|FIXED_SUN|BLOCK_TERRAFORM|SANDBOX|DISABLE_(COLLISIONS|PHYSICS)|BLOCK_FLY|ALLOW_DIRECT_TELEPORT|RESTRICT_PUSH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+ }, + { begin: "\\b(FALSE|TRUE)\\b" }, + { begin: "\\b(ZERO_ROTATION)\\b" }, + { + begin: + "\\b(EOF|JSON_(ARRAY|DELETE|FALSE|INVALID|NULL|NUMBER|OBJECT|STRING|TRUE)|NULL_KEY|TEXTURE_(BLANK|DEFAULT|MEDIA|PLYWOOD|TRANSPARENT)|URL_REQUEST_(GRANTED|DENIED))\\b", + }, + { begin: "\\b(ZERO_VECTOR|TOUCH_INVALID_(TEXCOORD|VECTOR))\\b" }, + ], + }, + { + className: "type", + begin: + "\\b(integer|float|string|key|vector|quaternion|rotation|list)\\b", + }, + ], + }; +}; +var pS = function (e) { + var t = "\\[=*\\[", + n = "\\]=*\\]", + a = { begin: t, end: n, contains: ["self"] }, + r = [ + e.COMMENT("--(?!\\[=*\\[)", "$"), + e.COMMENT("--\\[=*\\[", n, { contains: [a], relevance: 10 }), + ]; + return { + name: "Lua", + keywords: { + $pattern: e.UNDERSCORE_IDENT_RE, + literal: "true false nil", + keyword: + "and break do else elseif end for goto if in local not or repeat return then until while", + built_in: + "_G _ENV _VERSION __index __newindex __mode __call __metatable __tostring __len __gc 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"0'.\\|0[box][0-9a-fA-F]*" }, + e.NUMBER_MODE, + n, + a, + { begin: /:-/ }, + { begin: /\.$/ }, + ], + } + ); +}; +var hS = function (e) { + return { + name: "MIPS Assembly", + case_insensitive: !0, + aliases: ["mips"], + keywords: { + $pattern: "\\.?" + e.IDENT_RE, + meta: ".2byte .4byte .align .ascii .asciz .balign .byte .code .data .else .end .endif .endm .endr .equ .err .exitm .extern .global .hword .if .ifdef .ifndef .include .irp .long .macro .rept .req .section .set .skip .space .text .word .ltorg ", + built_in: + "$0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10 $11 $12 $13 $14 $15 $16 $17 $18 $19 $20 $21 $22 $23 $24 $25 $26 $27 $28 $29 $30 $31 zero at v0 v1 a0 a1 a2 a3 a4 a5 a6 a7 t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 s0 s1 s2 s3 s4 s5 s6 s7 s8 k0 k1 gp sp fp ra $f0 $f1 $f2 $f2 $f4 $f5 $f6 $f7 $f8 $f9 $f10 $f11 $f12 $f13 $f14 $f15 $f16 $f17 $f18 $f19 $f20 $f21 $f22 $f23 $f24 $f25 $f26 $f27 $f28 $f29 $f30 $f31 Context Random EntryLo0 EntryLo1 Context PageMask Wired EntryHi HWREna BadVAddr Count Compare SR IntCtl SRSCtl SRSMap Cause EPC PRId EBase Config Config1 Config2 Config3 LLAddr Debug DEPC DESAVE CacheErr ECC ErrorEPC TagLo DataLo TagHi DataHi WatchLo WatchHi PerfCtl PerfCnt ", + }, + contains: [ + { + className: "keyword", + begin: + "\\b(addi?u?|andi?|b(al)?|beql?|bgez(al)?l?|bgtzl?|blezl?|bltz(al)?l?|bnel?|cl[oz]|divu?|ext|ins|j(al)?|jalr(\\.hb)?|jr(\\.hb)?|lbu?|lhu?|ll|lui|lw[lr]?|maddu?|mfhi|mflo|movn|movz|move|msubu?|mthi|mtlo|mul|multu?|nop|nor|ori?|rotrv?|sb|sc|se[bh]|sh|sllv?|slti?u?|srav?|srlv?|subu?|sw[lr]?|xori?|wsbh|abs\\.[sd]|add\\.[sd]|alnv.ps|bc1[ft]l?|c\\.(s?f|un|u?eq|[ou]lt|[ou]le|ngle?|seq|l[et]|ng[et])\\.[sd]|(ceil|floor|round|trunc)\\.[lw]\\.[sd]|cfc1|cvt\\.d\\.[lsw]|cvt\\.l\\.[dsw]|cvt\\.ps\\.s|cvt\\.s\\.[dlw]|cvt\\.s\\.p[lu]|cvt\\.w\\.[dls]|div\\.[ds]|ldx?c1|luxc1|lwx?c1|madd\\.[sd]|mfc1|mov[fntz]?\\.[ds]|msub\\.[sd]|mth?c1|mul\\.[ds]|neg\\.[ds]|nmadd\\.[ds]|nmsub\\.[ds]|p[lu][lu]\\.ps|recip\\.fmt|r?sqrt\\.[ds]|sdx?c1|sub\\.[ds]|suxc1|swx?c1|break|cache|d?eret|[de]i|ehb|mfc0|mtc0|pause|prefx?|rdhwr|rdpgpr|sdbbp|ssnop|synci?|syscall|teqi?|tgei?u?|tlb(p|r|w[ir])|tlti?u?|tnei?|wait|wrpgpr)", + end: "\\s", + }, + e.COMMENT("[;#](?!\\s*$)", "$"), + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + { className: "string", begin: "'", end: "[^\\\\]'", relevance: 0 }, + { + className: "title", + begin: "\\|", + end: "\\|", + illegal: "\\n", + relevance: 0, + }, + { + className: "number", + variants: [{ begin: "0x[0-9a-f]+" }, { begin: "\\b-?\\d+" }], + relevance: 0, + }, + { + className: "symbol", + variants: [ + { begin: "^\\s*[a-z_\\.\\$][a-z0-9_\\.\\$]+:" }, + { begin: "^\\s*[0-9]+:" }, + { begin: "[0-9]+[bf]" }, + ], + relevance: 0, + }, + ], + illegal: /\//, + }; +}; +var yS = function (e) { + return { + name: "Mizar", + keywords: + "environ vocabularies notations constructors definitions registrations theorems schemes requirements begin end definition registration cluster existence pred func defpred deffunc theorem proof let take assume then thus hence ex for st holds consider reconsider such that and in provided of as from be being by means equals implies iff redefine define now not or attr is mode suppose per cases set thesis contradiction scheme reserve struct correctness compatibility coherence symmetry assymetry reflexivity irreflexivity connectedness uniqueness commutativity idempotence involutiveness projectivity", + contains: [e.COMMENT("::", "$")], + }; +}; +function IS(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function AS() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return IS(e); + }) + .join(""); + return a; +} +function DS() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return IS(e); + }) + .join("|") + + ")"; + return a; +} +var MS = function (e) { + var t = /[dualxmsipngr]{0,12}/, + n = { + $pattern: /[\w.]+/, + keyword: [ + "abs", + "accept", + "alarm", + "and", + "atan2", + "bind", + "binmode", + "bless", + "break", + "caller", + "chdir", + "chmod", + "chomp", + "chop", + "chown", + "chr", + "chroot", + "close", + "closedir", + "connect", + "continue", + "cos", + "crypt", + "dbmclose", + "dbmopen", + "defined", + "delete", + "die", + "do", + "dump", + "each", + "else", + "elsif", + "endgrent", + "endhostent", + "endnetent", + "endprotoent", + "endpwent", + "endservent", + "eof", + "eval", + "exec", + "exists", + "exit", + "exp", + "fcntl", + "fileno", + "flock", + "for", + "foreach", + "fork", + "format", + "formline", + "getc", + "getgrent", + "getgrgid", + "getgrnam", + "gethostbyaddr", + "gethostbyname", + "gethostent", + "getlogin", + "getnetbyaddr", + "getnetbyname", + "getnetent", + "getpeername", + "getpgrp", + "getpriority", + "getprotobyname", + "getprotobynumber", + "getprotoent", + "getpwent", + "getpwnam", + "getpwuid", + "getservbyname", + "getservbyport", + "getservent", + "getsockname", + "getsockopt", + "given", + "glob", + "gmtime", + "goto", + "grep", + "gt", + "hex", + "if", + "index", + "int", + "ioctl", + "join", + "keys", + "kill", + "last", + "lc", + "lcfirst", + "length", + "link", + "listen", + "local", + "localtime", + "log", + "lstat", + "lt", + "ma", + "map", + "mkdir", + "msgctl", + "msgget", + "msgrcv", + "msgsnd", + "my", + "ne", + "next", + "no", + "not", + "oct", + "open", + "opendir", + "or", + "ord", + "our", + "pack", + "package", + "pipe", + "pop", + "pos", + "print", + "printf", + "prototype", + "push", + "q|0", + "qq", + "quotemeta", + "qw", + "qx", + "rand", + "read", + "readdir", + "readline", + "readlink", + "readpipe", + "recv", + "redo", + "ref", + "rename", + "require", + "reset", + "return", + "reverse", + "rewinddir", + "rindex", + "rmdir", + "say", + "scalar", + "seek", + "seekdir", + "select", + "semctl", + "semget", + "semop", + "send", + "setgrent", + "sethostent", + "setnetent", + "setpgrp", + "setpriority", + "setprotoent", + "setpwent", + "setservent", + "setsockopt", + "shift", + "shmctl", + "shmget", + "shmread", + "shmwrite", + "shutdown", + "sin", + "sleep", + "socket", + "socketpair", + "sort", + "splice", + "split", + "sprintf", + "sqrt", + "srand", + "stat", + "state", + "study", + "sub", + "substr", + "symlink", + "syscall", + "sysopen", + "sysread", + "sysseek", + "system", + "syswrite", + "tell", + "telldir", + "tie", + "tied", + "time", + "times", + "tr", + "truncate", + "uc", + "ucfirst", + "umask", + "undef", + "unless", + "unlink", + "unpack", + "unshift", + "untie", + "until", + "use", + "utime", + "values", + "vec", + "wait", + "waitpid", + "wantarray", + "warn", + "when", + "while", + "write", + "x|0", + "xor", + "y|0", + ].join(" "), + }, + a = { className: "subst", begin: "[$@]\\{", end: "\\}", keywords: n }, + r = { begin: /->\{/, end: /\}/ }, + i = { + variants: [ + { begin: /\$\d/ }, + { + begin: AS( + /[$%@](\^\w\b|#\w+(::\w+)*|\{\w+\}|\w+(::\w*)*)/, + "(?![A-Za-z])(?![@$%])", + ), + }, + { begin: /[$%@][^\s\w{]/, relevance: 0 }, + ], + }, + o = [e.BACKSLASH_ESCAPE, a, i], + s = [/!/, /\//, /\|/, /\?/, /'/, /"/, /#/], + l = function (e, n) { + var a = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "\\1", + r = "\\1" === a ? a : AS(a, n); + return AS( + AS("(?:", e, ")"), + n, + /(?:\\.|[^\\\/])*?/, + r, + /(?:\\.|[^\\\/])*?/, + a, + t, + ); + }, + c = function (e, n, a) { + return AS(AS("(?:", e, ")"), n, /(?:\\.|[^\\\/])*?/, a, t); + }, + _ = [ + i, + e.HASH_COMMENT_MODE, + e.COMMENT(/^=\w/, /=cut/, { endsWithParent: !0 }), + r, + { + className: "string", + contains: o, + variants: [ + { begin: "q[qwxr]?\\s*\\(", end: "\\)", relevance: 5 }, + { begin: "q[qwxr]?\\s*\\[", end: "\\]", relevance: 5 }, + { begin: "q[qwxr]?\\s*\\{", end: "\\}", relevance: 5 }, + { begin: "q[qwxr]?\\s*\\|", end: "\\|", relevance: 5 }, + { begin: "q[qwxr]?\\s*<", end: ">", relevance: 5 }, + { begin: "qw\\s+q", end: "q", relevance: 5 }, + { begin: "'", end: "'", contains: [e.BACKSLASH_ESCAPE] }, + { begin: '"', end: '"' }, + { begin: "`", end: "`", contains: [e.BACKSLASH_ESCAPE] }, + { begin: /\{\w+\}/, relevance: 0 }, + { begin: "-?\\w+\\s*=>", relevance: 0 }, + ], + }, + { + className: "number", + begin: + "(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b", + relevance: 0, + }, + { + begin: + "(\\/\\/|" + + e.RE_STARTERS_RE + + "|\\b(split|return|print|reverse|grep)\\b)\\s*", + keywords: "split return print reverse grep", + relevance: 0, + contains: [ + e.HASH_COMMENT_MODE, + { + className: "regexp", + variants: [ + { begin: l("s|tr|y", DS.apply(void 0, s)) }, + { begin: l("s|tr|y", "\\(", "\\)") }, + { begin: l("s|tr|y", "\\[", "\\]") }, + { begin: l("s|tr|y", "\\{", "\\}") }, + ], + relevance: 2, + }, + { + className: "regexp", + variants: [ + { begin: /(m|qr)\/\//, relevance: 0 }, + { begin: c("(?:m|qr)?", /\//, /\//) }, + { begin: c("m|qr", DS.apply(void 0, s), /\1/) }, + { begin: c("m|qr", /\(/, /\)/) }, + { begin: c("m|qr", /\[/, /\]/) }, + { begin: c("m|qr", /\{/, /\}/) }, + ], + }, + ], + }, + { + className: "function", + beginKeywords: "sub", + end: "(\\s*\\(.*?\\))?[;{]", + excludeEnd: !0, + relevance: 5, + contains: [e.TITLE_MODE], + }, + { begin: "-\\w\\b", relevance: 0 }, + { + begin: "^__DATA__$", + end: "^__END__$", + subLanguage: "mojolicious", + contains: [{ begin: "^@@.*", end: "$", className: "comment" }], + }, + ]; + return ( + (a.contains = _), + (r.contains = _), + { name: "Perl", aliases: ["pl", "pm"], keywords: n, contains: _ } + ); +}; +var LS = function (e) { + return { + name: "Mojolicious", + subLanguage: "xml", + contains: [ + { className: "meta", begin: "^__(END|DATA)__$" }, + { begin: "^\\s*%{1,2}={0,2}", end: "$", subLanguage: "perl" }, + { + begin: "<%{1,2}={0,2}", + end: "={0,1}%>", + subLanguage: "perl", + excludeBegin: !0, + excludeEnd: !0, + }, + ], + }; +}; +var wS = function (e) { + var t = { + className: "number", + relevance: 0, + variants: [{ begin: "[$][a-fA-F0-9]+" }, e.NUMBER_MODE], + }; + return { + name: "Monkey", + case_insensitive: !0, + keywords: { + keyword: + "public private property continue exit extern new try catch eachin not abstract final select case default const local global field end if then else elseif endif while wend repeat until forever for to step next return module inline throw import", + built_in: + "DebugLog DebugStop Error Print ACos ACosr ASin ASinr ATan ATan2 ATan2r ATanr Abs Abs Ceil Clamp Clamp Cos Cosr Exp Floor Log Max Max Min Min Pow Sgn Sgn Sin Sinr Sqrt Tan Tanr Seed PI HALFPI TWOPI", + literal: "true false null and or shl shr mod", + }, + illegal: /\/\*/, + contains: [ + e.COMMENT("#rem", "#end"), + e.COMMENT("'", "$", { relevance: 0 }), + { + className: "function", + beginKeywords: "function method", + end: "[(=:]|$", + illegal: /\n/, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "class", + beginKeywords: "class interface", + end: "$", + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { className: "built_in", begin: "\\b(self|super)\\b" }, + { + className: "meta", + begin: "\\s*#", + end: "$", + keywords: { "meta-keyword": "if else elseif endif end then" }, + }, + { className: "meta", begin: "^\\s*strict\\b" }, + { beginKeywords: "alias", end: "=", contains: [e.UNDERSCORE_TITLE_MODE] }, + e.QUOTE_STRING_MODE, + t, + ], + }; +}; +var xS = function (e) { + var t = { + keyword: + "if then not for in while do return else elseif break continue switch and or unless when class extends super local import export from using", + literal: "true false nil", + built_in: + "_G _VERSION assert collectgarbage dofile error getfenv getmetatable ipairs load loadfile loadstring module next pairs pcall print rawequal rawget rawset require select setfenv setmetatable tonumber tostring type unpack xpcall coroutine debug io math os package string table", + }, + n = "[A-Za-z$_][0-9A-Za-z$_]*", + a = { className: "subst", begin: /#\{/, end: /\}/, keywords: t }, + r = [ + e.inherit(e.C_NUMBER_MODE, { starts: { end: "(\\s*/)?", relevance: 0 } }), + { + className: "string", + variants: [ + { begin: /'/, end: /'/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /"/, end: /"/, contains: [e.BACKSLASH_ESCAPE, a] }, + ], + }, + { className: "built_in", begin: "@__" + e.IDENT_RE }, + { begin: "@" + e.IDENT_RE }, + { begin: e.IDENT_RE + "\\\\" + e.IDENT_RE }, + ]; + a.contains = r; + var i = e.inherit(e.TITLE_MODE, { begin: n }), + o = "(\\(.*\\)\\s*)?\\B[-=]>", + s = { + className: "params", + begin: "\\([^\\(]", + returnBegin: !0, + contains: [ + { begin: /\(/, end: /\)/, keywords: t, contains: ["self"].concat(r) }, + ], + }; + return { + name: "MoonScript", + aliases: ["moon"], + keywords: t, + illegal: /\/\*/, + contains: r.concat([ + e.COMMENT("--", "$"), + { + className: "function", + begin: "^\\s*" + n + "\\s*=\\s*" + o, + end: "[-=]>", + returnBegin: !0, + contains: [i, s], + }, + { + begin: /[\(,:=]\s*/, + relevance: 0, + contains: [ + { + className: "function", + begin: o, + end: "[-=]>", + returnBegin: !0, + contains: [s], + }, + ], + }, + { + className: "class", + beginKeywords: "class", + end: "$", + illegal: /[:="\[\]]/, + contains: [ + { + beginKeywords: "extends", + endsWithParent: !0, + illegal: /[:="\[\]]/, + contains: [i], + }, + i, + ], + }, + { + className: "name", + begin: n + ":", + end: ":", + returnBegin: !0, + returnEnd: !0, + relevance: 0, + }, + ]), + }; +}; +var PS = function (e) { + return { + name: "N1QL", + case_insensitive: !0, + contains: [ + { + beginKeywords: + "build create index delete drop explain infer|10 insert merge prepare select update upsert|10", + end: /;/, + endsWithParent: !0, + keywords: { + keyword: + "all alter analyze and any array as asc begin between binary boolean break bucket build by call case cast cluster collate collection commit connect continue correlate cover create database dataset datastore declare decrement delete derived desc describe distinct do drop each element else end every except exclude execute exists explain fetch first flatten for force from function grant group gsi having if ignore ilike in include increment index infer inline inner insert intersect into is join key keys keyspace known last left let letting like limit lsm map mapping matched materialized merge minus namespace nest not number object offset on option or order outer over parse partition password path pool prepare primary private privilege procedure public raw realm reduce rename return returning revoke right role rollback satisfies schema select self semi set show some start statistics string system then to transaction trigger truncate under union unique unknown unnest unset update upsert use user using validate value valued values via view when where while with within work xor", + literal: "true false null missing|5", + built_in: + "array_agg array_append array_concat array_contains array_count array_distinct array_ifnull array_length array_max array_min array_position array_prepend array_put array_range array_remove array_repeat array_replace array_reverse array_sort array_sum avg count max min sum greatest least ifmissing ifmissingornull ifnull missingif nullif ifinf ifnan ifnanorinf naninf neginfif posinfif clock_millis clock_str date_add_millis date_add_str date_diff_millis date_diff_str date_part_millis date_part_str date_trunc_millis date_trunc_str duration_to_str millis str_to_millis millis_to_str millis_to_utc millis_to_zone_name now_millis now_str str_to_duration str_to_utc str_to_zone_name decode_json encode_json encoded_size poly_length base64 base64_encode base64_decode meta uuid abs acos asin atan atan2 ceil cos degrees e exp ln log floor pi power radians random round sign sin sqrt tan trunc object_length object_names object_pairs object_inner_pairs object_values object_inner_values object_add object_put object_remove object_unwrap regexp_contains regexp_like regexp_position regexp_replace contains initcap length lower ltrim position repeat replace rtrim split substr title trim upper isarray isatom isboolean isnumber isobject isstring type toarray toatom toboolean tonumber toobject tostring", + }, + contains: [ + { + className: "string", + begin: "'", + end: "'", + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "string", + begin: '"', + end: '"', + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "symbol", + begin: "`", + end: "`", + contains: [e.BACKSLASH_ESCAPE], + relevance: 2, + }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var kS = function (e) { + var t = { + className: "variable", + variants: [ + { begin: /\$\d+/ }, + { begin: /\$\{/, end: /\}/ }, + { begin: /[$@]/ + e.UNDERSCORE_IDENT_RE }, + ], + }, + n = { + endsWithParent: !0, + keywords: { + $pattern: "[a-z/_]+", + literal: + "on off yes no true false none blocked debug info notice warn error crit select break last permanent redirect kqueue rtsig epoll poll /dev/poll", + }, + relevance: 0, + illegal: "=>", + contains: [ + e.HASH_COMMENT_MODE, + { + className: "string", + contains: [e.BACKSLASH_ESCAPE, t], + variants: [ + { begin: /"/, end: /"/ }, + { begin: /'/, end: /'/ }, + ], + }, + { + begin: "([a-z]+):/", + end: "\\s", + endsWithParent: !0, + excludeEnd: !0, + contains: [t], + }, + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, t], + variants: [ + { begin: "\\s\\^", end: "\\s|\\{|;", returnEnd: !0 }, + { begin: "~\\*?\\s+", end: "\\s|\\{|;", returnEnd: !0 }, + { begin: "\\*(\\.[a-z\\-]+)+" }, + { begin: "([a-z\\-]+\\.)+\\*" }, + ], + }, + { + className: "number", + begin: "\\b\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}(:\\d{1,5})?\\b", + }, + { + className: "number", + begin: "\\b\\d+[kKmMgGdshdwy]*\\b", + relevance: 0, + }, + t, + ], + }; + return { + name: "Nginx config", + aliases: ["nginxconf"], + contains: [ + e.HASH_COMMENT_MODE, + { + begin: e.UNDERSCORE_IDENT_RE + "\\s+\\{", + returnBegin: !0, + end: /\{/, + contains: [{ className: "section", begin: e.UNDERSCORE_IDENT_RE }], + relevance: 0, + }, + { + begin: e.UNDERSCORE_IDENT_RE + "\\s", + end: ";|\\{", + returnBegin: !0, + contains: [ + { className: "attribute", begin: e.UNDERSCORE_IDENT_RE, starts: n }, + ], + relevance: 0, + }, + ], + illegal: "[^\\s\\}]", + }; +}; +var US = function (e) { + return { + name: "Nim", + keywords: { + keyword: + "addr and as asm bind block break case cast const continue converter discard distinct div do elif else end enum except export finally for from func generic if import in include interface is isnot iterator let macro method mixin mod nil not notin object of or out proc ptr raise ref return shl shr static template try tuple type using var when while with without xor yield", + literal: "shared guarded stdin stdout stderr result true false", + built_in: + "int int8 int16 int32 int64 uint uint8 uint16 uint32 uint64 float float32 float64 bool char string cstring pointer expr stmt void auto any range array openarray varargs seq set clong culong cchar cschar cshort cint csize clonglong cfloat cdouble clongdouble cuchar cushort cuint culonglong cstringarray semistatic", + }, + contains: [ + { className: "meta", begin: /\{\./, end: /\.\}/, relevance: 10 }, + { + className: "string", + begin: /[a-zA-Z]\w*"/, + end: /"/, + contains: [{ begin: /""/ }], + }, + { className: "string", begin: /([a-zA-Z]\w*)?"""/, end: /"""/ }, + e.QUOTE_STRING_MODE, + { className: "type", begin: /\b[A-Z]\w+\b/, relevance: 0 }, + { + className: "number", + relevance: 0, + variants: [ + { begin: /\b(0[xX][0-9a-fA-F][_0-9a-fA-F]*)('?[iIuU](8|16|32|64))?/ }, + { begin: /\b(0o[0-7][_0-7]*)('?[iIuUfF](8|16|32|64))?/ }, + { begin: /\b(0(b|B)[01][_01]*)('?[iIuUfF](8|16|32|64))?/ }, + { begin: /\b(\d[_\d]*)('?[iIuUfF](8|16|32|64))?/ }, + ], + }, + e.HASH_COMMENT_MODE, + ], + }; +}; +var FS = function (e) { + var t = { + keyword: "rec with let in inherit assert if else then", + literal: "true false or and null", + built_in: + "import abort baseNameOf dirOf isNull builtins map removeAttrs throw toString derivation", + }, + n = { className: "subst", begin: /\$\{/, end: /\}/, keywords: t }, + a = { + className: "string", + contains: [n], + variants: [ + { begin: "''", end: "''" }, + { begin: '"', end: '"' }, + ], + }, + r = [ + e.NUMBER_MODE, + e.HASH_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + a, + { + begin: /[a-zA-Z0-9-_]+(\s*=)/, + returnBegin: !0, + relevance: 0, + contains: [{ className: "attr", begin: /\S+/ }], + }, + ]; + return ( + (n.contains = r), + { name: "Nix", aliases: ["nixos"], keywords: t, contains: r } + ); +}; +var BS = function (e) { + return { + name: "Node REPL", + contains: [ + { + className: "meta", + starts: { end: / |$/, starts: { end: "$", subLanguage: "javascript" } }, + variants: [{ begin: /^>(?=[ ]|$)/ }, { begin: /^\.\.\.(?=[ ]|$)/ }], + }, + ], + }; +}; +var GS = function (e) { + var t = { className: "variable", begin: /\$+\{[\w.:-]+\}/ }, + n = { className: "variable", begin: /\$+\w+/, illegal: /\(\)\{\}/ }, + a = { className: "variable", begin: /\$+\([\w^.:-]+\)/ }, + r = { + className: "string", + variants: [ + { begin: '"', end: '"' }, + { begin: "'", end: "'" }, + { begin: "`", end: "`" }, + ], + illegal: /\n/, + contains: [ + { className: "meta", begin: /\$(\\[nrt]|\$)/ }, + { + className: "variable", + begin: + /\$(ADMINTOOLS|APPDATA|CDBURN_AREA|CMDLINE|COMMONFILES32|COMMONFILES64|COMMONFILES|COOKIES|DESKTOP|DOCUMENTS|EXEDIR|EXEFILE|EXEPATH|FAVORITES|FONTS|HISTORY|HWNDPARENT|INSTDIR|INTERNET_CACHE|LANGUAGE|LOCALAPPDATA|MUSIC|NETHOOD|OUTDIR|PICTURES|PLUGINSDIR|PRINTHOOD|PROFILE|PROGRAMFILES32|PROGRAMFILES64|PROGRAMFILES|QUICKLAUNCH|RECENT|RESOURCES_LOCALIZED|RESOURCES|SENDTO|SMPROGRAMS|SMSTARTUP|STARTMENU|SYSDIR|TEMP|TEMPLATES|VIDEOS|WINDIR)/, + }, + t, + n, + a, + ], + }; + return { + name: "NSIS", + case_insensitive: !1, + keywords: { + keyword: + "Abort AddBrandingImage AddSize AllowRootDirInstall AllowSkipFiles AutoCloseWindow BGFont BGGradient BrandingText BringToFront Call CallInstDLL Caption ChangeUI CheckBitmap ClearErrors CompletedText ComponentText CopyFiles CRCCheck CreateDirectory CreateFont CreateShortCut Delete DeleteINISec DeleteINIStr DeleteRegKey DeleteRegValue DetailPrint DetailsButtonText DirText DirVar DirVerify EnableWindow EnumRegKey EnumRegValue Exch Exec ExecShell ExecShellWait ExecWait ExpandEnvStrings File FileBufSize FileClose FileErrorText FileOpen FileRead FileReadByte FileReadUTF16LE FileReadWord FileWriteUTF16LE FileSeek FileWrite FileWriteByte FileWriteWord FindClose FindFirst FindNext FindWindow FlushINI GetCurInstType GetCurrentAddress GetDlgItem GetDLLVersion GetDLLVersionLocal GetErrorLevel GetFileTime GetFileTimeLocal GetFullPathName GetFunctionAddress GetInstDirError GetKnownFolderPath GetLabelAddress GetTempFileName Goto HideWindow Icon IfAbort IfErrors IfFileExists IfRebootFlag IfRtlLanguage IfShellVarContextAll IfSilent InitPluginsDir InstallButtonText InstallColors InstallDir InstallDirRegKey InstProgressFlags InstType InstTypeGetText InstTypeSetText Int64Cmp Int64CmpU Int64Fmt IntCmp IntCmpU IntFmt IntOp IntPtrCmp IntPtrCmpU IntPtrOp IsWindow LangString LicenseBkColor LicenseData LicenseForceSelection LicenseLangString LicenseText LoadAndSetImage LoadLanguageFile LockWindow LogSet LogText ManifestDPIAware ManifestLongPathAware ManifestMaxVersionTested ManifestSupportedOS MessageBox MiscButtonText Name Nop OutFile Page PageCallbacks PEAddResource PEDllCharacteristics PERemoveResource PESubsysVer Pop Push Quit ReadEnvStr ReadINIStr ReadRegDWORD ReadRegStr Reboot RegDLL Rename RequestExecutionLevel ReserveFile Return RMDir SearchPath SectionGetFlags SectionGetInstTypes SectionGetSize SectionGetText SectionIn SectionSetFlags SectionSetInstTypes SectionSetSize SectionSetText SendMessage SetAutoClose SetBrandingImage SetCompress SetCompressor SetCompressorDictSize SetCtlColors SetCurInstType SetDatablockOptimize SetDateSave SetDetailsPrint SetDetailsView SetErrorLevel SetErrors SetFileAttributes SetFont SetOutPath SetOverwrite SetRebootFlag SetRegView SetShellVarContext SetSilent ShowInstDetails ShowUninstDetails ShowWindow SilentInstall SilentUnInstall Sleep SpaceTexts StrCmp StrCmpS StrCpy StrLen SubCaption Unicode UninstallButtonText UninstallCaption UninstallIcon UninstallSubCaption UninstallText UninstPage UnRegDLL Var VIAddVersionKey VIFileVersion VIProductVersion WindowIcon WriteINIStr WriteRegBin WriteRegDWORD WriteRegExpandStr WriteRegMultiStr WriteRegNone WriteRegStr WriteUninstaller XPStyle", + literal: + "admin all auto both bottom bzip2 colored components current custom directory false force hide highest ifdiff ifnewer instfiles lastused leave left license listonly lzma nevershow none normal notset off on open print right show silent silentlog smooth textonly top true try un.components un.custom un.directory un.instfiles un.license uninstConfirm user Win10 Win7 Win8 WinVista zlib", + }, + contains: [ + e.HASH_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "function", + beginKeywords: "Function PageEx Section SectionGroup", + end: "$", + }, + r, + { + className: "keyword", + begin: + /!(addincludedir|addplugindir|appendfile|cd|define|delfile|echo|else|endif|error|execute|finalize|getdllversion|gettlbversion|if|ifdef|ifmacrodef|ifmacrondef|ifndef|include|insertmacro|macro|macroend|makensis|packhdr|searchparse|searchreplace|system|tempfile|undef|verbose|warning)/, + }, + t, + n, + a, + { + className: "params", + begin: + "(ARCHIVE|FILE_ATTRIBUTE_ARCHIVE|FILE_ATTRIBUTE_NORMAL|FILE_ATTRIBUTE_OFFLINE|FILE_ATTRIBUTE_READONLY|FILE_ATTRIBUTE_SYSTEM|FILE_ATTRIBUTE_TEMPORARY|HKCR|HKCU|HKDD|HKEY_CLASSES_ROOT|HKEY_CURRENT_CONFIG|HKEY_CURRENT_USER|HKEY_DYN_DATA|HKEY_LOCAL_MACHINE|HKEY_PERFORMANCE_DATA|HKEY_USERS|HKLM|HKPD|HKU|IDABORT|IDCANCEL|IDIGNORE|IDNO|IDOK|IDRETRY|IDYES|MB_ABORTRETRYIGNORE|MB_DEFBUTTON1|MB_DEFBUTTON2|MB_DEFBUTTON3|MB_DEFBUTTON4|MB_ICONEXCLAMATION|MB_ICONINFORMATION|MB_ICONQUESTION|MB_ICONSTOP|MB_OK|MB_OKCANCEL|MB_RETRYCANCEL|MB_RIGHT|MB_RTLREADING|MB_SETFOREGROUND|MB_TOPMOST|MB_USERICON|MB_YESNO|NORMAL|OFFLINE|READONLY|SHCTX|SHELL_CONTEXT|SYSTEM|TEMPORARY)", + }, + { className: "class", begin: /\w+::\w+/ }, + e.NUMBER_MODE, + ], + }; +}; +var YS = function (e) { + var t = /[a-zA-Z@][a-zA-Z0-9_]*/, + n = { $pattern: t, keyword: "@interface @class @protocol @implementation" }; + return { + name: "Objective-C", + aliases: ["mm", "objc", "obj-c", "obj-c++", "objective-c++"], + keywords: { + $pattern: t, + keyword: + "int float while char export sizeof typedef const struct for union unsigned long volatile static bool mutable if do return goto void enum else break extern asm case short default double register explicit signed typename this switch continue wchar_t inline readonly assign readwrite self @synchronized id typeof nonatomic super unichar IBOutlet IBAction strong weak copy in out inout bycopy byref oneway __strong __weak __block __autoreleasing @private @protected @public @try @property @end @throw @catch @finally @autoreleasepool @synthesize @dynamic @selector @optional @required @encode @package @import @defs @compatibility_alias __bridge __bridge_transfer __bridge_retained __bridge_retain __covariant __contravariant __kindof _Nonnull _Nullable _Null_unspecified __FUNCTION__ __PRETTY_FUNCTION__ __attribute__ getter setter retain unsafe_unretained nonnull nullable null_unspecified null_resettable class instancetype NS_DESIGNATED_INITIALIZER NS_UNAVAILABLE NS_REQUIRES_SUPER NS_RETURNS_INNER_POINTER NS_INLINE NS_AVAILABLE NS_DEPRECATED NS_ENUM NS_OPTIONS NS_SWIFT_UNAVAILABLE NS_ASSUME_NONNULL_BEGIN NS_ASSUME_NONNULL_END NS_REFINED_FOR_SWIFT NS_SWIFT_NAME NS_SWIFT_NOTHROW NS_DURING NS_HANDLER NS_ENDHANDLER NS_VALUERETURN NS_VOIDRETURN", + literal: "false true FALSE TRUE nil YES NO NULL", + built_in: + "BOOL dispatch_once_t dispatch_queue_t dispatch_sync dispatch_async dispatch_once", + }, + illegal: "/, + end: /$/, + illegal: "\\n", + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + { + className: "class", + begin: "(" + n.keyword.split(" ").join("|") + ")\\b", + end: /(\{|$)/, + excludeEnd: !0, + keywords: n, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { begin: "\\." + e.UNDERSCORE_IDENT_RE, relevance: 0 }, + ], + }; +}; +var HS = function (e) { + return { + name: "OCaml", + aliases: ["ml"], + keywords: { + $pattern: "[a-z_]\\w*!?", + keyword: + "and as assert asr begin class constraint do done downto else end exception external for fun function functor if in include inherit! inherit initializer land lazy let lor lsl lsr lxor match method!|10 method mod module mutable new object of open! open or private rec sig struct then to try type val! val virtual when while with parser value", + built_in: + "array bool bytes char exn|5 float int int32 int64 list lazy_t|5 nativeint|5 string unit in_channel out_channel ref", + literal: "true false", + }, + illegal: /\/\/|>>/, + contains: [ + { className: "literal", begin: "\\[(\\|\\|)?\\]|\\(\\)", relevance: 0 }, + e.COMMENT("\\(\\*", "\\*\\)", { contains: ["self"] }), + { className: "symbol", begin: "'[A-Za-z_](?!')[\\w']*" }, + { className: "type", begin: "`[A-Z][\\w']*" }, + { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + { begin: "[a-z_]\\w*'[\\w']*", relevance: 0 }, + e.inherit(e.APOS_STRING_MODE, { className: "string", relevance: 0 }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { + className: "number", + begin: + "\\b(0[xX][a-fA-F0-9_]+[Lln]?|0[oO][0-7_]+[Lln]?|0[bB][01_]+[Lln]?|[0-9][0-9_]*([Lln]|(\\.[0-9_]*)?([eE][-+]?[0-9_]+)?)?)", + relevance: 0, + }, + { begin: /->/ }, + ], + }; +}; +var VS = function (e) { + var t = { className: "keyword", begin: "\\$(f[asn]|t|vp[rtd]|children)" }, + n = { + className: "number", + begin: "\\b\\d+(\\.\\d+)?(e-?\\d+)?", + relevance: 0, + }, + a = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + r = { + className: "function", + beginKeywords: "module function", + end: /=|\{/, + contains: [ + { + className: "params", + begin: "\\(", + end: "\\)", + contains: [ + "self", + n, + a, + t, + { className: "literal", begin: "false|true|PI|undef" }, + ], + }, + e.UNDERSCORE_TITLE_MODE, + ], + }; + return { + name: "OpenSCAD", + aliases: ["scad"], + keywords: { + keyword: "function module include use for intersection_for if else \\%", + literal: "false true PI undef", + built_in: + "circle square polygon text sphere cube cylinder polyhedron translate rotate scale resize mirror multmatrix color offset hull minkowski union difference intersection abs sign sin cos tan acos asin atan atan2 floor round ceil ln log pow sqrt exp rands min max concat lookup str chr search version version_num norm cross parent_module echo import import_dxf dxf_linear_extrude linear_extrude rotate_extrude surface projection render children dxf_cross dxf_dim let assign", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + { + className: "meta", + keywords: { "meta-keyword": "include use" }, + begin: "include|use <", + end: ">", + }, + a, + t, + { begin: "[*!#%]", relevance: 0 }, + r, + ], + }; +}; +var qS = function (e) { + var t = { + $pattern: /\.?\w+/, + keyword: + "abstract add and array as asc aspect assembly async begin break block by case class concat const copy constructor continue create default delegate desc distinct div do downto dynamic each else empty end ensure enum equals event except exit extension external false final finalize finalizer finally flags for forward from function future global group has if implementation implements implies in index inherited inline interface into invariants is iterator join locked locking loop matching method mod module namespace nested new nil not notify nullable of old on operator or order out override parallel params partial pinned private procedure property protected public queryable raise read readonly record reintroduce remove repeat require result reverse sealed select self sequence set shl shr skip static step soft take then to true try tuple type union unit unsafe until uses using var virtual raises volatile where while with write xor yield await mapped deprecated stdcall cdecl pascal register safecall overload library platform reference packed strict published autoreleasepool selector strong weak unretained", + }, + n = e.COMMENT(/\{/, /\}/, { relevance: 0 }), + a = e.COMMENT("\\(\\*", "\\*\\)", { relevance: 10 }), + r = { + className: "string", + begin: "'", + end: "'", + contains: [{ begin: "''" }], + }, + i = { className: "string", begin: "(#\\d+)+" }, + o = { + className: "function", + beginKeywords: "function constructor destructor procedure method", + end: "[:;]", + keywords: "function constructor|10 destructor|10 procedure|10 method|10", + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: "\\(", + end: "\\)", + keywords: t, + contains: [r, i], + }, + n, + a, + ], + }; + return { + name: "Oxygene", + case_insensitive: !0, + keywords: t, + illegal: '("|\\$[G-Zg-z]|\\/\\*||->)', + contains: [ + n, + a, + e.C_LINE_COMMENT_MODE, + r, + i, + e.NUMBER_MODE, + o, + { + className: "class", + begin: "=\\bclass\\b", + end: "end;", + keywords: t, + contains: [r, i, n, a, e.C_LINE_COMMENT_MODE, o], + }, + ], + }; +}; +var zS = function (e) { + var t = e.COMMENT(/\{/, /\}/, { contains: ["self"] }); + return { + name: "Parser3", + subLanguage: "xml", + relevance: 0, + contains: [ + e.COMMENT("^#", "$"), + e.COMMENT(/\^rem\{/, /\}/, { relevance: 10, contains: [t] }), + { + className: "meta", + begin: "^@(?:BASE|USE|CLASS|OPTIONS)$", + relevance: 10, + }, + { + className: "title", + begin: "@[\\w\\-]+\\[[\\w^;\\-]*\\](?:\\[[\\w^;\\-]*\\])?(?:.*)$", + }, + { className: "variable", begin: /\$\{?[\w\-.:]+\}?/ }, + { className: "keyword", begin: /\^[\w\-.:]+/ }, + { className: "number", begin: "\\^#[0-9a-fA-F]+" }, + e.C_NUMBER_MODE, + ], + }; +}; +var WS = function (e) { + return { + name: "Packet Filter config", + aliases: ["pf.conf"], + keywords: { + $pattern: /[a-z0-9_<>-]+/, + built_in: "block match pass load anchor|5 antispoof|10 set table", + keyword: + "in out log quick on rdomain inet inet6 proto from port os to route allow-opts divert-packet divert-reply divert-to flags group icmp-type icmp6-type label once probability recieved-on rtable prio queue tos tag tagged user keep fragment for os drop af-to|10 binat-to|10 nat-to|10 rdr-to|10 bitmask least-stats random round-robin source-hash static-port dup-to reply-to route-to parent bandwidth default min max qlimit block-policy debug fingerprints hostid limit loginterface optimization reassemble ruleset-optimization basic none profile skip state-defaults state-policy timeout const counters persist no modulate synproxy state|5 floating if-bound no-sync pflow|10 sloppy source-track global rule max-src-nodes max-src-states max-src-conn max-src-conn-rate overload flush scrub|5 max-mss min-ttl no-df|10 random-id", + literal: "all any no-route self urpf-failed egress|5 unknown", + }, + contains: [ + e.HASH_COMMENT_MODE, + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + { className: "variable", begin: /\$[\w\d#@][\w\d_]*/ }, + { className: "variable", begin: /<(?!\/)/, end: />/ }, + ], + }; +}; +var $S = function (e) { + var t = e.COMMENT("--", "$"), + n = "\\$([a-zA-Z_]?|[a-zA-Z_][a-zA-Z_0-9]*)\\$", + a = + "BIGINT INT8 BIGSERIAL SERIAL8 BIT VARYING VARBIT BOOLEAN BOOL BOX BYTEA CHARACTER CHAR VARCHAR CIDR CIRCLE DATE DOUBLE PRECISION FLOAT8 FLOAT INET INTEGER INT INT4 INTERVAL JSON JSONB LINE LSEG|10 MACADDR MACADDR8 MONEY NUMERIC DEC DECIMAL PATH POINT POLYGON REAL FLOAT4 SMALLINT INT2 SMALLSERIAL|10 SERIAL2|10 SERIAL|10 SERIAL4|10 TEXT TIME ZONE TIMETZ|10 TIMESTAMP TIMESTAMPTZ|10 TSQUERY|10 TSVECTOR|10 TXID_SNAPSHOT|10 UUID XML NATIONAL NCHAR INT4RANGE|10 INT8RANGE|10 NUMRANGE|10 TSRANGE|10 TSTZRANGE|10 DATERANGE|10 ANYELEMENT ANYARRAY ANYNONARRAY ANYENUM ANYRANGE CSTRING INTERNAL RECORD PG_DDL_COMMAND VOID UNKNOWN OPAQUE REFCURSOR NAME OID REGPROC|10 REGPROCEDURE|10 REGOPER|10 REGOPERATOR|10 REGCLASS|10 REGTYPE|10 REGROLE|10 REGNAMESPACE|10 REGCONFIG|10 REGDICTIONARY|10 ", + r = a + .trim() + .split(" ") + .map(function (e) { + return e.split("|")[0]; + }) + .join("|"), + i = + "ARRAY_AGG AVG BIT_AND BIT_OR BOOL_AND BOOL_OR COUNT EVERY JSON_AGG JSONB_AGG JSON_OBJECT_AGG JSONB_OBJECT_AGG MAX MIN MODE STRING_AGG SUM XMLAGG CORR COVAR_POP COVAR_SAMP REGR_AVGX REGR_AVGY REGR_COUNT REGR_INTERCEPT REGR_R2 REGR_SLOPE REGR_SXX REGR_SXY REGR_SYY STDDEV STDDEV_POP STDDEV_SAMP VARIANCE VAR_POP VAR_SAMP PERCENTILE_CONT PERCENTILE_DISC ROW_NUMBER RANK DENSE_RANK PERCENT_RANK CUME_DIST NTILE LAG LEAD FIRST_VALUE LAST_VALUE NTH_VALUE NUM_NONNULLS NUM_NULLS ABS CBRT CEIL CEILING DEGREES DIV EXP FLOOR LN LOG MOD PI POWER RADIANS ROUND SCALE SIGN SQRT TRUNC WIDTH_BUCKET RANDOM SETSEED ACOS ACOSD ASIN ASIND ATAN ATAND ATAN2 ATAN2D COS COSD COT COTD SIN SIND TAN TAND BIT_LENGTH CHAR_LENGTH CHARACTER_LENGTH LOWER OCTET_LENGTH OVERLAY POSITION SUBSTRING TREAT TRIM UPPER ASCII BTRIM CHR CONCAT CONCAT_WS CONVERT CONVERT_FROM CONVERT_TO DECODE ENCODE INITCAP LEFT LENGTH LPAD LTRIM MD5 PARSE_IDENT PG_CLIENT_ENCODING QUOTE_IDENT|10 QUOTE_LITERAL|10 QUOTE_NULLABLE|10 REGEXP_MATCH REGEXP_MATCHES REGEXP_REPLACE REGEXP_SPLIT_TO_ARRAY REGEXP_SPLIT_TO_TABLE REPEAT REPLACE REVERSE RIGHT RPAD RTRIM SPLIT_PART STRPOS SUBSTR TO_ASCII TO_HEX TRANSLATE OCTET_LENGTH GET_BIT GET_BYTE SET_BIT SET_BYTE TO_CHAR TO_DATE TO_NUMBER TO_TIMESTAMP AGE CLOCK_TIMESTAMP|10 DATE_PART DATE_TRUNC ISFINITE JUSTIFY_DAYS JUSTIFY_HOURS JUSTIFY_INTERVAL MAKE_DATE MAKE_INTERVAL|10 MAKE_TIME MAKE_TIMESTAMP|10 MAKE_TIMESTAMPTZ|10 NOW STATEMENT_TIMESTAMP|10 TIMEOFDAY TRANSACTION_TIMESTAMP|10 ENUM_FIRST ENUM_LAST ENUM_RANGE AREA CENTER DIAMETER HEIGHT ISCLOSED ISOPEN NPOINTS PCLOSE POPEN RADIUS WIDTH BOX BOUND_BOX CIRCLE LINE LSEG PATH POLYGON ABBREV BROADCAST HOST HOSTMASK MASKLEN NETMASK NETWORK SET_MASKLEN TEXT INET_SAME_FAMILY INET_MERGE MACADDR8_SET7BIT ARRAY_TO_TSVECTOR GET_CURRENT_TS_CONFIG NUMNODE PLAINTO_TSQUERY PHRASETO_TSQUERY WEBSEARCH_TO_TSQUERY QUERYTREE SETWEIGHT STRIP TO_TSQUERY TO_TSVECTOR JSON_TO_TSVECTOR JSONB_TO_TSVECTOR TS_DELETE TS_FILTER TS_HEADLINE TS_RANK TS_RANK_CD TS_REWRITE TSQUERY_PHRASE TSVECTOR_TO_ARRAY TSVECTOR_UPDATE_TRIGGER TSVECTOR_UPDATE_TRIGGER_COLUMN XMLCOMMENT XMLCONCAT XMLELEMENT XMLFOREST XMLPI XMLROOT XMLEXISTS XML_IS_WELL_FORMED XML_IS_WELL_FORMED_DOCUMENT XML_IS_WELL_FORMED_CONTENT XPATH XPATH_EXISTS XMLTABLE XMLNAMESPACES TABLE_TO_XML TABLE_TO_XMLSCHEMA TABLE_TO_XML_AND_XMLSCHEMA QUERY_TO_XML QUERY_TO_XMLSCHEMA QUERY_TO_XML_AND_XMLSCHEMA CURSOR_TO_XML CURSOR_TO_XMLSCHEMA SCHEMA_TO_XML SCHEMA_TO_XMLSCHEMA SCHEMA_TO_XML_AND_XMLSCHEMA DATABASE_TO_XML DATABASE_TO_XMLSCHEMA DATABASE_TO_XML_AND_XMLSCHEMA XMLATTRIBUTES TO_JSON TO_JSONB ARRAY_TO_JSON ROW_TO_JSON JSON_BUILD_ARRAY JSONB_BUILD_ARRAY JSON_BUILD_OBJECT JSONB_BUILD_OBJECT JSON_OBJECT JSONB_OBJECT JSON_ARRAY_LENGTH JSONB_ARRAY_LENGTH JSON_EACH JSONB_EACH JSON_EACH_TEXT JSONB_EACH_TEXT JSON_EXTRACT_PATH JSONB_EXTRACT_PATH JSON_OBJECT_KEYS JSONB_OBJECT_KEYS JSON_POPULATE_RECORD JSONB_POPULATE_RECORD JSON_POPULATE_RECORDSET JSONB_POPULATE_RECORDSET JSON_ARRAY_ELEMENTS JSONB_ARRAY_ELEMENTS JSON_ARRAY_ELEMENTS_TEXT JSONB_ARRAY_ELEMENTS_TEXT JSON_TYPEOF JSONB_TYPEOF JSON_TO_RECORD JSONB_TO_RECORD JSON_TO_RECORDSET JSONB_TO_RECORDSET JSON_STRIP_NULLS JSONB_STRIP_NULLS JSONB_SET JSONB_INSERT JSONB_PRETTY CURRVAL LASTVAL NEXTVAL SETVAL COALESCE NULLIF GREATEST LEAST ARRAY_APPEND ARRAY_CAT ARRAY_NDIMS ARRAY_DIMS ARRAY_FILL ARRAY_LENGTH ARRAY_LOWER ARRAY_POSITION ARRAY_POSITIONS ARRAY_PREPEND ARRAY_REMOVE ARRAY_REPLACE ARRAY_TO_STRING ARRAY_UPPER CARDINALITY STRING_TO_ARRAY UNNEST ISEMPTY LOWER_INC UPPER_INC LOWER_INF UPPER_INF RANGE_MERGE GENERATE_SERIES GENERATE_SUBSCRIPTS CURRENT_DATABASE CURRENT_QUERY CURRENT_SCHEMA|10 CURRENT_SCHEMAS|10 INET_CLIENT_ADDR INET_CLIENT_PORT INET_SERVER_ADDR INET_SERVER_PORT ROW_SECURITY_ACTIVE FORMAT_TYPE TO_REGCLASS TO_REGPROC TO_REGPROCEDURE TO_REGOPER TO_REGOPERATOR TO_REGTYPE TO_REGNAMESPACE TO_REGROLE COL_DESCRIPTION OBJ_DESCRIPTION SHOBJ_DESCRIPTION TXID_CURRENT TXID_CURRENT_IF_ASSIGNED TXID_CURRENT_SNAPSHOT TXID_SNAPSHOT_XIP TXID_SNAPSHOT_XMAX TXID_SNAPSHOT_XMIN TXID_VISIBLE_IN_SNAPSHOT TXID_STATUS CURRENT_SETTING SET_CONFIG BRIN_SUMMARIZE_NEW_VALUES BRIN_SUMMARIZE_RANGE BRIN_DESUMMARIZE_RANGE GIN_CLEAN_PENDING_LIST SUPPRESS_REDUNDANT_UPDATES_TRIGGER LO_FROM_BYTEA LO_PUT LO_GET LO_CREAT LO_CREATE LO_UNLINK LO_IMPORT LO_EXPORT LOREAD LOWRITE GROUPING CAST " + .trim() + .split(" ") + .map(function (e) { + return e.split("|")[0]; + }) + .join("|"); + return { + name: "PostgreSQL", + aliases: ["postgres", "postgresql"], + case_insensitive: !0, + keywords: { + keyword: + "ABORT ALTER ANALYZE BEGIN CALL CHECKPOINT|10 CLOSE CLUSTER COMMENT COMMIT COPY CREATE DEALLOCATE DECLARE DELETE DISCARD DO DROP END EXECUTE EXPLAIN FETCH GRANT IMPORT INSERT LISTEN LOAD LOCK MOVE NOTIFY PREPARE REASSIGN|10 REFRESH REINDEX RELEASE RESET REVOKE ROLLBACK SAVEPOINT SECURITY SELECT SET SHOW START TRUNCATE UNLISTEN|10 UPDATE VACUUM|10 VALUES AGGREGATE COLLATION CONVERSION|10 DATABASE DEFAULT PRIVILEGES DOMAIN TRIGGER EXTENSION FOREIGN WRAPPER|10 TABLE FUNCTION GROUP LANGUAGE LARGE OBJECT MATERIALIZED VIEW OPERATOR CLASS FAMILY POLICY PUBLICATION|10 ROLE RULE SCHEMA SEQUENCE SERVER STATISTICS SUBSCRIPTION SYSTEM TABLESPACE CONFIGURATION DICTIONARY PARSER TEMPLATE TYPE USER MAPPING PREPARED ACCESS METHOD CAST AS TRANSFORM TRANSACTION OWNED TO INTO SESSION AUTHORIZATION INDEX PROCEDURE ASSERTION ALL ANALYSE AND ANY ARRAY ASC ASYMMETRIC|10 BOTH CASE CHECK COLLATE COLUMN CONCURRENTLY|10 CONSTRAINT CROSS DEFERRABLE RANGE DESC DISTINCT ELSE EXCEPT FOR FREEZE|10 FROM FULL HAVING ILIKE IN INITIALLY INNER INTERSECT IS ISNULL JOIN LATERAL LEADING LIKE LIMIT NATURAL NOT NOTNULL NULL OFFSET ON ONLY OR ORDER OUTER OVERLAPS PLACING PRIMARY REFERENCES RETURNING SIMILAR SOME SYMMETRIC TABLESAMPLE THEN TRAILING UNION UNIQUE USING VARIADIC|10 VERBOSE WHEN WHERE WINDOW WITH BY RETURNS INOUT OUT SETOF|10 IF STRICT CURRENT CONTINUE OWNER LOCATION OVER PARTITION WITHIN BETWEEN ESCAPE EXTERNAL INVOKER DEFINER WORK RENAME VERSION CONNECTION CONNECT TABLES TEMP TEMPORARY FUNCTIONS SEQUENCES TYPES SCHEMAS OPTION CASCADE RESTRICT ADD ADMIN EXISTS VALID VALIDATE ENABLE DISABLE REPLICA|10 ALWAYS PASSING COLUMNS PATH REF VALUE OVERRIDING IMMUTABLE STABLE VOLATILE BEFORE AFTER EACH ROW PROCEDURAL ROUTINE NO HANDLER VALIDATOR OPTIONS STORAGE OIDS|10 WITHOUT INHERIT DEPENDS CALLED INPUT LEAKPROOF|10 COST ROWS NOWAIT SEARCH UNTIL ENCRYPTED|10 PASSWORD CONFLICT|10 INSTEAD INHERITS CHARACTERISTICS WRITE CURSOR ALSO STATEMENT SHARE EXCLUSIVE INLINE ISOLATION REPEATABLE READ COMMITTED SERIALIZABLE UNCOMMITTED LOCAL GLOBAL SQL PROCEDURES RECURSIVE SNAPSHOT ROLLUP CUBE TRUSTED|10 INCLUDE FOLLOWING PRECEDING UNBOUNDED RANGE GROUPS UNENCRYPTED|10 SYSID FORMAT DELIMITER HEADER QUOTE ENCODING FILTER OFF FORCE_QUOTE FORCE_NOT_NULL FORCE_NULL COSTS BUFFERS TIMING SUMMARY DISABLE_PAGE_SKIPPING RESTART CYCLE GENERATED IDENTITY DEFERRED IMMEDIATE LEVEL LOGGED UNLOGGED OF NOTHING NONE EXCLUDE ATTRIBUTE USAGE ROUTINES TRUE FALSE NAN INFINITY ALIAS BEGIN CONSTANT DECLARE END EXCEPTION RETURN PERFORM|10 RAISE GET DIAGNOSTICS STACKED|10 FOREACH LOOP ELSIF EXIT WHILE REVERSE SLICE DEBUG LOG INFO NOTICE WARNING ASSERT OPEN SUPERUSER NOSUPERUSER CREATEDB NOCREATEDB CREATEROLE NOCREATEROLE INHERIT NOINHERIT LOGIN NOLOGIN REPLICATION NOREPLICATION BYPASSRLS NOBYPASSRLS ", + built_in: + "CURRENT_TIME CURRENT_TIMESTAMP CURRENT_USER CURRENT_CATALOG|10 CURRENT_DATE LOCALTIME LOCALTIMESTAMP CURRENT_ROLE|10 CURRENT_SCHEMA|10 SESSION_USER PUBLIC FOUND NEW OLD TG_NAME|10 TG_WHEN|10 TG_LEVEL|10 TG_OP|10 TG_RELID|10 TG_RELNAME|10 TG_TABLE_NAME|10 TG_TABLE_SCHEMA|10 TG_NARGS|10 TG_ARGV|10 TG_EVENT|10 TG_TAG|10 ROW_COUNT RESULT_OID|10 PG_CONTEXT|10 RETURNED_SQLSTATE COLUMN_NAME CONSTRAINT_NAME PG_DATATYPE_NAME|10 MESSAGE_TEXT TABLE_NAME SCHEMA_NAME PG_EXCEPTION_DETAIL|10 PG_EXCEPTION_HINT|10 PG_EXCEPTION_CONTEXT|10 SQLSTATE SQLERRM|10 SUCCESSFUL_COMPLETION WARNING DYNAMIC_RESULT_SETS_RETURNED IMPLICIT_ZERO_BIT_PADDING NULL_VALUE_ELIMINATED_IN_SET_FUNCTION PRIVILEGE_NOT_GRANTED PRIVILEGE_NOT_REVOKED STRING_DATA_RIGHT_TRUNCATION DEPRECATED_FEATURE NO_DATA NO_ADDITIONAL_DYNAMIC_RESULT_SETS_RETURNED SQL_STATEMENT_NOT_YET_COMPLETE CONNECTION_EXCEPTION CONNECTION_DOES_NOT_EXIST CONNECTION_FAILURE SQLCLIENT_UNABLE_TO_ESTABLISH_SQLCONNECTION SQLSERVER_REJECTED_ESTABLISHMENT_OF_SQLCONNECTION TRANSACTION_RESOLUTION_UNKNOWN PROTOCOL_VIOLATION TRIGGERED_ACTION_EXCEPTION FEATURE_NOT_SUPPORTED INVALID_TRANSACTION_INITIATION LOCATOR_EXCEPTION INVALID_LOCATOR_SPECIFICATION INVALID_GRANTOR INVALID_GRANT_OPERATION INVALID_ROLE_SPECIFICATION DIAGNOSTICS_EXCEPTION STACKED_DIAGNOSTICS_ACCESSED_WITHOUT_ACTIVE_HANDLER CASE_NOT_FOUND CARDINALITY_VIOLATION DATA_EXCEPTION ARRAY_SUBSCRIPT_ERROR CHARACTER_NOT_IN_REPERTOIRE DATETIME_FIELD_OVERFLOW DIVISION_BY_ZERO ERROR_IN_ASSIGNMENT ESCAPE_CHARACTER_CONFLICT INDICATOR_OVERFLOW INTERVAL_FIELD_OVERFLOW INVALID_ARGUMENT_FOR_LOGARITHM INVALID_ARGUMENT_FOR_NTILE_FUNCTION INVALID_ARGUMENT_FOR_NTH_VALUE_FUNCTION INVALID_ARGUMENT_FOR_POWER_FUNCTION INVALID_ARGUMENT_FOR_WIDTH_BUCKET_FUNCTION INVALID_CHARACTER_VALUE_FOR_CAST INVALID_DATETIME_FORMAT INVALID_ESCAPE_CHARACTER INVALID_ESCAPE_OCTET INVALID_ESCAPE_SEQUENCE NONSTANDARD_USE_OF_ESCAPE_CHARACTER INVALID_INDICATOR_PARAMETER_VALUE INVALID_PARAMETER_VALUE INVALID_REGULAR_EXPRESSION INVALID_ROW_COUNT_IN_LIMIT_CLAUSE INVALID_ROW_COUNT_IN_RESULT_OFFSET_CLAUSE INVALID_TABLESAMPLE_ARGUMENT INVALID_TABLESAMPLE_REPEAT INVALID_TIME_ZONE_DISPLACEMENT_VALUE INVALID_USE_OF_ESCAPE_CHARACTER MOST_SPECIFIC_TYPE_MISMATCH NULL_VALUE_NOT_ALLOWED NULL_VALUE_NO_INDICATOR_PARAMETER NUMERIC_VALUE_OUT_OF_RANGE SEQUENCE_GENERATOR_LIMIT_EXCEEDED STRING_DATA_LENGTH_MISMATCH STRING_DATA_RIGHT_TRUNCATION SUBSTRING_ERROR TRIM_ERROR UNTERMINATED_C_STRING ZERO_LENGTH_CHARACTER_STRING FLOATING_POINT_EXCEPTION INVALID_TEXT_REPRESENTATION INVALID_BINARY_REPRESENTATION BAD_COPY_FILE_FORMAT UNTRANSLATABLE_CHARACTER NOT_AN_XML_DOCUMENT INVALID_XML_DOCUMENT INVALID_XML_CONTENT INVALID_XML_COMMENT INVALID_XML_PROCESSING_INSTRUCTION INTEGRITY_CONSTRAINT_VIOLATION RESTRICT_VIOLATION NOT_NULL_VIOLATION FOREIGN_KEY_VIOLATION UNIQUE_VIOLATION CHECK_VIOLATION EXCLUSION_VIOLATION INVALID_CURSOR_STATE INVALID_TRANSACTION_STATE ACTIVE_SQL_TRANSACTION BRANCH_TRANSACTION_ALREADY_ACTIVE HELD_CURSOR_REQUIRES_SAME_ISOLATION_LEVEL INAPPROPRIATE_ACCESS_MODE_FOR_BRANCH_TRANSACTION INAPPROPRIATE_ISOLATION_LEVEL_FOR_BRANCH_TRANSACTION NO_ACTIVE_SQL_TRANSACTION_FOR_BRANCH_TRANSACTION READ_ONLY_SQL_TRANSACTION SCHEMA_AND_DATA_STATEMENT_MIXING_NOT_SUPPORTED NO_ACTIVE_SQL_TRANSACTION IN_FAILED_SQL_TRANSACTION IDLE_IN_TRANSACTION_SESSION_TIMEOUT INVALID_SQL_STATEMENT_NAME TRIGGERED_DATA_CHANGE_VIOLATION INVALID_AUTHORIZATION_SPECIFICATION INVALID_PASSWORD DEPENDENT_PRIVILEGE_DESCRIPTORS_STILL_EXIST DEPENDENT_OBJECTS_STILL_EXIST INVALID_TRANSACTION_TERMINATION SQL_ROUTINE_EXCEPTION FUNCTION_EXECUTED_NO_RETURN_STATEMENT MODIFYING_SQL_DATA_NOT_PERMITTED PROHIBITED_SQL_STATEMENT_ATTEMPTED READING_SQL_DATA_NOT_PERMITTED INVALID_CURSOR_NAME EXTERNAL_ROUTINE_EXCEPTION CONTAINING_SQL_NOT_PERMITTED MODIFYING_SQL_DATA_NOT_PERMITTED PROHIBITED_SQL_STATEMENT_ATTEMPTED READING_SQL_DATA_NOT_PERMITTED EXTERNAL_ROUTINE_INVOCATION_EXCEPTION INVALID_SQLSTATE_RETURNED NULL_VALUE_NOT_ALLOWED TRIGGER_PROTOCOL_VIOLATED SRF_PROTOCOL_VIOLATED EVENT_TRIGGER_PROTOCOL_VIOLATED SAVEPOINT_EXCEPTION INVALID_SAVEPOINT_SPECIFICATION INVALID_CATALOG_NAME INVALID_SCHEMA_NAME TRANSACTION_ROLLBACK TRANSACTION_INTEGRITY_CONSTRAINT_VIOLATION SERIALIZATION_FAILURE STATEMENT_COMPLETION_UNKNOWN DEADLOCK_DETECTED SYNTAX_ERROR_OR_ACCESS_RULE_VIOLATION SYNTAX_ERROR INSUFFICIENT_PRIVILEGE CANNOT_COERCE GROUPING_ERROR WINDOWING_ERROR INVALID_RECURSION INVALID_FOREIGN_KEY INVALID_NAME NAME_TOO_LONG RESERVED_NAME DATATYPE_MISMATCH INDETERMINATE_DATATYPE COLLATION_MISMATCH INDETERMINATE_COLLATION WRONG_OBJECT_TYPE GENERATED_ALWAYS UNDEFINED_COLUMN UNDEFINED_FUNCTION UNDEFINED_TABLE UNDEFINED_PARAMETER UNDEFINED_OBJECT DUPLICATE_COLUMN DUPLICATE_CURSOR DUPLICATE_DATABASE DUPLICATE_FUNCTION DUPLICATE_PREPARED_STATEMENT DUPLICATE_SCHEMA DUPLICATE_TABLE DUPLICATE_ALIAS DUPLICATE_OBJECT AMBIGUOUS_COLUMN AMBIGUOUS_FUNCTION AMBIGUOUS_PARAMETER AMBIGUOUS_ALIAS INVALID_COLUMN_REFERENCE INVALID_COLUMN_DEFINITION INVALID_CURSOR_DEFINITION INVALID_DATABASE_DEFINITION INVALID_FUNCTION_DEFINITION INVALID_PREPARED_STATEMENT_DEFINITION INVALID_SCHEMA_DEFINITION INVALID_TABLE_DEFINITION INVALID_OBJECT_DEFINITION WITH_CHECK_OPTION_VIOLATION INSUFFICIENT_RESOURCES DISK_FULL OUT_OF_MEMORY TOO_MANY_CONNECTIONS CONFIGURATION_LIMIT_EXCEEDED PROGRAM_LIMIT_EXCEEDED STATEMENT_TOO_COMPLEX TOO_MANY_COLUMNS TOO_MANY_ARGUMENTS OBJECT_NOT_IN_PREREQUISITE_STATE OBJECT_IN_USE CANT_CHANGE_RUNTIME_PARAM LOCK_NOT_AVAILABLE OPERATOR_INTERVENTION QUERY_CANCELED ADMIN_SHUTDOWN CRASH_SHUTDOWN CANNOT_CONNECT_NOW DATABASE_DROPPED SYSTEM_ERROR IO_ERROR UNDEFINED_FILE DUPLICATE_FILE SNAPSHOT_TOO_OLD CONFIG_FILE_ERROR LOCK_FILE_EXISTS FDW_ERROR FDW_COLUMN_NAME_NOT_FOUND FDW_DYNAMIC_PARAMETER_VALUE_NEEDED FDW_FUNCTION_SEQUENCE_ERROR FDW_INCONSISTENT_DESCRIPTOR_INFORMATION FDW_INVALID_ATTRIBUTE_VALUE FDW_INVALID_COLUMN_NAME FDW_INVALID_COLUMN_NUMBER FDW_INVALID_DATA_TYPE FDW_INVALID_DATA_TYPE_DESCRIPTORS FDW_INVALID_DESCRIPTOR_FIELD_IDENTIFIER FDW_INVALID_HANDLE FDW_INVALID_OPTION_INDEX FDW_INVALID_OPTION_NAME FDW_INVALID_STRING_LENGTH_OR_BUFFER_LENGTH FDW_INVALID_STRING_FORMAT FDW_INVALID_USE_OF_NULL_POINTER FDW_TOO_MANY_HANDLES FDW_OUT_OF_MEMORY FDW_NO_SCHEMAS FDW_OPTION_NAME_NOT_FOUND FDW_REPLY_HANDLE FDW_SCHEMA_NOT_FOUND FDW_TABLE_NOT_FOUND FDW_UNABLE_TO_CREATE_EXECUTION FDW_UNABLE_TO_CREATE_REPLY FDW_UNABLE_TO_ESTABLISH_CONNECTION PLPGSQL_ERROR RAISE_EXCEPTION NO_DATA_FOUND TOO_MANY_ROWS ASSERT_FAILURE INTERNAL_ERROR DATA_CORRUPTED INDEX_CORRUPTED ", + }, + illegal: /:==|\W\s*\(\*|(^|\s)\$[a-z]|\{\{|[a-z]:\s*$|\.\.\.|TO:|DO:/, + contains: [ + { + className: "keyword", + variants: [ + { begin: /\bTEXT\s*SEARCH\b/ }, + { begin: /\b(PRIMARY|FOREIGN|FOR(\s+NO)?)\s+KEY\b/ }, + { begin: /\bPARALLEL\s+(UNSAFE|RESTRICTED|SAFE)\b/ }, + { begin: /\bSTORAGE\s+(PLAIN|EXTERNAL|EXTENDED|MAIN)\b/ }, + { begin: /\bMATCH\s+(FULL|PARTIAL|SIMPLE)\b/ }, + { begin: /\bNULLS\s+(FIRST|LAST)\b/ }, + { begin: /\bEVENT\s+TRIGGER\b/ }, + { begin: /\b(MAPPING|OR)\s+REPLACE\b/ }, + { begin: /\b(FROM|TO)\s+(PROGRAM|STDIN|STDOUT)\b/ }, + { begin: /\b(SHARE|EXCLUSIVE)\s+MODE\b/ }, + { begin: /\b(LEFT|RIGHT)\s+(OUTER\s+)?JOIN\b/ }, + { + begin: + /\b(FETCH|MOVE)\s+(NEXT|PRIOR|FIRST|LAST|ABSOLUTE|RELATIVE|FORWARD|BACKWARD)\b/, + }, + { begin: /\bPRESERVE\s+ROWS\b/ }, + { begin: /\bDISCARD\s+PLANS\b/ }, + { begin: /\bREFERENCING\s+(OLD|NEW)\b/ }, + { begin: /\bSKIP\s+LOCKED\b/ }, + { begin: /\bGROUPING\s+SETS\b/ }, + { + begin: /\b(BINARY|INSENSITIVE|SCROLL|NO\s+SCROLL)\s+(CURSOR|FOR)\b/, + }, + { begin: /\b(WITH|WITHOUT)\s+HOLD\b/ }, + { begin: /\bWITH\s+(CASCADED|LOCAL)\s+CHECK\s+OPTION\b/ }, + { begin: /\bEXCLUDE\s+(TIES|NO\s+OTHERS)\b/ }, + { begin: /\bFORMAT\s+(TEXT|XML|JSON|YAML)\b/ }, + { begin: /\bSET\s+((SESSION|LOCAL)\s+)?NAMES\b/ }, + { begin: /\bIS\s+(NOT\s+)?UNKNOWN\b/ }, + { begin: /\bSECURITY\s+LABEL\b/ }, + { begin: /\bSTANDALONE\s+(YES|NO|NO\s+VALUE)\b/ }, + { begin: /\bWITH\s+(NO\s+)?DATA\b/ }, + { begin: /\b(FOREIGN|SET)\s+DATA\b/ }, + { begin: /\bSET\s+(CATALOG|CONSTRAINTS)\b/ }, + { begin: /\b(WITH|FOR)\s+ORDINALITY\b/ }, + { begin: /\bIS\s+(NOT\s+)?DOCUMENT\b/ }, + { begin: /\bXML\s+OPTION\s+(DOCUMENT|CONTENT)\b/ }, + { begin: /\b(STRIP|PRESERVE)\s+WHITESPACE\b/ }, + { begin: /\bNO\s+(ACTION|MAXVALUE|MINVALUE)\b/ }, + { begin: /\bPARTITION\s+BY\s+(RANGE|LIST|HASH)\b/ }, + { begin: /\bAT\s+TIME\s+ZONE\b/ }, + { begin: /\bGRANTED\s+BY\b/ }, + { begin: /\bRETURN\s+(QUERY|NEXT)\b/ }, + { begin: /\b(ATTACH|DETACH)\s+PARTITION\b/ }, + { begin: /\bFORCE\s+ROW\s+LEVEL\s+SECURITY\b/ }, + { + begin: + /\b(INCLUDING|EXCLUDING)\s+(COMMENTS|CONSTRAINTS|DEFAULTS|IDENTITY|INDEXES|STATISTICS|STORAGE|ALL)\b/, + }, + { + begin: + /\bAS\s+(ASSIGNMENT|IMPLICIT|PERMISSIVE|RESTRICTIVE|ENUM|RANGE)\b/, + }, + ], + }, + { begin: /\b(FORMAT|FAMILY|VERSION)\s*\(/ }, + { begin: /\bINCLUDE\s*\(/, keywords: "INCLUDE" }, + { begin: /\bRANGE(?!\s*(BETWEEN|UNBOUNDED|CURRENT|[-0-9]+))/ }, + { + begin: + /\b(VERSION|OWNER|TEMPLATE|TABLESPACE|CONNECTION\s+LIMIT|PROCEDURE|RESTRICT|JOIN|PARSER|COPY|START|END|COLLATION|INPUT|ANALYZE|STORAGE|LIKE|DEFAULT|DELIMITER|ENCODING|COLUMN|CONSTRAINT|TABLE|SCHEMA)\s*=/, + }, + { begin: /\b(PG_\w+?|HAS_[A-Z_]+_PRIVILEGE)\b/, relevance: 10 }, + { + begin: /\bEXTRACT\s*\(/, + end: /\bFROM\b/, + returnEnd: !0, + keywords: { + type: "CENTURY DAY DECADE DOW DOY EPOCH HOUR ISODOW ISOYEAR MICROSECONDS MILLENNIUM MILLISECONDS MINUTE MONTH QUARTER SECOND TIMEZONE TIMEZONE_HOUR TIMEZONE_MINUTE WEEK YEAR", + }, + }, + { + begin: /\b(XMLELEMENT|XMLPI)\s*\(\s*NAME/, + keywords: { keyword: "NAME" }, + }, + { + begin: /\b(XMLPARSE|XMLSERIALIZE)\s*\(\s*(DOCUMENT|CONTENT)/, + keywords: { keyword: "DOCUMENT CONTENT" }, + }, + { + beginKeywords: "CACHE INCREMENT MAXVALUE MINVALUE", + end: e.C_NUMBER_RE, + returnEnd: !0, + keywords: "BY CACHE INCREMENT MAXVALUE MINVALUE", + }, + { className: "type", begin: /\b(WITH|WITHOUT)\s+TIME\s+ZONE\b/ }, + { + className: "type", + begin: + /\bINTERVAL\s+(YEAR|MONTH|DAY|HOUR|MINUTE|SECOND)(\s+TO\s+(MONTH|HOUR|MINUTE|SECOND))?\b/, + }, + { + begin: + /\bRETURNS\s+(LANGUAGE_HANDLER|TRIGGER|EVENT_TRIGGER|FDW_HANDLER|INDEX_AM_HANDLER|TSM_HANDLER)\b/, + keywords: { + keyword: "RETURNS", + type: "LANGUAGE_HANDLER TRIGGER EVENT_TRIGGER FDW_HANDLER INDEX_AM_HANDLER TSM_HANDLER", + }, + }, + { begin: "\\b(" + i + ")\\s*\\(" }, + { begin: "\\.(" + r + ")\\b" }, + { + begin: "\\b(" + r + ")\\s+PATH\\b", + keywords: { keyword: "PATH", type: a.replace("PATH ", "") }, + }, + { className: "type", begin: "\\b(" + r + ")\\b" }, + { + className: "string", + begin: "'", + end: "'", + contains: [{ begin: "''" }], + }, + { + className: "string", + begin: "(e|E|u&|U&)'", + end: "'", + contains: [{ begin: "\\\\." }], + relevance: 10, + }, + e.END_SAME_AS_BEGIN({ + begin: n, + end: n, + contains: [ + { + subLanguage: [ + "pgsql", + "perl", + "python", + "tcl", + "r", + "lua", + "java", + "php", + "ruby", + "bash", + "scheme", + "xml", + "json", + ], + endsWithParent: !0, + }, + ], + }), + { begin: '"', end: '"', contains: [{ begin: '""' }] }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + { + className: "meta", + variants: [ + { begin: "%(ROW)?TYPE", relevance: 10 }, + { begin: "\\$\\d+" }, + { begin: "^#\\w", end: "$" }, + ], + }, + { + className: "symbol", + begin: "<<\\s*[a-zA-Z_][a-zA-Z_0-9$]*\\s*>>", + relevance: 10, + }, + ], + }; +}; +var QS = function (e) { + var t = { + className: "variable", + begin: "\\$+[a-zA-Z_-ÿ][a-zA-Z0-9_-ÿ]*(?![A-Za-z0-9])(?![$])", + }, + n = { + className: "meta", + variants: [ + { begin: /<\?php/, relevance: 10 }, + { begin: /<\?[=]?/ }, + { begin: /\?>/ }, + ], + }, + a = { + className: "subst", + variants: [{ begin: /\$\w+/ }, { begin: /\{\$/, end: /\}/ }], + }, + r = e.inherit(e.APOS_STRING_MODE, { illegal: null }), + i = e.inherit(e.QUOTE_STRING_MODE, { + illegal: null, + contains: e.QUOTE_STRING_MODE.contains.concat(a), + }), + o = e.END_SAME_AS_BEGIN({ + begin: /<<<[ \t]*(\w+)\n/, + end: /[ \t]*(\w+)\b/, + contains: e.QUOTE_STRING_MODE.contains.concat(a), + }), + s = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, n], + variants: [ + e.inherit(r, { begin: "b'", end: "'" }), + e.inherit(i, { begin: 'b"', end: '"' }), + i, + r, + o, + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b0b[01]+(?:_[01]+)*\\b" }, + { begin: "\\b0o[0-7]+(?:_[0-7]+)*\\b" }, + { begin: "\\b0x[\\da-f]+(?:_[\\da-f]+)*\\b" }, + { + begin: + "(?:\\b\\d+(?:_\\d+)*(\\.(?:\\d+(?:_\\d+)*))?|\\B\\.\\d+)(?:e[+-]?\\d+)?", + }, + ], + relevance: 0, + }, + c = { + keyword: + "__CLASS__ __DIR__ __FILE__ __FUNCTION__ __LINE__ __METHOD__ __NAMESPACE__ __TRAIT__ die echo exit include include_once print require require_once array abstract and as binary bool boolean break callable case catch class clone const continue declare default do double else elseif empty enddeclare endfor endforeach endif endswitch endwhile enum eval extends final finally float for foreach from global goto if implements instanceof insteadof int integer interface isset iterable list match|0 mixed new object or private protected public real return string switch throw trait try unset use var void while xor yield", + literal: "false null true", + built_in: + "Error|0 AppendIterator ArgumentCountError ArithmeticError ArrayIterator ArrayObject AssertionError BadFunctionCallException BadMethodCallException CachingIterator CallbackFilterIterator CompileError Countable DirectoryIterator DivisionByZeroError DomainException EmptyIterator ErrorException Exception FilesystemIterator FilterIterator GlobIterator InfiniteIterator InvalidArgumentException IteratorIterator LengthException LimitIterator LogicException MultipleIterator NoRewindIterator OutOfBoundsException OutOfRangeException OuterIterator OverflowException ParentIterator ParseError RangeException RecursiveArrayIterator RecursiveCachingIterator RecursiveCallbackFilterIterator RecursiveDirectoryIterator RecursiveFilterIterator RecursiveIterator RecursiveIteratorIterator RecursiveRegexIterator RecursiveTreeIterator RegexIterator RuntimeException SeekableIterator SplDoublyLinkedList SplFileInfo SplFileObject SplFixedArray SplHeap SplMaxHeap SplMinHeap SplObjectStorage SplObserver SplObserver SplPriorityQueue SplQueue SplStack SplSubject SplSubject SplTempFileObject TypeError UnderflowException UnexpectedValueException UnhandledMatchError ArrayAccess Closure Generator Iterator IteratorAggregate Serializable Stringable Throwable Traversable WeakReference WeakMap Directory __PHP_Incomplete_Class parent php_user_filter self static stdClass", + }; + return { + aliases: ["php3", "php4", "php5", "php6", "php7", "php8"], + case_insensitive: !0, + keywords: c, + contains: [ + e.HASH_COMMENT_MODE, + e.COMMENT("//", "$", { contains: [n] }), + e.COMMENT("/\\*", "\\*/", { + contains: [{ className: "doctag", begin: "@[A-Za-z]+" }], + }), + e.COMMENT("__halt_compiler.+?;", !1, { + endsWithParent: !0, + keywords: "__halt_compiler", + }), + n, + { className: "keyword", begin: /\$this\b/ }, + t, + { begin: /(::|->)+[a-zA-Z_\x7f-\xff][a-zA-Z0-9_\x7f-\xff]*/ }, + { + className: "function", + relevance: 0, + beginKeywords: "fn function", + end: /[;{]/, + excludeEnd: !0, + illegal: "[$%\\[]", + contains: [ + { beginKeywords: "use" }, + e.UNDERSCORE_TITLE_MODE, + { begin: "=>", endsParent: !0 }, + { + className: "params", + begin: "\\(", + end: "\\)", + excludeBegin: !0, + excludeEnd: !0, + keywords: c, + contains: ["self", t, e.C_BLOCK_COMMENT_MODE, s, l], + }, + ], + }, + { + className: "class", + variants: [ + { beginKeywords: "enum", illegal: /[($"]/ }, + { beginKeywords: "class interface trait", illegal: /[:($"]/ }, + ], + relevance: 0, + end: /\{/, + excludeEnd: !0, + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { + beginKeywords: "namespace", + relevance: 0, + end: ";", + illegal: /[.']/, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + beginKeywords: "use", + relevance: 0, + end: ";", + contains: [e.UNDERSCORE_TITLE_MODE], + }, + s, + l, + ], + }; +}; +var KS = function (e) { + return { + name: "PHP template", + subLanguage: "xml", + contains: [ + { + begin: /<\?(php|=)?/, + end: /\?>/, + subLanguage: "php", + contains: [ + { begin: "/\\*", end: "\\*/", skip: !0 }, + { begin: 'b"', end: '"', skip: !0 }, + { begin: "b'", end: "'", skip: !0 }, + e.inherit(e.APOS_STRING_MODE, { + illegal: null, + className: null, + contains: null, + skip: !0, + }), + e.inherit(e.QUOTE_STRING_MODE, { + illegal: null, + className: null, + contains: null, + skip: !0, + }), + ], + }, + ], + }; +}; +var jS = function (e) { + return { + name: "Plain text", + aliases: ["text", "txt"], + disableAutodetect: !0, + }; +}; +var XS = function (e) { + return { + name: "Pony", + keywords: { + keyword: + "actor addressof and as be break class compile_error compile_intrinsic consume continue delegate digestof do else elseif embed end error for fun if ifdef in interface is isnt lambda let match new not object or primitive recover repeat return struct then trait try type until use var where while with xor", + meta: "iso val tag trn box ref", + literal: "this false true", + }, + contains: [ + { className: "type", begin: "\\b_?[A-Z][\\w]*", relevance: 0 }, + { className: "string", begin: '"""', end: '"""', relevance: 10 }, + { + className: "string", + begin: '"', + end: '"', + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "string", + begin: "'", + end: "'", + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + { begin: e.IDENT_RE + "'", relevance: 0 }, + { + className: "number", + begin: + "(-?)(\\b0[xX][a-fA-F0-9]+|\\b0[bB][01]+|(\\b\\d+(_\\d+)?(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)", + relevance: 0, + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var ZS = function (e) { + var t = { + $pattern: /-?[A-z\.\-]+\b/, + keyword: + "if else foreach return do while until elseif begin for trap data dynamicparam end break throw param continue finally in switch exit filter try process catch hidden static parameter", + built_in: + "ac asnp cat cd CFS chdir clc clear clhy cli clp cls clv cnsn compare copy cp cpi cpp curl cvpa dbp del diff dir dnsn ebp echo|0 epal epcsv epsn erase etsn exsn fc fhx fl ft fw gal gbp gc gcb gci gcm gcs gdr gerr ghy gi gin gjb gl gm gmo gp gps gpv group gsn gsnp gsv gtz gu gv gwmi h history icm iex ihy ii ipal ipcsv ipmo ipsn irm ise iwmi iwr kill lp ls man md measure mi mount move mp mv nal ndr ni nmo npssc nsn nv ogv oh popd ps pushd pwd r rbp rcjb rcsn rd rdr ren ri rjb rm rmdir rmo rni rnp rp rsn rsnp rujb rv rvpa rwmi sajb sal saps sasv sbp sc scb select set shcm si sl sleep sls sort sp spjb spps spsv start stz sujb sv swmi tee trcm type wget where wjb write", + }, + n = { begin: "`[\\s\\S]", relevance: 0 }, + a = { + className: "variable", + variants: [ + { begin: /\$\B/ }, + { className: "keyword", begin: /\$this/ }, + { begin: /\$[\w\d][\w\d_:]*/ }, + ], + }, + r = { + className: "string", + variants: [ + { begin: /"/, end: /"/ }, + { begin: /@"/, end: /^"@/ }, + ], + contains: [ + n, + a, + { className: "variable", begin: /\$[A-z]/, end: /[^A-z]/ }, + ], + }, + i = { + className: "string", + variants: [ + { begin: /'/, end: /'/ }, + { begin: /@'/, end: /^'@/ }, + ], + }, + o = e.inherit(e.COMMENT(null, null), { + variants: [ + { begin: /#/, end: /$/ }, + { begin: /<#/, end: /#>/ }, + ], + contains: [ + { + className: "doctag", + variants: [ + { + begin: + /\.(synopsis|description|example|inputs|outputs|notes|link|component|role|functionality)/, + }, + { + begin: + /\.(parameter|forwardhelptargetname|forwardhelpcategory|remotehelprunspace|externalhelp)\s+\S+/, + }, + ], + }, + ], + }), + s = { + className: "built_in", + variants: [ + { + begin: "(".concat( + "Add|Clear|Close|Copy|Enter|Exit|Find|Format|Get|Hide|Join|Lock|Move|New|Open|Optimize|Pop|Push|Redo|Remove|Rename|Reset|Resize|Search|Select|Set|Show|Skip|Split|Step|Switch|Undo|Unlock|Watch|Backup|Checkpoint|Compare|Compress|Convert|ConvertFrom|ConvertTo|Dismount|Edit|Expand|Export|Group|Import|Initialize|Limit|Merge|Mount|Out|Publish|Restore|Save|Sync|Unpublish|Update|Approve|Assert|Build|Complete|Confirm|Deny|Deploy|Disable|Enable|Install|Invoke|Register|Request|Restart|Resume|Start|Stop|Submit|Suspend|Uninstall|Unregister|Wait|Debug|Measure|Ping|Repair|Resolve|Test|Trace|Connect|Disconnect|Read|Receive|Send|Write|Block|Grant|Protect|Revoke|Unblock|Unprotect|Use|ForEach|Sort|Tee|Where", + ")+(-)[\\w\\d]+", + ), + }, + ], + }, + l = { + className: "class", + beginKeywords: "class enum", + end: /\s*[{]/, + excludeEnd: !0, + relevance: 0, + contains: [e.TITLE_MODE], + }, + c = { + className: "function", + begin: /function\s+/, + end: /\s*\{|$/, + excludeEnd: !0, + returnBegin: !0, + relevance: 0, + contains: [ + { begin: "function", relevance: 0, className: "keyword" }, + { className: "title", begin: /\w[\w\d]*((-)[\w\d]+)*/, relevance: 0 }, + { + begin: /\(/, + end: /\)/, + className: "params", + relevance: 0, + contains: [a], + }, + ], + }, + _ = { + begin: /using\s/, + end: /$/, + returnBegin: !0, + contains: [ + r, + i, + { + className: "keyword", + begin: /(using|assembly|command|module|namespace|type)/, + }, + ], + }, + d = { + variants: [ + { + className: "operator", + begin: "(".concat( + "-and|-as|-band|-bnot|-bor|-bxor|-casesensitive|-ccontains|-ceq|-cge|-cgt|-cle|-clike|-clt|-cmatch|-cne|-cnotcontains|-cnotlike|-cnotmatch|-contains|-creplace|-csplit|-eq|-exact|-f|-file|-ge|-gt|-icontains|-ieq|-ige|-igt|-ile|-ilike|-ilt|-imatch|-in|-ine|-inotcontains|-inotlike|-inotmatch|-ireplace|-is|-isnot|-isplit|-join|-le|-like|-lt|-match|-ne|-not|-notcontains|-notin|-notlike|-notmatch|-or|-regex|-replace|-shl|-shr|-split|-wildcard|-xor", + ")\\b", + ), + }, + { className: "literal", begin: /(-)[\w\d]+/, relevance: 0 }, + ], + }, + u = { + className: "function", + begin: /\[.*\]\s*[\w]+[ ]??\(/, + end: /$/, + returnBegin: !0, + relevance: 0, + contains: [ + { + className: "keyword", + begin: "(".concat(t.keyword.toString().replace(/\s/g, "|"), ")\\b"), + endsParent: !0, + relevance: 0, + }, + e.inherit(e.TITLE_MODE, { endsParent: !0 }), + ], + }, + m = [ + u, + o, + n, + e.NUMBER_MODE, + r, + i, + s, + a, + { className: "literal", begin: /\$(null|true|false)\b/ }, + { className: "selector-tag", begin: /@\B/, relevance: 0 }, + ], + p = { + begin: /\[/, + end: /\]/, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + contains: [].concat( + "self", + m, + { + begin: + "(" + + [ + "string", + "char", + "byte", + "int", + "long", + "bool", + "decimal", + "single", + "double", + "DateTime", + "xml", + "array", + "hashtable", + "void", + ].join("|") + + ")", + className: "built_in", + relevance: 0, + }, + { className: "type", begin: /[\.\w\d]+/, relevance: 0 }, + ), + }; + return ( + u.contains.unshift(p), + { + name: "PowerShell", + aliases: ["ps", "ps1"], + case_insensitive: !0, + keywords: t, + contains: m.concat(l, c, _, d, p), + } + ); +}; +var JS = function (e) { + return { + name: "Processing", + keywords: { + keyword: + "BufferedReader PVector PFont PImage PGraphics HashMap boolean byte char color double float int long String Array FloatDict FloatList IntDict IntList JSONArray JSONObject Object StringDict StringList Table TableRow XML false synchronized int abstract float private char boolean static null if const for true while long throw strictfp finally protected import native final return void enum else break transient new catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private", + literal: "P2D P3D HALF_PI PI QUARTER_PI TAU TWO_PI", + title: "setup draw", + built_in: + "displayHeight displayWidth mouseY mouseX mousePressed pmouseX pmouseY key keyCode pixels focused frameCount frameRate height width size createGraphics beginDraw createShape loadShape PShape arc ellipse line point quad rect triangle bezier bezierDetail bezierPoint bezierTangent curve curveDetail curvePoint curveTangent curveTightness shape shapeMode beginContour beginShape bezierVertex curveVertex endContour endShape quadraticVertex vertex ellipseMode noSmooth rectMode smooth strokeCap strokeJoin strokeWeight mouseClicked mouseDragged mouseMoved mousePressed mouseReleased mouseWheel keyPressed keyPressedkeyReleased keyTyped print println save saveFrame day hour millis minute month second year background clear colorMode fill noFill noStroke stroke alpha blue brightness color green hue lerpColor red saturation modelX modelY modelZ screenX screenY screenZ ambient emissive shininess specular add createImage beginCamera camera endCamera frustum ortho perspective printCamera printProjection cursor frameRate noCursor exit loop noLoop popStyle pushStyle redraw binary boolean byte char float hex int str unbinary unhex join match matchAll nf nfc nfp nfs split splitTokens trim append arrayCopy concat expand reverse shorten sort splice subset box sphere sphereDetail createInput createReader loadBytes loadJSONArray loadJSONObject loadStrings loadTable loadXML open parseXML saveTable selectFolder selectInput beginRaw beginRecord createOutput createWriter endRaw endRecord PrintWritersaveBytes saveJSONArray saveJSONObject saveStream saveStrings saveXML selectOutput popMatrix printMatrix pushMatrix resetMatrix rotate rotateX rotateY rotateZ scale shearX shearY translate ambientLight directionalLight lightFalloff lights lightSpecular noLights normal pointLight spotLight image imageMode loadImage noTint requestImage tint texture textureMode textureWrap blend copy filter get loadPixels set updatePixels blendMode loadShader PShaderresetShader shader createFont loadFont text textFont textAlign textLeading textMode textSize textWidth textAscent textDescent abs ceil constrain dist exp floor lerp log mag map max min norm pow round sq sqrt acos asin atan atan2 cos degrees radians sin tan noise noiseDetail noiseSeed random randomGaussian randomSeed", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + ], + }; +}; +var eb = function (e) { + return { + name: "Python profiler", + contains: [ + e.C_NUMBER_MODE, + { + begin: "[a-zA-Z_][\\da-zA-Z_]+\\.[\\da-zA-Z_]{1,3}", + end: ":", + excludeEnd: !0, + }, + { + begin: "(ncalls|tottime|cumtime)", + end: "$", + keywords: "ncalls tottime|10 cumtime|10 filename", + relevance: 10, + }, + { + begin: "function calls", + end: "$", + contains: [e.C_NUMBER_MODE], + relevance: 10, + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { + className: "string", + begin: "\\(", + end: "\\)$", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + ], + }; +}; +var tb = function (e) { + var t = { begin: /\(/, end: /\)/, relevance: 0 }, + n = { begin: /\[/, end: /\]/ }, + a = { + className: "comment", + begin: /%/, + end: /$/, + contains: [e.PHRASAL_WORDS_MODE], + }, + r = { + className: "string", + begin: /`/, + end: /`/, + contains: [e.BACKSLASH_ESCAPE], + }, + i = [ + { begin: /[a-z][A-Za-z0-9_]*/, relevance: 0 }, + { + className: "symbol", + variants: [ + { begin: /[A-Z][a-zA-Z0-9_]*/ }, + { begin: /_[A-Za-z0-9_]*/ }, + ], + relevance: 0, + }, + t, + { begin: /:-/ }, + n, + a, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + r, + { className: "string", begin: /0'(\\'|.)/ }, + { className: "string", begin: /0'\\s/ }, + e.C_NUMBER_MODE, + ]; + return ( + (t.contains = i), + (n.contains = i), + { name: "Prolog", contains: i.concat([{ begin: /\.$/ }]) } + ); +}; +var nb = function (e) { + var t = "[ \\t\\f]*", + n = t + "[:=]" + t, + a = "[ \\t\\f]+", + r = "(" + n + "|" + "[ \\t\\f]+)", + i = "([^\\\\\\W:= \\t\\f\\n]|\\\\.)+", + o = "([^\\\\:= \\t\\f\\n]|\\\\.)+", + s = { + end: r, + relevance: 0, + starts: { + className: "string", + end: /$/, + relevance: 0, + contains: [{ begin: "\\\\\\\\" }, { begin: "\\\\\\n" }], + }, + }; + return { + name: ".properties", + case_insensitive: !0, + illegal: /\S/, + contains: [ + e.COMMENT("^\\s*[!#]", "$"), + { + returnBegin: !0, + variants: [ + { begin: i + n, relevance: 1 }, + { begin: i + a, relevance: 0 }, + ], + contains: [ + { className: "attr", begin: i, endsParent: !0, relevance: 0 }, + ], + starts: s, + }, + { + begin: o + r, + returnBegin: !0, + relevance: 0, + contains: [ + { className: "meta", begin: o, endsParent: !0, relevance: 0 }, + ], + starts: s, + }, + { className: "attr", relevance: 0, begin: o + t + "$" }, + ], + }; +}; +var ab = function (e) { + return { + name: "Protocol Buffers", + keywords: { + keyword: "package import option optional required repeated group oneof", + built_in: + "double float int32 int64 uint32 uint64 sint32 sint64 fixed32 fixed64 sfixed32 sfixed64 bool string bytes", + literal: "true false", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "class", + beginKeywords: "message enum service", + end: /\{/, + illegal: /\n/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, excludeEnd: !0 }, + }), + ], + }, + { + className: "function", + beginKeywords: "rpc", + end: /[{;]/, + excludeEnd: !0, + keywords: "rpc returns", + }, + { begin: /^\s*[A-Z_]+(?=\s*=[^\n]+;$)/ }, + ], + }; +}; +var rb = function (e) { + var t = e.COMMENT("#", "$"), + n = "([A-Za-z_]|::)(\\w|::)*", + a = e.inherit(e.TITLE_MODE, { begin: n }), + r = { className: "variable", begin: "\\$" + n }, + i = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, r], + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + ], + }; + return { + name: "Puppet", + aliases: ["pp"], + contains: [ + t, + r, + i, + { beginKeywords: "class", end: "\\{|;", illegal: /=/, contains: [a, t] }, + { + beginKeywords: "define", + end: /\{/, + contains: [{ className: "section", begin: e.IDENT_RE, endsParent: !0 }], + }, + { + begin: e.IDENT_RE + "\\s+\\{", + returnBegin: !0, + end: /\S/, + contains: [ + { className: "keyword", begin: e.IDENT_RE }, + { + begin: /\{/, + end: /\}/, + keywords: { + keyword: + "and case default else elsif false if in import enherits node or true undef unless main settings $string ", + literal: + "alias audit before loglevel noop require subscribe tag owner ensure group mode name|0 changes context force incl lens load_path onlyif provider returns root show_diff type_check en_address ip_address realname command environment hour monute month monthday special target weekday creates cwd ogoutput refresh refreshonly tries try_sleep umask backup checksum content ctime force ignore links mtime purge recurse recurselimit replace selinux_ignore_defaults selrange selrole seltype seluser source souirce_permissions sourceselect validate_cmd validate_replacement allowdupe attribute_membership auth_membership forcelocal gid ia_load_module members system host_aliases ip allowed_trunk_vlans description device_url duplex encapsulation etherchannel native_vlan speed principals allow_root auth_class auth_type authenticate_user k_of_n mechanisms rule session_owner shared options device fstype enable hasrestart directory present absent link atboot blockdevice device dump pass remounts poller_tag use message withpath adminfile allow_virtual allowcdrom category configfiles flavor install_options instance package_settings platform responsefile status uninstall_options vendor unless_system_user unless_uid binary control flags hasstatus manifest pattern restart running start stop allowdupe auths expiry gid groups home iterations key_membership keys managehome membership password password_max_age password_min_age profile_membership profiles project purge_ssh_keys role_membership roles salt shell uid baseurl cost descr enabled enablegroups exclude failovermethod gpgcheck gpgkey http_caching include includepkgs keepalive metadata_expire metalink mirrorlist priority protect proxy proxy_password proxy_username repo_gpgcheck s3_enabled skip_if_unavailable sslcacert sslclientcert sslclientkey sslverify mounted", + built_in: + "architecture augeasversion blockdevices boardmanufacturer boardproductname boardserialnumber cfkey dhcp_servers domain ec2_ ec2_userdata facterversion filesystems ldom fqdn gid hardwareisa hardwaremodel hostname id|0 interfaces ipaddress ipaddress_ ipaddress6 ipaddress6_ iphostnumber is_virtual kernel kernelmajversion kernelrelease kernelversion kernelrelease kernelversion lsbdistcodename lsbdistdescription lsbdistid lsbdistrelease lsbmajdistrelease lsbminordistrelease lsbrelease macaddress macaddress_ macosx_buildversion macosx_productname macosx_productversion macosx_productverson_major macosx_productversion_minor manufacturer memoryfree memorysize netmask metmask_ network_ operatingsystem operatingsystemmajrelease operatingsystemrelease osfamily partitions path physicalprocessorcount processor processorcount productname ps puppetversion rubysitedir rubyversion selinux selinux_config_mode selinux_config_policy selinux_current_mode selinux_current_mode selinux_enforced selinux_policyversion serialnumber sp_ sshdsakey sshecdsakey sshrsakey swapencrypted swapfree swapsize timezone type uniqueid uptime uptime_days uptime_hours uptime_seconds uuid virtual vlans xendomains zfs_version zonenae zones zpool_version", + }, + relevance: 0, + contains: [ + i, + t, + { + begin: "[a-zA-Z_]+\\s*=>", + returnBegin: !0, + end: "=>", + contains: [{ className: "attr", begin: e.IDENT_RE }], + }, + { + className: "number", + begin: + "(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b", + relevance: 0, + }, + r, + ], + }, + ], + relevance: 0, + }, + ], + }; +}; +var ib = function (e) { + return { + name: "PureBASIC", + aliases: ["pb", "pbi"], + keywords: + "Align And Array As Break CallDebugger Case CompilerCase CompilerDefault CompilerElse CompilerElseIf CompilerEndIf CompilerEndSelect CompilerError CompilerIf CompilerSelect CompilerWarning Continue Data DataSection Debug DebugLevel Declare DeclareC DeclareCDLL DeclareDLL DeclareModule Default Define Dim DisableASM DisableDebugger DisableExplicit Else ElseIf EnableASM EnableDebugger EnableExplicit End EndDataSection EndDeclareModule EndEnumeration EndIf EndImport EndInterface EndMacro EndModule EndProcedure EndSelect EndStructure EndStructureUnion EndWith Enumeration EnumerationBinary Extends FakeReturn For ForEach ForEver Global Gosub Goto If Import ImportC IncludeBinary IncludeFile IncludePath Interface List Macro MacroExpandedCount Map Module NewList NewMap Next Not Or Procedure ProcedureC ProcedureCDLL ProcedureDLL ProcedureReturn Protected Prototype PrototypeC ReDim Read Repeat Restore Return Runtime Select Shared Static Step Structure StructureUnion Swap Threaded To UndefineMacro Until Until UnuseModule UseModule Wend While With XIncludeFile XOr", + contains: [ + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "function", + begin: "\\b(Procedure|Declare)(C|CDLL|DLL)?\\b", + end: "\\(", + excludeEnd: !0, + returnBegin: !0, + contains: [ + { + className: "keyword", + begin: "(Procedure|Declare)(C|CDLL|DLL)?", + excludeEnd: !0, + }, + { className: "type", begin: "\\.\\w*" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { className: "string", begin: '(~)?"', end: '"', illegal: "\\n" }, + { className: "symbol", begin: "#[a-zA-Z_]\\w*\\$?" }, + ], + }; +}; +function ob(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function sb(e) { + return (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return t + .map(function (e) { + return ob(e); + }) + .join(""); + })("(?=", e, ")"); +} +var lb = function (e) { + var t = { + $pattern: /[A-Za-z]\w+|__\w+__/, + keyword: [ + "and", + "as", + "assert", + "async", + "await", + "break", + "class", + "continue", + "def", + "del", + "elif", + "else", + "except", + "finally", + "for", + "from", + "global", + "if", + "import", + "in", + "is", + "lambda", + "nonlocal|10", + "not", + "or", + "pass", + "raise", + "return", + "try", + "while", + "with", + "yield", + ], + built_in: [ + "__import__", + "abs", + "all", + "any", + "ascii", + "bin", + "bool", + "breakpoint", + "bytearray", + "bytes", + "callable", + "chr", + "classmethod", + "compile", + "complex", + "delattr", + "dict", + "dir", + "divmod", + "enumerate", + "eval", + "exec", + "filter", + "float", + "format", + "frozenset", + "getattr", + "globals", + "hasattr", + "hash", + "help", + "hex", + "id", + "input", + "int", + "isinstance", + "issubclass", + "iter", + "len", + "list", + "locals", + "map", + "max", + "memoryview", + "min", + "next", + "object", + "oct", + "open", + "ord", + "pow", + "print", + "property", + "range", + "repr", + "reversed", + "round", + "set", + "setattr", + "slice", + "sorted", + "staticmethod", + "str", + "sum", + "super", + "tuple", + "type", + "vars", + "zip", + ], + literal: [ + "__debug__", + "Ellipsis", + "False", + "None", + "NotImplemented", + "True", + ], + type: [ + "Any", + "Callable", + "Coroutine", + "Dict", + "List", + "Literal", + "Generic", + "Optional", + "Sequence", + "Set", + "Tuple", + "Type", + "Union", + ], + }, + n = { className: "meta", begin: /^(>>>|\.\.\.) / }, + a = { + className: "subst", + begin: /\{/, + end: /\}/, + keywords: t, + illegal: /#/, + }, + r = { begin: /\{\{/, relevance: 0 }, + i = { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { + begin: /([uU]|[bB]|[rR]|[bB][rR]|[rR][bB])?'''/, + end: /'''/, + contains: [e.BACKSLASH_ESCAPE, n], + relevance: 10, + }, + { + begin: /([uU]|[bB]|[rR]|[bB][rR]|[rR][bB])?"""/, + end: /"""/, + contains: [e.BACKSLASH_ESCAPE, n], + relevance: 10, + }, + { + begin: /([fF][rR]|[rR][fF]|[fF])'''/, + end: /'''/, + contains: [e.BACKSLASH_ESCAPE, n, r, a], + }, + { + begin: /([fF][rR]|[rR][fF]|[fF])"""/, + end: /"""/, + contains: [e.BACKSLASH_ESCAPE, n, r, a], + }, + { begin: /([uU]|[rR])'/, end: /'/, relevance: 10 }, + { begin: /([uU]|[rR])"/, end: /"/, relevance: 10 }, + { begin: /([bB]|[bB][rR]|[rR][bB])'/, end: /'/ }, + { begin: /([bB]|[bB][rR]|[rR][bB])"/, end: /"/ }, + { + begin: /([fF][rR]|[rR][fF]|[fF])'/, + end: /'/, + contains: [e.BACKSLASH_ESCAPE, r, a], + }, + { + begin: /([fF][rR]|[rR][fF]|[fF])"/, + end: /"/, + contains: [e.BACKSLASH_ESCAPE, r, a], + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + }, + o = "[0-9](_?[0-9])*", + s = "(\\b(".concat(o, "))?\\.(").concat(o, ")|\\b(").concat(o, ")\\."), + l = { + className: "number", + relevance: 0, + variants: [ + { + begin: "(\\b(" + .concat(o, ")|(") + .concat(s, "))[eE][+-]?(") + .concat(o, ")[jJ]?\\b"), + }, + { begin: "(".concat(s, ")[jJ]?") }, + { begin: "\\b([1-9](_?[0-9])*|0+(_?0)*)[lLjJ]?\\b" }, + { begin: "\\b0[bB](_?[01])+[lL]?\\b" }, + { begin: "\\b0[oO](_?[0-7])+[lL]?\\b" }, + { begin: "\\b0[xX](_?[0-9a-fA-F])+[lL]?\\b" }, + { begin: "\\b(".concat(o, ")[jJ]\\b") }, + ], + }, + c = { + className: "comment", + begin: sb(/# type:/), + end: /$/, + keywords: t, + contains: [ + { begin: /# type:/ }, + { begin: /#/, end: /\b\B/, endsWithParent: !0 }, + ], + }, + _ = { + className: "params", + variants: [ + { className: "", begin: /\(\s*\)/, skip: !0 }, + { + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: t, + contains: ["self", n, l, i, e.HASH_COMMENT_MODE], + }, + ], + }; + return ( + (a.contains = [i, l, n]), + { + name: "Python", + aliases: ["py", "gyp", "ipython"], + keywords: t, + illegal: /(<\/|->|\?)|=>/, + contains: [ + n, + l, + { begin: /\bself\b/ }, + { beginKeywords: "if", relevance: 0 }, + i, + c, + e.HASH_COMMENT_MODE, + { + variants: [ + { className: "function", beginKeywords: "def" }, + { className: "class", beginKeywords: "class" }, + ], + end: /:/, + illegal: /[${=;\n,]/, + contains: [ + e.UNDERSCORE_TITLE_MODE, + _, + { begin: /->/, endsWithParent: !0, keywords: t }, + ], + }, + { + className: "meta", + begin: /^[\t ]*@/, + end: /(?=#)|$/, + contains: [l, _, i], + }, + ], + } + ); +}; +var cb = function (e) { + return { + aliases: ["pycon"], + contains: [ + { + className: "meta", + starts: { end: / |$/, starts: { end: "$", subLanguage: "python" } }, + variants: [{ begin: /^>>>(?=[ ]|$)/ }, { begin: /^\.\.\.(?=[ ]|$)/ }], + }, + ], + }; +}; +var _b = function (e) { + return { + name: "Q", + aliases: ["k", "kdb"], + keywords: { + $pattern: /(`?)[A-Za-z0-9_]+\b/, + keyword: "do while select delete by update from", + literal: "0b 1b", + built_in: + "neg not null string reciprocal floor ceiling signum mod xbar xlog and or each scan over prior mmu lsq inv md5 ltime gtime count first var dev med cov cor all any rand sums prds mins maxs fills deltas ratios avgs differ prev next rank reverse iasc idesc asc desc msum mcount mavg mdev xrank mmin mmax xprev rotate distinct group where flip type key til get value attr cut set upsert raze union inter except cross sv vs sublist enlist read0 read1 hopen hclose hdel hsym hcount peach system ltrim rtrim trim lower upper ssr view tables views cols xcols keys xkey xcol xasc xdesc fkeys meta lj aj aj0 ij pj asof uj ww wj wj1 fby xgroup ungroup ej save load rsave rload show csv parse eval min max avg wavg wsum sin cos tan sum", + type: "`float `double int `timestamp `timespan `datetime `time `boolean `symbol `char `byte `short `long `real `month `date `minute `second `guid", + }, + contains: [e.C_LINE_COMMENT_MODE, e.QUOTE_STRING_MODE, e.C_NUMBER_MODE], + }; +}; +function db(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function ub() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return db(e); + }) + .join(""); + return a; +} +var mb = function (e) { + var t = "[a-zA-Z_][a-zA-Z0-9\\._]*", + n = { + className: "attribute", + begin: "\\bid\\s*:", + starts: { className: "string", end: t, returnEnd: !1 }, + }, + a = { + begin: t + "\\s*:", + returnBegin: !0, + contains: [ + { + className: "attribute", + begin: t, + end: "\\s*:", + excludeEnd: !0, + relevance: 0, + }, + ], + relevance: 0, + }, + r = { + begin: ub(t, /\s*\{/), + end: /\{/, + returnBegin: !0, + relevance: 0, + contains: [e.inherit(e.TITLE_MODE, { begin: t })], + }; + return { + name: "QML", + aliases: ["qt"], + case_insensitive: !1, + keywords: { + keyword: + "in of on if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await import", + literal: "true false null undefined NaN Infinity", + built_in: + "eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Behavior bool color coordinate date double enumeration font geocircle georectangle geoshape int list matrix4x4 parent point quaternion real rect size string url variant vector2d vector3d vector4d Promise", + }, + contains: [ + { className: "meta", begin: /^\s*['"]use (strict|asm)['"]/ }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { + className: "string", + begin: "`", + end: "`", + contains: [ + e.BACKSLASH_ESCAPE, + { className: "subst", begin: "\\$\\{", end: "\\}" }, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "number", + variants: [ + { begin: "\\b(0[bB][01]+)" }, + { begin: "\\b(0[oO][0-7]+)" }, + { begin: e.C_NUMBER_RE }, + ], + relevance: 0, + }, + { + begin: "(" + e.RE_STARTERS_RE + "|\\b(case|return|throw)\\b)\\s*", + keywords: "return throw case", + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.REGEXP_MODE, + { begin: /\s*[);\]]/, relevance: 0, subLanguage: "xml" }, + ], + relevance: 0, + }, + { + className: "keyword", + begin: "\\bsignal\\b", + starts: { + className: "string", + end: "(\\(|:|=|;|,|//|/\\*|$)", + returnEnd: !0, + }, + }, + { + className: "keyword", + begin: "\\bproperty\\b", + starts: { + className: "string", + end: "(:|=|;|,|//|/\\*|$)", + returnEnd: !0, + }, + }, + { + className: "function", + beginKeywords: "function", + end: /\{/, + excludeEnd: !0, + contains: [ + e.inherit(e.TITLE_MODE, { begin: /[A-Za-z$_][0-9A-Za-z$_]*/ }), + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + contains: [e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + }, + ], + illegal: /\[|%/, + }, + { begin: "\\." + e.IDENT_RE, relevance: 0 }, + n, + a, + r, + ], + illegal: /#/, + }; +}; +function pb(e) { + return e ? 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connection console customer default dhcp-client dhcp-server discovery dns e-mail ethernet filter firmware gps graphing group hardware health hotspot identity igmp-proxy incoming instance interface ip ipsec ipv6 irq l2tp-server lcd ldp logging mac-server mac-winbox mangle manual mirror mme mpls nat nd neighbor network note ntp ospf ospf-v3 ovpn-server page peer pim ping policy pool port ppp pppoe-client pptp-server prefix profile proposal proxy queue radius resource rip ripng route routing screen script security-profiles server service service-port settings shares smb sms sniffer snmp snooper socks sstp-server system tool tracking type upgrade upnp user-manager users user vlan secret vrrp watchdog web-access wireless pptp pppoe lan wan layer7-protocol lease simple raw" + .split(" ") + .join("|") + + ");?\\s)+", + }, + { begin: /\.\./, relevance: 0 }, + ], + }, + ], + }; +}; +var Cb = function (e) { + return { + name: "RenderMan RSL", + keywords: { + keyword: + "float color point normal vector matrix while for if do return else break extern continue", + built_in: + "abs acos ambient area asin atan atmosphere attribute calculatenormal ceil cellnoise clamp comp concat cos degrees depth Deriv diffuse distance Du Dv environment exp faceforward filterstep floor format fresnel incident length lightsource log match max min mod noise normalize ntransform opposite option phong pnoise pow printf ptlined radians random reflect refract renderinfo round setcomp setxcomp setycomp setzcomp shadow sign sin smoothstep specular specularbrdf spline sqrt step tan texture textureinfo trace transform vtransform xcomp ycomp zcomp", + }, + illegal: "" }, + ], + }; +}; +var vb = function (e) { + return { + name: "SAS", + case_insensitive: !0, + keywords: { + literal: + "null missing _all_ _automatic_ _character_ _infile_ _n_ _name_ _null_ _numeric_ _user_ _webout_", + meta: "do if then else end until while abort array attrib by call cards cards4 catname continue datalines datalines4 delete delim delimiter display dm drop endsas error file filename footnote format goto in infile informat input keep label leave length libname link list lostcard merge missing modify options output out page put redirect remove rename replace retain return select set skip startsas stop title update waitsas where window x systask add and alter as cascade check create delete describe distinct drop foreign from group having index insert into in key like message modify msgtype not null on or order primary references reset restrict select set table unique update validate view where", + }, + contains: [ + { className: "keyword", begin: /^\s*(proc [\w\d_]+|data|run|quit)[\s;]/ }, + { className: "variable", begin: /&[a-zA-Z_&][a-zA-Z0-9_]*\.?/ }, + { + className: "emphasis", + begin: /^\s*datalines|cards.*;/, + end: /^\s*;\s*$/, + }, + { + className: "built_in", + begin: + "%(bquote|nrbquote|cmpres|qcmpres|compstor|datatyp|display|do|else|end|eval|global|goto|if|index|input|keydef|label|left|length|let|local|lowcase|macro|mend|nrbquote|nrquote|nrstr|put|qcmpres|qleft|qlowcase|qscan|qsubstr|qsysfunc|qtrim|quote|qupcase|scan|str|substr|superq|syscall|sysevalf|sysexec|sysfunc|sysget|syslput|sysprod|sysrc|sysrput|then|to|trim|unquote|until|upcase|verify|while|window)", + }, + { className: "name", begin: /%[a-zA-Z_][a-zA-Z_0-9]*/ }, + { + className: "meta", + begin: + "[^%](abs|addr|airy|arcos|arsin|atan|attrc|attrn|band|betainv|blshift|bnot|bor|brshift|bxor|byte|cdf|ceil|cexist|cinv|close|cnonct|collate|compbl|compound|compress|cos|cosh|css|curobs|cv|daccdb|daccdbsl|daccsl|daccsyd|dacctab|dairy|date|datejul|datepart|datetime|day|dclose|depdb|depdbsl|depdbsl|depsl|depsl|depsyd|depsyd|deptab|deptab|dequote|dhms|dif|digamma|dim|dinfo|dnum|dopen|doptname|doptnum|dread|dropnote|dsname|erf|erfc|exist|exp|fappend|fclose|fcol|fdelete|fetch|fetchobs|fexist|fget|fileexist|filename|fileref|finfo|finv|fipname|fipnamel|fipstate|floor|fnonct|fnote|fopen|foptname|foptnum|fpoint|fpos|fput|fread|frewind|frlen|fsep|fuzz|fwrite|gaminv|gamma|getoption|getvarc|getvarn|hbound|hms|hosthelp|hour|ibessel|index|indexc|indexw|input|inputc|inputn|int|intck|intnx|intrr|irr|jbessel|juldate|kurtosis|lag|lbound|left|length|lgamma|libname|libref|log|log10|log2|logpdf|logpmf|logsdf|lowcase|max|mdy|mean|min|minute|mod|month|mopen|mort|n|netpv|nmiss|normal|note|npv|open|ordinal|pathname|pdf|peek|peekc|pmf|point|poisson|poke|probbeta|probbnml|probchi|probf|probgam|probhypr|probit|probnegb|probnorm|probt|put|putc|putn|qtr|quote|ranbin|rancau|ranexp|rangam|range|rank|rannor|ranpoi|rantbl|rantri|ranuni|repeat|resolve|reverse|rewind|right|round|saving|scan|sdf|second|sign|sin|sinh|skewness|soundex|spedis|sqrt|std|stderr|stfips|stname|stnamel|substr|sum|symget|sysget|sysmsg|sysprod|sysrc|system|tan|tanh|time|timepart|tinv|tnonct|today|translate|tranwrd|trigamma|trim|trimn|trunc|uniform|upcase|uss|var|varfmt|varinfmt|varlabel|varlen|varname|varnum|varray|varrayx|vartype|verify|vformat|vformatd|vformatdx|vformatn|vformatnx|vformatw|vformatwx|vformatx|vinarray|vinarrayx|vinformat|vinformatd|vinformatdx|vinformatn|vinformatnx|vinformatw|vinformatwx|vinformatx|vlabel|vlabelx|vlength|vlengthx|vname|vnamex|vtype|vtypex|weekday|year|yyq|zipfips|zipname|zipnamel|zipstate)[(]", + }, + { + className: "string", + variants: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + e.COMMENT("\\*", ";"), + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var Ob = function (e) { + var t = { + className: "subst", + variants: [{ begin: "\\$[A-Za-z0-9_]+" }, { begin: /\$\{/, end: /\}/ }], + }, + n = { + className: "string", + variants: [ + { begin: '"""', end: '"""' }, + { + begin: '"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: '[a-z]+"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, t], + }, + { + className: "string", + begin: '[a-z]+"""', + end: '"""', + contains: [t], + relevance: 10, + }, + ], + }, + a = { className: "type", begin: "\\b[A-Z][A-Za-z0-9_]*", relevance: 0 }, + r = { + className: "title", + begin: + /[^0-9\n\t "'(),.`{}\[\]:;][^\n\t "'(),.`{}\[\]:;]+|[^0-9\n\t "'(),.`{}\[\]:;=]/, + relevance: 0, + }, + i = { + className: "class", + beginKeywords: "class object trait type", + end: /[:={\[\n;]/, + excludeEnd: !0, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { beginKeywords: "extends with", relevance: 10 }, + { + begin: /\[/, + end: /\]/, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + contains: [a], + }, + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + contains: [a], + }, + r, + ], + }, + o = { + className: "function", + beginKeywords: "def", + end: /[:={\[(\n;]/, + excludeEnd: !0, + contains: [r], + }; + return { + name: "Scala", + keywords: { + literal: "true false null", + keyword: + "type yield lazy override def with val var sealed abstract private trait object if forSome for while throw finally protected extends import final return else break new catch super class case package default try this match continue throws implicit", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + { className: "symbol", begin: "'\\w[\\w\\d_]*(?!')" }, + a, + o, + i, + e.C_NUMBER_MODE, + { className: "meta", begin: "@[A-Za-z]+" }, + ], + }; +}; +var hb = function (e) { + var t = "[^\\(\\)\\[\\]\\{\\}\",'`;#|\\\\\\s]+", + n = "(-|\\+)?\\d+([./]\\d+)?", + a = { + $pattern: t, + "builtin-name": + "case-lambda call/cc class define-class exit-handler field import inherit init-field interface let*-values let-values let/ec mixin opt-lambda override protect provide public rename require require-for-syntax syntax syntax-case syntax-error unit/sig unless when with-syntax and begin call-with-current-continuation call-with-input-file call-with-output-file case cond define define-syntax delay do dynamic-wind else for-each if lambda let let* let-syntax letrec letrec-syntax map or syntax-rules ' * + , ,@ - ... / ; < <= = => > >= ` abs acos angle append apply asin assoc assq assv atan boolean? caar cadr call-with-input-file call-with-output-file call-with-values car cdddar cddddr cdr ceiling char->integer char-alphabetic? char-ci<=? char-ci=? char-ci>? char-downcase char-lower-case? char-numeric? char-ready? char-upcase char-upper-case? char-whitespace? char<=? char=? char>? char? close-input-port close-output-port complex? cons cos current-input-port current-output-port denominator display eof-object? eq? equal? eqv? eval even? exact->inexact exact? exp expt floor force gcd imag-part inexact->exact inexact? input-port? integer->char integer? interaction-environment lcm length list list->string list->vector list-ref list-tail list? load log magnitude make-polar make-rectangular make-string make-vector max member memq memv min modulo negative? newline not null-environment null? number->string number? numerator odd? open-input-file open-output-file output-port? pair? peek-char port? positive? procedure? quasiquote quote quotient rational? rationalize read read-char real-part real? remainder reverse round scheme-report-environment set! set-car! set-cdr! sin sqrt string string->list string->number string->symbol string-append string-ci<=? string-ci=? string-ci>? string-copy string-fill! string-length string-ref string-set! string<=? string=? string>? string? substring symbol->string symbol? tan transcript-off transcript-on truncate values vector vector->list vector-fill! vector-length vector-ref vector-set! with-input-from-file with-output-to-file write write-char zero?", + }, + r = { className: "literal", begin: "(#t|#f|#\\\\" + t + "|#\\\\.)" }, + i = { + className: "number", + variants: [ + { begin: n, relevance: 0 }, + { + begin: "(-|\\+)?\\d+([./]\\d+)?[+\\-](-|\\+)?\\d+([./]\\d+)?i", + relevance: 0, + }, + { begin: "#b[0-1]+(/[0-1]+)?" }, + { begin: "#o[0-7]+(/[0-7]+)?" }, + { begin: "#x[0-9a-f]+(/[0-9a-f]+)?" }, + ], + }, + o = e.QUOTE_STRING_MODE, + s = [e.COMMENT(";", "$", { relevance: 0 }), e.COMMENT("#\\|", "\\|#")], + l = { begin: t, relevance: 0 }, + c = { className: "symbol", begin: "'" + t }, + _ = { endsWithParent: !0, relevance: 0 }, + d = { + variants: [{ begin: /'/ }, { begin: "`" }], + contains: [ + { begin: "\\(", end: "\\)", contains: ["self", r, o, i, l, c] }, + ], + }, + u = { className: "name", relevance: 0, begin: t, keywords: a }, + m = { + variants: [ + { begin: "\\(", end: "\\)" }, + { begin: "\\[", end: "\\]" }, + ], + contains: [ + { + begin: /lambda/, + endsWithParent: !0, + returnBegin: !0, + contains: [ + u, + { + endsParent: !0, + variants: [ + { begin: /\(/, end: /\)/ }, + { begin: /\[/, end: /\]/ }, + ], + contains: [l], + }, + ], + }, + u, + _, + ], + }; + return ( + (_.contains = [r, i, o, l, c, d, m].concat(s)), + { + name: "Scheme", + illegal: /\S/, + contains: [e.SHEBANG(), i, o, c, d, m].concat(s), + } + ); +}; +var yb = function (e) { + var t = [ + e.C_NUMBER_MODE, + { + className: "string", + begin: "'|\"", + end: "'|\"", + contains: [e.BACKSLASH_ESCAPE, { begin: "''" }], + }, + ]; + return { + name: "Scilab", + aliases: ["sci"], + keywords: { + $pattern: /%?\w+/, + keyword: + "abort break case clear catch continue do elseif else endfunction end for function global if pause return resume select try then while", + literal: "%f %F %t %T %pi %eps %inf %nan %e %i %z %s", + built_in: + "abs and acos asin atan ceil cd chdir clearglobal cosh cos cumprod deff disp error exec execstr exists exp eye gettext floor fprintf fread fsolve imag isdef isempty isinfisnan isvector lasterror length load linspace list listfiles log10 log2 log max min msprintf mclose mopen ones or pathconvert poly printf prod pwd rand real round sinh sin size gsort sprintf sqrt strcat strcmps tring sum system tanh tan type typename warning zeros matrix", + }, + illegal: '("|#|/\\*|\\s+/\\w+)', + contains: [ + { + className: "function", + beginKeywords: "function", + end: "$", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }, + { begin: "[a-zA-Z_][a-zA-Z_0-9]*[\\.']+", relevance: 0 }, + { begin: "\\[", end: "\\][\\.']*", relevance: 0, contains: t }, + e.COMMENT("//", "$"), + ].concat(t), + }; + }, + Ib = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + Ab = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + Db = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + Mb = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + Lb = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(); +var wb = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = Mb, + a = Db, + r = "@[a-z-]+", + i = { className: "variable", begin: "(\\$[a-zA-Z-][a-zA-Z0-9_-]*)\\b" }; + return { + name: "SCSS", + case_insensitive: !0, + illegal: "[=/|']", + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "selector-id", begin: "#[A-Za-z0-9_-]+", relevance: 0 }, + { className: "selector-class", begin: "\\.[A-Za-z0-9_-]+", relevance: 0 }, + t.ATTRIBUTE_SELECTOR_MODE, + { + className: "selector-tag", + begin: "\\b(" + Ib.join("|") + ")\\b", + relevance: 0, + }, + { className: "selector-pseudo", begin: ":(" + a.join("|") + ")" }, + { className: "selector-pseudo", begin: "::(" + n.join("|") + ")" }, + i, + { begin: /\(/, end: /\)/, contains: [e.CSS_NUMBER_MODE] }, + { className: "attribute", begin: "\\b(" + Lb.join("|") + ")\\b" }, + { + begin: + "\\b(whitespace|wait|w-resize|visible|vertical-text|vertical-ideographic|uppercase|upper-roman|upper-alpha|underline|transparent|top|thin|thick|text|text-top|text-bottom|tb-rl|table-header-group|table-footer-group|sw-resize|super|strict|static|square|solid|small-caps|separate|se-resize|scroll|s-resize|rtl|row-resize|ridge|right|repeat|repeat-y|repeat-x|relative|progress|pointer|overline|outside|outset|oblique|nowrap|not-allowed|normal|none|nw-resize|no-repeat|no-drop|newspaper|ne-resize|n-resize|move|middle|medium|ltr|lr-tb|lowercase|lower-roman|lower-alpha|loose|list-item|line|line-through|line-edge|lighter|left|keep-all|justify|italic|inter-word|inter-ideograph|inside|inset|inline|inline-block|inherit|inactive|ideograph-space|ideograph-parenthesis|ideograph-numeric|ideograph-alpha|horizontal|hidden|help|hand|groove|fixed|ellipsis|e-resize|double|dotted|distribute|distribute-space|distribute-letter|distribute-all-lines|disc|disabled|default|decimal|dashed|crosshair|collapse|col-resize|circle|char|center|capitalize|break-word|break-all|bottom|both|bolder|bold|block|bidi-override|below|baseline|auto|always|all-scroll|absolute|table|table-cell)\\b", + }, + { + begin: ":", + end: ";", + contains: [ + i, + t.HEXCOLOR, + e.CSS_NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + t.IMPORTANT, + ], + }, + { begin: "@(page|font-face)", lexemes: r, keywords: "@page @font-face" }, + { + begin: "@", + end: "[{;]", + returnBegin: !0, + keywords: { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: Ab.join(" "), + }, + contains: [ + { begin: r, className: "keyword" }, + { begin: /[a-z-]+(?=:)/, className: "attribute" }, + i, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + t.HEXCOLOR, + e.CSS_NUMBER_MODE, + ], + }, + ], + }; +}; +var xb = function (e) { + return { + name: "Shell Session", + aliases: ["console"], + contains: [ + { + className: "meta", + begin: /^\s{0,3}[/~\w\d[\]()@-]*[>%$#]/, + starts: { end: /[^\\](?=\s*$)/, subLanguage: "bash" }, + }, + ], + }; +}; +var Pb = function (e) { + var t = [ + "add", + "and", + "cmp", + "cmpg", + "cmpl", + "const", + "div", + "double", + "float", + "goto", + "if", + "int", + "long", + "move", + "mul", + "neg", + "new", + "nop", + "not", + "or", + "rem", + "return", + "shl", + "shr", + "sput", + "sub", + "throw", + "ushr", + "xor", + ]; + return { + name: "Smali", + contains: [ + { className: "string", begin: '"', end: '"', relevance: 0 }, + e.COMMENT("#", "$", { relevance: 0 }), + { + className: "keyword", + variants: [ + { begin: "\\s*\\.end\\s[a-zA-Z0-9]*" }, + { begin: "^[ ]*\\.[a-zA-Z]*", relevance: 0 }, + { begin: "\\s:[a-zA-Z_0-9]*", relevance: 0 }, + { + begin: + "\\s(" + + [ + "transient", + "constructor", + "abstract", + "final", + "synthetic", + "public", + "private", + "protected", + "static", + "bridge", + "system", + ].join("|") + + ")", + }, + ], + }, + { + className: "built_in", + variants: [ + { begin: "\\s(" + t.join("|") + ")\\s" }, + { + begin: "\\s(" + t.join("|") + ")((-|/)[a-zA-Z0-9]+)+\\s", + relevance: 10, + }, + { + begin: + "\\s(" + + [ + "aget", + "aput", + "array", + "check", + "execute", + "fill", + "filled", + "goto/16", + "goto/32", + "iget", + "instance", + "invoke", + "iput", + "monitor", + "packed", + "sget", + "sparse", + ].join("|") + + ")((-|/)[a-zA-Z0-9]+)*\\s", + relevance: 10, + }, + ], + }, + { className: "class", begin: "L[^(;:\n]*;", relevance: 0 }, + { begin: "[vp][0-9]+" }, + ], + }; +}; +var kb = function (e) { + var t = "[a-z][a-zA-Z0-9_]*", + n = { className: "string", begin: "\\$.{1}" }, + a = { className: "symbol", begin: "#" + e.UNDERSCORE_IDENT_RE }; + return { + name: "Smalltalk", + aliases: ["st"], + keywords: "self super nil true false thisContext", + contains: [ + e.COMMENT('"', '"'), + e.APOS_STRING_MODE, + { className: "type", begin: "\\b[A-Z][A-Za-z0-9_]*", relevance: 0 }, + { begin: t + ":", relevance: 0 }, + e.C_NUMBER_MODE, + a, + n, + { + begin: "\\|[ ]*" + t + "([ ]+" + t + ")*[ ]*\\|", + returnBegin: !0, + end: /\|/, + illegal: /\S/, + contains: [{ begin: "(\\|[ ]*)?" + t }], + }, + { + begin: "#\\(", + end: "\\)", + contains: [e.APOS_STRING_MODE, n, e.C_NUMBER_MODE, a], + }, + ], + }; +}; +var Ub = function (e) { + return { + name: "SML (Standard ML)", + aliases: ["ml"], + keywords: { + $pattern: "[a-z_]\\w*!?", + keyword: + "abstype and andalso as case datatype do else end eqtype exception fn fun functor handle if in include infix infixr let local nonfix of op open orelse raise rec sharing sig signature struct structure then type val with withtype where while", + built_in: + "array bool char exn int list option order real ref string substring vector unit word", + literal: "true false NONE SOME LESS EQUAL GREATER nil", + }, + illegal: /\/\/|>>/, + contains: [ + { className: "literal", begin: /\[(\|\|)?\]|\(\)/, relevance: 0 }, + e.COMMENT("\\(\\*", "\\*\\)", { contains: ["self"] }), + { className: "symbol", begin: "'[A-Za-z_](?!')[\\w']*" }, + { className: "type", begin: "`[A-Z][\\w']*" }, + { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + { begin: "[a-z_]\\w*'[\\w']*" }, + e.inherit(e.APOS_STRING_MODE, { className: "string", relevance: 0 }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { + className: "number", + begin: + "\\b(0[xX][a-fA-F0-9_]+[Lln]?|0[oO][0-7_]+[Lln]?|0[bB][01_]+[Lln]?|[0-9][0-9_]*([Lln]|(\\.[0-9_]*)?([eE][-+]?[0-9_]+)?)?)", + relevance: 0, + }, + { begin: /[-=]>/ }, + ], + }; +}; +var Fb = function (e) { + var t = { + className: "string", + variants: [ + { begin: '"', end: '"', contains: [{ begin: '""', relevance: 0 }] }, + { begin: "'", end: "'", contains: [{ begin: "''", relevance: 0 }] }, + ], + }, + n = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": "define undef ifdef ifndef else endif include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(t, { className: "meta-string" }), + { + className: "meta-string", + begin: /<[^\n>]*>/, + end: /$/, + illegal: "\\n", + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; + return { + name: "SQF", + case_insensitive: !0, + keywords: { + keyword: + "case catch default do else exit exitWith for forEach from if private switch then throw to try waitUntil while with", + built_in: + "abs accTime acos action actionIDs actionKeys actionKeysImages actionKeysNames actionKeysNamesArray actionName actionParams activateAddons activatedAddons activateKey add3DENConnection add3DENEventHandler add3DENLayer addAction addBackpack addBackpackCargo addBackpackCargoGlobal addBackpackGlobal addCamShake addCuratorAddons addCuratorCameraArea addCuratorEditableObjects addCuratorEditingArea addCuratorPoints addEditorObject addEventHandler addForce addGoggles addGroupIcon addHandgunItem addHeadgear addItem addItemCargo addItemCargoGlobal addItemPool addItemToBackpack addItemToUniform addItemToVest addLiveStats addMagazine addMagazineAmmoCargo addMagazineCargo addMagazineCargoGlobal addMagazineGlobal addMagazinePool addMagazines addMagazineTurret addMenu addMenuItem addMissionEventHandler addMPEventHandler addMusicEventHandler addOwnedMine addPlayerScores addPrimaryWeaponItem addPublicVariableEventHandler addRating addResources addScore addScoreSide addSecondaryWeaponItem addSwitchableUnit addTeamMember addToRemainsCollector addTorque addUniform addVehicle addVest addWaypoint addWeapon addWeaponCargo addWeaponCargoGlobal addWeaponGlobal addWeaponItem addWeaponPool addWeaponTurret admin agent agents AGLToASL aimedAtTarget aimPos airDensityRTD airplaneThrottle airportSide AISFinishHeal alive all3DENEntities allAirports allControls allCurators allCutLayers allDead allDeadMen allDisplays allGroups allMapMarkers allMines allMissionObjects allow3DMode allowCrewInImmobile allowCuratorLogicIgnoreAreas allowDamage allowDammage allowFileOperations allowFleeing allowGetIn allowSprint allPlayers allSimpleObjects allSites allTurrets allUnits allUnitsUAV allVariables ammo ammoOnPylon and animate animateBay animateDoor animatePylon animateSource animationNames animationPhase animationSourcePhase animationState append apply armoryPoints arrayIntersect asin ASLToAGL ASLToATL assert assignAsCargo assignAsCargoIndex assignAsCommander assignAsDriver assignAsGunner assignAsTurret assignCurator assignedCargo assignedCommander assignedDriver assignedGunner assignedItems assignedTarget assignedTeam assignedVehicle assignedVehicleRole assignItem assignTeam assignToAirport atan atan2 atg ATLToASL attachedObject attachedObjects attachedTo attachObject attachTo attackEnabled backpack backpackCargo backpackContainer backpackItems backpackMagazines backpackSpaceFor behaviour benchmark binocular boundingBox boundingBoxReal boundingCenter breakOut breakTo briefingName buildingExit buildingPos buttonAction buttonSetAction cadetMode call callExtension camCommand camCommit camCommitPrepared camCommitted camConstuctionSetParams camCreate camDestroy cameraEffect cameraEffectEnableHUD cameraInterest cameraOn cameraView campaignConfigFile camPreload camPreloaded camPrepareBank camPrepareDir camPrepareDive camPrepareFocus camPrepareFov camPrepareFovRange camPreparePos camPrepareRelPos camPrepareTarget camSetBank camSetDir camSetDive camSetFocus camSetFov camSetFovRange camSetPos camSetRelPos camSetTarget camTarget camUseNVG canAdd canAddItemToBackpack canAddItemToUniform canAddItemToVest cancelSimpleTaskDestination canFire canMove canSlingLoad canStand canSuspend canTriggerDynamicSimulation canUnloadInCombat canVehicleCargo captive captiveNum cbChecked cbSetChecked ceil channelEnabled cheatsEnabled checkAIFeature checkVisibility className clearAllItemsFromBackpack clearBackpackCargo clearBackpackCargoGlobal clearGroupIcons clearItemCargo clearItemCargoGlobal clearItemPool clearMagazineCargo clearMagazineCargoGlobal clearMagazinePool clearOverlay clearRadio clearWeaponCargo clearWeaponCargoGlobal clearWeaponPool clientOwner closeDialog closeDisplay closeOverlay collapseObjectTree collect3DENHistory collectiveRTD combatMode commandArtilleryFire commandChat commander commandFire commandFollow commandFSM commandGetOut commandingMenu commandMove commandRadio commandStop commandSuppressiveFire commandTarget commandWatch comment commitOverlay compile compileFinal completedFSM composeText configClasses configFile configHierarchy configName configProperties configSourceAddonList configSourceMod configSourceModList confirmSensorTarget connectTerminalToUAV controlsGroupCtrl copyFromClipboard copyToClipboard copyWaypoints cos count countEnemy countFriendly countSide countType countUnknown create3DENComposition create3DENEntity createAgent createCenter createDialog createDiaryLink createDiaryRecord createDiarySubject createDisplay createGearDialog createGroup createGuardedPoint createLocation createMarker createMarkerLocal createMenu createMine createMissionDisplay createMPCampaignDisplay createSimpleObject createSimpleTask createSite createSoundSource createTask createTeam createTrigger createUnit createVehicle createVehicleCrew createVehicleLocal crew ctAddHeader ctAddRow ctClear ctCurSel ctData ctFindHeaderRows ctFindRowHeader ctHeaderControls ctHeaderCount ctRemoveHeaders ctRemoveRows ctrlActivate ctrlAddEventHandler ctrlAngle ctrlAutoScrollDelay ctrlAutoScrollRewind ctrlAutoScrollSpeed ctrlChecked ctrlClassName ctrlCommit ctrlCommitted ctrlCreate ctrlDelete ctrlEnable ctrlEnabled ctrlFade ctrlHTMLLoaded ctrlIDC ctrlIDD ctrlMapAnimAdd ctrlMapAnimClear ctrlMapAnimCommit ctrlMapAnimDone ctrlMapCursor ctrlMapMouseOver ctrlMapScale ctrlMapScreenToWorld ctrlMapWorldToScreen ctrlModel ctrlModelDirAndUp ctrlModelScale ctrlParent ctrlParentControlsGroup ctrlPosition ctrlRemoveAllEventHandlers ctrlRemoveEventHandler ctrlScale ctrlSetActiveColor ctrlSetAngle ctrlSetAutoScrollDelay ctrlSetAutoScrollRewind ctrlSetAutoScrollSpeed ctrlSetBackgroundColor ctrlSetChecked ctrlSetEventHandler ctrlSetFade ctrlSetFocus ctrlSetFont ctrlSetFontH1 ctrlSetFontH1B ctrlSetFontH2 ctrlSetFontH2B ctrlSetFontH3 ctrlSetFontH3B ctrlSetFontH4 ctrlSetFontH4B ctrlSetFontH5 ctrlSetFontH5B ctrlSetFontH6 ctrlSetFontH6B ctrlSetFontHeight ctrlSetFontHeightH1 ctrlSetFontHeightH2 ctrlSetFontHeightH3 ctrlSetFontHeightH4 ctrlSetFontHeightH5 ctrlSetFontHeightH6 ctrlSetFontHeightSecondary ctrlSetFontP ctrlSetFontPB ctrlSetFontSecondary ctrlSetForegroundColor ctrlSetModel ctrlSetModelDirAndUp ctrlSetModelScale ctrlSetPixelPrecision ctrlSetPosition ctrlSetScale ctrlSetStructuredText ctrlSetText ctrlSetTextColor ctrlSetTooltip ctrlSetTooltipColorBox ctrlSetTooltipColorShade ctrlSetTooltipColorText ctrlShow ctrlShown ctrlText ctrlTextHeight ctrlTextWidth ctrlType ctrlVisible ctRowControls ctRowCount ctSetCurSel ctSetData ctSetHeaderTemplate ctSetRowTemplate ctSetValue ctValue curatorAddons curatorCamera curatorCameraArea curatorCameraAreaCeiling curatorCoef curatorEditableObjects curatorEditingArea curatorEditingAreaType curatorMouseOver curatorPoints curatorRegisteredObjects curatorSelected curatorWaypointCost current3DENOperation currentChannel currentCommand currentMagazine currentMagazineDetail currentMagazineDetailTurret currentMagazineTurret currentMuzzle currentNamespace currentTask currentTasks currentThrowable currentVisionMode currentWaypoint currentWeapon currentWeaponMode currentWeaponTurret currentZeroing cursorObject cursorTarget customChat customRadio cutFadeOut cutObj cutRsc cutText damage date dateToNumber daytime deActivateKey debriefingText debugFSM debugLog deg delete3DENEntities deleteAt deleteCenter deleteCollection deleteEditorObject deleteGroup deleteGroupWhenEmpty deleteIdentity deleteLocation deleteMarker deleteMarkerLocal deleteRange deleteResources deleteSite deleteStatus deleteTeam deleteVehicle deleteVehicleCrew deleteWaypoint detach detectedMines diag_activeMissionFSMs diag_activeScripts diag_activeSQFScripts diag_activeSQSScripts diag_captureFrame diag_captureFrameToFile diag_captureSlowFrame diag_codePerformance diag_drawMode diag_enable diag_enabled diag_fps diag_fpsMin diag_frameNo diag_lightNewLoad diag_list diag_log diag_logSlowFrame diag_mergeConfigFile diag_recordTurretLimits diag_setLightNew diag_tickTime diag_toggle dialog diarySubjectExists didJIP didJIPOwner difficulty difficultyEnabled difficultyEnabledRTD difficultyOption direction directSay disableAI disableCollisionWith disableConversation disableDebriefingStats disableMapIndicators disableNVGEquipment disableRemoteSensors disableSerialization disableTIEquipment disableUAVConnectability disableUserInput displayAddEventHandler displayCtrl displayParent displayRemoveAllEventHandlers displayRemoveEventHandler displaySetEventHandler dissolveTeam distance distance2D distanceSqr distributionRegion do3DENAction doArtilleryFire doFire doFollow doFSM doGetOut doMove doorPhase doStop doSuppressiveFire doTarget doWatch drawArrow drawEllipse drawIcon drawIcon3D drawLine drawLine3D drawLink drawLocation drawPolygon drawRectangle drawTriangle driver drop dynamicSimulationDistance dynamicSimulationDistanceCoef dynamicSimulationEnabled dynamicSimulationSystemEnabled echo edit3DENMissionAttributes editObject editorSetEventHandler effectiveCommander emptyPositions enableAI enableAIFeature enableAimPrecision enableAttack enableAudioFeature enableAutoStartUpRTD enableAutoTrimRTD enableCamShake enableCaustics enableChannel enableCollisionWith enableCopilot enableDebriefingStats enableDiagLegend enableDynamicSimulation enableDynamicSimulationSystem enableEndDialog enableEngineArtillery enableEnvironment enableFatigue enableGunLights enableInfoPanelComponent enableIRLasers enableMimics enablePersonTurret enableRadio enableReload enableRopeAttach enableSatNormalOnDetail enableSaving enableSentences enableSimulation enableSimulationGlobal enableStamina enableTeamSwitch enableTraffic enableUAVConnectability enableUAVWaypoints enableVehicleCargo enableVehicleSensor enableWeaponDisassembly endLoadingScreen endMission engineOn enginesIsOnRTD enginesRpmRTD enginesTorqueRTD entities environmentEnabled estimatedEndServerTime estimatedTimeLeft evalObjectArgument everyBackpack everyContainer exec execEditorScript execFSM execVM exp expectedDestination exportJIPMessages eyeDirection eyePos face faction fadeMusic fadeRadio fadeSound fadeSpeech failMission fillWeaponsFromPool find findCover findDisplay findEditorObject findEmptyPosition findEmptyPositionReady findIf findNearestEnemy finishMissionInit finite fire fireAtTarget firstBackpack flag flagAnimationPhase flagOwner flagSide flagTexture fleeing floor flyInHeight flyInHeightASL fog fogForecast fogParams forceAddUniform forcedMap forceEnd forceFlagTexture forceFollowRoad forceMap forceRespawn forceSpeed forceWalk forceWeaponFire forceWeatherChange forEachMember forEachMemberAgent forEachMemberTeam forgetTarget format formation formationDirection formationLeader formationMembers formationPosition formationTask formatText formLeader freeLook fromEditor fuel fullCrew gearIDCAmmoCount gearSlotAmmoCount gearSlotData get3DENActionState get3DENAttribute get3DENCamera get3DENConnections get3DENEntity get3DENEntityID get3DENGrid get3DENIconsVisible get3DENLayerEntities get3DENLinesVisible get3DENMissionAttribute get3DENMouseOver get3DENSelected getAimingCoef getAllEnvSoundControllers getAllHitPointsDamage getAllOwnedMines getAllSoundControllers getAmmoCargo getAnimAimPrecision getAnimSpeedCoef getArray getArtilleryAmmo getArtilleryComputerSettings getArtilleryETA getAssignedCuratorLogic getAssignedCuratorUnit getBackpackCargo getBleedingRemaining getBurningValue getCameraViewDirection getCargoIndex getCenterOfMass getClientState getClientStateNumber getCompatiblePylonMagazines getConnectedUAV getContainerMaxLoad getCursorObjectParams getCustomAimCoef getDammage getDescription getDir getDirVisual getDLCAssetsUsage getDLCAssetsUsageByName getDLCs getEditorCamera getEditorMode getEditorObjectScope getElevationOffset getEnvSoundController getFatigue getForcedFlagTexture getFriend getFSMVariable getFuelCargo getGroupIcon getGroupIconParams getGroupIcons getHideFrom getHit getHitIndex getHitPointDamage getItemCargo getMagazineCargo getMarkerColor getMarkerPos getMarkerSize getMarkerType getMass getMissionConfig getMissionConfigValue getMissionDLCs getMissionLayerEntities getModelInfo getMousePosition getMusicPlayedTime getNumber getObjectArgument getObjectChildren getObjectDLC getObjectMaterials getObjectProxy getObjectTextures getObjectType getObjectViewDistance getOxygenRemaining getPersonUsedDLCs getPilotCameraDirection getPilotCameraPosition getPilotCameraRotation getPilotCameraTarget getPlateNumber getPlayerChannel getPlayerScores getPlayerUID getPos getPosASL getPosASLVisual getPosASLW getPosATL getPosATLVisual getPosVisual getPosWorld getPylonMagazines getRelDir getRelPos getRemoteSensorsDisabled getRepairCargo getResolution getShadowDistance getShotParents getSlingLoad getSoundController getSoundControllerResult getSpeed getStamina getStatValue getSuppression getTerrainGrid getTerrainHeightASL getText getTotalDLCUsageTime getUnitLoadout getUnitTrait getUserMFDText getUserMFDvalue getVariable getVehicleCargo getWeaponCargo getWeaponSway getWingsOrientationRTD getWingsPositionRTD getWPPos glanceAt globalChat globalRadio goggles goto group groupChat groupFromNetId groupIconSelectable groupIconsVisible groupId groupOwner groupRadio groupSelectedUnits groupSelectUnit gunner gusts halt handgunItems handgunMagazine handgunWeapon handsHit hasInterface hasPilotCamera hasWeapon hcAllGroups hcGroupParams hcLeader hcRemoveAllGroups hcRemoveGroup hcSelected hcSelectGroup hcSetGroup hcShowBar hcShownBar headgear hideBody hideObject hideObjectGlobal hideSelection hint hintC hintCadet hintSilent hmd hostMission htmlLoad HUDMovementLevels humidity image importAllGroups importance in inArea inAreaArray incapacitatedState inflame inflamed infoPanel infoPanelComponentEnabled infoPanelComponents infoPanels inGameUISetEventHandler inheritsFrom initAmbientLife inPolygon inputAction inRangeOfArtillery insertEditorObject intersect is3DEN is3DENMultiplayer isAbleToBreathe isAgent isArray isAutoHoverOn isAutonomous isAutotest isBleeding isBurning isClass isCollisionLightOn isCopilotEnabled isDamageAllowed isDedicated isDLCAvailable isEngineOn isEqualTo isEqualType isEqualTypeAll isEqualTypeAny isEqualTypeArray isEqualTypeParams isFilePatchingEnabled isFlashlightOn isFlatEmpty isForcedWalk isFormationLeader isGroupDeletedWhenEmpty isHidden isInRemainsCollector isInstructorFigureEnabled isIRLaserOn isKeyActive isKindOf isLaserOn isLightOn isLocalized isManualFire isMarkedForCollection isMultiplayer isMultiplayerSolo isNil isNull isNumber isObjectHidden isObjectRTD isOnRoad isPipEnabled isPlayer isRealTime isRemoteExecuted isRemoteExecutedJIP isServer isShowing3DIcons isSimpleObject isSprintAllowed isStaminaEnabled isSteamMission isStreamFriendlyUIEnabled isText isTouchingGround isTurnedOut isTutHintsEnabled isUAVConnectable isUAVConnected isUIContext isUniformAllowed isVehicleCargo isVehicleRadarOn isVehicleSensorEnabled isWalking isWeaponDeployed isWeaponRested itemCargo items itemsWithMagazines join joinAs joinAsSilent joinSilent joinString kbAddDatabase kbAddDatabaseTargets kbAddTopic kbHasTopic kbReact kbRemoveTopic kbTell kbWasSaid keyImage keyName knowsAbout land landAt landResult language laserTarget lbAdd lbClear lbColor lbColorRight lbCurSel lbData lbDelete lbIsSelected lbPicture lbPictureRight lbSelection lbSetColor lbSetColorRight lbSetCurSel lbSetData lbSetPicture lbSetPictureColor lbSetPictureColorDisabled lbSetPictureColorSelected lbSetPictureRight lbSetPictureRightColor lbSetPictureRightColorDisabled lbSetPictureRightColorSelected lbSetSelectColor lbSetSelectColorRight lbSetSelected lbSetText lbSetTextRight lbSetTooltip lbSetValue lbSize lbSort lbSortByValue lbText lbTextRight lbValue leader leaderboardDeInit leaderboardGetRows leaderboardInit leaderboardRequestRowsFriends leaderboardsRequestUploadScore leaderboardsRequestUploadScoreKeepBest leaderboardState leaveVehicle libraryCredits libraryDisclaimers lifeState lightAttachObject lightDetachObject lightIsOn lightnings limitSpeed linearConversion lineIntersects lineIntersectsObjs lineIntersectsSurfaces lineIntersectsWith linkItem list listObjects listRemoteTargets listVehicleSensors ln lnbAddArray lnbAddColumn lnbAddRow lnbClear lnbColor lnbCurSelRow lnbData lnbDeleteColumn lnbDeleteRow lnbGetColumnsPosition lnbPicture lnbSetColor lnbSetColumnsPos lnbSetCurSelRow lnbSetData lnbSetPicture lnbSetText lnbSetValue lnbSize lnbSort lnbSortByValue lnbText lnbValue load loadAbs loadBackpack loadFile loadGame loadIdentity loadMagazine loadOverlay loadStatus loadUniform loadVest local localize locationPosition lock lockCameraTo lockCargo lockDriver locked lockedCargo lockedDriver lockedTurret lockIdentity lockTurret lockWP log logEntities logNetwork logNetworkTerminate lookAt lookAtPos magazineCargo magazines magazinesAllTurrets magazinesAmmo magazinesAmmoCargo magazinesAmmoFull magazinesDetail magazinesDetailBackpack magazinesDetailUniform magazinesDetailVest magazinesTurret magazineTurretAmmo mapAnimAdd mapAnimClear mapAnimCommit mapAnimDone mapCenterOnCamera mapGridPosition markAsFinishedOnSteam markerAlpha markerBrush markerColor markerDir markerPos markerShape markerSize markerText markerType max members menuAction menuAdd menuChecked menuClear menuCollapse menuData menuDelete menuEnable menuEnabled menuExpand menuHover menuPicture menuSetAction menuSetCheck menuSetData menuSetPicture menuSetValue menuShortcut menuShortcutText menuSize menuSort menuText menuURL menuValue min mineActive mineDetectedBy missionConfigFile missionDifficulty missionName missionNamespace missionStart missionVersion mod modelToWorld modelToWorldVisual modelToWorldVisualWorld modelToWorldWorld modParams moonIntensity moonPhase morale move move3DENCamera moveInAny moveInCargo moveInCommander moveInDriver moveInGunner moveInTurret moveObjectToEnd moveOut moveTime moveTo moveToCompleted moveToFailed musicVolume name nameSound nearEntities nearestBuilding nearestLocation nearestLocations nearestLocationWithDubbing nearestObject nearestObjects nearestTerrainObjects nearObjects nearObjectsReady nearRoads nearSupplies nearTargets needReload netId netObjNull newOverlay nextMenuItemIndex nextWeatherChange nMenuItems not numberOfEnginesRTD numberToDate objectCurators objectFromNetId objectParent objStatus onBriefingGroup onBriefingNotes onBriefingPlan onBriefingTeamSwitch onCommandModeChanged onDoubleClick onEachFrame onGroupIconClick onGroupIconOverEnter onGroupIconOverLeave onHCGroupSelectionChanged onMapSingleClick onPlayerConnected onPlayerDisconnected onPreloadFinished onPreloadStarted onShowNewObject onTeamSwitch openCuratorInterface openDLCPage openMap openSteamApp openYoutubeVideo or orderGetIn overcast overcastForecast owner param params parseNumber parseSimpleArray parseText parsingNamespace particlesQuality pickWeaponPool pitch pixelGrid pixelGridBase pixelGridNoUIScale pixelH pixelW playableSlotsNumber playableUnits playAction playActionNow player playerRespawnTime playerSide playersNumber playGesture playMission playMove playMoveNow playMusic playScriptedMission playSound playSound3D position positionCameraToWorld posScreenToWorld posWorldToScreen ppEffectAdjust ppEffectCommit ppEffectCommitted ppEffectCreate ppEffectDestroy ppEffectEnable ppEffectEnabled ppEffectForceInNVG precision preloadCamera preloadObject preloadSound preloadTitleObj preloadTitleRsc preprocessFile preprocessFileLineNumbers primaryWeapon primaryWeaponItems primaryWeaponMagazine priority processDiaryLink productVersion profileName profileNamespace profileNameSteam progressLoadingScreen progressPosition progressSetPosition publicVariable publicVariableClient publicVariableServer pushBack pushBackUnique putWeaponPool queryItemsPool queryMagazinePool queryWeaponPool rad radioChannelAdd radioChannelCreate radioChannelRemove radioChannelSetCallSign radioChannelSetLabel radioVolume rain rainbow random rank rankId rating rectangular registeredTasks registerTask reload reloadEnabled remoteControl remoteExec remoteExecCall remoteExecutedOwner remove3DENConnection remove3DENEventHandler remove3DENLayer removeAction removeAll3DENEventHandlers removeAllActions removeAllAssignedItems removeAllContainers removeAllCuratorAddons removeAllCuratorCameraAreas removeAllCuratorEditingAreas removeAllEventHandlers removeAllHandgunItems removeAllItems removeAllItemsWithMagazines removeAllMissionEventHandlers removeAllMPEventHandlers removeAllMusicEventHandlers removeAllOwnedMines removeAllPrimaryWeaponItems removeAllWeapons removeBackpack removeBackpackGlobal removeCuratorAddons removeCuratorCameraArea removeCuratorEditableObjects removeCuratorEditingArea removeDrawIcon removeDrawLinks removeEventHandler removeFromRemainsCollector removeGoggles removeGroupIcon removeHandgunItem removeHeadgear removeItem removeItemFromBackpack removeItemFromUniform removeItemFromVest removeItems removeMagazine removeMagazineGlobal removeMagazines removeMagazinesTurret removeMagazineTurret removeMenuItem removeMissionEventHandler removeMPEventHandler removeMusicEventHandler removeOwnedMine removePrimaryWeaponItem removeSecondaryWeaponItem removeSimpleTask removeSwitchableUnit removeTeamMember removeUniform removeVest removeWeapon removeWeaponAttachmentCargo removeWeaponCargo removeWeaponGlobal removeWeaponTurret reportRemoteTarget requiredVersion resetCamShake resetSubgroupDirection resize resources respawnVehicle restartEditorCamera reveal revealMine reverse reversedMouseY roadAt roadsConnectedTo roleDescription ropeAttachedObjects ropeAttachedTo ropeAttachEnabled ropeAttachTo ropeCreate ropeCut ropeDestroy ropeDetach ropeEndPosition ropeLength ropes ropeUnwind ropeUnwound rotorsForcesRTD rotorsRpmRTD round runInitScript safeZoneH safeZoneW safeZoneWAbs safeZoneX safeZoneXAbs safeZoneY save3DENInventory saveGame saveIdentity saveJoysticks saveOverlay saveProfileNamespace saveStatus saveVar savingEnabled say say2D say3D scopeName score scoreSide screenshot screenToWorld scriptDone scriptName scudState secondaryWeapon secondaryWeaponItems secondaryWeaponMagazine select selectBestPlaces selectDiarySubject selectedEditorObjects selectEditorObject selectionNames selectionPosition selectLeader selectMax selectMin selectNoPlayer selectPlayer selectRandom selectRandomWeighted selectWeapon selectWeaponTurret sendAUMessage sendSimpleCommand sendTask sendTaskResult sendUDPMessage serverCommand serverCommandAvailable serverCommandExecutable serverName serverTime set set3DENAttribute set3DENAttributes set3DENGrid set3DENIconsVisible set3DENLayer set3DENLinesVisible set3DENLogicType set3DENMissionAttribute set3DENMissionAttributes set3DENModelsVisible set3DENObjectType set3DENSelected setAccTime setActualCollectiveRTD setAirplaneThrottle setAirportSide setAmmo setAmmoCargo setAmmoOnPylon setAnimSpeedCoef setAperture setApertureNew setArmoryPoints setAttributes setAutonomous setBehaviour setBleedingRemaining setBrakesRTD setCameraInterest setCamShakeDefParams setCamShakeParams setCamUseTI setCaptive setCenterOfMass setCollisionLight setCombatMode setCompassOscillation setConvoySeparation setCuratorCameraAreaCeiling setCuratorCoef setCuratorEditingAreaType setCuratorWaypointCost setCurrentChannel setCurrentTask setCurrentWaypoint setCustomAimCoef setCustomWeightRTD setDamage setDammage setDate setDebriefingText setDefaultCamera setDestination setDetailMapBlendPars setDir setDirection setDrawIcon setDriveOnPath setDropInterval setDynamicSimulationDistance setDynamicSimulationDistanceCoef setEditorMode setEditorObjectScope setEffectCondition setEngineRPMRTD setFace setFaceAnimation setFatigue setFeatureType setFlagAnimationPhase setFlagOwner setFlagSide setFlagTexture setFog setFormation setFormationTask setFormDir setFriend setFromEditor setFSMVariable setFuel setFuelCargo setGroupIcon setGroupIconParams setGroupIconsSelectable setGroupIconsVisible setGroupId setGroupIdGlobal setGroupOwner setGusts setHideBehind setHit setHitIndex setHitPointDamage setHorizonParallaxCoef setHUDMovementLevels setIdentity setImportance setInfoPanel setLeader setLightAmbient setLightAttenuation setLightBrightness setLightColor setLightDayLight setLightFlareMaxDistance setLightFlareSize setLightIntensity setLightnings setLightUseFlare setLocalWindParams setMagazineTurretAmmo setMarkerAlpha setMarkerAlphaLocal setMarkerBrush setMarkerBrushLocal setMarkerColor setMarkerColorLocal setMarkerDir setMarkerDirLocal setMarkerPos setMarkerPosLocal setMarkerShape setMarkerShapeLocal setMarkerSize setMarkerSizeLocal setMarkerText setMarkerTextLocal setMarkerType setMarkerTypeLocal setMass setMimic setMousePosition setMusicEffect setMusicEventHandler setName setNameSound setObjectArguments setObjectMaterial setObjectMaterialGlobal setObjectProxy setObjectTexture setObjectTextureGlobal setObjectViewDistance setOvercast setOwner setOxygenRemaining setParticleCircle setParticleClass setParticleFire setParticleParams setParticleRandom setPilotCameraDirection setPilotCameraRotation setPilotCameraTarget setPilotLight setPiPEffect setPitch setPlateNumber setPlayable setPlayerRespawnTime setPos setPosASL setPosASL2 setPosASLW setPosATL setPosition setPosWorld setPylonLoadOut setPylonsPriority setRadioMsg setRain setRainbow setRandomLip setRank setRectangular setRepairCargo setRotorBrakeRTD setShadowDistance setShotParents setSide setSimpleTaskAlwaysVisible setSimpleTaskCustomData setSimpleTaskDescription setSimpleTaskDestination setSimpleTaskTarget setSimpleTaskType setSimulWeatherLayers setSize setSkill setSlingLoad setSoundEffect setSpeaker setSpeech setSpeedMode setStamina setStaminaScheme setStatValue setSuppression setSystemOfUnits setTargetAge setTaskMarkerOffset setTaskResult setTaskState setTerrainGrid setText setTimeMultiplier setTitleEffect setTrafficDensity setTrafficDistance setTrafficGap setTrafficSpeed setTriggerActivation setTriggerArea setTriggerStatements setTriggerText setTriggerTimeout setTriggerType setType setUnconscious setUnitAbility setUnitLoadout setUnitPos setUnitPosWeak setUnitRank setUnitRecoilCoefficient setUnitTrait setUnloadInCombat setUserActionText setUserMFDText setUserMFDvalue setVariable setVectorDir setVectorDirAndUp setVectorUp setVehicleAmmo setVehicleAmmoDef setVehicleArmor setVehicleCargo setVehicleId setVehicleLock setVehiclePosition setVehicleRadar setVehicleReceiveRemoteTargets setVehicleReportOwnPosition setVehicleReportRemoteTargets setVehicleTIPars setVehicleVarName setVelocity setVelocityModelSpace setVelocityTransformation setViewDistance setVisibleIfTreeCollapsed setWantedRPMRTD setWaves setWaypointBehaviour setWaypointCombatMode setWaypointCompletionRadius setWaypointDescription setWaypointForceBehaviour setWaypointFormation setWaypointHousePosition setWaypointLoiterRadius setWaypointLoiterType setWaypointName setWaypointPosition setWaypointScript setWaypointSpeed setWaypointStatements setWaypointTimeout setWaypointType setWaypointVisible setWeaponReloadingTime setWind setWindDir setWindForce setWindStr setWingForceScaleRTD setWPPos show3DIcons showChat showCinemaBorder showCommandingMenu showCompass showCuratorCompass showGPS showHUD showLegend showMap shownArtilleryComputer shownChat shownCompass shownCuratorCompass showNewEditorObject shownGPS shownHUD shownMap shownPad shownRadio shownScoretable shownUAVFeed shownWarrant shownWatch showPad showRadio showScoretable showSubtitles showUAVFeed showWarrant showWatch showWaypoint showWaypoints side sideChat sideEnemy sideFriendly sideRadio simpleTasks simulationEnabled simulCloudDensity simulCloudOcclusion simulInClouds simulWeatherSync sin size sizeOf skill skillFinal skipTime sleep sliderPosition sliderRange sliderSetPosition sliderSetRange sliderSetSpeed sliderSpeed slingLoadAssistantShown soldierMagazines someAmmo sort soundVolume spawn speaker speed speedMode splitString sqrt squadParams stance startLoadingScreen step stop stopEngineRTD stopped str sunOrMoon supportInfo suppressFor surfaceIsWater surfaceNormal surfaceType swimInDepth switchableUnits switchAction switchCamera switchGesture switchLight switchMove synchronizedObjects synchronizedTriggers synchronizedWaypoints synchronizeObjectsAdd synchronizeObjectsRemove synchronizeTrigger synchronizeWaypoint systemChat systemOfUnits tan targetKnowledge targets targetsAggregate targetsQuery taskAlwaysVisible taskChildren taskCompleted taskCustomData taskDescription taskDestination taskHint taskMarkerOffset taskParent taskResult taskState taskType teamMember teamName teams teamSwitch teamSwitchEnabled teamType terminate terrainIntersect terrainIntersectASL terrainIntersectAtASL text textLog textLogFormat tg time timeMultiplier titleCut titleFadeOut titleObj titleRsc titleText toArray toFixed toLower toString toUpper triggerActivated triggerActivation triggerArea triggerAttachedVehicle triggerAttachObject triggerAttachVehicle triggerDynamicSimulation triggerStatements triggerText triggerTimeout triggerTimeoutCurrent triggerType turretLocal turretOwner turretUnit tvAdd tvClear tvCollapse tvCollapseAll tvCount tvCurSel tvData tvDelete tvExpand tvExpandAll tvPicture tvSetColor tvSetCurSel tvSetData tvSetPicture tvSetPictureColor tvSetPictureColorDisabled tvSetPictureColorSelected tvSetPictureRight tvSetPictureRightColor tvSetPictureRightColorDisabled tvSetPictureRightColorSelected tvSetText tvSetTooltip tvSetValue tvSort tvSortByValue tvText tvTooltip tvValue type typeName typeOf UAVControl uiNamespace uiSleep unassignCurator unassignItem unassignTeam unassignVehicle underwater uniform uniformContainer uniformItems uniformMagazines unitAddons unitAimPosition unitAimPositionVisual unitBackpack unitIsUAV unitPos unitReady unitRecoilCoefficient units unitsBelowHeight unlinkItem unlockAchievement unregisterTask updateDrawIcon updateMenuItem updateObjectTree useAISteeringComponent useAudioTimeForMoves userInputDisabled vectorAdd vectorCos vectorCrossProduct vectorDiff vectorDir vectorDirVisual vectorDistance vectorDistanceSqr vectorDotProduct vectorFromTo vectorMagnitude vectorMagnitudeSqr vectorModelToWorld vectorModelToWorldVisual vectorMultiply vectorNormalized vectorUp vectorUpVisual vectorWorldToModel vectorWorldToModelVisual vehicle vehicleCargoEnabled vehicleChat vehicleRadio vehicleReceiveRemoteTargets vehicleReportOwnPosition vehicleReportRemoteTargets vehicles vehicleVarName velocity velocityModelSpace verifySignature vest vestContainer vestItems vestMagazines viewDistance visibleCompass visibleGPS visibleMap visiblePosition visiblePositionASL visibleScoretable visibleWatch waves waypointAttachedObject waypointAttachedVehicle waypointAttachObject waypointAttachVehicle waypointBehaviour waypointCombatMode waypointCompletionRadius waypointDescription waypointForceBehaviour waypointFormation waypointHousePosition waypointLoiterRadius waypointLoiterType waypointName waypointPosition waypoints waypointScript waypointsEnabledUAV waypointShow waypointSpeed waypointStatements waypointTimeout waypointTimeoutCurrent waypointType waypointVisible weaponAccessories weaponAccessoriesCargo weaponCargo weaponDirection weaponInertia weaponLowered weapons weaponsItems weaponsItemsCargo weaponState weaponsTurret weightRTD WFSideText wind ", + literal: + "blufor civilian configNull controlNull displayNull east endl false grpNull independent lineBreak locationNull nil objNull opfor pi resistance scriptNull sideAmbientLife sideEmpty sideLogic sideUnknown taskNull teamMemberNull true west", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.NUMBER_MODE, + { className: "variable", begin: /\b_+[a-zA-Z]\w*/ }, + { className: "title", begin: /[a-zA-Z][a-zA-Z0-9]+_fnc_\w*/ }, + t, + n, + ], + illegal: /#|^\$ /, + }; +}; +var Bb = function (e) { + var t = e.COMMENT("--", "$"); + return { + name: "SQL (more)", + aliases: ["mysql", "oracle"], + disableAutodetect: !0, + case_insensitive: !0, + illegal: /[<>{}*]/, + contains: [ + { + beginKeywords: + "begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment values with", + end: /;/, + endsWithParent: !0, + keywords: { + $pattern: /[\w\.]+/, + keyword: + "as abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias all allocate allow alter always analyze ancillary and anti any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound bucket buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain explode export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force foreign form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour hours http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lateral lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minutes minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notnull notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second seconds section securefile security seed segment select self semi sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tablesample tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unnest unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace window with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek", + literal: "true false null unknown", + built_in: + "array bigint binary bit blob bool boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text time timestamp tinyint varchar varchar2 varying void", + }, + contains: [ + { + className: "string", + begin: "'", + end: "'", + contains: [{ begin: "''" }], + }, + { + className: "string", + begin: '"', + end: '"', + contains: [{ begin: '""' }], + }, + { className: "string", begin: "`", end: "`" }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + e.HASH_COMMENT_MODE, + ], + }, + e.C_BLOCK_COMMENT_MODE, + t, + e.HASH_COMMENT_MODE, + ], + }; +}; +function Gb(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Yb() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Gb(e); + }) + .join(""); + return a; +} +function Hb() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return Gb(e); + }) + .join("|") + + ")"; + return a; +} +var Vb = function (e) { + var t = e.COMMENT("--", "$"), + n = ["true", "false", "unknown"], + a = [ + "bigint", + "binary", + "blob", + "boolean", + "char", + "character", + "clob", + "date", + "dec", + "decfloat", + "decimal", + "float", + "int", + "integer", + "interval", + "nchar", + "nclob", + "national", + "numeric", + "real", + "row", + "smallint", + "time", + "timestamp", + "varchar", + "varying", + "varbinary", + ], + r = [ + "abs", + "acos", + "array_agg", + "asin", + "atan", + "avg", + "cast", + "ceil", + "ceiling", + "coalesce", + "corr", + "cos", + "cosh", + "count", + "covar_pop", + "covar_samp", + "cume_dist", + "dense_rank", + "deref", + "element", + "exp", + "extract", + "first_value", + "floor", + "json_array", + "json_arrayagg", + "json_exists", + "json_object", + "json_objectagg", + "json_query", + "json_table", + "json_table_primitive", + "json_value", + "lag", + "last_value", + "lead", + "listagg", + "ln", + "log", + "log10", + "lower", + "max", + "min", + "mod", + "nth_value", + "ntile", + "nullif", + "percent_rank", + "percentile_cont", + "percentile_disc", + "position", + "position_regex", + "power", + "rank", + "regr_avgx", + "regr_avgy", + "regr_count", + "regr_intercept", + "regr_r2", + "regr_slope", + "regr_sxx", + "regr_sxy", + "regr_syy", + "row_number", + "sin", + "sinh", + "sqrt", + "stddev_pop", + "stddev_samp", + "substring", + "substring_regex", + "sum", + "tan", + "tanh", + "translate", + "translate_regex", + "treat", + "trim", + "trim_array", + "unnest", + "upper", + "value_of", + "var_pop", + "var_samp", + "width_bucket", + ], + i = [ + "create table", + "insert into", + "primary key", + "foreign key", + "not null", + "alter table", + "add constraint", + "grouping sets", + "on overflow", + "character set", + "respect nulls", + "ignore nulls", + "nulls first", + "nulls last", + "depth first", + "breadth first", + ], + o = r, + s = [] + .concat( + [ + "abs", + "acos", + "all", + "allocate", + "alter", + "and", + "any", + "are", + "array", + "array_agg", + "array_max_cardinality", + "as", + "asensitive", + "asin", + "asymmetric", + "at", + "atan", + "atomic", + "authorization", + "avg", + "begin", + "begin_frame", + "begin_partition", + "between", + "bigint", + "binary", + "blob", + "boolean", + "both", + "by", + "call", + "called", + "cardinality", + "cascaded", + "case", + "cast", + "ceil", + "ceiling", + "char", + "char_length", + "character", + "character_length", + "check", + "classifier", + "clob", + "close", + "coalesce", + "collate", + "collect", + "column", + "commit", + "condition", + "connect", + "constraint", + "contains", + "convert", + "copy", + "corr", + "corresponding", + "cos", + "cosh", + "count", + "covar_pop", + "covar_samp", + "create", + "cross", + "cube", + "cume_dist", + "current", + "current_catalog", + "current_date", + "current_default_transform_group", + "current_path", + "current_role", + "current_row", + "current_schema", + "current_time", + "current_timestamp", + "current_path", + "current_role", + "current_transform_group_for_type", + "current_user", + "cursor", + "cycle", + "date", + "day", + "deallocate", + "dec", + "decimal", + "decfloat", + "declare", + "default", + "define", + "delete", + "dense_rank", + "deref", + "describe", + "deterministic", + "disconnect", + "distinct", + "double", + "drop", + "dynamic", + "each", + "element", + "else", + "empty", + "end", + "end_frame", + "end_partition", + "end-exec", + "equals", + "escape", + "every", + "except", + "exec", + "execute", + "exists", + "exp", + "external", + "extract", + "false", + "fetch", + "filter", + "first_value", + "float", + "floor", + "for", + "foreign", + "frame_row", + "free", + "from", + "full", + "function", + "fusion", + "get", + "global", + "grant", + "group", + "grouping", + "groups", + "having", + "hold", + "hour", + "identity", + "in", + "indicator", + "initial", + "inner", + "inout", + "insensitive", + "insert", + "int", + "integer", + "intersect", + "intersection", + "interval", + "into", + "is", + "join", + "json_array", + "json_arrayagg", + "json_exists", + "json_object", + "json_objectagg", + "json_query", + "json_table", + "json_table_primitive", + "json_value", + "lag", + "language", + "large", + "last_value", + "lateral", + "lead", + "leading", + "left", + "like", + "like_regex", + "listagg", + "ln", + "local", + "localtime", + "localtimestamp", + "log", + "log10", + "lower", + "match", + "match_number", + "match_recognize", + "matches", + "max", + "member", + "merge", + "method", + "min", + "minute", + "mod", + "modifies", + "module", + "month", + "multiset", + "national", + "natural", + "nchar", + "nclob", + "new", + "no", + "none", + "normalize", + "not", + "nth_value", + "ntile", + "null", + "nullif", + "numeric", + "octet_length", + "occurrences_regex", + "of", + "offset", + "old", + "omit", + "on", + "one", + "only", + "open", + "or", + "order", + "out", + "outer", + "over", + "overlaps", + "overlay", + "parameter", + "partition", + "pattern", + "per", + "percent", + "percent_rank", + "percentile_cont", + "percentile_disc", + "period", + "portion", + "position", + "position_regex", + "power", + "precedes", + "precision", + "prepare", + "primary", + "procedure", + "ptf", + "range", + "rank", + "reads", + "real", + "recursive", + "ref", + "references", + "referencing", + "regr_avgx", + "regr_avgy", + "regr_count", + "regr_intercept", + "regr_r2", + "regr_slope", + "regr_sxx", + "regr_sxy", + "regr_syy", + "release", + "result", + "return", + "returns", + "revoke", + "right", + "rollback", + "rollup", + "row", + "row_number", + "rows", + "running", + "savepoint", + "scope", + "scroll", + "search", + "second", + "seek", + "select", + "sensitive", + "session_user", + "set", + "show", + "similar", + "sin", + "sinh", + "skip", + "smallint", + "some", + "specific", + "specifictype", + "sql", + "sqlexception", + "sqlstate", + "sqlwarning", + "sqrt", + "start", + "static", + "stddev_pop", + "stddev_samp", + "submultiset", + "subset", + "substring", + "substring_regex", + "succeeds", + "sum", + "symmetric", + "system", + "system_time", + "system_user", + "table", + "tablesample", + "tan", + "tanh", + "then", + "time", + "timestamp", + "timezone_hour", + "timezone_minute", + "to", + "trailing", + "translate", + "translate_regex", + "translation", + "treat", + "trigger", + "trim", + "trim_array", + "true", + "truncate", + "uescape", + "union", + "unique", + "unknown", + "unnest", + "update ", + "upper", + "user", + "using", + "value", + "values", + "value_of", + "var_pop", + "var_samp", + "varbinary", + "varchar", + "varying", + "versioning", + "when", + "whenever", + "where", + "width_bucket", + "window", + "with", + "within", + "without", + "year", + ], + ["add", "asc", "collation", "desc", "final", "first", "last", "view"], + ) + .filter(function (e) { + return !r.includes(e); + }), + l = { + begin: Yb(/\b/, Hb.apply(void 0, o), /\s*\(/), + keywords: { built_in: o }, + }; + return { + name: "SQL", + case_insensitive: !0, + illegal: /[{}]|<\//, + keywords: { + $pattern: /\b[\w\.]+/, + keyword: (function (e) { + var t = + arguments.length > 1 && void 0 !== arguments[1] ? arguments[1] : {}, + n = t.exceptions, + a = t.when, + r = a; + return ( + (n = n || []), + e.map(function (e) { + return e.match(/\|\d+$/) || n.includes(e) + ? e + : r(e) + ? "".concat(e, "|0") + : e; + }) + ); + })(s, { + when: function (e) { + return e.length < 3; + }, + }), + literal: n, + type: a, + built_in: [ + "current_catalog", + "current_date", + "current_default_transform_group", + "current_path", + "current_role", + "current_schema", + "current_transform_group_for_type", + "current_user", + "session_user", + "system_time", + "system_user", + "current_time", + "localtime", + "current_timestamp", + "localtimestamp", + ], + }, + contains: [ + { + begin: Hb.apply(void 0, i), + keywords: { + $pattern: /[\w\.]+/, + keyword: s.concat(i), + literal: n, + type: a, + }, + }, + { + className: "type", + begin: Hb.apply(void 0, [ + "double precision", + "large object", + "with timezone", + "without timezone", + ]), + }, + l, + { className: "variable", begin: /@[a-z0-9]+/ }, + { + className: "string", + variants: [{ begin: /'/, end: /'/, contains: [{ begin: /''/ }] }], + }, + { begin: /"/, end: /"/, contains: [{ begin: /""/ }] }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + { + className: "operator", + begin: /[-+*/=%^~]|&&?|\|\|?|!=?|<(?:=>?|<|>)?|>[>=]?/, + relevance: 0, + }, + ], + }; +}; +var qb = function (e) { + return { + name: "Stan", + aliases: ["stanfuncs"], + keywords: { + $pattern: e.IDENT_RE, + title: [ + "functions", + "model", + "data", + "parameters", + "quantities", + "transformed", + "generated", + ], + keyword: [ + "for", + "in", + "if", + "else", + "while", + "break", + "continue", + "return", + ] + .concat([ + "int", + "real", + "vector", + "ordered", + "positive_ordered", + "simplex", + "unit_vector", + "row_vector", + "matrix", + "cholesky_factor_corr|10", + "cholesky_factor_cov|10", + "corr_matrix|10", + "cov_matrix|10", + "void", + ]) + .concat([ + "print", + "reject", + "increment_log_prob|10", + "integrate_ode|10", + "integrate_ode_rk45|10", + "integrate_ode_bdf|10", + "algebra_solver", + ]), + built_in: [ + "Phi", + "Phi_approx", + "abs", + "acos", + "acosh", + "algebra_solver", + "append_array", + "append_col", + "append_row", + "asin", + "asinh", + "atan", + "atan2", + "atanh", + "bernoulli_cdf", + "bernoulli_lccdf", + "bernoulli_lcdf", + "bernoulli_logit_lpmf", + "bernoulli_logit_rng", + "bernoulli_lpmf", + "bernoulli_rng", + "bessel_first_kind", + "bessel_second_kind", + "beta_binomial_cdf", + "beta_binomial_lccdf", + "beta_binomial_lcdf", + "beta_binomial_lpmf", + "beta_binomial_rng", + "beta_cdf", + "beta_lccdf", + "beta_lcdf", + "beta_lpdf", + "beta_rng", + "binary_log_loss", + "binomial_cdf", + "binomial_coefficient_log", + "binomial_lccdf", + "binomial_lcdf", + "binomial_logit_lpmf", + "binomial_lpmf", + "binomial_rng", + "block", + "categorical_logit_lpmf", + "categorical_logit_rng", + "categorical_lpmf", + "categorical_rng", + "cauchy_cdf", + "cauchy_lccdf", + "cauchy_lcdf", + "cauchy_lpdf", + "cauchy_rng", + "cbrt", + "ceil", + "chi_square_cdf", + "chi_square_lccdf", + "chi_square_lcdf", + "chi_square_lpdf", + "chi_square_rng", + "cholesky_decompose", + "choose", + "col", + "cols", + "columns_dot_product", + "columns_dot_self", + "cos", + "cosh", + "cov_exp_quad", + "crossprod", + "csr_extract_u", + "csr_extract_v", + "csr_extract_w", + "csr_matrix_times_vector", + "csr_to_dense_matrix", + "cumulative_sum", + "determinant", + "diag_matrix", + "diag_post_multiply", + "diag_pre_multiply", + "diagonal", + "digamma", + "dims", + "dirichlet_lpdf", + "dirichlet_rng", + "distance", + "dot_product", + "dot_self", + "double_exponential_cdf", + "double_exponential_lccdf", + "double_exponential_lcdf", + "double_exponential_lpdf", + "double_exponential_rng", + "e", + "eigenvalues_sym", + "eigenvectors_sym", + "erf", + "erfc", + "exp", + "exp2", + "exp_mod_normal_cdf", + "exp_mod_normal_lccdf", + "exp_mod_normal_lcdf", + "exp_mod_normal_lpdf", + "exp_mod_normal_rng", + "expm1", + "exponential_cdf", + "exponential_lccdf", + "exponential_lcdf", + "exponential_lpdf", + "exponential_rng", + "fabs", + "falling_factorial", + "fdim", + "floor", + "fma", + "fmax", + "fmin", + "fmod", + "frechet_cdf", + "frechet_lccdf", + "frechet_lcdf", + "frechet_lpdf", + "frechet_rng", + "gamma_cdf", + "gamma_lccdf", + "gamma_lcdf", + "gamma_lpdf", + "gamma_p", + "gamma_q", + "gamma_rng", + "gaussian_dlm_obs_lpdf", + "get_lp", + "gumbel_cdf", + "gumbel_lccdf", + "gumbel_lcdf", + "gumbel_lpdf", + "gumbel_rng", + "head", + "hypergeometric_lpmf", + "hypergeometric_rng", + "hypot", + "inc_beta", + "int_step", + "integrate_ode", + "integrate_ode_bdf", + "integrate_ode_rk45", + "inv", + "inv_Phi", + "inv_chi_square_cdf", + "inv_chi_square_lccdf", + "inv_chi_square_lcdf", + "inv_chi_square_lpdf", + "inv_chi_square_rng", + "inv_cloglog", + "inv_gamma_cdf", + "inv_gamma_lccdf", + "inv_gamma_lcdf", + "inv_gamma_lpdf", + "inv_gamma_rng", + "inv_logit", + "inv_sqrt", + "inv_square", + "inv_wishart_lpdf", + "inv_wishart_rng", + "inverse", + "inverse_spd", + "is_inf", + "is_nan", + "lbeta", + "lchoose", + "lgamma", + "lkj_corr_cholesky_lpdf", + "lkj_corr_cholesky_rng", + "lkj_corr_lpdf", + "lkj_corr_rng", + "lmgamma", + "lmultiply", + "log", + "log10", + "log1m", + "log1m_exp", + "log1m_inv_logit", + "log1p", + "log1p_exp", + "log2", + "log_determinant", + "log_diff_exp", + "log_falling_factorial", + "log_inv_logit", + "log_mix", + "log_rising_factorial", + "log_softmax", + "log_sum_exp", + "logistic_cdf", + "logistic_lccdf", + "logistic_lcdf", + "logistic_lpdf", + "logistic_rng", + "logit", + "lognormal_cdf", + "lognormal_lccdf", + "lognormal_lcdf", + "lognormal_lpdf", + "lognormal_rng", + "machine_precision", + "matrix_exp", + "max", + "mdivide_left_spd", + "mdivide_left_tri_low", + "mdivide_right_spd", + "mdivide_right_tri_low", + "mean", + "min", + "modified_bessel_first_kind", + "modified_bessel_second_kind", + "multi_gp_cholesky_lpdf", + "multi_gp_lpdf", + "multi_normal_cholesky_lpdf", + "multi_normal_cholesky_rng", + "multi_normal_lpdf", + "multi_normal_prec_lpdf", + "multi_normal_rng", + "multi_student_t_lpdf", + "multi_student_t_rng", + "multinomial_lpmf", + "multinomial_rng", + "multiply_log", + "multiply_lower_tri_self_transpose", + "neg_binomial_2_cdf", + "neg_binomial_2_lccdf", + "neg_binomial_2_lcdf", + "neg_binomial_2_log_lpmf", + "neg_binomial_2_log_rng", + "neg_binomial_2_lpmf", + "neg_binomial_2_rng", + "neg_binomial_cdf", + "neg_binomial_lccdf", + "neg_binomial_lcdf", + "neg_binomial_lpmf", + "neg_binomial_rng", + "negative_infinity", + "normal_cdf", + "normal_lccdf", + "normal_lcdf", + "normal_lpdf", + "normal_rng", + "not_a_number", + "num_elements", + "ordered_logistic_lpmf", + "ordered_logistic_rng", + "owens_t", + "pareto_cdf", + "pareto_lccdf", + "pareto_lcdf", + "pareto_lpdf", + "pareto_rng", + "pareto_type_2_cdf", + "pareto_type_2_lccdf", + "pareto_type_2_lcdf", + "pareto_type_2_lpdf", + "pareto_type_2_rng", + "pi", + "poisson_cdf", + "poisson_lccdf", + "poisson_lcdf", + "poisson_log_lpmf", + "poisson_log_rng", + "poisson_lpmf", + "poisson_rng", + "positive_infinity", + "pow", + "print", + "prod", + 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"tanh", + "target", + "tcrossprod", + "tgamma", + "to_array_1d", + "to_array_2d", + "to_matrix", + "to_row_vector", + "to_vector", + "trace", + "trace_gen_quad_form", + "trace_quad_form", + "trigamma", + "trunc", + "uniform_cdf", + "uniform_lccdf", + "uniform_lcdf", + "uniform_lpdf", + "uniform_rng", + "variance", + "von_mises_lpdf", + "von_mises_rng", + "weibull_cdf", + "weibull_lccdf", + "weibull_lcdf", + "weibull_lpdf", + "weibull_rng", + "wiener_lpdf", + "wishart_lpdf", + "wishart_rng", + ], + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/#/, /$/, { + relevance: 0, + keywords: { "meta-keyword": "include" }, + }), + e.COMMENT(/\/\*/, /\*\//, { + relevance: 0, + contains: [{ className: "doctag", begin: /@(return|param)/ }], + }), + { begin: /<\s*lower\s*=/, keywords: "lower" }, + { begin: /[<,]\s*upper\s*=/, keywords: "upper" }, + { className: "keyword", begin: /\btarget\s*\+=/, relevance: 10 }, + { + begin: "~\\s*(" + e.IDENT_RE + ")\\s*\\(", + keywords: [ + "bernoulli", + 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ztir_5 ztjoin_5 ztnb ztnb_p ztp ztp_p zts_5 ztset_5 ztspli_5 ztsum_5 zttoct_5 ztvary_5 ztweib_5", + contains: [ + { className: "symbol", begin: /`[a-zA-Z0-9_]+'/ }, + { className: "variable", begin: /\$\{?[a-zA-Z0-9_]+\}?/ }, + { + className: "string", + variants: [{ begin: '`"[^\r\n]*?"\'' }, { begin: '"[^\r\n"]*"' }], + }, + { + className: "built_in", + variants: [ + { + begin: + 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+ }, + ], + }, + e.COMMENT("^[ \t]*\\*.*$", !1), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var Wb = function (e) { + return { + name: "STEP Part 21", + aliases: ["p21", "step", "stp"], + case_insensitive: !0, + keywords: { + $pattern: "[A-Z_][A-Z0-9_.]*", + keyword: "HEADER ENDSEC DATA", + }, + contains: [ + { className: "meta", begin: "ISO-10303-21;", relevance: 10 }, + { className: "meta", begin: "END-ISO-10303-21;", relevance: 10 }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT("/\\*\\*!", "\\*/"), + e.C_NUMBER_MODE, + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { className: "string", begin: "'", end: "'" }, + { + className: "symbol", + variants: [{ begin: "#", end: "\\d+", illegal: "\\W" }], + }, + ], + }; + }, + $b = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + Qb = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + Kb = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + jb = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + Xb = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(); +var Zb = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = { className: "variable", begin: "\\$" + e.IDENT_RE }, + a = "(?=[.\\s\\n[:,(])"; + return { + name: "Stylus", + aliases: ["styl"], + case_insensitive: !1, + keywords: "if else for in", + illegal: + "(" + + [ + "\\?", + "(\\bReturn\\b)", + "(\\bEnd\\b)", + "(\\bend\\b)", + "(\\bdef\\b)", + ";", + "#\\s", + "\\*\\s", + "===\\s", + "\\|", + "%", + ].join("|") + + ")", + contains: [ + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + t.HEXCOLOR, + { + begin: "\\.[a-zA-Z][a-zA-Z0-9_-]*(?=[.\\s\\n[:,(])", + className: "selector-class", + }, + { + begin: "#[a-zA-Z][a-zA-Z0-9_-]*(?=[.\\s\\n[:,(])", + className: "selector-id", + }, + { begin: "\\b(" + $b.join("|") + ")" + a, className: "selector-tag" }, + { className: "selector-pseudo", begin: "&?:(" + Kb.join("|") + ")" + a }, + { className: "selector-pseudo", begin: "&?::(" + jb.join("|") + ")" + a }, + t.ATTRIBUTE_SELECTOR_MODE, + { + className: "keyword", + begin: /@media/, + starts: { + end: /[{;}]/, + keywords: { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: Qb.join(" "), + }, + contains: [e.CSS_NUMBER_MODE], + }, + }, + { + className: "keyword", + begin: + "@((-(o|moz|ms|webkit)-)?(" + + [ + "charset", + "css", + "debug", + "extend", + "font-face", + "for", + "import", + "include", + "keyframes", + "media", + "mixin", + "page", + "warn", + "while", + ].join("|") + + "))\\b", + }, + n, + e.CSS_NUMBER_MODE, + { + className: "function", + begin: "^[a-zA-Z][a-zA-Z0-9_-]*\\(.*\\)", + illegal: "[\\n]", + returnBegin: !0, + contains: [ + { className: "title", begin: "\\b[a-zA-Z][a-zA-Z0-9_-]*" }, + { + className: "params", + begin: /\(/, + end: /\)/, + contains: [ + t.HEXCOLOR, + n, + e.APOS_STRING_MODE, + e.CSS_NUMBER_MODE, + e.QUOTE_STRING_MODE, + ], + }, + ], + }, + { + className: "attribute", + begin: "\\b(" + Xb.join("|") + ")\\b", + starts: { + end: /;|$/, + contains: [ + t.HEXCOLOR, + n, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.CSS_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t.IMPORTANT, + ], + illegal: /\./, + relevance: 0, + }, + }, + ], + }; +}; +var Jb = function (e) { + return { + name: "SubUnit", + case_insensitive: !0, + contains: [ + { className: "string", begin: "\\[\n(multipart)?", end: "\\]\n" }, + { + className: "string", + begin: "\\d{4}-\\d{2}-\\d{2}(\\s+)\\d{2}:\\d{2}:\\d{2}.\\d+Z", + }, + { className: "string", begin: "(\\+|-)\\d+" }, + { + className: "keyword", + relevance: 10, + variants: [ + { + begin: + "^(test|testing|success|successful|failure|error|skip|xfail|uxsuccess)(:?)\\s+(test)?", + }, + { begin: "^progress(:?)(\\s+)?(pop|push)?" }, + { begin: "^tags:" }, + { begin: "^time:" }, + ], + }, + ], + }; +}; +function eT(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function tT(e) { + return nT("(?=", e, ")"); +} +function nT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return eT(e); + }) + .join(""); + return a; +} +function aT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return eT(e); + }) + .join("|") + + ")"; + return a; +} +var rT = function (e) { + return nT(/\b/, e, /\w$/.test(e) ? /\b/ : /\B/); + }, + iT = ["Protocol", "Type"].map(rT), + oT = ["init", "self"].map(rT), + sT = ["Any", "Self"], + lT = [ + "associatedtype", + "async", + "await", + /as\?/, + /as!/, + "as", + "break", + "case", + "catch", + "class", + "continue", + "convenience", + "default", + "defer", + "deinit", + "didSet", + "do", + "dynamic", + "else", + "enum", + "extension", + "fallthrough", + /fileprivate\(set\)/, + "fileprivate", + "final", + "for", + "func", + "get", + "guard", + "if", + "import", + "indirect", + "infix", + /init\?/, + /init!/, + "inout", + /internal\(set\)/, + "internal", + "in", + "is", + "lazy", + "let", + "mutating", + "nonmutating", + /open\(set\)/, + "open", + "operator", + "optional", + "override", + "postfix", + "precedencegroup", + "prefix", + /private\(set\)/, + "private", + "protocol", + /public\(set\)/, + "public", + "repeat", + "required", + "rethrows", + "return", + "set", + "some", + "static", + "struct", + "subscript", + "super", + "switch", + "throws", + "throw", + /try\?/, + /try!/, + "try", + "typealias", + /unowned\(safe\)/, + /unowned\(unsafe\)/, + "unowned", + "var", + "weak", + "where", + "while", + "willSet", + ], + cT = ["false", "nil", "true"], + _T = [ + "assignment", + "associativity", + "higherThan", + "left", + "lowerThan", + "none", + "right", + ], + dT = [ + "#colorLiteral", + "#column", + "#dsohandle", + "#else", + "#elseif", + "#endif", + "#error", + "#file", + "#fileID", + "#fileLiteral", + "#filePath", + "#function", + "#if", + "#imageLiteral", + "#keyPath", + "#line", + "#selector", + "#sourceLocation", + "#warn_unqualified_access", + "#warning", + ], + uT = [ + "abs", + "all", + "any", + "assert", + "assertionFailure", + "debugPrint", + "dump", + "fatalError", + "getVaList", + "isKnownUniquelyReferenced", + "max", + "min", + "numericCast", + "pointwiseMax", + "pointwiseMin", + "precondition", + "preconditionFailure", + "print", + "readLine", + "repeatElement", + "sequence", + "stride", + "swap", + "swift_unboxFromSwiftValueWithType", + "transcode", + "type", + "unsafeBitCast", + "unsafeDowncast", + "withExtendedLifetime", + "withUnsafeMutablePointer", + "withUnsafePointer", + "withVaList", + "withoutActuallyEscaping", + "zip", + ], + mT = aT( + /[/=\-+!*%<>&|^~?]/, + /[\u00A1-\u00A7]/, + /[\u00A9\u00AB]/, + /[\u00AC\u00AE]/, + /[\u00B0\u00B1]/, + /[\u00B6\u00BB\u00BF\u00D7\u00F7]/, + /[\u2016-\u2017]/, + /[\u2020-\u2027]/, + /[\u2030-\u203E]/, + /[\u2041-\u2053]/, + /[\u2055-\u205E]/, + /[\u2190-\u23FF]/, + /[\u2500-\u2775]/, + /[\u2794-\u2BFF]/, + /[\u2E00-\u2E7F]/, + /[\u3001-\u3003]/, + /[\u3008-\u3020]/, + /[\u3030]/, + ), + pT = aT( + mT, + /[\u0300-\u036F]/, + /[\u1DC0-\u1DFF]/, + /[\u20D0-\u20FF]/, + /[\uFE00-\uFE0F]/, + /[\uFE20-\uFE2F]/, + ), + gT = nT(mT, pT, "*"), + ET = aT( + /[a-zA-Z_]/, + /[\u00A8\u00AA\u00AD\u00AF\u00B2-\u00B5\u00B7-\u00BA]/, + /[\u00BC-\u00BE\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u00FF]/, + /[\u0100-\u02FF\u0370-\u167F\u1681-\u180D\u180F-\u1DBF]/, + /[\u1E00-\u1FFF]/, + /[\u200B-\u200D\u202A-\u202E\u203F-\u2040\u2054\u2060-\u206F]/, + /[\u2070-\u20CF\u2100-\u218F\u2460-\u24FF\u2776-\u2793]/, + /[\u2C00-\u2DFF\u2E80-\u2FFF]/, + /[\u3004-\u3007\u3021-\u302F\u3031-\u303F\u3040-\uD7FF]/, + /[\uF900-\uFD3D\uFD40-\uFDCF\uFDF0-\uFE1F\uFE30-\uFE44]/, + /[\uFE47-\uFEFE\uFF00-\uFFFD]/, + ), + ST = aT(ET, /\d/, /[\u0300-\u036F\u1DC0-\u1DFF\u20D0-\u20FF\uFE20-\uFE2F]/), + bT = nT(ET, ST, "*"), + TT = nT(/[A-Z]/, ST, "*"), + fT = [ + "autoclosure", + nT(/convention\(/, aT("swift", "block", "c"), /\)/), + "discardableResult", + "dynamicCallable", + "dynamicMemberLookup", + "escaping", + "frozen", + "GKInspectable", + "IBAction", + "IBDesignable", + "IBInspectable", + "IBOutlet", + "IBSegueAction", + "inlinable", + "main", + "nonobjc", + "NSApplicationMain", + "NSCopying", + "NSManaged", + nT(/objc\(/, bT, /\)/), + "objc", + "objcMembers", + "propertyWrapper", + "requires_stored_property_inits", + "testable", + "UIApplicationMain", + "unknown", + "usableFromInline", + ], + CT = [ + "iOS", + "iOSApplicationExtension", + "macOS", + "macOSApplicationExtension", + "macCatalyst", + "macCatalystApplicationExtension", + "watchOS", + "watchOSApplicationExtension", + "tvOS", + "tvOSApplicationExtension", + "swift", + ]; +var NT = function (e) { + var t = { match: /\s+/, relevance: 0 }, + n = e.COMMENT("/\\*", "\\*/", { contains: ["self"] }), + a = [e.C_LINE_COMMENT_MODE, n], + r = { + className: "keyword", + begin: nT(/\./, tT(aT.apply(void 0, c(iT).concat(c(oT))))), + end: aT.apply(void 0, c(iT).concat(c(oT))), + excludeBegin: !0, + }, + i = { match: nT(/\./, aT.apply(void 0, lT)), relevance: 0 }, + o = lT + .filter(function (e) { + return "string" == typeof e; + }) + .concat(["_|0"]), + s = lT + .filter(function (e) { + return "string" != typeof e; + }) + .concat(sT) + .map(rT), + l = { + variants: [ + { className: "keyword", match: aT.apply(void 0, c(s).concat(c(oT))) }, + ], + }, + d = { $pattern: aT(/\b\w+/, /#\w+/), keyword: o.concat(dT), literal: cT }, + u = [r, i, l], + m = [ + { match: nT(/\./, aT.apply(void 0, uT)), relevance: 0 }, + { + className: "built_in", + match: nT(/\b/, aT.apply(void 0, uT), /(?=\()/), + }, + ], + p = { match: /->/, relevance: 0 }, + g = [ + p, + { + className: "operator", + relevance: 0, + variants: [{ match: gT }, { match: "\\.(\\.|".concat(pT, ")+") }], + }, + ], + E = "([0-9]_*)+", + S = "([0-9a-fA-F]_*)+", + b = { + className: "number", + relevance: 0, + variants: [ + { + match: + "\\b(".concat(E, ")(\\.(").concat(E, "))?") + + "([eE][+-]?(".concat(E, "))?\\b"), + }, + { + match: + "\\b0x(".concat(S, ")(\\.(").concat(S, "))?") + + "([pP][+-]?(".concat(E, "))?\\b"), + }, + { match: /\b0o([0-7]_*)+\b/ }, + { match: /\b0b([01]_*)+\b/ }, + ], + }, + T = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + className: "subst", + variants: [ + { match: nT(/\\/, e, /[0\\tnr"']/) }, + { match: nT(/\\/, e, /u\{[0-9a-fA-F]{1,8}\}/) }, + ], + }; + }, + f = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + className: "subst", + match: nT(/\\/, e, /[\t ]*(?:[\r\n]|\r\n)/), + }; + }, + C = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + className: "subst", + label: "interpol", + begin: nT(/\\/, e, /\(/), + end: /\)/, + }; + }, + N = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + begin: nT(e, /"""/), + end: nT(/"""/, e), + contains: [T(e), f(e), C(e)], + }; + }, + R = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { begin: nT(e, /"/), end: nT(/"/, e), contains: [T(e), C(e)] }; + }, + v = { + className: "string", + variants: [ + N(), + N("#"), + N("##"), + N("###"), + R(), + R("#"), + R("##"), + R("###"), + ], + }, + O = { match: nT(/`/, bT, /`/) }, + h = [ + O, + { className: "variable", match: /\$\d+/ }, + { className: "variable", match: "\\$".concat(ST, "+") }, + ], + y = [ + { + match: /(@|#)available/, + className: "keyword", + starts: { + contains: [ + { + begin: /\(/, + end: /\)/, + keywords: CT, + contains: [].concat(g, [b, v]), + }, + ], + }, + }, + { className: "keyword", match: nT(/@/, aT.apply(void 0, fT)) }, + { className: "meta", match: nT(/@/, bT) }, + ], + I = { + match: tT(/\b[A-Z]/), + relevance: 0, + contains: [ + { + className: "type", + match: nT( + /(AV|CA|CF|CG|CI|CL|CM|CN|CT|MK|MP|MTK|MTL|NS|SCN|SK|UI|WK|XC)/, + ST, + "+", + ), + }, + { className: "type", match: TT, relevance: 0 }, + { match: /[?!]+/, relevance: 0 }, + { match: /\.\.\./, relevance: 0 }, + { match: nT(/\s+&\s+/, tT(TT)), relevance: 0 }, + ], + }, + A = { + begin: //, + keywords: d, + contains: [].concat(a, u, y, [p, I]), + }; + I.contains.push(A); + var D, + M = { + begin: /\(/, + end: /\)/, + relevance: 0, + keywords: d, + contains: [ + "self", + { match: nT(bT, /\s*:/), keywords: "_|0", relevance: 0 }, + ].concat(a, u, m, g, [b, v], h, y, [I]), + }, + L = { + beginKeywords: "func", + contains: [ + { + className: "title", + match: aT(O.match, bT, gT), + endsParent: !0, + relevance: 0, + }, + t, + ], + }, + w = { begin: //, contains: [].concat(a, [I]) }, + x = { + begin: /\(/, + end: /\)/, + keywords: d, + contains: [ + { + begin: aT(tT(nT(bT, /\s*:/)), tT(nT(bT, /\s+/, bT, /\s*:/))), + end: /:/, + relevance: 0, + contains: [ + { className: "keyword", match: /\b_\b/ }, + { className: "params", match: bT }, + ], + }, + ].concat(a, u, g, [b, v], y, [I, M]), + endsParent: !0, + illegal: /["']/, + }, + P = { + className: "function", + match: tT(/\bfunc\b/), + contains: [L, w, x, t], + illegal: [/\[/, /%/], + }, + k = { + className: "function", + match: /\b(subscript|init[?!]?)\s*(?=[<(])/, + keywords: { keyword: "subscript init init? init!", $pattern: /\w+[?!]?/ }, + contains: [w, x, t], + illegal: /\[|%/, + }, + U = { + beginKeywords: "operator", + end: e.MATCH_NOTHING_RE, + contains: [ + { className: "title", match: gT, endsParent: !0, relevance: 0 }, + ], + }, + F = { + beginKeywords: "precedencegroup", + end: e.MATCH_NOTHING_RE, + contains: [ + { className: "title", match: TT, relevance: 0 }, + { + begin: /{/, + end: /}/, + relevance: 0, + endsParent: !0, + keywords: [].concat(_T, cT), + contains: [I], + }, + ], + }, + B = (function (e, t) { + var n = + ("undefined" != typeof Symbol && e[Symbol.iterator]) || e["@@iterator"]; + if (!n) { + if ( + Array.isArray(e) || + (n = _(e)) || + (t && e && "number" == typeof e.length) + ) { + n && (e = n); + var a = 0, + r = function () {}; + return { + s: r, + n: function () { + return a >= e.length ? { done: !0 } : { done: !1, value: e[a++] }; + }, + e: function (e) { + throw e; + }, + f: r, + }; + } + throw new TypeError( + "Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.", + ); + } + var i, + o = !0, + s = !1; + return { + s: function () { + n = n.call(e); + }, + n: function () { + var e = n.next(); + return (o = e.done), e; + }, + e: function (e) { + (s = !0), (i = e); + }, + f: function () { + try { + o || null == n.return || n.return(); + } finally { + if (s) throw i; + } + }, + }; + })(v.variants); + try { + for (B.s(); !(D = B.n()).done; ) { + var G = D.value.contains.find(function (e) { + return "interpol" === e.label; + }); + G.keywords = d; + var Y = [].concat(u, m, g, [b, v], h); + G.contains = [].concat(c(Y), [ + { begin: /\(/, end: /\)/, contains: ["self"].concat(c(Y)) }, + ]); + } + } catch (e) { + B.e(e); + } finally { + B.f(); + } + return { + name: "Swift", + keywords: d, + contains: [].concat( + a, + [ + P, + k, + { + className: "class", + beginKeywords: "struct protocol class extension enum", + end: "\\{", + excludeEnd: !0, + keywords: d, + contains: [ + e.inherit(e.TITLE_MODE, { + begin: /[A-Za-z$_][\u00C0-\u02B80-9A-Za-z$_]*/, + }), + ].concat(u), + }, + U, + F, + { + beginKeywords: "import", + end: /$/, + contains: [].concat(a), + relevance: 0, + }, + ], + u, + m, + g, + [b, v], + h, + y, + [I, M], + ), + }; +}; +var RT = function (e) { + return { + name: "Tagger Script", + contains: [ + { + className: "comment", + begin: /\$noop\(/, + end: /\)/, + contains: [ + { begin: /\(/, end: /\)/, contains: ["self", { begin: /\\./ }] }, + ], + relevance: 10, + }, + { + className: "keyword", + begin: /\$(?!noop)[a-zA-Z][_a-zA-Z0-9]*/, + end: /\(/, + excludeEnd: !0, + }, + { className: "variable", begin: /%[_a-zA-Z0-9:]*/, end: "%" }, + { className: "symbol", begin: /\\./ }, + ], + }; +}; +var vT = function (e) { + var t = "true false yes no null", + n = "[\\w#;/?:@&=+$,.~*'()[\\]]+", + a = { + className: "string", + relevance: 0, + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /\S+/ }, + ], + contains: [ + e.BACKSLASH_ESCAPE, + { + className: "template-variable", + variants: [ + { begin: /\{\{/, end: /\}\}/ }, + { begin: /%\{/, end: /\}/ }, + ], + }, + ], + }, + r = e.inherit(a, { + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /[^\s,{}[\]]+/ }, + ], + }), + i = { + className: "number", + begin: + "\\b[0-9]{4}(-[0-9][0-9]){0,2}([Tt \\t][0-9][0-9]?(:[0-9][0-9]){2})?(\\.[0-9]*)?([ \\t])*(Z|[-+][0-9][0-9]?(:[0-9][0-9])?)?\\b", + }, + o = { + end: ",", + endsWithParent: !0, + excludeEnd: !0, + keywords: t, + relevance: 0, + }, + s = { begin: /\{/, end: /\}/, contains: [o], illegal: "\\n", relevance: 0 }, + l = { + begin: "\\[", + end: "\\]", + contains: [o], + illegal: "\\n", + relevance: 0, + }, + c = [ + { + className: "attr", + variants: [ + { begin: "\\w[\\w :\\/.-]*:(?=[ \t]|$)" }, + { begin: '"\\w[\\w :\\/.-]*":(?=[ \t]|$)' }, + { begin: "'\\w[\\w :\\/.-]*':(?=[ \t]|$)" }, + ], + }, + { className: "meta", begin: "^---\\s*$", relevance: 10 }, + { + className: "string", + begin: "[\\|>]([1-9]?[+-])?[ ]*\\n( +)[^ ][^\\n]*\\n(\\2[^\\n]+\\n?)*", + }, + { + begin: "<%[%=-]?", + end: "[%-]?%>", + subLanguage: "ruby", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + { className: "type", begin: "!\\w+!" + n }, + { className: "type", begin: "!<" + n + ">" }, + { className: "type", begin: "!" + n }, + { className: "type", begin: "!!" + n }, + { className: "meta", begin: "&" + e.UNDERSCORE_IDENT_RE + "$" }, + { className: "meta", begin: "\\*" + e.UNDERSCORE_IDENT_RE + "$" }, + { className: "bullet", begin: "-(?=[ ]|$)", relevance: 0 }, + e.HASH_COMMENT_MODE, + { beginKeywords: t, keywords: { literal: t } }, + i, + { className: "number", begin: e.C_NUMBER_RE + "\\b", relevance: 0 }, + s, + l, + a, + ], + _ = [].concat(c); + return ( + _.pop(), + _.push(r), + (o.contains = _), + { name: "YAML", case_insensitive: !0, aliases: ["yml"], contains: c } + ); +}; +var OT = function (e) { + return { + name: "Test Anything Protocol", + case_insensitive: !0, + contains: [ + e.HASH_COMMENT_MODE, + { + className: "meta", + variants: [ + { begin: "^TAP version (\\d+)$" }, + { begin: "^1\\.\\.(\\d+)$" }, + ], + }, + { begin: /---$/, end: "\\.\\.\\.$", subLanguage: "yaml", relevance: 0 }, + { className: "number", begin: " (\\d+) " }, + { + className: "symbol", + variants: [{ begin: "^ok" }, { begin: "^not ok" }], + }, + ], + }; +}; +function hT(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function yT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return hT(e); + }) + .join(""); + return a; +} +var IT = function (e) { + var t, + n = /[a-zA-Z_][a-zA-Z0-9_]*/, + a = { + className: "number", + variants: [e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE], + }; + return { + name: "Tcl", + aliases: ["tk"], + keywords: + "after append apply array auto_execok auto_import auto_load auto_mkindex auto_mkindex_old auto_qualify auto_reset bgerror binary break catch cd chan clock close concat continue dde dict encoding eof error eval exec exit expr fblocked fconfigure fcopy file fileevent filename flush for foreach format gets glob global history http if incr info interp join lappend|10 lassign|10 lindex|10 linsert|10 list llength|10 load lrange|10 lrepeat|10 lreplace|10 lreverse|10 lsearch|10 lset|10 lsort|10 mathfunc mathop memory msgcat namespace open package parray pid pkg::create pkg_mkIndex platform platform::shell proc puts pwd read refchan regexp registry regsub|10 rename return safe scan seek set socket source split string subst switch tcl_endOfWord tcl_findLibrary tcl_startOfNextWord tcl_startOfPreviousWord tcl_wordBreakAfter tcl_wordBreakBefore tcltest tclvars tell time tm trace unknown unload unset update uplevel upvar variable vwait while", + contains: [ + e.COMMENT(";[ \\t]*#", "$"), + e.COMMENT("^[ \\t]*#", "$"), + { + beginKeywords: "proc", + end: "[\\{]", + excludeEnd: !0, + contains: [ + { + className: "title", + begin: "[ \\t\\n\\r]+(::)?[a-zA-Z_]((::)?[a-zA-Z0-9_])*", + end: "[ \\t\\n\\r]", + endsWithParent: !0, + excludeEnd: !0, + }, + ], + }, + { + className: "variable", + variants: [ + { + begin: yT(/\$/, ((t = /::/), yT("(", t, ")?")), n, "(::", n, ")*"), + }, + { + begin: "\\$\\{(::)?[a-zA-Z_]((::)?[a-zA-Z0-9_])*", + end: "\\}", + contains: [a], + }, + ], + }, + { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [e.inherit(e.QUOTE_STRING_MODE, { illegal: null })], + }, + a, + ], + }; 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+function kT(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function UT(e) { + return FT("(?=", e, ")"); +} +function FT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return kT(e); + }) + .join(""); + return a; +} +var BT = function (e) { + var t = { + $pattern: LT, + keyword: wT.concat([ + "type", + "namespace", + "typedef", + "interface", + "public", + "private", + "protected", + "implements", + "declare", + "abstract", + "readonly", + ]), + literal: xT, + built_in: PT.concat([ + "any", + "void", + "number", + "boolean", + "string", + "object", + "never", + "enum", + ]), + }, + n = { className: "meta", begin: "@[A-Za-z$_][0-9A-Za-z$_]*" }, + a = function (e, t, n) { + var a = e.contains.findIndex(function (e) { + return e.label === t; + }); + if (-1 === a) throw new Error("can not find mode to replace"); + e.contains.splice(a, 1, n); + }, + r = (function (e) { + var t = LT, + n = "<>", + a = "", + r = { + begin: /<[A-Za-z0-9\\._:-]+/, + end: /\/[A-Za-z0-9\\._:-]+>|\/>/, + isTrulyOpeningTag: function (e, t) { + var n = e[0].length + e.index, + a = e.input[n]; + "<" !== a + ? 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("string" == typeof e ? e : e.source) : null; +} +function HT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return YT(e); + }) + .join(""); + return a; +} +function VT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return YT(e); + }) + .join("|") + + ")"; + return a; +} +var qT = function (e) { + var t = /\d{1,2}\/\d{1,2}\/\d{4}/, + n = /\d{4}-\d{1,2}-\d{1,2}/, + a = /(\d|1[012])(:\d+){0,2} *(AM|PM)/, + r = /\d{1,2}(:\d{1,2}){1,2}/, + i = { + className: "literal", + variants: [ + { begin: HT(/# */, VT(n, t), / *#/) }, + { begin: HT(/# */, r, / *#/) }, + { begin: HT(/# */, a, / *#/) }, + { begin: HT(/# */, VT(n, t), / +/, VT(a, r), / *#/) }, + ], + }, + o = e.COMMENT(/'''/, /$/, { + contains: [{ className: "doctag", begin: /<\/?/, end: />/ }], + }), + s = e.COMMENT(null, /$/, { + variants: [{ begin: /'/ }, { begin: /([\t ]|^)REM(?=\s)/ }], + }); + return { + name: "Visual Basic .NET", + aliases: ["vb"], + case_insensitive: !0, + classNameAliases: { label: "symbol" }, + keywords: { + keyword: + "addhandler alias aggregate ansi as async assembly auto binary by byref byval call case catch class compare const continue custom declare default delegate dim distinct do each equals else elseif end enum erase error event exit explicit finally for friend from function get global goto group handles if implements imports in inherits interface into iterator join key let lib loop me mid module mustinherit mustoverride mybase myclass namespace narrowing new next notinheritable notoverridable of off on operator option optional order overloads overridable overrides paramarray partial preserve private property protected public raiseevent readonly redim removehandler resume return select set shadows shared skip static step stop structure strict sub synclock take text then throw to try unicode until using when where while widening with withevents writeonly yield", + built_in: + "addressof and andalso await directcast gettype getxmlnamespace is isfalse isnot istrue like mod nameof new not or orelse trycast typeof xor cbool cbyte cchar cdate cdbl cdec cint clng cobj csbyte cshort csng cstr cuint culng cushort", + type: "boolean byte char date decimal double integer long object sbyte short single string uinteger ulong ushort", + literal: "true false nothing", + }, + illegal: "//|\\{|\\}|endif|gosub|variant|wend|^\\$ ", + contains: [ + { className: "string", begin: /"(""|[^/n])"C\b/ }, + { + className: "string", + begin: /"/, + end: /"/, + illegal: /\n/, + contains: [{ begin: /""/ }], + }, + i, + { + className: "number", + relevance: 0, + variants: [ + { + begin: + /\b\d[\d_]*((\.[\d_]+(E[+-]?[\d_]+)?)|(E[+-]?[\d_]+))[RFD@!#]?/, + }, + { begin: /\b\d[\d_]*((U?[SIL])|[%&])?/ }, + { begin: /&H[\dA-F_]+((U?[SIL])|[%&])?/ }, + { begin: /&O[0-7_]+((U?[SIL])|[%&])?/ }, + { begin: /&B[01_]+((U?[SIL])|[%&])?/ }, + ], + }, + { className: "label", begin: /^\w+:/ }, + o, + s, + { + className: "meta", + begin: + /[\t ]*#(const|disable|else|elseif|enable|end|externalsource|if|region)\b/, + end: /$/, + keywords: { + "meta-keyword": + "const disable else elseif enable end externalsource if region then", + }, + contains: [s], + }, + ], + }; +}; +function zT(e) { + return e ? 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$writeh $writeo $monitor $monitorb $monitorh $monitoro $writememb $dumpfile $dumpoff $dumpall $dumpflush $dumpportsoff $dumpportsall $dumpportsflush $fclose $fdisplay $fdisplayb $fdisplayh $fdisplayo $fstrobe $fstrobeb $fstrobeh $fstrobeo $swrite $swriteb $swriteh $swriteo $fscanf $fread $fseek $fflush $feof $fopen $fwrite $fwriteb $fwriteh $fwriteo $fmonitor $fmonitorb $fmonitorh $fmonitoro $sformat $sformatf $fgetc $ungetc $fgets $sscanf $rewind $ftell $ferror", + }, + contains: [ + e.C_BLOCK_COMMENT_MODE, + e.C_LINE_COMMENT_MODE, + e.QUOTE_STRING_MODE, + { + className: "number", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { begin: "\\b((\\d+'(b|h|o|d|B|H|O|D))[0-9xzXZa-fA-F_]+)" }, + { begin: "\\B(('(b|h|o|d|B|H|O|D))[0-9xzXZa-fA-F_]+)" }, + { begin: "\\b([0-9_])+", relevance: 0 }, + ], + }, + { + className: "variable", + variants: [ + { begin: "#\\((?!parameter).+\\)" }, + { begin: "\\.\\w+", relevance: 0 }, + ], + }, + { + className: "meta", + begin: "`", + end: "$", + keywords: { + "meta-keyword": + "define __FILE__ __LINE__ begin_keywords celldefine default_nettype define else elsif end_keywords endcelldefine endif ifdef ifndef include line nounconnected_drive pragma resetall timescale unconnected_drive undef undefineall", + }, + relevance: 0, + }, + ], + }; +}; +var XT = function (e) { + return { + name: "VHDL", + case_insensitive: !0, + keywords: { + keyword: + "abs access after alias all and architecture array assert assume assume_guarantee attribute begin block body buffer bus case component configuration constant context cover disconnect downto default else elsif end entity exit fairness file for force function generate generic group guarded if impure in inertial inout is label library linkage literal loop map mod nand new next nor not null of on open or others out package parameter port postponed procedure process property protected pure range record register reject release rem report restrict restrict_guarantee return rol ror select sequence severity shared signal sla sll sra srl strong subtype then to transport type unaffected units until use variable view vmode vprop vunit wait when while with xnor xor", + built_in: + "boolean bit character integer time delay_length natural positive string bit_vector file_open_kind file_open_status std_logic std_logic_vector unsigned signed boolean_vector integer_vector std_ulogic std_ulogic_vector unresolved_unsigned u_unsigned unresolved_signed u_signed real_vector time_vector", + literal: "false true note warning error failure line text side width", + }, + illegal: /\{/, + contains: [ + e.C_BLOCK_COMMENT_MODE, + e.COMMENT("--", "$"), + e.QUOTE_STRING_MODE, + { + className: "number", + begin: + "\\b(\\d(_|\\d)*#\\w+(\\.\\w+)?#([eE][-+]?\\d(_|\\d)*)?|\\d(_|\\d)*(\\.\\d(_|\\d)*)?([eE][-+]?\\d(_|\\d)*)?)", + relevance: 0, + }, + { + className: "string", + begin: "'(U|X|0|1|Z|W|L|H|-)'", + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "symbol", + begin: "'[A-Za-z](_?[A-Za-z0-9])*", + contains: [e.BACKSLASH_ESCAPE], + }, + ], + }; +}; +var ZT = function (e) { + return { + name: "Vim Script", + keywords: { + $pattern: /[!#@\w]+/, + keyword: + "N|0 P|0 X|0 a|0 ab abc abo al am an|0 ar arga argd arge argdo argg argl argu as au aug aun b|0 bN ba bad bd be bel bf bl bm bn bo bp br brea breaka breakd breakl bro bufdo buffers bun bw c|0 cN cNf ca cabc caddb cad caddf cal cat cb cc ccl cd ce cex cf cfir cgetb cgete cg changes chd che checkt cl cla clo cm cmapc cme cn cnew cnf cno cnorea cnoreme co col colo com comc comp con conf cope cp cpf cq cr cs cst cu cuna cunme cw delm deb debugg delc delf dif diffg diffo diffp diffpu diffs diffthis dig di dl dell dj dli do doautoa dp dr ds dsp e|0 ea ec echoe echoh echom echon el elsei em en endfo endf endt endw ene ex exe exi exu f|0 files filet fin fina fini fir fix fo foldc foldd folddoc foldo for fu go gr grepa gu gv ha helpf helpg helpt hi hid his ia iabc if ij il im imapc ime ino inorea inoreme int is isp iu iuna iunme j|0 ju k|0 keepa kee keepj lN lNf l|0 lad laddb laddf la lan lat lb lc lch lcl lcs le lefta let lex lf lfir lgetb lgete lg lgr lgrepa lh ll lla lli lmak lm lmapc lne lnew lnf ln loadk lo loc lockv lol lope lp lpf lr ls lt lu lua luad luaf lv lvimgrepa lw m|0 ma mak map mapc marks mat me menut mes mk mks mksp mkv mkvie mod mz mzf nbc nb nbs new nm nmapc nme nn nnoreme noa no noh norea noreme norm nu nun nunme ol o|0 om omapc ome on ono onoreme opt ou ounme ow p|0 profd prof pro promptr pc ped pe perld po popu pp pre prev ps pt ptN ptf ptj ptl ptn ptp ptr pts pu pw py3 python3 py3d py3f py pyd pyf quita qa rec red redi redr redraws reg res ret retu rew ri rightb rub rubyd rubyf rund ru rv sN san sa sal sav sb sbN sba sbf sbl sbm sbn sbp sbr scrip scripte scs se setf setg setl sf sfir sh sim sig sil sl sla sm smap smapc sme sn sni sno snor snoreme sor so spelld spe spelli spellr spellu spellw sp spr sre st sta startg startr star stopi stj sts sun sunm sunme sus sv sw sy synti sync tN tabN tabc tabdo tabe tabf tabfir tabl tabm tabnew tabn tabo tabp tabr tabs tab ta tags tc tcld tclf te tf th tj tl tm tn to tp tr try ts tu u|0 undoj undol una unh unl unlo unm unme uns up ve verb vert vim vimgrepa vi viu vie vm vmapc vme vne vn vnoreme vs vu vunme windo w|0 wN wa wh wi winc winp wn wp wq wqa ws wu wv x|0 xa xmapc xm xme xn xnoreme xu xunme y|0 z|0 ~ Next Print append abbreviate abclear aboveleft all amenu anoremenu args argadd argdelete argedit argglobal arglocal argument ascii autocmd augroup aunmenu buffer bNext ball badd bdelete behave belowright bfirst blast bmodified bnext botright bprevious brewind break breakadd breakdel breaklist browse bunload bwipeout change cNext cNfile cabbrev cabclear caddbuffer caddexpr caddfile call catch cbuffer cclose center cexpr cfile cfirst cgetbuffer cgetexpr cgetfile chdir checkpath checktime clist clast close cmap cmapclear cmenu cnext cnewer cnfile cnoremap cnoreabbrev cnoremenu copy colder colorscheme command comclear compiler continue confirm copen cprevious cpfile cquit crewind cscope cstag cunmap cunabbrev cunmenu cwindow delete delmarks debug debuggreedy delcommand delfunction diffupdate diffget diffoff diffpatch diffput diffsplit digraphs display deletel djump dlist doautocmd doautoall deletep drop dsearch dsplit edit earlier echo echoerr echohl echomsg else elseif emenu endif endfor endfunction endtry endwhile enew execute exit exusage file filetype find finally finish first fixdel fold foldclose folddoopen folddoclosed foldopen function global goto grep grepadd gui gvim hardcopy help helpfind helpgrep helptags highlight hide history insert iabbrev iabclear ijump ilist imap imapclear imenu inoremap inoreabbrev inoremenu intro isearch isplit iunmap iunabbrev iunmenu join jumps keepalt keepmarks keepjumps lNext lNfile list laddexpr laddbuffer laddfile last language later lbuffer lcd lchdir lclose lcscope left leftabove lexpr lfile lfirst lgetbuffer lgetexpr lgetfile lgrep lgrepadd lhelpgrep llast llist lmake lmap lmapclear lnext lnewer lnfile lnoremap loadkeymap loadview lockmarks lockvar lolder lopen lprevious lpfile lrewind ltag lunmap luado luafile lvimgrep lvimgrepadd lwindow move mark make mapclear match menu menutranslate messages mkexrc mksession mkspell mkvimrc mkview mode mzscheme mzfile nbclose nbkey nbsart next nmap nmapclear nmenu nnoremap nnoremenu noautocmd noremap nohlsearch noreabbrev noremenu normal number nunmap nunmenu oldfiles open omap omapclear omenu only onoremap onoremenu options ounmap ounmenu ownsyntax print profdel profile promptfind promptrepl pclose pedit perl perldo pop popup ppop preserve previous psearch ptag ptNext ptfirst ptjump ptlast ptnext ptprevious ptrewind ptselect put pwd py3do py3file python pydo pyfile quit quitall qall read recover redo redir redraw redrawstatus registers resize retab return rewind right rightbelow ruby rubydo rubyfile rundo runtime rviminfo substitute sNext sandbox sargument sall saveas sbuffer sbNext sball sbfirst sblast sbmodified sbnext sbprevious sbrewind scriptnames scriptencoding scscope set setfiletype setglobal setlocal sfind sfirst shell simalt sign silent sleep slast smagic smapclear smenu snext sniff snomagic snoremap snoremenu sort source spelldump spellgood spellinfo spellrepall spellundo spellwrong split sprevious srewind stop stag startgreplace startreplace startinsert stopinsert stjump stselect sunhide sunmap sunmenu suspend sview swapname syntax syntime syncbind tNext tabNext tabclose tabedit tabfind tabfirst tablast tabmove tabnext tabonly tabprevious tabrewind tag tcl tcldo tclfile tearoff tfirst throw tjump tlast tmenu tnext topleft tprevious trewind tselect tunmenu undo undojoin undolist unabbreviate unhide unlet unlockvar unmap unmenu unsilent update vglobal version verbose vertical vimgrep vimgrepadd visual viusage view vmap vmapclear vmenu vnew vnoremap vnoremenu vsplit vunmap vunmenu write wNext wall while winsize wincmd winpos wnext wprevious wqall wsverb wundo wviminfo xit xall xmapclear xmap xmenu xnoremap xnoremenu xunmap xunmenu yank", + built_in: + "synIDtrans atan2 range matcharg did_filetype asin feedkeys xor argv complete_check add getwinposx getqflist getwinposy screencol clearmatches empty extend getcmdpos mzeval garbagecollect setreg ceil sqrt diff_hlID inputsecret get getfperm getpid filewritable shiftwidth max sinh isdirectory synID system inputrestore winline atan visualmode inputlist tabpagewinnr round getregtype mapcheck hasmapto histdel argidx findfile sha256 exists toupper getcmdline taglist string getmatches bufnr strftime winwidth bufexists strtrans tabpagebuflist setcmdpos remote_read printf setloclist getpos getline bufwinnr float2nr len getcmdtype diff_filler luaeval resolve libcallnr foldclosedend reverse filter has_key bufname str2float strlen setline getcharmod setbufvar index searchpos shellescape undofile foldclosed setqflist buflisted strchars str2nr virtcol floor remove undotree remote_expr winheight gettabwinvar reltime cursor tabpagenr finddir localtime acos getloclist search tanh matchend rename gettabvar strdisplaywidth type abs py3eval setwinvar tolower wildmenumode log10 spellsuggest bufloaded synconcealed nextnonblank server2client complete settabwinvar executable input wincol setmatches getftype hlID inputsave searchpair or screenrow line settabvar histadd deepcopy strpart remote_peek and eval getftime submatch screenchar winsaveview matchadd mkdir screenattr getfontname libcall reltimestr getfsize winnr invert pow getbufline byte2line soundfold repeat fnameescape tagfiles sin strwidth spellbadword trunc maparg log lispindent hostname setpos globpath remote_foreground getchar synIDattr fnamemodify cscope_connection stridx winbufnr indent min complete_add nr2char searchpairpos inputdialog values matchlist items hlexists strridx browsedir expand fmod pathshorten line2byte argc count getwinvar glob foldtextresult getreg foreground cosh matchdelete has char2nr simplify histget searchdecl iconv winrestcmd pumvisible writefile foldlevel haslocaldir keys cos matchstr foldtext histnr tan tempname getcwd byteidx getbufvar islocked escape eventhandler remote_send serverlist winrestview synstack pyeval prevnonblank readfile cindent filereadable changenr exp", + }, + illegal: /;/, + contains: [ + e.NUMBER_MODE, + { className: "string", begin: "'", end: "'", illegal: "\\n" }, + { className: "string", begin: /"(\\"|\n\\|[^"\n])*"/ }, + e.COMMENT('"', "$"), + { className: "variable", begin: /[bwtglsav]:[\w\d_]*/ }, + { + className: "function", + beginKeywords: "function function!", + end: "$", + relevance: 0, + contains: [ + e.TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }, + { className: "symbol", begin: /<[\w-]+>/ }, + ], + }; +}; +var JT = function (e) { + return { + name: "Intel x86 Assembly", + case_insensitive: !0, + keywords: { + $pattern: "[.%]?" + e.IDENT_RE, + keyword: + "lock rep repe repz repne repnz xaquire xrelease bnd nobnd aaa aad aam aas adc add and arpl bb0_reset bb1_reset bound bsf bsr bswap bt btc btr bts call cbw cdq cdqe clc cld cli clts cmc cmp cmpsb cmpsd cmpsq cmpsw cmpxchg cmpxchg486 cmpxchg8b cmpxchg16b cpuid cpu_read cpu_write cqo cwd cwde daa das dec div dmint emms enter equ f2xm1 fabs fadd faddp fbld fbstp fchs fclex fcmovb fcmovbe fcmove fcmovnb fcmovnbe fcmovne fcmovnu fcmovu fcom fcomi fcomip fcomp fcompp fcos fdecstp fdisi fdiv fdivp fdivr fdivrp femms feni ffree ffreep fiadd ficom ficomp fidiv fidivr fild fimul fincstp finit fist fistp fisttp fisub fisubr fld fld1 fldcw fldenv fldl2e fldl2t fldlg2 fldln2 fldpi fldz fmul fmulp fnclex fndisi fneni fninit fnop fnsave fnstcw fnstenv fnstsw fpatan fprem fprem1 fptan frndint frstor fsave fscale fsetpm fsin fsincos fsqrt fst fstcw fstenv fstp fstsw fsub fsubp fsubr fsubrp ftst fucom fucomi fucomip fucomp fucompp fxam fxch fxtract fyl2x fyl2xp1 hlt ibts icebp idiv imul in inc incbin insb insd insw int int01 int1 int03 int3 into invd invpcid invlpg invlpga iret iretd iretq iretw jcxz jecxz jrcxz jmp jmpe lahf lar lds lea leave les lfence lfs lgdt lgs lidt lldt lmsw loadall loadall286 lodsb lodsd lodsq lodsw loop loope loopne loopnz loopz lsl lss ltr mfence monitor mov movd movq movsb movsd movsq movsw movsx movsxd movzx mul mwait neg nop not or out outsb outsd outsw packssdw packsswb packuswb paddb paddd paddsb paddsiw paddsw paddusb paddusw paddw pand pandn pause paveb pavgusb pcmpeqb pcmpeqd pcmpeqw pcmpgtb pcmpgtd pcmpgtw pdistib pf2id pfacc pfadd pfcmpeq pfcmpge pfcmpgt pfmax pfmin pfmul pfrcp pfrcpit1 pfrcpit2 pfrsqit1 pfrsqrt pfsub pfsubr pi2fd pmachriw pmaddwd pmagw pmulhriw pmulhrwa pmulhrwc pmulhw pmullw pmvgezb pmvlzb pmvnzb pmvzb pop popa popad popaw popf popfd popfq popfw por prefetch prefetchw pslld psllq psllw psrad psraw psrld psrlq psrlw psubb psubd psubsb psubsiw psubsw psubusb psubusw psubw punpckhbw punpckhdq punpckhwd punpcklbw punpckldq punpcklwd push pusha pushad pushaw pushf pushfd pushfq pushfw pxor rcl rcr rdshr rdmsr rdpmc rdtsc rdtscp ret retf retn rol ror rdm rsdc rsldt rsm rsts sahf sal salc sar sbb scasb scasd scasq scasw sfence sgdt shl shld shr shrd sidt sldt skinit smi smint smintold smsw stc std sti stosb stosd stosq stosw str sub svdc svldt svts swapgs syscall sysenter sysexit sysret test ud0 ud1 ud2b ud2 ud2a umov verr verw fwait wbinvd wrshr wrmsr xadd xbts xchg xlatb xlat xor cmove cmovz cmovne cmovnz cmova cmovnbe cmovae cmovnb cmovb cmovnae cmovbe cmovna cmovg cmovnle cmovge cmovnl cmovl cmovnge cmovle cmovng cmovc cmovnc cmovo cmovno cmovs cmovns cmovp cmovpe cmovnp cmovpo je jz jne jnz ja jnbe jae jnb jb jnae jbe jna jg jnle jge jnl jl jnge jle jng jc jnc jo jno js jns jpo jnp jpe jp sete setz setne setnz seta setnbe setae setnb setnc setb setnae setcset setbe setna setg setnle setge setnl setl setnge setle setng sets setns seto setno setpe setp setpo setnp addps addss andnps andps cmpeqps cmpeqss cmpleps cmpless cmpltps cmpltss cmpneqps cmpneqss cmpnleps cmpnless cmpnltps cmpnltss cmpordps cmpordss cmpunordps cmpunordss cmpps cmpss comiss cvtpi2ps cvtps2pi cvtsi2ss cvtss2si cvttps2pi cvttss2si divps divss ldmxcsr maxps maxss minps minss movaps movhps movlhps movlps movhlps movmskps movntps movss movups mulps mulss orps rcpps rcpss rsqrtps rsqrtss shufps sqrtps sqrtss stmxcsr subps subss ucomiss unpckhps unpcklps xorps fxrstor fxrstor64 fxsave fxsave64 xgetbv xsetbv xsave xsave64 xsaveopt xsaveopt64 xrstor xrstor64 prefetchnta prefetcht0 prefetcht1 prefetcht2 maskmovq movntq pavgb pavgw pextrw pinsrw pmaxsw pmaxub pminsw pminub pmovmskb pmulhuw psadbw pshufw pf2iw pfnacc pfpnacc pi2fw pswapd maskmovdqu clflush movntdq movnti movntpd movdqa movdqu movdq2q movq2dq paddq pmuludq pshufd pshufhw pshuflw pslldq psrldq psubq punpckhqdq punpcklqdq addpd addsd andnpd andpd cmpeqpd cmpeqsd cmplepd cmplesd cmpltpd cmpltsd cmpneqpd cmpneqsd cmpnlepd cmpnlesd cmpnltpd cmpnltsd cmpordpd cmpordsd cmpunordpd cmpunordsd cmppd comisd cvtdq2pd cvtdq2ps cvtpd2dq cvtpd2pi cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd haddps hsubpd hsubps lddqu movddup movshdup movsldup clgi stgi vmcall vmclear vmfunc vmlaunch vmload vmmcall vmptrld vmptrst vmread vmresume vmrun vmsave vmwrite vmxoff vmxon invept invvpid pabsb pabsw pabsd palignr phaddw phaddd phaddsw phsubw phsubd phsubsw pmaddubsw pmulhrsw pshufb psignb psignw psignd extrq insertq movntsd movntss lzcnt blendpd blendps blendvpd blendvps dppd dpps extractps insertps movntdqa mpsadbw packusdw pblendvb pblendw pcmpeqq pextrb pextrd pextrq phminposuw pinsrb pinsrd pinsrq pmaxsb pmaxsd pmaxud pmaxuw pminsb pminsd pminud pminuw pmovsxbw pmovsxbd pmovsxbq pmovsxwd pmovsxwq pmovsxdq pmovzxbw pmovzxbd pmovzxbq pmovzxwd pmovzxwq pmovzxdq pmuldq pmulld ptest roundpd roundps roundsd roundss crc32 pcmpestri pcmpestrm pcmpistri pcmpistrm pcmpgtq popcnt getsec pfrcpv pfrsqrtv movbe aesenc aesenclast aesdec aesdeclast aesimc aeskeygenassist vaesenc vaesenclast vaesdec vaesdeclast vaesimc vaeskeygenassist vaddpd vaddps vaddsd vaddss vaddsubpd vaddsubps vandpd vandps vandnpd vandnps vblendpd vblendps vblendvpd vblendvps vbroadcastss vbroadcastsd vbroadcastf128 vcmpeq_ospd vcmpeqpd vcmplt_ospd vcmpltpd vcmple_ospd vcmplepd vcmpunord_qpd vcmpunordpd vcmpneq_uqpd vcmpneqpd vcmpnlt_uspd vcmpnltpd vcmpnle_uspd vcmpnlepd vcmpord_qpd vcmpordpd vcmpeq_uqpd vcmpnge_uspd vcmpngepd vcmpngt_uspd vcmpngtpd vcmpfalse_oqpd vcmpfalsepd vcmpneq_oqpd vcmpge_ospd vcmpgepd vcmpgt_ospd vcmpgtpd vcmptrue_uqpd vcmptruepd vcmplt_oqpd vcmple_oqpd vcmpunord_spd vcmpneq_uspd vcmpnlt_uqpd vcmpnle_uqpd vcmpord_spd vcmpeq_uspd vcmpnge_uqpd vcmpngt_uqpd vcmpfalse_ospd vcmpneq_ospd vcmpge_oqpd vcmpgt_oqpd vcmptrue_uspd vcmppd vcmpeq_osps vcmpeqps vcmplt_osps vcmpltps vcmple_osps vcmpleps vcmpunord_qps vcmpunordps vcmpneq_uqps vcmpneqps vcmpnlt_usps vcmpnltps vcmpnle_usps vcmpnleps vcmpord_qps vcmpordps vcmpeq_uqps vcmpnge_usps vcmpngeps vcmpngt_usps vcmpngtps vcmpfalse_oqps vcmpfalseps vcmpneq_oqps vcmpge_osps vcmpgeps vcmpgt_osps vcmpgtps vcmptrue_uqps vcmptrueps vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oqsd vcmptrue_ussd vcmpsd vcmpeq_osss vcmpeqss vcmplt_osss vcmpltss vcmple_osss vcmpless vcmpunord_qss vcmpunordss vcmpneq_uqss vcmpneqss vcmpnlt_usss vcmpnltss vcmpnle_usss vcmpnless vcmpord_qss vcmpordss vcmpeq_uqss vcmpnge_usss vcmpngess vcmpngt_usss vcmpngtss vcmpfalse_oqss vcmpfalsess vcmpneq_oqss vcmpge_osss vcmpgess vcmpgt_osss vcmpgtss vcmptrue_uqss vcmptruess vcmplt_oqss vcmple_oqss vcmpunord_sss vcmpneq_usss vcmpnlt_uqss vcmpnle_uqss vcmpord_sss vcmpeq_usss vcmpnge_uqss vcmpngt_uqss vcmpfalse_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmovd vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmpeqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmulld vpmuludq vpmuldq vpor 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vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfnmadd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmaddsd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpmacswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq vpcmpleq vpcmpneqq vpcmpnltq vpcmpnleq vpcmpq vpcmpequd vpcmpltud vpcmpleud vpcmpnequd vpcmpnltud vpcmpnleud vpcmpud vpcmpequq vpcmpltuq vpcmpleuq vpcmpnequq vpcmpnltuq vpcmpnleuq vpcmpuq vpcompressd vpcompressq vpermi2d vpermi2pd vpermi2ps vpermi2q vpermt2d vpermt2pd vpermt2ps vpermt2q vpexpandd vpexpandq vpmaxsq vpmaxuq vpminsq vpminuq vpmovdb vpmovdw vpmovqb vpmovqd vpmovqw vpmovsdb vpmovsdw vpmovsqb vpmovsqd vpmovsqw vpmovusdb vpmovusdw vpmovusqb vpmovusqd vpmovusqw vpord vporq vprold vprolq vprolvd vprolvq vprord vprorq vprorvd vprorvq vpscatterdd vpscatterdq vpscatterqd vpscatterqq vpsraq vpsravq vpternlogd vpternlogq vptestmd vptestmq vptestnmd vptestnmq vpxord vpxorq vrcp14pd vrcp14ps vrcp14sd vrcp14ss vrndscalepd vrndscaleps vrndscalesd vrndscaless vrsqrt14pd vrsqrt14ps vrsqrt14sd vrsqrt14ss vscalefpd vscalefps vscalefsd vscalefss vscatterdpd vscatterdps vscatterqpd vscatterqps vshuff32x4 vshuff64x2 vshufi32x4 vshufi64x2 kandnw kandw kmovw knotw kortestw korw kshiftlw kshiftrw kunpckbw kxnorw kxorw vpbroadcastmb2q vpbroadcastmw2d vpconflictd vpconflictq vplzcntd vplzcntq vexp2pd vexp2ps vrcp28pd vrcp28ps vrcp28sd vrcp28ss vrsqrt28pd vrsqrt28ps vrsqrt28sd vrsqrt28ss vgatherpf0dpd vgatherpf0dps vgatherpf0qpd vgatherpf0qps vgatherpf1dpd vgatherpf1dps vgatherpf1qpd vgatherpf1qps vscatterpf0dpd vscatterpf0dps vscatterpf0qpd vscatterpf0qps vscatterpf1dpd vscatterpf1dps vscatterpf1qpd vscatterpf1qps prefetchwt1 bndmk bndcl bndcu bndcn bndmov bndldx bndstx sha1rnds4 sha1nexte sha1msg1 sha1msg2 sha256rnds2 sha256msg1 sha256msg2 hint_nop0 hint_nop1 hint_nop2 hint_nop3 hint_nop4 hint_nop5 hint_nop6 hint_nop7 hint_nop8 hint_nop9 hint_nop10 hint_nop11 hint_nop12 hint_nop13 hint_nop14 hint_nop15 hint_nop16 hint_nop17 hint_nop18 hint_nop19 hint_nop20 hint_nop21 hint_nop22 hint_nop23 hint_nop24 hint_nop25 hint_nop26 hint_nop27 hint_nop28 hint_nop29 hint_nop30 hint_nop31 hint_nop32 hint_nop33 hint_nop34 hint_nop35 hint_nop36 hint_nop37 hint_nop38 hint_nop39 hint_nop40 hint_nop41 hint_nop42 hint_nop43 hint_nop44 hint_nop45 hint_nop46 hint_nop47 hint_nop48 hint_nop49 hint_nop50 hint_nop51 hint_nop52 hint_nop53 hint_nop54 hint_nop55 hint_nop56 hint_nop57 hint_nop58 hint_nop59 hint_nop60 hint_nop61 hint_nop62 hint_nop63", + built_in: + "ip eip rip al ah bl bh cl ch dl dh sil dil bpl spl r8b r9b r10b r11b r12b r13b r14b r15b ax bx cx dx si di bp sp r8w r9w r10w r11w r12w r13w r14w r15w eax ebx ecx edx esi edi ebp esp eip r8d r9d r10d r11d r12d r13d r14d r15d rax rbx rcx rdx rsi rdi rbp rsp r8 r9 r10 r11 r12 r13 r14 r15 cs ds es fs gs ss st st0 st1 st2 st3 st4 st5 st6 st7 mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7 xmm0 xmm1 xmm2 xmm3 xmm4 xmm5 xmm6 xmm7 xmm8 xmm9 xmm10 xmm11 xmm12 xmm13 xmm14 xmm15 xmm16 xmm17 xmm18 xmm19 xmm20 xmm21 xmm22 xmm23 xmm24 xmm25 xmm26 xmm27 xmm28 xmm29 xmm30 xmm31 ymm0 ymm1 ymm2 ymm3 ymm4 ymm5 ymm6 ymm7 ymm8 ymm9 ymm10 ymm11 ymm12 ymm13 ymm14 ymm15 ymm16 ymm17 ymm18 ymm19 ymm20 ymm21 ymm22 ymm23 ymm24 ymm25 ymm26 ymm27 ymm28 ymm29 ymm30 ymm31 zmm0 zmm1 zmm2 zmm3 zmm4 zmm5 zmm6 zmm7 zmm8 zmm9 zmm10 zmm11 zmm12 zmm13 zmm14 zmm15 zmm16 zmm17 zmm18 zmm19 zmm20 zmm21 zmm22 zmm23 zmm24 zmm25 zmm26 zmm27 zmm28 zmm29 zmm30 zmm31 k0 k1 k2 k3 k4 k5 k6 k7 bnd0 bnd1 bnd2 bnd3 cr0 cr1 cr2 cr3 cr4 cr8 dr0 dr1 dr2 dr3 dr8 tr3 tr4 tr5 tr6 tr7 r0 r1 r2 r3 r4 r5 r6 r7 r0b r1b r2b r3b r4b r5b r6b r7b r0w r1w r2w r3w r4w r5w r6w r7w r0d r1d r2d r3d r4d r5d r6d r7d r0h r1h r2h r3h r0l r1l r2l r3l r4l r5l r6l r7l r8l r9l r10l r11l r12l r13l r14l r15l db dw dd dq dt ddq do dy dz resb resw resd resq rest resdq reso resy resz incbin equ times byte word dword qword nosplit rel abs seg wrt strict near far a32 ptr", + meta: "%define %xdefine %+ %undef %defstr %deftok %assign %strcat %strlen %substr %rotate %elif %else %endif %if %ifmacro %ifctx %ifidn %ifidni %ifid %ifnum %ifstr %iftoken %ifempty %ifenv %error %warning %fatal %rep %endrep %include %push %pop %repl %pathsearch %depend %use %arg %stacksize %local %line %comment %endcomment .nolist __FILE__ __LINE__ __SECT__ __BITS__ __OUTPUT_FORMAT__ __DATE__ __TIME__ __DATE_NUM__ __TIME_NUM__ __UTC_DATE__ __UTC_TIME__ __UTC_DATE_NUM__ __UTC_TIME_NUM__ __PASS__ struc endstruc istruc at iend align alignb sectalign daz nodaz up down zero default option assume public bits use16 use32 use64 default section segment absolute extern global common cpu float __utf16__ __utf16le__ __utf16be__ __utf32__ __utf32le__ __utf32be__ __float8__ __float16__ __float32__ __float64__ __float80m__ __float80e__ __float128l__ __float128h__ __Infinity__ __QNaN__ __SNaN__ Inf NaN QNaN SNaN float8 float16 float32 float64 float80m float80e float128l float128h __FLOAT_DAZ__ __FLOAT_ROUND__ __FLOAT__", + }, + contains: [ + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "number", + variants: [ + { + begin: + "\\b(?:([0-9][0-9_]*)?\\.[0-9_]*(?:[eE][+-]?[0-9_]+)?|(0[Xx])?[0-9][0-9_]*(\\.[0-9_]*)?(?:[pP](?:[+-]?[0-9_]+)?)?)\\b", + relevance: 0, + }, + { begin: "\\$[0-9][0-9A-Fa-f]*", relevance: 0 }, + { + begin: + "\\b(?:[0-9A-Fa-f][0-9A-Fa-f_]*[Hh]|[0-9][0-9_]*[DdTt]?|[0-7][0-7_]*[QqOo]|[0-1][0-1_]*[BbYy])\\b", + }, + { + begin: + "\\b(?:0[Xx][0-9A-Fa-f_]+|0[DdTt][0-9_]+|0[QqOo][0-7_]+|0[BbYy][0-1_]+)\\b", + }, + ], + }, + e.QUOTE_STRING_MODE, + { + className: "string", + variants: [ + { begin: "'", end: "[^\\\\]'" }, + { begin: "`", end: "[^\\\\]`" }, + ], + relevance: 0, + }, + { + className: "symbol", + variants: [ + { begin: "^\\s*[A-Za-z._?][A-Za-z0-9_$#@~.?]*(:|\\s+label)" }, + { begin: "^\\s*%%[A-Za-z0-9_$#@~.?]*:" }, + ], + relevance: 0, + }, + { className: "subst", begin: "%[0-9]+", relevance: 0 }, + { className: "subst", begin: "%!S+", relevance: 0 }, + { className: "meta", begin: /^\s*\.[\w_-]+/ }, + ], + }; +}; +var ef = function (e) { + var t = { + $pattern: /[a-zA-Z][a-zA-Z0-9_?]*/, + keyword: + "if then else do while until for loop import with is as where when by data constant integer real text name boolean symbol infix prefix postfix block tree", + literal: "true false nil", + built_in: + "in mod rem and or xor not abs sign floor ceil sqrt sin cos tan asin acos atan exp expm1 log log2 log10 log1p pi at text_length text_range text_find text_replace contains page slide basic_slide title_slide title subtitle fade_in fade_out fade_at clear_color color line_color line_width texture_wrap texture_transform texture scale_?x scale_?y scale_?z? translate_?x translate_?y translate_?z? rotate_?x rotate_?y rotate_?z? rectangle circle ellipse sphere path line_to move_to quad_to curve_to theme background contents locally time mouse_?x mouse_?y mouse_buttons ObjectLoader Animate MovieCredits Slides Filters Shading Materials LensFlare Mapping VLCAudioVideo StereoDecoder PointCloud NetworkAccess RemoteControl RegExp ChromaKey Snowfall NodeJS Speech Charts", + }, + n = { className: "string", begin: '"', end: '"', illegal: "\\n" }, + a = { beginKeywords: "import", end: "$", keywords: t, contains: [n] }, + r = { + className: "function", + begin: /[a-z][^\n]*->/, + returnBegin: !0, + end: /->/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, keywords: t }, + }), + ], + }; + return { + name: "XL", + aliases: ["tao"], + keywords: t, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + { className: "string", begin: "'", end: "'", illegal: "\\n" }, + { className: "string", begin: "<<", end: ">>" }, + r, + a, + { + className: "number", + begin: "[0-9]+#[0-9A-Z_]+(\\.[0-9-A-Z_]+)?#?([Ee][+-]?[0-9]+)?", + }, + e.NUMBER_MODE, + ], + }; +}; +var tf = function (e) { + return { + name: "XQuery", + aliases: ["xpath", "xq"], + case_insensitive: !1, + illegal: /(proc)|(abstract)|(extends)|(until)|(#)/, + keywords: { + $pattern: /[a-zA-Z$][a-zA-Z0-9_:-]*/, + keyword: + "module schema namespace boundary-space preserve no-preserve strip default collation base-uri ordering context decimal-format decimal-separator copy-namespaces empty-sequence except exponent-separator external grouping-separator inherit no-inherit lax minus-sign per-mille percent schema-attribute schema-element strict unordered zero-digit declare import option function validate variable for at in let where order group by return if then else tumbling sliding window start when only end previous next stable ascending descending allowing empty greatest least some every satisfies switch case typeswitch try catch and or to union intersect instance of treat as castable cast map array delete insert into replace value rename copy modify update", + type: "item document-node node attribute document element comment namespace namespace-node processing-instruction text construction xs:anyAtomicType xs:untypedAtomic xs:duration xs:time xs:decimal xs:float xs:double xs:gYearMonth xs:gYear xs:gMonthDay xs:gMonth xs:gDay xs:boolean xs:base64Binary xs:hexBinary xs:anyURI xs:QName xs:NOTATION xs:dateTime xs:dateTimeStamp xs:date xs:string xs:normalizedString xs:token xs:language xs:NMTOKEN xs:Name xs:NCName xs:ID xs:IDREF xs:ENTITY xs:integer xs:nonPositiveInteger xs:negativeInteger xs:long xs:int xs:short xs:byte xs:nonNegativeInteger xs:unisignedLong xs:unsignedInt xs:unsignedShort xs:unsignedByte xs:positiveInteger xs:yearMonthDuration xs:dayTimeDuration", + literal: + "eq ne lt le gt ge is self:: child:: descendant:: descendant-or-self:: attribute:: following:: following-sibling:: parent:: ancestor:: ancestor-or-self:: preceding:: preceding-sibling:: NaN", + }, + contains: [ + { className: "variable", begin: /[$][\w\-:]+/ }, + { + className: "built_in", + variants: [ + { + begin: /\barray:/, + end: /(?:append|filter|flatten|fold-(?:left|right)|for-each(?:-pair)?|get|head|insert-before|join|put|remove|reverse|size|sort|subarray|tail)\b/, + }, + { + begin: /\bmap:/, + end: /(?:contains|entry|find|for-each|get|keys|merge|put|remove|size)\b/, + }, + { + begin: /\bmath:/, + end: /(?:a(?:cos|sin|tan[2]?)|cos|exp(?:10)?|log(?:10)?|pi|pow|sin|sqrt|tan)\b/, + }, + { begin: /\bop:/, end: /\(/, excludeEnd: !0 }, + { begin: /\bfn:/, end: /\(/, excludeEnd: !0 }, + { + begin: + /[^/, + end: /(\/[\w._:-]+>)/, + subLanguage: "xml", + contains: [{ begin: /\{/, end: /\}/, subLanguage: "xquery" }, "self"], + }, + ], + }; +}; +var nf = function (e) { + var t = { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + ], + }, + n = e.UNDERSCORE_TITLE_MODE, + a = { variants: [e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE] }, + r = + "namespace class interface use extends function return abstract final public protected private static deprecated throw try catch Exception echo empty isset instanceof unset let var new const self require if else elseif switch case default do while loop for continue break likely unlikely __LINE__ __FILE__ __DIR__ __FUNCTION__ __CLASS__ __TRAIT__ __METHOD__ __NAMESPACE__ array boolean float double integer object resource string char long unsigned bool int uint ulong uchar true false null undefined"; + return { + name: "Zephir", + aliases: ["zep"], + keywords: r, + contains: [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/\/\*/, /\*\//, { + contains: [{ className: "doctag", begin: /@[A-Za-z]+/ }], + }), + { + className: "string", + begin: /<<<['"]?\w+['"]?$/, + end: /^\w+;/, + contains: [e.BACKSLASH_ESCAPE], + }, + { begin: /(::|->)+[a-zA-Z_\x7f-\xff][a-zA-Z0-9_\x7f-\xff]*/ }, + { + className: "function", + beginKeywords: "function fn", + end: /[;{]/, + excludeEnd: !0, + illegal: /\$|\[|%/, + contains: [ + n, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: r, + contains: ["self", e.C_BLOCK_COMMENT_MODE, t, a], + }, + ], + }, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + illegal: /[:($"]/, + contains: [{ beginKeywords: "extends implements" }, n], + }, + { + beginKeywords: "namespace", + end: /;/, + illegal: /[.']/, + contains: [n], + }, + { beginKeywords: "use", end: /;/, contains: [n] }, + { begin: /=>/ }, + t, + a, + ], + }; + }, + af = Pm; +af.registerLanguage("1c", km), + af.registerLanguage("abnf", Bm), + af.registerLanguage("accesslog", Vm), + af.registerLanguage("actionscript", Wm), + af.registerLanguage("ada", $m), + af.registerLanguage("angelscript", Qm), + af.registerLanguage("apache", Km), + af.registerLanguage("applescript", Jm), + af.registerLanguage("arcade", ep), + af.registerLanguage("arduino", rp), + af.registerLanguage("armasm", ip), + af.registerLanguage("xml", _p), + af.registerLanguage("asciidoc", mp), + af.registerLanguage("aspectj", Ep), + af.registerLanguage("autohotkey", Sp), + af.registerLanguage("autoit", bp), + af.registerLanguage("avrasm", Tp), + af.registerLanguage("awk", fp), + af.registerLanguage("axapta", Cp), + af.registerLanguage("bash", vp), + af.registerLanguage("basic", Op), + af.registerLanguage("bnf", hp), + af.registerLanguage("brainfuck", yp), + af.registerLanguage("c-like", Mp), + af.registerLanguage("c", xp), + af.registerLanguage("cal", Pp), + af.registerLanguage("capnproto", kp), + af.registerLanguage("ceylon", Up), + af.registerLanguage("clean", Fp), + af.registerLanguage("clojure", Bp), + af.registerLanguage("clojure-repl", Gp), + af.registerLanguage("cmake", Yp), + af.registerLanguage("coffeescript", zp), + af.registerLanguage("coq", Wp), + af.registerLanguage("cos", $p), + af.registerLanguage("cpp", Xp), + af.registerLanguage("crmsh", Zp), + af.registerLanguage("crystal", Jp), + af.registerLanguage("csharp", eg), + af.registerLanguage("csp", tg), + af.registerLanguage("css", cg), + af.registerLanguage("d", _g), + af.registerLanguage("markdown", mg), + af.registerLanguage("dart", pg), + af.registerLanguage("delphi", gg), + af.registerLanguage("diff", Eg), + af.registerLanguage("django", Sg), + af.registerLanguage("dns", bg), + af.registerLanguage("dockerfile", Tg), + af.registerLanguage("dos", fg), + af.registerLanguage("dsconfig", Cg), + af.registerLanguage("dts", Ng), + af.registerLanguage("dust", Rg), + af.registerLanguage("ebnf", vg), + af.registerLanguage("elixir", Og), + af.registerLanguage("elm", hg), + af.registerLanguage("ruby", Ag), + af.registerLanguage("erb", Dg), + af.registerLanguage("erlang-repl", wg), + af.registerLanguage("erlang", xg), + af.registerLanguage("excel", Pg), + af.registerLanguage("fix", kg), + af.registerLanguage("flix", Ug), + af.registerLanguage("fortran", Gg), + af.registerLanguage("fsharp", Yg), + af.registerLanguage("gams", qg), + af.registerLanguage("gauss", zg), + af.registerLanguage("gcode", Wg), + af.registerLanguage("gherkin", $g), + af.registerLanguage("glsl", Qg), + af.registerLanguage("gml", Kg), + af.registerLanguage("go", jg), + af.registerLanguage("golo", Xg), + af.registerLanguage("gradle", Zg), + af.registerLanguage("groovy", nE), + af.registerLanguage("haml", aE), + af.registerLanguage("handlebars", oE), + af.registerLanguage("haskell", sE), + af.registerLanguage("haxe", lE), + af.registerLanguage("hsp", cE), + af.registerLanguage("htmlbars", mE), + af.registerLanguage("http", EE), + af.registerLanguage("hy", SE), + af.registerLanguage("inform7", bE), + af.registerLanguage("ini", CE), + af.registerLanguage("irpf90", vE), + af.registerLanguage("isbl", OE), + af.registerLanguage("java", AE), + af.registerLanguage("javascript", kE), + af.registerLanguage("jboss-cli", UE), + af.registerLanguage("json", FE), + af.registerLanguage("julia", BE), + af.registerLanguage("julia-repl", GE), + af.registerLanguage("kotlin", qE), + af.registerLanguage("lasso", zE), + af.registerLanguage("latex", QE), + af.registerLanguage("ldif", KE), + af.registerLanguage("leaf", jE), + af.registerLanguage("less", aS), + af.registerLanguage("lisp", rS), + af.registerLanguage("livecodeserver", iS), + af.registerLanguage("livescript", cS), + af.registerLanguage("llvm", uS), + af.registerLanguage("lsl", mS), + af.registerLanguage("lua", pS), + af.registerLanguage("makefile", gS), + af.registerLanguage("mathematica", CS), + af.registerLanguage("matlab", NS), + af.registerLanguage("maxima", RS), + af.registerLanguage("mel", vS), + af.registerLanguage("mercury", OS), + af.registerLanguage("mipsasm", hS), + af.registerLanguage("mizar", yS), + af.registerLanguage("perl", MS), + af.registerLanguage("mojolicious", LS), + af.registerLanguage("monkey", wS), + af.registerLanguage("moonscript", xS), + af.registerLanguage("n1ql", PS), + af.registerLanguage("nginx", kS), + af.registerLanguage("nim", US), + af.registerLanguage("nix", FS), + af.registerLanguage("node-repl", BS), + af.registerLanguage("nsis", GS), + af.registerLanguage("objectivec", YS), + af.registerLanguage("ocaml", HS), + af.registerLanguage("openscad", VS), + af.registerLanguage("oxygene", qS), + af.registerLanguage("parser3", zS), + af.registerLanguage("pf", WS), + af.registerLanguage("pgsql", $S), + af.registerLanguage("php", QS), + af.registerLanguage("php-template", KS), + af.registerLanguage("plaintext", jS), + af.registerLanguage("pony", XS), + af.registerLanguage("powershell", ZS), + af.registerLanguage("processing", JS), + af.registerLanguage("profile", eb), + af.registerLanguage("prolog", tb), + af.registerLanguage("properties", nb), + af.registerLanguage("protobuf", ab), + af.registerLanguage("puppet", rb), + af.registerLanguage("purebasic", ib), + af.registerLanguage("python", lb), + af.registerLanguage("python-repl", cb), + af.registerLanguage("q", _b), + af.registerLanguage("qml", mb), + af.registerLanguage("r", Eb), + af.registerLanguage("reasonml", Sb), + af.registerLanguage("rib", bb), + af.registerLanguage("roboconf", Tb), + af.registerLanguage("routeros", fb), + af.registerLanguage("rsl", Cb), + af.registerLanguage("ruleslanguage", Nb), + af.registerLanguage("rust", Rb), + af.registerLanguage("sas", vb), + af.registerLanguage("scala", Ob), + af.registerLanguage("scheme", hb), + af.registerLanguage("scilab", yb), + af.registerLanguage("scss", wb), + af.registerLanguage("shell", xb), + af.registerLanguage("smali", Pb), + af.registerLanguage("smalltalk", kb), + af.registerLanguage("sml", Ub), + af.registerLanguage("sqf", Fb), + af.registerLanguage("sql_more", Bb), + af.registerLanguage("sql", Vb), + af.registerLanguage("stan", qb), + af.registerLanguage("stata", zb), + af.registerLanguage("step21", Wb), + af.registerLanguage("stylus", Zb), + af.registerLanguage("subunit", Jb), + af.registerLanguage("swift", NT), + af.registerLanguage("taggerscript", RT), + af.registerLanguage("yaml", vT), + af.registerLanguage("tap", OT), + af.registerLanguage("tcl", IT), + af.registerLanguage("thrift", AT), + af.registerLanguage("tp", DT), + af.registerLanguage("twig", MT), + af.registerLanguage("typescript", BT), + af.registerLanguage("vala", GT), + af.registerLanguage("vbnet", qT), + af.registerLanguage("vbscript", QT), + af.registerLanguage("vbscript-html", KT), + af.registerLanguage("verilog", jT), + af.registerLanguage("vhdl", XT), + af.registerLanguage("vim", ZT), + af.registerLanguage("x86asm", JT), + af.registerLanguage("xl", ef), + af.registerLanguage("xquery", tf), + af.registerLanguage("zephir", nf); +var rf = af; +!(function (t, n) { + var a, + r = "hljs-ln", + i = "hljs-ln-code", + o = "hljs-ln-n", + s = "data-line-number", + l = /\r\n|\r|\n/g; + function c(e) { + try { + var a = n.querySelectorAll("code.hljs,code.nohighlight"); + for (var r in a) + a.hasOwnProperty(r) && + (a[r].classList.contains("nohljsln") || _(a[r], e)); + } catch (e) { + t.console.error("LineNumbers error: ", e); + } + } + function _(t, n) { + "object" == e(t) && (t.innerHTML = d(t, n)); + } + function d(e, t) { + var n, + a, + c = + ((n = e), + { + singleLine: (function (e) { + return !!e.singleLine && e.singleLine; + })((a = (a = t) || {})), + startFrom: (function (e, t) { + var n = 1; + isFinite(t.startFrom) && (n = t.startFrom); + var a = (function (e, t) { + return e.hasAttribute(t) ? e.getAttribute(t) : null; + })(e, "data-ln-start-from"); + return ( + null !== a && + (n = (function (e, t) { + if (!e) return 1; + var n = Number(e); + return isFinite(n) ? n : 1; + })(a)), + n + ); + })(n, a), + }); + return ( + (function e(t) { + var n = t.childNodes; + for (var a in n) { + var r; + n.hasOwnProperty(a) && + 0 < ((r = n[a]).textContent.trim().match(l) || []).length && + (0 < r.childNodes.length ? e(r) : u(r.parentNode)); + } + })(e), + (function (e, t) { + var n = m(e); + if ( + ("" === n[n.length - 1].trim() && n.pop(), + 1 < n.length || t.singleLine) + ) { + for (var a = "", l = 0, c = n.length; l < c; l++) + a += p( + '
{6}', + [ + "hljs-ln-line", + "hljs-ln-numbers", + o, + s, + i, + l + t.startFrom, + 0 < n[l].length ? n[l] : " ", + ], + ); + return p('{1}
', [r, a]); + } + return e; + })(e.innerHTML, c) + ); + } + function u(e) { + var t = e.className; + if (/hljs-/.test(t)) { + for (var n = m(e.innerHTML), a = 0, r = ""; a < n.length; a++) + r += p('{1}\n', [ + t, + 0 < n[a].length ? n[a] : " ", + ]); + e.innerHTML = r.trim(); + } + } + function m(e) { + return 0 === e.length ? [] : e.split(l); + } + function p(e, t) { + return e.replace(/\{(\d+)\}/g, function (e, n) { + return void 0 !== t[n] ? t[n] : e; + }); + } + rf + ? ((rf.initLineNumbersOnLoad = function (e) { + "interactive" === n.readyState || "complete" === n.readyState + ? c(e) + : t.addEventListener("DOMContentLoaded", function () { + c(e); + }); + }), + (rf.lineNumbersBlock = _), + (rf.lineNumbersValue = function (e, t) { + if ("string" == typeof e) { + var n = document.createElement("code"); + return (n.innerHTML = e), d(n, t); + } + }), + ((a = n.createElement("style")).type = "text/css"), + (a.innerHTML = p( + ".{0}{border-collapse:collapse}.{0} td{padding:0}.{1}:before{content:attr({2})}", + [r, o, s], + )), + n.getElementsByTagName("head")[0].appendChild(a)) + : t.console.error("highlight.js not detected!"), + document.addEventListener("copy", function (e) { + var t, + n = window.getSelection(); + !(function (e) { + for (var t = e; t; ) { + if (t.className && -1 !== t.className.indexOf("hljs-ln-code")) + return 1; + t = t.parentNode; + } + })(n.anchorNode) || + ((t = + -1 !== window.navigator.userAgent.indexOf("Edge") + ? (function (e) { + for ( + var t = e.toString(), n = e.anchorNode; + "TD" !== n.nodeName; + + ) + n = n.parentNode; + for (var a = e.focusNode; "TD" !== a.nodeName; ) + a = a.parentNode; + var r = parseInt(n.dataset.lineNumber), + o = parseInt(a.dataset.lineNumber); + if (r == o) return t; + var l, + c = n.textContent, + _ = a.textContent; + for ( + o < r && + ((l = r), (r = o), (o = l), (l = c), (c = _), (_ = l)); + 0 !== t.indexOf(c); + + ) + c = c.slice(1); + for (; -1 === t.lastIndexOf(_); ) _ = _.slice(0, -1); + for ( + var d = c, + u = (function (e) { + for (var t = e; "TABLE" !== t.nodeName; ) + t = t.parentNode; + return t; + })(n), + m = r + 1; + m < o; + ++m + ) { + var g = p('.{0}[{1}="{2}"]', [i, s, m]); + d += "\n" + u.querySelector(g).textContent; + } + return d + "\n" + _; + })(n) + : n.toString()), + e.clipboardData.setData("text/plain", t), + e.preventDefault()); + }); +})(window, document); /*! * reveal.js plugin that adds syntax highlight support. */ -var of={id:"highlight",HIGHLIGHT_STEP_DELIMITER:"|",HIGHLIGHT_LINE_DELIMITER:",",HIGHLIGHT_LINE_RANGE_DELIMITER:"-",hljs:rf,init:function(e){var t=e.getConfig().highlight||{};t.highlightOnLoad="boolean"!=typeof t.highlightOnLoad||t.highlightOnLoad,t.escapeHTML="boolean"!=typeof t.escapeHTML||t.escapeHTML,Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach((function(e){e.parentNode.classList.add("code-wrapper");var n=e.querySelector('script[type="text/template"]');n&&(e.textContent=n.innerHTML),e.hasAttribute("data-trim")&&"function"==typeof e.innerHTML.trim&&(e.innerHTML=function(e){function t(e){return e.replace(/^[\s\uFEFF\xA0]+/g,"")}function n(e){for(var t=e.split("\n"),n=0;n=0&&""===t[n].trim();n--)t.splice(n,1);return t.join("\n")}return function(e){var a=n(e.innerHTML).split("\n"),r=a.reduce((function(e,n){return n.length>0&&t(n).length>0&&e>n.length-t(n).length?n.length-t(n).length:e}),Number.POSITIVE_INFINITY);return a.map((function(e,t){return e.slice(r)})).join("\n")}(e)}(e)),t.escapeHTML&&!e.hasAttribute("data-noescape")&&(e.innerHTML=e.innerHTML.replace(//g,">")),e.addEventListener("focusout",(function(e){rf.highlightElement(e.currentTarget)}),!1)})),"function"==typeof t.beforeHighlight&&t.beforeHighlight(rf),t.highlightOnLoad&&Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach((function(e){of.highlightBlock(e)})),e.on("pdf-ready",(function(){[].slice.call(e.getRevealElement().querySelectorAll("pre code[data-line-numbers].current-fragment")).forEach((function(e){of.scrollHighlightedLineIntoView(e,{},!0)}))}))},highlightBlock:function(e){if(rf.highlightElement(e),0!==e.innerHTML.trim().length&&e.hasAttribute("data-line-numbers")){rf.lineNumbersBlock(e,{singleLine:!0});var t={currentBlock:e},n=of.deserializeHighlightSteps(e.getAttribute("data-line-numbers"));if(n.length>1){var a=parseInt(e.getAttribute("data-fragment-index"),10);("number"!=typeof a||isNaN(a))&&(a=null),n.slice(1).forEach((function(n){var 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HIGHLIGHT_LINE_DELIMITER: ",", + HIGHLIGHT_LINE_RANGE_DELIMITER: "-", + hljs: rf, + init: function (e) { + var t = e.getConfig().highlight || {}; + (t.highlightOnLoad = + "boolean" != typeof t.highlightOnLoad || t.highlightOnLoad), + (t.escapeHTML = "boolean" != typeof t.escapeHTML || t.escapeHTML), + Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach( + function (e) { + e.parentNode.classList.add("code-wrapper"); + var n = e.querySelector('script[type="text/template"]'); + n && (e.textContent = n.innerHTML), + e.hasAttribute("data-trim") && + "function" == typeof e.innerHTML.trim && + (e.innerHTML = (function (e) { + function t(e) { + return e.replace(/^[\s\uFEFF\xA0]+/g, ""); + } + function n(e) { + for ( + var t = e.split("\n"), n = 0; + n < t.length && "" === t[n].trim(); + n++ + ) + t.splice(n--, 1); + for (n = t.length - 1; n >= 0 && "" === t[n].trim(); n--) + t.splice(n, 1); + return t.join("\n"); + } + return (function (e) { + var a = n(e.innerHTML).split("\n"), + r = a.reduce(function (e, n) { + return n.length > 0 && + t(n).length > 0 && + e > n.length - t(n).length + ? n.length - t(n).length + : e; + }, Number.POSITIVE_INFINITY); + return a + .map(function (e, t) { + return e.slice(r); + }) + .join("\n"); + })(e); + })(e)), + t.escapeHTML && + !e.hasAttribute("data-noescape") && + (e.innerHTML = e.innerHTML + .replace(//g, ">")), + e.addEventListener( + "focusout", + function (e) { + rf.highlightElement(e.currentTarget); + }, + !1, + ); + }, + ), + "function" == typeof t.beforeHighlight && t.beforeHighlight(rf), + t.highlightOnLoad && + Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach( + function (e) { + of.highlightBlock(e); + }, + ), + e.on("pdf-ready", function () { + [].slice + .call( + e + .getRevealElement() + .querySelectorAll("pre code[data-line-numbers].current-fragment"), + ) + .forEach(function (e) { + of.scrollHighlightedLineIntoView(e, {}, !0); + }); + }); + }, + highlightBlock: function (e) { + if ( + (rf.highlightElement(e), + 0 !== e.innerHTML.trim().length && e.hasAttribute("data-line-numbers")) + ) { + rf.lineNumbersBlock(e, { singleLine: !0 }); + var t = { currentBlock: e }, + n = of.deserializeHighlightSteps(e.getAttribute("data-line-numbers")); + if (n.length > 1) { + var a = parseInt(e.getAttribute("data-fragment-index"), 10); + ("number" != typeof a || isNaN(a)) && (a = null), + n.slice(1).forEach(function (n) { + var r = e.cloneNode(!0); + r.setAttribute( + "data-line-numbers", + of.serializeHighlightSteps([n]), + ), + r.classList.add("fragment"), + e.parentNode.appendChild(r), + of.highlightLines(r), + "number" == typeof a + ? (r.setAttribute("data-fragment-index", a), (a += 1)) + : r.removeAttribute("data-fragment-index"), + r.addEventListener( + "visible", + of.scrollHighlightedLineIntoView.bind(of, r, t), + ), + r.addEventListener( + "hidden", + of.scrollHighlightedLineIntoView.bind(of, r.previousSibling, t), + ); + }), + e.removeAttribute("data-fragment-index"), + e.setAttribute( + "data-line-numbers", + of.serializeHighlightSteps([n[0]]), + ); + } + var r = + "function" == typeof e.closest + ? e.closest("section:not(.stack)") + : null; + if (r) { + r.addEventListener("visible", function n() { + of.scrollHighlightedLineIntoView(e, t, !0), + r.removeEventListener("visible", n); + }); + } + of.highlightLines(e); + } + }, + scrollHighlightedLineIntoView: function (e, t, n) { + cancelAnimationFrame(t.animationFrameID), + t.currentBlock && (e.scrollTop = t.currentBlock.scrollTop), + (t.currentBlock = e); + var a = this.getHighlightedLineBounds(e), + r = e.offsetHeight, + i = getComputedStyle(e); + r -= parseInt(i.paddingTop) + parseInt(i.paddingBottom); + var o = e.scrollTop, + s = a.top + (Math.min(a.bottom - a.top, r) - r) / 2, + l = e.querySelector(".hljs-ln"); + if ( + (l && (s += l.offsetTop - parseInt(i.paddingTop)), + (s = Math.max(Math.min(s, e.scrollHeight - r), 0)), + !0 === n || o === s) + ) + e.scrollTop = s; + else { + if (e.scrollHeight <= r) return; + var c = 0; + !(function n() { + (c = Math.min(c + 0.02, 1)), + (e.scrollTop = o + (s - o) * of.easeInOutQuart(c)), + c < 1 && (t.animationFrameID = requestAnimationFrame(n)); + })(); + } + }, + easeInOutQuart: function (e) { + return e < 0.5 ? 8 * e * e * e * e : 1 - 8 * --e * e * e * e; + }, + getHighlightedLineBounds: function (e) { + var t = e.querySelectorAll(".highlight-line"); + if (0 === t.length) return { top: 0, bottom: 0 }; + var n = t[0], + a = t[t.length - 1]; + return { top: n.offsetTop, bottom: a.offsetTop + a.offsetHeight }; + }, + highlightLines: function (e, t) { + var n = of.deserializeHighlightSteps( + t || e.getAttribute("data-line-numbers"), + ); + n.length && + n[0].forEach(function (t) { + var n = []; + "number" == typeof t.end + ? (n = [].slice.call( + e.querySelectorAll( + "table tr:nth-child(n+" + + t.start + + "):nth-child(-n+" + + t.end + + ")", + ), + )) + : "number" == typeof t.start && + (n = [].slice.call( + e.querySelectorAll("table tr:nth-child(" + t.start + ")"), + )), + n.length && + (n.forEach(function (e) { + e.classList.add("highlight-line"); + }), + e.classList.add("has-highlights")); + }); + }, + deserializeHighlightSteps: function (e) { + return (e = (e = e.replace(/\s/g, "")).split( + of.HIGHLIGHT_STEP_DELIMITER, + )).map(function (e) { + return e.split(of.HIGHLIGHT_LINE_DELIMITER).map(function (e) { + if (/^[\d-]+$/.test(e)) { + e = e.split(of.HIGHLIGHT_LINE_RANGE_DELIMITER); + var t = parseInt(e[0], 10), + n = parseInt(e[1], 10); + return isNaN(n) ? { start: t } : { start: t, end: n }; + } + return {}; + }); + }); + }, + serializeHighlightSteps: function (e) { + return e + .map(function (e) { + return e + .map(function (e) { + return "number" == typeof e.end + ? e.start + of.HIGHLIGHT_LINE_RANGE_DELIMITER + e.end + : "number" == typeof e.start + ? e.start + : ""; + }) + .join(of.HIGHLIGHT_LINE_DELIMITER); + }) + .join(of.HIGHLIGHT_STEP_DELIMITER); + }, +}; +export default function () { + return of; +} diff --git a/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.js b/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.js index b2529bf..6038f0a 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.js +++ b/content/slides/slides_files/libs/revealjs/plugin/highlight/highlight.js @@ -1,5 +1,30333 @@ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).RevealHighlight=t()}(this,(function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof 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(n = r = 0) + : 1 === f + ? ((n = 0), (r = l - c)) + : ((n = f - 2), (r = Mc(Lc(Rc(t), 0), l - c))), + l + n - r > 9007199254740991) + ) + throw TypeError("Maximum allowed length exceeded"); + for (u = zc(s, r), i = 0; i < r; i++) (o = c + i) in s && $c(u, i, s[o]); + if (((u.length = r), n < r)) { + for (i = c; i < l - r; i++) + (a = i + n), (o = i + r) in s ? (s[a] = s[o]) : delete s[a]; + for (i = l; i > l - r + n; i--) delete s[i - 1]; + } else if (n > r) + for (i = l - r; i > c; i--) + (a = i + n - 1), (o = i + r - 1) in s ? (s[a] = s[o]) : delete s[a]; + for (i = 0; i < n; i++) s[i + c] = arguments[i + 2]; + return (s.length = l - r + n), u; + }, + }, +); +var Nc = ki.map; +en( + { target: "Array", proto: !0, forced: !ws("map") }, + { + map: function (e) { + return Nc(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +); +var Uc = en, + qc = ei.start, + Zc = ri("trimStart"), + Gc = Zc + ? function () { + return qc(this); + } + : "".trimStart; +Uc( + { target: "String", proto: !0, forced: Zc }, + { trimStart: Gc, trimLeft: Gc }, +); +var Hc = + Object.is || + function (e, t) { + return e === t ? 0 !== e || 1 / e == 1 / t : e != e && t != t; + }, + Qc = ee, + Vc = C, + Yc = Hc, + Kc = fr; +Xn("search", 1, function (e, t, n) { + return [ + function (t) { + var n = Vc(this), + r = null == t ? void 0 : t[e]; + return void 0 !== r ? r.call(t, n) : new RegExp(t)[e](String(n)); + }, + function (e) { + var r = n(t, e, this); + if (r.done) return r.value; + var u = Qc(e), + i = String(this), + o = u.lastIndex; + Yc(o, 0) || (u.lastIndex = 0); + var a = Kc(u, i); + return Yc(u.lastIndex, o) || (u.lastIndex = o), null === a ? -1 : a.index; + }, + ]; +}); +var Xc = en, + Wc = ei.end, + Jc = ri("trimEnd"), + ef = Jc + ? function () { + return Wc(this); + } + : "".trimEnd; +Xc({ target: "String", proto: !0, forced: Jc }, { trimEnd: ef, trimRight: ef }); +var tf = ki.filter; +en( + { target: "Array", proto: !0, forced: !ws("filter") }, + { + filter: function (e) { + return tf(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +); +var nf = C, + rf = /"/g, + uf = D, + of = function (e, t, n, r) { + var u = String(nf(e)), + i = "<" + t; + return ( + "" !== n && (i += " " + n + '="' + String(r).replace(rf, """) + '"'), + i + ">" + u + "" + ); + }; +en( + { + target: "String", + proto: !0, + forced: (function (e) { + return uf(function () { + var t = ""[e]('"'); + return t !== t.toLowerCase() || t.split('"').length > 3; + }); + })("link"), + }, + { + link: function (e) { + return of(this, "a", "href", e); + }, + }, +); +var af = I, + sf = qs; +en( + { + target: "Object", + stat: !0, + forced: D(function () { + sf(1); + }), + }, + { + keys: function (e) { + return sf(af(e)); + }, + }, +); +var lf = kt.includes, + cf = pl; +en( + { target: "Array", proto: !0 }, + { + includes: function (e) { + return lf(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +), + cf("includes"); +var ff = Tr, + pf = Pn("match"), + hf = function (e) { + if (ff(e)) throw TypeError("The method doesn't accept regular expressions"); + return e; + }, + Df = C; +function gf() { + return { + baseUrl: null, + breaks: !1, + extensions: null, + gfm: !0, + headerIds: !0, + headerPrefix: "", + highlight: null, + langPrefix: "language-", + mangle: !0, + pedantic: !1, + renderer: null, + sanitize: !1, + sanitizer: null, + silent: !1, + smartLists: !1, + smartypants: !1, + tokenizer: null, + walkTokens: null, + xhtml: !1, + }; +} +en( + { + target: "String", + proto: !0, + forced: !(function (e) { + var t = /./; + try { + "/./"[e](t); + } catch (n) { + try { + return (t[pf] = !1), "/./"[e](t); + } catch (e) {} + } + return !1; + })("includes"), + }, + { + includes: function (e) { + return !!~String(Df(this)).indexOf( + hf(e), + arguments.length > 1 ? arguments[1] : void 0, + ); + }, + }, +); +var df = { + baseUrl: null, + breaks: !1, + extensions: null, + gfm: !0, + headerIds: !0, + headerPrefix: "", + highlight: null, + langPrefix: "language-", + mangle: !0, + pedantic: !1, + renderer: null, + sanitize: !1, + sanitizer: null, + silent: !1, + smartLists: !1, + smartypants: !1, + tokenizer: null, + walkTokens: null, + xhtml: !1, +}; +var vf = /[&<>"']/, + yf = /[&<>"']/g, + Af = /[<>"']|&(?!#?\w+;)/, + mf = /[<>"']|&(?!#?\w+;)/g, + kf = { "&": "&", "<": "<", ">": ">", '"': """, "'": "'" }, + Ef = function (e) { + return kf[e]; + }; +function xf(e, t) { + if (t) { + if (vf.test(e)) return e.replace(yf, Ef); + } else if (Af.test(e)) return e.replace(mf, Ef); + return e; +} +var Ff = /&(#(?:\d+)|(?:#x[0-9A-Fa-f]+)|(?:\w+));?/gi; +function bf(e) { + return e.replace(Ff, function (e, t) { + return "colon" === (t = t.toLowerCase()) + ? ":" + : "#" === t.charAt(0) + ? "x" === t.charAt(1) + ? String.fromCharCode(parseInt(t.substring(2), 16)) + : String.fromCharCode(+t.substring(1)) + : ""; + }); +} +var Cf = /(^|[^\[])\^/g; +function wf(e, t) { + (e = e.source || e), (t = t || ""); + var n = { + replace: function (t, r) { + return ( + (r = (r = r.source || r).replace(Cf, "$1")), (e = e.replace(t, r)), n + ); + }, + getRegex: function () { + return new RegExp(e, t); + }, + }; + return n; +} +var Bf = /[^\w:]/g, + Sf = /^$|^[a-z][a-z0-9+.-]*:|^[?#]/i; +function _f(e, t, n) { + if (e) { + var r; + try { + r = decodeURIComponent(bf(n)).replace(Bf, "").toLowerCase(); + } catch (e) { + return null; + } + if ( + 0 === r.indexOf("javascript:") || + 0 === r.indexOf("vbscript:") || + 0 === r.indexOf("data:") + ) + return null; + } + t && + !Sf.test(n) && + (n = (function (e, t) { + Tf[" " + e] || + (Of.test(e) ? (Tf[" " + e] = e + "/") : (Tf[" " + e] = Pf(e, "/", !0))); + var n = -1 === (e = Tf[" " + e]).indexOf(":"); + return "//" === t.substring(0, 2) + ? n + ? t + : e.replace(Rf, "$1") + t + : "/" === t.charAt(0) + ? n + ? t + : e.replace(If, "$1") + t + : e + t; + })(t, n)); + try { + n = encodeURI(n).replace(/%25/g, "%"); + } catch (e) { + return null; + } + return n; +} +var Tf = {}, + Of = /^[^:]+:\/*[^/]*$/, + Rf = /^([^:]+:)[\s\S]*$/, + If = /^([^:]+:\/*[^/]*)[\s\S]*$/; +var jf = { exec: function () {} }; +function zf(e) { + for (var t, n, r = 1; r < arguments.length; r++) + for (n in (t = arguments[r])) + Object.prototype.hasOwnProperty.call(t, n) && (e[n] = t[n]); + return e; +} +function $f(e, t) { + var n = e + .replace(/\|/g, function (e, t, n) { + for (var r = !1, u = t; --u >= 0 && "\\" === n[u]; ) r = !r; + return r ? "|" : " |"; + }) + .split(/ \|/), + r = 0; + if ( + (n[0].trim() || n.shift(), + n.length > 0 && !n[n.length - 1].trim() && n.pop(), + n.length > t) + ) + n.splice(t); + else for (; n.length < t; ) n.push(""); + for (; r < n.length; r++) n[r] = n[r].trim().replace(/\\\|/g, "|"); + return n; +} +function Pf(e, t, n) { + var r = e.length; + if (0 === r) return ""; + for (var u = 0; u < r; ) { + var i = e.charAt(r - u - 1); + if (i !== t || n) { + if (i === t || !n) break; + u++; + } else u++; + } + return e.substr(0, r - u); +} +function Lf(e) { + e && + e.sanitize && + !e.silent && + console.warn( + "marked(): sanitize and sanitizer parameters are deprecated since version 0.7.0, should not be used and will be removed in the future. Read more here: https://marked.js.org/#/USING_ADVANCED.md#options", + ); +} +function Mf(e, t) { + if (t < 1) return ""; + for (var n = ""; t > 1; ) 1 & t && (n += e), (t >>= 1), (e += e); + return n + e; +} +function Nf(e, t, n, r) { + var u = t.href, + i = t.title ? xf(t.title) : null, + o = e[1].replace(/\\([\[\]])/g, "$1"); + if ("!" !== e[0].charAt(0)) { + r.state.inLink = !0; + var a = { + type: "link", + raw: n, + href: u, + title: i, + text: o, + tokens: r.inlineTokens(o, []), + }; + return (r.state.inLink = !1), a; + } + return { type: "image", raw: n, href: u, title: i, text: xf(o) }; +} +var Uf = (function () { + function e(n) { + t(this, e), (this.options = n || df); + } + return ( + r(e, [ + { + key: "space", + value: function (e) { + var t = this.rules.block.newline.exec(e); + if (t && t[0].length > 0) return { type: "space", raw: t[0] }; + }, + }, + { + key: "code", + value: function (e) { + var t = this.rules.block.code.exec(e); + if (t) { + var n = t[0].replace(/^ {1,4}/gm, ""); + return { + type: "code", + raw: t[0], + codeBlockStyle: "indented", + text: this.options.pedantic ? n : Pf(n, "\n"), + }; + } + }, + }, + { + key: "fences", + value: function (e) { + var t = this.rules.block.fences.exec(e); + if (t) { + var n = t[0], + r = (function (e, t) { + var n = e.match(/^(\s+)(?:```)/); + if (null === n) return t; + var r = n[1]; + return t + .split("\n") + .map(function (e) { + var t = e.match(/^\s+/); + return null === t + ? e + : o(t, 1)[0].length >= r.length + ? e.slice(r.length) + : e; + }) + .join("\n"); + })(n, t[3] || ""); + return { + type: "code", + raw: n, + lang: t[2] ? t[2].trim() : t[2], + text: r, + }; + } + }, + }, + { + key: "heading", + value: function (e) { + var t = this.rules.block.heading.exec(e); + if (t) { + var n = t[2].trim(); + if (/#$/.test(n)) { + var r = Pf(n, "#"); + this.options.pedantic + ? (n = r.trim()) + : (r && !/ $/.test(r)) || (n = r.trim()); + } + var u = { + type: "heading", + raw: t[0], + depth: t[1].length, + text: n, + tokens: [], + }; + return this.lexer.inline(u.text, u.tokens), u; + } + }, + }, + { + key: "hr", + value: function (e) { + var t = this.rules.block.hr.exec(e); + if (t) return { type: "hr", raw: t[0] }; + }, + }, + { + key: "blockquote", + value: function (e) { + var t = this.rules.block.blockquote.exec(e); + if (t) { + var n = t[0].replace(/^ *> ?/gm, ""); + return { + type: "blockquote", + raw: t[0], + tokens: this.lexer.blockTokens(n, []), + text: n, + }; + } + }, + }, + { + key: "list", + value: function (e) { + var t = this.rules.block.list.exec(e); + if (t) { + var n, + r, + u, + i, + o, + a, + s, + c, + f, + p, + h, + D, + g = t[1].trim(), + d = g.length > 1, + v = { + type: "list", + raw: "", + ordered: d, + start: d ? +g.slice(0, -1) : "", + loose: !1, + items: [], + }; + (g = d ? "\\d{1,9}\\".concat(g.slice(-1)) : "\\".concat(g)), + this.options.pedantic && (g = d ? g : "[*+-]"); + for ( + var y = new RegExp( + "^( {0,3}".concat(g, ")((?: [^\\n]*)?(?:\\n|$))"), + ); + e && + ((D = !1), (t = y.exec(e))) && + !this.rules.block.hr.test(e); + + ) { + if ( + ((n = t[0]), + (e = e.substring(n.length)), + (c = t[2].split("\n", 1)[0]), + (f = e.split("\n", 1)[0]), + this.options.pedantic + ? ((i = 2), (h = c.trimLeft())) + : ((i = (i = t[2].search(/[^ ]/)) > 4 ? 1 : i), + (h = c.slice(i)), + (i += t[1].length)), + (a = !1), + !c && + /^ *$/.test(f) && + ((n += f + "\n"), + (e = e.substring(f.length + 1)), + (D = !0)), + !D) + ) + for ( + var A = new RegExp( + "^ {0,".concat( + Math.min(3, i - 1), + "}(?:[*+-]|\\d{1,9}[.)])", + ), + ); + e && + ((c = p = e.split("\n", 1)[0]), + this.options.pedantic && + (c = c.replace(/^ {1,4}(?=( {4})*[^ ])/g, " ")), + !A.test(c)); + + ) { + if (c.search(/[^ ]/) >= i || !c.trim()) + h += "\n" + c.slice(i); + else { + if (a) break; + h += "\n" + c; + } + a || c.trim() || (a = !0), + (n += p + "\n"), + (e = e.substring(p.length + 1)); + } + v.loose || + (s ? (v.loose = !0) : /\n *\n *$/.test(n) && (s = !0)), + this.options.gfm && + (r = /^\[[ xX]\] /.exec(h)) && + ((u = "[ ] " !== r[0]), + (h = h.replace(/^\[[ xX]\] +/, ""))), + v.items.push({ + type: "list_item", + raw: n, + task: !!r, + checked: u, + loose: !1, + text: h, + }), + (v.raw += n); + } + (v.items[v.items.length - 1].raw = n.trimRight()), + (v.items[v.items.length - 1].text = h.trimRight()), + (v.raw = v.raw.trimRight()); + var m = v.items.length; + for (o = 0; o < m; o++) { + (this.lexer.state.top = !1), + (v.items[o].tokens = this.lexer.blockTokens( + v.items[o].text, + [], + )); + var k = v.items[o].tokens.filter(function (e) { + return "space" === e.type; + }), + E = k.every(function (e) { + var t, + n = 0, + r = l(e.raw.split("")); + try { + for (r.s(); !(t = r.n()).done; ) { + if (("\n" === t.value && (n += 1), n > 1)) return !0; + } + } catch (e) { + r.e(e); + } finally { + r.f(); + } + return !1; + }); + !v.loose && + k.length && + E && + ((v.loose = !0), (v.items[o].loose = !0)); + } + return v; + } + }, + }, + { + key: "html", + value: function (e) { + var t = this.rules.block.html.exec(e); + if (t) { + var n = { + type: "html", + raw: t[0], + pre: + !this.options.sanitizer && + ("pre" === t[1] || "script" === t[1] || "style" === t[1]), + text: t[0], + }; + return ( + this.options.sanitize && + ((n.type = "paragraph"), + (n.text = this.options.sanitizer + ? this.options.sanitizer(t[0]) + : xf(t[0])), + (n.tokens = []), + this.lexer.inline(n.text, n.tokens)), + n + ); + } + }, + }, + { + key: "def", + value: function (e) { + var t = this.rules.block.def.exec(e); + if (t) + return ( + t[3] && (t[3] = t[3].substring(1, t[3].length - 1)), + { + type: "def", + tag: t[1].toLowerCase().replace(/\s+/g, " "), + raw: t[0], + href: t[2], + title: t[3], + } + ); + }, + }, + { + key: "table", + value: function (e) { + var t = this.rules.block.table.exec(e); + if (t) { + var n = { + type: "table", + header: $f(t[1]).map(function (e) { + return { text: e }; + }), + align: t[2].replace(/^ *|\| *$/g, "").split(/ *\| */), + rows: + t[3] && t[3].trim() + ? t[3].replace(/\n[ \t]*$/, "").split("\n") + : [], + }; + if (n.header.length === n.align.length) { + n.raw = t[0]; + var r, + u, + i, + o, + a = n.align.length; + for (r = 0; r < a; r++) + /^ *-+: *$/.test(n.align[r]) + ? (n.align[r] = "right") + : /^ *:-+: *$/.test(n.align[r]) + ? (n.align[r] = "center") + : /^ *:-+ *$/.test(n.align[r]) + ? (n.align[r] = "left") + : (n.align[r] = null); + for (a = n.rows.length, r = 0; r < a; r++) + n.rows[r] = $f(n.rows[r], n.header.length).map(function (e) { + return { text: e }; + }); + for (a = n.header.length, u = 0; u < a; u++) + (n.header[u].tokens = []), + this.lexer.inlineTokens( + n.header[u].text, + n.header[u].tokens, + ); + for (a = n.rows.length, u = 0; u < a; u++) + for (o = n.rows[u], i = 0; i < o.length; i++) + (o[i].tokens = []), + this.lexer.inlineTokens(o[i].text, o[i].tokens); + return n; + } + } + }, + }, + { + key: "lheading", + value: function (e) { + var t = this.rules.block.lheading.exec(e); + if (t) { + var n = { + type: "heading", + raw: t[0], + depth: "=" === t[2].charAt(0) ? 1 : 2, + text: t[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "paragraph", + value: function (e) { + var t = this.rules.block.paragraph.exec(e); + if (t) { + var n = { + type: "paragraph", + raw: t[0], + text: + "\n" === t[1].charAt(t[1].length - 1) + ? t[1].slice(0, -1) + : t[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "text", + value: function (e) { + var t = this.rules.block.text.exec(e); + if (t) { + var n = { type: "text", raw: t[0], text: t[0], tokens: [] }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "escape", + value: function (e) { + var t = this.rules.inline.escape.exec(e); + if (t) return { type: "escape", raw: t[0], text: xf(t[1]) }; + }, + }, + { + key: "tag", + value: function (e) { + var t = this.rules.inline.tag.exec(e); + if (t) + return ( + !this.lexer.state.inLink && /^/i.test(t[0]) && + (this.lexer.state.inLink = !1), + !this.lexer.state.inRawBlock && + /^<(pre|code|kbd|script)(\s|>)/i.test(t[0]) + ? (this.lexer.state.inRawBlock = !0) + : this.lexer.state.inRawBlock && + /^<\/(pre|code|kbd|script)(\s|>)/i.test(t[0]) && + (this.lexer.state.inRawBlock = !1), + { + type: this.options.sanitize ? "text" : "html", + raw: t[0], + inLink: this.lexer.state.inLink, + inRawBlock: this.lexer.state.inRawBlock, + text: this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(t[0]) + : xf(t[0]) + : t[0], + } + ); + }, + }, + { + key: "link", + value: function (e) { + var t = this.rules.inline.link.exec(e); + if (t) { + var n = t[2].trim(); + if (!this.options.pedantic && /^$/.test(n)) return; + var r = Pf(n.slice(0, -1), "\\"); + if ((n.length - r.length) % 2 == 0) return; + } else { + var u = (function (e, t) { + if (-1 === e.indexOf(t[1])) return -1; + for (var n = e.length, r = 0, u = 0; u < n; u++) + if ("\\" === e[u]) u++; + else if (e[u] === t[0]) r++; + else if (e[u] === t[1] && --r < 0) return u; + return -1; + })(t[2], "()"); + if (u > -1) { + var i = (0 === t[0].indexOf("!") ? 5 : 4) + t[1].length + u; + (t[2] = t[2].substring(0, u)), + (t[0] = t[0].substring(0, i).trim()), + (t[3] = ""); + } + } + var o = t[2], + a = ""; + if (this.options.pedantic) { + var s = /^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(o); + s && ((o = s[1]), (a = s[3])); + } else a = t[3] ? t[3].slice(1, -1) : ""; + return ( + (o = o.trim()), + /^$/.test(n) + ? o.slice(1) + : o.slice(1, -1)), + Nf( + t, + { + href: o ? o.replace(this.rules.inline._escapes, "$1") : o, + title: a ? a.replace(this.rules.inline._escapes, "$1") : a, + }, + t[0], + this.lexer, + ) + ); + } + }, + }, + { + key: "reflink", + value: function (e, t) { + var n; + if ( + (n = this.rules.inline.reflink.exec(e)) || + (n = this.rules.inline.nolink.exec(e)) + ) { + var r = (n[2] || n[1]).replace(/\s+/g, " "); + if (!(r = t[r.toLowerCase()]) || !r.href) { + var u = n[0].charAt(0); + return { type: "text", raw: u, text: u }; + } + return Nf(n, r, n[0], this.lexer); + } + }, + }, + { + key: "emStrong", + value: function (e, t) { + var n = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "", + r = this.rules.inline.emStrong.lDelim.exec(e); + if ( + r && + (!r[3] || + !n.match( + 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+ )) + ) { + var u = r[1] || r[2] || ""; + if ( + !u || + (u && ("" === n || this.rules.inline.punctuation.exec(n))) + ) { + var i, + o, + a = r[0].length - 1, + s = a, + l = 0, + c = + "*" === r[0][0] + ? this.rules.inline.emStrong.rDelimAst + : this.rules.inline.emStrong.rDelimUnd; + for ( + c.lastIndex = 0, t = t.slice(-1 * e.length + a); + null != (r = c.exec(t)); + + ) + if ((i = r[1] || r[2] || r[3] || r[4] || r[5] || r[6])) + if (((o = i.length), r[3] || r[4])) s += o; + else if (!((r[5] || r[6]) && a % 3) || (a + o) % 3) { + if (!((s -= o) > 0)) { + if ( + ((o = Math.min(o, o + s + l)), Math.min(a, o) % 2) + ) { + var f = e.slice(1, a + r.index + o); + return { + type: "em", + raw: e.slice(0, a + r.index + o + 1), + text: f, + tokens: this.lexer.inlineTokens(f, []), + }; + } + var p = e.slice(2, a + r.index + o - 1); + return { + type: "strong", + raw: e.slice(0, a + r.index + o + 1), + text: p, + tokens: this.lexer.inlineTokens(p, []), + }; + } + } else l += o; + } + } + }, + }, + { + key: "codespan", + value: function (e) { + var t = this.rules.inline.code.exec(e); + if (t) { + var n = t[2].replace(/\n/g, " "), + r = /[^ ]/.test(n), + u = /^ /.test(n) && / $/.test(n); + return ( + r && u && (n = n.substring(1, n.length - 1)), + (n = xf(n, !0)), + { type: "codespan", raw: t[0], text: n } + ); + } + }, + }, + { + key: "br", + value: function (e) { + var t = this.rules.inline.br.exec(e); + if (t) return { type: "br", raw: t[0] }; + }, + }, + { + key: "del", + value: function (e) { + var t = this.rules.inline.del.exec(e); + if (t) + return { + type: "del", + raw: t[0], + text: t[2], + tokens: this.lexer.inlineTokens(t[2], []), + }; + }, + }, + { + key: "autolink", + value: function (e, t) { + var n, + r, + u = this.rules.inline.autolink.exec(e); + if (u) + return ( + (r = + "@" === u[2] + ? "mailto:" + (n = xf(this.options.mangle ? t(u[1]) : u[1])) + : (n = xf(u[1]))), + { + type: "link", + raw: u[0], + text: n, + href: r, + tokens: [{ type: "text", raw: n, text: n }], + } + ); + }, + }, + { + key: "url", + value: function (e, t) { + var n; + if ((n = this.rules.inline.url.exec(e))) { + var r, u; + if ("@" === n[2]) + u = "mailto:" + (r = xf(this.options.mangle ? t(n[0]) : n[0])); + else { + var i; + do { + (i = n[0]), + (n[0] = this.rules.inline._backpedal.exec(n[0])[0]); + } while (i !== n[0]); + (r = xf(n[0])), (u = "www." === n[1] ? "http://" + r : r); + } + return { + type: "link", + raw: n[0], + text: r, + href: u, + tokens: [{ type: "text", raw: r, text: r }], + }; + } + }, + }, + { + key: "inlineText", + value: function (e, t) { + var n, + r = this.rules.inline.text.exec(e); + if (r) + return ( + (n = this.lexer.state.inRawBlock + ? this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(r[0]) + : xf(r[0]) + : r[0] + : xf(this.options.smartypants ? t(r[0]) : r[0])), + { type: "text", raw: r[0], text: n } + ); + }, + }, + ]), + e + ); + })(), + qf = { + newline: /^(?: *(?:\n|$))+/, + code: /^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/, + fences: + /^ {0,3}(`{3,}(?=[^`\n]*\n)|~{3,})([^\n]*)\n(?:|([\s\S]*?)\n)(?: {0,3}\1[~`]* *(?=\n|$)|$)/, + hr: /^ {0,3}((?:- *){3,}|(?:_ *){3,}|(?:\* *){3,})(?:\n+|$)/, + heading: /^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/, + blockquote: /^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/, + list: /^( {0,3}bull)( [^\n]+?)?(?:\n|$)/, + html: "^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|\\n*|$)|\\n*|$)|)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))", + def: /^ {0,3}\[(label)\]: *(?:\n *)?]+)>?(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/, + table: jf, + lheading: /^([^\n]+)\n {0,3}(=+|-+) *(?:\n+|$)/, + _paragraph: + /^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/, + text: /^[^\n]+/, + _label: /(?!\s*\])(?:\\.|[^\[\]\\])+/, + _title: /(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/, + }; +(qf.def = wf(qf.def) + .replace("label", qf._label) + .replace("title", qf._title) + .getRegex()), + (qf.bullet = /(?:[*+-]|\d{1,9}[.)])/), + (qf.listItemStart = wf(/^( *)(bull) */) + .replace("bull", qf.bullet) + .getRegex()), + (qf.list = wf(qf.list) + .replace(/bull/g, qf.bullet) + .replace( + "hr", + "\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))", + ) + .replace("def", "\\n+(?=" + qf.def.source + ")") + .getRegex()), + (qf._tag = + "address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul"), + (qf._comment = /|$)/), + (qf.html = wf(qf.html, "i") + .replace("comment", qf._comment) + .replace("tag", qf._tag) + .replace( + "attribute", + / +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/, + ) + .getRegex()), + (qf.paragraph = wf(qf._paragraph) + .replace("hr", qf.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("|table", "") + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", qf._tag) + .getRegex()), + (qf.blockquote = wf(qf.blockquote) + .replace("paragraph", qf.paragraph) + .getRegex()), + (qf.normal = zf({}, qf)), + (qf.gfm = zf({}, qf.normal, { + table: + "^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)", + })), + (qf.gfm.table = wf(qf.gfm.table) + .replace("hr", qf.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("blockquote", " {0,3}>") + .replace("code", " {4}[^\\n]") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", qf._tag) + .getRegex()), + (qf.gfm.paragraph = wf(qf._paragraph) + .replace("hr", qf.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("table", qf.gfm.table) + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", qf._tag) + .getRegex()), + (qf.pedantic = zf({}, qf.normal, { + html: wf( + "^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+? *(?:\\n{2,}|\\s*$)|\\s]*)*?/?> *(?:\\n{2,}|\\s*$))", + ) + .replace("comment", qf._comment) + .replace( + /tag/g, + "(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b", + ) + .getRegex(), + def: /^ *\[([^\]]+)\]: *]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/, + heading: /^(#{1,6})(.*)(?:\n+|$)/, + fences: jf, + paragraph: wf(qf.normal._paragraph) + .replace("hr", qf.hr) + .replace("heading", " *#{1,6} *[^\n]") + .replace("lheading", qf.lheading) + .replace("blockquote", " {0,3}>") + .replace("|fences", "") + .replace("|list", "") + .replace("|html", "") + .getRegex(), + })); +var Zf = { + escape: /^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/, + autolink: /^<(scheme:[^\s\x00-\x1f<>]*|email)>/, + url: jf, + tag: "^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^", + link: /^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/, + reflink: /^!?\[(label)\]\[(ref)\]/, + nolink: /^!?\[(ref)\](?:\[\])?/, + reflinkSearch: "reflink|nolink(?!\\()", + emStrong: { + lDelim: /^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/, + rDelimAst: + /^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/, + rDelimUnd: + /^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/, + }, + code: /^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/, + br: /^( {2,}|\\)\n(?!\s*$)/, + del: jf, + text: /^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\ 0.5 && (n = "x" + n.toString(16)), + (r += "&#" + n + ";"); + return r; +} +(Zf._punctuation = "!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~"), + (Zf.punctuation = wf(Zf.punctuation) + .replace(/punctuation/g, Zf._punctuation) + .getRegex()), + (Zf.blockSkip = /\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g), + (Zf.escapedEmSt = /\\\*|\\_/g), + (Zf._comment = wf(qf._comment).replace("(?:--\x3e|$)", "--\x3e").getRegex()), + (Zf.emStrong.lDelim = wf(Zf.emStrong.lDelim) + .replace(/punct/g, Zf._punctuation) + .getRegex()), + (Zf.emStrong.rDelimAst = wf(Zf.emStrong.rDelimAst, "g") + .replace(/punct/g, Zf._punctuation) + .getRegex()), + (Zf.emStrong.rDelimUnd = wf(Zf.emStrong.rDelimUnd, "g") + .replace(/punct/g, Zf._punctuation) + .getRegex()), + (Zf._escapes = /\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g), + (Zf._scheme = /[a-zA-Z][a-zA-Z0-9+.-]{1,31}/), + (Zf._email = + /[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/), + (Zf.autolink = wf(Zf.autolink) + .replace("scheme", Zf._scheme) + .replace("email", Zf._email) + .getRegex()), + (Zf._attribute = + /\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/), + (Zf.tag = wf(Zf.tag) + .replace("comment", Zf._comment) + .replace("attribute", Zf._attribute) + .getRegex()), + (Zf._label = /(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/), + (Zf._href = /<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/), + (Zf._title = /"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/), + (Zf.link = wf(Zf.link) + .replace("label", Zf._label) + .replace("href", Zf._href) + .replace("title", Zf._title) + .getRegex()), + (Zf.reflink = wf(Zf.reflink) + .replace("label", Zf._label) + .replace("ref", qf._label) + .getRegex()), + (Zf.nolink = wf(Zf.nolink).replace("ref", qf._label).getRegex()), + (Zf.reflinkSearch = wf(Zf.reflinkSearch, "g") + .replace("reflink", Zf.reflink) + .replace("nolink", Zf.nolink) + .getRegex()), + (Zf.normal = zf({}, Zf)), + (Zf.pedantic = zf({}, Zf.normal, { + strong: { + start: /^__|\*\*/, + middle: /^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/, + endAst: /\*\*(?!\*)/g, + endUnd: /__(?!_)/g, + }, + em: { + start: /^_|\*/, + middle: /^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/, + endAst: /\*(?!\*)/g, + endUnd: /_(?!_)/g, + }, + link: wf(/^!?\[(label)\]\((.*?)\)/) + .replace("label", Zf._label) + .getRegex(), + reflink: wf(/^!?\[(label)\]\s*\[([^\]]*)\]/) + .replace("label", Zf._label) + .getRegex(), + })), + (Zf.gfm = zf({}, Zf.normal, { + escape: wf(Zf.escape).replace("])", "~|])").getRegex(), + _extended_email: + /[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/, + url: /^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/, + _backpedal: + /(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/, + del: /^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/, + text: /^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\ 1 && void 0 !== arguments[1] + ? arguments[1] + : []; + for (this.options.pedantic && (e = e.replace(/^ +$/gm, "")); e; ) + if ( + !( + this.options.extensions && + this.options.extensions.block && + this.options.extensions.block.some(function (n) { + return ( + !!(t = n.call({ lexer: i }, e, o)) && + ((e = e.substring(t.raw.length)), o.push(t), !0) + ); + }) + ) + ) + if ((t = this.tokenizer.space(e))) + (e = e.substring(t.raw.length)), + 1 === t.raw.length && o.length > 0 + ? (o[o.length - 1].raw += "\n") + : o.push(t); + else if ((t = this.tokenizer.code(e))) + (e = e.substring(t.raw.length)), + !(n = o[o.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? o.push(t) + : ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.text), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((t = this.tokenizer.fences(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.heading(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.hr(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.blockquote(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.list(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.html(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.def(e))) + (e = e.substring(t.raw.length)), + !(n = o[o.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? this.tokens.links[t.tag] || + (this.tokens.links[t.tag] = { + href: t.href, + title: t.title, + }) + : ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.raw), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((t = this.tokenizer.table(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.lheading(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ( + ((r = e), + this.options.extensions && + this.options.extensions.startBlock && + (function () { + var t = 1 / 0, + n = e.slice(1), + u = void 0; + i.options.extensions.startBlock.forEach(function (e) { + "number" == typeof (u = e.call({ lexer: this }, n)) && + u >= 0 && + (t = Math.min(t, u)); + }), + t < 1 / 0 && t >= 0 && (r = e.substring(0, t + 1)); + })(), + this.state.top && (t = this.tokenizer.paragraph(r))) + ) + (n = o[o.length - 1]), + u && "paragraph" === n.type + ? ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : o.push(t), + (u = r.length !== e.length), + (e = e.substring(t.raw.length)); + else if ((t = this.tokenizer.text(e))) + (e = e.substring(t.raw.length)), + (n = o[o.length - 1]) && "text" === n.type + ? ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : o.push(t); + else if (e) { + var a = "Infinite loop on byte: " + e.charCodeAt(0); + if (this.options.silent) { + console.error(a); + break; + } + throw new Error(a); + } + return (this.state.top = !0), o; + }, + }, + { + key: "inline", + value: function (e, t) { + this.inlineQueue.push({ src: e, tokens: t }); + }, + }, + { + key: "inlineTokens", + value: function (e) { + var t, + n, + r, + u, + i, + o, + a = this, + s = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : [], + l = e; + if (this.tokens.links) { + var c = Object.keys(this.tokens.links); + if (c.length > 0) + for ( + ; + null != + (u = this.tokenizer.rules.inline.reflinkSearch.exec(l)); + + ) + c.includes(u[0].slice(u[0].lastIndexOf("[") + 1, -1)) && + (l = + l.slice(0, u.index) + + "[" + + Mf("a", u[0].length - 2) + + "]" + + l.slice( + this.tokenizer.rules.inline.reflinkSearch.lastIndex, + )); + } + for ( + ; + null != (u = this.tokenizer.rules.inline.blockSkip.exec(l)); + + ) + l = + l.slice(0, u.index) + + "[" + + Mf("a", u[0].length - 2) + + "]" + + l.slice(this.tokenizer.rules.inline.blockSkip.lastIndex); + for ( + ; + null != (u = this.tokenizer.rules.inline.escapedEmSt.exec(l)); + + ) + l = + l.slice(0, u.index) + + "++" + + l.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex); + for (; e; ) + if ( + (i || (o = ""), + (i = !1), + !( + this.options.extensions && + this.options.extensions.inline && + this.options.extensions.inline.some(function (n) { + return ( + !!(t = n.call({ lexer: a }, e, s)) && + ((e = e.substring(t.raw.length)), s.push(t), !0) + ); + }) + )) + ) + if ((t = this.tokenizer.escape(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.tag(e))) + (e = e.substring(t.raw.length)), + (n = s[s.length - 1]) && + "text" === t.type && + "text" === n.type + ? ((n.raw += t.raw), (n.text += t.text)) + : s.push(t); + else if ((t = this.tokenizer.link(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.reflink(e, this.tokens.links))) + (e = e.substring(t.raw.length)), + (n = s[s.length - 1]) && + "text" === t.type && + "text" === n.type + ? ((n.raw += t.raw), (n.text += t.text)) + : s.push(t); + else if ((t = this.tokenizer.emStrong(e, l, o))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.codespan(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.br(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.del(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.autolink(e, Hf))) + (e = e.substring(t.raw.length)), s.push(t); + else if ( + this.state.inLink || + !(t = this.tokenizer.url(e, Hf)) + ) { + if ( + ((r = e), + this.options.extensions && + this.options.extensions.startInline && + (function () { + var t = 1 / 0, + n = e.slice(1), + u = void 0; + a.options.extensions.startInline.forEach( + function (e) { + "number" == + typeof (u = e.call({ lexer: this }, n)) && + u >= 0 && + (t = Math.min(t, u)); + }, + ), + t < 1 / 0 && t >= 0 && (r = e.substring(0, t + 1)); + })(), + (t = this.tokenizer.inlineText(r, Gf))) + ) + (e = e.substring(t.raw.length)), + "_" !== t.raw.slice(-1) && (o = t.raw.slice(-1)), + (i = !0), + (n = s[s.length - 1]) && "text" === n.type + ? ((n.raw += t.raw), (n.text += t.text)) + : s.push(t); + else if (e) { + var f = "Infinite loop on byte: " + e.charCodeAt(0); + if (this.options.silent) { + console.error(f); + break; + } + throw new Error(f); + } + } else (e = e.substring(t.raw.length)), s.push(t); + return s; + }, + }, + ], + [ + { + key: "rules", + get: function () { + return { block: qf, inline: Zf }; + }, + }, + { + key: "lex", + value: function (t, n) { + return new e(n).lex(t); + }, + }, + { + key: "lexInline", + value: function (t, n) { + return new e(n).inlineTokens(t); + }, + }, + ], + ), + e + ); + })(), + Vf = (function () { + function e(n) { + t(this, e), (this.options = n || df); + } + return ( + r(e, [ + { + key: "code", + value: function (e, t, n) { + var r = (t || "").match(/\S*/)[0]; + if (this.options.highlight) { + var u = this.options.highlight(e, r); + null != u && u !== e && ((n = !0), (e = u)); + } + return ( + (e = e.replace(/\n$/, "") + "\n"), + r + ? '
' +
+                  (n ? e : xf(e, !0)) +
+                  "
\n" + : "
" + (n ? e : xf(e, !0)) + "
\n" + ); + }, + }, + { + key: "blockquote", + value: function (e) { + return "
\n" + e + "
\n"; + }, + }, + { + key: "html", + value: function (e) { + return e; + }, + }, + { + key: "heading", + value: function (e, t, n, r) { + return this.options.headerIds + ? "' + + e + + "\n" + : "" + e + "\n"; + }, + }, + { + key: "hr", + value: function () { + return this.options.xhtml ? "
\n" : "
\n"; + }, + }, + { + key: "list", + value: function (e, t, n) { + var r = t ? "ol" : "ul"; + return ( + "<" + + r + + (t && 1 !== n ? ' start="' + n + '"' : "") + + ">\n" + + e + + "\n" + ); + }, + }, + { + key: "listitem", + value: function (e) { + return "
  • " + e + "
    • \n"; + }, + }, + { + key: "checkbox", + value: function (e) { + return ( + " " + ); + }, + }, + { + key: "paragraph", + value: function (e) { + return "

    " + e + "

    \n"; + }, + }, + { + key: "table", + value: function (e, t) { + return ( + t && (t = "" + t + ""), + "\n\n" + e + "\n" + t + "
    \n" + ); + }, + }, + { + key: "tablerow", + value: function (e) { + return "\n" + e + "\n"; + }, + }, + { + key: "tablecell", + value: function (e, t) { + var n = t.header ? "th" : "td"; + return ( + (t.align + ? "<" + n + ' align="' + t.align + '">' + : "<" + n + ">") + + e + + "\n" + ); + }, + }, + { + key: "strong", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "em", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "codespan", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "br", + value: function () { + return this.options.xhtml ? "
    " : "
    "; + }, + }, + { + key: "del", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "link", + value: function (e, t, n) { + if ( + null === (e = _f(this.options.sanitize, this.options.baseUrl, e)) + ) + return n; + var r = '    "); + }, + }, + { + key: "image", + value: function (e, t, n) { + if ( + null === (e = _f(this.options.sanitize, this.options.baseUrl, e)) + ) + return n; + var r = '' + n + '" : ">") + ); + }, + }, + { + key: "text", + value: function (e) { + return e; + }, + }, + ]), + e + ); + })(), + Yf = (function () { + function e() { + t(this, e); + } + return ( + r(e, [ + { + key: "strong", + value: function (e) { + return e; + }, + }, + { + key: "em", + value: function (e) { + return e; + }, + }, + { + key: "codespan", + value: function (e) { + return e; + }, + }, + { + key: "del", + value: function (e) { + return e; + }, + }, + { + key: "html", + value: function (e) { + return e; + }, + }, + { + key: "text", + value: function (e) { + return e; + }, + }, + { + key: "link", + value: function (e, t, n) { + return "" + n; + }, + }, + { + key: "image", + value: function (e, t, n) { + return "" + n; + }, + }, + { + key: "br", + value: function () { + return ""; + }, + }, + ]), + e + ); + })(), + Kf = (function () { + function e() { + t(this, e), (this.seen = {}); + } + return ( + r(e, [ + { + key: "serialize", + value: function (e) { + return e + .toLowerCase() + .trim() + .replace(/<[!\/a-z].*?>/gi, "") + .replace( + /[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g, + "", + ) + .replace(/\s/g, "-"); + }, + }, + { + key: "getNextSafeSlug", + value: function (e, t) { + var n = e, + r = 0; + if (this.seen.hasOwnProperty(n)) { + r = this.seen[e]; + do { + n = e + "-" + ++r; + } while (this.seen.hasOwnProperty(n)); + } + return t || ((this.seen[e] = r), (this.seen[n] = 0)), n; + }, + }, + { + key: "slug", + value: function (e) { + var t = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : {}, + n = this.serialize(e); + return this.getNextSafeSlug(n, t.dryrun); + }, + }, + ]), + e + ); + })(), + Xf = (function () { + function e(n) { + t(this, e), + (this.options = n || df), + (this.options.renderer = this.options.renderer || new Vf()), + (this.renderer = this.options.renderer), + (this.renderer.options = this.options), + (this.textRenderer = new Yf()), + (this.slugger = new Kf()); + } + return ( + r( + e, + [ + { + key: "parse", + value: function (e) { + var t, + n, + r, + u, + i, + o, + a, + s, + l, + c, + f, + p, + h, + D, + g, + d, + v, + y, + A, + m = + !(arguments.length > 1 && void 0 !== arguments[1]) || + arguments[1], + k = "", + E = e.length; + for (t = 0; t < E; t++) + if ( + ((c = e[t]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[c.type] + ) || + (!1 === + (A = this.options.extensions.renderers[c.type].call( + { parser: this }, + c, + )) && + [ + "space", + "hr", + "heading", + "code", + "table", + "blockquote", + "list", + "html", + "paragraph", + "text", + ].includes(c.type))) + ) + switch (c.type) { + case "space": + continue; + case "hr": + k += this.renderer.hr(); + continue; + case "heading": + k += this.renderer.heading( + this.parseInline(c.tokens), + c.depth, + bf(this.parseInline(c.tokens, this.textRenderer)), + this.slugger, + ); + continue; + case "code": + k += this.renderer.code(c.text, c.lang, c.escaped); + continue; + case "table": + for ( + s = "", a = "", u = c.header.length, n = 0; + n < u; + n++ + ) + a += this.renderer.tablecell( + this.parseInline(c.header[n].tokens), + { header: !0, align: c.align[n] }, + ); + for ( + s += this.renderer.tablerow(a), + l = "", + u = c.rows.length, + n = 0; + n < u; + n++ + ) { + for ( + a = "", i = (o = c.rows[n]).length, r = 0; + r < i; + r++ + ) + a += this.renderer.tablecell( + this.parseInline(o[r].tokens), + { header: !1, align: c.align[r] }, + ); + l += this.renderer.tablerow(a); + } + k += this.renderer.table(s, l); + continue; + case "blockquote": + (l = this.parse(c.tokens)), + (k += this.renderer.blockquote(l)); + continue; + case "list": + for ( + f = c.ordered, + p = c.start, + h = c.loose, + u = c.items.length, + l = "", + n = 0; + n < u; + n++ + ) + (d = (g = c.items[n]).checked), + (v = g.task), + (D = ""), + g.task && + ((y = this.renderer.checkbox(d)), + h + ? g.tokens.length > 0 && + "paragraph" === g.tokens[0].type + ? ((g.tokens[0].text = + y + " " + g.tokens[0].text), + g.tokens[0].tokens && + g.tokens[0].tokens.length > 0 && + "text" === g.tokens[0].tokens[0].type && + (g.tokens[0].tokens[0].text = + y + " " + g.tokens[0].tokens[0].text)) + : g.tokens.unshift({ type: "text", text: y }) + : (D += y)), + (D += this.parse(g.tokens, h)), + (l += this.renderer.listitem(D, v, d)); + k += this.renderer.list(l, f, p); + continue; + case "html": + k += this.renderer.html(c.text); + continue; + case "paragraph": + k += this.renderer.paragraph(this.parseInline(c.tokens)); + continue; + case "text": + for ( + l = c.tokens ? this.parseInline(c.tokens) : c.text; + t + 1 < E && "text" === e[t + 1].type; + + ) + l += + "\n" + + ((c = e[++t]).tokens + ? this.parseInline(c.tokens) + : c.text); + k += m ? this.renderer.paragraph(l) : l; + continue; + default: + var x = 'Token with "' + c.type + '" type was not found.'; + if (this.options.silent) return void console.error(x); + throw new Error(x); + } + else k += A || ""; + return k; + }, + }, + { + key: "parseInline", + value: function (e, t) { + t = t || this.renderer; + var n, + r, + u, + i = "", + o = e.length; + for (n = 0; n < o; n++) + if ( + ((r = e[n]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[r.type] + ) || + (!1 === + (u = this.options.extensions.renderers[r.type].call( + { parser: this }, + r, + )) && + [ + "escape", + "html", + "link", + "image", + "strong", + "em", + "codespan", + "br", + "del", + "text", + ].includes(r.type))) + ) + switch (r.type) { + case "escape": + i += t.text(r.text); + break; + case "html": + i += t.html(r.text); + break; + case "link": + i += t.link( + r.href, + r.title, + this.parseInline(r.tokens, t), + ); + break; + case "image": + i += t.image(r.href, r.title, r.text); + break; + case "strong": + i += t.strong(this.parseInline(r.tokens, t)); + break; + case "em": + i += t.em(this.parseInline(r.tokens, t)); + break; + case "codespan": + i += t.codespan(r.text); + break; + case "br": + i += t.br(); + break; + case "del": + i += t.del(this.parseInline(r.tokens, t)); + break; + case "text": + i += t.text(r.text); + break; + default: + var a = 'Token with "' + r.type + '" type was not found.'; + if (this.options.silent) return void console.error(a); + throw new Error(a); + } + else i += u || ""; + return i; + }, + }, + ], + [ + { + key: "parse", + value: function (t, n) { + return new e(n).parse(t); + }, + }, + { + key: "parseInline", + value: function (t, n) { + return new e(n).parseInline(t); + }, + }, + ], + ), + e + ); + })(); +function Wf(e, t, n) { + if (null == e) + throw new Error("marked(): input parameter is undefined or null"); + if ("string" != typeof e) + throw new Error( + "marked(): input parameter is of type " + + Object.prototype.toString.call(e) + + ", string expected", + ); + if ( + ("function" == typeof t && ((n = t), (t = null)), + Lf((t = zf({}, Wf.defaults, t || {}))), + n) + ) { + var r, + u = t.highlight; + try { + r = Qf.lex(e, t); + } catch (e) { + return n(e); + } + var i = function (e) { + var i; + if (!e) + try { + t.walkTokens && Wf.walkTokens(r, t.walkTokens), (i = Xf.parse(r, t)); + } catch (t) { + e = t; + } + return (t.highlight = u), e ? n(e) : n(null, i); + }; + if (!u || u.length < 3) return i(); + if ((delete t.highlight, !r.length)) return i(); + var o = 0; + return ( + Wf.walkTokens(r, function (e) { + "code" === e.type && + (o++, + setTimeout(function () { + u(e.text, e.lang, function (t, n) { + if (t) return i(t); + null != n && n !== e.text && ((e.text = n), (e.escaped = !0)), + 0 === --o && i(); + }); + }, 0)); + }), + void (0 === o && i()) + ); + } + try { + var a = Qf.lex(e, t); + return t.walkTokens && Wf.walkTokens(a, t.walkTokens), Xf.parse(a, t); + } catch (e) { + if ( + ((e.message += + "\nPlease report this to https://github.com/markedjs/marked."), + t.silent) + ) + return ( + "

    An error occurred:

    " + xf(e.message + "", !0) + "
    " + ); + throw e; + } +} +(Wf.options = Wf.setOptions = + function (e) { + var t; + return zf(Wf.defaults, e), (t = Wf.defaults), (df = t), Wf; + }), + (Wf.getDefaults = gf), + (Wf.defaults = df), + (Wf.use = function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var r, + u = zf.apply(void 0, [{}].concat(t)), + i = Wf.defaults.extensions || { renderers: {}, childTokens: {} }; + t.forEach(function (e) { + if ( + (e.extensions && + ((r = !0), + e.extensions.forEach(function (e) { + if (!e.name) throw new Error("extension name required"); + if (e.renderer) { + var t = i.renderers ? i.renderers[e.name] : null; + i.renderers[e.name] = t + ? function () { + for ( + var n = arguments.length, r = new Array(n), u = 0; + u < n; + u++ + ) + r[u] = arguments[u]; + var i = e.renderer.apply(this, r); + return !1 === i && (i = t.apply(this, r)), i; + } + : e.renderer; + } + if (e.tokenizer) { + if (!e.level || ("block" !== e.level && "inline" !== e.level)) + throw new Error("extension level must be 'block' or 'inline'"); + i[e.level] + ? i[e.level].unshift(e.tokenizer) + : (i[e.level] = [e.tokenizer]), + e.start && + ("block" === e.level + ? i.startBlock + ? i.startBlock.push(e.start) + : (i.startBlock = [e.start]) + : "inline" === e.level && + (i.startInline + ? i.startInline.push(e.start) + : (i.startInline = [e.start]))); + } + e.childTokens && (i.childTokens[e.name] = e.childTokens); + })), + e.renderer && + (function () { + var t = Wf.defaults.renderer || new Vf(), + n = function (n) { + var r = t[n]; + t[n] = function () { + for ( + var u = arguments.length, i = new Array(u), o = 0; + o < u; + o++ + ) + i[o] = arguments[o]; + var a = e.renderer[n].apply(t, i); + return !1 === a && (a = r.apply(t, i)), a; + }; + }; + for (var r in e.renderer) n(r); + u.renderer = t; + })(), + e.tokenizer && + (function () { + var t = Wf.defaults.tokenizer || new Uf(), + n = function (n) { + var r = t[n]; + t[n] = function () { + for ( + var u = arguments.length, i = new Array(u), o = 0; + o < u; + o++ + ) + i[o] = arguments[o]; + var a = e.tokenizer[n].apply(t, i); + return !1 === a && (a = r.apply(t, i)), a; + }; + }; + for (var r in e.tokenizer) n(r); + u.tokenizer = t; + })(), + e.walkTokens) + ) { + var t = Wf.defaults.walkTokens; + u.walkTokens = function (n) { + e.walkTokens.call(this, n), t && t.call(this, n); + }; + } + r && (u.extensions = i), Wf.setOptions(u); + }); + }), + (Wf.walkTokens = function (e, t) { + var n, + r = l(e); + try { + var u = function () { + var e = n.value; + switch ((t.call(Wf, e), e.type)) { + case "table": + var r, + u = l(e.header); + try { + for (u.s(); !(r = u.n()).done; ) { + var i = r.value; + Wf.walkTokens(i.tokens, t); + } + } catch (e) { + u.e(e); + } finally { + u.f(); + } + var o, + a = l(e.rows); + try { + for (a.s(); !(o = a.n()).done; ) { + var s, + c = l(o.value); + try { + for (c.s(); !(s = c.n()).done; ) { + var f = s.value; + Wf.walkTokens(f.tokens, t); + } + } catch (e) { + c.e(e); + } finally { + c.f(); + } + } + } catch (e) { + a.e(e); + } finally { + a.f(); + } + break; + case "list": + Wf.walkTokens(e.items, t); + break; + default: + Wf.defaults.extensions && + Wf.defaults.extensions.childTokens && + Wf.defaults.extensions.childTokens[e.type] + ? Wf.defaults.extensions.childTokens[e.type].forEach( + function (n) { + Wf.walkTokens(e[n], t); + }, + ) + : e.tokens && Wf.walkTokens(e.tokens, t); + } + }; + for (r.s(); !(n = r.n()).done; ) u(); + } catch (e) { + r.e(e); + } finally { + r.f(); + } + }), + (Wf.parseInline = function (e, t) { + if (null == e) + throw new Error( + "marked.parseInline(): input parameter is undefined or null", + ); + if ("string" != typeof e) + throw new Error( + "marked.parseInline(): input parameter is of type " + + Object.prototype.toString.call(e) + + ", string expected", + ); + Lf((t = zf({}, Wf.defaults, t || {}))); + try { + var n = Qf.lexInline(e, t); + return ( + t.walkTokens && Wf.walkTokens(n, t.walkTokens), Xf.parseInline(n, t) + ); + } catch (e) { + if ( + ((e.message += + "\nPlease report this to https://github.com/markedjs/marked."), + t.silent) + ) + return ( + "

    An error occurred:

    " + xf(e.message + "", !0) + "
    " + ); + throw e; + } + }), + (Wf.Parser = Xf), + (Wf.parser = Xf.parse), + (Wf.Renderer = Vf), + (Wf.TextRenderer = Yf), + (Wf.Lexer = Qf), + (Wf.lexer = Qf.lex), + (Wf.Tokenizer = Uf), + (Wf.Slugger = Kf), + (Wf.parse = Wf); +var Jf = /\[([\s\d,|-]*)\]/, + ep = { "&": "&", "<": "<", ">": ">", '"': """, "'": "'" }; +export default function () { + var t; + function n(e) { + var t = ( + e.querySelector("[data-template]") || + e.querySelector("script") || + e + ).textContent, + n = (t = t.replace(new RegExp("__SCRIPT_END__", "g"), "")).match( + /^\n?(\s*)/, + )[1].length, + r = t.match(/^\n?(\t*)/)[1].length; + return ( + r > 0 + ? (t = t.replace(new RegExp("\\n?\\t{" + r + "}", "g"), "\n")) + : n > 1 && (t = t.replace(new RegExp("\\n? {" + n + "}", "g"), "\n")), + t + ); + } + function r(e) { + for (var t = e.attributes, n = [], r = 0, u = t.length; r < u; r++) { + var i = t[r].name, + o = t[r].value; + /data\-(markdown|separator|vertical|notes)/gi.test(i) || + (o ? n.push(i + '="' + o + '"') : n.push(i)); + } + return n.join(" "); + } + function o(e) { + return ( + ((e = e || {}).separator = e.separator || "\r?\n---\r?\n"), + (e.notesSeparator = e.notesSeparator || "notes?:"), + (e.attributes = e.attributes || ""), + e + ); + } + function a(e, t) { + t = o(t); + var n = e.split(new RegExp(t.notesSeparator, "mgi")); + return ( + 2 === n.length && + (e = n[0] + '"), + '" + ); + } + function s(e, t) { + t = o(t); + for ( + var n, + r, + u, + i = new RegExp( + t.separator + (t.verticalSeparator ? "|" + t.verticalSeparator : ""), + "mg", + ), + s = new RegExp(t.separator), + l = 0, + c = !0, + f = []; + (n = i.exec(e)); + + ) + !(r = s.test(n[0])) && c && f.push([]), + (u = e.substring(l, n.index)), + r && c ? f.push(u) : f[f.length - 1].push(u), + (l = i.lastIndex), + (c = r); + (c ? f : f[f.length - 1]).push(e.substring(l)); + for (var p = "", h = 0, D = f.length; h < D; h++) + f[h] instanceof Array + ? ((p += "
    "), + f[h].forEach(function (e) { + p += "
    " + a(e, t) + "
    "; + }), + (p += "
    ")) + : (p += + "
    " + + a(f[h], t) + + "
    "); + return p; + } + function l(e) { + return new Promise(function (t) { + var u = []; + [].slice + .call( + e.querySelectorAll( + "section[data-markdown]:not([data-markdown-parsed])", + ), + ) + .forEach(function (e, t) { + e.getAttribute("data-markdown").length + ? u.push( + (function (e) { + return new Promise(function (t, n) { + var r = new XMLHttpRequest(), + u = e.getAttribute("data-markdown"), + i = e.getAttribute("data-charset"); + null != i && + "" != i && + r.overrideMimeType("text/html; charset=" + i), + (r.onreadystatechange = function (e, r) { + 4 === r.readyState && + ((r.status >= 200 && r.status < 300) || 0 === r.status + ? t(r, u) + : n(r, u)); + }.bind(this, e, r)), + r.open("GET", u, !0); + try { + r.send(); + } catch (e) { + console.warn( + "Failed to get the Markdown file " + + u + + ". Make sure that the presentation and the file are served by a HTTP server and the file can be found there. " + + e, + ), + t(r, u); + } + }); + })(e).then( + function (t, n) { + e.outerHTML = s(t.responseText, { + separator: e.getAttribute("data-separator"), + verticalSeparator: e.getAttribute( + "data-separator-vertical", + ), + notesSeparator: e.getAttribute("data-separator-notes"), + attributes: r(e), + }); + }, + function (t, n) { + e.outerHTML = + '
    ERROR: The attempt to fetch ' + + n + + " failed with HTTP status " + + t.status + + ".Check your browser's JavaScript console for more details.

    Remember that you need to serve the presentation HTML from a HTTP server.

    "; + }, + ), + ) + : (e.outerHTML = s(n(e), { + separator: e.getAttribute("data-separator"), + verticalSeparator: e.getAttribute("data-separator-vertical"), + notesSeparator: e.getAttribute("data-separator-notes"), + attributes: r(e), + })); + }), + Promise.all(u).then(t); + }); + } + function c(e, t, n) { + var r, + u, + i = new RegExp(n, "mg"), + o = new RegExp('([^"= ]+?)="([^"]+?)"|(data-[^"= ]+?)(?=[" ])', "mg"), + a = e.nodeValue; + if ((r = i.exec(a))) { + var s = r[1]; + for ( + a = a.substring(0, r.index) + a.substring(i.lastIndex), e.nodeValue = a; + (u = o.exec(s)); + + ) + u[2] ? t.setAttribute(u[1], u[2]) : t.setAttribute(u[3], ""); + return !0; + } + return !1; + } + function f(e, t, n, r, u) { + if (null != t && null != t.childNodes && t.childNodes.length > 0) + for (var i = t, o = 0; o < t.childNodes.length; o++) { + var a = t.childNodes[o]; + if (o > 0) + for (var s = o - 1; s >= 0; ) { + var l = t.childNodes[s]; + if ("function" == typeof l.setAttribute && "BR" != l.tagName) { + i = l; + break; + } + s -= 1; + } + var p = e; + "section" == a.nodeName && ((p = a), (i = a)), + ("function" != typeof a.setAttribute && + a.nodeType != Node.COMMENT_NODE) || + f(p, a, i, r, u); + } + t.nodeType == Node.COMMENT_NODE && 0 == c(t, n, r) && c(t, e, u); + } + function p() { + var e = t + .getRevealElement() + .querySelectorAll("[data-markdown]:not([data-markdown-parsed])"); + return ( + [].slice.call(e).forEach(function (e) { + e.setAttribute("data-markdown-parsed", !0); + var t = e.querySelector("aside.notes"), + r = n(e); + (e.innerHTML = Wf(r)), + f( + e, + e, + null, + e.getAttribute("data-element-attributes") || + e.parentNode.getAttribute("data-element-attributes") || + "\\.element\\s*?(.+?)$", + e.getAttribute("data-attributes") || + e.parentNode.getAttribute("data-attributes") || + "\\.slide:\\s*?(\\S.+?)$", + ), + t && e.appendChild(t); + }), + Promise.resolve() + ); + } + return { + id: "markdown", + init: function (n) { + var r = (t = n).getConfig().markdown || {}, + o = r.renderer, + a = r.animateLists, + s = i(r, ["renderer", "animateLists"]); + return ( + o || + ((o = new Wf.Renderer()).code = function (e, t) { + var n = ""; + return ( + Jf.test(t) && + ((n = t.match(Jf)[1].trim()), + (n = 'data-line-numbers="'.concat(n, '"')), + (t = t.replace(Jf, "").trim())), + (e = e.replace(/([&<>'"])/g, function (e) { + return ep[e]; + })), + "
    ')
+                .concat(e, "
    ") + ); + }), + !0 === a && + (o.listitem = function (e) { + return '
  • '.concat(e, "
    • "); + }), + Wf.setOptions( + (function (t) { + for (var n = 1; n < arguments.length; n++) { + var r = null != arguments[n] ? arguments[n] : {}; + n % 2 + ? e(Object(r), !0).forEach(function (e) { + u(t, e, r[e]); + }) + : Object.getOwnPropertyDescriptors + ? 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    ") + ); + }), + !0 === a && + (o.listitem = function (e) { + return '
  • '.concat(e, "
    • "); + }), + gp.setOptions( + (function (t) { + for (var n = 1; n < arguments.length; n++) { + var r = null != arguments[n] ? arguments[n] : {}; + n % 2 + ? e(Object(r), !0).forEach(function (e) { + u(t, e, r[e]); + }) + : Object.getOwnPropertyDescriptors + ? Object.defineProperties( + t, + Object.getOwnPropertyDescriptors(r), + ) + : e(Object(r)).forEach(function (e) { + Object.defineProperty( + t, + e, + Object.getOwnPropertyDescriptor(r, e), + ); + }); + } + return t; + })({ renderer: o }, s), + ), + l(t.getRevealElement()).then(p) + ); + }, + processSlides: l, + convertSlides: p, + slidify: s, + marked: gp, + }; + }; +}); diff --git a/content/slides/slides_files/libs/revealjs/plugin/markdown/plugin.js b/content/slides/slides_files/libs/revealjs/plugin/markdown/plugin.js index db1cbf2..e78356c 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/markdown/plugin.js +++ b/content/slides/slides_files/libs/revealjs/plugin/markdown/plugin.js @@ -4,472 +4,508 @@ * of external markdown documents. */ -import { marked } from 'marked'; +import { marked } from "marked"; -const DEFAULT_SLIDE_SEPARATOR = '\r?\n---\r?\n', - DEFAULT_NOTES_SEPARATOR = 'notes?:', - DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR = '\\\.element\\\s*?(.+?)$', - DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR = '\\\.slide:\\\s*?(\\\S.+?)$'; +const DEFAULT_SLIDE_SEPARATOR = "\r?\n---\r?\n", + DEFAULT_NOTES_SEPARATOR = "notes?:", + DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR = "\\.element\\s*?(.+?)$", + DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR = "\\.slide:\\s*?(\\S.+?)$"; -const SCRIPT_END_PLACEHOLDER = '__SCRIPT_END__'; +const SCRIPT_END_PLACEHOLDER = "__SCRIPT_END__"; const CODE_LINE_NUMBER_REGEX = /\[([\s\d,|-]*)\]/; const HTML_ESCAPE_MAP = { - '&': '&', - '<': '<', - '>': '>', - '"': '"', - "'": ''' + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", }; const Plugin = () => { - - // The reveal.js instance this plugin is attached to - let deck; - - /** - * Retrieves the markdown contents of a slide section - * element. Normalizes leading tabs/whitespace. - */ - function getMarkdownFromSlide( section ) { - - // look for a ' ); - - var leadingWs = text.match( /^\n?(\s*)/ )[1].length, - leadingTabs = text.match( /^\n?(\t*)/ )[1].length; - - if( leadingTabs > 0 ) { - text = text.replace( new RegExp('\\n?\\t{' + leadingTabs + '}','g'), '\n' ); - } - else if( leadingWs > 1 ) { - text = text.replace( new RegExp('\\n? {' + leadingWs + '}', 'g'), '\n' ); - } - - return text; - - } - - /** - * Given a markdown slide section element, this will - * return all arguments that aren't related to markdown - * parsing. Used to forward any other user-defined arguments - * to the output markdown slide. - */ - function getForwardedAttributes( section ) { - - var attributes = section.attributes; - var result = []; - - for( var i = 0, len = attributes.length; i < len; i++ ) { - var name = attributes[i].name, - value = attributes[i].value; - - // disregard attributes that are used for markdown loading/parsing - if( /data\-(markdown|separator|vertical|notes)/gi.test( name ) ) continue; - - if( value ) { - result.push( name + '="' + value + '"' ); - } - else { - result.push( name ); - } - } - - return result.join( ' ' ); - - } - - /** - * Inspects the given options and fills out default - * values for what's not defined. - */ - function getSlidifyOptions( options ) { - - options = options || {}; - options.separator = options.separator || DEFAULT_SLIDE_SEPARATOR; - options.notesSeparator = options.notesSeparator || DEFAULT_NOTES_SEPARATOR; - options.attributes = options.attributes || ''; - - return options; - - } - - /** - * Helper function for constructing a markdown slide. - */ - function createMarkdownSlide( content, options ) { - - options = getSlidifyOptions( options ); - - var notesMatch = content.split( new RegExp( options.notesSeparator, 'mgi' ) ); - - if( notesMatch.length === 2 ) { - content = notesMatch[0] + ''; - } - - // prevent script end tags in the content from interfering - // with parsing - content = content.replace( /<\/script>/g, SCRIPT_END_PLACEHOLDER ); - - return ''; - - } - - /** - * Parses a data string into multiple slides based - * on the passed in separator arguments. - */ - function slidify( markdown, options ) { - - options = getSlidifyOptions( options ); - - var separatorRegex = new RegExp( options.separator + ( options.verticalSeparator ? '|' + options.verticalSeparator : '' ), 'mg' ), - horizontalSeparatorRegex = new RegExp( options.separator ); - - var matches, - lastIndex = 0, - isHorizontal, - wasHorizontal = true, - content, - sectionStack = []; - - // iterate until all blocks between separators are stacked up - while( matches = separatorRegex.exec( markdown ) ) { - var notes = null; - - // determine direction (horizontal by default) - isHorizontal = horizontalSeparatorRegex.test( matches[0] ); - - if( !isHorizontal && wasHorizontal ) { - // create vertical stack - sectionStack.push( [] ); - } - - // pluck slide content from markdown input - content = markdown.substring( lastIndex, matches.index ); - - if( isHorizontal && wasHorizontal ) { - // add to horizontal stack - sectionStack.push( content ); - } - else { - // add to vertical stack - sectionStack[sectionStack.length-1].push( content ); - } - - lastIndex = separatorRegex.lastIndex; - wasHorizontal = isHorizontal; - } - - // add the remaining slide - ( wasHorizontal ? sectionStack : sectionStack[sectionStack.length-1] ).push( markdown.substring( lastIndex ) ); - - var markdownSections = ''; - - // flatten the hierarchical stack, and insert
    tags - for( var i = 0, len = sectionStack.length; i < len; i++ ) { - // vertical - if( sectionStack[i] instanceof Array ) { - markdownSections += '
    '; - - sectionStack[i].forEach( function( child ) { - markdownSections += '
    ' + createMarkdownSlide( child, options ) + '
    '; - } ); - - markdownSections += '
    '; - } - else { - markdownSections += '
    ' + createMarkdownSlide( sectionStack[i], options ) + '
    '; - } - } - - return markdownSections; - - } - - /** - * Parses any current data-markdown slides, splits - * multi-slide markdown into separate sections and - * handles loading of external markdown. - */ - function processSlides( scope ) { - - return new Promise( function( resolve ) { - - var externalPromises = []; - - [].slice.call( scope.querySelectorAll( 'section[data-markdown]:not([data-markdown-parsed])') ).forEach( function( section, i ) { - - if( section.getAttribute( 'data-markdown' ).length ) { - - externalPromises.push( loadExternalMarkdown( section ).then( - - // Finished loading external file - function( xhr, url ) { - section.outerHTML = slidify( xhr.responseText, { - separator: section.getAttribute( 'data-separator' ), - verticalSeparator: section.getAttribute( 'data-separator-vertical' ), - notesSeparator: section.getAttribute( 'data-separator-notes' ), - attributes: getForwardedAttributes( section ) - }); - }, - - // Failed to load markdown - function( xhr, url ) { - section.outerHTML = '
    ' + - 'ERROR: The attempt to fetch ' + url + ' failed with HTTP status ' + xhr.status + '.' + - 'Check your browser\'s JavaScript console for more details.' + - '

    Remember that you need to serve the presentation HTML from a HTTP server.

    ' + - '
    '; - } - - ) ); - - } - else { - - section.outerHTML = slidify( getMarkdownFromSlide( section ), { - separator: section.getAttribute( 'data-separator' ), - verticalSeparator: section.getAttribute( 'data-separator-vertical' ), - notesSeparator: section.getAttribute( 'data-separator-notes' ), - attributes: getForwardedAttributes( section ) - }); - - } - - }); - - Promise.all( externalPromises ).then( resolve ); - - } ); - - } - - function loadExternalMarkdown( section ) { - - return new Promise( function( resolve, reject ) { - - var xhr = new XMLHttpRequest(), - url = section.getAttribute( 'data-markdown' ); - - var datacharset = section.getAttribute( 'data-charset' ); - - // see https://developer.mozilla.org/en-US/docs/Web/API/element.getAttribute#Notes - if( datacharset != null && datacharset != '' ) { - xhr.overrideMimeType( 'text/html; charset=' + datacharset ); - } - - xhr.onreadystatechange = function( section, xhr ) { - if( xhr.readyState === 4 ) { - // file protocol yields status code 0 (useful for local debug, mobile applications etc.) - if ( ( xhr.status >= 200 && xhr.status < 300 ) || xhr.status === 0 ) { - - resolve( xhr, url ); - - } - else { - - reject( xhr, url ); - - } - } - }.bind( this, section, xhr ); - - xhr.open( 'GET', url, true ); - - try { - xhr.send(); - } - catch ( e ) { - console.warn( 'Failed to get the Markdown file ' + url + '. Make sure that the presentation and the file are served by a HTTP server and the file can be found there. ' + e ); - resolve( xhr, url ); - } - - } ); - - } - - /** - * Check if a node value has the attributes pattern. - * If yes, extract it and add that value as one or several attributes - * to the target element. - * - * You need Cache Killer on Chrome to see the effect on any FOM transformation - * directly on refresh (F5) - * http://stackoverflow.com/questions/5690269/disabling-chrome-cache-for-website-development/7000899#answer-11786277 - */ - function addAttributeInElement( node, elementTarget, separator ) { - - var mardownClassesInElementsRegex = new RegExp( separator, 'mg' ); - var mardownClassRegex = new RegExp( "([^\"= ]+?)=\"([^\"]+?)\"|(data-[^\"= ]+?)(?=[\" ])", 'mg' ); - var nodeValue = node.nodeValue; - var matches, - matchesClass; - if( matches = mardownClassesInElementsRegex.exec( nodeValue ) ) { - - var classes = matches[1]; - nodeValue = nodeValue.substring( 0, matches.index ) + nodeValue.substring( mardownClassesInElementsRegex.lastIndex ); - node.nodeValue = nodeValue; - while( matchesClass = mardownClassRegex.exec( classes ) ) { - if( matchesClass[2] ) { - elementTarget.setAttribute( matchesClass[1], matchesClass[2] ); - } else { - elementTarget.setAttribute( matchesClass[3], "" ); - } - } - return true; - } - return false; - } - - /** - * Add attributes to the parent element of a text node, - * or the element of an attribute node. - */ - function addAttributes( section, element, previousElement, separatorElementAttributes, separatorSectionAttributes ) { - - if ( element != null && element.childNodes != undefined && element.childNodes.length > 0 ) { - var previousParentElement = element; - for( var i = 0; i < element.childNodes.length; i++ ) { - var childElement = element.childNodes[i]; - if ( i > 0 ) { - var j = i - 1; - while ( j >= 0 ) { - var aPreviousChildElement = element.childNodes[j]; - if ( typeof aPreviousChildElement.setAttribute == 'function' && aPreviousChildElement.tagName != "BR" ) { - previousParentElement = aPreviousChildElement; - break; - } - j = j - 1; - } - } - var parentSection = section; - if( childElement.nodeName == "section" ) { - parentSection = childElement ; - previousParentElement = childElement ; - } - if ( typeof childElement.setAttribute == 'function' || childElement.nodeType == Node.COMMENT_NODE ) { - addAttributes( parentSection, childElement, previousParentElement, separatorElementAttributes, separatorSectionAttributes ); - } - } - } - - if ( element.nodeType == Node.COMMENT_NODE ) { - if ( addAttributeInElement( element, previousElement, separatorElementAttributes ) == false ) { - addAttributeInElement( element, section, separatorSectionAttributes ); - } - } - } - - /** - * Converts any current data-markdown slides in the - * DOM to HTML. - */ - function convertSlides() { - - var sections = deck.getRevealElement().querySelectorAll( '[data-markdown]:not([data-markdown-parsed])'); - - [].slice.call( sections ).forEach( function( section ) { - - section.setAttribute( 'data-markdown-parsed', true ) - - var notes = section.querySelector( 'aside.notes' ); - var markdown = getMarkdownFromSlide( section ); - - section.innerHTML = marked( markdown ); - addAttributes( section, section, null, section.getAttribute( 'data-element-attributes' ) || - section.parentNode.getAttribute( 'data-element-attributes' ) || - DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR, - section.getAttribute( 'data-attributes' ) || - section.parentNode.getAttribute( 'data-attributes' ) || - DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR); - - // If there were notes, we need to re-add them after - // having overwritten the section's HTML - if( notes ) { - section.appendChild( notes ); - } - - } ); - - return Promise.resolve(); - - } - - function escapeForHTML( input ) { - - return input.replace( /([&<>'"])/g, char => HTML_ESCAPE_MAP[char] ); - - } - - return { - id: 'markdown', - - /** - * Starts processing and converting Markdown within the - * current reveal.js deck. - */ - init: function( reveal ) { - - deck = reveal; - - let { renderer, animateLists, ...markedOptions } = deck.getConfig().markdown || {}; - - if( !renderer ) { - renderer = new marked.Renderer(); - - renderer.code = ( code, language ) => { - - // Off by default - let lineNumbers = ''; - - // Users can opt in to show line numbers and highlight - // specific lines. - // ```javascript [] show line numbers - // ```javascript [1,4-8] highlights lines 1 and 4-8 - if( CODE_LINE_NUMBER_REGEX.test( language ) ) { - lineNumbers = language.match( CODE_LINE_NUMBER_REGEX )[1].trim(); - lineNumbers = `data-line-numbers="${lineNumbers}"`; - language = language.replace( CODE_LINE_NUMBER_REGEX, '' ).trim(); - } - - // Escape before this gets injected into the DOM to - // avoid having the HTML parser alter our code before - // highlight.js is able to read it - code = escapeForHTML( code ); - - return `
    ${code}
    `; - }; - } - - if( animateLists === true ) { - renderer.listitem = text => `
  • ${text}
    • `; - } - - marked.setOptions( { - renderer, - ...markedOptions - } ); - - return processSlides( deck.getRevealElement() ).then( convertSlides ); - - }, - - // TODO: Do these belong in the API? - processSlides: processSlides, - convertSlides: convertSlides, - slidify: slidify, - marked: marked - } - + // The reveal.js instance this plugin is attached to + let deck; + + /** + * Retrieves the markdown contents of a slide section + * element. Normalizes leading tabs/whitespace. + */ + function getMarkdownFromSlide(section) { + // look for a "); + + var leadingWs = text.match(/^\n?(\s*)/)[1].length, + leadingTabs = text.match(/^\n?(\t*)/)[1].length; + + if (leadingTabs > 0) { + text = text.replace( + new RegExp("\\n?\\t{" + leadingTabs + "}", "g"), + "\n", + ); + } else if (leadingWs > 1) { + text = text.replace(new RegExp("\\n? {" + leadingWs + "}", "g"), "\n"); + } + + return text; + } + + /** + * Given a markdown slide section element, this will + * return all arguments that aren't related to markdown + * parsing. Used to forward any other user-defined arguments + * to the output markdown slide. + */ + function getForwardedAttributes(section) { + var attributes = section.attributes; + var result = []; + + for (var i = 0, len = attributes.length; i < len; i++) { + var name = attributes[i].name, + value = attributes[i].value; + + // disregard attributes that are used for markdown loading/parsing + if (/data\-(markdown|separator|vertical|notes)/gi.test(name)) continue; + + if (value) { + result.push(name + '="' + value + '"'); + } else { + result.push(name); + } + } + + return result.join(" "); + } + + /** + * Inspects the given options and fills out default + * values for what's not defined. + */ + function getSlidifyOptions(options) { + options = options || {}; + options.separator = options.separator || DEFAULT_SLIDE_SEPARATOR; + options.notesSeparator = options.notesSeparator || DEFAULT_NOTES_SEPARATOR; + options.attributes = options.attributes || ""; + + return options; + } + + /** + * Helper function for constructing a markdown slide. + */ + function createMarkdownSlide(content, options) { + options = getSlidifyOptions(options); + + var notesMatch = content.split(new RegExp(options.notesSeparator, "mgi")); + + if (notesMatch.length === 2) { + content = + notesMatch[0] + + '"; + } + + // prevent script end tags in the content from interfering + // with parsing + content = content.replace(/<\/script>/g, SCRIPT_END_PLACEHOLDER); + + return '"; + } + + /** + * Parses a data string into multiple slides based + * on the passed in separator arguments. + */ + function slidify(markdown, options) { + options = getSlidifyOptions(options); + + var separatorRegex = new RegExp( + options.separator + + (options.verticalSeparator ? "|" + options.verticalSeparator : ""), + "mg", + ), + horizontalSeparatorRegex = new RegExp(options.separator); + + var matches, + lastIndex = 0, + isHorizontal, + wasHorizontal = true, + content, + sectionStack = []; + + // iterate until all blocks between separators are stacked up + while ((matches = separatorRegex.exec(markdown))) { + var notes = null; + + // determine direction (horizontal by default) + isHorizontal = horizontalSeparatorRegex.test(matches[0]); + + if (!isHorizontal && wasHorizontal) { + // create vertical stack + sectionStack.push([]); + } + + // pluck slide content from markdown input + content = markdown.substring(lastIndex, matches.index); + + if (isHorizontal && wasHorizontal) { + // add to horizontal stack + sectionStack.push(content); + } else { + // add to vertical stack + sectionStack[sectionStack.length - 1].push(content); + } + + lastIndex = separatorRegex.lastIndex; + wasHorizontal = isHorizontal; + } + + // add the remaining slide + (wasHorizontal ? sectionStack : sectionStack[sectionStack.length - 1]).push( + markdown.substring(lastIndex), + ); + + var markdownSections = ""; + + // flatten the hierarchical stack, and insert
    tags + for (var i = 0, len = sectionStack.length; i < len; i++) { + // vertical + if (sectionStack[i] instanceof Array) { + markdownSections += "
    "; + + sectionStack[i].forEach(function (child) { + markdownSections += + "
    " + + createMarkdownSlide(child, options) + + "
    "; + }); + + markdownSections += "
    "; + } else { + markdownSections += + "
    " + + createMarkdownSlide(sectionStack[i], options) + + "
    "; + } + } + + return markdownSections; + } + + /** + * Parses any current data-markdown slides, splits + * multi-slide markdown into separate sections and + * handles loading of external markdown. + */ + function processSlides(scope) { + return new Promise(function (resolve) { + var externalPromises = []; + + [].slice + .call( + scope.querySelectorAll( + "section[data-markdown]:not([data-markdown-parsed])", + ), + ) + .forEach(function (section, i) { + if (section.getAttribute("data-markdown").length) { + externalPromises.push( + loadExternalMarkdown(section).then( + // Finished loading external file + function (xhr, url) { + section.outerHTML = slidify(xhr.responseText, { + separator: section.getAttribute("data-separator"), + verticalSeparator: section.getAttribute( + "data-separator-vertical", + ), + notesSeparator: section.getAttribute( + "data-separator-notes", + ), + attributes: getForwardedAttributes(section), + }); + }, + + // Failed to load markdown + function (xhr, url) { + section.outerHTML = + '
    ' + + "ERROR: The attempt to fetch " + + url + + " failed with HTTP status " + + xhr.status + + "." + + "Check your browser's JavaScript console for more details." + + "

    Remember that you need to serve the presentation HTML from a HTTP server.

    " + + "
    "; + }, + ), + ); + } else { + section.outerHTML = slidify(getMarkdownFromSlide(section), { + separator: section.getAttribute("data-separator"), + verticalSeparator: section.getAttribute( + "data-separator-vertical", + ), + notesSeparator: section.getAttribute("data-separator-notes"), + attributes: getForwardedAttributes(section), + }); + } + }); + + Promise.all(externalPromises).then(resolve); + }); + } + + function loadExternalMarkdown(section) { + return new Promise(function (resolve, reject) { + var xhr = new XMLHttpRequest(), + url = section.getAttribute("data-markdown"); + + var datacharset = section.getAttribute("data-charset"); + + // see https://developer.mozilla.org/en-US/docs/Web/API/element.getAttribute#Notes + if (datacharset != null && datacharset != "") { + xhr.overrideMimeType("text/html; charset=" + datacharset); + } + + xhr.onreadystatechange = function (section, xhr) { + if (xhr.readyState === 4) { + // file protocol yields status code 0 (useful for local debug, mobile applications etc.) + if ((xhr.status >= 200 && xhr.status < 300) || xhr.status === 0) { + resolve(xhr, url); + } else { + reject(xhr, url); + } + } + }.bind(this, section, xhr); + + xhr.open("GET", url, true); + + try { + xhr.send(); + } catch (e) { + console.warn( + "Failed to get the Markdown file " + + url + + ". Make sure that the presentation and the file are served by a HTTP server and the file can be found there. " + + e, + ); + resolve(xhr, url); + } + }); + } + + /** + * Check if a node value has the attributes pattern. + * If yes, extract it and add that value as one or several attributes + * to the target element. + * + * You need Cache Killer on Chrome to see the effect on any FOM transformation + * directly on refresh (F5) + * http://stackoverflow.com/questions/5690269/disabling-chrome-cache-for-website-development/7000899#answer-11786277 + */ + function addAttributeInElement(node, elementTarget, separator) { + var mardownClassesInElementsRegex = new RegExp(separator, "mg"); + var mardownClassRegex = new RegExp( + '([^"= ]+?)="([^"]+?)"|(data-[^"= ]+?)(?=[" ])', + "mg", + ); + var nodeValue = node.nodeValue; + var matches, matchesClass; + if ((matches = mardownClassesInElementsRegex.exec(nodeValue))) { + var classes = matches[1]; + nodeValue = + nodeValue.substring(0, matches.index) + + nodeValue.substring(mardownClassesInElementsRegex.lastIndex); + node.nodeValue = nodeValue; + while ((matchesClass = mardownClassRegex.exec(classes))) { + if (matchesClass[2]) { + elementTarget.setAttribute(matchesClass[1], matchesClass[2]); + } else { + elementTarget.setAttribute(matchesClass[3], ""); + } + } + return true; + } + return false; + } + + /** + * Add attributes to the parent element of a text node, + * or the element of an attribute node. + */ + function addAttributes( + section, + element, + previousElement, + separatorElementAttributes, + separatorSectionAttributes, + ) { + if ( + element != null && + element.childNodes != undefined && + element.childNodes.length > 0 + ) { + var previousParentElement = element; + for (var i = 0; i < element.childNodes.length; i++) { + var childElement = element.childNodes[i]; + if (i > 0) { + var j = i - 1; + while (j >= 0) { + var aPreviousChildElement = element.childNodes[j]; + if ( + typeof aPreviousChildElement.setAttribute == "function" && + aPreviousChildElement.tagName != "BR" + ) { + previousParentElement = aPreviousChildElement; + break; + } + j = j - 1; + } + } + var parentSection = section; + if (childElement.nodeName == "section") { + parentSection = childElement; + previousParentElement = childElement; + } + if ( + typeof childElement.setAttribute == "function" || + childElement.nodeType == Node.COMMENT_NODE + ) { + addAttributes( + parentSection, + childElement, + previousParentElement, + separatorElementAttributes, + separatorSectionAttributes, + ); + } + } + } + + if (element.nodeType == Node.COMMENT_NODE) { + if ( + addAttributeInElement( + element, + previousElement, + separatorElementAttributes, + ) == false + ) { + addAttributeInElement(element, section, separatorSectionAttributes); + } + } + } + + /** + * Converts any current data-markdown slides in the + * DOM to HTML. + */ + function convertSlides() { + var sections = deck + .getRevealElement() + .querySelectorAll("[data-markdown]:not([data-markdown-parsed])"); + + [].slice.call(sections).forEach(function (section) { + section.setAttribute("data-markdown-parsed", true); + + var notes = section.querySelector("aside.notes"); + var markdown = getMarkdownFromSlide(section); + + section.innerHTML = marked(markdown); + addAttributes( + section, + section, + null, + section.getAttribute("data-element-attributes") || + section.parentNode.getAttribute("data-element-attributes") || + DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR, + section.getAttribute("data-attributes") || + section.parentNode.getAttribute("data-attributes") || + DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR, + ); + + // If there were notes, we need to re-add them after + // having overwritten the section's HTML + if (notes) { + section.appendChild(notes); + } + }); + + return Promise.resolve(); + } + + function escapeForHTML(input) { + return input.replace(/([&<>'"])/g, (char) => HTML_ESCAPE_MAP[char]); + } + + return { + id: "markdown", + + /** + * Starts processing and converting Markdown within the + * current reveal.js deck. + */ + init: function (reveal) { + deck = reveal; + + let { renderer, animateLists, ...markedOptions } = + deck.getConfig().markdown || {}; + + if (!renderer) { + renderer = new marked.Renderer(); + + renderer.code = (code, language) => { + // Off by default + let lineNumbers = ""; + + // Users can opt in to show line numbers and highlight + // specific lines. + // ```javascript [] show line numbers + // ```javascript [1,4-8] highlights lines 1 and 4-8 + if (CODE_LINE_NUMBER_REGEX.test(language)) { + lineNumbers = language.match(CODE_LINE_NUMBER_REGEX)[1].trim(); + lineNumbers = `data-line-numbers="${lineNumbers}"`; + language = language.replace(CODE_LINE_NUMBER_REGEX, "").trim(); + } + + // Escape before this gets injected into the DOM to + // avoid having the HTML parser alter our code before + // highlight.js is able to read it + code = escapeForHTML(code); + + return `
    ${code}
    `; + }; + } + + if (animateLists === true) { + renderer.listitem = (text) => `
  • ${text}
    • `; + } + + marked.setOptions({ + renderer, + ...markedOptions, + }); + + return processSlides(deck.getRevealElement()).then(convertSlides); + }, + + // TODO: Do these belong in the API? + processSlides: processSlides, + convertSlides: convertSlides, + slidify: slidify, + marked: marked, + }; }; export default Plugin; diff --git a/content/slides/slides_files/libs/revealjs/plugin/math/katex.js b/content/slides/slides_files/libs/revealjs/plugin/math/katex.js index a8b47c4..3b927b6 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/math/katex.js +++ b/content/slides/slides_files/libs/revealjs/plugin/math/katex.js @@ -6,91 +6,87 @@ * @author Gerhard Burger */ export const KaTeX = () => { - let deck; - - let defaultOptions = { - version: 'latest', - delimiters: [ - {left: '$$', right: '$$', display: true}, // Note: $$ has to come before $ - {left: '$', right: '$', display: false}, - {left: '\\(', right: '\\)', display: false}, - {left: '\\[', right: '\\]', display: true} - ], - ignoredTags: ['script', 'noscript', 'style', 'textarea', 'pre'] - } - - const loadCss = src => { - let link = document.createElement('link'); - link.rel = 'stylesheet'; - link.href = src; - document.head.appendChild(link); - }; - - /** - * Loads a JavaScript file and returns a Promise for when it is loaded - * Credits: https://aaronsmith.online/easily-load-an-external-script-using-javascript/ - */ - const loadScript = src => { - return new Promise((resolve, reject) => { - const script = document.createElement('script') - script.type = 'text/javascript' - script.onload = resolve - script.onerror = reject - script.src = src - document.head.append(script) - }) - }; - - async function loadScripts(urls) { - for(const url of urls) { - await loadScript(url); - } - } - - return { - id: 'katex', - - init: function (reveal) { - - deck = reveal; - - let revealOptions = deck.getConfig().katex || {}; - - let options = {...defaultOptions, ...revealOptions}; - const {local, version, extensions, ...katexOptions} = options; - - let baseUrl = options.local || 'https://cdn.jsdelivr.net/npm/katex'; - let versionString = options.local ? '' : '@' + options.version; - - let cssUrl = baseUrl + versionString + '/dist/katex.min.css'; - let katexUrl = baseUrl + versionString + '/dist/katex.min.js'; - let mhchemUrl = baseUrl + versionString + '/dist/contrib/mhchem.min.js' - let karUrl = baseUrl + versionString + '/dist/contrib/auto-render.min.js'; - - let katexScripts = [katexUrl]; - if(options.extensions && options.extensions.includes("mhchem")) { - katexScripts.push(mhchemUrl); - } - katexScripts.push(karUrl); - - const renderMath = () => { - renderMathInElement(reveal.getSlidesElement(), katexOptions); - deck.layout(); - } - - loadCss(cssUrl); - - // For some reason dynamically loading with defer attribute doesn't result in the expected behavior, the below code does - loadScripts(katexScripts).then(() => { - if( deck.isReady() ) { - renderMath(); - } - else { - deck.on( 'ready', renderMath.bind( this ) ); - } - }); - - } - } - + let deck; + + let defaultOptions = { + version: "latest", + delimiters: [ + { left: "$$", right: "$$", display: true }, // Note: $$ has to come before $ + { left: "$", right: "$", display: false }, + { left: "\\(", right: "\\)", display: false }, + { left: "\\[", right: "\\]", display: true }, + ], + ignoredTags: ["script", "noscript", "style", "textarea", "pre"], + }; + + const loadCss = (src) => { + let link = document.createElement("link"); + link.rel = "stylesheet"; + link.href = src; + document.head.appendChild(link); + }; + + /** + * Loads a JavaScript file and returns a Promise for when it is loaded + * Credits: https://aaronsmith.online/easily-load-an-external-script-using-javascript/ + */ + const loadScript = (src) => { + return new Promise((resolve, reject) => { + const script = document.createElement("script"); + script.type = "text/javascript"; + script.onload = resolve; + script.onerror = reject; + script.src = src; + document.head.append(script); + }); + }; + + async function loadScripts(urls) { + for (const url of urls) { + await loadScript(url); + } + } + + return { + id: "katex", + + init: function (reveal) { + deck = reveal; + + let revealOptions = deck.getConfig().katex || {}; + + let options = { ...defaultOptions, ...revealOptions }; + const { local, version, extensions, ...katexOptions } = options; + + let baseUrl = options.local || "https://cdn.jsdelivr.net/npm/katex"; + let versionString = options.local ? "" : "@" + options.version; + + let cssUrl = baseUrl + versionString + "/dist/katex.min.css"; + let katexUrl = baseUrl + versionString + "/dist/katex.min.js"; + let mhchemUrl = baseUrl + versionString + "/dist/contrib/mhchem.min.js"; + let karUrl = baseUrl + versionString + "/dist/contrib/auto-render.min.js"; + + let katexScripts = [katexUrl]; + if (options.extensions && options.extensions.includes("mhchem")) { + katexScripts.push(mhchemUrl); + } + katexScripts.push(karUrl); + + const renderMath = () => { + renderMathInElement(reveal.getSlidesElement(), katexOptions); + deck.layout(); + }; + + loadCss(cssUrl); + + // For some reason dynamically loading with defer attribute doesn't result in the expected behavior, the below code does + loadScripts(katexScripts).then(() => { + if (deck.isReady()) { + renderMath(); + } else { + deck.on("ready", renderMath.bind(this)); + } + }); + }, + }; }; diff --git a/content/slides/slides_files/libs/revealjs/plugin/math/math.esm.js 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((Xi.prototype = Hi(t)), + (n = new Xi()), + (Xi.prototype = null), + (n[Qi] = t)) + : (n = Bi()), + void 0 === e ? n : zi(n, e) + ); + }, + Zi = H, + ta = In("unscopables"), + ea = Array.prototype; +null == ea[ta] && Zi.f(ea, ta, { configurable: !0, value: Vi(null) }); +var na = se.includes, + ra = function (t) { + ea[ta][t] = !0; + }; +ze( + { target: "Array", proto: !0 }, + { + includes: function (t) { + return na(this, t, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +), + ra("includes"); +var oa = w, + ia = p, + aa = In("match"), + ca = function (t) { + var e; + return oa(t) && (void 0 !== (e = t[aa]) ? !!e : "RegExp" == ia(t)); + }, + ua = In("match"), + fa = function (t) { + if (ca(t)) throw TypeError("The method doesn't accept regular expressions"); + return t; + }, + sa = y; +ze( + { + target: "String", + proto: !0, + forced: !(function (t) { + var e = /./; + try { + "/./"[t](e); + } catch (n) { + try { + return (e[ua] = !1), "/./"[t](e); + } catch (t) {} + } + return !1; + })("includes"), + }, + { + includes: function (t) { + return !!~String(sa(this)).indexOf( + fa(t), + arguments.length > 1 ? arguments[1] : void 0, + ); + }, + }, +); +var la = function () { + var t, + e = { + messageStyle: "none", + tex2jax: { + inlineMath: [ + ["$", "$"], + ["\\(", "\\)"], + ], + skipTags: ["script", "noscript", "style", "textarea", "pre"], + }, + skipStartupTypeset: !0, + }; + return { + id: "mathjax2", + init: function (n) { + var r = (t = n).getConfig().mathjax2 || t.getConfig().math || {}, + o = on(on({}, e), r), + i = + (o.mathjax || "https://cdn.jsdelivr.net/npm/mathjax@2/MathJax.js") + + "?config=" + + (o.config || "TeX-AMS_HTML-full"); + (o.tex2jax = on(on({}, e.tex2jax), r.tex2jax)), + (o.mathjax = o.config = null), + (function (t, e) { + var n = this, + r = document.querySelector("head"), + o = document.createElement("script"); + (o.type = "text/javascript"), (o.src = t); + var i = function () { + "function" == typeof e && (e.call(), (e = null)); + }; + (o.onload = i), + (o.onreadystatechange = function () { + "loaded" === n.readyState && i(); + }), + r.appendChild(o); + })(i, function () { + MathJax.Hub.Config(o), + MathJax.Hub.Queue(["Typeset", MathJax.Hub, t.getRevealElement()]), + MathJax.Hub.Queue(t.layout), + t.on("slidechanged", function (t) { + MathJax.Hub.Queue(["Typeset", MathJax.Hub, t.currentSlide]); + }); + }); + }, + }; + }, + pa = la, + ha = (Plugin = Object.assign(pa(), { + KaTeX: function () { + var t, + e = { + version: "latest", + delimiters: [ + { left: "$$", right: "$$", display: !0 }, + { left: "$", right: "$", display: !1 }, + { left: "\\(", right: "\\)", display: !1 }, + { left: "\\[", right: "\\]", display: !0 }, + ], + ignoredTags: ["script", "noscript", "style", "textarea", "pre"], + }, + n = function (t) { + return new Promise(function (e, n) { + var r = document.createElement("script"); + (r.type = "text/javascript"), + (r.onload = e), + (r.onerror = n), + (r.src = t), + document.head.append(r); + }); + }; + function r() { + return (r = cn( + regeneratorRuntime.mark(function t(e) { + var r, o, i; + return regeneratorRuntime.wrap( + function (t) { + for (;;) + switch ((t.prev = t.next)) { + case 0: + (r = ln(e)), (t.prev = 1), r.s(); + case 3: + if ((o = r.n()).done) { + t.next = 9; + break; + } + return (i = o.value), (t.next = 7), n(i); + case 7: + t.next = 3; + break; + case 9: + t.next = 14; + break; + case 11: + (t.prev = 11), (t.t0 = t.catch(1)), r.e(t.t0); + case 14: + return (t.prev = 14), r.f(), t.finish(14); + case 17: + case "end": + return t.stop(); + } + }, + t, + null, + [[1, 11, 14, 17]], + ); + }), + )).apply(this, arguments); + } + return { + id: "katex", + init: function (n) { + var o = this, + i = (t = n).getConfig().katex || {}, + a = on(on({}, e), i); + a.local, a.version, a.extensions; + var c = fn(a, ["local", "version", "extensions"]), + u = a.local || "https://cdn.jsdelivr.net/npm/katex", + f = a.local ? "" : "@" + a.version, + s = u + f + "/dist/katex.min.css", + l = u + f + "/dist/contrib/mhchem.min.js", + p = u + f + "/dist/contrib/auto-render.min.js", + h = [u + f + "/dist/katex.min.js"]; + a.extensions && a.extensions.includes("mhchem") && h.push(l), + h.push(p); + var v, + d, + y = function () { + renderMathInElement(n.getSlidesElement(), c), t.layout(); + }; + (v = s), + ((d = document.createElement("link")).rel = "stylesheet"), + (d.href = v), + document.head.appendChild(d), + (function (t) { + return r.apply(this, arguments); + })(h).then(function () { + t.isReady() ? y() : t.on("ready", y.bind(o)); + }); + }, + }; + }, + MathJax2: la, + MathJax3: function () { + var t = { + tex: { + inlineMath: [ + ["$", "$"], + ["\\(", "\\)"], + ], + }, + options: { + skipHtmlTags: ["script", "noscript", "style", "textarea", "pre"], + }, + startup: { + ready: function () { + MathJax.startup.defaultReady(), + MathJax.startup.promise.then(function () { + Reveal.layout(); + }); + }, + }, + }; + return { + id: "mathjax3", + init: function (e) { + var n = e.getConfig().mathjax3 || {}, + r = on(on({}, t), n); + (r.tex = on(on({}, t.tex), n.tex)), + (r.options = on(on({}, t.options), n.options)), + (r.startup = on(on({}, t.startup), n.startup)); + var o = + r.mathjax || + "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"; + (r.mathjax = null), + (window.MathJax = r), + (function (t, e) { + var n = document.createElement("script"); + (n.type = "text/javascript"), + (n.id = "MathJax-script"), + (n.src = t), + (n.async = !0), + (n.onload = function () { + "function" == typeof e && (e.call(), (e = null)); + }), + document.head.appendChild(n); + })(o, function () { + Reveal.addEventListener("slidechanged", function (t) { + MathJax.typeset(); + }); + }); + }, + }; + }, + })); +export default ha; diff --git a/content/slides/slides_files/libs/revealjs/plugin/math/math.js b/content/slides/slides_files/libs/revealjs/plugin/math/math.js index 4ad5234..fb0e43a 100644 --- 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"\\d{1,9}\\".concat(h.slice(-1)) : "\\".concat(h)), + this.options.pedantic && (h = g ? h : "[*+-]"); + for ( + var m = new RegExp( + "^( {0,3}".concat(h, ")((?: [^\\n]*)?(?:\\n|$))"), + ); + t && + ((f = !1), (e = m.exec(t))) && + !this.rules.block.hr.test(t); + + ) { + if ( + ((n = e[0]), + (t = t.substring(n.length)), + (l = e[2].split("\n", 1)[0]), + (c = t.split("\n", 1)[0]), + this.options.pedantic + ? ((u = 2), (d = l.trimLeft())) + : ((u = (u = e[2].search(/[^ ]/)) > 4 ? 1 : u), + (d = l.slice(u)), + (u += e[1].length)), + (o = !1), + !l && + /^ *$/.test(c) && + ((n += c + "\n"), + (t = t.substring(c.length + 1)), + (f = !0)), + !f) + ) + for ( + var v = new RegExp( + "^ {0,".concat( + Math.min(3, u - 1), + "}(?:[*+-]|\\d{1,9}[.)])", + ), + ); + t && + ((l = p = t.split("\n", 1)[0]), + this.options.pedantic && + (l = l.replace(/^ {1,4}(?=( {4})*[^ ])/g, " ")), + !v.test(l)); + + ) { + if (l.search(/[^ ]/) >= u || !l.trim()) + d += "\n" + l.slice(u); + else { + if (o) break; + d += "\n" + l; + } + o || l.trim() || (o = !0), + (n += p + "\n"), + (t = t.substring(p.length + 1)); + } + D.loose || + (s ? (D.loose = !0) : /\n *\n *$/.test(n) && (s = !0)), + this.options.gfm && + (r = /^\[[ xX]\] /.exec(d)) && + ((i = "[ ] " !== r[0]), + (d = d.replace(/^\[[ xX]\] +/, ""))), + D.items.push({ + type: "list_item", + raw: n, + task: !!r, + checked: i, + loose: !1, + text: d, + }), + (D.raw += n); + } + (D.items[D.items.length - 1].raw = n.trimRight()), + (D.items[D.items.length - 1].text = d.trimRight()), + (D.raw = D.raw.trimRight()); + var y = D.items.length; + for (a = 0; a < y; a++) { + (this.lexer.state.top = !1), + (D.items[a].tokens = this.lexer.blockTokens( + D.items[a].text, + [], + )); + var k = D.items[a].tokens.filter(function (t) { + return "space" === t.type; + }), + E = k.every(function (t) { + var e, + n = 0, + r = pr(t.raw.split("")); + try { + for (r.s(); !(e = r.n()).done; ) { + if (("\n" === e.value && (n += 1), n > 1)) return !0; + } + } catch (t) { + r.e(t); + } finally { + r.f(); + } + return !1; + }); + !D.loose && + k.length && + E && + ((D.loose = !0), (D.items[a].loose = !0)); + } + return D; + } + }, + }, + { + key: "html", + value: function (t) { + var e = this.rules.block.html.exec(t); + if (e) { + var n = { + type: "html", + raw: e[0], + pre: + !this.options.sanitizer && + ("pre" === e[1] || "script" === e[1] || "style" === e[1]), + text: e[0], + }; + return ( + this.options.sanitize && + ((n.type = "paragraph"), + (n.text = this.options.sanitizer + ? this.options.sanitizer(e[0]) + : Fo(e[0])), + (n.tokens = []), + this.lexer.inline(n.text, n.tokens)), + n + ); + } + }, + }, + { + key: "def", + value: function (t) { + var e = this.rules.block.def.exec(t); + if (e) + return ( + e[3] && (e[3] = e[3].substring(1, e[3].length - 1)), + { + type: "def", + tag: e[1].toLowerCase().replace(/\s+/g, " "), + raw: e[0], + href: e[2], + title: e[3], + } + ); + }, + }, + { + key: "table", + value: function (t) { + var e = this.rules.block.table.exec(t); + if (e) { + var n = { + type: "table", + header: No(e[1]).map(function (t) { + return { text: t }; + }), + align: e[2].replace(/^ *|\| *$/g, "").split(/ *\| */), + rows: + e[3] && e[3].trim() + ? e[3].replace(/\n[ \t]*$/, "").split("\n") + : [], + }; + if (n.header.length === n.align.length) { + n.raw = e[0]; + var r, + i, + u, + a, + o = n.align.length; + for (r = 0; r < o; r++) + /^ *-+: *$/.test(n.align[r]) + ? (n.align[r] = "right") + : /^ *:-+: *$/.test(n.align[r]) + ? (n.align[r] = "center") + : /^ *:-+ *$/.test(n.align[r]) + ? (n.align[r] = "left") + : (n.align[r] = null); + for (o = n.rows.length, r = 0; r < o; r++) + n.rows[r] = No(n.rows[r], n.header.length).map(function (t) { + return { text: t }; + }); + for (o = n.header.length, i = 0; i < o; i++) + (n.header[i].tokens = []), + this.lexer.inlineTokens( + n.header[i].text, + n.header[i].tokens, + ); + for (o = n.rows.length, i = 0; i < o; i++) + for (a = n.rows[i], u = 0; u < a.length; u++) + (a[u].tokens = []), + this.lexer.inlineTokens(a[u].text, a[u].tokens); + return n; + } + } + }, + }, + { + key: "lheading", + value: function (t) { + var e = this.rules.block.lheading.exec(t); + if (e) { + var n = { + type: "heading", + raw: e[0], + depth: "=" === e[2].charAt(0) ? 1 : 2, + text: e[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "paragraph", + value: function (t) { + var e = this.rules.block.paragraph.exec(t); + if (e) { + var n = { + type: "paragraph", + raw: e[0], + text: + "\n" === e[1].charAt(e[1].length - 1) + ? e[1].slice(0, -1) + : e[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "text", + value: function (t) { + var e = this.rules.block.text.exec(t); + if (e) { + var n = { type: "text", raw: e[0], text: e[0], tokens: [] }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "escape", + value: function (t) { + var e = this.rules.inline.escape.exec(t); + if (e) return { type: "escape", raw: e[0], text: Fo(e[1]) }; + }, + }, + { + key: "tag", + value: function (t) { + var e = this.rules.inline.tag.exec(t); + if (e) + return ( + !this.lexer.state.inLink && /^    /i.test(e[0]) && + (this.lexer.state.inLink = !1), + !this.lexer.state.inRawBlock && + /^<(pre|code|kbd|script)(\s|>)/i.test(e[0]) + ? (this.lexer.state.inRawBlock = !0) + : this.lexer.state.inRawBlock && + /^<\/(pre|code|kbd|script)(\s|>)/i.test(e[0]) && + (this.lexer.state.inRawBlock = !1), + { + type: this.options.sanitize ? "text" : "html", + raw: e[0], + inLink: this.lexer.state.inLink, + inRawBlock: this.lexer.state.inRawBlock, + text: this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(e[0]) + : Fo(e[0]) + : e[0], + } + ); + }, + }, + { + key: "link", + value: function (t) { + var e = this.rules.inline.link.exec(t); + if (e) { + var n = e[2].trim(); + if (!this.options.pedantic && /^$/.test(n)) return; + var r = Uo(n.slice(0, -1), "\\"); + if ((n.length - r.length) % 2 == 0) return; + } else { + var i = (function (t, e) { + if (-1 === t.indexOf(e[1])) return -1; + for (var n = t.length, r = 0, i = 0; i < n; i++) + if ("\\" === t[i]) i++; + else if (t[i] === e[0]) r++; + else if (t[i] === e[1] && --r < 0) return i; + return -1; + })(e[2], "()"); + if (i > -1) { + var u = (0 === e[0].indexOf("!") ? 5 : 4) + e[1].length + i; + (e[2] = e[2].substring(0, i)), + (e[0] = e[0].substring(0, u).trim()), + (e[3] = ""); + } + } + var a = e[2], + o = ""; + if (this.options.pedantic) { + var s = /^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(a); + s && ((a = s[1]), (o = s[3])); + } else o = e[3] ? e[3].slice(1, -1) : ""; + return ( + (a = a.trim()), + /^$/.test(n) + ? a.slice(1) + : a.slice(1, -1)), + Ho( + e, + { + href: a ? a.replace(this.rules.inline._escapes, "$1") : a, + title: o ? o.replace(this.rules.inline._escapes, "$1") : o, + }, + e[0], + this.lexer, + ) + ); + } + }, + }, + { + key: "reflink", + value: function (t, e) { + var n; + if ( + (n = this.rules.inline.reflink.exec(t)) || + (n = this.rules.inline.nolink.exec(t)) + ) { + var r = (n[2] || n[1]).replace(/\s+/g, " "); + if (!(r = e[r.toLowerCase()]) || !r.href) { + var i = n[0].charAt(0); + return { type: "text", raw: i, text: i }; + } + return Ho(n, r, n[0], this.lexer); + } + }, + }, + { + key: "emStrong", + value: function (t, e) { + var n = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "", + r = this.rules.inline.emStrong.lDelim.exec(t); + if ( + r && + (!r[3] || + !n.match( + 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+ )) + ) { + var i = r[1] || r[2] || ""; + if ( + !i || + (i && ("" === n || this.rules.inline.punctuation.exec(n))) + ) { + var u, + a, + o = r[0].length - 1, + s = o, + l = 0, + c = + "*" === r[0][0] + ? this.rules.inline.emStrong.rDelimAst + : this.rules.inline.emStrong.rDelimUnd; + for ( + c.lastIndex = 0, e = e.slice(-1 * t.length + o); + null != (r = c.exec(e)); + + ) + if ((u = r[1] || r[2] || r[3] || r[4] || r[5] || r[6])) + if (((a = u.length), r[3] || r[4])) s += a; + else if (!((r[5] || r[6]) && o % 3) || (o + a) % 3) { + if (!((s -= a) > 0)) { + if ( + ((a = Math.min(a, a + s + l)), Math.min(o, a) % 2) + ) { + var p = t.slice(1, o + r.index + a); + return { + type: "em", + raw: t.slice(0, o + r.index + a + 1), + text: p, + tokens: this.lexer.inlineTokens(p, []), + }; + } + var d = t.slice(2, o + r.index + a - 1); + return { + type: "strong", + raw: t.slice(0, o + r.index + a + 1), + text: d, + tokens: this.lexer.inlineTokens(d, []), + }; + } + } else l += a; + } + } + }, + }, + { + key: "codespan", + value: function (t) { + var e = this.rules.inline.code.exec(t); + if (e) { + var n = e[2].replace(/\n/g, " "), + r = /[^ ]/.test(n), + i = /^ /.test(n) && / $/.test(n); + return ( + r && i && (n = n.substring(1, n.length - 1)), + (n = Fo(n, !0)), + { type: "codespan", raw: e[0], text: n } + ); + } + }, + }, + { + key: "br", + value: function (t) { + var e = this.rules.inline.br.exec(t); + if (e) return { type: "br", raw: e[0] }; + }, + }, + { + key: "del", + value: function (t) { + var e = this.rules.inline.del.exec(t); + if (e) + return { + type: "del", + raw: e[0], + text: e[2], + tokens: this.lexer.inlineTokens(e[2], []), + }; + }, + }, + { + key: "autolink", + value: function (t, e) { + var n, + r, + i = this.rules.inline.autolink.exec(t); + if (i) + return ( + (r = + "@" === i[2] + ? "mailto:" + (n = Fo(this.options.mangle ? e(i[1]) : i[1])) + : (n = Fo(i[1]))), + { + type: "link", + raw: i[0], + text: n, + href: r, + tokens: [{ type: "text", raw: n, text: n }], + } + ); + }, + }, + { + key: "url", + value: function (t, e) { + var n; + if ((n = this.rules.inline.url.exec(t))) { + var r, i; + if ("@" === n[2]) + i = "mailto:" + (r = Fo(this.options.mangle ? e(n[0]) : n[0])); + else { + var u; + do { + (u = n[0]), + (n[0] = this.rules.inline._backpedal.exec(n[0])[0]); + } while (u !== n[0]); + (r = Fo(n[0])), (i = "www." === n[1] ? "http://" + r : r); + } + return { + type: "link", + raw: n[0], + text: r, + href: i, + tokens: [{ type: "text", raw: r, text: r }], + }; + } + }, + }, + { + key: "inlineText", + value: function (t, e) { + var n, + r = this.rules.inline.text.exec(t); + if (r) + return ( + (n = this.lexer.state.inRawBlock + ? this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(r[0]) + : Fo(r[0]) + : r[0] + : Fo(this.options.smartypants ? e(r[0]) : r[0])), + { type: "text", raw: r[0], text: n } + ); + }, + }, + ]), + t + ); + })(), + Jo = { + newline: /^(?: *(?:\n|$))+/, + code: /^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/, + fences: + /^ {0,3}(`{3,}(?=[^`\n]*\n)|~{3,})([^\n]*)\n(?:|([\s\S]*?)\n)(?: {0,3}\1[~`]* *(?=\n|$)|$)/, + hr: /^ {0,3}((?:- *){3,}|(?:_ *){3,}|(?:\* *){3,})(?:\n+|$)/, + heading: /^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/, + blockquote: /^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/, + list: /^( {0,3}bull)( [^\n]+?)?(?:\n|$)/, + html: "^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|\\n*|$)|\\n*|$)|)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))", + def: /^ {0,3}\[(label)\]: *(?:\n *)?]+)>?(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/, + table: Mo, + lheading: /^([^\n]+)\n {0,3}(=+|-+) *(?:\n+|$)/, + _paragraph: + /^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/, + text: /^[^\n]+/, + _label: /(?!\s*\])(?:\\.|[^\[\]\\])+/, + _title: /(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/, + }; +(Jo.def = _o(Jo.def) + .replace("label", Jo._label) + .replace("title", Jo._title) + .getRegex()), + (Jo.bullet = /(?:[*+-]|\d{1,9}[.)])/), + (Jo.listItemStart = _o(/^( *)(bull) */) + .replace("bull", Jo.bullet) + .getRegex()), + (Jo.list = _o(Jo.list) + .replace(/bull/g, Jo.bullet) + .replace( + "hr", + "\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))", + ) + .replace("def", "\\n+(?=" + Jo.def.source + ")") + .getRegex()), + (Jo._tag = + "address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul"), + (Jo._comment = /|$)/), + (Jo.html = _o(Jo.html, "i") + .replace("comment", Jo._comment) + .replace("tag", Jo._tag) + .replace( + "attribute", + / +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/, + ) + .getRegex()), + (Jo.paragraph = _o(Jo._paragraph) + .replace("hr", Jo.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("|table", "") + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", Jo._tag) + .getRegex()), + (Jo.blockquote = _o(Jo.blockquote) + .replace("paragraph", Jo.paragraph) + .getRegex()), + (Jo.normal = jo({}, Jo)), + (Jo.gfm = jo({}, Jo.normal, { + table: + "^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)", + })), + (Jo.gfm.table = _o(Jo.gfm.table) + .replace("hr", Jo.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("blockquote", " {0,3}>") + .replace("code", " {4}[^\\n]") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", Jo._tag) + .getRegex()), + (Jo.gfm.paragraph = _o(Jo._paragraph) + .replace("hr", Jo.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("table", Jo.gfm.table) + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", Jo._tag) + .getRegex()), + (Jo.pedantic = jo({}, Jo.normal, { + html: _o( + "^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+? *(?:\\n{2,}|\\s*$)|\\s]*)*?/?> *(?:\\n{2,}|\\s*$))", + ) + .replace("comment", Jo._comment) + .replace( + /tag/g, + "(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b", + ) + .getRegex(), + def: /^ *\[([^\]]+)\]: *]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/, + heading: /^(#{1,6})(.*)(?:\n+|$)/, + fences: Mo, + paragraph: _o(Jo.normal._paragraph) + .replace("hr", Jo.hr) + .replace("heading", " *#{1,6} *[^\n]") + .replace("lheading", Jo.lheading) + .replace("blockquote", " {0,3}>") + .replace("|fences", "") + .replace("|list", "") + .replace("|html", "") + .getRegex(), + })); +var Vo = { + escape: /^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/, + autolink: /^<(scheme:[^\s\x00-\x1f<>]*|email)>/, + url: Mo, + tag: "^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^", + link: /^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/, + reflink: /^!?\[(label)\]\[(ref)\]/, + nolink: /^!?\[(ref)\](?:\[\])?/, + reflinkSearch: "reflink|nolink(?!\\()", + emStrong: { + lDelim: /^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/, + rDelimAst: + /^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/, + rDelimUnd: + /^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/, + }, + code: /^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/, + br: /^( {2,}|\\)\n(?!\s*$)/, + del: Mo, + text: /^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\ 0.5 && (n = "x" + n.toString(16)), + (r += "&#" + n + ";"); + return r; +} +(Vo._punctuation = "!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~"), + (Vo.punctuation = _o(Vo.punctuation) + .replace(/punctuation/g, Vo._punctuation) + .getRegex()), + (Vo.blockSkip = /\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g), + (Vo.escapedEmSt = /\\\*|\\_/g), + (Vo._comment = _o(Jo._comment).replace("(?:--\x3e|$)", "--\x3e").getRegex()), + (Vo.emStrong.lDelim = _o(Vo.emStrong.lDelim) + .replace(/punct/g, Vo._punctuation) + .getRegex()), + (Vo.emStrong.rDelimAst = _o(Vo.emStrong.rDelimAst, "g") + .replace(/punct/g, Vo._punctuation) + .getRegex()), + (Vo.emStrong.rDelimUnd = _o(Vo.emStrong.rDelimUnd, "g") + .replace(/punct/g, Vo._punctuation) + .getRegex()), + (Vo._escapes = /\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g), + (Vo._scheme = /[a-zA-Z][a-zA-Z0-9+.-]{1,31}/), + (Vo._email = + /[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/), + (Vo.autolink = _o(Vo.autolink) + .replace("scheme", Vo._scheme) + .replace("email", Vo._email) + .getRegex()), + (Vo._attribute = + /\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/), + (Vo.tag = _o(Vo.tag) + .replace("comment", Vo._comment) + .replace("attribute", Vo._attribute) + .getRegex()), + (Vo._label = /(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/), + (Vo._href = /<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/), + (Vo._title = /"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/), + (Vo.link = _o(Vo.link) + .replace("label", Vo._label) + .replace("href", Vo._href) + .replace("title", Vo._title) + .getRegex()), + (Vo.reflink = _o(Vo.reflink) + .replace("label", Vo._label) + .replace("ref", Jo._label) + .getRegex()), + (Vo.nolink = _o(Vo.nolink).replace("ref", Jo._label).getRegex()), + (Vo.reflinkSearch = _o(Vo.reflinkSearch, "g") + .replace("reflink", Vo.reflink) + .replace("nolink", Vo.nolink) + .getRegex()), + (Vo.normal = jo({}, Vo)), + (Vo.pedantic = jo({}, Vo.normal, { + strong: { + start: /^__|\*\*/, + middle: /^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/, + endAst: /\*\*(?!\*)/g, + endUnd: /__(?!_)/g, + }, + em: { + start: /^_|\*/, + middle: /^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/, + endAst: /\*(?!\*)/g, + endUnd: /_(?!_)/g, + }, + link: _o(/^!?\[(label)\]\((.*?)\)/) + .replace("label", Vo._label) + .getRegex(), + reflink: _o(/^!?\[(label)\]\s*\[([^\]]*)\]/) + .replace("label", Vo._label) + .getRegex(), + })), + (Vo.gfm = jo({}, Vo.normal, { + escape: _o(Vo.escape).replace("])", "~|])").getRegex(), + _extended_email: + /[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/, + url: /^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/, + _backpedal: + /(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/, + del: /^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/, + text: /^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\ 1 && void 0 !== arguments[1] + ? arguments[1] + : []; + for (this.options.pedantic && (t = t.replace(/^ +$/gm, "")); t; ) + if ( + !( + this.options.extensions && + this.options.extensions.block && + this.options.extensions.block.some(function (n) { + return ( + !!(e = n.call({ lexer: u }, t, a)) && + ((t = t.substring(e.raw.length)), a.push(e), !0) + ); + }) + ) + ) + if ((e = this.tokenizer.space(t))) + (t = t.substring(e.raw.length)), + 1 === e.raw.length && a.length > 0 + ? (a[a.length - 1].raw += "\n") + : a.push(e); + else if ((e = this.tokenizer.code(t))) + (t = t.substring(e.raw.length)), + !(n = a[a.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? a.push(e) + : ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.text), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((e = this.tokenizer.fences(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.heading(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.hr(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.blockquote(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.list(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.html(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.def(t))) + (t = t.substring(e.raw.length)), + !(n = a[a.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? this.tokens.links[e.tag] || + (this.tokens.links[e.tag] = { + href: e.href, + title: e.title, + }) + : ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.raw), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((e = this.tokenizer.table(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.lheading(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ( + ((r = t), + this.options.extensions && + this.options.extensions.startBlock && + (function () { + var e = 1 / 0, + n = t.slice(1), + i = void 0; + u.options.extensions.startBlock.forEach(function (t) { + "number" == typeof (i = t.call({ lexer: this }, n)) && + i >= 0 && + (e = Math.min(e, i)); + }), + e < 1 / 0 && e >= 0 && (r = t.substring(0, e + 1)); + })(), + this.state.top && (e = this.tokenizer.paragraph(r))) + ) + (n = a[a.length - 1]), + i && "paragraph" === n.type + ? ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : a.push(e), + (i = r.length !== t.length), + (t = t.substring(e.raw.length)); + else if ((e = this.tokenizer.text(t))) + (t = t.substring(e.raw.length)), + (n = a[a.length - 1]) && "text" === n.type + ? ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : a.push(e); + else if (t) { + var o = "Infinite loop on byte: " + t.charCodeAt(0); + if (this.options.silent) { + console.error(o); + break; + } + throw new Error(o); + } + return (this.state.top = !0), a; + }, + }, + { + key: "inline", + value: function (t, e) { + this.inlineQueue.push({ src: t, tokens: e }); + }, + }, + { + key: "inlineTokens", + value: function (t) { + var e, + n, + r, + i, + u, + a, + o = this, + s = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : [], + l = t; + if (this.tokens.links) { + var c = Object.keys(this.tokens.links); + if (c.length > 0) + for ( + ; + null != + (i = this.tokenizer.rules.inline.reflinkSearch.exec(l)); + + ) + c.includes(i[0].slice(i[0].lastIndexOf("[") + 1, -1)) && + (l = + l.slice(0, i.index) + + "[" + + Zo("a", i[0].length - 2) + + "]" + + l.slice( + this.tokenizer.rules.inline.reflinkSearch.lastIndex, + )); + } + for ( + ; + null != (i = this.tokenizer.rules.inline.blockSkip.exec(l)); + + ) + l = + l.slice(0, i.index) + + "[" + + Zo("a", i[0].length - 2) + + "]" + + l.slice(this.tokenizer.rules.inline.blockSkip.lastIndex); + for ( + ; + null != (i = this.tokenizer.rules.inline.escapedEmSt.exec(l)); + + ) + l = + l.slice(0, i.index) + + "++" + + l.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex); + for (; t; ) + if ( + (u || (a = ""), + (u = !1), + !( + this.options.extensions && + this.options.extensions.inline && + this.options.extensions.inline.some(function (n) { + return ( + !!(e = n.call({ lexer: o }, t, s)) && + ((t = t.substring(e.raw.length)), s.push(e), !0) + ); + }) + )) + ) + if ((e = this.tokenizer.escape(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.tag(t))) + (t = t.substring(e.raw.length)), + (n = s[s.length - 1]) && + "text" === e.type && + "text" === n.type + ? ((n.raw += e.raw), (n.text += e.text)) + : s.push(e); + else if ((e = this.tokenizer.link(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.reflink(t, this.tokens.links))) + (t = t.substring(e.raw.length)), + (n = s[s.length - 1]) && + "text" === e.type && + "text" === n.type + ? ((n.raw += e.raw), (n.text += e.text)) + : s.push(e); + else if ((e = this.tokenizer.emStrong(t, l, a))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.codespan(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.br(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.del(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.autolink(t, Qo))) + (t = t.substring(e.raw.length)), s.push(e); + else if ( + this.state.inLink || + !(e = this.tokenizer.url(t, Qo)) + ) { + if ( + ((r = t), + this.options.extensions && + this.options.extensions.startInline && + (function () { + var e = 1 / 0, + n = t.slice(1), + i = void 0; + o.options.extensions.startInline.forEach( + function (t) { + "number" == + typeof (i = t.call({ lexer: this }, n)) && + i >= 0 && + (e = Math.min(e, i)); + }, + ), + e < 1 / 0 && e >= 0 && (r = t.substring(0, e + 1)); + })(), + (e = this.tokenizer.inlineText(r, Ko))) + ) + (t = t.substring(e.raw.length)), + "_" !== e.raw.slice(-1) && (a = e.raw.slice(-1)), + (u = !0), + (n = s[s.length - 1]) && "text" === n.type + ? ((n.raw += e.raw), (n.text += e.text)) + : s.push(e); + else if (t) { + var p = "Infinite loop on byte: " + t.charCodeAt(0); + if (this.options.silent) { + console.error(p); + break; + } + throw new Error(p); + } + } else (t = t.substring(e.raw.length)), s.push(e); + return s; + }, + }, + ], + [ + { + key: "rules", + get: function () { + return { block: Jo, inline: Vo }; + }, + }, + { + key: "lex", + value: function (e, n) { + return new t(n).lex(e); + }, + }, + { + key: "lexInline", + value: function (e, n) { + return new t(n).inlineTokens(e); + }, + }, + ], + ), + t + ); + })(), + Yo = (function () { + function t(e) { + ur(this, t), (this.options = e || ko); + } + return ( + or(t, [ + { + key: "code", + value: function (t, e, n) { + var r = (e || "").match(/\S*/)[0]; + if (this.options.highlight) { + var i = this.options.highlight(t, r); + null != i && i !== t && ((n = !0), (t = i)); + } + return ( + (t = t.replace(/\n$/, "") + "\n"), + r + ? '
    ' +
+                  (n ? t : Fo(t, !0)) +
+                  "
    \n" + : "
    " + (n ? t : Fo(t, !0)) + "
    \n" + ); + }, + }, + { + key: "blockquote", + value: function (t) { + return "
    \n" + t + "
    \n"; + }, + }, + { + key: "html", + value: function (t) { + return t; + }, + }, + { + key: "heading", + value: function (t, e, n, r) { + return this.options.headerIds + ? "' + + t + + "\n" + : "" + t + "\n"; + }, + }, + { + key: "hr", + value: function () { + return this.options.xhtml ? "
    \n" : "
    \n"; + }, + }, + { + key: "list", + value: function (t, e, n) { + var r = e ? "ol" : "ul"; + return ( + "<" + + r + + (e && 1 !== n ? ' start="' + n + '"' : "") + + ">\n" + + t + + "\n" + ); + }, + }, + { + key: "listitem", + value: function (t) { + return "
  • " + t + "
    • \n"; + }, + }, + { + key: "checkbox", + value: function (t) { + return ( + " " + ); + }, + }, + { + key: "paragraph", + value: function (t) { + return "

    " + t + "

    \n"; + }, + }, + { + key: "table", + value: function (t, e) { + return ( + e && (e = "" + e + ""), + "\n\n" + t + "\n" + e + "
    \n" + ); + }, + }, + { + key: "tablerow", + value: function (t) { + return "\n" + t + "\n"; + }, + }, + { + key: "tablecell", + value: function (t, e) { + var n = e.header ? "th" : "td"; + return ( + (e.align + ? "<" + n + ' align="' + e.align + '">' + : "<" + n + ">") + + t + + "\n" + ); + }, + }, + { + key: "strong", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "em", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "codespan", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "br", + value: function () { + return this.options.xhtml ? "
    " : "
    "; + }, + }, + { + key: "del", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "link", + value: function (t, e, n) { + if ( + null === (t = Io(this.options.sanitize, this.options.baseUrl, t)) + ) + return n; + var r = '    "); + }, + }, + { + key: "image", + value: function (t, e, n) { + if ( + null === (t = Io(this.options.sanitize, this.options.baseUrl, t)) + ) + return n; + var r = '' + n + '" : ">") + ); + }, + }, + { + key: "text", + value: function (t) { + return t; + }, + }, + ]), + t + ); + })(), + Xo = (function () { + function t() { + ur(this, t); + } + return ( + or(t, [ + { + key: "strong", + value: function (t) { + return t; + }, + }, + { + key: "em", + value: function (t) { + return t; + }, + }, + { + key: "codespan", + value: function (t) { + return t; + }, + }, + { + key: "del", + value: function (t) { + return t; + }, + }, + { + key: "html", + value: function (t) { + return t; + }, + }, + { + key: "text", + value: function (t) { + return t; + }, + }, + { + key: "link", + value: function (t, e, n) { + return "" + n; + }, + }, + { + key: "image", + value: function (t, e, n) { + return "" + n; + }, + }, + { + key: "br", + value: function () { + return ""; + }, + }, + ]), + t + ); + })(), + ts = (function () { + function t() { + ur(this, t), (this.seen = {}); + } + return ( + or(t, [ + { + key: "serialize", + value: function (t) { + return t + .toLowerCase() + .trim() + .replace(/<[!\/a-z].*?>/gi, "") + .replace( + /[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g, + "", + ) + .replace(/\s/g, "-"); + }, + }, + { + key: "getNextSafeSlug", + value: function (t, e) { + var n = t, + r = 0; + if (this.seen.hasOwnProperty(n)) { + r = this.seen[t]; + do { + n = t + "-" + ++r; + } while (this.seen.hasOwnProperty(n)); + } + return e || ((this.seen[t] = r), (this.seen[n] = 0)), n; + }, + }, + { + key: "slug", + value: function (t) { + var e = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : {}, + n = this.serialize(t); + return this.getNextSafeSlug(n, e.dryrun); + }, + }, + ]), + t + ); + })(), + es = (function () { + function t(e) { + ur(this, t), + (this.options = e || ko), + (this.options.renderer = this.options.renderer || new Yo()), + (this.renderer = this.options.renderer), + (this.renderer.options = this.options), + (this.textRenderer = new Xo()), + (this.slugger = new ts()); + } + return ( + or( + t, + [ + { + key: "parse", + value: function (t) { + var e, + n, + r, + i, + u, + a, + o, + s, + l, + c, + p, + d, + f, + h, + g, + D, + m, + v, + y, + k = + !(arguments.length > 1 && void 0 !== arguments[1]) || + arguments[1], + E = "", + x = t.length; + for (e = 0; e < x; e++) + if ( + ((c = t[e]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[c.type] + ) || + (!1 === + (y = this.options.extensions.renderers[c.type].call( + { parser: this }, + c, + )) && + [ + "space", + "hr", + "heading", + "code", + "table", + "blockquote", + "list", + "html", + "paragraph", + "text", + ].includes(c.type))) + ) + switch (c.type) { + case "space": + continue; + case "hr": + E += this.renderer.hr(); + continue; + case "heading": + E += this.renderer.heading( + this.parseInline(c.tokens), + c.depth, + Bo(this.parseInline(c.tokens, this.textRenderer)), + this.slugger, + ); + continue; + case "code": + E += this.renderer.code(c.text, c.lang, c.escaped); + continue; + case "table": + for ( + s = "", o = "", i = c.header.length, n = 0; + n < i; + n++ + ) + o += this.renderer.tablecell( + this.parseInline(c.header[n].tokens), + { header: !0, align: c.align[n] }, + ); + for ( + s += this.renderer.tablerow(o), + l = "", + i = c.rows.length, + n = 0; + n < i; + n++ + ) { + for ( + o = "", u = (a = c.rows[n]).length, r = 0; + r < u; + r++ + ) + o += this.renderer.tablecell( + this.parseInline(a[r].tokens), + { header: !1, align: c.align[r] }, + ); + l += this.renderer.tablerow(o); + } + E += this.renderer.table(s, l); + continue; + case "blockquote": + (l = this.parse(c.tokens)), + (E += this.renderer.blockquote(l)); + continue; + case "list": + for ( + p = c.ordered, + d = c.start, + f = c.loose, + i = c.items.length, + l = "", + n = 0; + n < i; + n++ + ) + (D = (g = c.items[n]).checked), + (m = g.task), + (h = ""), + g.task && + ((v = this.renderer.checkbox(D)), + f + ? g.tokens.length > 0 && + "paragraph" === g.tokens[0].type + ? ((g.tokens[0].text = + v + " " + g.tokens[0].text), + g.tokens[0].tokens && + g.tokens[0].tokens.length > 0 && + "text" === g.tokens[0].tokens[0].type && + (g.tokens[0].tokens[0].text = + v + " " + g.tokens[0].tokens[0].text)) + : g.tokens.unshift({ type: "text", text: v }) + : (h += v)), + (h += this.parse(g.tokens, f)), + (l += this.renderer.listitem(h, m, D)); + E += this.renderer.list(l, p, d); + continue; + case "html": + E += this.renderer.html(c.text); + continue; + case "paragraph": + E += this.renderer.paragraph(this.parseInline(c.tokens)); + continue; + case "text": + for ( + l = c.tokens ? this.parseInline(c.tokens) : c.text; + e + 1 < x && "text" === t[e + 1].type; + + ) + l += + "\n" + + ((c = t[++e]).tokens + ? this.parseInline(c.tokens) + : c.text); + E += k ? this.renderer.paragraph(l) : l; + continue; + default: + var A = 'Token with "' + c.type + '" type was not found.'; + if (this.options.silent) return void console.error(A); + throw new Error(A); + } + else E += y || ""; + return E; + }, + }, + { + key: "parseInline", + value: function (t, e) { + e = e || this.renderer; + var n, + r, + i, + u = "", + a = t.length; + for (n = 0; n < a; n++) + if ( + ((r = t[n]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[r.type] + ) || + (!1 === + (i = this.options.extensions.renderers[r.type].call( + { parser: this }, + r, + )) && + [ + "escape", + "html", + "link", + "image", + "strong", + "em", + "codespan", + "br", + "del", + "text", + ].includes(r.type))) + ) + switch (r.type) { + case "escape": + u += e.text(r.text); + break; + case "html": + u += e.html(r.text); + break; + case "link": + u += e.link( + r.href, + r.title, + this.parseInline(r.tokens, e), + ); + break; + case "image": + u += e.image(r.href, r.title, r.text); + break; + case "strong": + u += e.strong(this.parseInline(r.tokens, e)); + break; + case "em": + u += e.em(this.parseInline(r.tokens, e)); + break; + case "codespan": + u += e.codespan(r.text); + break; + case "br": + u += e.br(); + break; + case "del": + u += e.del(this.parseInline(r.tokens, e)); + break; + case "text": + u += e.text(r.text); + break; + default: + var o = 'Token with "' + r.type + '" type was not found.'; + if (this.options.silent) return void console.error(o); + throw new Error(o); + } + else u += i || ""; + return u; + }, + }, + ], + [ + { + key: "parse", + value: function (e, n) { + return new t(n).parse(e); + }, + }, + { + key: "parseInline", + value: function (e, n) { + return new t(n).parseInline(e); + }, + }, + ], + ), + t + ); + })(); +function ns(t, e, n) { + if (null == t) + throw new Error("marked(): input parameter is undefined or null"); + if ("string" != typeof t) + throw new Error( + "marked(): input parameter is of type " + + Object.prototype.toString.call(t) + + ", string expected", + ); + if ( + ("function" == typeof e && ((n = e), (e = null)), + qo((e = jo({}, ns.defaults, e || {}))), + n) + ) { + var r, + i = e.highlight; + try { + r = Go.lex(t, e); + } catch (t) { + return n(t); + } + var u = function (t) { + var u; + if (!t) + try { + e.walkTokens && ns.walkTokens(r, e.walkTokens), (u = es.parse(r, e)); + } catch (e) { + t = e; + } + return (e.highlight = i), t ? n(t) : n(null, u); + }; + if (!i || i.length < 3) return u(); + if ((delete e.highlight, !r.length)) return u(); + var a = 0; + return ( + ns.walkTokens(r, function (t) { + "code" === t.type && + (a++, + setTimeout(function () { + i(t.text, t.lang, function (e, n) { + if (e) return u(e); + null != n && n !== t.text && ((t.text = n), (t.escaped = !0)), + 0 === --a && u(); + }); + }, 0)); + }), + void (0 === a && u()) + ); + } + try { + var o = Go.lex(t, e); + return e.walkTokens && ns.walkTokens(o, e.walkTokens), es.parse(o, e); + } catch (t) { + if ( + ((t.message += + "\nPlease report this to https://github.com/markedjs/marked."), + e.silent) + ) + return ( + "

    An error occurred:

    " + Fo(t.message + "", !0) + "
    " + ); + throw t; + } +} +(ns.options = ns.setOptions = + function (t) { + var e; + return jo(ns.defaults, t), (e = ns.defaults), (ko = e), ns; + }), + (ns.getDefaults = yo), + (ns.defaults = ko), + (ns.use = function () { + for (var t = arguments.length, e = new Array(t), n = 0; n < t; n++) + e[n] = arguments[n]; + var r, + i = jo.apply(void 0, [{}].concat(e)), + u = ns.defaults.extensions || { renderers: {}, childTokens: {} }; + e.forEach(function (t) { + if ( + (t.extensions && + ((r = !0), + t.extensions.forEach(function (t) { + if (!t.name) throw new Error("extension name required"); + if (t.renderer) { + var e = u.renderers ? u.renderers[t.name] : null; + u.renderers[t.name] = e + ? function () { + for ( + var n = arguments.length, r = new Array(n), i = 0; + i < n; + i++ + ) + r[i] = arguments[i]; + var u = t.renderer.apply(this, r); + return !1 === u && (u = e.apply(this, r)), u; + } + : t.renderer; + } + if (t.tokenizer) { + if (!t.level || ("block" !== t.level && "inline" !== t.level)) + throw new Error("extension level must be 'block' or 'inline'"); + u[t.level] + ? u[t.level].unshift(t.tokenizer) + : (u[t.level] = [t.tokenizer]), + t.start && + ("block" === t.level + ? u.startBlock + ? u.startBlock.push(t.start) + : (u.startBlock = [t.start]) + : "inline" === t.level && + (u.startInline + ? u.startInline.push(t.start) + : (u.startInline = [t.start]))); + } + t.childTokens && (u.childTokens[t.name] = t.childTokens); + })), + t.renderer && + (function () { + var e = ns.defaults.renderer || new Yo(), + n = function (n) { + var r = e[n]; + e[n] = function () { + for ( + var i = arguments.length, u = new Array(i), a = 0; + a < i; + a++ + ) + u[a] = arguments[a]; + var o = t.renderer[n].apply(e, u); + return !1 === o && (o = r.apply(e, u)), o; + }; + }; + for (var r in t.renderer) n(r); + i.renderer = e; + })(), + t.tokenizer && + (function () { + var e = ns.defaults.tokenizer || new Wo(), + n = function (n) { + var r = e[n]; + e[n] = function () { + for ( + var i = arguments.length, u = new Array(i), a = 0; + a < i; + a++ + ) + u[a] = arguments[a]; + var o = t.tokenizer[n].apply(e, u); + return !1 === o && (o = r.apply(e, u)), o; + }; + }; + for (var r in t.tokenizer) n(r); + i.tokenizer = e; + })(), + t.walkTokens) + ) { + var e = ns.defaults.walkTokens; + i.walkTokens = function (n) { + t.walkTokens.call(this, n), e && e.call(this, n); + }; + } + r && (i.extensions = u), ns.setOptions(i); + }); + }), + (ns.walkTokens = function (t, e) { + var n, + r = pr(t); + try { + var i = function () { + var t = n.value; + switch ((e.call(ns, t), t.type)) { + case "table": + var r, + i = pr(t.header); + try { + for (i.s(); !(r = i.n()).done; ) { + var u = r.value; + ns.walkTokens(u.tokens, e); + } + } catch (t) { + i.e(t); + } finally { + i.f(); + } + var a, + o = pr(t.rows); + try { + for (o.s(); !(a = o.n()).done; ) { + var s, + l = pr(a.value); + try { + for (l.s(); !(s = l.n()).done; ) { + var c = s.value; + ns.walkTokens(c.tokens, e); + } + } catch (t) { + l.e(t); + } finally { + l.f(); + } + } + } catch (t) { + o.e(t); + } finally { + o.f(); + } + break; + case "list": + ns.walkTokens(t.items, e); + break; + default: + ns.defaults.extensions && + ns.defaults.extensions.childTokens && + ns.defaults.extensions.childTokens[t.type] + ? ns.defaults.extensions.childTokens[t.type].forEach( + function (n) { + ns.walkTokens(t[n], e); + }, + ) + : t.tokens && ns.walkTokens(t.tokens, e); + } + }; + for (r.s(); !(n = r.n()).done; ) i(); + } catch (t) { + r.e(t); + } finally { + r.f(); + } + }), + (ns.parseInline = function (t, e) { + if (null == t) + throw new Error( + "marked.parseInline(): input parameter is undefined or null", + ); + if ("string" != typeof t) + throw new Error( + "marked.parseInline(): input parameter is of type " + + Object.prototype.toString.call(t) + + ", string expected", + ); + qo((e = jo({}, ns.defaults, e || {}))); + try { + var n = Go.lexInline(t, e); + return ( + e.walkTokens && ns.walkTokens(n, e.walkTokens), es.parseInline(n, e) + ); + } catch (t) { + if ( + ((t.message += + "\nPlease report this to https://github.com/markedjs/marked."), + e.silent) + ) + return ( + "

    An error occurred:

    " + Fo(t.message + "", !0) + "
    " + ); + throw t; + } + }), + (ns.Parser = es), + (ns.parser = es.parse), + (ns.Renderer = Yo), + (ns.TextRenderer = Xo), + (ns.Lexer = Go), + (ns.lexer = Go.lex), + (ns.Tokenizer = Wo), + (ns.Slugger = ts), + (ns.parse = ns); +export default function () { + var t, + e, + n = null; + function r() { + if (n && !n.closed) n.focus(); + else { + if ( + (((n = window.open( + "about:blank", + "reveal.js - Notes", + "width=1100,height=700", + )).marked = ns), + n.document.write( + "\x3c!--\n\tNOTE: You need to build the notes plugin after making changes to this file.\n--\x3e\n\n\t\n\t\t\n\n\t\treveal.js - Speaker View\n\n\t\t\n\t\n\n\t\n\n\t\t
    Loading speaker view...
    \n\n\t\t
    \n\t\t
    Upcoming
    \n\t\t
    \n\t\t\t
    \n\t\t\t\t

    Time Click to Reset

    \n\t\t\t\t
    \n\t\t\t\t\t0:00 AM\n\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t00:00:00\n\t\t\t\t
    \n\t\t\t\t
    \n\n\t\t\t\t

    Pacing – Time to finish current slide

    \n\t\t\t\t
    \n\t\t\t\t\t00:00:00\n\t\t\t\t
    \n\t\t\t
    \n\n\t\t\t
    \n\t\t\t\t

    Notes

    \n\t\t\t\t
    \n\t\t\t
    \n\t\t
    \n\t\t
    \n\t\t\t\n\t\t\t\n\t\t
    \n\n\t\t\n\t\n", + ), + !n) + ) + return void alert( + "Speaker view popup failed to open. Please make sure popups are allowed and reopen the speaker view.", + ); + (r = e.getConfig().url), + (i = + "string" == typeof r + ? r + : window.location.protocol + + "//" + + window.location.host + + window.location.pathname + + window.location.search), + (t = setInterval(function () { + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "connect", + state: e.getState(), + url: i, + }), + "*", + ); + }, 500)), + window.addEventListener("message", u); + } + var r, i; + } + function i(t) { + var r = e.getCurrentSlide(), + i = r.querySelector("aside.notes"), + u = r.querySelector(".current-fragment"), + a = { + namespace: "reveal-notes", + type: "state", + notes: "", + markdown: !1, + whitespace: "normal", + state: e.getState(), + }; + if ( + (r.hasAttribute("data-notes") && + ((a.notes = r.getAttribute("data-notes")), (a.whitespace = "pre-wrap")), + u) + ) { + var o = u.querySelector("aside.notes"); + o + ? (i = o) + : u.hasAttribute("data-notes") && + ((a.notes = u.getAttribute("data-notes")), + (a.whitespace = "pre-wrap"), + (i = null)); + } + i && + ((a.notes = i.innerHTML), + (a.markdown = "string" == typeof i.getAttribute("data-markdown"))), + n.postMessage(JSON.stringify(a), "*"); + } + function u(r) { + var i, + u, + o, + s, + l = JSON.parse(r.data); + l && "reveal-notes" === l.namespace && "connected" === l.type + ? (clearInterval(t), a()) + : l && + "reveal-notes" === l.namespace && + "call" === l.type && + ((i = l.methodName), + (u = l.arguments), + (o = l.callId), + (s = e[i].apply(e, u)), + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "return", + result: s, + callId: o, + }), + "*", + )); + } + function a() { + e.on("slidechanged", i), + e.on("fragmentshown", i), + e.on("fragmenthidden", i), + e.on("overviewhidden", i), + e.on("overviewshown", i), + e.on("paused", i), + e.on("resumed", i), + i(); + } + return { + id: "notes", + init: function (t) { + (e = t), + /receiver/i.test(window.location.search) || + (null !== window.location.search.match(/(\?|\&)notes/gi) + ? r() + : window.addEventListener("message", function (t) { + if (!n && "string" == typeof t.data) { + var e; + try { + e = JSON.parse(t.data); + } catch (t) {} + e && + "reveal-notes" === e.namespace && + "heartbeat" === e.type && + ((r = t.source), + n && !n.closed + ? n.focus() + : ((n = r), window.addEventListener("message", u), a())); + } + var r; + }), + e.addKeyBinding( + { keyCode: 83, key: "S", description: "Speaker notes view" }, + function () { + r(); + }, + )); + }, + open: r, + }; +} diff --git a/content/slides/slides_files/libs/revealjs/plugin/notes/notes.js b/content/slides/slides_files/libs/revealjs/plugin/notes/notes.js index ddbc03b..5024838 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/notes/notes.js +++ b/content/slides/slides_files/libs/revealjs/plugin/notes/notes.js @@ -1 +1,4401 @@ -!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).RevealNotes=e()}(this,(function(){"use strict";var t="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{},e=function(t){return t&&t.Math==Math&&t},n=e("object"==typeof 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Please make sure popups are allowed and reopen the speaker view.", + ); + (r = e.getConfig().url), + (i = + "string" == typeof r + ? r + : window.location.protocol + + "//" + + window.location.host + + window.location.pathname + + window.location.search), + (t = setInterval(function () { + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "connect", + state: e.getState(), + url: i, + }), + "*", + ); + }, 500)), + window.addEventListener("message", u); + } + var r, i; + } + function i(t) { + var r = e.getCurrentSlide(), + i = r.querySelector("aside.notes"), + u = r.querySelector(".current-fragment"), + a = { + namespace: "reveal-notes", + type: "state", + notes: "", + markdown: !1, + whitespace: "normal", + state: e.getState(), + }; + if ( + (r.hasAttribute("data-notes") && + ((a.notes = r.getAttribute("data-notes")), + (a.whitespace = "pre-wrap")), + u) + ) { + var o = u.querySelector("aside.notes"); + o + ? (i = o) + : u.hasAttribute("data-notes") && + ((a.notes = u.getAttribute("data-notes")), + (a.whitespace = "pre-wrap"), + (i = null)); + } + i && + ((a.notes = i.innerHTML), + (a.markdown = "string" == typeof i.getAttribute("data-markdown"))), + n.postMessage(JSON.stringify(a), "*"); + } + function u(r) { + var i, + u, + o, + s, + l = JSON.parse(r.data); + l && "reveal-notes" === l.namespace && "connected" === l.type + ? (clearInterval(t), a()) + : l && + "reveal-notes" === l.namespace && + "call" === l.type && + ((i = l.methodName), + (u = l.arguments), + (o = l.callId), + (s = e[i].apply(e, u)), + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "return", + result: s, + callId: o, + }), + "*", + )); + } + function a() { + e.on("slidechanged", i), + e.on("fragmentshown", i), + e.on("fragmenthidden", i), + e.on("overviewhidden", i), + e.on("overviewshown", i), + e.on("paused", i), + e.on("resumed", i), + i(); + } + return { + id: "notes", + init: function (t) { + (e = t), + /receiver/i.test(window.location.search) || + (null !== window.location.search.match(/(\?|\&)notes/gi) + ? r() + : window.addEventListener("message", function (t) { + if (!n && "string" == typeof t.data) { + var e; + try { + e = JSON.parse(t.data); + } catch (t) {} + e && + "reveal-notes" === e.namespace && + "heartbeat" === e.type && + ((r = t.source), + n && !n.closed + ? n.focus() + : ((n = r), + window.addEventListener("message", u), + a())); + } + var r; + }), + e.addKeyBinding( + { keyCode: 83, key: "S", description: "Speaker notes view" }, + function () { + r(); + }, + )); + }, + open: r, + }; + }; +}); diff --git a/content/slides/slides_files/libs/revealjs/plugin/notes/plugin.js b/content/slides/slides_files/libs/revealjs/plugin/notes/plugin.js index c80afa8..be08c6e 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/notes/plugin.js +++ b/content/slides/slides_files/libs/revealjs/plugin/notes/plugin.js @@ -1,6 +1,6 @@ -import speakerViewHTML from './speaker-view.html'; +import speakerViewHTML from "./speaker-view.html"; -import { marked } from 'marked'; +import { marked } from "marked"; /** * Handles opening of and synchronization with the reveal.js @@ -14,223 +14,230 @@ import { marked } from 'marked'; * to the notes window */ const Plugin = () => { - - let connectInterval; - let speakerWindow = null; - let deck; - - /** - * Opens a new speaker view window. - */ - function openSpeakerWindow() { - - // If a window is already open, focus it - if( speakerWindow && !speakerWindow.closed ) { - speakerWindow.focus(); - } - else { - speakerWindow = window.open( 'about:blank', 'reveal.js - Notes', 'width=1100,height=700' ); - speakerWindow.marked = marked; - speakerWindow.document.write( speakerViewHTML ); - - if( !speakerWindow ) { - alert( 'Speaker view popup failed to open. Please make sure popups are allowed and reopen the speaker view.' ); - return; - } - - connect(); - } - - } - - /** - * Reconnect with an existing speaker view window. - */ - function reconnectSpeakerWindow( reconnectWindow ) { - - if( speakerWindow && !speakerWindow.closed ) { - speakerWindow.focus(); - } - else { - speakerWindow = reconnectWindow; - window.addEventListener( 'message', onPostMessage ); - onConnected(); - } - - } - - /** - * Connect to the notes window through a postmessage handshake. - * Using postmessage enables us to work in situations where the - * origins differ, such as a presentation being opened from the - * file system. - */ - function connect() { - - const presentationURL = deck.getConfig().url; - - const url = typeof presentationURL === 'string' ? presentationURL : - window.location.protocol + '//' + window.location.host + window.location.pathname + window.location.search; - - // Keep trying to connect until we get a 'connected' message back - connectInterval = setInterval( function() { - speakerWindow.postMessage( JSON.stringify( { - namespace: 'reveal-notes', - type: 'connect', - state: deck.getState(), - url - } ), '*' ); - }, 500 ); - - window.addEventListener( 'message', onPostMessage ); - - } - - /** - * Calls the specified Reveal.js method with the provided argument - * and then pushes the result to the notes frame. - */ - function callRevealApi( methodName, methodArguments, callId ) { - - let result = deck[methodName].apply( deck, methodArguments ); - speakerWindow.postMessage( JSON.stringify( { - namespace: 'reveal-notes', - type: 'return', - result, - callId - } ), '*' ); - - } - - /** - * Posts the current slide data to the notes window. - */ - function post( event ) { - - let slideElement = deck.getCurrentSlide(), - notesElement = slideElement.querySelector( 'aside.notes' ), - fragmentElement = slideElement.querySelector( '.current-fragment' ); - - let messageData = { - namespace: 'reveal-notes', - type: 'state', - notes: '', - markdown: false, - whitespace: 'normal', - state: deck.getState() - }; - - // Look for notes defined in a slide attribute - if( slideElement.hasAttribute( 'data-notes' ) ) { - messageData.notes = slideElement.getAttribute( 'data-notes' ); - messageData.whitespace = 'pre-wrap'; - } - - // Look for notes defined in a fragment - if( fragmentElement ) { - let fragmentNotes = fragmentElement.querySelector( 'aside.notes' ); - if( fragmentNotes ) { - notesElement = fragmentNotes; - } - else if( fragmentElement.hasAttribute( 'data-notes' ) ) { - messageData.notes = fragmentElement.getAttribute( 'data-notes' ); - messageData.whitespace = 'pre-wrap'; - - // In case there are slide notes - notesElement = null; - } - } - - // Look for notes defined in an aside element - if( notesElement ) { - messageData.notes = notesElement.innerHTML; - messageData.markdown = typeof notesElement.getAttribute( 'data-markdown' ) === 'string'; - } - - speakerWindow.postMessage( JSON.stringify( messageData ), '*' ); - - } - - function onPostMessage( event ) { - - let data = JSON.parse( event.data ); - if( data && data.namespace === 'reveal-notes' && data.type === 'connected' ) { - clearInterval( connectInterval ); - onConnected(); - } - else if( data && data.namespace === 'reveal-notes' && data.type === 'call' ) { - callRevealApi( data.methodName, data.arguments, data.callId ); - } - - } - - /** - * Called once we have established a connection to the notes - * window. - */ - function onConnected() { - - // Monitor events that trigger a change in state - deck.on( 'slidechanged', post ); - deck.on( 'fragmentshown', post ); - deck.on( 'fragmenthidden', post ); - deck.on( 'overviewhidden', post ); - deck.on( 'overviewshown', post ); - deck.on( 'paused', post ); - deck.on( 'resumed', post ); - - // Post the initial state - post(); - - } - - return { - id: 'notes', - - init: function( reveal ) { - - deck = reveal; - - if( !/receiver/i.test( window.location.search ) ) { - - // If the there's a 'notes' query set, open directly - if( window.location.search.match( /(\?|\&)notes/gi ) !== null ) { - openSpeakerWindow(); - } - else { - // Keep listening for speaker view hearbeats. If we receive a - // heartbeat from an orphaned window, reconnect it. This ensures - // that we remain connected to the notes even if the presentation - // is reloaded. - window.addEventListener( 'message', event => { - - if( !speakerWindow && typeof event.data === 'string' ) { - let data; - - try { - data = JSON.parse( event.data ); - } - catch( error ) {} - - if( data && data.namespace === 'reveal-notes' && data.type === 'heartbeat' ) { - reconnectSpeakerWindow( event.source ); - } - } - }); - } - - // Open the notes when the 's' key is hit - deck.addKeyBinding({keyCode: 83, key: 'S', description: 'Speaker notes view'}, function() { - openSpeakerWindow(); - } ); - - } - - }, - - open: openSpeakerWindow - }; - + let connectInterval; + let speakerWindow = null; + let deck; + + /** + * Opens a new speaker view window. + */ + function openSpeakerWindow() { + // If a window is already open, focus it + if (speakerWindow && !speakerWindow.closed) { + speakerWindow.focus(); + } else { + speakerWindow = window.open( + "about:blank", + "reveal.js - Notes", + "width=1100,height=700", + ); + speakerWindow.marked = marked; + speakerWindow.document.write(speakerViewHTML); + + if (!speakerWindow) { + alert( + "Speaker view popup failed to open. Please make sure popups are allowed and reopen the speaker view.", + ); + return; + } + + connect(); + } + } + + /** + * Reconnect with an existing speaker view window. + */ + function reconnectSpeakerWindow(reconnectWindow) { + if (speakerWindow && !speakerWindow.closed) { + speakerWindow.focus(); + } else { + speakerWindow = reconnectWindow; + window.addEventListener("message", onPostMessage); + onConnected(); + } + } + + /** + * Connect to the notes window through a postmessage handshake. + * Using postmessage enables us to work in situations where the + * origins differ, such as a presentation being opened from the + * file system. + */ + function connect() { + const presentationURL = deck.getConfig().url; + + const url = + typeof presentationURL === "string" + ? presentationURL + : window.location.protocol + + "//" + + window.location.host + + window.location.pathname + + window.location.search; + + // Keep trying to connect until we get a 'connected' message back + connectInterval = setInterval(function () { + speakerWindow.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "connect", + state: deck.getState(), + url, + }), + "*", + ); + }, 500); + + window.addEventListener("message", onPostMessage); + } + + /** + * Calls the specified Reveal.js method with the provided argument + * and then pushes the result to the notes frame. + */ + function callRevealApi(methodName, methodArguments, callId) { + let result = deck[methodName].apply(deck, methodArguments); + speakerWindow.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "return", + result, + callId, + }), + "*", + ); + } + + /** + * Posts the current slide data to the notes window. + */ + function post(event) { + let slideElement = deck.getCurrentSlide(), + notesElement = slideElement.querySelector("aside.notes"), + fragmentElement = slideElement.querySelector(".current-fragment"); + + let messageData = { + namespace: "reveal-notes", + type: "state", + notes: "", + markdown: false, + whitespace: "normal", + state: deck.getState(), + }; + + // Look for notes defined in a slide attribute + if (slideElement.hasAttribute("data-notes")) { + messageData.notes = slideElement.getAttribute("data-notes"); + messageData.whitespace = "pre-wrap"; + } + + // Look for notes defined in a fragment + if (fragmentElement) { + let fragmentNotes = fragmentElement.querySelector("aside.notes"); + if (fragmentNotes) { + notesElement = fragmentNotes; + } else if (fragmentElement.hasAttribute("data-notes")) { + messageData.notes = fragmentElement.getAttribute("data-notes"); + messageData.whitespace = "pre-wrap"; + + // In case there are slide notes + notesElement = null; + } + } + + // Look for notes defined in an aside element + if (notesElement) { + messageData.notes = notesElement.innerHTML; + messageData.markdown = + typeof notesElement.getAttribute("data-markdown") === "string"; + } + + speakerWindow.postMessage(JSON.stringify(messageData), "*"); + } + + function onPostMessage(event) { + let data = JSON.parse(event.data); + if ( + data && + data.namespace === "reveal-notes" && + data.type === "connected" + ) { + clearInterval(connectInterval); + onConnected(); + } else if ( + data && + data.namespace === "reveal-notes" && + data.type === "call" + ) { + callRevealApi(data.methodName, data.arguments, data.callId); + } + } + + /** + * Called once we have established a connection to the notes + * window. + */ + function onConnected() { + // Monitor events that trigger a change in state + deck.on("slidechanged", post); + deck.on("fragmentshown", post); + deck.on("fragmenthidden", post); + deck.on("overviewhidden", post); + deck.on("overviewshown", post); + deck.on("paused", post); + deck.on("resumed", post); + + // Post the initial state + post(); + } + + return { + id: "notes", + + init: function (reveal) { + deck = reveal; + + if (!/receiver/i.test(window.location.search)) { + // If the there's a 'notes' query set, open directly + if (window.location.search.match(/(\?|\&)notes/gi) !== null) { + openSpeakerWindow(); + } else { + // Keep listening for speaker view hearbeats. If we receive a + // heartbeat from an orphaned window, reconnect it. This ensures + // that we remain connected to the notes even if the presentation + // is reloaded. + window.addEventListener("message", (event) => { + if (!speakerWindow && typeof event.data === "string") { + let data; + + try { + data = JSON.parse(event.data); + } catch (error) {} + + if ( + data && + data.namespace === "reveal-notes" && + data.type === "heartbeat" + ) { + reconnectSpeakerWindow(event.source); + } + } + }); + } + + // Open the notes when the 's' key is hit + deck.addKeyBinding( + { keyCode: 83, key: "S", description: "Speaker notes view" }, + function () { + openSpeakerWindow(); + }, + ); + } + }, + + open: openSpeakerWindow, + }; }; export default Plugin; diff --git a/content/slides/slides_files/libs/revealjs/plugin/notes/speaker-view.html b/content/slides/slides_files/libs/revealjs/plugin/notes/speaker-view.html index 93ac3c0..dcb838c 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/notes/speaker-view.html +++ b/content/slides/slides_files/libs/revealjs/plugin/notes/speaker-view.html @@ -2,883 +2,912 @@ NOTE: You need to build the notes plugin after making changes to this file. --> - - - - reveal.js - Speaker View - - - - - - -
    Loading speaker view...
    - -
    -
    Upcoming
    -
    -
    -

    Time Click to Reset

    -
    - 0:00 AM -
    -
    - 00:00:00 -
    -
    - - - -
    - - -
    -
    - - -
    - - - - \ No newline at end of file + + + + reveal.js - Speaker View + + + + + +
    Loading speaker view...
    + +
    +
    + Upcoming +
    +
    +
    +

    + Time Click to Reset +

    +
    + 0:00 AM +
    +
    + 00:00:00 +
    +
    + + + +
    + + +
    +
    + + +
    + + + + diff --git a/content/slides/slides_files/libs/revealjs/plugin/pdf-export/pdfexport.js b/content/slides/slides_files/libs/revealjs/plugin/pdf-export/pdfexport.js index bf9104c..d052cc6 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/pdf-export/pdfexport.js +++ b/content/slides/slides_files/libs/revealjs/plugin/pdf-export/pdfexport.js @@ -1,111 +1,125 @@ -var PdfExport = ( function( _Reveal ){ +var PdfExport = (function (_Reveal) { + var Reveal = _Reveal; + var setStylesheet = null; + var installAltKeyBindings = null; - var Reveal = _Reveal; - var setStylesheet = null; - var installAltKeyBindings = null; + function getRevealJsPath() { + var regex = /\b[^/]+\/reveal.css$/i; + var script = Array.from(document.querySelectorAll("link")).find( + function (e) { + return e.attributes.href && e.attributes.href.value.search(regex) >= 0; + }, + ); + if (!script) { + console.error( + "reveal.css could not be found in included elements. Did you rename this file?", + ); + return ""; + } + return script.attributes.href.value.replace(regex, ""); + } - function getRevealJsPath(){ - var regex = /\b[^/]+\/reveal.css$/i; - var script = Array.from( document.querySelectorAll( 'link' ) ).find( function( e ){ - return e.attributes.href && e.attributes.href.value.search( regex ) >= 0; - }); - if( !script ){ - console.error( 'reveal.css could not be found in included elements. Did you rename this file?' ); - return ''; - } - return script.attributes.href.value.replace( regex, '' ); - } + function setStylesheet3(pdfExport) { + var link = document.querySelector("#print"); + if (!link) { + link = document.createElement("link"); + link.rel = "stylesheet"; + link.id = "print"; + document.querySelector("head").appendChild(link); + } + var style = "paper"; + if (pdfExport) { + style = "pdf"; + } + link.href = getRevealJsPath() + "css/print/" + style + ".css"; + } - function setStylesheet3( pdfExport ){ - var link = document.querySelector( '#print' ); - if( !link ){ - link = document.createElement( 'link' ); - link.rel = 'stylesheet'; - link.id = 'print'; - document.querySelector( 'head' ).appendChild( link ); - } - var style = 'paper'; - if( pdfExport ){ - style = 'pdf'; - } - link.href = getRevealJsPath() + 'css/print/' + style + '.css'; - } + function setStylesheet4(pdfExport) {} - function setStylesheet4( pdfExport ){ - } + function installAltKeyBindings3() {} - function installAltKeyBindings3(){ - } + function installAltKeyBindings4() { + if (isPrintingPDF()) { + var config = Reveal.getConfig(); + var shortcut = config.pdfExportShortcut || "E"; + window.addEventListener( + "keydown", + function (e) { + if ( + e.target.nodeName.toUpperCase() == "BODY" && + (e.key.toUpperCase() == shortcut.toUpperCase() || + e.keyCode == shortcut.toUpperCase().charCodeAt(0)) + ) { + e.preventDefault(); + togglePdfExport(); + return false; + } + }, + true, + ); + } + } - function installAltKeyBindings4(){ - if( isPrintingPDF() ){ - var config = Reveal.getConfig(); - var shortcut = config.pdfExportShortcut || 'E'; - window.addEventListener( 'keydown', function( e ){ - if( e.target.nodeName.toUpperCase() == 'BODY' - && ( e.key.toUpperCase() == shortcut.toUpperCase() || e.keyCode == shortcut.toUpperCase().charCodeAt( 0 ) ) ){ - e.preventDefault(); - togglePdfExport(); - return false; - } - }, true ); - } - } - - function isPrintingPDF(){ - return ( /print-pdf/gi ).test( window.location.search ); - } + function isPrintingPDF() { + return /print-pdf/gi.test(window.location.search); + } - function togglePdfExport(){ - var url_doc = new URL( document.URL ); - var query_doc = new URLSearchParams( url_doc.searchParams ); - if( isPrintingPDF() ){ - query_doc.delete( 'print-pdf' ); - }else{ - query_doc.set( 'print-pdf', '' ); - } - url_doc.search = ( query_doc.toString() ? '?' + query_doc.toString() : '' ); - window.location.href = url_doc.toString(); - } + function togglePdfExport() { + var url_doc = new URL(document.URL); + var query_doc = new URLSearchParams(url_doc.searchParams); + if (isPrintingPDF()) { + query_doc.delete("print-pdf"); + } else { + query_doc.set("print-pdf", ""); + } + url_doc.search = query_doc.toString() ? "?" + query_doc.toString() : ""; + window.location.href = url_doc.toString(); + } - function installKeyBindings(){ - var config = Reveal.getConfig(); - var shortcut = config.pdfExportShortcut || 'E'; - Reveal.addKeyBinding({ - keyCode: shortcut.toUpperCase().charCodeAt( 0 ), - key: shortcut.toUpperCase(), - description: 'PDF export mode' - }, togglePdfExport ); - installAltKeyBindings(); - } + function installKeyBindings() { + var config = Reveal.getConfig(); + var shortcut = config.pdfExportShortcut || "E"; + Reveal.addKeyBinding( + { + keyCode: shortcut.toUpperCase().charCodeAt(0), + key: shortcut.toUpperCase(), + description: "PDF export mode", + }, + togglePdfExport, + ); + installAltKeyBindings(); + } - function install(){ - installKeyBindings(); - setStylesheet( isPrintingPDF() ); - } + function install() { + installKeyBindings(); + setStylesheet(isPrintingPDF()); + } - var Plugin = { - } + var Plugin = {}; - if( Reveal && Reveal.VERSION && Reveal.VERSION.length && Reveal.VERSION[ 0 ] == '3' ){ - // reveal 3.x - setStylesheet = setStylesheet3; - installAltKeyBindings = installAltKeyBindings3; - install(); - }else{ - // must be reveal 4.x - setStylesheet = setStylesheet4; - installAltKeyBindings = installAltKeyBindings4; - Plugin.id = 'pdf-export'; - Plugin.init = function( _Reveal ){ - Reveal = _Reveal; - install(); - }; - Plugin.togglePdfExport = function () { + if ( + Reveal && + Reveal.VERSION && + Reveal.VERSION.length && + Reveal.VERSION[0] == "3" + ) { + // reveal 3.x + setStylesheet = setStylesheet3; + installAltKeyBindings = installAltKeyBindings3; + install(); + } else { + // must be reveal 4.x + setStylesheet = setStylesheet4; + installAltKeyBindings = installAltKeyBindings4; + Plugin.id = "pdf-export"; + Plugin.init = function (_Reveal) { + Reveal = _Reveal; + install(); + }; + Plugin.togglePdfExport = function () { togglePdfExport(); }; - } - - return Plugin; + } -})( Reveal ); + return Plugin; +})(Reveal); diff --git a/content/slides/slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js b/content/slides/slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js index a69ca1d..6ea7afc 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js +++ b/content/slides/slides_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js @@ -10,7 +10,7 @@ window.QuartoLineHighlight = function () { }; const regex = new RegExp( - "^[\\d" + Object.values(delimiters).join("") + "]+$" + "^[\\d" + Object.values(delimiters).join("") + "]+$", ); function handleLinesSelector(deck, attr) { @@ -56,7 +56,7 @@ window.QuartoLineHighlight = function () { // each clone should follow in an incremental sequence let fragmentIndex = parseInt( code.getAttribute(kFragmentIndex), - 10 + 10, ); fragmentIndex = typeof fragmentIndex !== "number" || isNaN(fragmentIndex) @@ -70,7 +70,7 @@ window.QuartoLineHighlight = function () { var fragmentBlock = code.cloneNode(true); fragmentBlock.setAttribute( "data-code-line-numbers", - joinLineNumbers([step]) + joinLineNumbers([step]), ); fragmentBlock.classList.add("fragment"); @@ -81,7 +81,7 @@ window.QuartoLineHighlight = function () { if (span.hasAttribute("id")) { span.setAttribute( "id", - span.getAttribute("id").concat("-" + stepN) + span.getAttribute("id").concat("-" + stepN), ); } }); @@ -106,23 +106,23 @@ window.QuartoLineHighlight = function () { scrollHighlightedLineIntoView.bind( this, fragmentBlock, - scrollState - ) + scrollState, + ), ); fragmentBlock.addEventListener( "hidden", scrollHighlightedLineIntoView.bind( this, fragmentBlock.previousSibling, - scrollState - ) + scrollState, + ), ); - } + }, ); code.removeAttribute(kFragmentIndex); code.setAttribute( kCodeLineNumbersAttr, - joinLineNumbers([highlightSteps[0]]) + joinLineNumbers([highlightSteps[0]]), ); } @@ -136,7 +136,7 @@ window.QuartoLineHighlight = function () { scrollHighlightedLineIntoView(code, scrollState, true); slide.removeEventListener( "visible", - scrollFirstHighlightIntoView + scrollFirstHighlightIntoView, ); }; slide.addEventListener("visible", scrollFirstHighlightIntoView); @@ -151,7 +151,7 @@ window.QuartoLineHighlight = function () { function highlightCodeBlock(codeBlock) { const highlightSteps = splitLineNumbers( - codeBlock.getAttribute(kCodeLineNumbersAttr) + codeBlock.getAttribute(kCodeLineNumbersAttr), ); if (highlightSteps.length) { @@ -169,20 +169,20 @@ window.QuartoLineHighlight = function () { highlight.first + "):nth-of-type(-n+" + highlight.last + - ")" - ) + ")", + ), ); } else if (typeof highlight.first === "number") { spanToHighlight = [].slice.call( codeBlock.querySelectorAll( - ":scope > span:nth-of-type(" + highlight.first + ")" - ) + ":scope > span:nth-of-type(" + highlight.first + ")", + ), ); } if (spanToHighlight.length) { // Add a class on and to select line to highlight spanToHighlight.forEach((span) => - span.classList.add("highlight-line") + span.classList.add("highlight-line"), ); codeBlock.classList.add("has-line-highlights"); } @@ -226,7 +226,7 @@ window.QuartoLineHighlight = function () { // Make sure the scroll target is within bounds targetTop = Math.max( Math.min(targetTop, block.scrollHeight - viewportHeight), - 0 + 0, ); if (skipAnimation === true || startTop === targetTop) { @@ -339,8 +339,8 @@ window.QuartoLineHighlight = function () { deck .getRevealElement() .querySelectorAll( - "pre code[data-code-line-numbers].current-fragment" - ) + "pre code[data-code-line-numbers].current-fragment", + ), ) .forEach(function (block) { scrollHighlightedLineIntoView(block, {}, true); diff --git a/content/slides/slides_files/libs/revealjs/plugin/quarto-support/support.js b/content/slides/slides_files/libs/revealjs/plugin/quarto-support/support.js index 25a0bc0..d69acb8 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/quarto-support/support.js +++ b/content/slides/slides_files/libs/revealjs/plugin/quarto-support/support.js @@ -7,14 +7,14 @@ window.QuartoSupport = function () { // helper for theme toggling function toggleBackgroundTheme(el, onDarkBackground, onLightBackground) { if (onDarkBackground) { - el.classList.add('has-dark-background') + el.classList.add("has-dark-background"); } else { - el.classList.remove('has-dark-background') + el.classList.remove("has-dark-background"); } if (onLightBackground) { - el.classList.add('has-light-background') + el.classList.add("has-light-background"); } else { - el.classList.remove('has-light-background') + el.classList.remove("has-light-background"); } } @@ -96,7 +96,7 @@ window.QuartoSupport = function () { return false; } }, - false + false, ); } }); @@ -130,14 +130,20 @@ window.QuartoSupport = function () { deck.on("slidechanged", function (ev) { const revealParent = deck.getRevealElement(); const slideNumberEl = revealParent.querySelector(".slide-number"); - const onDarkBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-dark-background'); - const onLightBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-light-background'); + const onDarkBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-dark-background"); + const onLightBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-light-background"); toggleBackgroundTheme(slideNumberEl, onDarkBackground, onLightBackground); - }) + }); } - // add footer text - function addFooter(deck) { + // add footer text + function addFooter(deck) { const revealParent = deck.getRevealElement(); const defaultFooterDiv = document.querySelector(".footer-default"); if (defaultFooterDiv) { @@ -146,23 +152,37 @@ window.QuartoSupport = function () { if (!isPrintView()) { deck.on("slidechanged", function (ev) { const prevSlideFooter = document.querySelector( - ".reveal > .footer:not(.footer-default)" + ".reveal > .footer:not(.footer-default)", ); if (prevSlideFooter) { prevSlideFooter.remove(); } const currentSlideFooter = ev.currentSlide.querySelector(".footer"); - const onDarkBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-dark-background') - const onLightBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-light-background') + const onDarkBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-dark-background"); + const onLightBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-light-background"); if (currentSlideFooter) { defaultFooterDiv.style.display = "none"; const slideFooter = currentSlideFooter.cloneNode(true); handleLinkClickEvents(deck, slideFooter); deck.getRevealElement().appendChild(slideFooter); - toggleBackgroundTheme(slideFooter, onDarkBackground, onLightBackground) + toggleBackgroundTheme( + slideFooter, + onDarkBackground, + onLightBackground, + ); } else { defaultFooterDiv.style.display = "block"; - toggleBackgroundTheme(defaultFooterDiv, onDarkBackground, onLightBackground) + toggleBackgroundTheme( + defaultFooterDiv, + onDarkBackground, + onLightBackground, + ); } }); } @@ -216,7 +236,7 @@ window.QuartoSupport = function () { const config = deck.getConfig(); let buttons = !!config.chalkboard.buttons; const slideButtons = ev.currentSlide.getAttribute( - "data-chalkboard-buttons" + "data-chalkboard-buttons", ); if (slideButtons) { if (slideButtons === "true" || slideButtons === "1") { @@ -306,7 +326,7 @@ window.QuartoSupport = function () { // remove all whitespace text nodes // whitespace nodes cause the columns to be misaligned // since they have inline-block layout - // + // // Quarto emits no whitespace nodes, but third-party tooling // has bugs that can cause whitespace nodes to be emitted. // See https://github.com/quarto-dev/quarto-cli/issues/8382 diff --git a/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.css b/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.css index 5a300fd..60b0f8d 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.css +++ b/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.css @@ -1,5 +1,5 @@ .slide-menu-wrapper { - font-family: 'Source Sans Pro', Helvetica, sans-serif; + font-family: "Source Sans Pro", Helvetica, sans-serif; } .slide-menu-wrapper .slide-menu { @@ -293,8 +293,8 @@ * Theme and Transitions buttons */ -.slide-menu-wrapper div[data-panel='Themes'] li, -.slide-menu-wrapper div[data-panel='Transitions'] li { +.slide-menu-wrapper div[data-panel="Themes"] li, +.slide-menu-wrapper div[data-panel="Transitions"] li { display: block; text-align: left; cursor: pointer; @@ -326,7 +326,10 @@ height: 0; background-color: #000; opacity: 0; - transition: opacity 0.3s, width 0s 0.3s, height 0s 0.3s; + transition: + opacity 0.3s, + width 0s 0.3s, + height 0s 0.3s; } .slide-menu-wrapper .slide-menu-overlay.active { diff --git a/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.js b/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.js index 5369df3..684d236 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.js +++ b/content/slides/slides_files/libs/revealjs/plugin/reveal-menu/menu.js @@ -1 +1,2256 @@ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e=e||self).RevealMenu=t()}(this,(function(){"use strict";var e="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function t(e,t,n){return e(n={path:t,exports:{},require:function(e,t){return function(){throw new Error("Dynamic requires are not currently supported by 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Array : n)(0 === t ? 0 : t) + ); + }, + We = [].push, + He = function (e) { + var t = 1 == e, + n = 2 == e, + r = 3 == e, + i = 4 == e, + a = 6 == e, + o = 5 == e || a; + return function (s, l, c, u) { + for ( + var f, + d, + h = Ae(s), + m = p(h), + v = (function (e, t, n) { + if ((Oe(e), void 0 === t)) return e; + switch (n) { + case 0: + return function () { + return e.call(t); + }; + case 1: + return function (n) { + return e.call(t, n); + }; + case 2: + return function (n, r) { + return e.call(t, n, r); + }; + case 3: + return function (n, r, i) { + return e.call(t, n, r, i); + }; + } + return function () { + return e.apply(t, arguments); + }; + })(l, c, 3), + g = oe(m.length), + y = 0, + b = u || Fe, + S = t ? b(s, g) : n ? b(s, 0) : void 0; + g > y; + y++ + ) + if ((o || y in m) && ((d = v((f = m[y]), y, h)), e)) + if (t) S[y] = d; + else if (d) + switch (e) { + case 3: + return !0; + case 5: + return f; + case 6: + return y; + case 2: + We.call(S, f); + } + else if (i) return !1; + return a ? -1 : r || i ? i : S; + }; + }, + Ue = { + forEach: He(0), + map: He(1), + filter: He(2), + some: He(3), + every: He(4), + find: He(5), + findIndex: He(6), + }, + $e = function (e, t) { + var n = [][e]; + return ( + !!n && + i(function () { + n.call( + null, + t || + function () { + throw 1; + }, + 1, + ); + }) + ); + }, + De = Object.defineProperty, + qe = {}, + Be = function (e) { + throw e; + }, + Ge = function (e, t) { + if (b(qe, e)) return qe[e]; + t || (t = {}); + var n = [][e], + r = !!b(t, "ACCESSORS") && t.ACCESSORS, + o = b(t, 0) ? t[0] : Be, + s = b(t, 1) ? t[1] : void 0; + return (qe[e] = + !!n && + !i(function () { + if (r && !a) return !0; + var e = { length: -1 }; + r ? De(e, 1, { enumerable: !0, get: Be }) : (e[1] = 1), + n.call(e, o, s); + })); + }, + Ve = Ue.every, + Ke = $e("every"), + ze = Ge("every"); + Ce( + { target: "Array", proto: !0, forced: !Ke || !ze }, + { + every: function (e) { + return Ve(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ); + var Xe, + Ye, + Je = te("navigator", "userAgent") || "", + Ze = r.process, + Qe = Ze && Ze.versions, + et = Qe && Qe.v8; + et + ? (Ye = (Xe = et.split("."))[0] + Xe[1]) + : Je && + (!(Xe = Je.match(/Edge\/(\d+)/)) || Xe[1] >= 74) && + (Xe = Je.match(/Chrome\/(\d+)/)) && + (Ye = Xe[1]); + var tt = Ye && +Ye, + nt = Ne("species"), + rt = function (e) { + return ( + tt >= 51 || + !i(function () { + var t = []; + return ( + ((t.constructor = {})[nt] = function () { + return { foo: 1 }; + }), + 1 !== t[e](Boolean).foo + ); + }) + ); + }, + it = Ue.filter, + at = rt("filter"), + ot = Ge("filter"); + Ce( + { target: "Array", proto: !0, forced: !at || !ot }, + { + filter: function (e) { + return it(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ); + var st = Ue.forEach, + lt = $e("forEach"), + ct = Ge("forEach"), + ut = + lt && ct + ? [].forEach + : function (e) { + return st(this, e, arguments.length > 1 ? arguments[1] : void 0); + }; + Ce({ target: "Array", proto: !0, forced: [].forEach != ut }, { forEach: ut }); + var ft = fe.indexOf, + dt = [].indexOf, + pt = !!dt && 1 / [1].indexOf(1, -0) < 0, + ht = $e("indexOf"), + mt = Ge("indexOf", { ACCESSORS: !0, 1: 0 }); + Ce( + { target: "Array", proto: !0, forced: pt || !ht || !mt }, + { + indexOf: function (e) { + return pt + ? dt.apply(this, arguments) || 0 + : ft(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ), + Ce({ target: "Array", stat: !0 }, { isArray: ke }); + var vt = [].join, + gt = p != Object, + yt = $e("join", ","); + Ce( + { target: "Array", proto: !0, forced: gt || !yt }, + { + join: function (e) { + return vt.call(m(this), void 0 === e ? "," : e); + }, + }, + ); + var bt = Math.min, + St = [].lastIndexOf, + Et = !!St && 1 / [1].lastIndexOf(1, -0) < 0, + xt = $e("lastIndexOf"), + wt = Ge("indexOf", { ACCESSORS: !0, 1: 0 }), + Lt = + Et || !xt || !wt + ? function (e) { + if (Et) return St.apply(this, arguments) || 0; + var t = m(this), + n = oe(t.length), + r = n - 1; + for ( + arguments.length > 1 && (r = bt(r, ie(arguments[1]))), + r < 0 && (r = n + r); + r >= 0; + r-- + ) + if (r in t && t[r] === e) return r || 0; + return -1; + } + : St; + Ce( + { target: "Array", proto: !0, forced: Lt !== [].lastIndexOf }, + { lastIndexOf: Lt }, + ); + var Tt = Ue.map, + Ct = rt("map"), + Ot = Ge("map"); + Ce( + { target: "Array", proto: !0, forced: !Ct || !Ot }, + { + map: function (e) { + return Tt(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ); + var At = function (e, t, n) { + var r = g(t); + r in e ? O.f(e, r, c(0, n)) : (e[r] = n); + }, + kt = rt("slice"), + It = Ge("slice", { ACCESSORS: !0, 0: 0, 1: 2 }), + Pt = Ne("species"), + Mt = [].slice, + Rt = Math.max; + Ce( + { target: "Array", proto: !0, forced: !kt || !It }, + { + slice: function (e, t) { + var n, + r, + i, + a = m(this), + o = oe(a.length), + s = ce(e, o), + l = ce(void 0 === t ? o : t, o); + if ( + ke(a) && + ("function" != typeof (n = a.constructor) || + (n !== Array && !ke(n.prototype)) + ? v(n) && null === (n = n[Pt]) && (n = void 0) + : (n = void 0), + n === Array || void 0 === n) + ) + return Mt.call(a, s, l); + for ( + r = new (void 0 === n ? Array : n)(Rt(l - s, 0)), i = 0; + s < l; + s++, i++ + ) + s in a && At(r, i, a[s]); + return (r.length = i), r; + }, + }, + ); + var jt = O.f, + Nt = Function.prototype, + _t = Nt.toString, + Ft = /^\s*function ([^ (]*)/; + a && + !("name" in Nt) && + jt(Nt, "name", { + configurable: !0, + get: function () { + try { + return _t.call(this).match(Ft)[1]; + } catch (e) { + return ""; + } + }, + }); + var Wt = he.f, + Ht = {}.toString, + Ut = + "object" == typeof window && window && Object.getOwnPropertyNames + ? Object.getOwnPropertyNames(window) + : [], + $t = function (e) { + return Ut && "[object Window]" == Ht.call(e) + ? (function (e) { + try { + return Wt(e); + } catch (e) { + return Ut.slice(); + } + })(e) + : Wt(m(e)); + }; + Ce( + { + target: "Object", + stat: !0, + forced: i(function () { + return !Object.getOwnPropertyNames(1); + }), + }, + { getOwnPropertyNames: $t }, + ); + var Dt = "\t\n\v\f\r                　\u2028\u2029\ufeff", + qt = "[" + Dt + "]", + Bt = RegExp("^" + qt + qt + "*"), + Gt = RegExp(qt + qt + "*$"), + Vt = function (e) { + return function (t) { + var n = String(h(t)); + return ( + 1 & e && (n = n.replace(Bt, "")), 2 & e && (n = n.replace(Gt, "")), n + ); + }; + }, + Kt = { start: Vt(1), end: Vt(2), trim: Vt(3) }, + zt = Kt.trim, + Xt = r.parseFloat, + Yt = + 1 / Xt(Dt + "-0") != -1 / 0 + ? function (e) { + var t = zt(String(e)), + n = Xt(t); + return 0 === n && "-" == t.charAt(0) ? -0 : n; + } + : Xt; + Ce({ global: !0, forced: parseFloat != Yt }, { parseFloat: Yt }); + var Jt = Kt.trim, + Zt = r.parseInt, + Qt = /^[+-]?0[Xx]/, + en = + 8 !== Zt(Dt + "08") || 22 !== Zt(Dt + "0x16") + ? function (e, t) { + var n = Jt(String(e)); + return Zt(n, t >>> 0 || (Qt.test(n) ? 16 : 10)); + } + : Zt; + Ce({ global: !0, forced: parseInt != en }, { parseInt: en }); + var tn = function () { + var e = T(this), + t = ""; + return ( + e.global && (t += "g"), + e.ignoreCase && (t += "i"), + e.multiline && (t += "m"), + e.dotAll && (t += "s"), + e.unicode && (t += "u"), + e.sticky && (t += "y"), + t + ); + }; + function nn(e, t) { + return RegExp(e, t); + } + var rn, + an, + on = { + UNSUPPORTED_Y: i(function () { + var e = nn("a", "y"); + return (e.lastIndex = 2), null != e.exec("abcd"); + }), + BROKEN_CARET: i(function () { + var e = nn("^r", "gy"); + return (e.lastIndex = 2), null != e.exec("str"); + }), + }, + sn = RegExp.prototype.exec, + ln = String.prototype.replace, + cn = sn, + un = + ((rn = /a/), + (an = /b*/g), + sn.call(rn, "a"), + sn.call(an, "a"), + 0 !== rn.lastIndex || 0 !== an.lastIndex), + fn = on.UNSUPPORTED_Y || on.BROKEN_CARET, + dn = void 0 !== /()??/.exec("")[1]; + (un || dn || fn) && + (cn = function (e) { + var t, + n, + r, + i, + a = this, + o = fn && a.sticky, + s = tn.call(a), + l = a.source, + c = 0, + u = e; + return ( + o && + (-1 === (s = s.replace("y", "")).indexOf("g") && (s += "g"), + (u = String(e).slice(a.lastIndex)), + a.lastIndex > 0 && + (!a.multiline || (a.multiline && "\n" !== e[a.lastIndex - 1])) && + ((l = "(?: " + l + ")"), (u = " " + u), c++), + (n = new RegExp("^(?:" + l + ")", s))), + dn && (n = new RegExp("^" + l + "$(?!\\s)", s)), + un && (t = a.lastIndex), + (r = sn.call(o ? n : a, u)), + o + ? r + ? ((r.input = r.input.slice(c)), + (r[0] = r[0].slice(c)), + (r.index = a.lastIndex), + (a.lastIndex += r[0].length)) + : (a.lastIndex = 0) + : un && r && (a.lastIndex = a.global ? r.index + r[0].length : t), + dn && + r && + r.length > 1 && + ln.call(r[0], n, function () { + for (i = 1; i < arguments.length - 2; i++) + void 0 === arguments[i] && (r[i] = void 0); + }), + r + ); + }); + var pn = cn; + Ce({ target: "RegExp", proto: !0, forced: /./.exec !== pn }, { exec: pn }); + var hn, + mn = Ne("match"), + vn = function (e) { + var t; + return v(e) && (void 0 !== (t = e[mn]) ? !!t : "RegExp" == f(e)); + }, + gn = function (e) { + if (vn(e)) + throw TypeError("The method doesn't accept regular expressions"); + return e; + }, + yn = Ne("match"), + bn = function (e) { + var t = /./; + try { + "/./"[e](t); + } catch (n) { + try { + return (t[yn] = !1), "/./"[e](t); + } catch (e) {} + } + return !1; + }, + Sn = L.f, + En = "".endsWith, + xn = Math.min, + wn = bn("endsWith"); + Ce( + { + target: "String", + proto: !0, + forced: + !!( + wn || ((hn = Sn(String.prototype, "endsWith")), !hn || hn.writable) + ) && !wn, + }, + { + endsWith: function (e) { + var t = String(h(this)); + gn(e); + var n = arguments.length > 1 ? arguments[1] : void 0, + r = oe(t.length), + i = void 0 === n ? r : xn(oe(n), r), + a = String(e); + return En ? En.call(t, a, i) : t.slice(i - a.length, i) === a; + }, + }, + ); + var Ln = Ne("species"), + Tn = !i(function () { + var e = /./; + return ( + (e.exec = function () { + var e = []; + return (e.groups = { a: "7" }), e; + }), + "7" !== "".replace(e, "$    ") + ); + }), + Cn = "$0" === "a".replace(/./, "$0"), + On = Ne("replace"), + An = !!/./[On] && "" === /./[On]("a", "$0"), + kn = !i(function () { + var e = /(?:)/, + t = e.exec; + e.exec = function () { + return t.apply(this, arguments); + }; + var n = "ab".split(e); + return 2 !== n.length || "a" !== n[0] || "b" !== n[1]; + }), + In = function (e, t, n, r) { + var a = Ne(e), + o = !i(function () { + var t = {}; + return ( + (t[a] = function () { + return 7; + }), + 7 != ""[e](t) + ); + }), + s = + o && + !i(function () { + var t = !1, + n = /a/; + return ( + "split" === e && + (((n = {}).constructor = {}), + (n.constructor[Ln] = function () { + return n; + }), + (n.flags = ""), + (n[a] = /./[a])), + (n.exec = function () { + return (t = !0), null; + }), + n[a](""), + !t + ); + }); + if ( + !o || + !s || + ("replace" === e && (!Tn || !Cn || An)) || + ("split" === e && !kn) + ) { + var l = /./[a], + c = n( + a, + ""[e], + function (e, t, n, r, i) { + return t.exec === pn + ? o && !i + ? { done: !0, value: l.call(t, n, r) } + : { done: !0, value: e.call(n, t, r) } + : { done: !1 }; + }, + { + REPLACE_KEEPS_$0: Cn, + REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE: An, + }, + ), + u = c[0], + f = c[1]; + Z(String.prototype, e, u), + Z( + RegExp.prototype, + a, + 2 == t + ? function (e, t) { + return f.call(e, this, t); + } + : function (e) { + return f.call(e, this); + }, + ); + } + r && A(RegExp.prototype[a], "sham", !0); + }, + Pn = function (e) { + return function (t, n) { + var r, + i, + a = String(h(t)), + o = ie(n), + s = a.length; + return o < 0 || o >= s + ? e + ? "" + : void 0 + : (r = a.charCodeAt(o)) < 55296 || + r > 56319 || + o + 1 === s || + (i = a.charCodeAt(o + 1)) < 56320 || + i > 57343 + ? e + ? a.charAt(o) + : r + : e + ? a.slice(o, o + 2) + : i - 56320 + ((r - 55296) << 10) + 65536; + }; + }, + Mn = { codeAt: Pn(!1), charAt: Pn(!0) }.charAt, + Rn = function (e, t, n) { + return t + (n ? Mn(e, t).length : 1); + }, + jn = function (e, t) { + var n = e.exec; + if ("function" == typeof n) { + var r = n.call(e, t); + if ("object" != typeof r) + throw TypeError( + "RegExp exec method returned something other than an Object or null", + ); + return r; + } + if ("RegExp" !== f(e)) + throw TypeError("RegExp#exec called on incompatible receiver"); + return pn.call(e, t); + }, + Nn = Math.max, + _n = Math.min, + Fn = Math.floor, + Wn = /\$([$&'`]|\d\d?|<[^>]*>)/g, + Hn = /\$([$&'`]|\d\d?)/g; + In("replace", 2, function (e, t, n, r) { + var i = r.REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE, + a = r.REPLACE_KEEPS_$0, + o = i ? "$" : "$0"; + return [ + function (n, r) { + var i = h(this), + a = null == n ? void 0 : n[e]; + return void 0 !== a ? a.call(n, i, r) : t.call(String(i), n, r); + }, + function (e, r) { + if ((!i && a) || ("string" == typeof r && -1 === r.indexOf(o))) { + var l = n(t, e, this, r); + if (l.done) return l.value; + } + var c = T(e), + u = String(this), + f = "function" == typeof r; + f || (r = String(r)); + var d = c.global; + if (d) { + var p = c.unicode; + c.lastIndex = 0; + } + for (var h = []; ; ) { + var m = jn(c, u); + if (null === m) break; + if ((h.push(m), !d)) break; + "" === String(m[0]) && (c.lastIndex = Rn(u, oe(c.lastIndex), p)); + } + for (var v, g = "", y = 0, b = 0; b < h.length; b++) { + m = h[b]; + for ( + var S = String(m[0]), + E = Nn(_n(ie(m.index), u.length), 0), + x = [], + w = 1; + w < m.length; + w++ + ) + x.push(void 0 === (v = m[w]) ? v : String(v)); + var L = m.groups; + if (f) { + var C = [S].concat(x, E, u); + void 0 !== L && C.push(L); + var O = String(r.apply(void 0, C)); + } else O = s(S, u, E, x, L, r); + E >= y && ((g += u.slice(y, E) + O), (y = E + S.length)); + } + return g + u.slice(y); + }, + ]; + function s(e, n, r, i, a, o) { + var s = r + e.length, + l = i.length, + c = Hn; + return ( + void 0 !== a && ((a = Ae(a)), (c = Wn)), + t.call(o, c, function (t, o) { + var c; + switch (o.charAt(0)) { + case "$": + return "$"; + case "&": + return e; + case "`": + return n.slice(0, r); + case "'": + return n.slice(s); + case "<": + c = a[o.slice(1, -1)]; + break; + default: + var u = +o; + if (0 === u) return t; + if (u > l) { + var f = Fn(u / 10); + return 0 === f + ? t + : f <= l + ? void 0 === i[f - 1] + ? o.charAt(1) + : i[f - 1] + o.charAt(1) + : t; + } + c = i[u - 1]; + } + return void 0 === c ? "" : c; + }) + ); + } + }); + var Un = + Object.is || + function (e, t) { + return e === t ? 0 !== e || 1 / e == 1 / t : e != e && t != t; + }; + In("search", 1, function (e, t, n) { + return [ + function (t) { + var n = h(this), + r = null == t ? void 0 : t[e]; + return void 0 !== r ? r.call(t, n) : new RegExp(t)[e](String(n)); + }, + function (e) { + var r = n(t, e, this); + if (r.done) return r.value; + var i = T(e), + a = String(this), + o = i.lastIndex; + Un(o, 0) || (i.lastIndex = 0); + var s = jn(i, a); + return ( + Un(i.lastIndex, o) || (i.lastIndex = o), null === s ? -1 : s.index + ); + }, + ]; + }); + var $n = Ne("species"), + Dn = [].push, + qn = Math.min, + Bn = !i(function () { + return !RegExp(4294967295, "y"); + }); + In( + "split", + 2, + function (e, t, n) { + var r; + return ( + (r = + "c" == "abbc".split(/(b)*/)[1] || + 4 != "test".split(/(?:)/, -1).length || + 2 != "ab".split(/(?:ab)*/).length || + 4 != ".".split(/(.?)(.?)/).length || + ".".split(/()()/).length > 1 || + "".split(/.?/).length + ? function (e, n) { + var r = String(h(this)), + i = void 0 === n ? 4294967295 : n >>> 0; + if (0 === i) return []; + if (void 0 === e) return [r]; + if (!vn(e)) return t.call(r, e, i); + for ( + var a, + o, + s, + l = [], + c = + (e.ignoreCase ? "i" : "") + + (e.multiline ? "m" : "") + + (e.unicode ? "u" : "") + + (e.sticky ? "y" : ""), + u = 0, + f = new RegExp(e.source, c + "g"); + (a = pn.call(f, r)) && + !( + (o = f.lastIndex) > u && + (l.push(r.slice(u, a.index)), + a.length > 1 && + a.index < r.length && + Dn.apply(l, a.slice(1)), + (s = a[0].length), + (u = o), + l.length >= i) + ); + + ) + f.lastIndex === a.index && f.lastIndex++; + return ( + u === r.length + ? (!s && f.test("")) || l.push("") + : l.push(r.slice(u)), + l.length > i ? l.slice(0, i) : l + ); + } + : "0".split(void 0, 0).length + ? function (e, n) { + return void 0 === e && 0 === n ? [] : t.call(this, e, n); + } + : t), + [ + function (t, n) { + var i = h(this), + a = null == t ? void 0 : t[e]; + return void 0 !== a ? a.call(t, i, n) : r.call(String(i), t, n); + }, + function (e, i) { + var a = n(r, e, this, i, r !== t); + if (a.done) return a.value; + var o = T(e), + s = String(this), + l = (function (e, t) { + var n, + r = T(e).constructor; + return void 0 === r || null == (n = T(r)[$n]) ? t : Oe(n); + })(o, RegExp), + c = o.unicode, + u = + (o.ignoreCase ? "i" : "") + + (o.multiline ? "m" : "") + + (o.unicode ? "u" : "") + + (Bn ? "y" : "g"), + f = new l(Bn ? o : "^(?:" + o.source + ")", u), + d = void 0 === i ? 4294967295 : i >>> 0; + if (0 === d) return []; + if (0 === s.length) return null === jn(f, s) ? [s] : []; + for (var p = 0, h = 0, m = []; h < s.length; ) { + f.lastIndex = Bn ? h : 0; + var v, + g = jn(f, Bn ? s : s.slice(h)); + if ( + null === g || + (v = qn(oe(f.lastIndex + (Bn ? 0 : h)), s.length)) === p + ) + h = Rn(s, h, c); + else { + if ((m.push(s.slice(p, h)), m.length === d)) return m; + for (var y = 1; y <= g.length - 1; y++) + if ((m.push(g[y]), m.length === d)) return m; + h = p = v; + } + } + return m.push(s.slice(p)), m; + }, + ] + ); + }, + !Bn, + ); + var Gn = L.f, + Vn = "".startsWith, + Kn = Math.min, + zn = bn("startsWith"); + Ce( + { + target: "String", + proto: !0, + forced: + !( + !zn && + !!(function () { + var e = Gn(String.prototype, "startsWith"); + return e && !e.writable; + })() + ) && !zn, + }, + { + startsWith: function (e) { + var t = String(h(this)); + gn(e); + var n = oe(Kn(arguments.length > 1 ? arguments[1] : void 0, t.length)), + r = String(e); + return Vn ? Vn.call(t, r, n) : t.slice(n, n + r.length) === r; + }, + }, + ); + var Xn, + Yn = Kt.trim; + Ce( + { + target: "String", + proto: !0, + forced: + ((Xn = "trim"), + i(function () { + return !!Dt[Xn]() || "​…᠎" != "​…᠎"[Xn]() || Dt[Xn].name !== Xn; + })), + }, + { + trim: function () { + return Yn(this); + }, + }, + ); + for (var Jn in { + CSSRuleList: 0, + CSSStyleDeclaration: 0, + CSSValueList: 0, + ClientRectList: 0, + DOMRectList: 0, + DOMStringList: 0, + DOMTokenList: 1, + DataTransferItemList: 0, + FileList: 0, + HTMLAllCollection: 0, + HTMLCollection: 0, + HTMLFormElement: 0, + HTMLSelectElement: 0, + MediaList: 0, + MimeTypeArray: 0, + NamedNodeMap: 0, + NodeList: 1, + PaintRequestList: 0, + Plugin: 0, + PluginArray: 0, + SVGLengthList: 0, + SVGNumberList: 0, + SVGPathSegList: 0, + SVGPointList: 0, + SVGStringList: 0, + SVGTransformList: 0, + SourceBufferList: 0, + StyleSheetList: 0, + TextTrackCueList: 0, + TextTrackList: 0, + TouchList: 0, + }) { + var Zn = r[Jn], + Qn = Zn && Zn.prototype; + if (Qn && Qn.forEach !== ut) + try { + A(Qn, "forEach", ut); + } catch (e) { + Qn.forEach = ut; + } + } + var er = [].slice, + tr = function (e) { + return function (t, n) { + var r = arguments.length > 2, + i = r ? er.call(arguments, 2) : void 0; + return e( + r + ? function () { + ("function" == typeof t ? t : Function(t)).apply(this, i); + } + : t, + n, + ); + }; + }; + Ce( + { global: !0, bind: !0, forced: /MSIE .\./.test(Je) }, + { setTimeout: tr(r.setTimeout), setInterval: tr(r.setInterval) }, + ); + return ( + String.prototype.startsWith || + (String.prototype.startsWith = function (e, t) { + return this.substr(t || 0, e.length) === e; + }), + String.prototype.endsWith || + (String.prototype.endsWith = function (e, t) { + return ( + (void 0 === t || t > this.length) && (t = this.length), + this.substring(t - e.length, t) === e + ); + }), + function () { + var e, + t, + n, + r, + i = + (e = /(msie) ([\w.]+)/.exec( + window.navigator.userAgent.toLowerCase(), + )) && "msie" === e[1] + ? parseFloat(e[2]) + : null, + a = !1; + function o(e) { + ((r = e.menu || {}).path = + r.path || + (function () { + var e; + if (document.querySelector('script[src$="menu.js"]')) { + var t = document.querySelector('script[src$="menu.js"]'); + t && (e = t.src.slice(0, -7)); + } else + e = ( + "undefined" == typeof document + ? new (require("url").URL)("file:" + __filename).href + : (document.currentScript && document.currentScript.src) || + new URL("menu.js", document.baseURI).href + ).slice( + 0, + ("undefined" == typeof document + ? new (require("url").URL)("file:" + __filename).href + : (document.currentScript && document.currentScript.src) || + new URL("menu.js", document.baseURI).href + ).lastIndexOf("/") + 1, + ); + return e; + })() || + "plugin/menu/"), + r.path.endsWith("/") || (r.path += "/"), + void 0 === r.side && (r.side = "left"), + void 0 === r.numbers && (r.numbers = !1), + "string" != typeof r.titleSelector && + (r.titleSelector = "h1, h2, h3, h4, h5"), + void 0 === r.hideMissingTitles && (r.hideMissingTitles = !1), + void 0 === r.useTextContentForMissingTitles && + (r.useTextContentForMissingTitles = !1), + void 0 === r.markers && (r.markers = !0), + "string" != typeof r.themesPath && (r.themesPath = "dist/theme/"), + r.themesPath.endsWith("/") || (r.themesPath += "/"), + O("link#theme") || (r.themes = !1), + !0 === r.themes + ? (r.themes = [ + { name: "Black", theme: r.themesPath + "black.css" }, + { name: "White", theme: r.themesPath + "white.css" }, + { name: "League", theme: r.themesPath + "league.css" }, + { name: "Sky", theme: r.themesPath + "sky.css" }, + { name: "Beige", theme: r.themesPath + "beige.css" }, + { name: "Simple", theme: r.themesPath + "simple.css" }, + { name: "Serif", theme: r.themesPath + "serif.css" }, + { name: "Blood", theme: r.themesPath + "blood.css" }, + { name: "Night", theme: r.themesPath + "night.css" }, + { name: "Moon", theme: r.themesPath + "moon.css" }, + { name: "Solarized", theme: r.themesPath + "solarized.css" }, + ]) + : Array.isArray(r.themes) || (r.themes = !1), + void 0 === r.transitions && (r.transitions = !1), + !0 === r.transitions + ? (r.transitions = [ + "None", + "Fade", + "Slide", + "Convex", + "Concave", + "Zoom", + ]) + : !1 === r.transitions || + (Array.isArray(r.transitions) && + r.transitions.every(function (e) { + return "string" == typeof e; + })) || + (console.error( + "reveal.js-menu error: transitions config value must be 'true' or an array of strings, eg ['None', 'Fade', 'Slide')", + ), + (r.transitions = !1)), + i && i <= 9 && (r.transitions = !1), + void 0 === r.openButton && (r.openButton = !0), + void 0 === r.openSlideNumber && (r.openSlideNumber = !1), + void 0 === r.keyboard && (r.keyboard = !0), + void 0 === r.sticky && (r.sticky = !1), + void 0 === r.autoOpen && (r.autoOpen = !0), + void 0 === r.delayInit && (r.delayInit = !1), + void 0 === r.openOnInit && (r.openOnInit = !1); + } + var s = !0; + function l() { + s = !1; + } + function c() { + O("nav.slide-menu").addEventListener("mousemove", function e(t) { + O("nav.slide-menu").removeEventListener("mousemove", e), (s = !0); + }); + } + function u(e) { + var t = + (function (e) { + for ( + var t = 0, n = 0; + e && !isNaN(e.offsetLeft) && !isNaN(e.offsetTop); + + ) + (t += e.offsetLeft - e.scrollLeft), + (n += e.offsetTop - e.scrollTop), + (e = e.offsetParent); + return { top: n, left: t }; + })(e).top - e.offsetParent.offsetTop; + if (t < 0) return -t; + var n = + e.offsetParent.offsetHeight - + (e.offsetTop - e.offsetParent.scrollTop + e.offsetHeight); + return n < 0 ? n : 0; + } + function f(e) { + var t = u(e); + t && (l(), e.scrollIntoView(t > 0), c()); + } + function d(e) { + l(), (e.offsetParent.scrollTop = e.offsetTop), c(); + } + function p(e) { + l(), + (e.offsetParent.scrollTop = + e.offsetTop - e.offsetParent.offsetHeight + e.offsetHeight), + c(); + } + function h(e) { + e.classList.add("selected"), f(e), r.sticky && r.autoOpen && E(e); + } + function m(e) { + if (b()) + switch ((e.stopImmediatePropagation(), e.keyCode)) { + case 72: + case 37: + !(function () { + var e = + parseInt( + O(".active-toolbar-button").getAttribute("data-button"), + ) - 1; + e < 0 && (e = T - 1); + S( + null, + O( + '.toolbar-panel-button[data-button="' + e + '"]', + ).getAttribute("data-panel"), + ); + })(); + break; + case 76: + case 39: + (l = + (parseInt( + O(".active-toolbar-button").getAttribute("data-button"), + ) + + 1) % + T), + S( + null, + O( + '.toolbar-panel-button[data-button="' + l + '"]', + ).getAttribute("data-panel"), + ); + break; + case 75: + case 38: + if ( + (s = + O(".active-menu-panel .slide-menu-items li.selected") || + O(".active-menu-panel .slide-menu-items li.active")) + ) + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h( + O( + '.active-menu-panel .slide-menu-items li[data-item="' + + (parseInt(s.getAttribute("data-item")) - 1) + + '"]', + ) || s, + ); + else + (o = O( + ".active-menu-panel .slide-menu-items li.slide-menu-item", + )) && h(o); + break; + case 74: + case 40: + if ( + (s = + O(".active-menu-panel .slide-menu-items li.selected") || + O(".active-menu-panel .slide-menu-items li.active")) + ) + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h( + O( + '.active-menu-panel .slide-menu-items li[data-item="' + + (parseInt(s.getAttribute("data-item")) + 1) + + '"]', + ) || s, + ); + else + (o = O( + ".active-menu-panel .slide-menu-items li.slide-menu-item", + )) && h(o); + break; + case 33: + case 85: + var t = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return u(e) > 0; + }, + ), + n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ), + r = + t.length > 0 && + Math.abs(u(t[t.length - 1])) < t[t.length - 1].clientHeight + ? t[t.length - 1] + : n[0]; + r && + (r.classList.contains("selected") && + t.length > 0 && + (p(r), + (r = + (n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ))[0] == r + ? t[t.length - 1] + : n[0])), + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h(r), + d(r)); + break; + case 34: + case 68: + n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ); + var i = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return u(e) < 0; + }, + ), + a = + i.length > 0 && Math.abs(u(i[0])) < i[0].clientHeight + ? i[0] + : n[n.length - 1]; + a && + (a.classList.contains("selected") && + i.length > 0 && + (d(a), + (a = + (n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ))[n.length - 1] == a + ? i[0] + : n[n.length - 1])), + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h(a), + p(a)); + break; + case 36: + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + (o = O( + ".active-menu-panel .slide-menu-items li:first-of-type", + )) && (o.classList.add("selected"), f(o)); + break; + case 35: + var o; + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + (o = O( + ".active-menu-panel .slide-menu-items:last-of-type li:last-of-type", + )) && (o.classList.add("selected"), f(o)); + break; + case 32: + case 13: + var s; + (s = O(".active-menu-panel .slide-menu-items li.selected")) && + E(s, !0); + break; + case 27: + g(null, !0); + } + var l; + } + function v(e) { + (e && e.preventDefault(), b()) || + (O("body").classList.add("slide-menu-active"), + O(".reveal").classList.add("has-" + r.effect + "-" + r.side), + O(".slide-menu").classList.add("active"), + O(".slide-menu-overlay").classList.add("active"), + r.themes && + (A('div[data-panel="Themes"] li').forEach(function (e) { + e.classList.remove("active"); + }), + A( + 'li[data-theme="' + O("link#theme").getAttribute("href") + '"]', + ).forEach(function (e) { + e.classList.add("active"); + })), + r.transitions && + (A('div[data-panel="Transitions"] li').forEach(function (e) { + e.classList.remove("active"); + }), + A('li[data-transition="' + n.transition + '"]').forEach( + function (e) { + e.classList.add("active"); + }, + )), + A(".slide-menu-panel li.active").forEach(function (e) { + e.classList.add("selected"), f(e); + })); + } + function g(e, t) { + e && e.preventDefault(), + (r.sticky && !t) || + (O("body").classList.remove("slide-menu-active"), + O(".reveal").classList.remove("has-" + r.effect + "-" + r.side), + O(".slide-menu").classList.remove("active"), + O(".slide-menu-overlay").classList.remove("active"), + A(".slide-menu-panel li.selected").forEach(function (e) { + e.classList.remove("selected"); + })); + } + function y(e) { + b() ? g(e, !0) : v(e); + } + function b() { + return O("body").classList.contains("slide-menu-active"); + } + function S(e, t) { + v(e); + var n = t; + "string" != typeof t && + (n = e.currentTarget.getAttribute("data-panel")), + O(".slide-menu-toolbar > li.active-toolbar-button").classList.remove( + "active-toolbar-button", + ), + O('li[data-panel="' + n + '"]').classList.add( + "active-toolbar-button", + ), + O(".slide-menu-panel.active-menu-panel").classList.remove( + "active-menu-panel", + ), + O('div[data-panel="' + n + '"]').classList.add("active-menu-panel"); + } + function E(e, n) { + var i = parseInt(e.getAttribute("data-slide-h")), + a = parseInt(e.getAttribute("data-slide-v")), + o = e.getAttribute("data-theme"), + s = e.getAttribute("data-highlight-theme"), + l = e.getAttribute("data-transition"); + isNaN(i) || isNaN(a) || t.slide(i, a), + o && I("theme", o), + s && I("highlight-theme", s), + l && t.configure({ transition: l }); + var c = O("a", e); + c && + (n || + !r.sticky || + (r.autoOpen && c.href.startsWith("#")) || + c.href.startsWith( + window.location.origin + window.location.pathname + "#", + )) && + c.click(), + g(); + } + function x(e) { + "A" !== e.target.nodeName && e.preventDefault(), E(e.currentTarget); + } + function w() { + var e = t.getState(); + A("li.slide-menu-item, li.slide-menu-item-vertical").forEach( + function (t) { + t.classList.remove("past"), + t.classList.remove("active"), + t.classList.remove("future"); + var n = parseInt(t.getAttribute("data-slide-h")), + r = parseInt(t.getAttribute("data-slide-v")); + n < e.indexh || (n === e.indexh && r < e.indexv) + ? t.classList.add("past") + : n === e.indexh && r === e.indexv + ? t.classList.add("active") + : t.classList.add("future"); + }, + ); + } + function L() { + var e = window.getComputedStyle(O(".reveal")); + O(".slide-menu").style.fontFamily = e.fontFamily; + } + var T = 0; + function C() { + if (!a) { + var e = function (e, t, n, r, i, a) { + var o = { + "data-button": "" + T++, + class: + "toolbar-panel-button" + (a ? " active-toolbar-button" : ""), + }; + t && (o["data-panel"] = t); + var s = k("li", o); + return ( + n.startsWith("fa-") + ? s.appendChild(k("i", { class: r + " " + n })) + : (s.innerHTML = n + ""), + s.appendChild(k("br"), O("i", s)), + s.appendChild( + k("span", { class: "slide-menu-toolbar-label" }, e), + O("i", s), + ), + (s.onclick = i), + d.appendChild(s), + s + ); + }, + i = function (e, i, a, o, s) { + function l(e, t) { + if ("" === e) return null; + var n = t ? O(e, i) : O(e); + return n ? n.textContent : null; + } + var c = + i.getAttribute("data-menu-title") || + l(".menu-title", i) || + l(r.titleSelector, i); + if ( + (!c && + r.useTextContentForMissingTitles && + (c = i.textContent.trim()) && + (c = + c + .split("\n") + .map(function (e) { + return e.trim(); + }) + .join(" ") + .trim() + .replace(/^(.{16}[^\s]*).*/, "$1") + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/'/g, "'") + "..."), + !c) + ) { + if (r.hideMissingTitles) return ""; + (e += " no-title"), (c = "Slide " + (a + 1)); + } + var u = k("li", { + class: e, + "data-item": a, + "data-slide-h": o, + "data-slide-v": void 0 === s ? 0 : s, + }); + if ( + (r.markers && + (u.appendChild( + k("i", { class: "fas fa-check-circle fa-fw past" }), + ), + u.appendChild( + k("i", { + class: "fas fa-arrow-alt-circle-right fa-fw active", + }), + ), + u.appendChild( + k("i", { class: "far fa-circle fa-fw future" }), + )), + r.numbers) + ) { + var f = [], + d = "h.v"; + switch ( + ("string" == typeof r.numbers + ? (d = r.numbers) + : "string" == typeof n.slideNumber && (d = n.slideNumber), + d) + ) { + case "c": + f.push(a + 1); + break; + case "c/t": + f.push(a + 1, "/", t.getTotalSlides()); + break; + case "h/v": + f.push(o + 1), + "number" != typeof s || isNaN(s) || f.push("/", s + 1); + break; + default: + f.push(o + 1), + "number" != typeof s || isNaN(s) || f.push(".", s + 1); + } + u.appendChild( + k( + "span", + { class: "slide-menu-item-number" }, + f.join("") + ". ", + ), + ); + } + return ( + u.appendChild(k("span", { class: "slide-menu-item-title" }, c)), + u + ); + }, + o = function (e) { + s && + (A(".active-menu-panel .slide-menu-items li.selected").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + e.currentTarget.classList.add("selected")); + }, + l = O(".reveal").parentElement, + c = k("div", { class: "slide-menu-wrapper" }); + l.appendChild(c); + var u = k("nav", { class: "slide-menu slide-menu--" + r.side }); + "string" == typeof r.width && + (-1 != ["normal", "wide", "third", "half", "full"].indexOf(r.width) + ? u.classList.add("slide-menu--" + r.width) + : (u.classList.add("slide-menu--custom"), + (u.style.width = r.width))), + c.appendChild(u), + L(); + var f = k("div", { class: "slide-menu-overlay" }); + c.appendChild(f), + (f.onclick = function () { + g(null, !0); + }); + var d = k("ol", { class: "slide-menu-toolbar" }); + O(".slide-menu").appendChild(d), + e("Slides", "Slides", "fa-images", "fas", S, !0), + r.custom && + r.custom.forEach(function (t, n, r) { + e(t.title, "Custom" + n, t.icon, null, S); + }), + r.themes && e("Themes", "Themes", "fa-adjust", "fas", S), + r.transitions && + e("Transitions", "Transitions", "fa-sticky-note", "fas", S); + var p = k("li", { id: "close", class: "toolbar-panel-button" }); + if ( + (p.appendChild(k("i", { class: "fas fa-times" })), + p.appendChild(k("br")), + p.appendChild( + k("span", { class: "slide-menu-toolbar-label" }, "Close"), + ), + (p.onclick = function () { + g(null, !0); + }), + d.appendChild(p), + (function e() { + if ( + document.querySelector( + "section[data-markdown]:not([data-markdown-parsed])", + ) + ) + setTimeout(e, 100); + else { + var t = k("div", { + "data-panel": "Slides", + class: "slide-menu-panel active-menu-panel", + }); + t.appendChild(k("ul", { class: "slide-menu-items" })), + u.appendChild(t); + var n = O( + '.slide-menu-panel[data-panel="Slides"] > .slide-menu-items', + ), + r = 0; + A(".slides > section").forEach(function (e, t) { + var a = A("section", e); + if (a.length > 0) + a.forEach(function (e, a) { + var o = i( + 0 === a + ? "slide-menu-item" + : "slide-menu-item-vertical", + e, + r, + t, + a, + ); + o && n.appendChild(o), r++; + }); + else { + var o = i("slide-menu-item", e, r, t); + o && n.appendChild(o), r++; + } + }), + A(".slide-menu-item, .slide-menu-item-vertical").forEach( + function (e) { + e.onclick = x; + }, + ), + w(); + } + })(), + t.addEventListener("slidechanged", w), + r.custom) + ) { + var h = function () { + this.status >= 200 && this.status < 300 + ? ((this.panel.innerHTML = this.responseText), C(this.panel)) + : I(this); + }, + E = function () { + I(this); + }, + C = function (e) { + A("ul.slide-menu-items li.slide-menu-item", e).forEach( + function (e, t) { + e.setAttribute("data-item", t + 1), + (e.onclick = x), + e.addEventListener("mouseenter", o); + }, + ); + }, + I = function (e) { + var t = + "

    ERROR: The attempt to fetch " + + e.responseURL + + " failed with HTTP status " + + e.status + + " (" + + e.statusText + + ").

    Remember that you need to serve the presentation HTML from a HTTP server.

    "; + e.panel.innerHTML = t; + }; + r.custom.forEach(function (e, t, n) { + var r = k("div", { + "data-panel": "Custom" + t, + class: "slide-menu-panel slide-menu-custom-panel", + }); + e.content + ? ((r.innerHTML = e.content), C(r)) + : e.src && + (function (e, t) { + var n = new XMLHttpRequest(); + (n.panel = e), + (n.arguments = Array.prototype.slice.call(arguments, 2)), + (n.onload = h), + (n.onerror = E), + n.open("get", t, !0), + n.send(null); + })(r, e.src), + u.appendChild(r); + }); + } + if (r.themes) { + var P = k("div", { + class: "slide-menu-panel", + "data-panel": "Themes", + }); + u.appendChild(P); + var M = k("ul", { class: "slide-menu-items" }); + P.appendChild(M), + r.themes.forEach(function (e, t) { + var n = { class: "slide-menu-item", "data-item": "" + (t + 1) }; + e.theme && (n["data-theme"] = e.theme), + e.highlightTheme && + (n["data-highlight-theme"] = e.highlightTheme); + var r = k("li", n, e.name); + M.appendChild(r), (r.onclick = x); + }); + } + if (r.transitions) { + P = k("div", { + class: "slide-menu-panel", + "data-panel": "Transitions", + }); + u.appendChild(P); + M = k("ul", { class: "slide-menu-items" }); + P.appendChild(M), + r.transitions.forEach(function (e, t) { + var n = k( + "li", + { + class: "slide-menu-item", + "data-transition": e.toLowerCase(), + "data-item": "" + (t + 1), + }, + e, + ); + M.appendChild(n), (n.onclick = x); + }); + } + if (r.openButton) { + var R = k("div", { class: "slide-menu-button" }), + j = k("a", { href: "#" }); + j.appendChild(k("i", { class: "fas fa-bars" })), + R.appendChild(j), + O(".reveal").appendChild(R), + (R.onclick = v); + } + if (r.openSlideNumber) O("div.slide-number").onclick = v; + A(".slide-menu-panel .slide-menu-items li").forEach(function (e) { + e.addEventListener("mouseenter", o); + }); + } + if (r.keyboard) { + if ( + (document.addEventListener("keydown", m, !1), + window.addEventListener("message", function (e) { + var t; + try { + t = JSON.parse(e.data); + } catch (e) {} + t && + "triggerKey" === t.method && + m({ + keyCode: t.args[0], + stopImmediatePropagation: function () {}, + }); + }), + n.keyboardCondition && "function" == typeof n.keyboardCondition) + ) { + var N = n.keyboardCondition; + n.keyboardCondition = function (e) { + return N(e) && (!b() || 77 == e.keyCode); + }; + } else + n.keyboardCondition = function (e) { + return !b() || 77 == e.keyCode; + }; + t.addKeyBinding( + { keyCode: 77, key: "M", description: "Toggle menu" }, + y, + ); + } + r.openOnInit && v(), (a = !0); + } + function O(e, t) { + return t || (t = document), t.querySelector(e); + } + function A(e, t) { + return ( + t || (t = document), Array.prototype.slice.call(t.querySelectorAll(e)) + ); + } + function k(e, t, n) { + var r = document.createElement(e); + return ( + t && + Object.getOwnPropertyNames(t).forEach(function (e) { + r.setAttribute(e, t[e]); + }), + n && (r.innerHTML = n), + r + ); + } + function I(e, t) { + var n = O("link#" + e), + r = n.parentElement, + i = n.nextElementSibling; + n.remove(); + var a = n.cloneNode(); + a.setAttribute("href", t), + (a.onload = function () { + L(); + }), + r.insertBefore(a, i); + } + function P(e, t, n) { + n.call(); + } + function M() { + var e, + a, + o, + s = !i || i >= 9; + t.isSpeakerNotes() && + window.location.search.endsWith("controls=false") && + (s = !1), + s && + (r.delayInit || C(), + (e = "menu-ready"), + (o = document.createEvent("HTMLEvents", 1, 2)).initEvent(e, !0, !0), + (function (e, t) { + for (var n in t) e[n] = t[n]; + })(o, a), + document.querySelector(".reveal").dispatchEvent(o), + n.postMessageEvents && + window.parent !== window.self && + window.parent.postMessage( + JSON.stringify({ + namespace: "reveal", + eventName: e, + state: t.getState(), + }), + "*", + )); + } + return { + id: "menu", + init: function (e) { + o((n = (t = e).getConfig())), + P(r.path + "menu.css", "stylesheet", function () { + void 0 === r.loadIcons || r.loadIcons + ? P(r.path + "font-awesome/css/all.css", "stylesheet", M) + : M(); + }); + }, + toggle: y, + openMenu: v, + closeMenu: g, + openPanel: S, + isOpen: b, + initialiseMenu: C, + isMenuInitialised: function () { + return a; + }, + }; + } + ); +}); diff --git a/content/slides/slides_files/libs/revealjs/plugin/search/plugin.js b/content/slides/slides_files/libs/revealjs/plugin/search/plugin.js index 5d09ce6..e6b25f9 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/search/plugin.js +++ b/content/slides/slides_files/libs/revealjs/plugin/search/plugin.js @@ -6,238 +6,242 @@ */ const Plugin = () => { - - // The reveal.js instance this plugin is attached to - let deck; - - let searchElement; - let searchButton; - let searchInput; - - let matchedSlides; - let currentMatchedIndex; - let searchboxDirty; - let hilitor; - - function render() { - - searchElement = document.createElement( 'div' ); - searchElement.classList.add( 'searchbox' ); - searchElement.style.position = 'absolute'; - searchElement.style.top = '10px'; - searchElement.style.right = '10px'; - searchElement.style.zIndex = 10; - - //embedded base64 search icon Designed by Sketchdock - http://www.sketchdock.com/: - searchElement.innerHTML = ` + // The reveal.js instance this plugin is attached to + let deck; + + let searchElement; + let searchButton; + let searchInput; + + let matchedSlides; + let currentMatchedIndex; + let searchboxDirty; + let hilitor; + + function render() { + searchElement = document.createElement("div"); + searchElement.classList.add("searchbox"); + searchElement.style.position = "absolute"; + searchElement.style.top = "10px"; + searchElement.style.right = "10px"; + searchElement.style.zIndex = 10; + + //embedded base64 search icon Designed by Sketchdock - http://www.sketchdock.com/: + searchElement.innerHTML = ` `; - searchInput = searchElement.querySelector( '.searchinput' ); - searchInput.style.width = '240px'; - searchInput.style.fontSize = '14px'; - searchInput.style.padding = '4px 6px'; - searchInput.style.color = '#000'; - searchInput.style.background = '#fff'; - searchInput.style.borderRadius = '2px'; - searchInput.style.border = '0'; - searchInput.style.outline = '0'; - searchInput.style.boxShadow = '0 2px 18px rgba(0, 0, 0, 0.2)'; - searchInput.style['-webkit-appearance'] = 'none'; - - deck.getRevealElement().appendChild( searchElement ); - - // searchButton.addEventListener( 'click', function(event) { - // doSearch(); - // }, false ); - - searchInput.addEventListener( 'keyup', function( event ) { - switch (event.keyCode) { - case 13: - event.preventDefault(); - doSearch(); - searchboxDirty = false; - break; - default: - searchboxDirty = true; - } - }, false ); - - closeSearch(); - - } - - function openSearch() { - if( !searchElement ) render(); - - searchElement.style.display = 'inline'; - searchInput.focus(); - searchInput.select(); - } - - function closeSearch() { - if( !searchElement ) render(); - - searchElement.style.display = 'none'; - if(hilitor) hilitor.remove(); - } - - function toggleSearch() { - if( !searchElement ) render(); - - if (searchElement.style.display !== 'inline') { - openSearch(); - } - else { - closeSearch(); - } - } - - function doSearch() { - //if there's been a change in the search term, perform a new search: - if (searchboxDirty) { - var searchstring = searchInput.value; - - if (searchstring === '') { - if(hilitor) hilitor.remove(); - matchedSlides = null; - } - else { - //find the keyword amongst the slides - hilitor = new Hilitor("slidecontent"); - matchedSlides = hilitor.apply(searchstring); - currentMatchedIndex = 0; - } - } - - if (matchedSlides) { - //navigate to the next slide that has the keyword, wrapping to the first if necessary - if (matchedSlides.length && (matchedSlides.length <= currentMatchedIndex)) { - currentMatchedIndex = 0; - } - if (matchedSlides.length > currentMatchedIndex) { - deck.slide(matchedSlides[currentMatchedIndex].h, matchedSlides[currentMatchedIndex].v); - currentMatchedIndex++; - } - } - } - - // Original JavaScript code by Chirp Internet: www.chirp.com.au - // Please acknowledge use of this code by including this header. - // 2/2013 jon: modified regex to display any match, not restricted to word boundaries. - function Hilitor(id, tag) { - - var targetNode = document.getElementById(id) || document.body; - var hiliteTag = tag || "EM"; - var skipTags = new RegExp("^(?:" + hiliteTag + "|SCRIPT|FORM)$"); - var colors = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"]; - var wordColor = []; - var colorIdx = 0; - var matchRegex = ""; - var matchingSlides = []; - - this.setRegex = function(input) - { - input = input.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|"); - matchRegex = new RegExp("(" + input + ")","i"); - } - - this.getRegex = function() - { - return matchRegex.toString().replace(/^\/\\b\(|\)\\b\/i$/g, "").replace(/\|/g, " "); - } - - // recursively apply word highlighting - this.hiliteWords = function(node) - { - if(node == undefined || !node) return; - if(!matchRegex) return; - if(skipTags.test(node.nodeName)) return; - - if(node.hasChildNodes()) { - for(var i=0; i < node.childNodes.length; i++) - this.hiliteWords(node.childNodes[i]); - } - if(node.nodeType == 3) { // NODE_TEXT - var nv, regs; - if((nv = node.nodeValue) && (regs = matchRegex.exec(nv))) { - //find the slide's section element and save it in our list of matching slides - var secnode = node; - while (secnode != null && secnode.nodeName != 'SECTION') { - secnode = secnode.parentNode; - } - - var slideIndex = deck.getIndices(secnode); - var slidelen = matchingSlides.length; - var alreadyAdded = false; - for (var i=0; i < slidelen; i++) { - if ( (matchingSlides[i].h === slideIndex.h) && (matchingSlides[i].v === slideIndex.v) ) { - alreadyAdded = true; - } - } - if (! alreadyAdded) { - matchingSlides.push(slideIndex); - } - - if(!wordColor[regs[0].toLowerCase()]) { - wordColor[regs[0].toLowerCase()] = colors[colorIdx++ % colors.length]; - } - - var match = document.createElement(hiliteTag); - match.appendChild(document.createTextNode(regs[0])); - match.style.backgroundColor = wordColor[regs[0].toLowerCase()]; - match.style.fontStyle = "inherit"; - match.style.color = "#000"; - - var after = node.splitText(regs.index); - after.nodeValue = after.nodeValue.substring(regs[0].length); - node.parentNode.insertBefore(match, after); - } - } - }; - - // remove highlighting - this.remove = function() - { - var arr = document.getElementsByTagName(hiliteTag); - var el; - while(arr.length && (el = arr[0])) { - el.parentNode.replaceChild(el.firstChild, el); - } - }; - - // start highlighting at target node - this.apply = function(input) - { - if(input == undefined || !input) return; - this.remove(); - this.setRegex(input); - this.hiliteWords(targetNode); - return matchingSlides; - }; - - } - - return { - - id: 'search', - - init: reveal => { - - deck = reveal; - deck.registerKeyboardShortcut( 'CTRL + Shift + F', 'Search' ); - - document.addEventListener( 'keydown', function( event ) { - if( event.key == "F" && (event.ctrlKey || event.metaKey) ) { //Control+Shift+f - event.preventDefault(); - toggleSearch(); - } - }, false ); - - }, - - open: openSearch - - } + searchInput = searchElement.querySelector(".searchinput"); + searchInput.style.width = "240px"; + searchInput.style.fontSize = "14px"; + searchInput.style.padding = "4px 6px"; + searchInput.style.color = "#000"; + searchInput.style.background = "#fff"; + searchInput.style.borderRadius = "2px"; + searchInput.style.border = "0"; + searchInput.style.outline = "0"; + searchInput.style.boxShadow = "0 2px 18px rgba(0, 0, 0, 0.2)"; + searchInput.style["-webkit-appearance"] = "none"; + + deck.getRevealElement().appendChild(searchElement); + + // searchButton.addEventListener( 'click', function(event) { + // doSearch(); + // }, false ); + + searchInput.addEventListener( + "keyup", + function (event) { + switch (event.keyCode) { + case 13: + event.preventDefault(); + doSearch(); + searchboxDirty = false; + break; + default: + searchboxDirty = true; + } + }, + false, + ); + + closeSearch(); + } + + function openSearch() { + if (!searchElement) render(); + + searchElement.style.display = "inline"; + searchInput.focus(); + searchInput.select(); + } + + function closeSearch() { + if (!searchElement) render(); + + searchElement.style.display = "none"; + if (hilitor) hilitor.remove(); + } + + function toggleSearch() { + if (!searchElement) render(); + + if (searchElement.style.display !== "inline") { + openSearch(); + } else { + closeSearch(); + } + } + + function doSearch() { + //if there's been a change in the search term, perform a new search: + if (searchboxDirty) { + var searchstring = searchInput.value; + + if (searchstring === "") { + if (hilitor) hilitor.remove(); + matchedSlides = null; + } else { + //find the keyword amongst the slides + hilitor = new Hilitor("slidecontent"); + matchedSlides = hilitor.apply(searchstring); + currentMatchedIndex = 0; + } + } + + if (matchedSlides) { + //navigate to the next slide that has the keyword, wrapping to the first if necessary + if (matchedSlides.length && matchedSlides.length <= currentMatchedIndex) { + currentMatchedIndex = 0; + } + if (matchedSlides.length > currentMatchedIndex) { + deck.slide( + matchedSlides[currentMatchedIndex].h, + matchedSlides[currentMatchedIndex].v, + ); + currentMatchedIndex++; + } + } + } + + // Original JavaScript code by Chirp Internet: www.chirp.com.au + // Please acknowledge use of this code by including this header. + // 2/2013 jon: modified regex to display any match, not restricted to word boundaries. + function Hilitor(id, tag) { + var targetNode = document.getElementById(id) || document.body; + var hiliteTag = tag || "EM"; + var skipTags = new RegExp("^(?:" + hiliteTag + "|SCRIPT|FORM)$"); + var colors = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"]; + var wordColor = []; + var colorIdx = 0; + var matchRegex = ""; + var matchingSlides = []; + + this.setRegex = function (input) { + input = input.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|"); + matchRegex = new RegExp("(" + input + ")", "i"); + }; + + this.getRegex = function () { + return matchRegex + .toString() + .replace(/^\/\\b\(|\)\\b\/i$/g, "") + .replace(/\|/g, " "); + }; + + // recursively apply word highlighting + this.hiliteWords = function (node) { + if (node == undefined || !node) return; + if (!matchRegex) return; + if (skipTags.test(node.nodeName)) return; + + if (node.hasChildNodes()) { + for (var i = 0; i < node.childNodes.length; i++) + this.hiliteWords(node.childNodes[i]); + } + if (node.nodeType == 3) { + // NODE_TEXT + var nv, regs; + if ((nv = node.nodeValue) && (regs = matchRegex.exec(nv))) { + //find the slide's section element and save it in our list of matching slides + var secnode = node; + while (secnode != null && secnode.nodeName != "SECTION") { + secnode = secnode.parentNode; + } + + var slideIndex = deck.getIndices(secnode); + var slidelen = matchingSlides.length; + var alreadyAdded = false; + for (var i = 0; i < slidelen; i++) { + if ( + matchingSlides[i].h === slideIndex.h && + matchingSlides[i].v === slideIndex.v + ) { + alreadyAdded = true; + } + } + if (!alreadyAdded) { + matchingSlides.push(slideIndex); + } + + if (!wordColor[regs[0].toLowerCase()]) { + wordColor[regs[0].toLowerCase()] = + colors[colorIdx++ % colors.length]; + } + + var match = document.createElement(hiliteTag); + match.appendChild(document.createTextNode(regs[0])); + match.style.backgroundColor = wordColor[regs[0].toLowerCase()]; + match.style.fontStyle = "inherit"; + match.style.color = "#000"; + + var after = node.splitText(regs.index); + after.nodeValue = after.nodeValue.substring(regs[0].length); + node.parentNode.insertBefore(match, after); + } + } + }; + + // remove highlighting + this.remove = function () { + var arr = document.getElementsByTagName(hiliteTag); + var el; + while (arr.length && (el = arr[0])) { + el.parentNode.replaceChild(el.firstChild, el); + } + }; + + // start highlighting at target node + this.apply = function (input) { + if (input == undefined || !input) return; + this.remove(); + this.setRegex(input); + this.hiliteWords(targetNode); + return matchingSlides; + }; + } + + return { + id: "search", + + init: (reveal) => { + deck = reveal; + deck.registerKeyboardShortcut("CTRL + Shift + F", "Search"); + + document.addEventListener( + "keydown", + function (event) { + if (event.key == "F" && (event.ctrlKey || event.metaKey)) { + //Control+Shift+f + event.preventDefault(); + toggleSearch(); + } + }, + false, + ); + }, + + open: openSearch, + }; }; -export default Plugin; \ No newline at end of file +export default Plugin; diff --git a/content/slides/slides_files/libs/revealjs/plugin/search/search.esm.js b/content/slides/slides_files/libs/revealjs/plugin/search/search.esm.js index b401a70..80e9e97 100644 --- 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(e[t] = n) + : Ut(t, n); +})(Function.prototype, "toString", function () { + return ("function" == typeof this && Ft(this).source) || Dt(this); +}); +var Bt = $e, + Wt = S, + Gt = n, + Vt = Ze("species"), + Yt = n, + qt = o, + Xt = p, + Ht = function (e, t, n) { + var r, o; + return ( + m && + "function" == typeof (r = t.constructor) && + r !== n && + E((o = r.prototype)) && + o !== n.prototype && + m(e, o), + e + ); + }, + Jt = S.f, + Qt = $.f, + Zt = function (e) { + var t; + return et(e) && (void 0 !== (t = e[nt]) ? !!t : "RegExp" == tt(e)); + }, + en = ot, + tn = it, + nn = ut.exports, + rn = t, + on = kt.enforce, + cn = function (e) { + var t = Bt(e), + n = Wt.f; + Gt && + t && + !t[Vt] && + n(t, Vt, { + configurable: !0, + get: function () { + return this; + }, + }); + }, + an = Ze("match"), + un = qt.RegExp, + ln = un.prototype, + fn = /a/g, + sn = /a/g, + pn = new un(fn) !== fn, + gn = tn.UNSUPPORTED_Y; +if ( + Yt && + Xt( + "RegExp", + !pn || + gn || + rn(function () { + return ( + (sn[an] = !1), un(fn) != fn || un(sn) == sn || "/a/i" != un(fn, "i") + ); + }), + ) +) { + for ( + var dn = function (e, t) { + var n, + r = this instanceof dn, + o = Zt(e), + i = void 0 === t; + if (!r && o && e.constructor === dn && i) return e; + pn + ? o && !i && (e = e.source) + : e instanceof dn && (i && (t = en.call(e)), (e = e.source)), + gn && (n = !!t && t.indexOf("y") > -1) && (t = t.replace(/y/g, "")); + var c = Ht(pn ? new un(e, t) : un(e, t), r ? this : ln, dn); + gn && n && (on(c).sticky = !0); + return c; + }, + hn = function (e) { + (e in dn) || + Jt(dn, e, { + configurable: !0, + get: function () { + return un[e]; + }, + set: function (t) { + un[e] = t; + }, + }); + }, + yn = Qt(un), + vn = 0; + yn.length > vn; + + ) + hn(yn[vn++]); + (ln.constructor = dn), (dn.prototype = ln), nn(qt, "RegExp", dn); +} +cn("RegExp"); +var xn = {}, + bn = {}, + En = {}.propertyIsEnumerable, + mn = Object.getOwnPropertyDescriptor, + Sn = mn && !En.call({ 1: 2 }, 1); +bn.f = Sn + ? function (e) { + var t = mn(this, e); + return !!t && t.enumerable; + } + : En; +var wn = n, + On = bn, + Rn = ve, + Tn = q, + _n = P, + jn = z, + Pn = _, + In = Object.getOwnPropertyDescriptor; +xn.f = wn + ? In + : function (e, t) { + if (((e = Tn(e)), (t = _n(t, !0)), Pn)) + try { + return In(e, t); + } catch (e) {} + if (jn(e, t)) return Rn(!On.f.call(e, t), e[t]); + }; +var Cn = {}; +Cn.f = Object.getOwnPropertySymbols; +var Nn = $, + An = Cn, + kn = h, + $n = + $e("Reflect", "ownKeys") || + function (e) { + var t = Nn.f(kn(e)), + n = An.f; + return n ? t.concat(n(e)) : t; + }, + Ln = z, + Mn = $n, + Un = xn, + Dn = S, + Fn = o, + zn = xn.f, + Kn = Ee, + Bn = ut.exports, + Wn = we, + Gn = function (e, t) { + for (var n = Mn(t), r = Dn.f, o = Un.f, i = 0; i < n.length; i++) { + var c = n[i]; + Ln(e, c) || r(e, c, o(t, c)); + } + }, + Vn = p, + Yn = ot, + qn = it, + Xn = ye.exports, + Hn = RegExp.prototype.exec, + Jn = Xn("native-string-replace", String.prototype.replace), + Qn = Hn, + Zn = (function () { + var e = /a/, + t = /b*/g; + return ( + Hn.call(e, "a"), Hn.call(t, "a"), 0 !== e.lastIndex || 0 !== t.lastIndex + ); + })(), + er = qn.UNSUPPORTED_Y || qn.BROKEN_CARET, + tr = void 0 !== /()??/.exec("")[1]; +(Zn || tr || er) && + (Qn = function (e) { + var t, + n, + r, + o, + i = this, + c = er && i.sticky, + a = Yn.call(i), + u = i.source, + l = 0, + f = e; + return ( + c && + (-1 === (a = a.replace("y", "")).indexOf("g") && (a += "g"), + (f = String(e).slice(i.lastIndex)), + i.lastIndex > 0 && + (!i.multiline || (i.multiline && "\n" !== e[i.lastIndex - 1])) && + ((u = "(?: " + u + ")"), (f = " " + f), l++), + (n = new RegExp("^(?:" + u + ")", a))), + tr && (n = new RegExp("^" + u + "$(?!\\s)", a)), + Zn && (t = i.lastIndex), + (r = Hn.call(c ? n : i, f)), + c + ? r + ? ((r.input = r.input.slice(l)), + (r[0] = r[0].slice(l)), + (r.index = i.lastIndex), + (i.lastIndex += r[0].length)) + : (i.lastIndex = 0) + : Zn && r && (i.lastIndex = i.global ? r.index + r[0].length : t), + tr && + r && + r.length > 1 && + Jn.call(r[0], n, function () { + for (o = 1; o < arguments.length - 2; o++) + void 0 === arguments[o] && (r[o] = void 0); + }), + r + ); + }); +var nr = Qn; +(function (e, t) { + var n, + r, + o, + i, + c, + a = e.target, + u = e.global, + l = e.stat; + if ((n = u ? Fn : l ? Fn[a] || Wn(a, {}) : (Fn[a] || {}).prototype)) + for (r in t) { + if ( + ((i = t[r]), + (o = e.noTargetGet ? (c = zn(n, r)) && c.value : n[r]), + !Vn(u ? r : a + (l ? "." : "#") + r, e.forced) && void 0 !== o) + ) { + if (typeof i == typeof o) continue; + Gn(i, o); + } + (e.sham || (o && o.sham)) && Kn(i, "sham", !0), Bn(n, r, i, e); + } +})({ target: "RegExp", proto: !0, forced: /./.exec !== nr }, { exec: nr }); +var rr = ut.exports, + or = h, + ir = t, + cr = ot, + ar = RegExp.prototype, + ur = ar.toString, + lr = ir(function () { + return "/a/b" != ur.call({ source: "a", flags: "b" }); + }), + fr = "toString" != ur.name; +(lr || fr) && + rr( + RegExp.prototype, + "toString", + function () { + var e = or(this), + t = String(e.source), + n = e.flags; + return ( + "/" + + t + + "/" + + String( + void 0 === n && e instanceof RegExp && !("flags" in ar) + ? cr.call(e) + : n, + ) + ); + }, + { unsafe: !0 }, + ); +var sr = ut.exports, + pr = nr, + gr = t, + dr = Ze, + hr = Ee, + yr = dr("species"), + vr = RegExp.prototype, + xr = !gr(function () { + var e = /./; + return ( + (e.exec = function () { + var e = []; + return (e.groups = { a: "7" }), e; + }), + "7" !== "".replace(e, "$    ") + ); + }), + br = "$0" === "a".replace(/./, "$0"), + Er = dr("replace"), + mr = !!/./[Er] && "" === /./[Er]("a", "$0"), + Sr = !gr(function () { + var e = /(?:)/, + t = e.exec; + e.exec = function () { + return t.apply(this, arguments); + }; + var n = "ab".split(e); + return 2 !== n.length || "a" !== n[0] || "b" !== n[1]; + }), + wr = J, + Or = L, + Rr = function (e) { + return function (t, n) { + var r, + o, + i = String(Or(t)), + c = wr(n), + a = i.length; + return c < 0 || c >= a + ? e + ? "" + : void 0 + : (r = i.charCodeAt(c)) < 55296 || + r > 56319 || + c + 1 === a || + (o = i.charCodeAt(c + 1)) < 56320 || + o > 57343 + ? e + ? i.charAt(c) + : r + : e + ? i.slice(c, c + 2) + : o - 56320 + ((r - 55296) << 10) + 65536; + }; + }, + Tr = { codeAt: Rr(!1), charAt: Rr(!0) }.charAt, + _r = U, + jr = Math.floor, + Pr = "".replace, + Ir = /\$([$&'`]|\d{1,2}|<[^>]*>)/g, + Cr = /\$([$&'`]|\d{1,2})/g, + Nr = B, + Ar = nr, + kr = function (e, t, n, r) { + var o = dr(e), + i = !gr(function () { + var t = {}; + return ( + (t[o] = function () { + return 7; + }), + 7 != ""[e](t) + ); + }), + c = + i && + !gr(function () { + var t = !1, + n = /a/; + return ( + "split" === e && + (((n = {}).constructor = {}), + (n.constructor[yr] = function () { + return n; + }), + (n.flags = ""), + (n[o] = /./[o])), + (n.exec = function () { + return (t = !0), null; + }), + n[o](""), + !t + ); + }); + if ( + !i || + !c || + ("replace" === e && (!xr || !br || mr)) || + ("split" === e && !Sr) + ) { + var a = /./[o], + u = n( + o, + ""[e], + function (e, t, n, r, o) { + var c = t.exec; + return c === pr || c === vr.exec + ? i && !o + ? { done: !0, value: a.call(t, n, r) } + : { done: !0, value: e.call(n, t, r) } + : { done: !1 }; + }, + { + REPLACE_KEEPS_$0: br, + REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE: mr, + }, + ), + l = u[0], + f = u[1]; + sr(String.prototype, e, l), + sr( + vr, + o, + 2 == t + ? function (e, t) { + return f.call(e, this, t); + } + : function (e) { + return f.call(e, this); + }, + ); + } + r && hr(vr[o], "sham", !0); + }, + $r = h, + Lr = ee, + Mr = J, + Ur = L, + Dr = function (e, t, n) { + return t + (n ? Tr(e, t).length : 1); + }, + Fr = function (e, t, n, r, o, i) { + var c = n + e.length, + a = r.length, + u = Cr; + return ( + void 0 !== o && ((o = _r(o)), (u = Ir)), + Pr.call(i, u, function (i, u) { + var l; + switch (u.charAt(0)) { + case "$": + return "$"; + case "&": + return e; + case "`": + return t.slice(0, n); + case "'": + return t.slice(c); + case "<": + l = o[u.slice(1, -1)]; + break; + default: + var f = +u; + if (0 === f) return i; + if (f > a) { + var s = jr(f / 10); + return 0 === s + ? i + : s <= a + ? void 0 === r[s - 1] + ? u.charAt(1) + : r[s - 1] + u.charAt(1) + : i; + } + l = r[f - 1]; + } + return void 0 === l ? "" : l; + }) + ); + }, + zr = function (e, t) { + var n = e.exec; + if ("function" == typeof n) { + var r = n.call(e, t); + if ("object" != typeof r) + throw TypeError( + "RegExp exec method returned something other than an Object or null", + ); + return r; + } + if ("RegExp" !== Nr(e)) + throw TypeError("RegExp#exec called on incompatible receiver"); + return Ar.call(e, t); + }, + Kr = Math.max, + Br = Math.min; +kr("replace", 2, function (e, t, n, r) { + var o = r.REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE, + i = r.REPLACE_KEEPS_$0, + c = o ? "$" : "$0"; + return [ + function (n, r) { + var o = Ur(this), + i = null == n ? void 0 : n[e]; + return void 0 !== i ? i.call(n, o, r) : t.call(String(o), n, r); + }, + function (e, r) { + if ((!o && i) || ("string" == typeof r && -1 === r.indexOf(c))) { + var a = n(t, e, this, r); + if (a.done) return a.value; + } + var u = $r(e), + l = String(this), + f = "function" == typeof r; + f || (r = String(r)); + var s = u.global; + if (s) { + var p = u.unicode; + u.lastIndex = 0; + } + for (var g = []; ; ) { + var d = zr(u, l); + if (null === d) break; + if ((g.push(d), !s)) break; + "" === String(d[0]) && (u.lastIndex = Dr(l, Lr(u.lastIndex), p)); + } + for (var h, y = "", v = 0, x = 0; x < g.length; x++) { + d = g[x]; + for ( + var b = String(d[0]), + E = Kr(Br(Mr(d.index), l.length), 0), + m = [], + S = 1; + S < d.length; + S++ + ) + m.push(void 0 === (h = d[S]) ? h : String(h)); + var w = d.groups; + if (f) { + var O = [b].concat(m, E, l); + void 0 !== w && O.push(w); + var R = String(r.apply(void 0, O)); + } else R = Fr(b, l, E, m, w, r); + E >= v && ((y += l.slice(v, E) + R), (v = E + b.length)); + } + return y + l.slice(v); + }, + ]; +}); +var Wr = {}; +Wr[Ze("toStringTag")] = "z"; +var Gr = "[object z]" === String(Wr), + Vr = Gr, + Yr = B, + qr = Ze("toStringTag"), + Xr = + "Arguments" == + Yr( + (function () { + return arguments; + })(), + ), + Hr = Vr + ? Yr + : function (e) { + var t, n, r; + return void 0 === e + ? "Undefined" + : null === e + ? "Null" + : "string" == + typeof (n = (function (e, t) { + try { + return e[t]; + } catch (e) {} + })((t = Object(e)), qr)) + ? n + : Xr + ? Yr(t) + : "Object" == (r = Yr(t)) && "function" == typeof t.callee + ? "Arguments" + : r; + }, + Jr = Gr + ? {}.toString + : function () { + return "[object " + Hr(this) + "]"; + }, + Qr = Gr, + Zr = ut.exports, + eo = Jr; +Qr || Zr(Object.prototype, "toString", eo, { unsafe: !0 }); /*! * Handles finding a text string anywhere in the slides and showing the next occurrence to the user * by navigatating to that slide and highlighting it. * * @author Jon Snyder , February 2013 - */;export default function(){var e,t,n,r,o,i,c;function a(){(t=document.createElement("div")).classList.add("searchbox"),t.style.position="absolute",t.style.top="10px",t.style.right="10px",t.style.zIndex=10,t.innerHTML='\n\t\t',(n=t.querySelector(".searchinput")).style.width="240px",n.style.fontSize="14px",n.style.padding="4px 6px",n.style.color="#000",n.style.background="#fff",n.style.borderRadius="2px",n.style.border="0",n.style.outline="0",n.style.boxShadow="0 2px 18px rgba(0, 0, 0, 0.2)",n.style["-webkit-appearance"]="none",e.getRevealElement().appendChild(t),n.addEventListener("keyup",(function(t){switch(t.keyCode){case 13:t.preventDefault(),function(){if(i){var t=n.value;""===t?(c&&c.remove(),r=null):(c=new f("slidecontent"),r=c.apply(t),o=0)}r&&(r.length&&r.length<=o&&(o=0),r.length>o&&(e.slide(r[o].h,r[o].v),o++))}(),i=!1;break;default:i=!0}}),!1),l()}function u(){t||a(),t.style.display="inline",n.focus(),n.select()}function l(){t||a(),t.style.display="none",c&&c.remove()}function f(t,n){var r=document.getElementById(t)||document.body,o=n||"EM",i=new RegExp("^(?:"+o+"|SCRIPT|FORM)$"),c=["#ff6","#a0ffff","#9f9","#f99","#f6f"],a=[],u=0,l="",f=[];this.setRegex=function(e){e=e.replace(/^[^\w]+|[^\w]+$/g,"").replace(/[^\w'-]+/g,"|"),l=new RegExp("("+e+")","i")},this.getRegex=function(){return l.toString().replace(/^\/\\b\(|\)\\b\/i$/g,"").replace(/\|/g," ")},this.hiliteWords=function(t){if(null!=t&&t&&l&&!i.test(t.nodeName)){if(t.hasChildNodes())for(var n=0;n\n\t\t'), + ((n = t.querySelector(".searchinput")).style.width = "240px"), + (n.style.fontSize = "14px"), + (n.style.padding = "4px 6px"), + (n.style.color = "#000"), + (n.style.background = "#fff"), + (n.style.borderRadius = "2px"), + (n.style.border = "0"), + (n.style.outline = "0"), + (n.style.boxShadow = "0 2px 18px rgba(0, 0, 0, 0.2)"), + (n.style["-webkit-appearance"] = "none"), + e.getRevealElement().appendChild(t), + n.addEventListener( + "keyup", + function (t) { + switch (t.keyCode) { + case 13: + t.preventDefault(), + (function () { + if (i) { + var t = n.value; + "" === t + ? (c && c.remove(), (r = null)) + : ((c = new f("slidecontent")), + (r = c.apply(t)), + (o = 0)); + } + r && + (r.length && r.length <= o && (o = 0), + r.length > o && (e.slide(r[o].h, r[o].v), o++)); + })(), + (i = !1); + break; + default: + i = !0; + } + }, + !1, + ), + l(); + } + function u() { + t || a(), (t.style.display = "inline"), n.focus(), n.select(); + } + function l() { + t || a(), (t.style.display = "none"), c && c.remove(); + } + function f(t, n) { + var r = document.getElementById(t) || document.body, + o = n || "EM", + i = new RegExp("^(?:" + o + "|SCRIPT|FORM)$"), + c = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"], + a = [], + u = 0, + l = "", + f = []; + (this.setRegex = function (e) { + (e = e.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|")), + (l = new RegExp("(" + e + ")", "i")); + }), + (this.getRegex = function () { + return l + .toString() + .replace(/^\/\\b\(|\)\\b\/i$/g, "") + .replace(/\|/g, " "); + }), + (this.hiliteWords = function (t) { + if (null != t && t && l && !i.test(t.nodeName)) { + if (t.hasChildNodes()) + for (var n = 0; n < t.childNodes.length; n++) + this.hiliteWords(t.childNodes[n]); + var r, s; + if (3 == t.nodeType) + if ((r = t.nodeValue) && (s = l.exec(r))) { + for (var p = t; null != p && "SECTION" != p.nodeName; ) + p = p.parentNode; + var g = e.getIndices(p), + d = f.length, + h = !1; + for (n = 0; n < d; n++) + f[n].h === g.h && f[n].v === g.v && (h = !0); + h || f.push(g), + a[s[0].toLowerCase()] || + (a[s[0].toLowerCase()] = c[u++ % c.length]); + var y = document.createElement(o); + y.appendChild(document.createTextNode(s[0])), + (y.style.backgroundColor = a[s[0].toLowerCase()]), + (y.style.fontStyle = "inherit"), + (y.style.color = "#000"); + var v = t.splitText(s.index); + (v.nodeValue = v.nodeValue.substring(s[0].length)), + t.parentNode.insertBefore(y, v); + } + } + }), + (this.remove = function () { + for ( + var e, t = document.getElementsByTagName(o); + t.length && (e = t[0]); + + ) + e.parentNode.replaceChild(e.firstChild, e); + }), + (this.apply = function (e) { + if (null != e && e) + return this.remove(), this.setRegex(e), this.hiliteWords(r), f; + }); + } + return { + id: "search", + init: function (n) { + (e = n).registerKeyboardShortcut("CTRL + Shift + F", "Search"), + document.addEventListener( + "keydown", + function (e) { + "F" == e.key && + (e.ctrlKey || e.metaKey) && + (e.preventDefault(), + t || a(), + "inline" !== t.style.display ? u() : l()); + }, + !1, + ); + }, + open: u, + }; +} diff --git a/content/slides/slides_files/libs/revealjs/plugin/search/search.js b/content/slides/slides_files/libs/revealjs/plugin/search/search.js index bcabf72..1a3c93b 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/search/search.js +++ b/content/slides/slides_files/libs/revealjs/plugin/search/search.js @@ -1,7 +1,1163 @@ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof 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(e[t] = n) + : Ft(t, n); + })(Function.prototype, "toString", function () { + return ("function" == typeof this && Kt(this).source) || zt(this); + }); + var Gt = Le, + Vt = S, + Yt = n, + qt = et("species"), + Xt = n, + Ht = o, + Jt = p, + Qt = function (e, t, n) { + var r, o; + return ( + E && + "function" == typeof (r = t.constructor) && + r !== n && + m((o = r.prototype)) && + o !== n.prototype && + E(e, o), + e + ); + }, + Zt = S.f, + en = $.f, + tn = function (e) { + var t; + return tt(e) && (void 0 !== (t = e[rt]) ? !!t : "RegExp" == nt(e)); + }, + nn = it, + rn = ct, + on = lt.exports, + cn = t, + an = Lt.enforce, + un = function (e) { + var t = Gt(e), + n = Vt.f; + Yt && + t && + !t[qt] && + n(t, qt, { + configurable: !0, + get: function () { + return this; + }, + }); + }, + ln = et("match"), + fn = Ht.RegExp, + sn = fn.prototype, + pn = /a/g, + dn = /a/g, + gn = new fn(pn) !== pn, + hn = rn.UNSUPPORTED_Y; + if ( + Xt && + Jt( + "RegExp", + !gn || + hn || + cn(function () { + return ( + (dn[ln] = !1), fn(pn) != pn || fn(dn) == dn || "/a/i" != fn(pn, "i") + ); + }), + ) + ) { + for ( + var yn = function (e, t) { + var n, + r = this instanceof yn, + o = tn(e), + i = void 0 === t; + if (!r && o && e.constructor === yn && i) return e; + gn + ? o && !i && (e = e.source) + : e instanceof yn && (i && (t = nn.call(e)), (e = e.source)), + hn && (n = !!t && t.indexOf("y") > -1) && (t = t.replace(/y/g, "")); + var c = Qt(gn ? new fn(e, t) : fn(e, t), r ? this : sn, yn); + hn && n && (an(c).sticky = !0); + return c; + }, + vn = function (e) { + (e in yn) || + Zt(yn, e, { + configurable: !0, + get: function () { + return fn[e]; + }, + set: function (t) { + fn[e] = t; + }, + }); + }, + xn = en(fn), + bn = 0; + xn.length > bn; + + ) + vn(xn[bn++]); + (sn.constructor = yn), (yn.prototype = sn), on(Ht, "RegExp", yn); + } + un("RegExp"); + var mn = {}, + En = {}, + Sn = {}.propertyIsEnumerable, + wn = Object.getOwnPropertyDescriptor, + On = wn && !Sn.call({ 1: 2 }, 1); + En.f = On + ? function (e) { + var t = wn(this, e); + return !!t && t.enumerable; + } + : Sn; + var Rn = n, + Tn = En, + _n = ve, + jn = q, + Pn = P, + In = z, + Cn = _, + Nn = Object.getOwnPropertyDescriptor; + mn.f = Rn + ? Nn + : function (e, t) { + if (((e = jn(e)), (t = Pn(t, !0)), Cn)) + try { + return Nn(e, t); + } catch (e) {} + if (In(e, t)) return _n(!Tn.f.call(e, t), e[t]); + }; + var An = {}; + An.f = Object.getOwnPropertySymbols; + var kn = $, + $n = An, + Ln = h, + Mn = + Le("Reflect", "ownKeys") || + function (e) { + var t = kn.f(Ln(e)), + n = $n.f; + return n ? t.concat(n(e)) : t; + }, + Un = z, + Dn = Mn, + Fn = mn, + zn = S, + Kn = o, + Bn = mn.f, + Wn = me, + Gn = lt.exports, + Vn = we, + Yn = function (e, t) { + for (var n = Dn(t), r = zn.f, o = Fn.f, i = 0; i < n.length; i++) { + var c = n[i]; + Un(e, c) || r(e, c, o(t, c)); + } + }, + qn = p, + Xn = it, + Hn = ct, + Jn = ye.exports, + Qn = RegExp.prototype.exec, + Zn = Jn("native-string-replace", String.prototype.replace), + er = Qn, + tr = (function () { + var e = /a/, + t = /b*/g; + return ( + Qn.call(e, "a"), Qn.call(t, "a"), 0 !== e.lastIndex || 0 !== t.lastIndex + ); + })(), + nr = Hn.UNSUPPORTED_Y || Hn.BROKEN_CARET, + rr = void 0 !== /()??/.exec("")[1]; + (tr || rr || nr) && + (er = function (e) { + var t, + n, + r, + o, + i = this, + c = nr && i.sticky, + a = Xn.call(i), + u = i.source, + l = 0, + f = e; + return ( + c && + (-1 === (a = a.replace("y", "")).indexOf("g") && (a += "g"), + (f = String(e).slice(i.lastIndex)), + i.lastIndex > 0 && + (!i.multiline || (i.multiline && "\n" !== e[i.lastIndex - 1])) && + ((u = "(?: " + u + ")"), (f = " " + f), l++), + (n = new RegExp("^(?:" + u + ")", a))), + rr && (n = new RegExp("^" + u + "$(?!\\s)", a)), + tr && (t = i.lastIndex), + (r = Qn.call(c ? n : i, f)), + c + ? r + ? ((r.input = r.input.slice(l)), + (r[0] = r[0].slice(l)), + (r.index = i.lastIndex), + (i.lastIndex += r[0].length)) + : (i.lastIndex = 0) + : tr && r && (i.lastIndex = i.global ? r.index + r[0].length : t), + rr && + r && + r.length > 1 && + Zn.call(r[0], n, function () { + for (o = 1; o < arguments.length - 2; o++) + void 0 === arguments[o] && (r[o] = void 0); + }), + r + ); + }); + var or = er; + (function (e, t) { + var n, + r, + o, + i, + c, + a = e.target, + u = e.global, + l = e.stat; + if ((n = u ? Kn : l ? Kn[a] || Vn(a, {}) : (Kn[a] || {}).prototype)) + for (r in t) { + if ( + ((i = t[r]), + (o = e.noTargetGet ? (c = Bn(n, r)) && c.value : n[r]), + !qn(u ? r : a + (l ? "." : "#") + r, e.forced) && void 0 !== o) + ) { + if (typeof i == typeof o) continue; + Yn(i, o); + } + (e.sham || (o && o.sham)) && Wn(i, "sham", !0), Gn(n, r, i, e); + } + })({ target: "RegExp", proto: !0, forced: /./.exec !== or }, { exec: or }); + var ir = lt.exports, + cr = h, + ar = t, + ur = it, + lr = "toString", + fr = RegExp.prototype, + sr = fr.toString, + pr = ar(function () { + return "/a/b" != sr.call({ source: "a", flags: "b" }); + }), + dr = sr.name != lr; + (pr || dr) && + ir( + RegExp.prototype, + lr, + function () { + var e = cr(this), + t = String(e.source), + n = e.flags; + return ( + "/" + + t + + "/" + + String( + void 0 === n && e instanceof RegExp && !("flags" in fr) + ? ur.call(e) + : n, + ) + ); + }, + { unsafe: !0 }, + ); + var gr = lt.exports, + hr = or, + yr = t, + vr = et, + xr = me, + br = vr("species"), + mr = RegExp.prototype, + Er = !yr(function () { + var e = /./; + return ( + (e.exec = function () { + var e = []; + return (e.groups = { a: "7" }), e; + }), + "7" !== "".replace(e, "$") + ); + }), + Sr = "$0" === "a".replace(/./, "$0"), + wr = vr("replace"), + Or = !!/./[wr] && "" === /./[wr]("a", "$0"), + Rr = !yr(function () { + var e = /(?:)/, + t = e.exec; + e.exec = function () { + return t.apply(this, arguments); + }; + var n = "ab".split(e); + return 2 !== n.length || "a" !== n[0] || "b" !== n[1]; + }), + Tr = J, + _r = L, + jr = function (e) { + return function (t, n) { + var r, + o, + i = String(_r(t)), + c = Tr(n), + a = i.length; + return c < 0 || c >= a + ? e + ? "" + : void 0 + : (r = i.charCodeAt(c)) < 55296 || + r > 56319 || + c + 1 === a || + (o = i.charCodeAt(c + 1)) < 56320 || + o > 57343 + ? e + ? i.charAt(c) + : r + : e + ? i.slice(c, c + 2) + : o - 56320 + ((r - 55296) << 10) + 65536; + }; + }, + Pr = { codeAt: jr(!1), charAt: jr(!0) }.charAt, + Ir = U, + Cr = Math.floor, + Nr = "".replace, + Ar = /\$([$&'`]|\d{1,2}|<[^>]*>)/g, + kr = /\$([$&'`]|\d{1,2})/g, + $r = B, + Lr = or, + Mr = function (e, t, n, r) { + var o = vr(e), + i = !yr(function () { + var t = {}; + return ( + (t[o] = function () { + return 7; + }), + 7 != ""[e](t) + ); + }), + c = + i && + !yr(function () { + var t = !1, + n = /a/; + return ( + "split" === e && + (((n = {}).constructor = {}), + (n.constructor[br] = function () { + return n; + }), + (n.flags = ""), + (n[o] = /./[o])), + (n.exec = function () { + return (t = !0), null; + }), + n[o](""), + !t + ); + }); + if ( + !i || + !c || + ("replace" === e && (!Er || !Sr || Or)) || + ("split" === e && !Rr) + ) { + var a = /./[o], + u = n( + o, + ""[e], + function (e, t, n, r, o) { + var c = t.exec; + return c === hr || c === mr.exec + ? i && !o + ? { done: !0, value: a.call(t, n, r) } + : { done: !0, value: e.call(n, t, r) } + : { done: !1 }; + }, + { + REPLACE_KEEPS_$0: Sr, + REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE: Or, + }, + ), + l = u[0], + f = u[1]; + gr(String.prototype, e, l), + gr( + mr, + o, + 2 == t + ? function (e, t) { + return f.call(e, this, t); + } + : function (e) { + return f.call(e, this); + }, + ); + } + r && xr(mr[o], "sham", !0); + }, + Ur = h, + Dr = ee, + Fr = J, + zr = L, + Kr = function (e, t, n) { + return t + (n ? Pr(e, t).length : 1); + }, + Br = function (e, t, n, r, o, i) { + var c = n + e.length, + a = r.length, + u = kr; + return ( + void 0 !== o && ((o = Ir(o)), (u = Ar)), + Nr.call(i, u, function (i, u) { + var l; + switch (u.charAt(0)) { + case "$": + return "$"; + case "&": + return e; + case "`": + return t.slice(0, n); + case "'": + return t.slice(c); + case "<": + l = o[u.slice(1, -1)]; + break; + default: + var f = +u; + if (0 === f) return i; + if (f > a) { + var s = Cr(f / 10); + return 0 === s + ? i + : s <= a + ? void 0 === r[s - 1] + ? u.charAt(1) + : r[s - 1] + u.charAt(1) + : i; + } + l = r[f - 1]; + } + return void 0 === l ? "" : l; + }) + ); + }, + Wr = function (e, t) { + var n = e.exec; + if ("function" == typeof n) { + var r = n.call(e, t); + if ("object" != typeof r) + throw TypeError( + "RegExp exec method returned something other than an Object or null", + ); + return r; + } + if ("RegExp" !== $r(e)) + throw TypeError("RegExp#exec called on incompatible receiver"); + return Lr.call(e, t); + }, + Gr = Math.max, + Vr = Math.min; + Mr("replace", 2, function (e, t, n, r) { + var o = r.REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE, + i = r.REPLACE_KEEPS_$0, + c = o ? "$" : "$0"; + return [ + function (n, r) { + var o = zr(this), + i = null == n ? void 0 : n[e]; + return void 0 !== i ? i.call(n, o, r) : t.call(String(o), n, r); + }, + function (e, r) { + if ((!o && i) || ("string" == typeof r && -1 === r.indexOf(c))) { + var a = n(t, e, this, r); + if (a.done) return a.value; + } + var u = Ur(e), + l = String(this), + f = "function" == typeof r; + f || (r = String(r)); + var s = u.global; + if (s) { + var p = u.unicode; + u.lastIndex = 0; + } + for (var d = []; ; ) { + var g = Wr(u, l); + if (null === g) break; + if ((d.push(g), !s)) break; + "" === String(g[0]) && (u.lastIndex = Kr(l, Dr(u.lastIndex), p)); + } + for (var h, y = "", v = 0, x = 0; x < d.length; x++) { + g = d[x]; + for ( + var b = String(g[0]), + m = Gr(Vr(Fr(g.index), l.length), 0), + E = [], + S = 1; + S < g.length; + S++ + ) + E.push(void 0 === (h = g[S]) ? h : String(h)); + var w = g.groups; + if (f) { + var O = [b].concat(E, m, l); + void 0 !== w && O.push(w); + var R = String(r.apply(void 0, O)); + } else R = Br(b, l, m, E, w, r); + m >= v && ((y += l.slice(v, m) + R), (v = m + b.length)); + } + return y + l.slice(v); + }, + ]; + }); + var Yr = {}; + Yr[et("toStringTag")] = "z"; + var qr = "[object z]" === String(Yr), + Xr = qr, + Hr = B, + Jr = et("toStringTag"), + Qr = + "Arguments" == + Hr( + (function () { + return arguments; + })(), + ), + Zr = Xr + ? Hr + : function (e) { + var t, n, r; + return void 0 === e + ? "Undefined" + : null === e + ? "Null" + : "string" == + typeof (n = (function (e, t) { + try { + return e[t]; + } catch (e) {} + })((t = Object(e)), Jr)) + ? n + : Qr + ? Hr(t) + : "Object" == (r = Hr(t)) && "function" == typeof t.callee + ? "Arguments" + : r; + }, + eo = qr + ? {}.toString + : function () { + return "[object " + Zr(this) + "]"; + }, + to = qr, + no = lt.exports, + ro = eo; + to || no(Object.prototype, "toString", ro, { unsafe: !0 }); + /*! + * Handles finding a text string anywhere in the slides and showing the next occurrence to the user + * by navigatating to that slide and highlighting it. + * + * @author Jon Snyder , February 2013 + */ + return function () { + var e, t, n, r, o, i, c; + function a() { + (t = document.createElement("div")).classList.add("searchbox"), + (t.style.position = "absolute"), + (t.style.top = "10px"), + (t.style.right = "10px"), + (t.style.zIndex = 10), + (t.innerHTML = + '\n\t\t'), + ((n = t.querySelector(".searchinput")).style.width = "240px"), + (n.style.fontSize = "14px"), + (n.style.padding = "4px 6px"), + (n.style.color = "#000"), + (n.style.background = "#fff"), + (n.style.borderRadius = "2px"), + (n.style.border = "0"), + (n.style.outline = "0"), + (n.style.boxShadow = "0 2px 18px rgba(0, 0, 0, 0.2)"), + (n.style["-webkit-appearance"] = "none"), + e.getRevealElement().appendChild(t), + n.addEventListener( + "keyup", + function (t) { + switch (t.keyCode) { + case 13: + t.preventDefault(), + (function () { + if (i) { + var t = n.value; + "" === t + ? (c && c.remove(), (r = null)) + : ((c = new f("slidecontent")), + (r = c.apply(t)), + (o = 0)); + } + r && + (r.length && r.length <= o && (o = 0), + r.length > o && (e.slide(r[o].h, r[o].v), o++)); + })(), + (i = !1); + break; + default: + i = !0; + } + }, + !1, + ), + l(); + } + function u() { + t || a(), (t.style.display = "inline"), n.focus(), n.select(); + } + function l() { + t || a(), (t.style.display = "none"), c && c.remove(); + } + function f(t, n) { + var r = document.getElementById(t) || document.body, + o = n || "EM", + i = new RegExp("^(?:" + o + "|SCRIPT|FORM)$"), + c = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"], + a = [], + u = 0, + l = "", + f = []; + (this.setRegex = function (e) { + (e = e.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|")), + (l = new RegExp("(" + e + ")", "i")); + }), + (this.getRegex = function () { + return l + .toString() + .replace(/^\/\\b\(|\)\\b\/i$/g, "") + .replace(/\|/g, " "); + }), + (this.hiliteWords = function (t) { + if (null != t && t && l && !i.test(t.nodeName)) { + if (t.hasChildNodes()) + for (var n = 0; n < t.childNodes.length; n++) + this.hiliteWords(t.childNodes[n]); + var r, s; + if (3 == t.nodeType) + if ((r = t.nodeValue) && (s = l.exec(r))) { + for (var p = t; null != p && "SECTION" != p.nodeName; ) + p = p.parentNode; + var d = e.getIndices(p), + g = f.length, + h = !1; + for (n = 0; n < g; n++) + f[n].h === d.h && f[n].v === d.v && (h = !0); + h || f.push(d), + a[s[0].toLowerCase()] || + (a[s[0].toLowerCase()] = c[u++ % c.length]); + var y = document.createElement(o); + y.appendChild(document.createTextNode(s[0])), + (y.style.backgroundColor = a[s[0].toLowerCase()]), + (y.style.fontStyle = "inherit"), + (y.style.color = "#000"); + var v = t.splitText(s.index); + (v.nodeValue = v.nodeValue.substring(s[0].length)), + t.parentNode.insertBefore(y, v); + } + } + }), + (this.remove = function () { + for ( + var e, t = document.getElementsByTagName(o); + t.length && (e = t[0]); + + ) + e.parentNode.replaceChild(e.firstChild, e); + }), + (this.apply = function (e) { + if (null != e && e) + return this.remove(), this.setRegex(e), this.hiliteWords(r), f; + }); + } + return { + id: "search", + init: function (n) { + (e = n).registerKeyboardShortcut("CTRL + Shift + F", "Search"), + document.addEventListener( + "keydown", + function (e) { + "F" == e.key && + (e.ctrlKey || e.metaKey) && + (e.preventDefault(), + t || a(), + "inline" !== t.style.display ? u() : l()); + }, + !1, + ); + }, + open: u, + }; + }; +}); diff --git a/content/slides/slides_files/libs/revealjs/plugin/zoom/plugin.js b/content/slides/slides_files/libs/revealjs/plugin/zoom/plugin.js index 960fb81..4a0ec82 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/zoom/plugin.js +++ b/content/slides/slides_files/libs/revealjs/plugin/zoom/plugin.js @@ -2,37 +2,38 @@ * reveal.js Zoom plugin */ const Plugin = { - - id: 'zoom', - - init: function( reveal ) { - - reveal.getRevealElement().addEventListener( 'mousedown', function( event ) { - var defaultModifier = /Linux/.test( window.navigator.platform ) ? 'ctrl' : 'alt'; - - var modifier = ( reveal.getConfig().zoomKey ? reveal.getConfig().zoomKey : defaultModifier ) + 'Key'; - var zoomLevel = ( reveal.getConfig().zoomLevel ? reveal.getConfig().zoomLevel : 2 ); - - if( event[ modifier ] && !reveal.isOverview() ) { - event.preventDefault(); - - zoom.to({ - x: event.clientX, - y: event.clientY, - scale: zoomLevel, - pan: false - }); - } - } ); - - }, - - destroy: () => { - - zoom.reset(); - - } - + id: "zoom", + + init: function (reveal) { + reveal.getRevealElement().addEventListener("mousedown", function (event) { + var defaultModifier = /Linux/.test(window.navigator.platform) + ? "ctrl" + : "alt"; + + var modifier = + (reveal.getConfig().zoomKey + ? reveal.getConfig().zoomKey + : defaultModifier) + "Key"; + var zoomLevel = reveal.getConfig().zoomLevel + ? reveal.getConfig().zoomLevel + : 2; + + if (event[modifier] && !reveal.isOverview()) { + event.preventDefault(); + + zoom.to({ + x: event.clientX, + y: event.clientY, + scale: zoomLevel, + pan: false, + }); + } + }); + }, + + destroy: () => { + zoom.reset(); + }, }; export default () => Plugin; @@ -44,221 +45,243 @@ export default () => Plugin; * * Copyright (C) 2011-2014 Hakim El Hattab, http://hakim.se */ -var zoom = (function(){ - - // The current zoom level (scale) - var level = 1; - - // The current mouse position, used for panning - var mouseX = 0, - mouseY = 0; - - // Timeout before pan is activated - var panEngageTimeout = -1, - panUpdateInterval = -1; - - // Check for transform support so that we can fallback otherwise - var supportsTransforms = 'transform' in document.body.style; - - if( supportsTransforms ) { - // The easing that will be applied when we zoom in/out - document.body.style.transition = 'transform 0.8s ease'; - } - - // Zoom out if the user hits escape - document.addEventListener( 'keyup', function( event ) { - if( level !== 1 && event.keyCode === 27 ) { - zoom.out(); - } - } ); - - // Monitor mouse movement for panning - document.addEventListener( 'mousemove', function( event ) { - if( level !== 1 ) { - mouseX = event.clientX; - mouseY = event.clientY; - } - } ); - - /** - * Applies the CSS required to zoom in, prefers the use of CSS3 - * transforms but falls back on zoom for IE. - * - * @param {Object} rect - * @param {Number} scale - */ - function magnify( rect, scale ) { - - var scrollOffset = getScrollOffset(); - - // Ensure a width/height is set - rect.width = rect.width || 1; - rect.height = rect.height || 1; - - // Center the rect within the zoomed viewport - rect.x -= ( window.innerWidth - ( rect.width * scale ) ) / 2; - rect.y -= ( window.innerHeight - ( rect.height * scale ) ) / 2; - - if( supportsTransforms ) { - // Reset - if( scale === 1 ) { - document.body.style.transform = ''; - } - // Scale - else { - var origin = scrollOffset.x +'px '+ scrollOffset.y +'px', - transform = 'translate('+ -rect.x +'px,'+ -rect.y +'px) scale('+ scale +')'; - - document.body.style.transformOrigin = origin; - document.body.style.transform = transform; - } - } - else { - // Reset - if( scale === 1 ) { - document.body.style.position = ''; - document.body.style.left = ''; - document.body.style.top = ''; - document.body.style.width = ''; - document.body.style.height = ''; - document.body.style.zoom = ''; - } - // Scale - else { - document.body.style.position = 'relative'; - document.body.style.left = ( - ( scrollOffset.x + rect.x ) / scale ) + 'px'; - document.body.style.top = ( - ( scrollOffset.y + rect.y ) / scale ) + 'px'; - document.body.style.width = ( scale * 100 ) + '%'; - document.body.style.height = ( scale * 100 ) + '%'; - document.body.style.zoom = scale; - } - } - - level = scale; - - if( document.documentElement.classList ) { - if( level !== 1 ) { - document.documentElement.classList.add( 'zoomed' ); - } - else { - document.documentElement.classList.remove( 'zoomed' ); - } - } - } - - /** - * Pan the document when the mosue cursor approaches the edges - * of the window. - */ - function pan() { - var range = 0.12, - rangeX = window.innerWidth * range, - rangeY = window.innerHeight * range, - scrollOffset = getScrollOffset(); - - // Up - if( mouseY < rangeY ) { - window.scroll( scrollOffset.x, scrollOffset.y - ( 1 - ( mouseY / rangeY ) ) * ( 14 / level ) ); - } - // Down - else if( mouseY > window.innerHeight - rangeY ) { - window.scroll( scrollOffset.x, scrollOffset.y + ( 1 - ( window.innerHeight - mouseY ) / rangeY ) * ( 14 / level ) ); - } - - // Left - if( mouseX < rangeX ) { - window.scroll( scrollOffset.x - ( 1 - ( mouseX / rangeX ) ) * ( 14 / level ), scrollOffset.y ); - } - // Right - else if( mouseX > window.innerWidth - rangeX ) { - window.scroll( scrollOffset.x + ( 1 - ( window.innerWidth - mouseX ) / rangeX ) * ( 14 / level ), scrollOffset.y ); - } - } - - function getScrollOffset() { - return { - x: window.scrollX !== undefined ? window.scrollX : window.pageXOffset, - y: window.scrollY !== undefined ? window.scrollY : window.pageYOffset - } - } - - return { - /** - * Zooms in on either a rectangle or HTML element. - * - * @param {Object} options - * - element: HTML element to zoom in on - * OR - * - x/y: coordinates in non-transformed space to zoom in on - * - width/height: the portion of the screen to zoom in on - * - scale: can be used instead of width/height to explicitly set scale - */ - to: function( options ) { - - // Due to an implementation limitation we can't zoom in - // to another element without zooming out first - if( level !== 1 ) { - zoom.out(); - } - else { - options.x = options.x || 0; - options.y = options.y || 0; - - // If an element is set, that takes precedence - if( !!options.element ) { - // Space around the zoomed in element to leave on screen - var padding = 20; - var bounds = options.element.getBoundingClientRect(); - - options.x = bounds.left - padding; - options.y = bounds.top - padding; - options.width = bounds.width + ( padding * 2 ); - options.height = bounds.height + ( padding * 2 ); - } - - // If width/height values are set, calculate scale from those values - if( options.width !== undefined && options.height !== undefined ) { - options.scale = Math.max( Math.min( window.innerWidth / options.width, window.innerHeight / options.height ), 1 ); - } - - if( options.scale > 1 ) { - options.x *= options.scale; - options.y *= options.scale; - - magnify( options, options.scale ); - - if( options.pan !== false ) { - - // Wait with engaging panning as it may conflict with the - // zoom transition - panEngageTimeout = setTimeout( function() { - panUpdateInterval = setInterval( pan, 1000 / 60 ); - }, 800 ); - - } - } - } - }, - - /** - * Resets the document zoom state to its default. - */ - out: function() { - clearTimeout( panEngageTimeout ); - clearInterval( panUpdateInterval ); - - magnify( { x: 0, y: 0 }, 1 ); - - level = 1; - }, - - // Alias - magnify: function( options ) { this.to( options ) }, - reset: function() { this.out() }, - - zoomLevel: function() { - return level; - } - } - +var zoom = (function () { + // The current zoom level (scale) + var level = 1; + + // The current mouse position, used for panning + var mouseX = 0, + mouseY = 0; + + // Timeout before pan is activated + var panEngageTimeout = -1, + panUpdateInterval = -1; + + // Check for transform support so that we can fallback otherwise + var supportsTransforms = "transform" in document.body.style; + + if (supportsTransforms) { + // The easing that will be applied when we zoom in/out + document.body.style.transition = "transform 0.8s ease"; + } + + // Zoom out if the user hits escape + document.addEventListener("keyup", function (event) { + if (level !== 1 && event.keyCode === 27) { + zoom.out(); + } + }); + + // Monitor mouse movement for panning + document.addEventListener("mousemove", function (event) { + if (level !== 1) { + mouseX = event.clientX; + mouseY = event.clientY; + } + }); + + /** + * Applies the CSS required to zoom in, prefers the use of CSS3 + * transforms but falls back on zoom for IE. + * + * @param {Object} rect + * @param {Number} scale + */ + function magnify(rect, scale) { + var scrollOffset = getScrollOffset(); + + // Ensure a width/height is set + rect.width = rect.width || 1; + rect.height = rect.height || 1; + + // Center the rect within the zoomed viewport + rect.x -= (window.innerWidth - rect.width * scale) / 2; + rect.y -= (window.innerHeight - rect.height * scale) / 2; + + if (supportsTransforms) { + // Reset + if (scale === 1) { + document.body.style.transform = ""; + } + // Scale + else { + var origin = scrollOffset.x + "px " + scrollOffset.y + "px", + transform = + "translate(" + + -rect.x + + "px," + + -rect.y + + "px) scale(" + + scale + + ")"; + + document.body.style.transformOrigin = origin; + document.body.style.transform = transform; + } + } else { + // Reset + if (scale === 1) { + document.body.style.position = ""; + document.body.style.left = ""; + document.body.style.top = ""; + document.body.style.width = ""; + document.body.style.height = ""; + document.body.style.zoom = ""; + } + // Scale + else { + document.body.style.position = "relative"; + document.body.style.left = -(scrollOffset.x + rect.x) / scale + "px"; + document.body.style.top = -(scrollOffset.y + rect.y) / scale + "px"; + document.body.style.width = scale * 100 + "%"; + document.body.style.height = scale * 100 + "%"; + document.body.style.zoom = scale; + } + } + + level = scale; + + if (document.documentElement.classList) { + if (level !== 1) { + document.documentElement.classList.add("zoomed"); + } else { + document.documentElement.classList.remove("zoomed"); + } + } + } + + /** + * Pan the document when the mosue cursor approaches the edges + * of the window. + */ + function pan() { + var range = 0.12, + rangeX = window.innerWidth * range, + rangeY = window.innerHeight * range, + scrollOffset = getScrollOffset(); + + // Up + if (mouseY < rangeY) { + window.scroll( + scrollOffset.x, + scrollOffset.y - (1 - mouseY / rangeY) * (14 / level), + ); + } + // Down + else if (mouseY > window.innerHeight - rangeY) { + window.scroll( + scrollOffset.x, + scrollOffset.y + + (1 - (window.innerHeight - mouseY) / rangeY) * (14 / level), + ); + } + + // Left + if (mouseX < rangeX) { + window.scroll( + scrollOffset.x - (1 - mouseX / rangeX) * (14 / level), + scrollOffset.y, + ); + } + // Right + else if (mouseX > window.innerWidth - rangeX) { + window.scroll( + scrollOffset.x + + (1 - (window.innerWidth - mouseX) / rangeX) * (14 / level), + scrollOffset.y, + ); + } + } + + function getScrollOffset() { + return { + x: window.scrollX !== undefined ? window.scrollX : window.pageXOffset, + y: window.scrollY !== undefined ? window.scrollY : window.pageYOffset, + }; + } + + return { + /** + * Zooms in on either a rectangle or HTML element. + * + * @param {Object} options + * - element: HTML element to zoom in on + * OR + * - x/y: coordinates in non-transformed space to zoom in on + * - width/height: the portion of the screen to zoom in on + * - scale: can be used instead of width/height to explicitly set scale + */ + to: function (options) { + // Due to an implementation limitation we can't zoom in + // to another element without zooming out first + if (level !== 1) { + zoom.out(); + } else { + options.x = options.x || 0; + options.y = options.y || 0; + + // If an element is set, that takes precedence + if (!!options.element) { + // Space around the zoomed in element to leave on screen + var padding = 20; + var bounds = options.element.getBoundingClientRect(); + + options.x = bounds.left - padding; + options.y = bounds.top - padding; + options.width = bounds.width + padding * 2; + options.height = bounds.height + padding * 2; + } + + // If width/height values are set, calculate scale from those values + if (options.width !== undefined && options.height !== undefined) { + options.scale = Math.max( + Math.min( + window.innerWidth / options.width, + window.innerHeight / options.height, + ), + 1, + ); + } + + if (options.scale > 1) { + options.x *= options.scale; + options.y *= options.scale; + + magnify(options, options.scale); + + if (options.pan !== false) { + // Wait with engaging panning as it may conflict with the + // zoom transition + panEngageTimeout = setTimeout(function () { + panUpdateInterval = setInterval(pan, 1000 / 60); + }, 800); + } + } + } + }, + + /** + * Resets the document zoom state to its default. + */ + out: function () { + clearTimeout(panEngageTimeout); + clearInterval(panUpdateInterval); + + magnify({ x: 0, y: 0 }, 1); + + level = 1; + }, + + // Alias + magnify: function (options) { + this.to(options); + }, + reset: function () { + this.out(); + }, + + zoomLevel: function () { + return level; + }, + }; })(); diff --git a/content/slides/slides_files/libs/revealjs/plugin/zoom/zoom.esm.js b/content/slides/slides_files/libs/revealjs/plugin/zoom/zoom.esm.js index c0e8d7b..fcb3807 100644 --- a/content/slides/slides_files/libs/revealjs/plugin/zoom/zoom.esm.js +++ b/content/slides/slides_files/libs/revealjs/plugin/zoom/zoom.esm.js @@ -1,4 +1,144 @@ /*! * reveal.js Zoom plugin */ -var e={id:"zoom",init:function(e){e.getRevealElement().addEventListener("mousedown",(function(n){var o=/Linux/.test(window.navigator.platform)?"ctrl":"alt",i=(e.getConfig().zoomKey?e.getConfig().zoomKey:o)+"Key",d=e.getConfig().zoomLevel?e.getConfig().zoomLevel:2;n[i]&&!e.isOverview()&&(n.preventDefault(),t.to({x:n.clientX,y:n.clientY,scale:d,pan:!1}))}))},destroy:function(){t.reset()}},t=function(){var e=1,n=0,o=0,i=-1,d=-1,l="transform"in document.body.style;function s(t,n){var o=r();if(t.width=t.width||1,t.height=t.height||1,t.x-=(window.innerWidth-t.width*n)/2,t.y-=(window.innerHeight-t.height*n)/2,l)if(1===n)document.body.style.transform="";else{var i=o.x+"px "+o.y+"px",d="translate("+-t.x+"px,"+-t.y+"px) scale("+n+")";document.body.style.transformOrigin=i,document.body.style.transform=d}else 1===n?(document.body.style.position="",document.body.style.left="",document.body.style.top="",document.body.style.width="",document.body.style.height="",document.body.style.zoom=""):(document.body.style.position="relative",document.body.style.left=-(o.x+t.x)/n+"px",document.body.style.top=-(o.y+t.y)/n+"px",document.body.style.width=100*n+"%",document.body.style.height=100*n+"%",document.body.style.zoom=n);e=n,document.documentElement.classList&&(1!==e?document.documentElement.classList.add("zoomed"):document.documentElement.classList.remove("zoomed"))}function c(){var t=.12*window.innerWidth,i=.12*window.innerHeight,d=r();owindow.innerHeight-i&&window.scroll(d.x,d.y+(1-(window.innerHeight-o)/i)*(14/e)),nwindow.innerWidth-t&&window.scroll(d.x+(1-(window.innerWidth-n)/t)*(14/e),d.y)}function r(){return{x:void 0!==window.scrollX?window.scrollX:window.pageXOffset,y:void 0!==window.scrollY?window.scrollY:window.pageYOffset}}return l&&(document.body.style.transition="transform 0.8s ease"),document.addEventListener("keyup",(function(n){1!==e&&27===n.keyCode&&t.out()})),document.addEventListener("mousemove",(function(t){1!==e&&(n=t.clientX,o=t.clientY)})),{to:function(n){if(1!==e)t.out();else{if(n.x=n.x||0,n.y=n.y||0,n.element){var o=n.element.getBoundingClientRect();n.x=o.left-20,n.y=o.top-20,n.width=o.width+40,n.height=o.height+40}void 0!==n.width&&void 0!==n.height&&(n.scale=Math.max(Math.min(window.innerWidth/n.width,window.innerHeight/n.height),1)),n.scale>1&&(n.x*=n.scale,n.y*=n.scale,s(n,n.scale),!1!==n.pan&&(i=setTimeout((function(){d=setInterval(c,1e3/60)}),800)))}},out:function(){clearTimeout(i),clearInterval(d),s({x:0,y:0},1),e=1},magnify:function(e){this.to(e)},reset:function(){this.out()},zoomLevel:function(){return e}}}();export default function(){return e} +var e = { + id: "zoom", + init: function (e) { + e.getRevealElement().addEventListener("mousedown", function (n) { + var o = /Linux/.test(window.navigator.platform) ? "ctrl" : "alt", + i = (e.getConfig().zoomKey ? e.getConfig().zoomKey : o) + "Key", + d = e.getConfig().zoomLevel ? e.getConfig().zoomLevel : 2; + n[i] && + !e.isOverview() && + (n.preventDefault(), + t.to({ x: n.clientX, y: n.clientY, scale: d, pan: !1 })); + }); + }, + destroy: function () { + t.reset(); + }, + }, + t = (function () { + var e = 1, + n = 0, + o = 0, + i = -1, + d = -1, + l = "transform" in document.body.style; + function s(t, n) { + var o = r(); + if ( + ((t.width = t.width || 1), + (t.height = t.height || 1), + (t.x -= (window.innerWidth - t.width * n) / 2), + (t.y -= (window.innerHeight - t.height * n) / 2), + l) + ) + if (1 === n) document.body.style.transform = ""; + else { + var i = o.x + "px " + o.y + "px", + d = "translate(" + -t.x + "px," + -t.y + "px) scale(" + n + ")"; + (document.body.style.transformOrigin = i), + (document.body.style.transform = d); + } + else + 1 === n + ? ((document.body.style.position = ""), + (document.body.style.left = ""), + (document.body.style.top = ""), + (document.body.style.width = ""), + (document.body.style.height = ""), + (document.body.style.zoom = "")) + : ((document.body.style.position = "relative"), + (document.body.style.left = -(o.x + t.x) / n + "px"), + (document.body.style.top = -(o.y + t.y) / n + "px"), + (document.body.style.width = 100 * n + "%"), + (document.body.style.height = 100 * n + "%"), + (document.body.style.zoom = n)); + (e = n), + document.documentElement.classList && + (1 !== e + ? document.documentElement.classList.add("zoomed") + : document.documentElement.classList.remove("zoomed")); + } + function c() { + var t = 0.12 * window.innerWidth, + i = 0.12 * window.innerHeight, + d = r(); + o < i + ? window.scroll(d.x, d.y - (14 / e) * (1 - o / i)) + : o > window.innerHeight - i && + window.scroll( + d.x, + d.y + (1 - (window.innerHeight - o) / i) * (14 / e), + ), + n < t + ? window.scroll(d.x - (14 / e) * (1 - n / t), d.y) + : n > window.innerWidth - t && + window.scroll( + d.x + (1 - (window.innerWidth - n) / t) * (14 / e), + d.y, + ); + } + function r() { + return { + x: void 0 !== window.scrollX ? 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[2021]) - Will also help speed up sensitivity analysis - Implement policy experiments and derive impulse response functions by life cycle diff --git a/content/slides/strucutral_estimation.html b/content/slides/strucutral_estimation.html index 46fb00f..c4918b7 100644 --- a/content/slides/strucutral_estimation.html +++ b/content/slides/strucutral_estimation.html @@ -1,4 +1,4 @@ - + Structural Estimation of Life Cycle Models @@ -106,7 +106,7 @@ - the rich have higher lifetime savings rates - models of consumption smoothing and precautionary savings can not explain this - propose a model where wealth is in the utility function - - households derive utility from wealth itself OR + - households derive utility from wealth itself OR - wealth provides a flow of services such as political power or social status -- @@ -141,8 +141,8 @@ `$$\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} ~ \uFunc(\cNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\underbrace{\left(\frac{\Rfree}{\PermShk_{t+1}\PermGroFac_{t+1}}\right)}_{\equiv \RNrm_{t+1}} + \TranShkEmp_{t+1} \end{aligned}$$` @@ -169,10 +169,10 @@ \\ \TranShkEmp_{t+1} & : \text{mean-one transitory shock to permanent income} \end{aligned}$$` -where +where `$$\begin{aligned} -\TranShkEmp_{s} = & \begin{cases} 0 & \text{with probability } \pZero>0 \\ +\TranShkEmp_{s} = & \begin{cases} 0 & \text{with probability } \pZero>0 \\ \xi_{s}/\pZero & \text{with probability } (1-\pZero) \text{, where } \log \xi_{s}\thicksim \mathcal{N}(-\sigma_{[\xi, t]}^{2}/2,\sigma_{[\xi, t]}^{2}) \end{cases} \\ \phantom{/\pZero} \\ & \text{and } \log \PermShk_{s} \thicksim \mathcal{N}(-\sigma_{[\PermShk, t]}^{2}/2,\sigma_{[\PermShk, t]}^{2}). \end{aligned}$$` @@ -181,13 +181,13 @@ # The WUFIM model -#### Wealth in the Utility Function Incomplete Markets Model +#### Wealth in the Utility Function Incomplete Markets Model `$$\begin{aligned} {\vFunc}_{t}({m}_{t}) & = \max_{\cNrm_{t}} \uFunc(\cNrm_{t}, \aNrm_{t})+\beth\Alive_{t+1}\hat{\DiscFac}_{t+1} \Ex_{t}[(\PermShk_{t+1}\PermGroFac_{t+1})^{1-\CRRA}{\vFunc}_{t+1}({m}_{t+1})] - \\ & \text{s.t.} & - \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} + \\ & \text{s.t.} & + \\ \aNrm_{t} & = {m}_{t}-\cNrm_{t} \\ {m}_{t+1} & = \aNrm_{t}\RNrm_{t+1}+ ~\TranShkEmp_{t+1} \end{aligned}$$` @@ -221,12 +221,12 @@ # Calibration and Estimation -Calibration +Calibration | Parameter | Description | Values | |----------|----------|----------| | `\(\sigma_{[\xi, t]}, \sigma_{[\PermShk, t]}\)` | Std. dev. of trans. and perm. shocks |Sabelhaus and Song [2010] | -| `\(\pZero = 0.005\)` | Probability of zero income | Carroll [1992] | +| `\(\pZero = 0.005\)` | Probability of zero income | Carroll [1992] | | `\(\Alive_{t},\hat{\DiscFac}_{t}\)` | Survival and discount factors | Caggetti [2003] | | `\(\Rfree = 1.03\)` | Risk free interest rate | Caggetti [2003] | @@ -302,7 +302,7 @@ #### Conclusion -- Need wealth in the utility function to better capture distribution of wealth +- Need wealth in the utility function to better capture distribution of wealth - Need life cycle structure to understand effect of policies on: - young parents with children and low income - working middle aged @@ -311,145 +311,179 @@ -- #### Future Work - + - Better estimation techniques such as Sequence Space Jacobians (Auclert et al. [2021]) - Will also help speed up sensitivity analysis - Implement policy experiments and derive impulse response functions by life cycle - Analyze the effect of policy experiments on different segments of the population - Evaluate optimal policy to minimize differential harm - - - - - - - + + + + + + + diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/highlight/highlight.esm.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/highlight/highlight.esm.js index 20f35d7..ea14c3e 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/highlight/highlight.esm.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/highlight/highlight.esm.js @@ -1,5 +1,30063 @@ -function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e 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((e.target = void 0), { value: void 0, done: !0 }) + : "keys" == n + ? { value: a, done: !1 } + : "values" == n + ? { value: t[a], done: !1 } + : { value: [a, t[a]], done: !1 }; + }, + "values", + ); +(el.Arguments = el.Array), Js("keys"), Js("values"), Js("entries"); +var ol = { exports: {} }, + sl = !E(function () { + return Object.isExtensible(Object.preventExtensions({})); + }), + ll = He, + cl = T, + _l = Z, + dl = b.f, + ul = sl, + ml = te("meta"), + pl = 0, + gl = + Object.isExtensible || + function () { + return !0; + }, + El = function (e) { + dl(e, ml, { value: { objectID: "O" + ++pl, weakData: {} } }); + }, + Sl = (ol.exports = { + REQUIRED: !1, + fastKey: function (e, t) { + if (!cl(e)) + return "symbol" == typeof e + ? e + : ("string" == typeof e ? 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(bl(e), m ? p(e[0], e[1], g) : p(e[0], e[1])) + : m + ? p(e, g) + : p(e); + }; + if (u) a = e; + else { + if ("function" != typeof (r = Nl(e))) + throw TypeError("Target is not iterable"); + if (Tl(r)) { + for (i = 0, o = fl(e.length); o > i; i++) + if ((s = E(e[i])) && s instanceof vl) return s; + return new vl(!1); + } + a = r.call(e); + } + for (l = a.next; !(c = l.call(a)).done; ) { + try { + s = E(c.value); + } catch (e) { + throw (Rl(a), e); + } + if ("object" == typeof s && s && s instanceof vl) return s; + } + return new vl(!1); + }, + hl = function (e, t, n) { + if (!(e instanceof t)) + throw TypeError("Incorrect " + (n ? n + " " : "") + "invocation"); + return e; + }, + yl = Qn, + Il = p, + Al = Gn, + Dl = Ie.exports, + Ml = ol.exports, + Ll = Ol, + wl = hl, + xl = T, + Pl = E, + kl = Fo, + Ul = os, + Fl = va, + Bl = function (e, t, n) { + var a = -1 !== e.indexOf("Map"), + r = -1 !== e.indexOf("Weak"), + i = a ? "set" : "add", + o = Il[e], + s = o && o.prototype, + l = o, + c = {}, + _ = function (e) { + var t = s[e]; + Dl( + s, + e, + "add" == e + ? function (e) { + return t.call(this, 0 === e ? 0 : e), this; + } + : "delete" == e + ? function (e) { + return !(r && !xl(e)) && t.call(this, 0 === e ? 0 : e); + } + : "get" == e + ? function (e) { + return r && !xl(e) ? void 0 : t.call(this, 0 === e ? 0 : e); + } + : "has" == e + ? function (e) { + return !(r && !xl(e)) && t.call(this, 0 === e ? 0 : e); + } + : function (e, n) { + return t.call(this, 0 === e ? 0 : e, n), this; + }, + ); + }; + if ( + Al( + e, + "function" != typeof o || + !( + r || + (s.forEach && + !Pl(function () { + new o().entries().next(); + })) + ), + ) + ) + (l = n.getConstructor(t, e, a, i)), (Ml.REQUIRED = !0); + else if (Al(e, !0)) { + var d = new l(), + u = d[i](r ? {} : -0, 1) != d, + m = Pl(function () { + d.has(1); + }), + p = kl(function (e) { + new o(e); + }), + g = + !r && + Pl(function () { + for (var e = new o(), t = 5; t--; ) e[i](t, t); + return !e.has(-0); + }); + p || + (((l = t(function (t, n) { + wl(t, l, e); + var r = Fl(new o(), t, l); + return null != n && Ll(n, r[i], { that: r, AS_ENTRIES: a }), r; + })).prototype = s), + (s.constructor = l)), + (m || g) && (_("delete"), _("has"), a && _("get")), + (g || u) && _(i), + r && s.clear && delete s.clear; + } + return ( + (c[e] = l), + yl({ global: !0, forced: l != o }, c), + Ul(l, e), + r || n.setStrong(l, e, a), + l + ); + }, + Gl = Ie.exports, + Yl = oe, + Hl = b, + Vl = S, + ql = Oe("species"), + zl = function (e) { + var t = Yl(e), + n = Hl.f; + Vl && + t && + !t[ql] && + n(t, ql, { + configurable: !0, + get: function () { + return this; + }, + }); + }, + Wl = b.f, + $l = Va, + Ql = function (e, t, n) { + for (var a in t) Gl(e, a, t[a], n); + return e; + }, + Kl = qi, + jl = hl, + Xl = Ol, + Zl = Os, + Jl = zl, + ec = S, + tc = ol.exports.fastKey, + nc = nt.set, + ac = nt.getterFor, + rc = { + getConstructor: function (e, t, n, a) { + var r = e(function (e, i) { + jl(e, r, t), + nc(e, { + type: t, + index: $l(null), + first: void 0, + last: void 0, + size: 0, + }), + ec || (e.size = 0), + null != i && Xl(i, e[a], { that: e, AS_ENTRIES: n }); + }), + i = ac(t), + o = function (e, t, n) { + var a, + r, + o = i(e), + l = s(e, t); + return ( + l + ? (l.value = n) + : ((o.last = l = + { + index: (r = tc(t, !0)), + key: t, + value: n, + previous: (a = o.last), + next: void 0, + removed: !1, + }), + o.first || (o.first = l), + a && (a.next = l), + ec ? o.size++ : e.size++, + "F" !== r && (o.index[r] = l)), + e + ); + }, + s = function (e, t) { + var n, + a = i(e), + r = tc(t); + if ("F" !== r) return a.index[r]; + for (n = a.first; n; n = n.next) if (n.key == t) return n; + }; + return ( + Ql(r.prototype, { + clear: function () { + for (var e = i(this), t = e.index, n = e.first; n; ) + (n.removed = !0), + n.previous && (n.previous = n.previous.next = void 0), + delete t[n.index], + (n = n.next); + (e.first = e.last = void 0), ec ? (e.size = 0) : (this.size = 0); + }, + delete: function (e) { + var t = this, + n = i(t), + a = s(t, e); + if (a) { + var r = a.next, + o = a.previous; + delete n.index[a.index], + (a.removed = !0), + o && (o.next = r), + r && (r.previous = o), + n.first == a && (n.first = r), + n.last == a && (n.last = o), + ec ? n.size-- : t.size--; + } + return !!a; + }, + forEach: function (e) { + for ( + var t, + n = i(this), + a = Kl(e, arguments.length > 1 ? arguments[1] : void 0, 3); + (t = t ? t.next : n.first); + + ) + for (a(t.value, t.key, this); t && t.removed; ) t = t.previous; + }, + has: function (e) { + return !!s(this, e); + }, + }), + Ql( + r.prototype, + n + ? { + get: function (e) { + var t = s(this, e); + return t && t.value; + }, + set: function (e, t) { + return o(this, 0 === e ? 0 : e, t); + }, + } + : { + add: function (e) { + return o(this, (e = 0 === e ? 0 : e), e); + }, + }, + ), + ec && + Wl(r.prototype, "size", { + get: function () { + return i(this).size; + }, + }), + r + ); + }, + setStrong: function (e, t, n) { + var a = t + " Iterator", + r = ac(t), + i = ac(a); + Zl( + e, + t, + function (e, t) { + nc(this, { type: a, target: e, state: r(e), kind: t, last: void 0 }); + }, + function () { + for (var e = i(this), t = e.kind, n = e.last; n && n.removed; ) + n = n.previous; + return e.target && (e.last = n = n ? n.next : e.state.first) + ? "keys" == t + ? { value: n.key, done: !1 } + : "values" == t + ? { value: n.value, done: !1 } + : { value: [n.key, n.value], done: !1 } + : ((e.target = void 0), { value: void 0, done: !0 }); + }, + n ? "entries" : "values", + !n, + !0, + ), + Jl(t); + }, + }; +Bl( + "Map", + function (e) { + return function () { + return e(this, arguments.length ? arguments[0] : void 0); + }; + }, + rc, +); +var ic = p, + oc = Hi, + sc = il, + lc = F, + cc = Oe, + _c = cc("iterator"), + dc = cc("toStringTag"), + uc = sc.values; +for (var mc in oc) { + var pc = ic[mc], + gc = pc && pc.prototype; + if (gc) { + if (gc[_c] !== uc) + try { + lc(gc, _c, uc); + } catch (Am) { + gc[_c] = uc; + } + if ((gc[dc] || lc(gc, dc, mc), oc[mc])) + for (var Ec in sc) + if (gc[Ec] !== sc[Ec]) + try { + lc(gc, Ec, sc[Ec]); + } catch (Am) { + gc[Ec] = sc[Ec]; + } + } +} +Bl( + "Set", + function (e) { + return function () { + return e(this, arguments.length ? arguments[0] : void 0); + }; + }, + rc, +); +var Sc = Qn, + bc = sl, + Tc = E, + fc = T, + Cc = ol.exports.onFreeze, + Nc = Object.freeze; +Sc( + { + target: "Object", + stat: !0, + forced: Tc(function () { + Nc(1); + }), + sham: !bc, + }, + { + freeze: function (e) { + return Nc && fc(e) ? Nc(Cc(e)) : e; + }, + }, +); +var Rc = {}, + vc = Vt, + Oc = Zt.f, + hc = {}.toString, + yc = + "object" == typeof window && window && Object.getOwnPropertyNames + ? Object.getOwnPropertyNames(window) + : []; +Rc.f = function (e) { + return yc && "[object Window]" == hc.call(e) + ? (function (e) { + try { + return Oc(e); + } catch (e) { + return yc.slice(); + } + })(e) + : Oc(vc(e)); +}; +var Ic = Qn, + Ac = E, + Dc = Rc.f; +Ic( + { + target: "Object", + stat: !0, + forced: Ac(function () { + return !Object.getOwnPropertyNames(1); + }), + }, + { getOwnPropertyNames: Dc }, +); +var Mc = Qn, + Lc = E, + wc = T, + xc = Object.isFrozen; +Mc( + { + target: "Object", + stat: !0, + forced: Lc(function () { + xc(1); + }), + }, + { + isFrozen: function (e) { + return !wc(e) || (!!xc && xc(e)); + }, + }, +); +var Pc = Qn, + kc = E, + Uc = jn, + Fc = T, + Bc = K, + Gc = rn, + Yc = ea, + Hc = Qi, + Vc = ra, + qc = de, + zc = Oe("isConcatSpreadable"), + Wc = + qc >= 51 || + !kc(function () { + var e = []; + return (e[zc] = !1), e.concat()[0] !== e; + }), + $c = Vc("concat"), + Qc = function (e) { + if (!Fc(e)) return !1; + var t = e[zc]; + return void 0 !== t ? !!t : Uc(e); + }; +Pc( + { target: "Array", proto: !0, forced: !Wc || !$c }, + { + concat: function (e) { + var t, + n, + a, + r, + i, + o = Bc(this), + s = Hc(o, 0), + l = 0; + for (t = -1, a = arguments.length; t < a; t++) + if (Qc((i = -1 === t ? o : arguments[t]))) { + if (l + (r = Gc(i.length)) > 9007199254740991) + throw TypeError("Maximum allowed index exceeded"); + for (n = 0; n < r; n++, l++) n in i && Yc(s, l, i[n]); + } else { + if (l >= 9007199254740991) + throw TypeError("Maximum allowed index exceeded"); + Yc(s, l++, i); + } + return (s.length = l), s; + }, + }, +); +var Kc = S, + jc = p, + Xc = Gn, + Zc = va, + Jc = b.f, + e_ = Zt.f, + t_ = _i, + n_ = vt, + a_ = br, + r_ = Ie.exports, + i_ = E, + o_ = nt.enforce, + s_ = zl, + l_ = Oe("match"), + c_ = jc.RegExp, + __ = c_.prototype, + d_ = /a/g, + u_ = /a/g, + m_ = new c_(d_) !== d_, + p_ = a_.UNSUPPORTED_Y; +if ( + Kc && + Xc( + "RegExp", + !m_ || + p_ || + i_(function () { + return ( + (u_[l_] = !1), c_(d_) != d_ || c_(u_) == u_ || "/a/i" != c_(d_, "i") + ); + }), + ) +) { + for ( + var g_ = function (e, t) { + var n, + a = this instanceof g_, + r = t_(e), + i = void 0 === t; + if (!a && r && e.constructor === g_ && i) return e; + m_ + ? r && !i && (e = e.source) + : e instanceof g_ && (i && (t = n_.call(e)), (e = e.source)), + p_ && (n = !!t && t.indexOf("y") > -1) && (t = t.replace(/y/g, "")); + var o = Zc(m_ ? new c_(e, t) : c_(e, t), a ? this : __, g_); + p_ && n && (o_(o).sticky = !0); + return o; + }, + E_ = function (e) { + (e in g_) || + Jc(g_, e, { + configurable: !0, + get: function () { + return c_[e]; + }, + set: function (t) { + c_[e] = t; + }, + }); + }, + S_ = e_(c_), + b_ = 0; + S_.length > b_; + + ) + E_(S_[b_++]); + (__.constructor = g_), (g_.prototype = __), r_(jc, "RegExp", g_); +} +s_("RegExp"); +var T_ = S, + f_ = E, + C_ = ya, + N_ = Rn, + R_ = xt, + v_ = K, + O_ = Gt, + h_ = Object.assign, + y_ = Object.defineProperty, + I_ = + !h_ || + f_(function () { + if ( + T_ && + 1 !== + h_( + { b: 1 }, + h_( + y_({}, "a", { + enumerable: !0, + get: function () { + y_(this, "b", { value: 3, enumerable: !1 }); + }, + }), + { b: 2 }, + ), + ).b + ) + return !0; + var e = {}, + t = {}, + n = Symbol(), + a = "abcdefghijklmnopqrst"; + return ( + (e[n] = 7), + a.split("").forEach(function (e) { + t[e] = e; + }), + 7 != h_({}, e)[n] || C_(h_({}, t)).join("") != a + ); + }) + ? function (e, t) { + for ( + var n = v_(e), a = arguments.length, r = 1, i = N_.f, o = R_.f; + a > r; + + ) + for ( + var s, + l = O_(arguments[r++]), + c = i ? C_(l).concat(i(l)) : C_(l), + _ = c.length, + d = 0; + _ > d; + + ) + (s = c[d++]), (T_ && !o.call(l, s)) || (n[s] = l[s]); + return n; + } + : h_; +Qn( + { target: "Object", stat: !0, forced: Object.assign !== I_ }, + { assign: I_ }, +); +var A_ = K, + D_ = ya; +Qn( + { + target: "Object", + stat: !0, + forced: E(function () { + D_(1); + }), + }, + { + keys: function (e) { + return D_(A_(e)); + }, + }, +); +var M_ = pn.includes, + L_ = Xs; +Qn( + { target: "Array", proto: !0 }, + { + includes: function (e) { + return M_(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +), + L_("includes"); +var w_ = Qn, + x_ = no.findIndex, + P_ = Xs, + k_ = !0; +"findIndex" in [] && + Array(1).findIndex(function () { + k_ = !1; + }), + w_( + { target: "Array", proto: !0, forced: k_ }, + { + findIndex: function (e) { + return x_(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ), + P_("findIndex"); +var U_ = _i, + F_ = function (e) { + if (U_(e)) throw TypeError("The method doesn't accept regular expressions"); + return e; + }, + B_ = Oe("match"), + G_ = function (e) { + var t = /./; + try { + "/./"[e](t); + } catch (n) { + try { + return (t[B_] = !1), "/./"[e](t); + } catch (e) {} + } + return !1; + }, + Y_ = F_, + H_ = $; +Qn( + { target: "String", proto: !0, forced: !G_("includes") }, + { + includes: function (e) { + return !!~String(H_(this)).indexOf( + Y_(e), + arguments.length > 1 ? arguments[1] : void 0, + ); + }, + }, +); +var V_ = {}, + q_ = Oe; +V_.f = q_; +var z_ = ne, + W_ = Z, + $_ = V_, + Q_ = b.f, + K_ = Qn, + j_ = p, + X_ = oe, + Z_ = S, + J_ = pe, + ed = ge, + td = E, + nd = Z, + ad = jn, + rd = T, + id = y, + od = K, + sd = Vt, + ld = A, + cd = P, + _d = Va, + dd = ya, + ud = Zt, + md = Rc, + pd = Rn, + gd = wt, + Ed = b, + Sd = xt, + bd = F, + Td = Ie.exports, + fd = g.exports, + Cd = He, + Nd = te, + Rd = Oe, + vd = V_, + Od = function (e) { + var t = z_.Symbol || (z_.Symbol = {}); + W_(t, e) || Q_(t, e, { value: $_.f(e) }); + }, + hd = os, + yd = nt, + Id = no.forEach, + Ad = Ye("hidden"), + Dd = Rd("toPrimitive"), + Md = yd.set, + Ld = yd.getterFor("Symbol"), + wd = Object.prototype, + xd = j_.Symbol, + Pd = X_("JSON", "stringify"), + kd = gd.f, + Ud = Ed.f, + Fd = md.f, + Bd = Sd.f, + Gd = fd("symbols"), + Yd = fd("op-symbols"), + Hd = fd("string-to-symbol-registry"), + Vd = fd("symbol-to-string-registry"), + qd = fd("wks"), + zd = j_.QObject, + Wd = !zd || !zd.prototype || !zd.prototype.findChild, + $d = + Z_ && + td(function () { + return ( + 7 != + _d( + Ud({}, "a", { + get: function () { + return Ud(this, "a", { value: 7 }).a; + }, + }), + ).a + ); + }) + ? function (e, t, n) { + var a = kd(wd, t); + a && delete wd[t], Ud(e, t, n), a && e !== wd && Ud(wd, t, a); + } + : Ud, + Qd = function (e, t) { + var n = (Gd[e] = _d(xd.prototype)); + return ( + Md(n, { type: "Symbol", tag: e, description: t }), + Z_ || (n.description = t), + n + ); + }, + Kd = ed + ? function (e) { + return "symbol" == typeof e; + } + : function (e) { + return Object(e) instanceof xd; + }, + jd = function (e, t, n) { + e === wd && jd(Yd, t, n), id(e); + var a = ld(t, !0); + return ( + id(n), + nd(Gd, a) + ? (n.enumerable + ? (nd(e, Ad) && e[Ad][a] && (e[Ad][a] = !1), + (n = _d(n, { enumerable: cd(0, !1) }))) + : (nd(e, Ad) || Ud(e, Ad, cd(1, {})), (e[Ad][a] = !0)), + $d(e, a, n)) + : Ud(e, a, n) + ); + }, + Xd = function (e, t) { + id(e); + var n = sd(t), + a = dd(n).concat(tu(n)); + return ( + Id(a, function (t) { + (Z_ && !Zd.call(n, t)) || jd(e, t, n[t]); + }), + e + ); + }, + Zd = function (e) { + var t = ld(e, !0), + n = Bd.call(this, t); + return ( + !(this === wd && nd(Gd, t) && !nd(Yd, t)) && + (!(n || !nd(this, t) || !nd(Gd, t) || (nd(this, Ad) && this[Ad][t])) || n) + ); + }, + Jd = function (e, t) { + var n = sd(e), + a = ld(t, !0); + if (n !== wd || !nd(Gd, a) || nd(Yd, a)) { + var r = kd(n, a); + return ( + !r || !nd(Gd, a) || (nd(n, Ad) && n[Ad][a]) || (r.enumerable = !0), r + ); + } + }, + eu = function (e) { + var t = Fd(sd(e)), + n = []; + return ( + Id(t, function (e) { + nd(Gd, e) || nd(Cd, e) || n.push(e); + }), + n + ); + }, + tu = function (e) { + var t = e === wd, + n = Fd(t ? Yd : sd(e)), + a = []; + return ( + Id(n, function (e) { + !nd(Gd, e) || (t && !nd(wd, e)) || a.push(Gd[e]); + }), + a + ); + }; +(J_ || + (Td( + (xd = function () { + if (this instanceof xd) throw TypeError("Symbol is not a constructor"); + var e = + arguments.length && void 0 !== arguments[0] + ? String(arguments[0]) + : void 0, + t = Nd(e), + n = function (e) { + this === wd && n.call(Yd, e), + nd(this, Ad) && nd(this[Ad], t) && (this[Ad][t] = !1), + $d(this, t, cd(1, e)); + }; + return Z_ && Wd && $d(wd, t, { configurable: !0, set: n }), Qd(t, e); + }).prototype, + "toString", + function () { + return Ld(this).tag; + }, + ), + Td(xd, "withoutSetter", function (e) { + return Qd(Nd(e), e); + }), + (Sd.f = Zd), + (Ed.f = jd), + (gd.f = Jd), + (ud.f = md.f = eu), + (pd.f = tu), + (vd.f = function (e) { + return Qd(Rd(e), e); + }), + Z_ && + (Ud(xd.prototype, "description", { + configurable: !0, + get: function () { + return Ld(this).description; + }, + }), + Td(wd, "propertyIsEnumerable", Zd, { unsafe: !0 }))), +K_({ global: !0, wrap: !0, forced: !J_, sham: !J_ }, { Symbol: xd }), +Id(dd(qd), function (e) { + Od(e); +}), +K_( + { target: "Symbol", stat: !0, forced: !J_ }, + { + for: function (e) { + var t = String(e); + if (nd(Hd, t)) return Hd[t]; + var n = xd(t); + return (Hd[t] = n), (Vd[n] = t), n; + }, + keyFor: function (e) { + if (!Kd(e)) throw TypeError(e + " is not a symbol"); + if (nd(Vd, e)) return Vd[e]; + }, + useSetter: function () { + Wd = !0; + }, + useSimple: function () { + Wd = !1; + }, + }, +), +K_( + { target: "Object", stat: !0, forced: !J_, sham: !Z_ }, + { + create: function (e, t) { + return void 0 === t ? _d(e) : Xd(_d(e), t); + }, + defineProperty: jd, + defineProperties: Xd, + getOwnPropertyDescriptor: Jd, + }, +), +K_( + { target: "Object", stat: !0, forced: !J_ }, + { getOwnPropertyNames: eu, getOwnPropertySymbols: tu }, +), +K_( + { + target: "Object", + stat: !0, + forced: td(function () { + pd.f(1); + }), + }, + { + getOwnPropertySymbols: function (e) { + return pd.f(od(e)); + }, + }, +), +Pd) && + K_( + { + target: "JSON", + stat: !0, + forced: + !J_ || + td(function () { + var e = xd(); + return ( + "[null]" != Pd([e]) || "{}" != Pd({ a: e }) || "{}" != Pd(Object(e)) + ); + }), + }, + { + stringify: function (e, t, n) { + for (var a, r = [e], i = 1; arguments.length > i; ) + r.push(arguments[i++]); + if (((a = t), (rd(t) || void 0 !== e) && !Kd(e))) + return ( + ad(t) || + (t = function (e, t) { + if ( + ("function" == typeof a && (t = a.call(this, e, t)), !Kd(t)) + ) + return t; + }), + (r[1] = t), + Pd.apply(null, r) + ); + }, + }, + ); +xd.prototype[Dd] || bd(xd.prototype, Dd, xd.prototype.valueOf), + hd(xd, "Symbol"), + (Cd[Ad] = !0); +var nu = Qn, + au = S, + ru = p, + iu = Z, + ou = T, + su = b.f, + lu = Ln, + cu = ru.Symbol; +if ( + au && + "function" == typeof cu && + (!("description" in cu.prototype) || void 0 !== cu().description) +) { + var _u = {}, + du = function () { + var e = + arguments.length < 1 || void 0 === arguments[0] + ? void 0 + : String(arguments[0]), + t = this instanceof du ? new cu(e) : void 0 === e ? cu() : cu(e); + return "" === e && (_u[t] = !0), t; + }; + lu(du, cu); + var uu = (du.prototype = cu.prototype); + uu.constructor = du; + var mu = uu.toString, + pu = "Symbol(test)" == String(cu("test")), + gu = /^Symbol\((.*)\)[^)]+$/; + su(uu, "description", { + configurable: !0, + get: function () { + var e = ou(this) ? this.valueOf() : this, + t = mu.call(e); + if (iu(_u, e)) return ""; + var n = pu ? t.slice(7, -1) : t.replace(gu, "$1"); + return "" === n ? void 0 : n; + }, + }), + nu({ global: !0, forced: !0 }, { Symbol: du }); +} +var Eu = Qn, + Su = no.find, + bu = Xs, + Tu = !0; +"find" in [] && + Array(1).find(function () { + Tu = !1; + }), + Eu( + { target: "Array", proto: !0, forced: Tu }, + { + find: function (e) { + return Su(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ), + bu("find"); +var fu, + Cu = Qn, + Nu = wt.f, + Ru = rn, + vu = F_, + Ou = $, + hu = G_, + yu = "".startsWith, + Iu = Math.min, + Au = hu("startsWith"); +Cu( + { + target: "String", + proto: !0, + forced: + !!( + Au || ((fu = Nu(String.prototype, "startsWith")), !fu || fu.writable) + ) && !Au, + }, + { + startsWith: function (e) { + var t = String(Ou(this)); + vu(e); + var n = Ru(Iu(arguments.length > 1 ? arguments[1] : void 0, t.length)), + a = String(e); + return yu ? yu.call(t, a, n) : t.slice(n, n + a.length) === a; + }, + }, +); +var Du = no.filter; +Qn( + { target: "Array", proto: !0, forced: !ra("filter") }, + { + filter: function (e) { + return Du(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +); +var Mu = S, + Lu = b.f, + wu = Function.prototype, + xu = wu.toString, + Pu = /^\s*function ([^ (]*)/; +function ku(t) { + return ( + t instanceof Map + ? (t.clear = + t.delete = + t.set = + function () { + throw new Error("map is read-only"); + }) + : t instanceof Set && + (t.add = + t.clear = + t.delete = + function () { + throw new Error("set is read-only"); + }), + Object.freeze(t), + Object.getOwnPropertyNames(t).forEach(function (n) { + var a = t[n]; + "object" != e(a) || Object.isFrozen(a) || ku(a); + }), + t + ); +} +Mu && + !("name" in wu) && + Lu(wu, "name", { + configurable: !0, + get: function () { + try { + return xu.call(this).match(Pu)[1]; + } catch (e) { + return ""; + } + }, + }); +var Uu = ku, + Fu = ku; +Uu.default = Fu; +var Bu = (function () { + function e(n) { + t(this, e), + void 0 === n.data && (n.data = {}), + (this.data = n.data), + (this.isMatchIgnored = !1); + } + return ( + a(e, [ + { + key: "ignoreMatch", + value: function () { + this.isMatchIgnored = !0; + }, + }, + ]), + e + ); +})(); +function Gu(e) { + return e + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/'/g, "'"); +} +function Yu(e) { + var t = Object.create(null); + for (var n in e) t[n] = e[n]; + for ( + var a = arguments.length, r = new Array(a > 1 ? a - 1 : 0), i = 1; + i < a; + i++ + ) + r[i - 1] = arguments[i]; + return ( + r.forEach(function (e) { + for (var n in e) t[n] = e[n]; + }), + t + ); +} +var Hu = function (e) { + return !!e.kind; + }, + Vu = (function () { + function e(n, a) { + t(this, e), + (this.buffer = ""), + (this.classPrefix = a.classPrefix), + n.walk(this); + } + return ( + a(e, [ + { + key: "addText", + value: function (e) { + this.buffer += Gu(e); + }, + }, + { + key: "openNode", + value: function (e) { + if (Hu(e)) { + var t = e.kind; + e.sublanguage || (t = "".concat(this.classPrefix).concat(t)), + this.span(t); + } + }, + }, + { + key: "closeNode", + value: function (e) { + Hu(e) && (this.buffer += ""); + }, + }, + { + key: "value", + value: function () { + return this.buffer; + }, + }, + { + key: "span", + value: function (e) { + this.buffer += ''); + }, + }, + ]), + e + ); + })(), + qu = (function () { + function e() { + t(this, e), + (this.rootNode = { children: [] }), + (this.stack = [this.rootNode]); + } + return ( + a( + e, + [ + { + key: "top", + get: function () { + return this.stack[this.stack.length - 1]; + }, + }, + { + key: "root", + get: function () { + return this.rootNode; + }, + }, + { + key: "add", + value: function (e) { + this.top.children.push(e); + }, + }, + { + key: "openNode", + value: function (e) { + var t = { kind: e, children: [] }; + this.add(t), this.stack.push(t); + }, + }, + { + key: "closeNode", + value: function () { + if (this.stack.length > 1) return this.stack.pop(); + }, + }, + { + key: "closeAllNodes", + value: function () { + for (; this.closeNode(); ); + }, + }, + { + key: "toJSON", + value: function () { + return JSON.stringify(this.rootNode, null, 4); + }, + }, + { + key: "walk", + value: function (e) { + return this.constructor._walk(e, this.rootNode); + }, + }, + ], + [ + { + key: "_walk", + value: function (e, t) { + var n = this; + return ( + "string" == typeof t + ? e.addText(t) + : t.children && + (e.openNode(t), + t.children.forEach(function (t) { + return n._walk(e, t); + }), + e.closeNode(t)), + e + ); + }, + }, + { + key: "_collapse", + value: function (t) { + "string" != typeof t && + t.children && + (t.children.every(function (e) { + return "string" == typeof e; + }) + ? (t.children = [t.children.join("")]) + : t.children.forEach(function (t) { + e._collapse(t); + })); + }, + }, + ], + ), + e + ); + })(), + zu = (function (e) { + !(function (e, t) { + if ("function" != typeof t && null !== t) + throw new TypeError( + "Super expression must either be null or a function", + ); + (e.prototype = Object.create(t && t.prototype, { + constructor: { value: e, writable: !0, configurable: !0 }, + })), + t && i(e, t); + })(r, qu); + var n = s(r); + function r(e) { + var a; + return t(this, r), ((a = n.call(this)).options = e), a; + } + return ( + a(r, [ + { + key: "addKeyword", + value: function (e, t) { + "" !== e && (this.openNode(t), this.addText(e), this.closeNode()); + }, + }, + { + key: "addText", + value: function (e) { + "" !== e && this.add(e); + }, + }, + { + key: "addSublanguage", + value: function (e, t) { + var n = e.root; + (n.kind = t), (n.sublanguage = !0), this.add(n); + }, + }, + { + key: "toHTML", + value: function () { + return new Vu(this, this.options).value(); + }, + }, + { + key: "finalize", + value: function () { + return !0; + }, + }, + ]), + r + ); + })(); +function Wu(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function $u() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Wu(e); + }) + .join(""); + return a; +} +function Qu() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return Wu(e); + }) + .join("|") + + ")"; + return a; +} +var Ku = /\[(?:[^\\\]]|\\.)*\]|\(\??|\\([1-9][0-9]*)|\\./; +var ju = + "(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)", + Xu = { begin: "\\\\[\\s\\S]", relevance: 0 }, + Zu = { + className: "string", + begin: "'", + end: "'", + illegal: "\\n", + contains: [Xu], + }, + Ju = { + className: "string", + begin: '"', + end: '"', + illegal: "\\n", + contains: [Xu], + }, + em = { + begin: + /\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/, + }, + tm = function (e, t) { + var n = arguments.length > 2 && void 0 !== arguments[2] ? arguments[2] : {}, + a = Yu({ className: "comment", begin: e, end: t, contains: [] }, n); + return ( + a.contains.push(em), + a.contains.push({ + className: "doctag", + begin: "(?:TODO|FIXME|NOTE|BUG|OPTIMIZE|HACK|XXX):", + relevance: 0, + }), + a + ); + }, + nm = tm("//", "$"), + am = tm("/\\*", "\\*/"), + rm = tm("#", "$"), + im = { className: "number", begin: "\\b\\d+(\\.\\d+)?", relevance: 0 }, + om = { className: "number", begin: ju, relevance: 0 }, + sm = { className: "number", begin: "\\b(0b[01]+)", relevance: 0 }, + lm = { + className: "number", + begin: + "\\b\\d+(\\.\\d+)?(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?", + relevance: 0, + }, + cm = { + begin: /(?=\/[^/\n]*\/)/, + contains: [ + { + className: "regexp", + begin: /\//, + end: /\/[gimuy]*/, + illegal: /\n/, + contains: [ + Xu, + { begin: /\[/, end: /\]/, relevance: 0, contains: [Xu] }, + ], + }, + ], + }, + _m = { className: "title", begin: "[a-zA-Z]\\w*", relevance: 0 }, + dm = { className: "title", begin: "[a-zA-Z_]\\w*", relevance: 0 }, + um = { begin: "\\.\\s*[a-zA-Z_]\\w*", relevance: 0 }, + mm = Object.freeze({ + __proto__: null, + MATCH_NOTHING_RE: /\b\B/, + IDENT_RE: "[a-zA-Z]\\w*", + UNDERSCORE_IDENT_RE: "[a-zA-Z_]\\w*", + NUMBER_RE: "\\b\\d+(\\.\\d+)?", + C_NUMBER_RE: ju, + BINARY_NUMBER_RE: "\\b(0b[01]+)", + RE_STARTERS_RE: + "!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~", + SHEBANG: function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : {}, + t = /^#![ ]*\//; + return ( + e.binary && (e.begin = $u(t, /.*\b/, e.binary, /\b.*/)), + Yu( + { + className: "meta", + begin: t, + end: /$/, + relevance: 0, + "on:begin": function (e, t) { + 0 !== e.index && t.ignoreMatch(); + }, + }, + e, + ) + ); + }, + BACKSLASH_ESCAPE: Xu, + APOS_STRING_MODE: Zu, + QUOTE_STRING_MODE: Ju, + PHRASAL_WORDS_MODE: em, + COMMENT: tm, + C_LINE_COMMENT_MODE: nm, + C_BLOCK_COMMENT_MODE: am, + HASH_COMMENT_MODE: rm, + NUMBER_MODE: im, + C_NUMBER_MODE: om, + BINARY_NUMBER_MODE: sm, + CSS_NUMBER_MODE: lm, + REGEXP_MODE: cm, + TITLE_MODE: _m, + UNDERSCORE_TITLE_MODE: dm, + METHOD_GUARD: um, + END_SAME_AS_BEGIN: function (e) { + return Object.assign(e, { + "on:begin": function (e, t) { + t.data._beginMatch = e[1]; + }, + "on:end": function (e, t) { + t.data._beginMatch !== e[1] && t.ignoreMatch(); + }, + }); + }, + }); +function pm(e, t) { + "." === e.input[e.index - 1] && t.ignoreMatch(); +} +function gm(e, t) { + t && + e.beginKeywords && + ((e.begin = + "\\b(" + e.beginKeywords.split(" ").join("|") + ")(?!\\.)(?=\\b|\\s)"), + (e.__beforeBegin = pm), + (e.keywords = e.keywords || e.beginKeywords), + delete e.beginKeywords, + void 0 === e.relevance && (e.relevance = 0)); +} +function Em(e, t) { + Array.isArray(e.illegal) && (e.illegal = Qu.apply(void 0, c(e.illegal))); +} +function Sm(e, t) { + if (e.match) { + if (e.begin || e.end) + throw new Error("begin & end are not supported with match"); + (e.begin = e.match), delete e.match; + } +} +function bm(e, t) { + void 0 === e.relevance && (e.relevance = 1); +} +var Tm = [ + "of", + "and", + "for", + "in", + "not", + "or", + "if", + "then", + "parent", + "list", + "value", +]; +function fm(e, t) { + var n = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "keyword", + a = {}; + return ( + "string" == typeof e + ? r(n, e.split(" ")) + : Array.isArray(e) + ? r(n, e) + : Object.keys(e).forEach(function (n) { + Object.assign(a, fm(e[n], t, n)); + }), + a + ); + function r(e, n) { + t && + (n = n.map(function (e) { + return e.toLowerCase(); + })), + n.forEach(function (t) { + var n = t.split("|"); + a[n[0]] = [e, Cm(n[0], n[1])]; + }); + } +} +function Cm(e, t) { + return t + ? Number(t) + : (function (e) { + return Tm.includes(e.toLowerCase()); + })(e) + ? 0 + : 1; +} +function Nm(n, r) { + function i(e, t) { + return new RegExp( + Wu(e), + "m" + (n.case_insensitive ? "i" : "") + (t ? "g" : ""), + ); + } + r.plugins; + var o = (function () { + function e() { + t(this, e), + (this.matchIndexes = {}), + (this.regexes = []), + (this.matchAt = 1), + (this.position = 0); + } + return ( + a(e, [ + { + key: "addRule", + value: function (e, t) { + (t.position = this.position++), + (this.matchIndexes[this.matchAt] = t), + this.regexes.push([t, e]), + (this.matchAt += + (function (e) { + return new RegExp(e.toString() + "|").exec("").length - 1; + })(e) + 1); + }, + }, + { + key: "compile", + value: function () { + 0 === this.regexes.length && + (this.exec = function () { + return null; + }); + var e = this.regexes.map(function (e) { + return e[1]; + }); + (this.matcherRe = i( + (function (e) { + var t = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : "|", + n = 0; + return e + .map(function (e) { + for ( + var t = (n += 1), a = Wu(e), r = ""; + a.length > 0; + + ) { + var i = Ku.exec(a); + if (!i) { + r += a; + break; + } + (r += a.substring(0, i.index)), + (a = a.substring(i.index + i[0].length)), + "\\" === i[0][0] && i[1] + ? (r += "\\" + String(Number(i[1]) + t)) + : ((r += i[0]), "(" === i[0] && n++); + } + return r; + }) + .map(function (e) { + return "(".concat(e, ")"); + }) + .join(t); + })(e), + !0, + )), + (this.lastIndex = 0); + }, + }, + { + key: "exec", + value: function (e) { + this.matcherRe.lastIndex = this.lastIndex; + var t = this.matcherRe.exec(e); + if (!t) return null; + var n = t.findIndex(function (e, t) { + return t > 0 && void 0 !== e; + }), + a = this.matchIndexes[n]; + return t.splice(0, n), Object.assign(t, a); + }, + }, + ]), + e + ); + })(), + s = (function () { + function e() { + t(this, e), + (this.rules = []), + (this.multiRegexes = []), + (this.count = 0), + (this.lastIndex = 0), + (this.regexIndex = 0); + } + return ( + a(e, [ + { + key: "getMatcher", + value: function (e) { + if (this.multiRegexes[e]) return this.multiRegexes[e]; + var t = new o(); + return ( + this.rules.slice(e).forEach(function (e) { + var n = l(e, 2), + a = n[0], + r = n[1]; + return t.addRule(a, r); + }), + t.compile(), + (this.multiRegexes[e] = t), + t + ); + }, + }, + { + key: "resumingScanAtSamePosition", + value: function () { + return 0 !== this.regexIndex; + }, + }, + { + key: "considerAll", + value: function () { + this.regexIndex = 0; + }, + }, + { + key: "addRule", + value: function (e, t) { + this.rules.push([e, t]), "begin" === t.type && this.count++; + }, + }, + { + key: "exec", + value: function (e) { + var t = this.getMatcher(this.regexIndex); + t.lastIndex = this.lastIndex; + var n = t.exec(e); + if (this.resumingScanAtSamePosition()) + if (n && n.index === this.lastIndex); + else { + var a = this.getMatcher(0); + (a.lastIndex = this.lastIndex + 1), (n = a.exec(e)); + } + return ( + n && + ((this.regexIndex += n.position + 1), + this.regexIndex === this.count && this.considerAll()), + n + ); + }, + }, + ]), + e + ); + })(); + if ( + (n.compilerExtensions || (n.compilerExtensions = []), + n.contains && n.contains.includes("self")) + ) + throw new Error( + "ERR: contains `self` is not supported at the top-level of a language. See documentation.", + ); + return ( + (n.classNameAliases = Yu(n.classNameAliases || {})), + (function t(a, r) { + var o, + l = a; + if (a.isCompiled) return l; + [Sm].forEach(function (e) { + return e(a, r); + }), + n.compilerExtensions.forEach(function (e) { + return e(a, r); + }), + (a.__beforeBegin = null), + [gm, Em, bm].forEach(function (e) { + return e(a, r); + }), + (a.isCompiled = !0); + var _ = null; + if ( + ("object" === e(a.keywords) && + ((_ = a.keywords.$pattern), delete a.keywords.$pattern), + a.keywords && (a.keywords = fm(a.keywords, n.case_insensitive)), + a.lexemes && _) + ) + throw new Error( + "ERR: Prefer `keywords.$pattern` to `mode.lexemes`, BOTH are not allowed. (see mode reference) ", + ); + return ( + (_ = _ || a.lexemes || /\w+/), + (l.keywordPatternRe = i(_, !0)), + r && + (a.begin || (a.begin = /\B|\b/), + (l.beginRe = i(a.begin)), + a.endSameAsBegin && (a.end = a.begin), + a.end || a.endsWithParent || (a.end = /\B|\b/), + a.end && (l.endRe = i(a.end)), + (l.terminatorEnd = Wu(a.end) || ""), + a.endsWithParent && + r.terminatorEnd && + (l.terminatorEnd += (a.end ? "|" : "") + r.terminatorEnd)), + a.illegal && (l.illegalRe = i(a.illegal)), + a.contains || (a.contains = []), + (a.contains = (o = []).concat.apply( + o, + c( + a.contains.map(function (e) { + return (function (e) { + e.variants && + !e.cachedVariants && + (e.cachedVariants = e.variants.map(function (t) { + return Yu(e, { variants: null }, t); + })); + if (e.cachedVariants) return e.cachedVariants; + if (Rm(e)) + return Yu(e, { starts: e.starts ? Yu(e.starts) : null }); + if (Object.isFrozen(e)) return Yu(e); + return e; + })("self" === e ? a : e); + }), + ), + )), + a.contains.forEach(function (e) { + t(e, l); + }), + a.starts && t(a.starts, r), + (l.matcher = (function (e) { + var t = new s(); + return ( + e.contains.forEach(function (e) { + return t.addRule(e.begin, { rule: e, type: "begin" }); + }), + e.terminatorEnd && t.addRule(e.terminatorEnd, { type: "end" }), + e.illegal && t.addRule(e.illegal, { type: "illegal" }), + t + ); + })(l)), + l + ); + })(n) + ); +} +function Rm(e) { + return !!e && (e.endsWithParent || Rm(e.starts)); +} +function vm(e) { + var t = { + props: ["language", "code", "autodetect"], + data: function () { + return { detectedLanguage: "", unknownLanguage: !1 }; + }, + computed: { + className: function () { + return this.unknownLanguage ? "" : "hljs " + this.detectedLanguage; + }, + highlighted: function () { + if (!this.autoDetect && !e.getLanguage(this.language)) + return ( + console.warn( + 'The language "'.concat( + this.language, + '" you specified could not be found.', + ), + ), + (this.unknownLanguage = !0), + Gu(this.code) + ); + var t = {}; + return ( + this.autoDetect + ? 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коннедели лог лог10 максимальноеколичествосубконто названиеинтерфейса названиенабораправ назначитьвид назначитьсчет найтиссылки началопериодаби началостандартногоинтервала начгода начквартала начмесяца начнедели номерднягода номерднянедели номернеделигода обработкаожидания основнойжурналрасчетов основнойплансчетов основнойязык очиститьокносообщений периодстр получитьвремята получитьдатута получитьдокументта получитьзначенияотбора получитьпозициюта получитьпустоезначение получитьта префиксавтонумерации пропись пустоезначение разм разобратьпозициюдокумента рассчитатьрегистрына рассчитатьрегистрыпо симв создатьобъект статусвозврата стрколичествострок сформироватьпозициюдокумента счетпокоду текущеевремя типзначения типзначениястр установитьтана установитьтапо фиксшаблон шаблон acos asin atan base64значение base64строка cos exp log log10 pow sin sqrt tan xmlзначение xmlстрока xmlтип xmlтипзнч активноеокно безопасныйрежим безопасныйрежимразделенияданных булево ввестидату ввестизначение ввестистроку ввестичисло возможностьчтенияxml вопрос восстановитьзначение врег выгрузитьжурналрегистрации выполнитьобработкуоповещения выполнитьпроверкуправдоступа вычислить год данныеформывзначение дата день деньгода деньнедели добавитьмесяц заблокироватьданныедляредактирования заблокироватьработупользователя завершитьработусистемы загрузитьвнешнююкомпоненту закрытьсправку записатьjson записатьxml записатьдатуjson записьжурналарегистрации заполнитьзначениясвойств запроситьразрешениепользователя запуститьприложение запуститьсистему зафиксироватьтранзакцию значениевданныеформы значениевстрокувнутр значениевфайл значениезаполнено значениеизстрокивнутр значениеизфайла изxmlтипа импортмоделиxdto имякомпьютера имяпользователя инициализироватьпредопределенныеданные информацияобошибке каталогбиблиотекимобильногоустройства каталогвременныхфайлов каталогдокументов каталогпрограммы кодироватьстроку кодлокализацииинформационнойбазы кодсимвола командасистемы конецгода конецдня конецквартала 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сохранитьнастройкипользователя сред стрдлина стрзаканчиваетсяна стрзаменить стрнайти стрначинаетсяс строка строкасоединенияинформационнойбазы стрполучитьстроку стрразделить стрсоединить стрсравнить стрчисловхождений стрчислострок стршаблон текущаядата текущаядатасеанса текущаяуниверсальнаядата текущаяуниверсальнаядатавмиллисекундах текущийвариантинтерфейсаклиентскогоприложения текущийвариантосновногошрифтаклиентскогоприложения текущийкодлокализации текущийрежимзапуска текущийязык текущийязыксистемы тип типзнч транзакцияактивна трег удалитьданныеинформационнойбазы удалитьизвременногохранилища удалитьобъекты удалитьфайлы универсальноевремя установитьбезопасныйрежим установитьбезопасныйрежимразделенияданных установитьблокировкусеансов установитьвнешнююкомпоненту установитьвремязавершенияспящегосеанса установитьвремязасыпанияпассивногосеанса установитьвремяожиданияблокировкиданных установитьзаголовокклиентскогоприложения установитьзаголовоксистемы установитьиспользованиежурналарегистрации 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документы доставляемыеуведомления журналыдокументов задачи информацияобинтернетсоединении использованиерабочейдаты историяработыпользователя константы критерииотбора метаданные обработки отображениерекламы отправкадоставляемыхуведомлений отчеты панельзадачос параметрзапуска параметрысеанса перечисления планывидоврасчета планывидовхарактеристик планыобмена планысчетов полнотекстовыйпоиск пользователиинформационнойбазы последовательности проверкавстроенныхпокупок рабочаядата расширенияконфигурации регистрыбухгалтерии регистрынакопления регистрырасчета регистрысведений регламентныезадания сериализаторxdto справочники средствагеопозиционирования средствакриптографии средствамультимедиа средстваотображениярекламы средствапочты средствателефонии фабрикаxdto файловыепотоки фоновыезадания хранилищанастроек хранилищевариантовотчетов хранилищенастроекданныхформ хранилищеобщихнастроек хранилищепользовательскихнастроекдинамическихсписков хранилищепользовательскихнастроекотчетов хранилищесистемныхнастроек ", + class: + "webцвета windowsцвета windowsшрифты библиотекакартинок рамкистиля символы цветастиля шрифтыстиля автоматическоесохранениеданныхформывнастройках автонумерациявформе автораздвижениесерий анимациядиаграммы вариантвыравниванияэлементовизаголовков вариантуправлениявысотойтаблицы вертикальнаяпрокруткаформы вертикальноеположение вертикальноеположениеэлемента видгруппыформы виддекорацииформы виддополненияэлементаформы видизмененияданных видкнопкиформы видпереключателя видподписейкдиаграмме видполяформы видфлажка влияниеразмеранапузырекдиаграммы горизонтальноеположение горизонтальноеположениеэлемента группировкаколонок группировкаподчиненныхэлементовформы группыиэлементы действиеперетаскивания дополнительныйрежимотображения допустимыедействияперетаскивания интервалмеждуэлементамиформы использованиевывода использованиеполосыпрокрутки используемоезначениеточкибиржевойдиаграммы историявыборапривводе источникзначенийоситочекдиаграммы 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("string" == typeof e ? e : e.source) : null; +} +function Fm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Um(e); + }) + .join(""); + return a; +} +var Bm = function (e) { + var t = { + ruleDeclaration: /^[a-zA-Z][a-zA-Z0-9-]*/, + unexpectedChars: /[!@#$^&',?+~`|:]/, + }, + n = e.COMMENT(/;/, /$/), + a = { className: "attribute", begin: Fm(t.ruleDeclaration, /(?=\s*=)/) }; + return { + name: "Augmented Backus-Naur Form", + illegal: t.unexpectedChars, + keywords: [ + "ALPHA", + "BIT", + "CHAR", + "CR", + "CRLF", + "CTL", + "DIGIT", + "DQUOTE", + "HEXDIG", + "HTAB", + "LF", + "LWSP", + "OCTET", + "SP", + "VCHAR", + "WSP", + ], + contains: [ + a, + n, + { className: "symbol", begin: /%b[0-1]+(-[0-1]+|(\.[0-1]+)+){0,1}/ }, + { className: "symbol", begin: /%d[0-9]+(-[0-9]+|(\.[0-9]+)+){0,1}/ }, + { + className: "symbol", + begin: /%x[0-9A-F]+(-[0-9A-F]+|(\.[0-9A-F]+)+){0,1}/, + }, + { className: "symbol", begin: /%[si]/ }, + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + ], + }; +}; +function Gm(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Ym() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Gm(e); + }) + .join(""); + return a; +} +function Hm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return Gm(e); + }) + .join("|") + + ")"; + return a; +} +var Vm = function (e) { + var t = [ + "GET", + "POST", + "HEAD", + "PUT", + "DELETE", + "CONNECT", + "OPTIONS", + "PATCH", + "TRACE", + ]; + return { + name: "Apache Access Log", + contains: [ + { + className: "number", + begin: /^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}(:\d{1,5})?\b/, + relevance: 5, + }, + { className: "number", begin: /\b\d+\b/, relevance: 0 }, + { + className: "string", + begin: Ym(/"/, Hm.apply(void 0, t)), + end: /"/, + keywords: t, + illegal: /\n/, + relevance: 5, + contains: [{ begin: /HTTP\/[12]\.\d'/, relevance: 5 }], + }, + { + className: "string", + begin: /\[\d[^\]\n]{8,}\]/, + illegal: /\n/, + relevance: 1, + }, + { + className: "string", + begin: /\[/, + end: /\]/, + illegal: /\n/, + relevance: 0, + }, + { + className: "string", + begin: /"Mozilla\/\d\.\d \(/, + end: /"/, + illegal: /\n/, + relevance: 3, + }, + { + className: "string", + begin: /"/, + end: /"/, + illegal: /\n/, + relevance: 0, + }, + ], + }; +}; +function qm(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function zm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return qm(e); + }) + .join(""); + return a; +} +var Wm = function (e) { + var t = { + className: "rest_arg", + begin: /[.]{3}/, + end: /[a-zA-Z_$][a-zA-Z0-9_$]*/, + relevance: 10, + }; + return { + name: "ActionScript", + aliases: ["as"], + keywords: { + keyword: + "as break case catch class const continue default delete do dynamic each else extends final finally for function get if implements import in include instanceof interface internal is namespace native new override package private protected public return set static super switch this throw try typeof use var void while with", + literal: "true false null undefined", + }, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.C_NUMBER_MODE, + { + className: "class", + beginKeywords: "package", + end: /\{/, + contains: [e.TITLE_MODE], + }, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + contains: [{ beginKeywords: "extends implements" }, e.TITLE_MODE], + }, + { + className: "meta", + beginKeywords: "import include", + end: /;/, + keywords: { "meta-keyword": "import include" }, + }, + { + className: "function", + beginKeywords: "function", + end: /[{;]/, + excludeEnd: !0, + illegal: /\S/, + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: /\(/, + end: /\)/, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + ], + }, + { begin: zm(/:\s*/, /([*]|[a-zA-Z_$][a-zA-Z0-9_$]*)/) }, + ], + }, + e.METHOD_GUARD, + ], + illegal: /#/, + }; +}; +var $m = function (e) { + var t = "[A-Za-z](_?[A-Za-z0-9.])*", + n = "[]\\{\\}%#'\"", + a = e.COMMENT("--", "$"), + r = { + begin: "\\s+:\\s+", + end: "\\s*(:=|;|\\)|=>|$)", + illegal: n, + contains: [ + { beginKeywords: "loop for declare others", endsParent: !0 }, + { + className: "keyword", + beginKeywords: + "not null constant access function procedure in out aliased exception", + }, + { className: "type", begin: t, endsParent: !0, relevance: 0 }, + ], + }; + return { + name: "Ada", + case_insensitive: !0, + keywords: { + keyword: + "abort else new return abs elsif not reverse abstract end accept entry select access exception of separate aliased exit or some all others subtype and for out synchronized array function overriding at tagged generic package task begin goto pragma terminate body private then if procedure type case in protected constant interface is raise use declare range delay limited record when delta loop rem while digits renames with do mod requeue xor", + literal: "True False", + }, + contains: [ + a, + { + className: "string", + begin: /"/, + end: /"/, + contains: [{ begin: /""/, relevance: 0 }], + }, + { className: "string", begin: /'.'/ }, + { + className: "number", + begin: + "\\b(\\d(_|\\d)*#\\w+(\\.\\w+)?#([eE][-+]?\\d(_|\\d)*)?|\\d(_|\\d)*(\\.\\d(_|\\d)*)?([eE][-+]?\\d(_|\\d)*)?)", + relevance: 0, + }, + { className: "symbol", begin: "'" + t }, + { + className: "title", + begin: "(\\bwith\\s+)?(\\bprivate\\s+)?\\bpackage\\s+(\\bbody\\s+)?", + end: "(is|$)", + keywords: "package body", + excludeBegin: !0, + excludeEnd: !0, + illegal: n, + }, + { + begin: "(\\b(with|overriding)\\s+)?\\b(function|procedure)\\s+", + end: "(\\bis|\\bwith|\\brenames|\\)\\s*;)", + keywords: "overriding function procedure with is renames return", + returnBegin: !0, + contains: [ + a, + { + className: "title", + begin: "(\\bwith\\s+)?\\b(function|procedure)\\s+", + end: "(\\(|\\s+|$)", + excludeBegin: !0, + excludeEnd: !0, + illegal: n, + }, + r, + { + className: "type", + begin: "\\breturn\\s+", + end: "(\\s+|;|$)", + keywords: "return", + excludeBegin: !0, + excludeEnd: !0, + endsParent: !0, + illegal: n, + }, + ], + }, + { + className: "type", + begin: "\\b(sub)?type\\s+", + end: "\\s+", + keywords: "type", + excludeBegin: !0, + illegal: n, + }, + r, + ], + }; +}; +var Qm = function (e) { + var t = { + className: "built_in", + begin: + "\\b(void|bool|int|int8|int16|int32|int64|uint|uint8|uint16|uint32|uint64|string|ref|array|double|float|auto|dictionary)", + }, + n = { className: "symbol", begin: "[a-zA-Z0-9_]+@" }, + a = { className: "keyword", begin: "<", end: ">", contains: [t, n] }; + return ( + (t.contains = [a]), + (n.contains = [a]), + { + name: "AngelScript", + aliases: ["asc"], + keywords: + "for in|0 break continue while do|0 return if else case switch namespace is cast or and xor not get|0 in inout|10 out override set|0 private public const default|0 final shared external mixin|10 enum typedef funcdef this super import from interface abstract|0 try catch protected explicit property", + illegal: "(^using\\s+[A-Za-z0-9_\\.]+;$|\\bfunction\\s*[^\\(])", + contains: [ + { + className: "string", + begin: "'", + end: "'", + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + { className: "string", begin: '"""', end: '"""' }, + { + className: "string", + begin: '"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "string", begin: "^\\s*\\[", end: "\\]" }, + { + beginKeywords: "interface namespace", + end: /\{/, + illegal: "[;.\\-]", + contains: [{ className: "symbol", begin: "[a-zA-Z0-9_]+" }], + }, + { + beginKeywords: "class", + end: /\{/, + illegal: "[;.\\-]", + contains: [ + { + className: "symbol", + begin: "[a-zA-Z0-9_]+", + contains: [ + { + begin: "[:,]\\s*", + contains: [{ className: "symbol", begin: "[a-zA-Z0-9_]+" }], + }, + ], + }, + ], + }, + t, + n, + { className: "literal", begin: "\\b(null|true|false)" }, + { + className: "number", + relevance: 0, + begin: + "(-?)(\\b0[xXbBoOdD][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?f?|\\.\\d+f?)([eE][-+]?\\d+f?)?)", + }, + ], + } + ); +}; +var Km = function (e) { + var t = { + className: "number", + begin: /\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}(:\d{1,5})?/, + }; + return { + name: "Apache config", + aliases: ["apacheconf"], + case_insensitive: !0, + contains: [ + e.HASH_COMMENT_MODE, + { + className: "section", + begin: /<\/?/, + end: />/, + contains: [ + t, + { className: "number", begin: /:\d{1,5}/ }, + e.inherit(e.QUOTE_STRING_MODE, { relevance: 0 }), + ], + }, + { + className: "attribute", + begin: /\w+/, + relevance: 0, + keywords: { + nomarkup: + "order deny allow setenv rewriterule rewriteengine rewritecond documentroot sethandler errordocument loadmodule options header listen serverroot servername", + }, + starts: { + end: /$/, + relevance: 0, + keywords: { literal: "on off all deny allow" }, + contains: [ + { className: "meta", begin: /\s\[/, end: /\]$/ }, + { + className: "variable", + begin: /[\$%]\{/, + end: /\}/, + contains: ["self", { className: "number", begin: /[$%]\d+/ }], + }, + t, + { className: "number", begin: /\d+/ }, + e.QUOTE_STRING_MODE, + ], + }, + }, + ], + illegal: /\S/, + }; +}; +function jm(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Xm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return jm(e); + }) + .join(""); + return a; +} +function Zm() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return jm(e); + }) + .join("|") + + ")"; + return a; +} +var Jm = function (e) { + var t = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + n = { + className: "params", + begin: /\(/, + end: /\)/, + contains: ["self", e.C_NUMBER_MODE, t], + }, + a = e.COMMENT(/--/, /$/), + r = [ + a, + e.COMMENT(/\(\*/, /\*\)/, { contains: ["self", a] }), + e.HASH_COMMENT_MODE, + ]; + return { + name: "AppleScript", + aliases: ["osascript"], + keywords: { + keyword: + "about above after against and around as at back before beginning behind below beneath beside between but by considering contain contains continue copy div does eighth else end equal equals error every exit fifth first for fourth from front get given global if ignoring in into is it its last local me middle mod my ninth not of on onto or over prop property put ref reference repeat returning script second set seventh since sixth some tell tenth that the|0 then third through thru timeout times to transaction try until where while whose with without", + literal: + "AppleScript false linefeed return pi quote result space tab true", + built_in: + "alias application boolean class constant date file integer list number real record string text activate beep count delay launch log offset read round run say summarize write character characters contents day frontmost id item length month name paragraph paragraphs rest reverse running time version weekday word words year", + }, + contains: [ + t, + e.C_NUMBER_MODE, + { + className: "built_in", + begin: Xm( + /\b/, + Zm.apply(void 0, [ + /clipboard info/, + /the clipboard/, + /info for/, + /list (disks|folder)/, + /mount volume/, + /path to/, + /(close|open for) access/, + /(get|set) eof/, + /current date/, + /do shell script/, + /get volume settings/, + /random number/, + /set volume/, + /system attribute/, + /system info/, + /time to GMT/, + /(load|run|store) script/, + /scripting components/, + /ASCII (character|number)/, + /localized string/, + /choose (application|color|file|file name|folder|from list|remote application|URL)/, + /display (alert|dialog)/, + ]), + /\b/, + ), + }, + { className: "built_in", begin: /^\s*return\b/ }, + { + className: "literal", + begin: /\b(text item delimiters|current application|missing value)\b/, + }, + { + className: "keyword", + begin: Xm( + /\b/, + Zm.apply(void 0, [ + /apart from/, + /aside from/, + /instead of/, + /out of/, + /greater than/, + /isn't|(doesn't|does not) (equal|come before|come after|contain)/, + /(greater|less) than( or equal)?/, + /(starts?|ends|begins?) with/, + /contained by/, + /comes (before|after)/, + /a (ref|reference)/, + /POSIX (file|path)/, + /(date|time) string/, + /quoted form/, + ]), + /\b/, + ), + }, + { + beginKeywords: "on", + illegal: /[${=;\n]/, + contains: [e.UNDERSCORE_TITLE_MODE, n], + }, + ].concat(r), + illegal: /\/\/|->|=>|\[\[/, + }; +}; +var ep = function (e) { + var t = "[A-Za-z_][0-9A-Za-z_]*", + n = { + keyword: "if for while var new function do return void else break", + literal: + "BackSlash DoubleQuote false ForwardSlash Infinity NaN NewLine null PI SingleQuote Tab TextFormatting true undefined", + built_in: + "Abs Acos Angle Attachments Area AreaGeodetic Asin Atan Atan2 Average Bearing Boolean Buffer BufferGeodetic Ceil Centroid Clip Console Constrain Contains Cos Count Crosses Cut Date DateAdd DateDiff Day Decode DefaultValue Dictionary Difference Disjoint Distance DistanceGeodetic Distinct DomainCode DomainName Equals Exp Extent Feature FeatureSet FeatureSetByAssociation FeatureSetById FeatureSetByPortalItem FeatureSetByRelationshipName FeatureSetByTitle FeatureSetByUrl Filter First Floor Geometry GroupBy Guid HasKey Hour IIf IndexOf Intersection Intersects IsEmpty IsNan IsSelfIntersecting Length LengthGeodetic Log Max Mean Millisecond Min Minute Month MultiPartToSinglePart Multipoint NextSequenceValue Now Number OrderBy Overlaps Point Polygon Polyline Portal Pow Random Relate Reverse RingIsClockWise Round Second SetGeometry Sin Sort Sqrt Stdev Sum SymmetricDifference Tan Text Timestamp Today ToLocal Top Touches ToUTC TrackCurrentTime TrackGeometryWindow TrackIndex TrackStartTime TrackWindow TypeOf Union UrlEncode Variance Weekday When Within Year ", + }, + a = { + className: "number", + variants: [ + { begin: "\\b(0[bB][01]+)" }, + { begin: "\\b(0[oO][0-7]+)" }, + { begin: e.C_NUMBER_RE }, + ], + relevance: 0, + }, + r = { + className: "subst", + begin: "\\$\\{", + end: "\\}", + keywords: n, + contains: [], + }, + i = { + className: "string", + begin: "`", + end: "`", + contains: [e.BACKSLASH_ESCAPE, r], + }; + r.contains = [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE, i, a, e.REGEXP_MODE]; + var o = r.contains.concat([e.C_BLOCK_COMMENT_MODE, e.C_LINE_COMMENT_MODE]); + return { + name: "ArcGIS Arcade", + keywords: n, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + i, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "symbol", + begin: + "\\$[datastore|feature|layer|map|measure|sourcefeature|sourcelayer|targetfeature|targetlayer|value|view]+", + }, + a, + { + begin: /[{,]\s*/, + relevance: 0, + contains: [ + { + begin: t + "\\s*:", + returnBegin: !0, + relevance: 0, + contains: [{ className: "attr", begin: t, relevance: 0 }], + }, + ], + }, + { + begin: "(" + e.RE_STARTERS_RE + "|\\b(return)\\b)\\s*", + keywords: "return", + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.REGEXP_MODE, + { + className: "function", + begin: "(\\(.*?\\)|" + t + ")\\s*=>", + returnBegin: !0, + end: "\\s*=>", + contains: [ + { + className: "params", + variants: [ + { begin: t }, + { begin: /\(\s*\)/ }, + { + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: n, + contains: o, + }, + ], + }, + ], + }, + ], + relevance: 0, + }, + { + className: "function", + beginKeywords: "function", + end: /\{/, + excludeEnd: !0, + contains: [ + e.inherit(e.TITLE_MODE, { begin: t }), + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + contains: o, + }, + ], + illegal: /\[|%/, + }, + { begin: /\$[(.]/ }, + ], + illegal: /#(?!!)/, + }; +}; +function tp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function np(e) { + return ap("(", e, ")?"); +} +function ap() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return tp(e); + }) + .join(""); + return a; +} +var rp = function (e) { + var t = "boolean byte word String", + n = + "KeyboardController MouseController SoftwareSerial EthernetServer EthernetClient LiquidCrystal RobotControl GSMVoiceCall EthernetUDP EsploraTFT HttpClient RobotMotor WiFiClient GSMScanner FileSystem Scheduler GSMServer YunClient YunServer IPAddress GSMClient GSMModem Keyboard Ethernet Console GSMBand Esplora Stepper Process WiFiUDP GSM_SMS Mailbox USBHost Firmata PImage Client Server GSMPIN FileIO Bridge Serial EEPROM Stream Mouse Audio Servo File Task GPRS WiFi Wire TFT GSM SPI SD ", + a = + "setup loop runShellCommandAsynchronously analogWriteResolution retrieveCallingNumber printFirmwareVersion analogReadResolution sendDigitalPortPair noListenOnLocalhost readJoystickButton setFirmwareVersion readJoystickSwitch scrollDisplayRight getVoiceCallStatus scrollDisplayLeft writeMicroseconds delayMicroseconds beginTransmission getSignalStrength runAsynchronously getAsynchronously listenOnLocalhost getCurrentCarrier readAccelerometer messageAvailable sendDigitalPorts lineFollowConfig countryNameWrite runShellCommand readStringUntil rewindDirectory readTemperature setClockDivider readLightSensor endTransmission analogReference detachInterrupt countryNameRead attachInterrupt encryptionType readBytesUntil robotNameWrite readMicrophone robotNameRead cityNameWrite userNameWrite readJoystickY readJoystickX mouseReleased openNextFile scanNetworks noInterrupts digitalWrite beginSpeaker mousePressed isActionDone mouseDragged displayLogos noAutoscroll addParameter remoteNumber getModifiers keyboardRead userNameRead waitContinue processInput parseCommand printVersion readNetworks writeMessage blinkVersion 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(e) { + var t, + n = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + a = "decltype\\(auto\\)", + r = "[a-zA-Z_]\\w*::", + i = + "(decltype\\(auto\\)|" + + np(r) + + "[a-zA-Z_]\\w*" + + np("<[^<>]+>") + + ")", + o = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + s = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + c = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(s, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + n, + e.C_BLOCK_COMMENT_MODE, + ], + }, + _ = { className: "title", begin: np(r) + e.IDENT_RE, relevance: 0 }, + d = np(r) + e.IDENT_RE + "\\s*\\(", + u = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: "_Bool _Complex _Imaginary", + _relevance_hints: [ + "asin", + "atan2", + "atan", + "calloc", + "ceil", + "cosh", + "cos", + "exit", + "exp", + "fabs", + "floor", + "fmod", + "fprintf", + "fputs", + "free", + "frexp", + "auto_ptr", + "deque", + "list", + "queue", + "stack", + "vector", + "map", + "set", + "pair", + "bitset", + "multiset", + "multimap", + "unordered_set", + "fscanf", + "future", + "isalnum", + "isalpha", + "iscntrl", + "isdigit", + "isgraph", + "islower", + "isprint", + "ispunct", + "isspace", + "isupper", + "isxdigit", + "tolower", + "toupper", + "labs", + "ldexp", + "log10", + "log", + "malloc", + "realloc", + "memchr", + "memcmp", + "memcpy", + "memset", + "modf", + "pow", + "printf", + "putchar", + "puts", + "scanf", + "sinh", + "sin", + "snprintf", + "sprintf", + "sqrt", + "sscanf", + "strcat", + "strchr", + "strcmp", + "strcpy", + "strcspn", + "strlen", + "strncat", + "strncmp", + "strncpy", + "strpbrk", + "strrchr", + "strspn", + "strstr", + "tanh", + "tan", + "unordered_map", + "unordered_multiset", + "unordered_multimap", + "priority_queue", + "make_pair", + "array", + "shared_ptr", + "abort", + "terminate", + "abs", + "acos", + "vfprintf", + "vprintf", + "vsprintf", + "endl", + "initializer_list", + "unique_ptr", + "complex", + "imaginary", + "std", + "string", + "wstring", + "cin", + "cout", + "cerr", + "clog", + "stdin", + "stdout", + "stderr", + "stringstream", + "istringstream", + "ostringstream", + ], + literal: "true false nullptr NULL", + }, + m = { + className: "function.dispatch", + relevance: 0, + keywords: u, + begin: ap( + /\b/, + /(?!decltype)/, + /(?!if)/, + /(?!for)/, + /(?!while)/, + e.IDENT_RE, + ((t = /\s*\(/), ap("(?=", t, ")")), + ), + }, + p = [m, c, o, n, e.C_BLOCK_COMMENT_MODE, l, s], + g = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: u, + contains: p.concat([ + { + begin: /\(/, + end: /\)/, + keywords: u, + contains: p.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + E = { + className: "function", + begin: "(" + i + "[\\*&\\s]+)+" + d, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: u, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: a, keywords: u, relevance: 0 }, + { begin: d, returnBegin: !0, contains: [_], relevance: 0 }, + { begin: /::/, relevance: 0 }, + { begin: /:/, endsWithParent: !0, contains: [s, l] }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: [ + n, + e.C_BLOCK_COMMENT_MODE, + s, + l, + o, + { + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: ["self", n, e.C_BLOCK_COMMENT_MODE, s, l, o], + }, + ], + }, + o, + n, + e.C_BLOCK_COMMENT_MODE, + c, + ], + }; + return { + name: "C++", + aliases: ["cc", "c++", "h++", "hpp", "hh", "hxx", "cxx"], + keywords: u, + illegal: "", + keywords: u, + contains: ["self", o], + }, + { begin: e.IDENT_RE + "::", keywords: u }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: c, strings: s, keywords: u }, + }; + })(e), + o = i.keywords; + return ( + (o.keyword += " " + t), + (o.literal += " " + r), + (o.built_in += " " + n), + (o._ += " " + a), + (i.name = "Arduino"), + (i.aliases = ["ino"]), + (i.supersetOf = "cpp"), + i + ); +}; +var ip = function (e) { + var t = { + variants: [ + e.COMMENT("^[ \\t]*(?=#)", "$", { relevance: 0, excludeBegin: !0 }), + e.COMMENT("[;@]", "$", { relevance: 0 }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; + return { + name: "ARM Assembly", + case_insensitive: !0, + aliases: ["arm"], + keywords: { + $pattern: "\\.?" + e.IDENT_RE, + meta: ".2byte .4byte .align .ascii .asciz .balign .byte .code .data .else .end .endif .endm .endr .equ .err .exitm .extern .global .hword .if .ifdef .ifndef .include .irp .long .macro .rept .req .section .set .skip .space .text .word .arm .thumb .code16 .code32 .force_thumb .thumb_func .ltorg ALIAS ALIGN ARM AREA ASSERT ATTR CN CODE CODE16 CODE32 COMMON CP DATA DCB DCD DCDU DCDO DCFD DCFDU DCI DCQ DCQU DCW DCWU DN ELIF ELSE END ENDFUNC ENDIF ENDP ENTRY EQU EXPORT EXPORTAS EXTERN FIELD FILL FUNCTION GBLA GBLL GBLS GET GLOBAL IF IMPORT INCBIN INCLUDE INFO KEEP LCLA LCLL LCLS LTORG MACRO MAP MEND MEXIT NOFP OPT PRESERVE8 PROC QN READONLY RELOC REQUIRE REQUIRE8 RLIST FN ROUT SETA SETL SETS SN SPACE SUBT THUMB THUMBX TTL WHILE WEND ", + built_in: + "r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 pc lr sp ip sl sb fp a1 a2 a3 a4 v1 v2 v3 v4 v5 v6 v7 v8 f0 f1 f2 f3 f4 f5 f6 f7 p0 p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 c0 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 cpsr_c cpsr_x cpsr_s cpsr_f cpsr_cx cpsr_cxs cpsr_xs cpsr_xsf cpsr_sf cpsr_cxsf spsr_c spsr_x spsr_s spsr_f spsr_cx spsr_cxs spsr_xs spsr_xsf spsr_sf spsr_cxsf s0 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 d0 d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 d13 d14 d15 d16 d17 d18 d19 d20 d21 d22 d23 d24 d25 d26 d27 d28 d29 d30 d31 {PC} {VAR} {TRUE} {FALSE} {OPT} {CONFIG} {ENDIAN} {CODESIZE} {CPU} {FPU} {ARCHITECTURE} {PCSTOREOFFSET} {ARMASM_VERSION} {INTER} {ROPI} {RWPI} {SWST} {NOSWST} . @", + }, + contains: [ + { + className: "keyword", + begin: + "\\b(adc|(qd?|sh?|u[qh]?)?add(8|16)?|usada?8|(q|sh?|u[qh]?)?(as|sa)x|and|adrl?|sbc|rs[bc]|asr|b[lx]?|blx|bxj|cbn?z|tb[bh]|bic|bfc|bfi|[su]bfx|bkpt|cdp2?|clz|clrex|cmp|cmn|cpsi[ed]|cps|setend|dbg|dmb|dsb|eor|isb|it[te]{0,3}|lsl|lsr|ror|rrx|ldm(([id][ab])|f[ds])?|ldr((s|ex)?[bhd])?|movt?|mvn|mra|mar|mul|[us]mull|smul[bwt][bt]|smu[as]d|smmul|smmla|mla|umlaal|smlal?([wbt][bt]|d)|mls|smlsl?[ds]|smc|svc|sev|mia([bt]{2}|ph)?|mrr?c2?|mcrr2?|mrs|msr|orr|orn|pkh(tb|bt)|rbit|rev(16|sh)?|sel|[su]sat(16)?|nop|pop|push|rfe([id][ab])?|stm([id][ab])?|str(ex)?[bhd]?|(qd?)?sub|(sh?|q|u[qh]?)?sub(8|16)|[su]xt(a?h|a?b(16)?)|srs([id][ab])?|swpb?|swi|smi|tst|teq|wfe|wfi|yield)(eq|ne|cs|cc|mi|pl|vs|vc|hi|ls|ge|lt|gt|le|al|hs|lo)?[sptrx]?(?=\\s)", + }, + t, + e.QUOTE_STRING_MODE, + { className: "string", begin: "'", end: "[^\\\\]'", relevance: 0 }, + { + className: "title", + begin: "\\|", + end: "\\|", + illegal: "\\n", + relevance: 0, + }, + { + className: "number", + variants: [ + { begin: "[#$=]?0x[0-9a-f]+" }, + { begin: "[#$=]?0b[01]+" }, + { begin: "[#$=]\\d+" }, + { begin: "\\b\\d+" }, + ], + relevance: 0, + }, + { + className: "symbol", + variants: [ + { begin: "^[ \\t]*[a-z_\\.\\$][a-z0-9_\\.\\$]+:" }, + { begin: "^[a-z_\\.\\$][a-z0-9_\\.\\$]+" }, + { begin: "[=#]\\w+" }, + ], + relevance: 0, + }, + ], + }; +}; +function op(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function sp(e) { + return lp("(?=", e, ")"); +} +function lp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return op(e); + }) + .join(""); + return a; +} +function cp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return op(e); + }) + .join("|") + + ")"; + return a; +} +var _p = function (e) { + var t = lp(/[A-Z_]/, lp("(", /[A-Z0-9_.-]*:/, ")?"), /[A-Z0-9_.-]*/), + n = { className: "symbol", begin: /&[a-z]+;|&#[0-9]+;|&#x[a-f0-9]+;/ }, + a = { + begin: /\s/, + contains: [ + { + className: "meta-keyword", + begin: /#?[a-z_][a-z1-9_-]+/, + illegal: /\n/, + }, + ], + }, + r = e.inherit(a, { begin: /\(/, end: /\)/ }), + i = e.inherit(e.APOS_STRING_MODE, { className: "meta-string" }), + o = e.inherit(e.QUOTE_STRING_MODE, { className: "meta-string" }), + s = { + endsWithParent: !0, + illegal: /`]+/ }, + ], + }, + ], + }, + ], + }; + return { + name: "HTML, XML", + aliases: [ + "html", + "xhtml", + "rss", + "atom", + "xjb", + "xsd", + "xsl", + "plist", + "wsf", + "svg", + ], + case_insensitive: !0, + contains: [ + { + className: "meta", + begin: //, + relevance: 10, + contains: [ + a, + o, + i, + r, + { + begin: /\[/, + end: /\]/, + contains: [ + { + className: "meta", + begin: //, + contains: [a, r, o, i], + }, + ], + }, + ], + }, + e.COMMENT(//, { relevance: 10 }), + { begin: //, relevance: 10 }, + n, + { className: "meta", begin: /<\?xml/, end: /\?>/, relevance: 10 }, + { + className: "tag", + begin: /)/, + end: />/, + keywords: { name: "style" }, + contains: [s], + starts: { + end: /<\/style>/, + returnEnd: !0, + subLanguage: ["css", "xml"], + }, + }, + { + className: "tag", + begin: /)/, + end: />/, + keywords: { name: "script" }, + contains: [s], + starts: { + end: /<\/script>/, + returnEnd: !0, + subLanguage: ["javascript", "handlebars", "xml"], + }, + }, + { className: "tag", begin: /<>|<\/>/ }, + { + className: "tag", + begin: lp(//, />/, /\s/)))), + end: /\/?>/, + contains: [{ className: "name", begin: t, relevance: 0, starts: s }], + }, + { + className: "tag", + begin: lp(/<\//, sp(lp(t, />/))), + contains: [ + { className: "name", begin: t, relevance: 0 }, + { begin: />/, relevance: 0, endsParent: !0 }, + ], + }, + ], + }; +}; +function dp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function up() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return dp(e); + }) + .join(""); + return a; +} +var mp = function (e) { + var t = [ + { className: "strong", begin: /\*{2}([^\n]+?)\*{2}/ }, + { + className: "strong", + begin: up( + /\*\*/, + /((\*(?!\*)|\\[^\n]|[^*\n\\])+\n)+/, + /(\*(?!\*)|\\[^\n]|[^*\n\\])*/, + /\*\*/, + ), + relevance: 0, + }, + { className: "strong", begin: /\B\*(\S|\S[^\n]*?\S)\*(?!\w)/ }, + { className: "strong", begin: /\*[^\s]([^\n]+\n)+([^\n]+)\*/ }, + ], + n = [ + { className: "emphasis", begin: /_{2}([^\n]+?)_{2}/ }, + { + className: "emphasis", + begin: up( + /__/, + /((_(?!_)|\\[^\n]|[^_\n\\])+\n)+/, + /(_(?!_)|\\[^\n]|[^_\n\\])*/, + /__/, + ), + relevance: 0, + }, + { className: "emphasis", begin: /\b_(\S|\S[^\n]*?\S)_(?!\w)/ }, + { className: "emphasis", begin: /_[^\s]([^\n]+\n)+([^\n]+)_/ }, + { + className: "emphasis", + begin: "\\B'(?!['\\s])", + end: "(\\n{2}|')", + contains: [{ begin: "\\\\'\\w", relevance: 0 }], + relevance: 0, + }, + ]; + return { + name: "AsciiDoc", + aliases: ["adoc"], + contains: [ + e.COMMENT("^/{4,}\\n", "\\n/{4,}$", { relevance: 10 }), + e.COMMENT("^//", "$", { relevance: 0 }), + { className: "title", begin: "^\\.\\w.*$" }, + { begin: "^[=\\*]{4,}\\n", end: "\\n^[=\\*]{4,}$", relevance: 10 }, + { + className: "section", + relevance: 10, + variants: [ + { begin: "^(={1,6})[ \t].+?([ \t]\\1)?$" }, + { begin: "^[^\\[\\]\\n]+?\\n[=\\-~\\^\\+]{2,}$" }, + ], + }, + { + className: "meta", + begin: "^:.+?:", + end: "\\s", + excludeEnd: !0, + relevance: 10, + }, + { className: "meta", begin: "^\\[.+?\\]$", relevance: 0 }, + { + className: "quote", + begin: "^_{4,}\\n", + end: "\\n_{4,}$", + relevance: 10, + }, + { + className: "code", + begin: "^[\\-\\.]{4,}\\n", + end: "\\n[\\-\\.]{4,}$", + relevance: 10, + }, + { + begin: "^\\+{4,}\\n", + end: "\\n\\+{4,}$", + contains: [{ begin: "<", end: ">", subLanguage: "xml", relevance: 0 }], + relevance: 10, + }, + { className: "bullet", begin: "^(\\*+|-+|\\.+|[^\\n]+?::)\\s+" }, + { + className: "symbol", + begin: "^(NOTE|TIP|IMPORTANT|WARNING|CAUTION):\\s+", + relevance: 10, + }, + ].concat( + [ + { begin: /\\[*_`]/ }, + { begin: /\\\\\*{2}[^\n]*?\*{2}/ }, + { begin: /\\\\_{2}[^\n]*_{2}/ }, + { begin: /\\\\`{2}[^\n]*`{2}/ }, + { begin: /[:;}][*_`](?![*_`])/ }, + ], + t, + n, + [ + { + className: "string", + variants: [{ begin: "``.+?''" }, { begin: "`.+?'" }], + }, + { className: "code", begin: /`{2}/, end: /(\n{2}|`{2})/ }, + { className: "code", begin: "(`.+?`|\\+.+?\\+)", relevance: 0 }, + { className: "code", begin: "^[ \\t]", end: "$", relevance: 0 }, + { begin: "^'{3,}[ \\t]*$", relevance: 10 }, + { + begin: "(link:)?(http|https|ftp|file|irc|image:?):\\S+?\\[[^[]*?\\]", + returnBegin: !0, + contains: [ + { begin: "(link|image:?):", relevance: 0 }, + { className: "link", begin: "\\w", end: "[^\\[]+", relevance: 0 }, + { + className: "string", + begin: "\\[", + end: "\\]", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + ], + relevance: 10, + }, + ], + ), + }; +}; +function pp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function gp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return pp(e); + }) + .join(""); + return a; +} +var Ep = function (e) { + var t = + "false synchronized int abstract float private char boolean static null if const for true while long throw strictfp finally protected import native final return void enum else extends implements break transient new catch instanceof byte super volatile case assert short package default double public try this switch continue throws privileged aspectOf adviceexecution proceed cflowbelow cflow initialization preinitialization staticinitialization withincode target within execution getWithinTypeName handler thisJoinPoint thisJoinPointStaticPart thisEnclosingJoinPointStaticPart declare parents warning error soft precedence thisAspectInstance", + n = "get set args call"; + return { + name: "AspectJ", + keywords: t, + illegal: /<\/|#/, + contains: [ + e.COMMENT(/\/\*\*/, /\*\//, { + relevance: 0, + contains: [ + { begin: /\w+@/, relevance: 0 }, + { className: "doctag", begin: /@[A-Za-z]+/ }, + ], + }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { + className: "class", + beginKeywords: "aspect", + end: /[{;=]/, + excludeEnd: !0, + illegal: /[:;"\[\]]/, + contains: [ + { + beginKeywords: + "extends implements pertypewithin perthis pertarget percflowbelow percflow issingleton", + }, + e.UNDERSCORE_TITLE_MODE, + { + begin: /\([^\)]*/, + end: /[)]+/, + keywords: t + " " + n, + excludeEnd: !1, + }, + ], + }, + { + className: "class", + beginKeywords: "class interface", + end: /[{;=]/, + excludeEnd: !0, + relevance: 0, + keywords: "class interface", + illegal: /[:"\[\]]/, + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { + beginKeywords: "pointcut after before around throwing returning", + end: /[)]/, + excludeEnd: !1, + illegal: /["\[\]]/, + contains: [ + { + begin: gp(e.UNDERSCORE_IDENT_RE, /\s*\(/), + returnBegin: !0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + ], + }, + { + begin: /[:]/, + returnBegin: !0, + end: /[{;]/, + relevance: 0, + excludeEnd: !1, + keywords: t, + illegal: /["\[\]]/, + contains: [ + { + begin: gp(e.UNDERSCORE_IDENT_RE, /\s*\(/), + keywords: t + " " + n, + relevance: 0, + }, + e.QUOTE_STRING_MODE, + ], + }, + { beginKeywords: "new throw", relevance: 0 }, + { + className: "function", + begin: /\w+ +\w+(\.\w+)?\s*\([^\)]*\)\s*((throws)[\w\s,]+)?[\{;]/, + returnBegin: !0, + end: /[{;=]/, + keywords: t, + excludeEnd: !0, + contains: [ + { + begin: gp(e.UNDERSCORE_IDENT_RE, /\s*\(/), + returnBegin: !0, + relevance: 0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "params", + begin: /\(/, + end: /\)/, + relevance: 0, + keywords: t, + contains: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_NUMBER_MODE, + { className: "meta", begin: /@[A-Za-z]+/ }, + ], + }; +}; +var Sp = function (e) { + var t = { begin: "`[\\s\\S]" }; + return { + name: "AutoHotkey", + case_insensitive: !0, + aliases: ["ahk"], + keywords: { + keyword: + "Break Continue Critical Exit ExitApp Gosub Goto New OnExit Pause return SetBatchLines SetTimer Suspend Thread Throw Until ahk_id ahk_class ahk_pid ahk_exe ahk_group", + literal: "true false NOT AND OR", + built_in: "ComSpec Clipboard ClipboardAll ErrorLevel", + }, + contains: [ + t, + e.inherit(e.QUOTE_STRING_MODE, { contains: [t] }), + e.COMMENT(";", "$", { relevance: 0 }), + e.C_BLOCK_COMMENT_MODE, + { className: "number", begin: e.NUMBER_RE, relevance: 0 }, + { className: "variable", begin: "%[a-zA-Z0-9#_$@]+%" }, + { className: "built_in", begin: "^\\s*\\w+\\s*(,|%)" }, + { + className: "title", + variants: [ + { begin: '^[^\\n";]+::(?!=)' }, + { begin: '^[^\\n";]+:(?!=)', relevance: 0 }, + ], + }, + { className: "meta", begin: "^\\s*#\\w+", end: "$", relevance: 0 }, + { className: "built_in", begin: "A_[a-zA-Z0-9]+" }, + { begin: ",\\s*," }, + ], + }; +}; +var bp = function (e) { + var t = { + variants: [ + e.COMMENT(";", "$", { relevance: 0 }), + e.COMMENT("#cs", "#ce"), + e.COMMENT("#comments-start", "#comments-end"), + ], + }, + n = { begin: "\\$[A-z0-9_]+" }, + a = { + className: "string", + variants: [ + { begin: /"/, end: /"/, contains: [{ begin: /""/, relevance: 0 }] }, + { begin: /'/, end: /'/, contains: [{ begin: /''/, relevance: 0 }] }, + ], + }, + r = { variants: [e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE] }; + return { + name: "AutoIt", + case_insensitive: !0, + illegal: /\/\*/, + keywords: { + keyword: + "ByRef Case Const ContinueCase ContinueLoop Dim Do Else ElseIf EndFunc EndIf EndSelect EndSwitch EndWith Enum Exit ExitLoop For Func Global If In Local Next ReDim Return Select Static Step Switch Then To Until Volatile WEnd While With", + built_in: + "Abs ACos AdlibRegister AdlibUnRegister Asc AscW ASin Assign ATan AutoItSetOption AutoItWinGetTitle AutoItWinSetTitle Beep Binary BinaryLen BinaryMid BinaryToString BitAND BitNOT BitOR BitRotate BitShift BitXOR BlockInput Break Call CDTray Ceiling Chr ChrW ClipGet ClipPut ConsoleRead ConsoleWrite ConsoleWriteError ControlClick ControlCommand ControlDisable ControlEnable ControlFocus ControlGetFocus ControlGetHandle ControlGetPos ControlGetText ControlHide ControlListView ControlMove ControlSend ControlSetText ControlShow ControlTreeView Cos Dec DirCopy DirCreate DirGetSize DirMove DirRemove DllCall DllCallAddress DllCallbackFree DllCallbackGetPtr DllCallbackRegister DllClose DllOpen DllStructCreate DllStructGetData DllStructGetPtr DllStructGetSize DllStructSetData DriveGetDrive DriveGetFileSystem DriveGetLabel DriveGetSerial DriveGetType DriveMapAdd DriveMapDel DriveMapGet DriveSetLabel DriveSpaceFree DriveSpaceTotal DriveStatus EnvGet EnvSet EnvUpdate Eval Execute Exp FileChangeDir FileClose FileCopy FileCreateNTFSLink FileCreateShortcut FileDelete FileExists FileFindFirstFile FileFindNextFile FileFlush FileGetAttrib FileGetEncoding FileGetLongName FileGetPos FileGetShortcut FileGetShortName FileGetSize FileGetTime FileGetVersion FileInstall FileMove FileOpen FileOpenDialog FileRead FileReadLine FileReadToArray FileRecycle FileRecycleEmpty FileSaveDialog FileSelectFolder FileSetAttrib FileSetEnd FileSetPos FileSetTime FileWrite FileWriteLine Floor FtpSetProxy FuncName GUICreate GUICtrlCreateAvi GUICtrlCreateButton GUICtrlCreateCheckbox GUICtrlCreateCombo GUICtrlCreateContextMenu GUICtrlCreateDate GUICtrlCreateDummy GUICtrlCreateEdit GUICtrlCreateGraphic GUICtrlCreateGroup GUICtrlCreateIcon GUICtrlCreateInput GUICtrlCreateLabel GUICtrlCreateList GUICtrlCreateListView GUICtrlCreateListViewItem GUICtrlCreateMenu GUICtrlCreateMenuItem GUICtrlCreateMonthCal GUICtrlCreateObj GUICtrlCreatePic GUICtrlCreateProgress GUICtrlCreateRadio GUICtrlCreateSlider GUICtrlCreateTab GUICtrlCreateTabItem GUICtrlCreateTreeView GUICtrlCreateTreeViewItem GUICtrlCreateUpdown GUICtrlDelete GUICtrlGetHandle GUICtrlGetState GUICtrlRead GUICtrlRecvMsg GUICtrlRegisterListViewSort GUICtrlSendMsg GUICtrlSendToDummy GUICtrlSetBkColor GUICtrlSetColor GUICtrlSetCursor GUICtrlSetData GUICtrlSetDefBkColor GUICtrlSetDefColor GUICtrlSetFont GUICtrlSetGraphic GUICtrlSetImage GUICtrlSetLimit GUICtrlSetOnEvent GUICtrlSetPos GUICtrlSetResizing GUICtrlSetState GUICtrlSetStyle GUICtrlSetTip GUIDelete GUIGetCursorInfo GUIGetMsg GUIGetStyle GUIRegisterMsg GUISetAccelerators GUISetBkColor GUISetCoord GUISetCursor GUISetFont GUISetHelp GUISetIcon GUISetOnEvent GUISetState GUISetStyle GUIStartGroup GUISwitch Hex HotKeySet HttpSetProxy HttpSetUserAgent HWnd InetClose InetGet InetGetInfo InetGetSize InetRead IniDelete IniRead IniReadSection IniReadSectionNames IniRenameSection IniWrite IniWriteSection InputBox Int IsAdmin IsArray IsBinary IsBool IsDeclared IsDllStruct IsFloat IsFunc IsHWnd IsInt IsKeyword IsNumber IsObj IsPtr IsString Log MemGetStats Mod MouseClick MouseClickDrag MouseDown MouseGetCursor MouseGetPos MouseMove MouseUp MouseWheel MsgBox Number ObjCreate ObjCreateInterface ObjEvent ObjGet ObjName OnAutoItExitRegister OnAutoItExitUnRegister Ping PixelChecksum PixelGetColor PixelSearch ProcessClose ProcessExists ProcessGetStats ProcessList ProcessSetPriority ProcessWait ProcessWaitClose ProgressOff ProgressOn ProgressSet Ptr Random RegDelete RegEnumKey RegEnumVal RegRead RegWrite Round Run RunAs RunAsWait RunWait Send SendKeepActive SetError SetExtended ShellExecute ShellExecuteWait Shutdown Sin Sleep SoundPlay SoundSetWaveVolume SplashImageOn SplashOff SplashTextOn Sqrt SRandom StatusbarGetText StderrRead StdinWrite StdioClose StdoutRead String StringAddCR StringCompare StringFormat StringFromASCIIArray StringInStr StringIsAlNum StringIsAlpha StringIsASCII StringIsDigit StringIsFloat StringIsInt StringIsLower StringIsSpace StringIsUpper StringIsXDigit StringLeft StringLen StringLower StringMid StringRegExp StringRegExpReplace StringReplace StringReverse StringRight StringSplit StringStripCR StringStripWS StringToASCIIArray StringToBinary StringTrimLeft StringTrimRight StringUpper Tan TCPAccept TCPCloseSocket TCPConnect TCPListen TCPNameToIP TCPRecv TCPSend TCPShutdown, UDPShutdown TCPStartup, UDPStartup TimerDiff TimerInit ToolTip TrayCreateItem TrayCreateMenu TrayGetMsg TrayItemDelete TrayItemGetHandle TrayItemGetState TrayItemGetText TrayItemSetOnEvent TrayItemSetState TrayItemSetText TraySetClick TraySetIcon TraySetOnEvent TraySetPauseIcon TraySetState TraySetToolTip TrayTip UBound UDPBind UDPCloseSocket UDPOpen UDPRecv UDPSend VarGetType WinActivate WinActive WinClose WinExists WinFlash WinGetCaretPos WinGetClassList WinGetClientSize WinGetHandle WinGetPos WinGetProcess WinGetState WinGetText WinGetTitle WinKill WinList WinMenuSelectItem WinMinimizeAll WinMinimizeAllUndo WinMove WinSetOnTop WinSetState WinSetTitle WinSetTrans WinWait WinWaitActive WinWaitClose WinWaitNotActive", + literal: "True False And Null Not Or Default", + }, + contains: [ + t, + n, + a, + r, + { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": [ + "EndRegion", + "forcedef", + "forceref", + "ignorefunc", + "include", + "include-once", + "NoTrayIcon", + "OnAutoItStartRegister", + "pragma", + "Region", + "RequireAdmin", + "Tidy_Off", + "Tidy_On", + "Tidy_Parameters", + ], + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + { + beginKeywords: "include", + keywords: { "meta-keyword": "include" }, + end: "$", + contains: [ + a, + { + className: "meta-string", + variants: [ + { begin: "<", end: ">" }, + { + begin: /"/, + end: /"/, + contains: [{ begin: /""/, relevance: 0 }], + }, + { + begin: /'/, + end: /'/, + contains: [{ begin: /''/, relevance: 0 }], + }, + ], + }, + ], + }, + a, + t, + ], + }, + { className: "symbol", begin: "@[A-z0-9_]+" }, + { + className: "function", + beginKeywords: "Func", + end: "$", + illegal: "\\$|\\[|%", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { + className: "params", + begin: "\\(", + end: "\\)", + contains: [n, a, r], + }, + ], + }, + ], + }; +}; +var Tp = function (e) { + return { + name: "AVR Assembly", + case_insensitive: !0, + keywords: { + $pattern: "\\.?" + e.IDENT_RE, + keyword: + "adc add adiw and andi asr bclr bld brbc brbs brcc brcs break breq brge brhc brhs brid brie brlo brlt brmi brne brpl brsh brtc brts brvc brvs bset bst call cbi cbr clc clh cli cln clr cls clt clv clz com cp cpc cpi cpse dec eicall eijmp elpm eor fmul fmuls fmulsu icall ijmp in inc jmp ld ldd ldi lds lpm lsl lsr mov movw mul muls mulsu neg nop or ori out pop push rcall ret reti rjmp rol ror sbc sbr sbrc sbrs sec seh sbi sbci sbic sbis sbiw sei sen ser ses set sev sez sleep spm st std sts sub subi swap tst wdr", + built_in: + "r0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 x|0 xh xl y|0 yh yl z|0 zh zl ucsr1c udr1 ucsr1a ucsr1b ubrr1l ubrr1h ucsr0c ubrr0h tccr3c tccr3a tccr3b tcnt3h tcnt3l ocr3ah ocr3al ocr3bh ocr3bl ocr3ch ocr3cl icr3h icr3l etimsk etifr tccr1c ocr1ch ocr1cl twcr twdr twar twsr twbr osccal xmcra xmcrb eicra spmcsr spmcr portg ddrg ping portf ddrf sreg sph spl xdiv rampz eicrb eimsk gimsk gicr eifr gifr timsk tifr mcucr mcucsr tccr0 tcnt0 ocr0 assr tccr1a tccr1b tcnt1h tcnt1l ocr1ah ocr1al ocr1bh ocr1bl icr1h icr1l tccr2 tcnt2 ocr2 ocdr wdtcr sfior eearh eearl eedr eecr porta ddra pina portb ddrb pinb portc ddrc pinc portd ddrd pind spdr spsr spcr udr0 ucsr0a ucsr0b ubrr0l acsr admux adcsr adch adcl porte ddre pine pinf", + meta: ".byte .cseg .db .def .device .dseg .dw .endmacro .equ .eseg .exit .include .list .listmac .macro .nolist .org .set", + }, + contains: [ + e.C_BLOCK_COMMENT_MODE, + e.COMMENT(";", "$", { relevance: 0 }), + e.C_NUMBER_MODE, + e.BINARY_NUMBER_MODE, + { className: "number", begin: "\\b(\\$[a-zA-Z0-9]+|0o[0-7]+)" }, + e.QUOTE_STRING_MODE, + { + className: "string", + begin: "'", + end: "[^\\\\]'", + illegal: "[^\\\\][^']", + }, + { className: "symbol", begin: "^[A-Za-z0-9_.$]+:" }, + { className: "meta", begin: "#", end: "$" }, + { className: "subst", begin: "@[0-9]+" }, + ], + }; +}; +var fp = function (e) { + return { + name: "Awk", + keywords: { + keyword: + "BEGIN END if else while do for in break continue delete next nextfile function func exit|10", + }, + contains: [ + { + className: "variable", + variants: [{ begin: /\$[\w\d#@][\w\d_]*/ }, { begin: /\$\{(.*?)\}/ }], + }, + { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { begin: /(u|b)?r?'''/, end: /'''/, relevance: 10 }, + { begin: /(u|b)?r?"""/, end: /"""/, relevance: 10 }, + { begin: /(u|r|ur)'/, end: /'/, relevance: 10 }, + { begin: /(u|r|ur)"/, end: /"/, relevance: 10 }, + { begin: /(b|br)'/, end: /'/ }, + { begin: /(b|br)"/, end: /"/ }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + }, + e.REGEXP_MODE, + e.HASH_COMMENT_MODE, + e.NUMBER_MODE, + ], + }; +}; +var Cp = function (e) { + return { + name: "X++", + aliases: ["x++"], + keywords: { + keyword: [ + "abstract", + "as", + "asc", + "avg", + "break", + "breakpoint", + "by", + "byref", + "case", + "catch", + "changecompany", + "class", + "client", + "client", + "common", + "const", + "continue", + "count", + "crosscompany", + "delegate", + "delete_from", + "desc", + "display", + "div", + "do", + "edit", + "else", + "eventhandler", + "exists", + "extends", + "final", + "finally", + "firstfast", + "firstonly", + "firstonly1", + "firstonly10", + "firstonly100", + "firstonly1000", + "flush", + "for", + "forceliterals", + "forcenestedloop", + "forceplaceholders", + "forceselectorder", + "forupdate", + "from", + "generateonly", + "group", + "hint", + "if", + "implements", + "in", + "index", + "insert_recordset", + "interface", + "internal", + "is", + "join", + "like", + "maxof", + "minof", + "mod", + "namespace", + "new", + "next", + "nofetch", + "notexists", + "optimisticlock", + "order", + "outer", + "pessimisticlock", + "print", + "private", + "protected", + "public", + "readonly", + "repeatableread", + "retry", + "return", + "reverse", + "select", + "server", + "setting", + "static", + "sum", + "super", + "switch", + "this", + "throw", + "try", + "ttsabort", + "ttsbegin", + "ttscommit", + "unchecked", + "update_recordset", + "using", + "validtimestate", + "void", + "where", + "while", + ], + built_in: [ + "anytype", + "boolean", + "byte", + "char", + "container", + "date", + "double", + "enum", + "guid", + "int", + "int64", + "long", + "real", + "short", + "str", + "utcdatetime", + "var", + ], + literal: ["default", "false", "null", "true"], + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + { className: "meta", begin: "#", end: "$" }, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + illegal: ":", + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + ], + }; +}; +function Np(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Rp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Np(e); + }) + .join(""); + return a; +} +var vp = function (e) { + var t = {}, + n = { + begin: /\$\{/, + end: /\}/, + contains: ["self", { begin: /:-/, contains: [t] }], + }; + Object.assign(t, { + className: "variable", + variants: [{ begin: Rp(/\$[\w\d#@][\w\d_]*/, "(?![\\w\\d])(?![$])") }, n], + }); + var a = { + className: "subst", + begin: /\$\(/, + end: /\)/, + contains: [e.BACKSLASH_ESCAPE], + }, + r = { + begin: /<<-?\s*(?=\w+)/, + starts: { + contains: [ + e.END_SAME_AS_BEGIN({ + begin: /(\w+)/, + end: /(\w+)/, + className: "string", + }), + ], + }, + }, + i = { + className: "string", + begin: /"/, + end: /"/, + contains: [e.BACKSLASH_ESCAPE, t, a], + }; + a.contains.push(i); + var o = { + begin: /\$\(\(/, + end: /\)\)/, + contains: [ + { begin: /\d+#[0-9a-f]+/, className: "number" }, + e.NUMBER_MODE, + t, + ], + }, + s = e.SHEBANG({ + binary: "(".concat( + [ + "fish", + "bash", + "zsh", + "sh", + "csh", + "ksh", + "tcsh", + "dash", + "scsh", + ].join("|"), + ")", + ), + relevance: 10, + }), + l = { + className: "function", + begin: /\w[\w\d_]*\s*\(\s*\)\s*\{/, + returnBegin: !0, + contains: [e.inherit(e.TITLE_MODE, { begin: /\w[\w\d_]*/ })], + relevance: 0, + }; + return { + name: "Bash", + aliases: ["sh", "zsh"], + keywords: { + $pattern: /\b[a-z._-]+\b/, + keyword: "if then else elif fi for while in do done case esac function", + literal: "true false", + built_in: + "break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp", + }, + contains: [ + s, + e.SHEBANG(), + l, + o, + e.HASH_COMMENT_MODE, + r, + i, + { className: "", begin: /\\"/ }, + { className: "string", begin: /'/, end: /'/ }, + t, + ], + }; +}; +var Op = function (e) { + return { + name: "BASIC", + case_insensitive: !0, + illegal: "^.", + keywords: { + $pattern: "[a-zA-Z][a-zA-Z0-9_$%!#]*", + keyword: + "ABS ASC AND ATN AUTO|0 BEEP BLOAD|10 BSAVE|10 CALL CALLS CDBL CHAIN CHDIR CHR$|10 CINT CIRCLE CLEAR CLOSE CLS COLOR COM COMMON CONT COS CSNG CSRLIN CVD CVI CVS DATA DATE$ DEFDBL DEFINT DEFSNG DEFSTR DEF|0 SEG USR DELETE DIM DRAW EDIT END ENVIRON ENVIRON$ EOF EQV ERASE ERDEV ERDEV$ ERL ERR ERROR EXP FIELD FILES FIX FOR|0 FRE GET GOSUB|10 GOTO HEX$ IF THEN ELSE|0 INKEY$ INP INPUT INPUT# INPUT$ INSTR IMP INT IOCTL IOCTL$ KEY ON OFF LIST KILL LEFT$ LEN LET LINE LLIST LOAD LOC LOCATE LOF LOG LPRINT USING LSET MERGE MID$ MKDIR MKD$ MKI$ MKS$ MOD NAME NEW NEXT NOISE NOT OCT$ ON OR PEN PLAY STRIG OPEN OPTION BASE OUT PAINT PALETTE PCOPY PEEK PMAP POINT POKE POS PRINT PRINT] PSET PRESET PUT RANDOMIZE READ REM RENUM RESET|0 RESTORE RESUME RETURN|0 RIGHT$ RMDIR RND RSET RUN SAVE SCREEN SGN SHELL SIN SOUND SPACE$ SPC SQR STEP STICK STOP STR$ STRING$ SWAP SYSTEM TAB TAN TIME$ TIMER TROFF TRON TO USR VAL VARPTR VARPTR$ VIEW WAIT WHILE WEND WIDTH WINDOW WRITE XOR", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.COMMENT("REM", "$", { relevance: 10 }), + e.COMMENT("'", "$", { relevance: 0 }), + { className: "symbol", begin: "^[0-9]+ ", relevance: 10 }, + { + className: "number", + begin: "\\b\\d+(\\.\\d+)?([edED]\\d+)?[#!]?", + relevance: 0, + }, + { className: "number", begin: "(&[hH][0-9a-fA-F]{1,4})" }, + { className: "number", begin: "(&[oO][0-7]{1,6})" }, + ], + }; +}; +var hp = function (e) { + return { + name: "Backus–Naur Form", + contains: [ + { className: "attribute", begin: // }, + { + begin: /::=/, + end: /$/, + contains: [ + { begin: // }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + }, + ], + }; +}; +var yp = function (e) { + var t = { className: "literal", begin: /[+-]/, relevance: 0 }; + return { + name: "Brainfuck", + aliases: ["bf"], + contains: [ + e.COMMENT("[^\\[\\]\\.,\\+\\-<> \r\n]", "[\\[\\]\\.,\\+\\-<> \r\n]", { + returnEnd: !0, + relevance: 0, + }), + { className: "title", begin: "[\\[\\]]", relevance: 0 }, + { className: "string", begin: "[\\.,]", relevance: 0 }, + { begin: /(?:\+\+|--)/, contains: [t] }, + t, + ], + }; +}; +function Ip(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Ap(e) { + return Dp("(", e, ")?"); +} +function Dp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Ip(e); + }) + .join(""); + return a; +} +var Mp = function (e) { + var t, + n, + a = (function (e) { + var t, + n = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + a = "decltype\\(auto\\)", + r = "[a-zA-Z_]\\w*::", + i = + "(decltype\\(auto\\)|" + + Ap(r) + + "[a-zA-Z_]\\w*" + + Ap("<[^<>]+>") + + ")", + o = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + s = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + c = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(s, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + n, + e.C_BLOCK_COMMENT_MODE, + ], + }, + _ = { className: "title", begin: Ap(r) + e.IDENT_RE, relevance: 0 }, + d = Ap(r) + e.IDENT_RE + "\\s*\\(", + u = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: "_Bool _Complex _Imaginary", + _relevance_hints: [ + "asin", + "atan2", + "atan", + "calloc", + "ceil", + "cosh", + "cos", + "exit", + "exp", + "fabs", + "floor", + "fmod", + "fprintf", + "fputs", + "free", + "frexp", + "auto_ptr", + "deque", + "list", + "queue", + "stack", + "vector", + "map", + "set", + "pair", + "bitset", + "multiset", + "multimap", + "unordered_set", + "fscanf", + "future", + "isalnum", + "isalpha", + "iscntrl", + "isdigit", + "isgraph", + "islower", + "isprint", + "ispunct", + "isspace", + "isupper", + "isxdigit", + "tolower", + "toupper", + "labs", + "ldexp", + "log10", + "log", + "malloc", + "realloc", + "memchr", + "memcmp", + "memcpy", + "memset", + "modf", + "pow", + "printf", + "putchar", + "puts", + "scanf", + "sinh", + "sin", + "snprintf", + "sprintf", + "sqrt", + "sscanf", + "strcat", + "strchr", + "strcmp", + "strcpy", + "strcspn", + "strlen", + "strncat", + "strncmp", + "strncpy", + "strpbrk", + "strrchr", + "strspn", + "strstr", + "tanh", + "tan", + "unordered_map", + "unordered_multiset", + "unordered_multimap", + "priority_queue", + "make_pair", + "array", + "shared_ptr", + "abort", + "terminate", + "abs", + "acos", + "vfprintf", + "vprintf", + "vsprintf", + "endl", + "initializer_list", + "unique_ptr", + "complex", + "imaginary", + "std", + "string", + "wstring", + "cin", + "cout", + "cerr", + "clog", + "stdin", + "stdout", + "stderr", + "stringstream", + "istringstream", + "ostringstream", + ], + literal: "true false nullptr NULL", + }, + m = { + className: "function.dispatch", + relevance: 0, + keywords: u, + begin: Dp( + /\b/, + /(?!decltype)/, + /(?!if)/, + /(?!for)/, + /(?!while)/, + e.IDENT_RE, + ((t = /\s*\(/), Dp("(?=", t, ")")), + ), + }, + p = [m, c, o, n, e.C_BLOCK_COMMENT_MODE, l, s], + g = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: u, + contains: p.concat([ + { + begin: /\(/, + end: /\)/, + keywords: u, + contains: p.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + E = { + className: "function", + begin: "(" + i + "[\\*&\\s]+)+" + d, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: u, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: a, keywords: u, relevance: 0 }, + { begin: d, returnBegin: !0, contains: [_], relevance: 0 }, + { begin: /::/, relevance: 0 }, + { begin: /:/, endsWithParent: !0, contains: [s, l] }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: [ + n, + e.C_BLOCK_COMMENT_MODE, + s, + l, + o, + { + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: ["self", n, e.C_BLOCK_COMMENT_MODE, s, l, o], + }, + ], + }, + o, + n, + e.C_BLOCK_COMMENT_MODE, + c, + ], + }; + return { + name: "C++", + aliases: ["cc", "c++", "h++", "hpp", "hh", "hxx", "cxx"], + keywords: u, + illegal: "", + keywords: u, + contains: ["self", o], + }, + { begin: e.IDENT_RE + "::", keywords: u }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: c, strings: s, keywords: u }, + }; + })(e); + return ( + (a.disableAutodetect = !0), + (a.aliases = []), + e.getLanguage("c") || (t = a.aliases).push.apply(t, ["c", "h"]), + e.getLanguage("cpp") || + (n = a.aliases).push.apply(n, [ + "cc", + "c++", + "h++", + "hpp", + "hh", + "hxx", + "cxx", + ]), + a + ); +}; +function Lp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function wp(e) { + return (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return t + .map(function (e) { + return Lp(e); + }) + .join(""); + })("(", e, ")?"); +} +var xp = function (e) { + var t = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + n = "decltype\\(auto\\)", + a = "[a-zA-Z_]\\w*::", + r = "(decltype\\(auto\\)|" + wp(a) + "[a-zA-Z_]\\w*" + wp("<[^<>]+>") + ")", + i = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + o = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + s = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + l = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(o, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + t, + e.C_BLOCK_COMMENT_MODE, + ], + }, + c = { className: "title", begin: wp(a) + e.IDENT_RE, relevance: 0 }, + _ = wp(a) + e.IDENT_RE + "\\s*\\(", + d = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: + "std string wstring cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set pair bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap priority_queue make_pair array shared_ptr abort terminate abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf future isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr _Bool complex _Complex imaginary _Imaginary", + literal: "true false nullptr NULL", + }, + u = [l, i, t, e.C_BLOCK_COMMENT_MODE, s, o], + m = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: d, + contains: u.concat([ + { + begin: /\(/, + end: /\)/, + keywords: d, + contains: u.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + p = { + className: "function", + begin: "(" + r + "[\\*&\\s]+)+" + _, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: d, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: n, keywords: d, relevance: 0 }, + { begin: _, returnBegin: !0, contains: [c], relevance: 0 }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: d, + relevance: 0, + contains: [ + t, + e.C_BLOCK_COMMENT_MODE, + o, + s, + i, + { + begin: /\(/, + end: /\)/, + keywords: d, + relevance: 0, + contains: ["self", t, e.C_BLOCK_COMMENT_MODE, o, s, i], + }, + ], + }, + i, + t, + e.C_BLOCK_COMMENT_MODE, + l, + ], + }; + return { + name: "C", + aliases: ["h"], + keywords: d, + disableAutodetect: !0, + illegal: "", + keywords: d, + contains: ["self", i], + }, + { begin: e.IDENT_RE + "::", keywords: d }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: l, strings: o, keywords: d }, + }; +}; +var Pp = function (e) { + var t = + "div mod in and or not xor asserterror begin case do downto else end exit for if of repeat then to until while with var", + n = [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/\{/, /\}/, { relevance: 0 }), + e.COMMENT(/\(\*/, /\*\)/, { relevance: 10 }), + ], + a = { + className: "string", + begin: /'/, + end: /'/, + contains: [{ begin: /''/ }], + }, + r = { className: "string", begin: /(#\d+)+/ }, + i = { + className: "function", + beginKeywords: "procedure", + end: /[:;]/, + keywords: "procedure|10", + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: t, + contains: [a, r], + }, + ].concat(n), + }, + o = { + className: "class", + begin: + "OBJECT (Table|Form|Report|Dataport|Codeunit|XMLport|MenuSuite|Page|Query) (\\d+) ([^\\r\\n]+)", + returnBegin: !0, + contains: [e.TITLE_MODE, i], + }; + return { + name: "C/AL", + case_insensitive: !0, + keywords: { keyword: t, literal: "false true" }, + illegal: /\/\*/, + contains: [ + a, + r, + { className: "number", begin: "\\b\\d+(\\.\\d+)?(DT|D|T)", relevance: 0 }, + { className: "string", begin: '"', end: '"' }, + e.NUMBER_MODE, + o, + i, + ], + }; +}; +var kp = function (e) { + return { + name: "Cap’n Proto", + aliases: ["capnp"], + keywords: { + keyword: + "struct enum interface union group import using const annotation extends in of on as with from fixed", + built_in: + "Void Bool Int8 Int16 Int32 Int64 UInt8 UInt16 UInt32 UInt64 Float32 Float64 Text Data AnyPointer AnyStruct Capability List", + literal: "true false", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + e.HASH_COMMENT_MODE, + { className: "meta", begin: /@0x[\w\d]{16};/, illegal: /\n/ }, + { className: "symbol", begin: /@\d+\b/ }, + { + className: "class", + beginKeywords: "struct enum", + end: /\{/, + illegal: /\n/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, excludeEnd: !0 }, + }), + ], + }, + { + className: "class", + beginKeywords: "interface", + end: /\{/, + illegal: /\n/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, excludeEnd: !0 }, + }), + ], + }, + ], + }; +}; +var Up = function (e) { + var t = + "assembly module package import alias class interface object given value assign void function new of extends satisfies abstracts in out return break continue throw assert dynamic if else switch case for while try catch finally then let this outer super is exists nonempty", + n = { + className: "subst", + excludeBegin: !0, + excludeEnd: !0, + begin: /``/, + end: /``/, + keywords: t, + relevance: 10, + }, + a = [ + { className: "string", begin: '"""', end: '"""', relevance: 10 }, + { className: "string", begin: '"', end: '"', contains: [n] }, + { className: "string", begin: "'", end: "'" }, + { + className: "number", + begin: + "#[0-9a-fA-F_]+|\\$[01_]+|[0-9_]+(?:\\.[0-9_](?:[eE][+-]?\\d+)?)?[kMGTPmunpf]?", + relevance: 0, + }, + ]; + return ( + (n.contains = a), + { + name: "Ceylon", + keywords: { + keyword: + t + + " shared abstract formal default actual variable late native deprecated final sealed annotation suppressWarnings small", + meta: "doc by license see throws tagged", + }, + illegal: "\\$[^01]|#[^0-9a-fA-F]", + contains: [ + e.C_LINE_COMMENT_MODE, + e.COMMENT("/\\*", "\\*/", { contains: ["self"] }), + { className: "meta", begin: '@[a-z]\\w*(?::"[^"]*")?' }, + ].concat(a), + } + ); +}; +var Fp = function (e) { + return { + name: "Clean", + aliases: ["icl", "dcl"], + keywords: { + keyword: + "if let in with where case of class instance otherwise implementation definition system module from import qualified as special code inline foreign export ccall stdcall generic derive infix infixl infixr", + built_in: "Int Real Char Bool", + literal: "True False", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + { begin: "->|<-[|:]?|#!?|>>=|\\{\\||\\|\\}|:==|=:|<>" }, + ], + }; +}; +var Bp = function (e) { + var t = "a-zA-Z_\\-!.?+*=<>&#'", + n = "[" + t + "][" + t + "0-9/;:]*", + a = + "def defonce defprotocol defstruct defmulti defmethod defn- defn defmacro deftype defrecord", + r = { + $pattern: n, + "builtin-name": + a + + " cond apply if-not if-let if not not= =|0 <|0 >|0 <=|0 >=|0 ==|0 +|0 /|0 *|0 -|0 rem quot neg? pos? delay? symbol? keyword? true? false? integer? empty? coll? list? set? ifn? fn? associative? sequential? sorted? counted? reversible? number? decimal? class? distinct? isa? float? rational? reduced? ratio? odd? even? char? seq? vector? string? map? nil? contains? zero? instance? not-every? not-any? libspec? -> ->> .. . inc compare do dotimes mapcat take remove take-while drop letfn drop-last take-last drop-while while intern condp case reduced cycle split-at split-with repeat replicate iterate range merge zipmap declare line-seq sort comparator sort-by dorun doall nthnext nthrest partition eval doseq await await-for let agent atom send send-off release-pending-sends add-watch mapv filterv remove-watch agent-error restart-agent set-error-handler error-handler set-error-mode! error-mode shutdown-agents quote var fn loop recur throw try monitor-enter monitor-exit macroexpand macroexpand-1 for dosync and or when when-not when-let comp juxt partial sequence memoize constantly complement identity assert peek pop doto proxy first rest cons cast coll last butlast sigs reify second ffirst fnext nfirst nnext meta with-meta ns in-ns create-ns import refer keys select-keys vals key val rseq name namespace promise into transient persistent! conj! assoc! dissoc! pop! disj! use class type num float double short byte boolean bigint biginteger bigdec print-method print-dup throw-if printf format load compile get-in update-in pr pr-on newline flush read slurp read-line subvec with-open memfn time re-find re-groups rand-int rand mod locking assert-valid-fdecl alias resolve ref deref refset swap! reset! set-validator! compare-and-set! alter-meta! reset-meta! commute get-validator alter ref-set ref-history-count ref-min-history ref-max-history ensure sync io! new next conj set! to-array future future-call into-array aset gen-class reduce map filter find empty hash-map hash-set sorted-map sorted-map-by sorted-set sorted-set-by vec vector seq flatten reverse assoc dissoc list disj get union difference intersection extend extend-type extend-protocol int nth delay count concat chunk chunk-buffer chunk-append chunk-first chunk-rest max min dec unchecked-inc-int unchecked-inc unchecked-dec-inc unchecked-dec unchecked-negate unchecked-add-int unchecked-add unchecked-subtract-int unchecked-subtract chunk-next chunk-cons chunked-seq? prn vary-meta lazy-seq spread list* str find-keyword keyword symbol gensym force rationalize", + }, + i = { begin: n, relevance: 0 }, + o = { className: "number", begin: "[-+]?\\d+(\\.\\d+)?", relevance: 0 }, + s = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + l = e.COMMENT(";", "$", { relevance: 0 }), + c = { className: "literal", begin: /\b(true|false|nil)\b/ }, + _ = { begin: "[\\[\\{]", end: "[\\]\\}]" }, + d = { className: "comment", begin: "\\^" + n }, + u = e.COMMENT("\\^\\{", "\\}"), + m = { className: "symbol", begin: "[:]{1,2}" + n }, + p = { begin: "\\(", end: "\\)" }, + g = { endsWithParent: !0, relevance: 0 }, + E = { keywords: r, className: "name", begin: n, relevance: 0, starts: g }, + S = [p, s, d, u, l, m, _, o, c, i], + b = { + beginKeywords: a, + lexemes: n, + end: '(\\[|#|\\d|"|:|\\{|\\)|\\(|$)', + contains: [ + { + className: "title", + begin: n, + relevance: 0, + excludeEnd: !0, + endsParent: !0, + }, + ].concat(S), + }; + return ( + (p.contains = [e.COMMENT("comment", ""), b, E, g]), + (g.contains = S), + (_.contains = S), + (u.contains = [_]), + { + name: "Clojure", + aliases: ["clj"], + illegal: /\S/, + contains: [p, s, d, u, l, m, _, o, c], + } + ); +}; +var Gp = function (e) { + return { + name: "Clojure REPL", + contains: [ + { + className: "meta", + begin: /^([\w.-]+|\s*#_)?=>/, + starts: { end: /$/, subLanguage: "clojure" }, + }, + ], + }; +}; +var Yp = function (e) { + return { + name: "CMake", + aliases: ["cmake.in"], + case_insensitive: !0, + keywords: { + keyword: + "break cmake_host_system_information cmake_minimum_required cmake_parse_arguments cmake_policy configure_file continue elseif else endforeach endfunction endif endmacro endwhile execute_process file find_file find_library find_package find_path find_program foreach function get_cmake_property get_directory_property get_filename_component get_property if include include_guard list macro mark_as_advanced math message option return separate_arguments set_directory_properties set_property set site_name string unset variable_watch while add_compile_definitions add_compile_options add_custom_command add_custom_target add_definitions add_dependencies add_executable add_library add_link_options add_subdirectory add_test aux_source_directory build_command create_test_sourcelist define_property enable_language enable_testing export fltk_wrap_ui get_source_file_property get_target_property get_test_property include_directories include_external_msproject include_regular_expression install link_directories link_libraries load_cache project qt_wrap_cpp qt_wrap_ui remove_definitions set_source_files_properties set_target_properties set_tests_properties source_group target_compile_definitions target_compile_features target_compile_options target_include_directories target_link_directories target_link_libraries target_link_options target_sources try_compile try_run ctest_build ctest_configure ctest_coverage ctest_empty_binary_directory ctest_memcheck ctest_read_custom_files ctest_run_script ctest_sleep ctest_start ctest_submit ctest_test ctest_update ctest_upload build_name exec_program export_library_dependencies install_files install_programs install_targets load_command make_directory output_required_files remove subdir_depends subdirs use_mangled_mesa utility_source variable_requires write_file qt5_use_modules qt5_use_package qt5_wrap_cpp on off true false and or not command policy target test exists is_newer_than is_directory is_symlink is_absolute matches less greater equal less_equal greater_equal strless strgreater strequal strless_equal strgreater_equal version_less version_greater version_equal version_less_equal version_greater_equal in_list defined", + }, + contains: [ + { className: "variable", begin: /\$\{/, end: /\}/ }, + e.HASH_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + ], + }; + }, + Hp = [ + "as", + "in", + "of", + "if", + "for", + "while", + "finally", + "var", + "new", + "function", + "do", + "return", + "void", + "else", + "break", + "catch", + "instanceof", + "with", + "throw", + "case", + "default", + "try", + "switch", + "continue", + "typeof", + "delete", + "let", + "yield", + "const", + "class", + "debugger", + "async", + "await", + "static", + "import", + "from", + "export", + "extends", + ], + Vp = ["true", "false", "null", "undefined", "NaN", "Infinity"], + qp = [].concat( + [ + "setInterval", + "setTimeout", + "clearInterval", + "clearTimeout", + "require", + "exports", + "eval", + "isFinite", + "isNaN", + "parseFloat", + "parseInt", + "decodeURI", + "decodeURIComponent", + "encodeURI", + "encodeURIComponent", + "escape", + "unescape", + ], + [ + "arguments", + "this", + "super", + "console", + "window", + "document", + "localStorage", + "module", + "global", + ], + [ + "Intl", + "DataView", + "Number", + "Math", + "Date", + "String", + "RegExp", + "Object", + "Function", + "Boolean", + "Error", + "Symbol", + "Set", + "Map", + "WeakSet", + "WeakMap", + "Proxy", + "Reflect", + "JSON", + "Promise", + "Float64Array", + "Int16Array", + "Int32Array", + "Int8Array", + "Uint16Array", + "Uint32Array", + "Float32Array", + "Array", + "Uint8Array", + "Uint8ClampedArray", + "ArrayBuffer", + "BigInt64Array", + "BigUint64Array", + "BigInt", + ], + [ + "EvalError", + "InternalError", + "RangeError", + "ReferenceError", + "SyntaxError", + "TypeError", + "URIError", + ], + ); +var zp = function (e) { + var t, + n = { + keyword: Hp.concat([ + "then", + "unless", + "until", + "loop", + "by", + "when", + "and", + "or", + "is", + "isnt", + "not", + ]).filter( + ((t = ["var", "const", "let", "function", "static"]), + function (e) { + return !t.includes(e); + }), + ), + literal: Vp.concat(["yes", "no", "on", "off"]), + built_in: qp.concat(["npm", "print"]), + }, + a = "[A-Za-z$_][0-9A-Za-z$_]*", + r = { className: "subst", begin: /#\{/, end: /\}/, keywords: n }, + i = [ + e.BINARY_NUMBER_MODE, + e.inherit(e.C_NUMBER_MODE, { starts: { end: "(\\s*/)?", relevance: 0 } }), + { + className: "string", + variants: [ + { begin: /'''/, end: /'''/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /'/, end: /'/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /"""/, end: /"""/, contains: [e.BACKSLASH_ESCAPE, r] }, + { begin: /"/, end: /"/, contains: [e.BACKSLASH_ESCAPE, r] }, + ], + }, + { + className: "regexp", + variants: [ + { begin: "///", end: "///", contains: [r, e.HASH_COMMENT_MODE] }, + { begin: "//[gim]{0,3}(?=\\W)", relevance: 0 }, + { begin: /\/(?![ *]).*?(?![\\]).\/[gim]{0,3}(?=\W)/ }, + ], + }, + { begin: "@" + a }, + { + subLanguage: "javascript", + excludeBegin: !0, + excludeEnd: !0, + variants: [ + { begin: "```", end: "```" }, + { begin: "`", end: "`" }, + ], + }, + ]; + r.contains = i; + var o = e.inherit(e.TITLE_MODE, { begin: a }), + s = "(\\(.*\\)\\s*)?\\B[-=]>", + l = { + className: "params", + begin: "\\([^\\(]", + returnBegin: !0, + contains: [ + { begin: /\(/, end: /\)/, keywords: n, contains: ["self"].concat(i) }, + ], + }; + return { + name: "CoffeeScript", + aliases: ["coffee", "cson", "iced"], + keywords: n, + illegal: /\/\*/, + contains: i.concat([ + e.COMMENT("###", "###"), + e.HASH_COMMENT_MODE, + { + className: "function", + begin: "^\\s*" + a + "\\s*=\\s*" + s, + end: "[-=]>", + returnBegin: !0, + contains: [o, l], + }, + { + begin: /[:\(,=]\s*/, + relevance: 0, + contains: [ + { + className: "function", + begin: s, + end: "[-=]>", + returnBegin: !0, + contains: [l], + }, + ], + }, + { + className: "class", + beginKeywords: "class", + end: "$", + illegal: /[:="\[\]]/, + contains: [ + { + beginKeywords: "extends", + endsWithParent: !0, + illegal: /[:="\[\]]/, + contains: [o], + }, + o, + ], + }, + { + begin: a + ":", + end: ":", + returnBegin: !0, + returnEnd: !0, + relevance: 0, + }, + ]), + }; +}; +var Wp = function (e) { + return { + name: "Coq", + keywords: { + keyword: + "_|0 as at cofix else end exists exists2 fix for forall fun if IF in let match mod Prop return Set then Type using where with Abort About Add Admit Admitted All Arguments Assumptions Axiom Back BackTo Backtrack Bind Blacklist Canonical Cd Check Class Classes Close Coercion Coercions CoFixpoint CoInductive Collection Combined Compute Conjecture Conjectures Constant constr Constraint Constructors Context Corollary CreateHintDb Cut Declare Defined Definition Delimit Dependencies Dependent Derive Drop eauto End Equality Eval Example Existential Existentials Existing Export exporting Extern Extract Extraction Fact Field Fields File Fixpoint Focus for From Function Functional Generalizable Global Goal Grab Grammar Graph Guarded Heap Hint HintDb Hints Hypotheses Hypothesis ident Identity If Immediate Implicit Import Include Inductive Infix Info Initial Inline Inspect Instance Instances Intro Intros Inversion Inversion_clear Language Left Lemma Let Libraries Library Load LoadPath Local Locate Ltac ML Mode Module Modules Monomorphic Morphism Next NoInline Notation Obligation Obligations Opaque Open Optimize Options Parameter Parameters Parametric Path Paths pattern Polymorphic Preterm Print Printing Program Projections Proof Proposition Pwd Qed Quit Rec Record Recursive Redirect Relation Remark Remove Require Reserved Reset Resolve Restart Rewrite Right Ring Rings Save Scheme Scope Scopes Script Search SearchAbout SearchHead SearchPattern SearchRewrite Section Separate Set Setoid Show Solve Sorted Step Strategies Strategy Structure SubClass Table Tables Tactic Term Test Theorem Time Timeout Transparent Type Typeclasses Types Undelimit Undo Unfocus Unfocused Unfold Universe Universes Unset Unshelve using Variable Variables Variant Verbose Visibility where with", + built_in: + "abstract absurd admit after apply as assert assumption at auto autorewrite autounfold before bottom btauto by case case_eq cbn cbv change classical_left classical_right clear clearbody cofix compare compute congruence constr_eq constructor contradict contradiction cut cutrewrite cycle decide decompose dependent destruct destruction dintuition discriminate discrR do double dtauto eapply eassumption eauto ecase econstructor edestruct ediscriminate eelim eexact eexists einduction einjection eleft elim elimtype enough equality erewrite eright esimplify_eq esplit evar exact exactly_once exfalso exists f_equal fail field field_simplify field_simplify_eq first firstorder fix fold fourier functional generalize generalizing gfail give_up has_evar hnf idtac in induction injection instantiate intro intro_pattern intros intuition inversion inversion_clear is_evar is_var lapply lazy left lia lra move native_compute nia nsatz omega once pattern pose progress proof psatz quote record red refine reflexivity remember rename repeat replace revert revgoals rewrite rewrite_strat right ring ring_simplify rtauto set setoid_reflexivity setoid_replace setoid_rewrite setoid_symmetry setoid_transitivity shelve shelve_unifiable simpl simple simplify_eq solve specialize split split_Rabs split_Rmult stepl stepr subst sum swap symmetry tactic tauto time timeout top transitivity trivial try tryif unfold unify until using vm_compute with", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.COMMENT("\\(\\*", "\\*\\)"), + e.C_NUMBER_MODE, + { className: "type", excludeBegin: !0, begin: "\\|\\s*", end: "\\w+" }, + { begin: /[-=]>/ }, + ], + }; +}; +var $p = function (e) { + return { + name: "Caché Object Script", + case_insensitive: !0, + aliases: ["cls"], + keywords: + "property parameter class classmethod clientmethod extends as break catch close continue do d|0 else elseif for goto halt hang h|0 if job j|0 kill k|0 lock l|0 merge new open quit q|0 read r|0 return set s|0 tcommit throw trollback try tstart use view while write w|0 xecute x|0 zkill znspace zn ztrap zwrite zw zzdump zzwrite print zbreak zinsert zload zprint zremove zsave zzprint mv mvcall mvcrt mvdim mvprint zquit zsync ascii", + contains: [ + { + className: "number", + begin: "\\b(\\d+(\\.\\d*)?|\\.\\d+)", + relevance: 0, + }, + { + className: "string", + variants: [ + { begin: '"', end: '"', contains: [{ begin: '""', relevance: 0 }] }, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "comment", begin: /;/, end: "$", relevance: 0 }, + { className: "built_in", begin: /(?:\$\$?|\.\.)\^?[a-zA-Z]+/ }, + { className: "built_in", begin: /\$\$\$[a-zA-Z]+/ }, + { className: "built_in", begin: /%[a-z]+(?:\.[a-z]+)*/ }, + { className: "symbol", begin: /\^%?[a-zA-Z][\w]*/ }, + { className: "keyword", begin: /##class|##super|#define|#dim/ }, + { + begin: /&sql\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + subLanguage: "sql", + }, + { + begin: /&(js|jscript|javascript)/, + excludeBegin: !0, + excludeEnd: !0, + subLanguage: "javascript", + }, + { begin: /&html<\s*\s*>/, subLanguage: "xml" }, + ], + }; +}; +function Qp(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Kp(e) { + return jp("(", e, ")?"); +} +function jp() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Qp(e); + }) + .join(""); + return a; +} +var Xp = function (e) { + var t, + n = e.COMMENT("//", "$", { contains: [{ begin: /\\\n/ }] }), + a = "decltype\\(auto\\)", + r = "[a-zA-Z_]\\w*::", + i = "(decltype\\(auto\\)|" + Kp(r) + "[a-zA-Z_]\\w*" + Kp("<[^<>]+>") + ")", + o = { className: "keyword", begin: "\\b[a-z\\d_]*_t\\b" }, + s = { + className: "string", + variants: [ + { + begin: '(u8?|U|L)?"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: + "(u8?|U|L)?'(\\\\(x[0-9A-Fa-f]{2}|u[0-9A-Fa-f]{4,8}|[0-7]{3}|\\S)|.)", + end: "'", + illegal: ".", + }, + e.END_SAME_AS_BEGIN({ + begin: /(?:u8?|U|L)?R"([^()\\ ]{0,16})\(/, + end: /\)([^()\\ ]{0,16})"/, + }), + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)((ll|LL|l|L)(u|U)?|(u|U)(ll|LL|l|L)?|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + c = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line pragma _Pragma ifdef ifndef include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(s, { className: "meta-string" }), + { className: "meta-string", begin: /<.*?>/ }, + n, + e.C_BLOCK_COMMENT_MODE, + ], + }, + _ = { className: "title", begin: Kp(r) + e.IDENT_RE, relevance: 0 }, + d = Kp(r) + e.IDENT_RE + "\\s*\\(", + u = { + keyword: + "int float while private char char8_t char16_t char32_t catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid wchar_t short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignas alignof constexpr consteval constinit decltype concept co_await co_return co_yield requires noexcept static_assert thread_local restrict final override atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and and_eq bitand bitor compl not not_eq or or_eq xor xor_eq", + built_in: "_Bool _Complex _Imaginary", + _relevance_hints: [ + "asin", + "atan2", + "atan", + "calloc", + "ceil", + "cosh", + "cos", + "exit", + "exp", + "fabs", + "floor", + "fmod", + "fprintf", + "fputs", + "free", + "frexp", + "auto_ptr", + "deque", + "list", + "queue", + "stack", + "vector", + "map", + "set", + "pair", + "bitset", + "multiset", + "multimap", + "unordered_set", + "fscanf", + "future", + "isalnum", + "isalpha", + "iscntrl", + "isdigit", + "isgraph", + "islower", + "isprint", + "ispunct", + "isspace", + "isupper", + "isxdigit", + "tolower", + "toupper", + "labs", + "ldexp", + "log10", + "log", + "malloc", + "realloc", + "memchr", + "memcmp", + "memcpy", + "memset", + "modf", + "pow", + "printf", + "putchar", + "puts", + "scanf", + "sinh", + "sin", + "snprintf", + "sprintf", + "sqrt", + "sscanf", + "strcat", + "strchr", + "strcmp", + "strcpy", + "strcspn", + "strlen", + "strncat", + "strncmp", + "strncpy", + "strpbrk", + "strrchr", + "strspn", + "strstr", + "tanh", + "tan", + "unordered_map", + "unordered_multiset", + "unordered_multimap", + "priority_queue", + "make_pair", + "array", + "shared_ptr", + "abort", + "terminate", + "abs", + "acos", + "vfprintf", + "vprintf", + "vsprintf", + "endl", + "initializer_list", + "unique_ptr", + "complex", + "imaginary", + "std", + "string", + "wstring", + "cin", + "cout", + "cerr", + "clog", + "stdin", + "stdout", + "stderr", + "stringstream", + "istringstream", + "ostringstream", + ], + literal: "true false nullptr NULL", + }, + m = { + className: "function.dispatch", + relevance: 0, + keywords: u, + begin: jp( + /\b/, + /(?!decltype)/, + /(?!if)/, + /(?!for)/, + /(?!while)/, + e.IDENT_RE, + ((t = /\s*\(/), jp("(?=", t, ")")), + ), + }, + p = [m, c, o, n, e.C_BLOCK_COMMENT_MODE, l, s], + g = { + variants: [ + { begin: /=/, end: /;/ }, + { begin: /\(/, end: /\)/ }, + { beginKeywords: "new throw return else", end: /;/ }, + ], + keywords: u, + contains: p.concat([ + { + begin: /\(/, + end: /\)/, + keywords: u, + contains: p.concat(["self"]), + relevance: 0, + }, + ]), + relevance: 0, + }, + E = { + className: "function", + begin: "(" + i + "[\\*&\\s]+)+" + d, + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: u, + illegal: /[^\w\s\*&:<>.]/, + contains: [ + { begin: a, keywords: u, relevance: 0 }, + { begin: d, returnBegin: !0, contains: [_], relevance: 0 }, + { begin: /::/, relevance: 0 }, + { begin: /:/, endsWithParent: !0, contains: [s, l] }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: [ + n, + e.C_BLOCK_COMMENT_MODE, + s, + l, + o, + { + begin: /\(/, + end: /\)/, + keywords: u, + relevance: 0, + contains: ["self", n, e.C_BLOCK_COMMENT_MODE, s, l, o], + }, + ], + }, + o, + n, + e.C_BLOCK_COMMENT_MODE, + c, + ], + }; + return { + name: "C++", + aliases: ["cc", "c++", "h++", "hpp", "hh", "hxx", "cxx"], + keywords: u, + illegal: "", + keywords: u, + contains: ["self", o], + }, + { begin: e.IDENT_RE + "::", keywords: u }, + { + className: "class", + beginKeywords: "enum class struct union", + end: /[{;:<>=]/, + contains: [{ beginKeywords: "final class struct" }, e.TITLE_MODE], + }, + ]), + exports: { preprocessor: c, strings: s, keywords: u }, + }; +}; +var Zp = function (e) { + var t = + "group clone ms master location colocation order fencing_topology rsc_ticket acl_target acl_group user role tag xml"; + return { + name: "crmsh", + aliases: ["crm", "pcmk"], + case_insensitive: !0, + keywords: { + keyword: + "params meta operations op rule attributes utilization read write deny defined not_defined in_range date spec in ref reference attribute type xpath version and or lt gt tag lte gte eq ne \\ number string", + literal: + "Master Started Slave Stopped start promote demote stop monitor true false", + }, + contains: [ + e.HASH_COMMENT_MODE, + { + beginKeywords: "node", + starts: { + end: "\\s*([\\w_-]+:)?", + starts: { className: "title", end: "\\s*[\\$\\w_][\\w_-]*" }, + }, + }, + { + beginKeywords: "primitive rsc_template", + starts: { + className: "title", + end: "\\s*[\\$\\w_][\\w_-]*", + starts: { end: "\\s*@?[\\w_][\\w_\\.:-]*" }, + }, + }, + { + begin: "\\b(" + t.split(" ").join("|") + ")\\s+", + keywords: t, + starts: { className: "title", end: "[\\$\\w_][\\w_-]*" }, + }, + { + beginKeywords: "property rsc_defaults op_defaults", + starts: { className: "title", end: "\\s*([\\w_-]+:)?" }, + }, + e.QUOTE_STRING_MODE, + { + className: "meta", + begin: "(ocf|systemd|service|lsb):[\\w_:-]+", + relevance: 0, + }, + { + className: "number", + begin: "\\b\\d+(\\.\\d+)?(ms|s|h|m)?", + relevance: 0, + }, + { className: "literal", begin: "[-]?(infinity|inf)", relevance: 0 }, + { className: "attr", begin: /([A-Za-z$_#][\w_-]+)=/, relevance: 0 }, + { className: "tag", begin: "", relevance: 0 }, + ], + }; +}; +var Jp = function (e) { + var t = "(_?[ui](8|16|32|64|128))?", + n = + "[a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|[=!]~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~|]|//|//=|&[-+*]=?|&\\*\\*|\\[\\][=?]?", + a = "[A-Za-z_]\\w*(::\\w+)*(\\?|!)?", + r = { + $pattern: "[a-zA-Z_]\\w*[!?=]?", + keyword: + "abstract alias annotation as as? asm begin break case class def do else elsif end ensure enum extend for fun if include instance_sizeof is_a? lib macro module next nil? of out pointerof private protected rescue responds_to? return require select self sizeof struct super then type typeof union uninitialized unless until verbatim when while with yield __DIR__ __END_LINE__ __FILE__ __LINE__", + literal: "false nil true", + }, + i = { className: "subst", begin: /#\{/, end: /\}/, keywords: r }, + o = { + className: "template-variable", + variants: [ + { begin: "\\{\\{", end: "\\}\\}" }, + { begin: "\\{%", end: "%\\}" }, + ], + keywords: r, + }; + function s(e, t) { + var n = [{ begin: e, end: t }]; + return (n[0].contains = n), n; + } + var l = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, i], + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /`/, end: /`/ }, + { begin: "%[Qwi]?\\(", end: "\\)", contains: s("\\(", "\\)") }, + { begin: "%[Qwi]?\\[", end: "\\]", contains: s("\\[", "\\]") }, + { begin: "%[Qwi]?\\{", end: /\}/, contains: s(/\{/, /\}/) }, + { begin: "%[Qwi]?<", end: ">", contains: s("<", ">") }, + { begin: "%[Qwi]?\\|", end: "\\|" }, + { begin: /<<-\w+$/, end: /^\s*\w+$/ }, + ], + relevance: 0, + }, + c = { + className: "string", + variants: [ + { begin: "%q\\(", end: "\\)", contains: s("\\(", "\\)") }, + { begin: "%q\\[", end: "\\]", contains: s("\\[", "\\]") }, + { begin: "%q\\{", end: /\}/, contains: s(/\{/, /\}/) }, + { begin: "%q<", end: ">", contains: s("<", ">") }, + { begin: "%q\\|", end: "\\|" }, + { begin: /<<-'\w+'$/, end: /^\s*\w+$/ }, + ], + relevance: 0, + }, + _ = { + begin: + "(?!%\\})(" + + e.RE_STARTERS_RE + + "|\\n|\\b(case|if|select|unless|until|when|while)\\b)\\s*", + keywords: "case if select unless until when while", + contains: [ + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, i], + variants: [ + { begin: "//[a-z]*", relevance: 0 }, + { begin: "/(?!\\/)", end: "/[a-z]*" }, + ], + }, + ], + relevance: 0, + }, + d = [ + o, + l, + c, + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, i], + variants: [ + { begin: "%r\\(", end: "\\)", contains: s("\\(", "\\)") }, + { begin: "%r\\[", end: "\\]", contains: s("\\[", "\\]") }, + { begin: "%r\\{", end: /\}/, contains: s(/\{/, /\}/) }, + { begin: "%r<", end: ">", contains: s("<", ">") }, + { begin: "%r\\|", end: "\\|" }, + ], + relevance: 0, + }, + _, + { + className: "meta", + begin: "@\\[", + end: "\\]", + contains: [ + e.inherit(e.QUOTE_STRING_MODE, { className: "meta-string" }), + ], + }, + e.HASH_COMMENT_MODE, + { + className: "class", + beginKeywords: "class module struct", + end: "$|;", + illegal: /=/, + contains: [ + e.HASH_COMMENT_MODE, + e.inherit(e.TITLE_MODE, { begin: a }), + { begin: "<" }, + ], + }, + { + className: "class", + beginKeywords: "lib enum union", + end: "$|;", + illegal: /=/, + contains: [e.HASH_COMMENT_MODE, e.inherit(e.TITLE_MODE, { begin: a })], + }, + { + beginKeywords: "annotation", + end: "$|;", + illegal: /=/, + contains: [e.HASH_COMMENT_MODE, e.inherit(e.TITLE_MODE, { begin: a })], + relevance: 2, + }, + { + className: "function", + beginKeywords: "def", + end: /\B\b/, + contains: [e.inherit(e.TITLE_MODE, { begin: n, endsParent: !0 })], + }, + { + className: "function", + beginKeywords: "fun macro", + end: /\B\b/, + contains: [e.inherit(e.TITLE_MODE, { begin: n, endsParent: !0 })], + relevance: 2, + }, + { + className: "symbol", + begin: e.UNDERSCORE_IDENT_RE + "(!|\\?)?:", + relevance: 0, + }, + { + className: "symbol", + begin: ":", + contains: [l, { begin: n }], + relevance: 0, + }, + { + className: "number", + variants: [ + { begin: "\\b0b([01_]+)" + t }, + { begin: "\\b0o([0-7_]+)" + t }, + { begin: "\\b0x([A-Fa-f0-9_]+)" + t }, + { + begin: + "\\b([1-9][0-9_]*[0-9]|[0-9])(\\.[0-9][0-9_]*)?([eE]_?[-+]?[0-9_]*)?(_?f(32|64))?(?!_)", + }, + { begin: "\\b([1-9][0-9_]*|0)" + t }, + ], + relevance: 0, + }, + ]; + return ( + (i.contains = d), + (o.contains = d.slice(1)), + { name: "Crystal", aliases: ["cr"], keywords: r, contains: d } + ); +}; +var eg = function (e) { + var t = { + keyword: [ + "abstract", + "as", + "base", + "break", + "case", + "class", + "const", + "continue", + "do", + "else", + "event", + "explicit", + "extern", + "finally", + "fixed", + "for", + "foreach", + "goto", + "if", + "implicit", + "in", + "interface", + "internal", + "is", + "lock", + "namespace", + "new", + "operator", + "out", + "override", + "params", + "private", + "protected", + "public", + "readonly", + "record", + "ref", + "return", + "sealed", + "sizeof", + "stackalloc", + "static", + "struct", + "switch", + "this", + "throw", + "try", + "typeof", + "unchecked", + "unsafe", + "using", + "virtual", + "void", + "volatile", + "while", + ].concat([ + "add", + "alias", + "and", + "ascending", + "async", + "await", + "by", + "descending", + "equals", + "from", + "get", + "global", + "group", + "init", + "into", + "join", + "let", + "nameof", + "not", + "notnull", + "on", + "or", + "orderby", + "partial", + "remove", + "select", + "set", + "unmanaged", + "value|0", + "var", + "when", + "where", + "with", + "yield", + ]), + built_in: [ + "bool", + "byte", + "char", + "decimal", + "delegate", + "double", + "dynamic", + "enum", + "float", + "int", + "long", + "nint", + "nuint", + "object", + "sbyte", + "short", + "string", + "ulong", + "uint", + "ushort", + ], + literal: ["default", "false", "null", "true"], + }, + n = e.inherit(e.TITLE_MODE, { begin: "[a-zA-Z](\\.?\\w)*" }), + a = { + className: "number", + variants: [ + { begin: "\\b(0b[01']+)" }, + { + begin: + "(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)", + }, + { + begin: + "(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)", + }, + ], + relevance: 0, + }, + r = { + className: "string", + begin: '@"', + end: '"', + contains: [{ begin: '""' }], + }, + i = e.inherit(r, { illegal: /\n/ }), + o = { className: "subst", begin: /\{/, end: /\}/, keywords: t }, + s = e.inherit(o, { illegal: /\n/ }), + l = { + className: "string", + begin: /\$"/, + end: '"', + illegal: /\n/, + contains: [{ begin: /\{\{/ }, { begin: /\}\}/ }, e.BACKSLASH_ESCAPE, s], + }, + c = { + className: "string", + begin: /\$@"/, + end: '"', + contains: [{ begin: /\{\{/ }, { begin: /\}\}/ }, { begin: '""' }, o], + }, + _ = e.inherit(c, { + illegal: /\n/, + contains: [{ begin: /\{\{/ }, { begin: /\}\}/ }, { begin: '""' }, s], + }); + (o.contains = [ + c, + l, + r, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + a, + e.C_BLOCK_COMMENT_MODE, + ]), + (s.contains = [ + _, + l, + i, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + a, + e.inherit(e.C_BLOCK_COMMENT_MODE, { illegal: /\n/ }), + ]); + var d = { variants: [c, l, r, e.APOS_STRING_MODE, e.QUOTE_STRING_MODE] }, + u = { begin: "<", end: ">", contains: [{ beginKeywords: "in out" }, n] }, + m = + e.IDENT_RE + + "(<" + + e.IDENT_RE + + "(\\s*,\\s*" + + e.IDENT_RE + + ")*>)?(\\[\\])?", + p = { begin: "@" + e.IDENT_RE, relevance: 0 }; + return { + name: "C#", + aliases: ["cs", "c#"], + keywords: t, + illegal: /::/, + contains: [ + e.COMMENT("///", "$", { + returnBegin: !0, + contains: [ + { + className: "doctag", + variants: [ + { begin: "///", relevance: 0 }, + { begin: "\x3c!--|--\x3e" }, + { begin: "" }, + ], + }, + ], + }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": + "if else elif endif define undef warning error line region endregion pragma checksum", + }, + }, + d, + a, + { + beginKeywords: "class interface", + relevance: 0, + end: /[{;=]/, + illegal: /[^\s:,]/, + contains: [ + { beginKeywords: "where class" }, + n, + u, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + { + beginKeywords: "namespace", + relevance: 0, + end: /[{;=]/, + illegal: /[^\s:]/, + contains: [n, e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + }, + { + beginKeywords: "record", + relevance: 0, + end: /[{;=]/, + illegal: /[^\s:]/, + contains: [n, u, e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + }, + { + className: "meta", + begin: "^\\s*\\[", + excludeBegin: !0, + end: "\\]", + excludeEnd: !0, + contains: [{ className: "meta-string", begin: /"/, end: /"/ }], + }, + { beginKeywords: "new return throw await else", relevance: 0 }, + { + className: "function", + begin: "(" + m + "\\s+)+" + e.IDENT_RE + "\\s*(<.+>\\s*)?\\(", + returnBegin: !0, + end: /\s*[{;=]/, + excludeEnd: !0, + keywords: t, + contains: [ + { + beginKeywords: [ + "public", + "private", + "protected", + "static", + "internal", + "protected", + "abstract", + "async", + "extern", + "override", + "unsafe", + "virtual", + "new", + "sealed", + "partial", + ].join(" "), + relevance: 0, + }, + { + begin: e.IDENT_RE + "\\s*(<.+>\\s*)?\\(", + returnBegin: !0, + contains: [e.TITLE_MODE, u], + relevance: 0, + }, + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: t, + relevance: 0, + contains: [d, a, e.C_BLOCK_COMMENT_MODE], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + p, + ], + }; +}; +var tg = function (e) { + return { + name: "CSP", + case_insensitive: !1, + keywords: { + $pattern: "[a-zA-Z][a-zA-Z0-9_-]*", + keyword: + "base-uri child-src connect-src default-src font-src form-action frame-ancestors frame-src img-src media-src object-src plugin-types report-uri sandbox script-src style-src", + }, + contains: [ + { className: "string", begin: "'", end: "'" }, + { className: "attribute", begin: "^Content", end: ":", excludeEnd: !0 }, + ], + }; + }, + ng = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + ag = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + rg = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + ig = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + og = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(); +function sg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function lg(e) { + return (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return t + .map(function (e) { + return sg(e); + }) + .join(""); + })("(?=", e, ")"); +} +var cg = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE]; + return { + name: "CSS", + case_insensitive: !0, + illegal: /[=|'\$]/, + keywords: { keyframePosition: "from to" }, + classNameAliases: { keyframePosition: "selector-tag" }, + contains: [ + e.C_BLOCK_COMMENT_MODE, + { begin: /-(webkit|moz|ms|o)-(?=[a-z])/ }, + e.CSS_NUMBER_MODE, + { className: "selector-id", begin: /#[A-Za-z0-9_-]+/, relevance: 0 }, + { + className: "selector-class", + begin: "\\.[a-zA-Z-][a-zA-Z0-9_-]*", + relevance: 0, + }, + t.ATTRIBUTE_SELECTOR_MODE, + { + className: "selector-pseudo", + variants: [ + { begin: ":(" + rg.join("|") + ")" }, + { begin: "::(" + ig.join("|") + ")" }, + ], + }, + { className: "attribute", begin: "\\b(" + og.join("|") + ")\\b" }, + { + begin: ":", + end: "[;}]", + contains: [t.HEXCOLOR, t.IMPORTANT, e.CSS_NUMBER_MODE].concat(n, [ + { + begin: /(url|data-uri)\(/, + end: /\)/, + relevance: 0, + keywords: { built_in: "url data-uri" }, + contains: [ + { + className: "string", + begin: /[^)]/, + endsWithParent: !0, + excludeEnd: !0, + }, + ], + }, + { className: "built_in", begin: /[\w-]+(?=\()/ }, + ]), + }, + { + begin: lg(/@/), + end: "[{;]", + relevance: 0, + illegal: /:/, + contains: [ + { className: "keyword", begin: /@-?\w[\w]*(-\w+)*/ }, + { + begin: /\s/, + endsWithParent: !0, + excludeEnd: !0, + relevance: 0, + keywords: { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: ag.join(" "), + }, + contains: [ + { begin: /[a-z-]+(?=:)/, className: "attribute" }, + ].concat(n, [e.CSS_NUMBER_MODE]), + }, + ], + }, + { className: "selector-tag", begin: "\\b(" + ng.join("|") + ")\\b" }, + ], + }; +}; +var _g = function (e) { + var t = { + $pattern: e.UNDERSCORE_IDENT_RE, + keyword: + "abstract alias align asm assert auto body break byte case cast catch class const continue debug default delete deprecated do else enum export extern final finally for foreach foreach_reverse|10 goto if immutable import in inout int interface invariant is lazy macro mixin module new nothrow out override package pragma private protected public pure ref return scope shared static struct super switch synchronized template this throw try typedef typeid typeof union unittest version void volatile while with __FILE__ __LINE__ __gshared|10 __thread __traits __DATE__ __EOF__ __TIME__ __TIMESTAMP__ __VENDOR__ __VERSION__", + built_in: + "bool cdouble cent cfloat char creal dchar delegate double dstring float function idouble ifloat ireal long real short string ubyte ucent uint ulong ushort wchar wstring", + literal: "false null true", + }, + n = + "((0|[1-9][\\d_]*)|0[bB][01_]+|0[xX]([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*))", + a = + "\\\\(['\"\\?\\\\abfnrtv]|u[\\dA-Fa-f]{4}|[0-7]{1,3}|x[\\dA-Fa-f]{2}|U[\\dA-Fa-f]{8})|&[a-zA-Z\\d]{2,};", + r = { + className: "number", + begin: "\\b" + n + "(L|u|U|Lu|LU|uL|UL)?", + relevance: 0, + }, + i = { + className: "number", + begin: + "\\b(((0[xX](([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*)\\.([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*)|\\.?([\\da-fA-F][\\da-fA-F_]*|_[\\da-fA-F][\\da-fA-F_]*))[pP][+-]?(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d))|((0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d)(\\.\\d*|([eE][+-]?(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d)))|\\d+\\.(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d)|\\.(0|[1-9][\\d_]*)([eE][+-]?(0|[1-9][\\d_]*|\\d[\\d_]*|[\\d_]+?\\d))?))([fF]|L|i|[fF]i|Li)?|" + + n + + "(i|[fF]i|Li))", + relevance: 0, + }, + o = { + className: "string", + begin: "'(" + a + "|.)", + end: "'", + illegal: ".", + }, + s = { + className: "string", + begin: '"', + contains: [{ begin: a, relevance: 0 }], + end: '"[cwd]?', + }, + l = e.COMMENT("\\/\\+", "\\+\\/", { contains: ["self"], relevance: 10 }); + return { + name: "D", + keywords: t, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + l, + { + className: "string", + begin: 'x"[\\da-fA-F\\s\\n\\r]*"[cwd]?', + relevance: 10, + }, + s, + { className: "string", begin: '[rq]"', end: '"[cwd]?', relevance: 5 }, + { className: "string", begin: "`", end: "`[cwd]?" }, + { className: "string", begin: 'q"\\{', end: '\\}"' }, + i, + r, + o, + { className: "meta", begin: "^#!", end: "$", relevance: 5 }, + { className: "meta", begin: "#(line)", end: "$", relevance: 5 }, + { className: "keyword", begin: "@[a-zA-Z_][a-zA-Z_\\d]*" }, + ], + }; +}; +function dg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function ug() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return dg(e); + }) + .join(""); + return a; +} +var mg = function (e) { + var t = { + begin: /<\/?[A-Za-z_]/, + end: ">", + subLanguage: "xml", + relevance: 0, + }, + n = { + variants: [ + { begin: /\[.+?\]\[.*?\]/, relevance: 0 }, + { + begin: + /\[.+?\]\(((data|javascript|mailto):|(?:http|ftp)s?:\/\/).*?\)/, + relevance: 2, + }, + { + begin: ug(/\[.+?\]\(/, /[A-Za-z][A-Za-z0-9+.-]*/, /:\/\/.*?\)/), + relevance: 2, + }, + { begin: /\[.+?\]\([./?&#].*?\)/, relevance: 1 }, + { begin: /\[.+?\]\(.*?\)/, relevance: 0 }, + ], + returnBegin: !0, + contains: [ + { + className: "string", + relevance: 0, + begin: "\\[", + end: "\\]", + excludeBegin: !0, + returnEnd: !0, + }, + { + className: "link", + relevance: 0, + begin: "\\]\\(", + end: "\\)", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "symbol", + relevance: 0, + begin: "\\]\\[", + end: "\\]", + excludeBegin: !0, + excludeEnd: !0, + }, + ], + }, + a = { + className: "strong", + contains: [], + variants: [ + { begin: /_{2}/, end: /_{2}/ }, + { begin: /\*{2}/, end: /\*{2}/ }, + ], + }, + r = { + className: "emphasis", + contains: [], + variants: [ + { begin: /\*(?!\*)/, end: /\*/ }, + { begin: /_(?!_)/, end: /_/, relevance: 0 }, + ], + }; + a.contains.push(r), r.contains.push(a); + var i = [t, n]; + return ( + (a.contains = a.contains.concat(i)), + (r.contains = r.contains.concat(i)), + { + name: "Markdown", + aliases: ["md", "mkdown", "mkd"], + contains: [ + { + className: "section", + variants: [ + { begin: "^#{1,6}", end: "$", contains: (i = i.concat(a, r)) }, + { + begin: "(?=^.+?\\n[=-]{2,}$)", + contains: [ + { begin: "^[=-]*$" }, + { begin: "^", end: "\\n", contains: i }, + ], + }, + ], + }, + t, + { + className: "bullet", + begin: "^[ \t]*([*+-]|(\\d+\\.))(?=\\s+)", + end: "\\s+", + excludeEnd: !0, + }, + a, + r, + { className: "quote", begin: "^>\\s+", contains: i, end: "$" }, + { + className: "code", + variants: [ + { begin: "(`{3,})[^`](.|\\n)*?\\1`*[ ]*" }, + { begin: "(~{3,})[^~](.|\\n)*?\\1~*[ ]*" }, + { begin: "```", end: "```+[ ]*$" }, + { begin: "~~~", end: "~~~+[ ]*$" }, + { begin: "`.+?`" }, + { + begin: "(?=^( {4}|\\t))", + contains: [{ begin: "^( {4}|\\t)", end: "(\\n)$" }], + relevance: 0, + }, + ], + }, + { begin: "^[-\\*]{3,}", end: "$" }, + n, + { + begin: /^\[[^\n]+\]:/, + returnBegin: !0, + contains: [ + { + className: "symbol", + begin: /\[/, + end: /\]/, + excludeBegin: !0, + excludeEnd: !0, + }, + { className: "link", begin: /:\s*/, end: /$/, excludeBegin: !0 }, + ], + }, + ], + } + ); +}; +var pg = function (e) { + var t = { className: "subst", variants: [{ begin: "\\$[A-Za-z0-9_]+" }] }, + n = { + className: "subst", + variants: [{ begin: /\$\{/, end: /\}/ }], + keywords: "true false null this is new super", + }, + a = { + className: "string", + variants: [ + { begin: "r'''", end: "'''" }, + { begin: 'r"""', end: '"""' }, + { begin: "r'", end: "'", illegal: "\\n" }, + { begin: 'r"', end: '"', illegal: "\\n" }, + { begin: "'''", end: "'''", contains: [e.BACKSLASH_ESCAPE, t, n] }, + { begin: '"""', end: '"""', contains: [e.BACKSLASH_ESCAPE, t, n] }, + { + begin: "'", + end: "'", + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, t, n], + }, + { + begin: '"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, t, n], + }, + ], + }; + n.contains = [e.C_NUMBER_MODE, a]; + var r = [ + "Comparable", + "DateTime", + "Duration", + "Function", + "Iterable", + "Iterator", + "List", + "Map", + "Match", + "Object", + "Pattern", + "RegExp", + "Set", + "Stopwatch", + "String", + "StringBuffer", + "StringSink", + "Symbol", + "Type", + "Uri", + "bool", + "double", + "int", + "num", + "Element", + "ElementList", + ], + i = r.map(function (e) { + return "".concat(e, "?"); + }); + return { + name: "Dart", + keywords: { + keyword: + "abstract as assert async await break case catch class const continue covariant default deferred do dynamic else enum export extends extension external factory false final finally for Function get hide if implements import in inferface is late library mixin new null on operator part required rethrow return set show static super switch sync this throw true try typedef var void while with yield", + built_in: r + .concat(i) + .concat([ + "Never", + "Null", + "dynamic", + "print", + "document", + "querySelector", + "querySelectorAll", + "window", + ]), + $pattern: /[A-Za-z][A-Za-z0-9_]*\??/, + }, + contains: [ + a, + e.COMMENT(/\/\*\*(?!\/)/, /\*\//, { + subLanguage: "markdown", + relevance: 0, + }), + e.COMMENT(/\/{3,} ?/, /$/, { + contains: [ + { subLanguage: "markdown", begin: ".", end: "$", relevance: 0 }, + ], + }), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + e.C_NUMBER_MODE, + { className: "meta", begin: "@[A-Za-z]+" }, + { begin: "=>" }, + ], + }; +}; +var gg = function (e) { + var t = + "exports register file shl array record property for mod while set ally label uses raise not stored class safecall var interface or private static exit index inherited to else stdcall override shr asm far resourcestring finalization packed virtual out and protected library do xorwrite goto near function end div overload object unit begin string on inline repeat until destructor write message program with read initialization except default nil if case cdecl in downto threadvar of try pascal const external constructor type public then implementation finally published procedure absolute reintroduce operator as is abstract alias assembler bitpacked break continue cppdecl cvar enumerator experimental platform deprecated unimplemented dynamic export far16 forward generic helper implements interrupt iochecks local name nodefault noreturn nostackframe oldfpccall otherwise saveregisters softfloat specialize strict unaligned varargs ", + n = [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/\{/, /\}/, { relevance: 0 }), + e.COMMENT(/\(\*/, /\*\)/, { relevance: 10 }), + ], + a = { + className: "meta", + variants: [ + { begin: /\{\$/, end: /\}/ }, + { begin: /\(\*\$/, end: /\*\)/ }, + ], + }, + r = { + className: "string", + begin: /'/, + end: /'/, + contains: [{ begin: /''/ }], + }, + i = { className: "string", begin: /(#\d+)+/ }, + o = { + begin: e.IDENT_RE + "\\s*=\\s*class\\s*\\(", + returnBegin: !0, + contains: [e.TITLE_MODE], + }, + s = { + className: "function", + beginKeywords: "function constructor destructor procedure", + end: /[:;]/, + keywords: "function constructor|10 destructor|10 procedure|10", + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: t, + contains: [r, i, a].concat(n), + }, + a, + ].concat(n), + }; + return { + name: "Delphi", + aliases: [ + "dpr", + "dfm", + "pas", + "pascal", + "freepascal", + "lazarus", + "lpr", + "lfm", + ], + case_insensitive: !0, + keywords: t, + illegal: /"|\$[G-Zg-z]|\/\*|<\/|\|/, + contains: [ + r, + i, + e.NUMBER_MODE, + { + className: "number", + relevance: 0, + variants: [ + { begin: "\\$[0-9A-Fa-f]+" }, + { begin: "&[0-7]+" }, + { begin: "%[01]+" }, + ], + }, + o, + s, + a, + ].concat(n), + }; +}; +var Eg = function (e) { + return { + name: "Diff", + aliases: ["patch"], + contains: [ + { + className: "meta", + relevance: 10, + variants: [ + { begin: /^@@ +-\d+,\d+ +\+\d+,\d+ +@@/ }, + { begin: /^\*\*\* +\d+,\d+ +\*\*\*\*$/ }, + { begin: /^--- +\d+,\d+ +----$/ }, + ], + }, + { + className: "comment", + variants: [ + { begin: /Index: /, end: /$/ }, + { begin: /^index/, end: /$/ }, + { begin: /={3,}/, end: /$/ }, + { begin: /^-{3}/, end: /$/ }, + { begin: /^\*{3} /, end: /$/ }, + { begin: /^\+{3}/, end: /$/ }, + { begin: /^\*{15}$/ }, + { begin: /^diff --git/, end: /$/ }, + ], + }, + { className: "addition", begin: /^\+/, end: /$/ }, + { className: "deletion", begin: /^-/, end: /$/ }, + { className: "addition", begin: /^!/, end: /$/ }, + ], + }; +}; +var Sg = function (e) { + var t = { + begin: /\|[A-Za-z]+:?/, + keywords: { + name: "truncatewords removetags linebreaksbr yesno get_digit timesince random striptags filesizeformat escape linebreaks length_is ljust rjust cut urlize fix_ampersands title floatformat capfirst pprint divisibleby add make_list unordered_list urlencode timeuntil urlizetrunc wordcount stringformat linenumbers slice date dictsort dictsortreversed default_if_none pluralize lower join center default truncatewords_html upper length phone2numeric wordwrap time addslashes slugify first escapejs force_escape iriencode last safe safeseq truncatechars localize unlocalize localtime utc timezone", + }, + contains: [e.QUOTE_STRING_MODE, e.APOS_STRING_MODE], + }; + return { + name: "Django", + aliases: ["jinja"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + e.COMMENT(/\{%\s*comment\s*%\}/, /\{%\s*endcomment\s*%\}/), + e.COMMENT(/\{#/, /#\}/), + { + className: "template-tag", + begin: /\{%/, + end: /%\}/, + contains: [ + { + className: "name", + begin: /\w+/, + keywords: { + name: "comment endcomment load templatetag ifchanged endifchanged if endif firstof for endfor ifnotequal endifnotequal widthratio extends include spaceless endspaceless regroup ifequal endifequal ssi now with cycle url filter endfilter debug block endblock else autoescape endautoescape csrf_token empty elif endwith static trans blocktrans endblocktrans get_static_prefix get_media_prefix plural get_current_language language get_available_languages get_current_language_bidi get_language_info get_language_info_list localize endlocalize localtime endlocaltime timezone endtimezone get_current_timezone verbatim", + }, + starts: { + endsWithParent: !0, + keywords: "in by as", + contains: [t], + relevance: 0, + }, + }, + ], + }, + { + className: "template-variable", + begin: /\{\{/, + end: /\}\}/, + contains: [t], + }, + ], + }; +}; +var bg = function (e) { + return { + name: "DNS Zone", + aliases: ["bind", "zone"], + keywords: { + keyword: + "IN A AAAA AFSDB APL CAA CDNSKEY CDS CERT CNAME DHCID DLV DNAME DNSKEY DS HIP IPSECKEY KEY KX LOC MX NAPTR NS NSEC NSEC3 NSEC3PARAM PTR RRSIG RP SIG SOA SRV SSHFP TA TKEY TLSA TSIG TXT", + }, + contains: [ + e.COMMENT(";", "$", { relevance: 0 }), + { className: "meta", begin: /^\$(TTL|GENERATE|INCLUDE|ORIGIN)\b/ }, + { + className: "number", + begin: + "((([0-9A-Fa-f]{1,4}:){7}([0-9A-Fa-f]{1,4}|:))|(([0-9A-Fa-f]{1,4}:){6}(:[0-9A-Fa-f]{1,4}|((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){5}(((:[0-9A-Fa-f]{1,4}){1,2})|:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){4}(((:[0-9A-Fa-f]{1,4}){1,3})|((:[0-9A-Fa-f]{1,4})?:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){3}(((:[0-9A-Fa-f]{1,4}){1,4})|((:[0-9A-Fa-f]{1,4}){0,2}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){2}(((:[0-9A-Fa-f]{1,4}){1,5})|((:[0-9A-Fa-f]{1,4}){0,3}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){1}(((:[0-9A-Fa-f]{1,4}){1,6})|((:[0-9A-Fa-f]{1,4}){0,4}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(:(((:[0-9A-Fa-f]{1,4}){1,7})|((:[0-9A-Fa-f]{1,4}){0,5}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:)))\\b", + }, + { + className: "number", + begin: + "((25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9]).){3,3}(25[0-5]|(2[0-4]|1{0,1}[0-9]){0,1}[0-9])\\b", + }, + e.inherit(e.NUMBER_MODE, { begin: /\b\d+[dhwm]?/ }), + ], + }; +}; +var Tg = function (e) { + return { + name: "Dockerfile", + aliases: ["docker"], + case_insensitive: !0, + keywords: "from maintainer expose env arg user onbuild stopsignal", + contains: [ + e.HASH_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + { + beginKeywords: + "run cmd entrypoint volume add copy workdir label healthcheck shell", + starts: { end: /[^\\]$/, subLanguage: "bash" }, + }, + ], + illegal: "", illegal: "\\n" }, + ], + }, + t, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + r = { className: "variable", begin: /&[a-z\d_]*\b/ }, + i = { className: "meta-keyword", begin: "/[a-z][a-z\\d-]*/" }, + o = { className: "symbol", begin: "^\\s*[a-zA-Z_][a-zA-Z\\d_]*:" }, + s = { className: "params", begin: "<", end: ">", contains: [n, r] }, + l = { + className: "class", + begin: /[a-zA-Z_][a-zA-Z\d_@]*\s\{/, + end: /[{;=]/, + returnBegin: !0, + excludeEnd: !0, + }; + return { + name: "Device Tree", + keywords: "", + contains: [ + { + className: "class", + begin: "/\\s*\\{", + end: /\};/, + relevance: 10, + contains: [ + r, + i, + o, + l, + s, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + t, + ], + }, + r, + i, + o, + l, + s, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + t, + a, + { begin: e.IDENT_RE + "::", keywords: "" }, + ], + }; +}; +var Rg = function (e) { + return { + name: "Dust", + aliases: ["dst"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + { + className: "template-tag", + begin: /\{[#\/]/, + end: /\}/, + illegal: /;/, + contains: [ + { + className: "name", + begin: /[a-zA-Z\.-]+/, + starts: { + endsWithParent: !0, + relevance: 0, + contains: [e.QUOTE_STRING_MODE], + }, + }, + ], + }, + { + className: "template-variable", + begin: /\{/, + end: /\}/, + illegal: /;/, + keywords: "if eq ne lt lte gt gte select default math sep", + }, + ], + }; +}; +var vg = function (e) { + var t = e.COMMENT(/\(\*/, /\*\)/); + return { + name: "Extended Backus-Naur Form", + illegal: /\S/, + contains: [ + t, + { className: "attribute", begin: /^[ ]*[a-zA-Z]+([\s_-]+[a-zA-Z]+)*/ }, + { + begin: /=/, + end: /[.;]/, + contains: [ + t, + { className: "meta", begin: /\?.*\?/ }, + { + className: "string", + variants: [ + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: "`", end: "`" }, + ], + }, + ], + }, + ], + }; +}; +var Og = function (e) { + var t = "[a-zA-Z_][a-zA-Z0-9_.]*(!|\\?)?", + n = { + $pattern: t, + keyword: + "and false then defined module in return redo retry end for true self when next until do begin unless nil break not case cond alias while ensure or include use alias fn quote require import with|0", + }, + a = { className: "subst", begin: /#\{/, end: /\}/, keywords: n }, + r = { + className: "number", + begin: + "(\\b0o[0-7_]+)|(\\b0b[01_]+)|(\\b0x[0-9a-fA-F_]+)|(-?\\b[1-9][0-9_]*(\\.[0-9_]+([eE][-+]?[0-9]+)?)?)", + relevance: 0, + }, + i = { + className: "string", + begin: "~[a-z](?=[/|([{<\"'])", + contains: [ + { + endsParent: !0, + contains: [ + { + contains: [e.BACKSLASH_ESCAPE, a], + variants: [ + { begin: /"/, end: /"/ }, + { begin: /'/, end: /'/ }, + { begin: /\//, end: /\// }, + { begin: /\|/, end: /\|/ }, + { begin: /\(/, end: /\)/ }, + { begin: /\[/, end: /\]/ }, + { begin: /\{/, end: /\}/ }, + { begin: // }, + ], + }, + ], + }, + ], + }, + o = { + className: "string", + begin: "~[A-Z](?=[/|([{<\"'])", + contains: [ + { begin: /"/, end: /"/ }, + { begin: /'/, end: /'/ }, + { begin: /\//, end: /\// }, + { begin: /\|/, end: /\|/ }, + { begin: /\(/, end: /\)/ }, + { begin: /\[/, end: /\]/ }, + { begin: /\{/, end: /\}/ }, + { begin: // }, + ], + }, + s = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, a], + variants: [ + { begin: /"""/, end: /"""/ }, + { begin: /'''/, end: /'''/ }, + { begin: /~S"""/, end: /"""/, contains: [] }, + { begin: /~S"/, end: /"/, contains: [] }, + { begin: /~S'''/, end: /'''/, contains: [] }, + { begin: /~S'/, end: /'/, contains: [] }, + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + ], + }, + l = { + className: "function", + beginKeywords: "def defp defmacro", + end: /\B\b/, + contains: [e.inherit(e.TITLE_MODE, { begin: t, endsParent: !0 })], + }, + c = e.inherit(l, { + className: "class", + beginKeywords: "defimpl defmodule defprotocol defrecord", + end: /\bdo\b|$|;/, + }), + _ = [ + s, + o, + i, + e.HASH_COMMENT_MODE, + c, + l, + { begin: "::" }, + { + className: "symbol", + begin: ":(?![\\s:])", + contains: [ + s, + { + begin: + "[a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?", + }, + ], + relevance: 0, + }, + { className: "symbol", begin: t + ":(?!:)", relevance: 0 }, + r, + { className: "variable", begin: "(\\$\\W)|((\\$|@@?)(\\w+))" }, + { begin: "->" }, + { + begin: "(" + e.RE_STARTERS_RE + ")\\s*", + contains: [ + e.HASH_COMMENT_MODE, + { begin: /\/: (?=\d+\s*[,\]])/, relevance: 0, contains: [r] }, + { + className: "regexp", + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, a], + variants: [ + { begin: "/", end: "/[a-z]*" }, + { begin: "%r\\[", end: "\\][a-z]*" }, + ], + }, + ], + relevance: 0, + }, + ]; + return (a.contains = _), { name: "Elixir", keywords: n, contains: _ }; +}; +var hg = function (e) { + var t = { + variants: [ + e.COMMENT("--", "$"), + e.COMMENT(/\{-/, /-\}/, { contains: ["self"] }), + ], + }, + n = { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + a = { + begin: "\\(", + end: "\\)", + illegal: '"', + contains: [ + { className: "type", begin: "\\b[A-Z][\\w]*(\\((\\.\\.|,|\\w+)\\))?" }, + t, + ], + }; + return { + name: "Elm", + keywords: + "let in if then else case of where module import exposing type alias as infix infixl infixr port effect command subscription", + contains: [ + { + beginKeywords: "port effect module", + end: "exposing", + keywords: "port effect module where command subscription exposing", + contains: [a, t], + illegal: "\\W\\.|;", + }, + { + begin: "import", + end: "$", + keywords: "import as exposing", + contains: [a, t], + illegal: "\\W\\.|;", + }, + { + begin: "type", + end: "$", + keywords: "type alias", + contains: [n, a, { begin: /\{/, end: /\}/, contains: a.contains }, t], + }, + { + beginKeywords: "infix infixl infixr", + end: "$", + contains: [e.C_NUMBER_MODE, t], + }, + { begin: "port", end: "$", keywords: "port", contains: [t] }, + { className: "string", begin: "'\\\\?.", end: "'", illegal: "." }, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + n, + e.inherit(e.TITLE_MODE, { begin: "^[_a-z][\\w']*" }), + t, + { begin: "->|<-" }, + ], + illegal: /;/, + }; +}; +function yg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Ig() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return yg(e); + }) + .join(""); + return a; +} +var Ag = function (e) { + var t, + n = + "([a-zA-Z_]\\w*[!?=]?|[-+~]@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?)", + a = { + keyword: + "and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor __FILE__", + built_in: "proc lambda", + literal: "true false nil", + }, + r = { className: "doctag", begin: "@[A-Za-z]+" }, + i = { begin: "#<", end: ">" }, + o = [ + e.COMMENT("#", "$", { contains: [r] }), + e.COMMENT("^=begin", "^=end", { contains: [r], relevance: 10 }), + e.COMMENT("^__END__", "\\n$"), + ], + s = { className: "subst", begin: /#\{/, end: /\}/, keywords: a }, + l = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, s], + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /`/, end: /`/ }, + { begin: /%[qQwWx]?\(/, end: /\)/ }, + { begin: /%[qQwWx]?\[/, end: /\]/ }, + { begin: /%[qQwWx]?\{/, end: /\}/ }, + { begin: /%[qQwWx]?/ }, + { begin: /%[qQwWx]?\//, end: /\// }, + { begin: /%[qQwWx]?%/, end: /%/ }, + { begin: /%[qQwWx]?-/, end: /-/ }, + { begin: /%[qQwWx]?\|/, end: /\|/ }, + { begin: /\B\?(\\\d{1,3})/ }, + { begin: /\B\?(\\x[A-Fa-f0-9]{1,2})/ }, + { begin: /\B\?(\\u\{?[A-Fa-f0-9]{1,6}\}?)/ }, + { begin: /\B\?(\\M-\\C-|\\M-\\c|\\c\\M-|\\M-|\\C-\\M-)[\x20-\x7e]/ }, + { begin: /\B\?\\(c|C-)[\x20-\x7e]/ }, + { begin: /\B\?\\?\S/ }, + { + begin: /<<[-~]?'?(\w+)\n(?:[^\n]*\n)*?\s*\1\b/, + returnBegin: !0, + contains: [ + { begin: /<<[-~]?'?/ }, + e.END_SAME_AS_BEGIN({ + begin: /(\w+)/, + end: /(\w+)/, + contains: [e.BACKSLASH_ESCAPE, s], + }), + ], + }, + ], + }, + c = "[0-9](_?[0-9])*", + _ = { + className: "number", + relevance: 0, + variants: [ + { + begin: "\\b(" + .concat("[1-9](_?[0-9])*|0", ")(\\.(") + .concat(c, "))?([eE][+-]?(") + .concat(c, ")|r)?i?\\b"), + }, + { begin: "\\b0[dD][0-9](_?[0-9])*r?i?\\b" }, + { begin: "\\b0[bB][0-1](_?[0-1])*r?i?\\b" }, + { begin: "\\b0[oO][0-7](_?[0-7])*r?i?\\b" }, + { begin: "\\b0[xX][0-9a-fA-F](_?[0-9a-fA-F])*r?i?\\b" }, + { begin: "\\b0(_?[0-7])+r?i?\\b" }, + ], + }, + d = { + className: "params", + begin: "\\(", + end: "\\)", + endsParent: !0, + keywords: a, + }, + u = [ + l, + { + className: "class", + beginKeywords: "class module", + end: "$|;", + illegal: /=/, + contains: [ + e.inherit(e.TITLE_MODE, { begin: "[A-Za-z_]\\w*(::\\w+)*(\\?|!)?" }), + { + begin: "<\\s*", + contains: [ + { begin: "(" + e.IDENT_RE + "::)?" + e.IDENT_RE, relevance: 0 }, + ], + }, + ].concat(o), + }, + { + className: "function", + begin: Ig(/def\s+/, ((t = n + "\\s*(\\(|;|$)"), Ig("(?=", t, ")"))), + relevance: 0, + keywords: "def", + end: "$|;", + contains: [e.inherit(e.TITLE_MODE, { begin: n }), d].concat(o), + }, + { begin: e.IDENT_RE + "::" }, + { + className: "symbol", + begin: e.UNDERSCORE_IDENT_RE + "(!|\\?)?:", + relevance: 0, + }, + { + className: "symbol", + begin: ":(?!\\s)", + contains: [l, { begin: n }], + relevance: 0, + }, + _, + { + className: "variable", + begin: "(\\$\\W)|((\\$|@@?)(\\w+))(?=[^@$?])(?![A-Za-z])(?![@$?'])", + }, + { + className: "params", + begin: /\|/, + end: /\|/, + relevance: 0, + keywords: a, + }, + { + begin: "(" + e.RE_STARTERS_RE + "|unless)\\s*", + keywords: "unless", + contains: [ + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, s], + illegal: /\n/, + variants: [ + { begin: "/", end: "/[a-z]*" }, + { begin: /%r\{/, end: /\}[a-z]*/ }, + { begin: "%r\\(", end: "\\)[a-z]*" }, + { begin: "%r!", end: "![a-z]*" }, + { begin: "%r\\[", end: "\\][a-z]*" }, + ], + }, + ].concat(i, o), + relevance: 0, + }, + ].concat(i, o); + (s.contains = u), (d.contains = u); + var m = [ + { begin: /^\s*=>/, starts: { end: "$", contains: u } }, + { + className: "meta", + begin: + "^([>?]>|[\\w#]+\\(\\w+\\):\\d+:\\d+>|(\\w+-)?\\d+\\.\\d+\\.\\d+(p\\d+)?[^\\d][^>]+>)(?=[ ])", + starts: { end: "$", contains: u }, + }, + ]; + return ( + o.unshift(i), + { + name: "Ruby", + aliases: ["rb", "gemspec", "podspec", "thor", "irb"], + keywords: a, + illegal: /\/\*/, + contains: [e.SHEBANG({ binary: "ruby" })].concat(m).concat(o).concat(u), + } + ); +}; +var Dg = function (e) { + return { + name: "ERB", + subLanguage: "xml", + contains: [ + e.COMMENT("<%#", "%>"), + { + begin: "<%[%=-]?", + end: "[%-]?%>", + subLanguage: "ruby", + excludeBegin: !0, + excludeEnd: !0, + }, + ], + }; +}; +function Mg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Lg() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Mg(e); + }) + .join(""); + return a; +} +var wg = function (e) { + return { + name: "Erlang REPL", + keywords: { + built_in: "spawn spawn_link self", + keyword: + "after and andalso|10 band begin bnot bor bsl bsr bxor case catch cond div end fun if let not of or orelse|10 query receive rem try when xor", + }, + contains: [ + { className: "meta", begin: "^[0-9]+> ", relevance: 10 }, + e.COMMENT("%", "$"), + { + className: "number", + begin: + "\\b(\\d+(_\\d+)*#[a-fA-F0-9]+(_[a-fA-F0-9]+)*|\\d+(_\\d+)*(\\.\\d+(_\\d+)*)?([eE][-+]?\\d+)?)", + relevance: 0, + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: Lg(/\?(::)?/, /([A-Z]\w*)/, /((::)[A-Z]\w*)*/) }, + { begin: "->" }, + { begin: "ok" }, + { begin: "!" }, + { + begin: + "(\\b[a-z'][a-zA-Z0-9_']*:[a-z'][a-zA-Z0-9_']*)|(\\b[a-z'][a-zA-Z0-9_']*)", + relevance: 0, + }, + { begin: "[A-Z][a-zA-Z0-9_']*", relevance: 0 }, + ], + }; +}; +var xg = function (e) { + var t = "[a-z'][a-zA-Z0-9_']*", + n = "(" + t + ":" + t + "|" + t + ")", + a = { + keyword: + "after and andalso|10 band begin bnot bor bsl bzr bxor case catch cond div end fun if let not of orelse|10 query receive rem try when xor", + literal: "false true", + }, + r = e.COMMENT("%", "$"), + i = { + className: "number", + begin: + "\\b(\\d+(_\\d+)*#[a-fA-F0-9]+(_[a-fA-F0-9]+)*|\\d+(_\\d+)*(\\.\\d+(_\\d+)*)?([eE][-+]?\\d+)?)", + relevance: 0, + }, + o = { begin: "fun\\s+" + t + "/\\d+" }, + s = { + begin: n + "\\(", + end: "\\)", + returnBegin: !0, + relevance: 0, + contains: [ + { begin: n, relevance: 0 }, + { + begin: "\\(", + end: "\\)", + endsWithParent: !0, + returnEnd: !0, + relevance: 0, + }, + ], + }, + l = { begin: /\{/, end: /\}/, relevance: 0 }, + c = { begin: "\\b_([A-Z][A-Za-z0-9_]*)?", relevance: 0 }, + _ = { begin: "[A-Z][a-zA-Z0-9_]*", relevance: 0 }, + d = { + begin: "#" + e.UNDERSCORE_IDENT_RE, + relevance: 0, + returnBegin: !0, + contains: [ + { begin: "#" + e.UNDERSCORE_IDENT_RE, relevance: 0 }, + { begin: /\{/, end: /\}/, relevance: 0 }, + ], + }, + u = { beginKeywords: "fun receive if try case", end: "end", keywords: a }; + u.contains = [ + r, + o, + e.inherit(e.APOS_STRING_MODE, { className: "" }), + u, + s, + e.QUOTE_STRING_MODE, + i, + l, + c, + _, + d, + ]; + var m = [r, o, u, s, e.QUOTE_STRING_MODE, i, l, c, _, d]; + (s.contains[1].contains = m), (l.contains = m), (d.contains[1].contains = m); + var p = { className: "params", begin: "\\(", end: "\\)", contains: m }; + return { + name: "Erlang", + aliases: ["erl"], + keywords: a, + illegal: "(", + returnBegin: !0, + illegal: "\\(|#|//|/\\*|\\\\|:|;", + contains: [p, e.inherit(e.TITLE_MODE, { begin: t })], + starts: { end: ";|\\.", keywords: a, contains: m }, + }, + r, + { + begin: "^-", + end: "\\.", + relevance: 0, + excludeEnd: !0, + returnBegin: !0, + keywords: { + $pattern: "-" + e.IDENT_RE, + keyword: [ + "-module", + "-record", + "-undef", + "-export", + "-ifdef", + "-ifndef", + "-author", + "-copyright", + "-doc", + "-vsn", + "-import", + "-include", + "-include_lib", + "-compile", + "-define", + "-else", + "-endif", + "-file", + "-behaviour", + "-behavior", + "-spec", + ] + .map(function (e) { + return "".concat(e, "|1.5"); + }) + .join(" "), + }, + contains: [p], + }, + i, + e.QUOTE_STRING_MODE, + d, + c, + _, + l, + { begin: /\.$/ }, + ], + }; +}; +var Pg = function (e) { + return { + name: "Excel formulae", + aliases: ["xlsx", "xls"], + case_insensitive: !0, + keywords: { + $pattern: /[a-zA-Z][\w\.]*/, + built_in: + "ABS ACCRINT ACCRINTM ACOS ACOSH ACOT ACOTH AGGREGATE ADDRESS AMORDEGRC AMORLINC AND ARABIC AREAS ASC ASIN ASINH ATAN ATAN2 ATANH AVEDEV AVERAGE AVERAGEA AVERAGEIF AVERAGEIFS BAHTTEXT BASE BESSELI BESSELJ BESSELK BESSELY BETADIST BETA.DIST BETAINV BETA.INV BIN2DEC BIN2HEX BIN2OCT BINOMDIST BINOM.DIST BINOM.DIST.RANGE BINOM.INV BITAND BITLSHIFT BITOR BITRSHIFT BITXOR CALL CEILING CEILING.MATH CEILING.PRECISE CELL CHAR CHIDIST CHIINV CHITEST CHISQ.DIST CHISQ.DIST.RT CHISQ.INV CHISQ.INV.RT CHISQ.TEST CHOOSE CLEAN CODE COLUMN COLUMNS COMBIN COMBINA COMPLEX CONCAT CONCATENATE CONFIDENCE CONFIDENCE.NORM CONFIDENCE.T CONVERT CORREL COS COSH COT COTH COUNT COUNTA COUNTBLANK COUNTIF COUNTIFS COUPDAYBS COUPDAYS COUPDAYSNC COUPNCD COUPNUM COUPPCD COVAR COVARIANCE.P COVARIANCE.S CRITBINOM CSC CSCH CUBEKPIMEMBER CUBEMEMBER CUBEMEMBERPROPERTY CUBERANKEDMEMBER CUBESET CUBESETCOUNT CUBEVALUE CUMIPMT CUMPRINC DATE DATEDIF DATEVALUE DAVERAGE DAY DAYS DAYS360 DB DBCS DCOUNT DCOUNTA DDB DEC2BIN DEC2HEX DEC2OCT DECIMAL DEGREES DELTA DEVSQ DGET DISC DMAX DMIN DOLLAR DOLLARDE DOLLARFR DPRODUCT DSTDEV DSTDEVP DSUM DURATION DVAR DVARP EDATE EFFECT ENCODEURL EOMONTH ERF ERF.PRECISE ERFC ERFC.PRECISE ERROR.TYPE EUROCONVERT EVEN EXACT EXP EXPON.DIST EXPONDIST FACT FACTDOUBLE FALSE|0 F.DIST FDIST F.DIST.RT FILTERXML FIND FINDB F.INV F.INV.RT FINV FISHER FISHERINV FIXED FLOOR FLOOR.MATH FLOOR.PRECISE FORECAST FORECAST.ETS FORECAST.ETS.CONFINT FORECAST.ETS.SEASONALITY FORECAST.ETS.STAT FORECAST.LINEAR FORMULATEXT FREQUENCY F.TEST FTEST FV FVSCHEDULE GAMMA GAMMA.DIST GAMMADIST GAMMA.INV GAMMAINV GAMMALN GAMMALN.PRECISE GAUSS GCD GEOMEAN GESTEP GETPIVOTDATA GROWTH HARMEAN HEX2BIN HEX2DEC HEX2OCT HLOOKUP HOUR HYPERLINK HYPGEOM.DIST HYPGEOMDIST IF IFERROR IFNA IFS IMABS IMAGINARY IMARGUMENT IMCONJUGATE IMCOS IMCOSH IMCOT IMCSC IMCSCH IMDIV IMEXP IMLN IMLOG10 IMLOG2 IMPOWER IMPRODUCT IMREAL IMSEC IMSECH IMSIN IMSINH IMSQRT IMSUB IMSUM IMTAN INDEX INDIRECT INFO INT INTERCEPT INTRATE IPMT IRR ISBLANK ISERR ISERROR ISEVEN ISFORMULA ISLOGICAL ISNA ISNONTEXT ISNUMBER ISODD ISREF ISTEXT ISO.CEILING ISOWEEKNUM ISPMT JIS KURT LARGE LCM LEFT LEFTB LEN LENB LINEST LN LOG LOG10 LOGEST LOGINV LOGNORM.DIST LOGNORMDIST LOGNORM.INV LOOKUP LOWER MATCH MAX MAXA MAXIFS MDETERM MDURATION MEDIAN MID MIDBs MIN MINIFS MINA MINUTE MINVERSE MIRR MMULT MOD MODE MODE.MULT MODE.SNGL MONTH MROUND MULTINOMIAL MUNIT N NA NEGBINOM.DIST NEGBINOMDIST NETWORKDAYS NETWORKDAYS.INTL NOMINAL NORM.DIST NORMDIST NORMINV NORM.INV NORM.S.DIST NORMSDIST NORM.S.INV NORMSINV NOT NOW NPER NPV NUMBERVALUE OCT2BIN OCT2DEC OCT2HEX ODD ODDFPRICE ODDFYIELD ODDLPRICE ODDLYIELD OFFSET OR PDURATION PEARSON PERCENTILE.EXC PERCENTILE.INC PERCENTILE PERCENTRANK.EXC PERCENTRANK.INC PERCENTRANK PERMUT PERMUTATIONA PHI PHONETIC PI PMT POISSON.DIST POISSON POWER PPMT PRICE PRICEDISC PRICEMAT PROB PRODUCT PROPER PV QUARTILE QUARTILE.EXC QUARTILE.INC QUOTIENT RADIANS RAND RANDBETWEEN RANK.AVG RANK.EQ RANK RATE RECEIVED REGISTER.ID REPLACE REPLACEB REPT RIGHT RIGHTB ROMAN ROUND ROUNDDOWN ROUNDUP ROW ROWS RRI RSQ RTD SEARCH SEARCHB SEC SECH SECOND SERIESSUM SHEET SHEETS SIGN SIN SINH SKEW SKEW.P SLN SLOPE SMALL SQL.REQUEST SQRT SQRTPI STANDARDIZE STDEV STDEV.P STDEV.S STDEVA STDEVP STDEVPA STEYX SUBSTITUTE SUBTOTAL SUM SUMIF SUMIFS SUMPRODUCT SUMSQ SUMX2MY2 SUMX2PY2 SUMXMY2 SWITCH SYD T TAN TANH TBILLEQ TBILLPRICE TBILLYIELD T.DIST T.DIST.2T T.DIST.RT TDIST TEXT TEXTJOIN TIME TIMEVALUE T.INV T.INV.2T TINV TODAY TRANSPOSE TREND TRIM TRIMMEAN TRUE|0 TRUNC T.TEST TTEST TYPE UNICHAR UNICODE UPPER VALUE VAR VAR.P VAR.S VARA VARP VARPA VDB VLOOKUP WEBSERVICE WEEKDAY WEEKNUM WEIBULL WEIBULL.DIST WORKDAY WORKDAY.INTL XIRR XNPV XOR YEAR YEARFRAC YIELD YIELDDISC YIELDMAT Z.TEST ZTEST", + }, + contains: [ + { begin: /^=/, end: /[^=]/, returnEnd: !0, illegal: /=/, relevance: 10 }, + { + className: "symbol", + begin: /\b[A-Z]{1,2}\d+\b/, + end: /[^\d]/, + excludeEnd: !0, + relevance: 0, + }, + { + className: "symbol", + begin: /[A-Z]{0,2}\d*:[A-Z]{0,2}\d*/, + relevance: 0, + }, + e.BACKSLASH_ESCAPE, + e.QUOTE_STRING_MODE, + { className: "number", begin: e.NUMBER_RE + "(%)?", relevance: 0 }, + e.COMMENT(/\bN\(/, /\)/, { + excludeBegin: !0, + excludeEnd: !0, + illegal: /\n/, + }), + ], + }; +}; +var kg = function (e) { + return { + name: "FIX", + contains: [ + { + begin: /[^\u2401\u0001]+/, + end: /[\u2401\u0001]/, + excludeEnd: !0, + returnBegin: !0, + returnEnd: !1, + contains: [ + { + begin: /([^\u2401\u0001=]+)/, + end: /=([^\u2401\u0001=]+)/, + returnEnd: !0, + returnBegin: !1, + className: "attr", + }, + { + begin: /=/, + end: /([\u2401\u0001])/, + excludeEnd: !0, + excludeBegin: !0, + className: "string", + }, + ], + }, + ], + case_insensitive: !0, + }; +}; +var Ug = function (e) { + var t = { + className: "function", + beginKeywords: "def", + end: /[:={\[(\n;]/, + excludeEnd: !0, + contains: [ + { + className: "title", + relevance: 0, + begin: + /[^0-9\n\t "'(),.`{}\[\]:;][^\n\t "'(),.`{}\[\]:;]+|[^0-9\n\t "'(),.`{}\[\]:;=]/, + }, + ], + }; + return { + name: "Flix", + keywords: { + literal: "true false", + keyword: + "case class def else enum if impl import in lat rel index let match namespace switch type yield with", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "string", begin: /'(.|\\[xXuU][a-zA-Z0-9]+)'/ }, + { className: "string", variants: [{ begin: '"', end: '"' }] }, + t, + e.C_NUMBER_MODE, + ], + }; +}; +function Fg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Bg() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Fg(e); + }) + .join(""); + return a; +} +var Gg = function (e) { + var t = { + variants: [ + e.COMMENT("!", "$", { relevance: 0 }), + e.COMMENT("^C[ ]", "$", { relevance: 0 }), + e.COMMENT("^C$", "$", { relevance: 0 }), + ], + }, + n = /(_[a-z_\d]+)?/, + a = /([de][+-]?\d+)?/, + r = { + className: "number", + variants: [ + { begin: Bg(/\b\d+/, /\.(\d*)/, a, n) }, + { begin: Bg(/\b\d+/, a, n) }, + { begin: Bg(/\.\d+/, a, n) }, + ], + relevance: 0, + }, + i = { + className: "function", + beginKeywords: "subroutine function program", + illegal: "[${=\\n]", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }; + return { + name: "Fortran", + case_insensitive: !0, + aliases: ["f90", "f95"], + keywords: { + literal: ".False. .True.", + keyword: + "kind do concurrent local shared while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then block endblock endassociate public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure impure integer real character complex logical codimension dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data", + built_in: + "alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_of acosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image sync change team co_broadcast co_max co_min co_sum co_reduce", + }, + illegal: /\/\*/, + contains: [ + { + className: "string", + relevance: 0, + variants: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + i, + { begin: /^C\s*=(?!=)/, relevance: 0 }, + t, + r, + ], + }; +}; +var Yg = function (e) { + var t = { + begin: "<", + end: ">", + contains: [e.inherit(e.TITLE_MODE, { begin: /'[a-zA-Z0-9_]+/ })], + }; + return { + name: "F#", + aliases: ["fs"], + keywords: + "abstract and as assert base begin class default delegate do done downcast downto elif else end exception extern false finally for fun function global if in inherit inline interface internal lazy let match member module mutable namespace new null of open or override private public rec return sig static struct then to true try type upcast use val void when while with yield", + illegal: /\/\*/, + contains: [ + { className: "keyword", begin: /\b(yield|return|let|do)!/ }, + { + className: "string", + begin: '@"', + end: '"', + contains: [{ begin: '""' }], + }, + { className: "string", begin: '"""', end: '"""' }, + e.COMMENT("\\(\\*(\\s)", "\\*\\)", { contains: ["self"] }), + { + className: "class", + beginKeywords: "type", + end: "\\(|=|$", + excludeEnd: !0, + contains: [e.UNDERSCORE_TITLE_MODE, t], + }, + { className: "meta", begin: "\\[<", end: ">\\]", relevance: 10 }, + { + className: "symbol", + begin: "\\B('[A-Za-z])\\b", + contains: [e.BACKSLASH_ESCAPE], + }, + e.C_LINE_COMMENT_MODE, + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + e.C_NUMBER_MODE, + ], + }; +}; +function Hg(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Vg() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Hg(e); + }) + .join(""); + return a; +} +var qg = function (e) { + var t, + n = { + keyword: + "abort acronym acronyms alias all and assign binary card diag display else eq file files for free ge gt if integer le loop lt maximizing minimizing model models ne negative no not option options or ord positive prod put putpage puttl repeat sameas semicont semiint smax smin solve sos1 sos2 sum system table then until using while xor yes", + literal: "eps inf na", + built_in: + "abs arccos arcsin arctan arctan2 Beta betaReg binomial ceil centropy cos cosh cvPower div div0 eDist entropy errorf execSeed exp fact floor frac gamma gammaReg log logBeta logGamma log10 log2 mapVal max min mod ncpCM ncpF ncpVUpow ncpVUsin normal pi poly power randBinomial randLinear randTriangle round rPower sigmoid sign signPower sin sinh slexp sllog10 slrec sqexp sqlog10 sqr sqrec sqrt tan tanh trunc uniform uniformInt vcPower bool_and bool_eqv bool_imp bool_not bool_or bool_xor ifThen rel_eq rel_ge rel_gt rel_le rel_lt rel_ne gday gdow ghour gleap gmillisec gminute gmonth gsecond gyear jdate jnow jstart jtime errorLevel execError gamsRelease gamsVersion handleCollect handleDelete handleStatus handleSubmit heapFree heapLimit heapSize jobHandle jobKill jobStatus jobTerminate licenseLevel licenseStatus maxExecError sleep timeClose timeComp timeElapsed timeExec timeStart", + }, + a = { + className: "symbol", + variants: [{ begin: /=[lgenxc]=/ }, { begin: /\$/ }], + }, + r = { + className: "comment", + variants: [ + { begin: "'", end: "'" }, + { begin: '"', end: '"' }, + ], + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + i = { + begin: "/", + end: "/", + keywords: n, + contains: [ + r, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + e.C_NUMBER_MODE, + ], + }, + o = /[a-z0-9&#*=?@\\><:,()$[\]_.{}!+%^-]+/, + s = { + begin: /[a-z][a-z0-9_]*(\([a-z0-9_, ]*\))?[ \t]+/, + excludeBegin: !0, + end: "$", + endsWithParent: !0, + contains: [ + r, + i, + { + className: "comment", + begin: Vg(o, ((t = Vg(/[ ]+/, o)), Vg("(", t, ")*"))), + relevance: 0, + }, + ], + }; + return { + name: "GAMS", + aliases: ["gms"], + case_insensitive: !0, + keywords: n, + contains: [ + e.COMMENT(/^\$ontext/, /^\$offtext/), + { + className: "meta", + begin: "^\\$[a-z0-9]+", + end: "$", + returnBegin: !0, + contains: [{ className: "meta-keyword", begin: "^\\$[a-z0-9]+" }], + }, + e.COMMENT("^\\*", "$"), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + beginKeywords: + "set sets parameter parameters variable variables scalar scalars equation equations", + end: ";", + contains: [ + e.COMMENT("^\\*", "$"), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + i, + s, + ], + }, + { + beginKeywords: "table", + end: ";", + returnBegin: !0, + contains: [ + { beginKeywords: "table", end: "$", contains: [s] }, + e.COMMENT("^\\*", "$"), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + e.C_NUMBER_MODE, + ], + }, + { + className: "function", + begin: /^[a-z][a-z0-9_,\-+' ()$]+\.{2}/, + returnBegin: !0, + contains: [ + { className: "title", begin: /^[a-z0-9_]+/ }, + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + }, + a, + ], + }, + e.C_NUMBER_MODE, + a, + ], + }; +}; +var zg = function (e) { + var t = { + keyword: + "bool break call callexe checkinterrupt clear clearg closeall cls comlog compile continue create debug declare delete disable dlibrary dllcall do dos ed edit else elseif enable end endfor endif endp endo errorlog errorlogat expr external fn for format goto gosub graph if keyword let lib library line load loadarray loadexe loadf loadk loadm loadp loads loadx local locate loopnextindex lprint lpwidth lshow matrix msym ndpclex new open output outwidth plot plotsym pop prcsn print printdos proc push retp return rndcon rndmod rndmult rndseed run save saveall screen scroll setarray show sparse stop string struct system trace trap threadfor threadendfor threadbegin threadjoin threadstat threadend until use while winprint ne ge le gt lt and xor or not eq eqv", + built_in: + "abs acf aconcat aeye amax amean AmericanBinomCall AmericanBinomCall_Greeks AmericanBinomCall_ImpVol AmericanBinomPut AmericanBinomPut_Greeks AmericanBinomPut_ImpVol AmericanBSCall AmericanBSCall_Greeks AmericanBSCall_ImpVol AmericanBSPut AmericanBSPut_Greeks AmericanBSPut_ImpVol amin amult annotationGetDefaults annotationSetBkd annotationSetFont annotationSetLineColor annotationSetLineStyle annotationSetLineThickness annualTradingDays arccos arcsin areshape arrayalloc arrayindex arrayinit arraytomat asciiload asclabel astd astds asum atan atan2 atranspose axmargin balance band bandchol bandcholsol bandltsol bandrv bandsolpd bar base10 begwind besselj bessely beta box boxcox cdfBeta cdfBetaInv cdfBinomial cdfBinomialInv cdfBvn cdfBvn2 cdfBvn2e cdfCauchy cdfCauchyInv cdfChic cdfChii cdfChinc cdfChincInv cdfExp cdfExpInv cdfFc cdfFnc cdfFncInv cdfGam cdfGenPareto cdfHyperGeo cdfLaplace cdfLaplaceInv cdfLogistic cdfLogisticInv cdfmControlCreate cdfMvn cdfMvn2e cdfMvnce cdfMvne cdfMvt2e cdfMvtce cdfMvte cdfN cdfN2 cdfNc cdfNegBinomial cdfNegBinomialInv cdfNi cdfPoisson cdfPoissonInv cdfRayleigh cdfRayleighInv cdfTc cdfTci cdfTnc cdfTvn cdfWeibull cdfWeibullInv cdir ceil ChangeDir chdir chiBarSquare chol choldn cholsol cholup chrs close code cols colsf combinate combinated complex con cond conj cons ConScore contour conv convertsatostr convertstrtosa corrm corrms corrvc corrx corrxs cos cosh counts countwts crossprd crout croutp csrcol csrlin csvReadM csvReadSA cumprodc cumsumc curve cvtos datacreate datacreatecomplex datalist dataload dataloop dataopen datasave date datestr datestring datestrymd dayinyr dayofweek dbAddDatabase dbClose dbCommit dbCreateQuery dbExecQuery dbGetConnectOptions dbGetDatabaseName dbGetDriverName dbGetDrivers dbGetHostName dbGetLastErrorNum dbGetLastErrorText dbGetNumericalPrecPolicy dbGetPassword dbGetPort dbGetTableHeaders dbGetTables dbGetUserName dbHasFeature dbIsDriverAvailable dbIsOpen dbIsOpenError dbOpen dbQueryBindValue dbQueryClear dbQueryCols dbQueryExecPrepared dbQueryFetchAllM dbQueryFetchAllSA dbQueryFetchOneM dbQueryFetchOneSA dbQueryFinish dbQueryGetBoundValue dbQueryGetBoundValues dbQueryGetField dbQueryGetLastErrorNum dbQueryGetLastErrorText dbQueryGetLastInsertID dbQueryGetLastQuery dbQueryGetPosition dbQueryIsActive dbQueryIsForwardOnly dbQueryIsNull dbQueryIsSelect dbQueryIsValid dbQueryPrepare dbQueryRows dbQuerySeek dbQuerySeekFirst dbQuerySeekLast dbQuerySeekNext dbQuerySeekPrevious dbQuerySetForwardOnly dbRemoveDatabase dbRollback dbSetConnectOptions dbSetDatabaseName dbSetHostName dbSetNumericalPrecPolicy dbSetPort dbSetUserName dbTransaction DeleteFile delif delrows denseToSp denseToSpRE denToZero design det detl dfft dffti diag diagrv digamma doswin DOSWinCloseall DOSWinOpen dotfeq dotfeqmt dotfge dotfgemt dotfgt dotfgtmt dotfle dotflemt dotflt dotfltmt dotfne dotfnemt draw drop dsCreate dstat dstatmt dstatmtControlCreate dtdate dtday dttime dttodtv dttostr dttoutc dtvnormal dtvtodt dtvtoutc dummy dummybr dummydn eig eigh eighv eigv elapsedTradingDays endwind envget eof eqSolve eqSolvemt eqSolvemtControlCreate eqSolvemtOutCreate eqSolveset erf erfc erfccplx erfcplx error etdays ethsec etstr EuropeanBinomCall EuropeanBinomCall_Greeks EuropeanBinomCall_ImpVol EuropeanBinomPut EuropeanBinomPut_Greeks EuropeanBinomPut_ImpVol EuropeanBSCall EuropeanBSCall_Greeks EuropeanBSCall_ImpVol EuropeanBSPut EuropeanBSPut_Greeks EuropeanBSPut_ImpVol exctsmpl exec execbg exp extern eye fcheckerr fclearerr feq feqmt fflush fft ffti fftm fftmi fftn fge fgemt fgets fgetsa fgetsat fgetst fgt fgtmt fileinfo filesa fle flemt floor flt fltmt fmod fne fnemt fonts fopen formatcv formatnv fputs fputst fseek fstrerror ftell ftocv ftos ftostrC gamma gammacplx gammaii gausset gdaAppend gdaCreate gdaDStat gdaDStatMat gdaGetIndex gdaGetName gdaGetNames gdaGetOrders gdaGetType gdaGetTypes gdaGetVarInfo gdaIsCplx gdaLoad gdaPack gdaRead gdaReadByIndex gdaReadSome gdaReadSparse gdaReadStruct gdaReportVarInfo gdaSave gdaUpdate gdaUpdateAndPack gdaVars gdaWrite gdaWrite32 gdaWriteSome getarray getdims getf getGAUSShome getmatrix getmatrix4D getname getnamef getNextTradingDay getNextWeekDay getnr getorders getpath getPreviousTradingDay getPreviousWeekDay getRow getscalar3D getscalar4D getTrRow getwind glm gradcplx gradMT gradMTm gradMTT gradMTTm gradp graphprt graphset hasimag header headermt hess hessMT hessMTg hessMTgw hessMTm hessMTmw hessMTT hessMTTg hessMTTgw hessMTTm hessMTw hessp hist histf histp hsec imag indcv indexcat 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nextnevn nextwind ntos null null1 numCombinations ols olsmt olsmtControlCreate olsqr olsqr2 olsqrmt ones optn optnevn orth outtyp pacf packedToSp packr parse pause pdfCauchy pdfChi pdfExp pdfGenPareto pdfHyperGeo pdfLaplace pdfLogistic pdfn pdfPoisson pdfRayleigh pdfWeibull pi pinv pinvmt plotAddArrow plotAddBar plotAddBox plotAddHist plotAddHistF plotAddHistP plotAddPolar plotAddScatter plotAddShape plotAddTextbox plotAddTS plotAddXY plotArea plotBar plotBox plotClearLayout plotContour plotCustomLayout plotGetDefaults plotHist plotHistF plotHistP plotLayout plotLogLog plotLogX plotLogY plotOpenWindow plotPolar plotSave plotScatter plotSetAxesPen plotSetBar plotSetBarFill plotSetBarStacked plotSetBkdColor plotSetFill plotSetGrid plotSetLegend plotSetLineColor plotSetLineStyle plotSetLineSymbol plotSetLineThickness plotSetNewWindow plotSetTitle plotSetWhichYAxis plotSetXAxisShow plotSetXLabel plotSetXRange plotSetXTicInterval plotSetXTicLabel plotSetYAxisShow plotSetYLabel plotSetYRange 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submat subscat substute subvec sumc sumr surface svd svd1 svd2 svdcusv svds svdusv sysstate tab tan tanh tempname time timedt timestr timeutc title tkf2eps tkf2ps tocart todaydt toeplitz token topolar trapchk trigamma trimr trunc type typecv typef union unionsa uniqindx uniqindxsa unique uniquesa upmat upmat1 upper utctodt utctodtv utrisol vals varCovMS varCovXS varget vargetl varmall varmares varput varputl vartypef vcm vcms vcx vcxs vec vech vecr vector vget view viewxyz vlist vnamecv volume vput vread vtypecv wait waitc walkindex where window writer xlabel xlsGetSheetCount xlsGetSheetSize xlsGetSheetTypes xlsMakeRange xlsReadM xlsReadSA xlsWrite xlsWriteM xlsWriteSA xpnd xtics xy xyz ylabel ytics zeros zeta zlabel ztics cdfEmpirical dot h5create h5open h5read h5readAttribute h5write h5writeAttribute ldl plotAddErrorBar plotAddSurface plotCDFEmpirical plotSetColormap plotSetContourLabels plotSetLegendFont plotSetTextInterpreter plotSetXTicCount plotSetYTicCount plotSetZLevels powerm strjoin sylvester strtrim", + literal: + "DB_AFTER_LAST_ROW DB_ALL_TABLES DB_BATCH_OPERATIONS DB_BEFORE_FIRST_ROW DB_BLOB DB_EVENT_NOTIFICATIONS DB_FINISH_QUERY DB_HIGH_PRECISION DB_LAST_INSERT_ID DB_LOW_PRECISION_DOUBLE DB_LOW_PRECISION_INT32 DB_LOW_PRECISION_INT64 DB_LOW_PRECISION_NUMBERS DB_MULTIPLE_RESULT_SETS DB_NAMED_PLACEHOLDERS DB_POSITIONAL_PLACEHOLDERS DB_PREPARED_QUERIES DB_QUERY_SIZE DB_SIMPLE_LOCKING DB_SYSTEM_TABLES DB_TABLES DB_TRANSACTIONS DB_UNICODE DB_VIEWS __STDIN __STDOUT __STDERR __FILE_DIR", + }, + n = e.COMMENT("@", "@"), + a = { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": + "define definecs|10 undef ifdef ifndef iflight ifdllcall ifmac ifos2win ifunix else endif lineson linesoff srcfile srcline", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + { + beginKeywords: "include", + end: "$", + keywords: { "meta-keyword": "include" }, + contains: [ + { className: "meta-string", begin: '"', end: '"', illegal: "\\n" }, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + ], + }, + r = { + begin: /\bstruct\s+/, + end: /\s/, + keywords: "struct", + contains: [ + { className: "type", begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + ], + }, + i = [ + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + endsWithParent: !0, + relevance: 0, + contains: [ + { className: "literal", begin: /\.\.\./ }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + r, + ], + }, + ], + o = { className: "title", begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + s = function (t, a, r) { + var s = e.inherit( + { + className: "function", + beginKeywords: t, + end: a, + excludeEnd: !0, + contains: [].concat(i), + }, + r || {}, + ); + return ( + s.contains.push(o), + s.contains.push(e.C_NUMBER_MODE), + s.contains.push(e.C_BLOCK_COMMENT_MODE), + s.contains.push(n), + s + ); + }, + l = { + className: "built_in", + begin: "\\b(" + t.built_in.split(" ").join("|") + ")\\b", + }, + c = { + className: "string", + begin: '"', + end: '"', + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + _ = { + begin: e.UNDERSCORE_IDENT_RE + "\\s*\\(", + returnBegin: !0, + keywords: t, + relevance: 0, + contains: [ + { beginKeywords: t.keyword }, + l, + { className: "built_in", begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + ], + }, + d = { + begin: /\(/, + end: /\)/, + relevance: 0, + keywords: { built_in: t.built_in, literal: t.literal }, + contains: [e.C_NUMBER_MODE, e.C_BLOCK_COMMENT_MODE, n, l, _, c, "self"], + }; + return ( + _.contains.push(d), + { + name: "GAUSS", + aliases: ["gss"], + case_insensitive: !0, + keywords: t, + illegal: /(\{[%#]|[%#]\}| <- )/, + contains: [ + e.C_NUMBER_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + c, + a, + { + className: "keyword", + begin: + /\bexternal (matrix|string|array|sparse matrix|struct|proc|keyword|fn)/, + }, + s("proc keyword", ";"), + s("fn", "="), + { + beginKeywords: "for threadfor", + end: /;/, + relevance: 0, + contains: [e.C_BLOCK_COMMENT_MODE, n, d], + }, + { + variants: [ + { begin: e.UNDERSCORE_IDENT_RE + "\\." + e.UNDERSCORE_IDENT_RE }, + { begin: e.UNDERSCORE_IDENT_RE + "\\s*=" }, + ], + relevance: 0, + }, + _, + r, + ], + } + ); +}; +var Wg = function (e) { + var t = { + $pattern: "[A-Z_][A-Z0-9_.]*", + keyword: + "IF DO WHILE ENDWHILE CALL ENDIF SUB ENDSUB GOTO REPEAT ENDREPEAT EQ LT GT NE GE LE OR XOR", + }, + n = e.inherit(e.C_NUMBER_MODE, { + begin: "([-+]?((\\.\\d+)|(\\d+)(\\.\\d*)?))|" + e.C_NUMBER_RE, + }), + a = [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT(/\(/, /\)/), + n, + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { className: "name", begin: "([G])([0-9]+\\.?[0-9]?)" }, + { className: "name", begin: "([M])([0-9]+\\.?[0-9]?)" }, + { className: "attr", begin: "(VC|VS|#)", end: "(\\d+)" }, + { className: "attr", begin: "(VZOFX|VZOFY|VZOFZ)" }, + { + className: "built_in", + begin: "(ATAN|ABS|ACOS|ASIN|SIN|COS|EXP|FIX|FUP|ROUND|LN|TAN)(\\[)", + contains: [n], + end: "\\]", + }, + { + className: "symbol", + variants: [{ begin: "N", end: "\\d+", illegal: "\\W" }], + }, + ]; + return { + name: "G-code (ISO 6983)", + aliases: ["nc"], + case_insensitive: !0, + keywords: t, + contains: [ + { className: "meta", begin: "%" }, + { className: "meta", begin: "([O])([0-9]+)" }, + ].concat(a), + }; +}; +var $g = function (e) { + return { + name: "Gherkin", + aliases: ["feature"], + keywords: + "Feature Background Ability Business Need Scenario Scenarios Scenario Outline Scenario Template Examples Given And Then But When", + contains: [ + { className: "symbol", begin: "\\*", relevance: 0 }, + { className: "meta", begin: "@[^@\\s]+" }, + { + begin: "\\|", + end: "\\|\\w*$", + contains: [{ className: "string", begin: "[^|]+" }], + }, + { className: "variable", begin: "<", end: ">" }, + e.HASH_COMMENT_MODE, + { className: "string", begin: '"""', end: '"""' }, + e.QUOTE_STRING_MODE, + ], + }; +}; +var Qg = function (e) { + return { + name: "GLSL", + keywords: { + keyword: + "break continue discard do else for if return while switch case default attribute binding buffer ccw centroid centroid varying coherent column_major const cw depth_any depth_greater depth_less depth_unchanged early_fragment_tests equal_spacing flat fractional_even_spacing fractional_odd_spacing highp in index inout invariant invocations isolines layout line_strip lines lines_adjacency local_size_x local_size_y local_size_z location lowp max_vertices mediump noperspective offset origin_upper_left out packed patch pixel_center_integer point_mode points precise precision quads r11f_g11f_b10f r16 r16_snorm r16f r16i r16ui r32f r32i r32ui r8 r8_snorm r8i r8ui readonly restrict rg16 rg16_snorm rg16f rg16i rg16ui rg32f rg32i rg32ui rg8 rg8_snorm rg8i rg8ui rgb10_a2 rgb10_a2ui rgba16 rgba16_snorm rgba16f rgba16i rgba16ui rgba32f rgba32i rgba32ui rgba8 rgba8_snorm rgba8i rgba8ui row_major sample shared smooth std140 std430 stream triangle_strip triangles triangles_adjacency uniform varying vertices volatile writeonly", + type: "atomic_uint bool bvec2 bvec3 bvec4 dmat2 dmat2x2 dmat2x3 dmat2x4 dmat3 dmat3x2 dmat3x3 dmat3x4 dmat4 dmat4x2 dmat4x3 dmat4x4 double dvec2 dvec3 dvec4 float iimage1D iimage1DArray iimage2D iimage2DArray iimage2DMS iimage2DMSArray iimage2DRect iimage3D iimageBuffer iimageCube iimageCubeArray image1D image1DArray image2D image2DArray image2DMS image2DMSArray image2DRect image3D imageBuffer imageCube imageCubeArray int isampler1D isampler1DArray isampler2D isampler2DArray isampler2DMS isampler2DMSArray isampler2DRect isampler3D isamplerBuffer isamplerCube isamplerCubeArray ivec2 ivec3 ivec4 mat2 mat2x2 mat2x3 mat2x4 mat3 mat3x2 mat3x3 mat3x4 mat4 mat4x2 mat4x3 mat4x4 sampler1D sampler1DArray sampler1DArrayShadow sampler1DShadow sampler2D sampler2DArray sampler2DArrayShadow sampler2DMS sampler2DMSArray sampler2DRect sampler2DRectShadow sampler2DShadow sampler3D samplerBuffer samplerCube samplerCubeArray samplerCubeArrayShadow samplerCubeShadow image1D uimage1DArray uimage2D uimage2DArray uimage2DMS uimage2DMSArray uimage2DRect uimage3D uimageBuffer uimageCube uimageCubeArray uint usampler1D usampler1DArray usampler2D usampler2DArray usampler2DMS usampler2DMSArray usampler2DRect usampler3D samplerBuffer usamplerCube usamplerCubeArray uvec2 uvec3 uvec4 vec2 vec3 vec4 void", + built_in: + "gl_MaxAtomicCounterBindings gl_MaxAtomicCounterBufferSize gl_MaxClipDistances gl_MaxClipPlanes gl_MaxCombinedAtomicCounterBuffers gl_MaxCombinedAtomicCounters gl_MaxCombinedImageUniforms gl_MaxCombinedImageUnitsAndFragmentOutputs gl_MaxCombinedTextureImageUnits gl_MaxComputeAtomicCounterBuffers gl_MaxComputeAtomicCounters gl_MaxComputeImageUniforms gl_MaxComputeTextureImageUnits gl_MaxComputeUniformComponents gl_MaxComputeWorkGroupCount gl_MaxComputeWorkGroupSize gl_MaxDrawBuffers gl_MaxFragmentAtomicCounterBuffers gl_MaxFragmentAtomicCounters gl_MaxFragmentImageUniforms gl_MaxFragmentInputComponents gl_MaxFragmentInputVectors gl_MaxFragmentUniformComponents gl_MaxFragmentUniformVectors gl_MaxGeometryAtomicCounterBuffers gl_MaxGeometryAtomicCounters gl_MaxGeometryImageUniforms gl_MaxGeometryInputComponents gl_MaxGeometryOutputComponents gl_MaxGeometryOutputVertices gl_MaxGeometryTextureImageUnits gl_MaxGeometryTotalOutputComponents gl_MaxGeometryUniformComponents gl_MaxGeometryVaryingComponents gl_MaxImageSamples gl_MaxImageUnits gl_MaxLights gl_MaxPatchVertices gl_MaxProgramTexelOffset gl_MaxTessControlAtomicCounterBuffers gl_MaxTessControlAtomicCounters gl_MaxTessControlImageUniforms gl_MaxTessControlInputComponents gl_MaxTessControlOutputComponents gl_MaxTessControlTextureImageUnits gl_MaxTessControlTotalOutputComponents gl_MaxTessControlUniformComponents gl_MaxTessEvaluationAtomicCounterBuffers gl_MaxTessEvaluationAtomicCounters gl_MaxTessEvaluationImageUniforms gl_MaxTessEvaluationInputComponents gl_MaxTessEvaluationOutputComponents gl_MaxTessEvaluationTextureImageUnits gl_MaxTessEvaluationUniformComponents gl_MaxTessGenLevel gl_MaxTessPatchComponents gl_MaxTextureCoords gl_MaxTextureImageUnits gl_MaxTextureUnits gl_MaxVaryingComponents gl_MaxVaryingFloats gl_MaxVaryingVectors gl_MaxVertexAtomicCounterBuffers gl_MaxVertexAtomicCounters gl_MaxVertexAttribs gl_MaxVertexImageUniforms gl_MaxVertexOutputComponents gl_MaxVertexOutputVectors gl_MaxVertexTextureImageUnits gl_MaxVertexUniformComponents gl_MaxVertexUniformVectors gl_MaxViewports gl_MinProgramTexelOffset gl_BackColor gl_BackLightModelProduct gl_BackLightProduct gl_BackMaterial gl_BackSecondaryColor gl_ClipDistance gl_ClipPlane gl_ClipVertex gl_Color gl_DepthRange gl_EyePlaneQ gl_EyePlaneR gl_EyePlaneS gl_EyePlaneT gl_Fog gl_FogCoord gl_FogFragCoord gl_FragColor gl_FragCoord gl_FragData gl_FragDepth gl_FrontColor gl_FrontFacing gl_FrontLightModelProduct gl_FrontLightProduct gl_FrontMaterial gl_FrontSecondaryColor gl_GlobalInvocationID gl_InstanceID gl_InvocationID gl_Layer gl_LightModel gl_LightSource gl_LocalInvocationID gl_LocalInvocationIndex gl_ModelViewMatrix gl_ModelViewMatrixInverse gl_ModelViewMatrixInverseTranspose gl_ModelViewMatrixTranspose gl_ModelViewProjectionMatrix gl_ModelViewProjectionMatrixInverse gl_ModelViewProjectionMatrixInverseTranspose gl_ModelViewProjectionMatrixTranspose gl_MultiTexCoord0 gl_MultiTexCoord1 gl_MultiTexCoord2 gl_MultiTexCoord3 gl_MultiTexCoord4 gl_MultiTexCoord5 gl_MultiTexCoord6 gl_MultiTexCoord7 gl_Normal gl_NormalMatrix gl_NormalScale gl_NumSamples gl_NumWorkGroups gl_ObjectPlaneQ gl_ObjectPlaneR gl_ObjectPlaneS gl_ObjectPlaneT gl_PatchVerticesIn gl_Point gl_PointCoord gl_PointSize gl_Position gl_PrimitiveID gl_PrimitiveIDIn gl_ProjectionMatrix gl_ProjectionMatrixInverse gl_ProjectionMatrixInverseTranspose gl_ProjectionMatrixTranspose gl_SampleID gl_SampleMask gl_SampleMaskIn gl_SamplePosition gl_SecondaryColor gl_TessCoord gl_TessLevelInner gl_TessLevelOuter gl_TexCoord gl_TextureEnvColor gl_TextureMatrix gl_TextureMatrixInverse gl_TextureMatrixInverseTranspose gl_TextureMatrixTranspose gl_Vertex gl_VertexID gl_ViewportIndex gl_WorkGroupID gl_WorkGroupSize gl_in gl_out EmitStreamVertex EmitVertex EndPrimitive EndStreamPrimitive abs acos acosh all any asin asinh atan atanh atomicAdd atomicAnd atomicCompSwap atomicCounter atomicCounterDecrement atomicCounterIncrement atomicExchange atomicMax atomicMin atomicOr atomicXor barrier bitCount bitfieldExtract bitfieldInsert bitfieldReverse ceil clamp cos cosh cross dFdx dFdy degrees determinant distance dot equal exp exp2 faceforward findLSB findMSB floatBitsToInt floatBitsToUint floor fma fract frexp ftransform fwidth greaterThan greaterThanEqual groupMemoryBarrier imageAtomicAdd imageAtomicAnd imageAtomicCompSwap imageAtomicExchange imageAtomicMax imageAtomicMin imageAtomicOr imageAtomicXor imageLoad imageSize imageStore imulExtended intBitsToFloat interpolateAtCentroid interpolateAtOffset interpolateAtSample inverse inversesqrt isinf isnan ldexp length lessThan lessThanEqual log log2 matrixCompMult max memoryBarrier memoryBarrierAtomicCounter memoryBarrierBuffer memoryBarrierImage memoryBarrierShared min mix mod modf noise1 noise2 noise3 noise4 normalize not notEqual outerProduct packDouble2x32 packHalf2x16 packSnorm2x16 packSnorm4x8 packUnorm2x16 packUnorm4x8 pow radians reflect refract round roundEven shadow1D shadow1DLod shadow1DProj shadow1DProjLod shadow2D shadow2DLod shadow2DProj shadow2DProjLod sign sin sinh smoothstep sqrt step tan tanh texelFetch texelFetchOffset texture texture1D texture1DLod texture1DProj texture1DProjLod texture2D texture2DLod texture2DProj texture2DProjLod texture3D texture3DLod texture3DProj texture3DProjLod textureCube textureCubeLod textureGather textureGatherOffset textureGatherOffsets textureGrad textureGradOffset textureLod textureLodOffset textureOffset textureProj textureProjGrad 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ds_grid_multiply_region ds_grid_set_disk ds_grid_add_disk ds_grid_multiply_disk ds_grid_set_grid_region ds_grid_add_grid_region ds_grid_multiply_grid_region ds_grid_get ds_grid_get_sum ds_grid_get_max ds_grid_get_min ds_grid_get_mean ds_grid_get_disk_sum ds_grid_get_disk_min ds_grid_get_disk_max ds_grid_get_disk_mean ds_grid_value_exists ds_grid_value_x ds_grid_value_y ds_grid_value_disk_exists ds_grid_value_disk_x ds_grid_value_disk_y ds_grid_shuffle ds_grid_write ds_grid_read ds_grid_sort ds_grid_set ds_grid_get effect_create_below effect_create_above effect_clear part_type_create part_type_destroy part_type_exists part_type_clear part_type_shape part_type_sprite part_type_size part_type_scale part_type_orientation part_type_life part_type_step part_type_death part_type_speed part_type_direction part_type_gravity part_type_colour1 part_type_colour2 part_type_colour3 part_type_colour_mix part_type_colour_rgb part_type_colour_hsv part_type_color1 part_type_color2 part_type_color3 part_type_color_mix part_type_color_rgb part_type_color_hsv part_type_alpha1 part_type_alpha2 part_type_alpha3 part_type_blend part_system_create part_system_create_layer part_system_destroy part_system_exists part_system_clear part_system_draw_order part_system_depth part_system_position part_system_automatic_update part_system_automatic_draw part_system_update part_system_drawit part_system_get_layer part_system_layer part_particles_create part_particles_create_colour part_particles_create_color part_particles_clear part_particles_count part_emitter_create part_emitter_destroy part_emitter_destroy_all part_emitter_exists part_emitter_clear part_emitter_region part_emitter_burst part_emitter_stream external_call external_define external_free window_handle window_device matrix_get matrix_set matrix_build_identity matrix_build matrix_build_lookat matrix_build_projection_ortho matrix_build_projection_perspective matrix_build_projection_perspective_fov matrix_multiply matrix_transform_vertex matrix_stack_push matrix_stack_pop matrix_stack_multiply matrix_stack_set matrix_stack_clear matrix_stack_top matrix_stack_is_empty browser_input_capture os_get_config os_get_info os_get_language os_get_region os_lock_orientation display_get_dpi_x display_get_dpi_y display_set_gui_size display_set_gui_maximise display_set_gui_maximize device_mouse_dbclick_enable display_set_timing_method display_get_timing_method display_set_sleep_margin display_get_sleep_margin virtual_key_add virtual_key_hide virtual_key_delete virtual_key_show draw_enable_drawevent draw_enable_swf_aa draw_set_swf_aa_level draw_get_swf_aa_level draw_texture_flush draw_flush gpu_set_blendenable gpu_set_ztestenable gpu_set_zfunc gpu_set_zwriteenable gpu_set_lightingenable gpu_set_fog gpu_set_cullmode gpu_set_blendmode gpu_set_blendmode_ext gpu_set_blendmode_ext_sepalpha gpu_set_colorwriteenable gpu_set_colourwriteenable gpu_set_alphatestenable gpu_set_alphatestref gpu_set_alphatestfunc gpu_set_texfilter gpu_set_texfilter_ext gpu_set_texrepeat gpu_set_texrepeat_ext gpu_set_tex_filter gpu_set_tex_filter_ext gpu_set_tex_repeat gpu_set_tex_repeat_ext gpu_set_tex_mip_filter gpu_set_tex_mip_filter_ext gpu_set_tex_mip_bias gpu_set_tex_mip_bias_ext gpu_set_tex_min_mip gpu_set_tex_min_mip_ext gpu_set_tex_max_mip gpu_set_tex_max_mip_ext gpu_set_tex_max_aniso gpu_set_tex_max_aniso_ext gpu_set_tex_mip_enable gpu_set_tex_mip_enable_ext gpu_get_blendenable gpu_get_ztestenable gpu_get_zfunc gpu_get_zwriteenable gpu_get_lightingenable gpu_get_fog gpu_get_cullmode gpu_get_blendmode gpu_get_blendmode_ext gpu_get_blendmode_ext_sepalpha gpu_get_blendmode_src gpu_get_blendmode_dest gpu_get_blendmode_srcalpha gpu_get_blendmode_destalpha gpu_get_colorwriteenable gpu_get_colourwriteenable gpu_get_alphatestenable gpu_get_alphatestref gpu_get_alphatestfunc gpu_get_texfilter gpu_get_texfilter_ext gpu_get_texrepeat gpu_get_texrepeat_ext gpu_get_tex_filter gpu_get_tex_filter_ext gpu_get_tex_repeat gpu_get_tex_repeat_ext gpu_get_tex_mip_filter gpu_get_tex_mip_filter_ext gpu_get_tex_mip_bias gpu_get_tex_mip_bias_ext gpu_get_tex_min_mip gpu_get_tex_min_mip_ext gpu_get_tex_max_mip gpu_get_tex_max_mip_ext gpu_get_tex_max_aniso gpu_get_tex_max_aniso_ext gpu_get_tex_mip_enable gpu_get_tex_mip_enable_ext gpu_push_state gpu_pop_state gpu_get_state gpu_set_state draw_light_define_ambient draw_light_define_direction draw_light_define_point draw_light_enable draw_set_lighting draw_light_get_ambient draw_light_get draw_get_lighting shop_leave_rating url_get_domain url_open url_open_ext url_open_full get_timer achievement_login achievement_logout achievement_post achievement_increment achievement_post_score achievement_available achievement_show_achievements achievement_show_leaderboards achievement_load_friends achievement_load_leaderboard achievement_send_challenge achievement_load_progress achievement_reset achievement_login_status achievement_get_pic achievement_show_challenge_notifications achievement_get_challenges achievement_event achievement_show achievement_get_info cloud_file_save cloud_string_save cloud_synchronise ads_enable ads_disable ads_setup ads_engagement_launch ads_engagement_available ads_engagement_active ads_event ads_event_preload ads_set_reward_callback ads_get_display_height ads_get_display_width ads_move ads_interstitial_available ads_interstitial_display device_get_tilt_x device_get_tilt_y device_get_tilt_z device_is_keypad_open device_mouse_check_button device_mouse_check_button_pressed device_mouse_check_button_released device_mouse_x device_mouse_y device_mouse_raw_x device_mouse_raw_y device_mouse_x_to_gui device_mouse_y_to_gui iap_activate iap_status iap_enumerate_products iap_restore_all iap_acquire iap_consume iap_product_details iap_purchase_details facebook_init facebook_login facebook_status facebook_graph_request facebook_dialog facebook_logout facebook_launch_offerwall facebook_post_message facebook_send_invite facebook_user_id facebook_accesstoken facebook_check_permission facebook_request_read_permissions facebook_request_publish_permissions gamepad_is_supported gamepad_get_device_count gamepad_is_connected gamepad_get_description gamepad_get_button_threshold gamepad_set_button_threshold gamepad_get_axis_deadzone gamepad_set_axis_deadzone gamepad_button_count gamepad_button_check gamepad_button_check_pressed gamepad_button_check_released gamepad_button_value gamepad_axis_count gamepad_axis_value gamepad_set_vibration gamepad_set_colour gamepad_set_color os_is_paused window_has_focus code_is_compiled http_get http_get_file http_post_string http_request json_encode json_decode zip_unzip load_csv base64_encode base64_decode md5_string_unicode md5_string_utf8 md5_file os_is_network_connected sha1_string_unicode sha1_string_utf8 sha1_file os_powersave_enable analytics_event analytics_event_ext win8_livetile_tile_notification win8_livetile_tile_clear win8_livetile_badge_notification win8_livetile_badge_clear win8_livetile_queue_enable win8_secondarytile_pin win8_secondarytile_badge_notification win8_secondarytile_delete win8_livetile_notification_begin win8_livetile_notification_secondary_begin win8_livetile_notification_expiry win8_livetile_notification_tag win8_livetile_notification_text_add win8_livetile_notification_image_add win8_livetile_notification_end win8_appbar_enable win8_appbar_add_element win8_appbar_remove_element win8_settingscharm_add_entry win8_settingscharm_add_html_entry win8_settingscharm_add_xaml_entry win8_settingscharm_set_xaml_property win8_settingscharm_get_xaml_property win8_settingscharm_remove_entry win8_share_image win8_share_screenshot win8_share_file win8_share_url win8_share_text win8_search_enable win8_search_disable win8_search_add_suggestions win8_device_touchscreen_available win8_license_initialize_sandbox win8_license_trial_version winphone_license_trial_version winphone_tile_title winphone_tile_count winphone_tile_back_title winphone_tile_back_content winphone_tile_back_content_wide winphone_tile_front_image winphone_tile_front_image_small winphone_tile_front_image_wide winphone_tile_back_image winphone_tile_back_image_wide winphone_tile_background_colour winphone_tile_background_color winphone_tile_icon_image winphone_tile_small_icon_image winphone_tile_wide_content winphone_tile_cycle_images winphone_tile_small_background_image physics_world_create physics_world_gravity physics_world_update_speed physics_world_update_iterations physics_world_draw_debug physics_pause_enable physics_fixture_create physics_fixture_set_kinematic physics_fixture_set_density physics_fixture_set_awake physics_fixture_set_restitution physics_fixture_set_friction physics_fixture_set_collision_group physics_fixture_set_sensor physics_fixture_set_linear_damping physics_fixture_set_angular_damping physics_fixture_set_circle_shape physics_fixture_set_box_shape physics_fixture_set_edge_shape physics_fixture_set_polygon_shape physics_fixture_set_chain_shape physics_fixture_add_point physics_fixture_bind physics_fixture_bind_ext physics_fixture_delete physics_apply_force physics_apply_impulse physics_apply_angular_impulse physics_apply_local_force physics_apply_local_impulse physics_apply_torque physics_mass_properties physics_draw_debug physics_test_overlap physics_remove_fixture physics_set_friction physics_set_density physics_set_restitution physics_get_friction physics_get_density physics_get_restitution physics_joint_distance_create physics_joint_rope_create physics_joint_revolute_create physics_joint_prismatic_create physics_joint_pulley_create physics_joint_wheel_create physics_joint_weld_create physics_joint_friction_create physics_joint_gear_create physics_joint_enable_motor physics_joint_get_value physics_joint_set_value physics_joint_delete physics_particle_create physics_particle_delete physics_particle_delete_region_circle physics_particle_delete_region_box physics_particle_delete_region_poly physics_particle_set_flags physics_particle_set_category_flags physics_particle_draw physics_particle_draw_ext physics_particle_count physics_particle_get_data physics_particle_get_data_particle physics_particle_group_begin physics_particle_group_circle physics_particle_group_box physics_particle_group_polygon physics_particle_group_add_point physics_particle_group_end physics_particle_group_join physics_particle_group_delete physics_particle_group_count physics_particle_group_get_data physics_particle_group_get_mass physics_particle_group_get_inertia physics_particle_group_get_centre_x physics_particle_group_get_centre_y physics_particle_group_get_vel_x physics_particle_group_get_vel_y physics_particle_group_get_ang_vel physics_particle_group_get_x physics_particle_group_get_y physics_particle_group_get_angle physics_particle_set_group_flags physics_particle_get_group_flags physics_particle_get_max_count physics_particle_get_radius physics_particle_get_density physics_particle_get_damping physics_particle_get_gravity_scale physics_particle_set_max_count physics_particle_set_radius physics_particle_set_density physics_particle_set_damping physics_particle_set_gravity_scale network_create_socket network_create_socket_ext network_create_server network_create_server_raw network_connect network_connect_raw network_send_packet network_send_raw network_send_broadcast network_send_udp network_send_udp_raw network_set_timeout network_set_config network_resolve network_destroy buffer_create buffer_write buffer_read buffer_seek buffer_get_surface buffer_set_surface buffer_delete buffer_exists buffer_get_type buffer_get_alignment buffer_poke buffer_peek buffer_save buffer_save_ext buffer_load buffer_load_ext buffer_load_partial buffer_copy buffer_fill buffer_get_size buffer_tell buffer_resize buffer_md5 buffer_sha1 buffer_base64_encode buffer_base64_decode buffer_base64_decode_ext buffer_sizeof buffer_get_address buffer_create_from_vertex_buffer buffer_create_from_vertex_buffer_ext buffer_copy_from_vertex_buffer buffer_async_group_begin buffer_async_group_option buffer_async_group_end buffer_load_async buffer_save_async gml_release_mode gml_pragma steam_activate_overlay steam_is_overlay_enabled steam_is_overlay_activated steam_get_persona_name steam_initialised steam_is_cloud_enabled_for_app steam_is_cloud_enabled_for_account steam_file_persisted steam_get_quota_total steam_get_quota_free steam_file_write steam_file_write_file steam_file_read steam_file_delete steam_file_exists steam_file_size steam_file_share steam_is_screenshot_requested steam_send_screenshot steam_is_user_logged_on steam_get_user_steam_id steam_user_owns_dlc steam_user_installed_dlc steam_set_achievement steam_get_achievement steam_clear_achievement steam_set_stat_int steam_set_stat_float steam_set_stat_avg_rate steam_get_stat_int steam_get_stat_float steam_get_stat_avg_rate steam_reset_all_stats steam_reset_all_stats_achievements steam_stats_ready steam_create_leaderboard steam_upload_score steam_upload_score_ext steam_download_scores_around_user steam_download_scores steam_download_friends_scores steam_upload_score_buffer steam_upload_score_buffer_ext steam_current_game_language steam_available_languages steam_activate_overlay_browser steam_activate_overlay_user steam_activate_overlay_store steam_get_user_persona_name steam_get_app_id steam_get_user_account_id steam_ugc_download steam_ugc_create_item steam_ugc_start_item_update steam_ugc_set_item_title steam_ugc_set_item_description steam_ugc_set_item_visibility steam_ugc_set_item_tags steam_ugc_set_item_content steam_ugc_set_item_preview steam_ugc_submit_item_update steam_ugc_get_item_update_progress steam_ugc_subscribe_item steam_ugc_unsubscribe_item steam_ugc_num_subscribed_items steam_ugc_get_subscribed_items steam_ugc_get_item_install_info steam_ugc_get_item_update_info steam_ugc_request_item_details steam_ugc_create_query_user steam_ugc_create_query_user_ex steam_ugc_create_query_all steam_ugc_create_query_all_ex steam_ugc_query_set_cloud_filename_filter steam_ugc_query_set_match_any_tag steam_ugc_query_set_search_text steam_ugc_query_set_ranked_by_trend_days steam_ugc_query_add_required_tag steam_ugc_query_add_excluded_tag steam_ugc_query_set_return_long_description steam_ugc_query_set_return_total_only steam_ugc_query_set_allow_cached_response steam_ugc_send_query shader_set shader_get_name shader_reset shader_current shader_is_compiled shader_get_sampler_index shader_get_uniform shader_set_uniform_i shader_set_uniform_i_array shader_set_uniform_f shader_set_uniform_f_array shader_set_uniform_matrix shader_set_uniform_matrix_array shader_enable_corner_id texture_set_stage texture_get_texel_width texture_get_texel_height shaders_are_supported vertex_format_begin vertex_format_end vertex_format_delete vertex_format_add_position vertex_format_add_position_3d vertex_format_add_colour vertex_format_add_color vertex_format_add_normal vertex_format_add_texcoord vertex_format_add_textcoord vertex_format_add_custom vertex_create_buffer vertex_create_buffer_ext vertex_delete_buffer vertex_begin vertex_end vertex_position vertex_position_3d vertex_colour vertex_color vertex_argb vertex_texcoord vertex_normal vertex_float1 vertex_float2 vertex_float3 vertex_float4 vertex_ubyte4 vertex_submit vertex_freeze vertex_get_number vertex_get_buffer_size vertex_create_buffer_from_buffer vertex_create_buffer_from_buffer_ext push_local_notification push_get_first_local_notification push_get_next_local_notification push_cancel_local_notification skeleton_animation_set skeleton_animation_get skeleton_animation_mix skeleton_animation_set_ext skeleton_animation_get_ext skeleton_animation_get_duration skeleton_animation_get_frames skeleton_animation_clear skeleton_skin_set skeleton_skin_get skeleton_attachment_set skeleton_attachment_get skeleton_attachment_create skeleton_collision_draw_set skeleton_bone_data_get skeleton_bone_data_set skeleton_bone_state_get skeleton_bone_state_set skeleton_get_minmax skeleton_get_num_bounds skeleton_get_bounds skeleton_animation_get_frame skeleton_animation_set_frame draw_skeleton draw_skeleton_time draw_skeleton_instance draw_skeleton_collision skeleton_animation_list skeleton_skin_list skeleton_slot_data layer_get_id layer_get_id_at_depth layer_get_depth layer_create layer_destroy layer_destroy_instances layer_add_instance layer_has_instance layer_set_visible layer_get_visible layer_exists layer_x layer_y layer_get_x layer_get_y layer_hspeed layer_vspeed layer_get_hspeed layer_get_vspeed layer_script_begin layer_script_end layer_shader layer_get_script_begin layer_get_script_end layer_get_shader layer_set_target_room layer_get_target_room layer_reset_target_room layer_get_all layer_get_all_elements layer_get_name layer_depth layer_get_element_layer layer_get_element_type layer_element_move layer_force_draw_depth layer_is_draw_depth_forced layer_get_forced_depth layer_background_get_id layer_background_exists layer_background_create layer_background_destroy layer_background_visible layer_background_change layer_background_sprite layer_background_htiled layer_background_vtiled layer_background_stretch layer_background_yscale layer_background_xscale layer_background_blend layer_background_alpha layer_background_index layer_background_speed layer_background_get_visible layer_background_get_sprite layer_background_get_htiled layer_background_get_vtiled layer_background_get_stretch layer_background_get_yscale layer_background_get_xscale layer_background_get_blend layer_background_get_alpha layer_background_get_index layer_background_get_speed layer_sprite_get_id layer_sprite_exists layer_sprite_create layer_sprite_destroy layer_sprite_change layer_sprite_index layer_sprite_speed layer_sprite_xscale layer_sprite_yscale layer_sprite_angle layer_sprite_blend layer_sprite_alpha layer_sprite_x layer_sprite_y layer_sprite_get_sprite layer_sprite_get_index layer_sprite_get_speed layer_sprite_get_xscale layer_sprite_get_yscale layer_sprite_get_angle layer_sprite_get_blend layer_sprite_get_alpha layer_sprite_get_x layer_sprite_get_y layer_tilemap_get_id layer_tilemap_exists layer_tilemap_create layer_tilemap_destroy tilemap_tileset tilemap_x tilemap_y tilemap_set tilemap_set_at_pixel tilemap_get_tileset tilemap_get_tile_width tilemap_get_tile_height tilemap_get_width tilemap_get_height tilemap_get_x tilemap_get_y tilemap_get tilemap_get_at_pixel tilemap_get_cell_x_at_pixel tilemap_get_cell_y_at_pixel tilemap_clear draw_tilemap draw_tile tilemap_set_global_mask tilemap_get_global_mask tilemap_set_mask tilemap_get_mask tilemap_get_frame tile_set_empty tile_set_index tile_set_flip tile_set_mirror tile_set_rotate tile_get_empty tile_get_index tile_get_flip tile_get_mirror tile_get_rotate layer_tile_exists layer_tile_create layer_tile_destroy layer_tile_change layer_tile_xscale layer_tile_yscale layer_tile_blend layer_tile_alpha layer_tile_x layer_tile_y layer_tile_region layer_tile_visible layer_tile_get_sprite layer_tile_get_xscale layer_tile_get_yscale layer_tile_get_blend layer_tile_get_alpha layer_tile_get_x layer_tile_get_y layer_tile_get_region layer_tile_get_visible layer_instance_get_instance instance_activate_layer instance_deactivate_layer camera_create camera_create_view camera_destroy camera_apply camera_get_active camera_get_default camera_set_default camera_set_view_mat camera_set_proj_mat camera_set_update_script camera_set_begin_script camera_set_end_script camera_set_view_pos camera_set_view_size camera_set_view_speed camera_set_view_border camera_set_view_angle camera_set_view_target camera_get_view_mat camera_get_proj_mat camera_get_update_script camera_get_begin_script camera_get_end_script camera_get_view_x camera_get_view_y camera_get_view_width camera_get_view_height camera_get_view_speed_x camera_get_view_speed_y camera_get_view_border_x camera_get_view_border_y camera_get_view_angle camera_get_view_target view_get_camera view_get_visible view_get_xport view_get_yport view_get_wport view_get_hport view_get_surface_id view_set_camera view_set_visible view_set_xport view_set_yport view_set_wport view_set_hport view_set_surface_id gesture_drag_time gesture_drag_distance gesture_flick_speed gesture_double_tap_time gesture_double_tap_distance gesture_pinch_distance gesture_pinch_angle_towards gesture_pinch_angle_away gesture_rotate_time gesture_rotate_angle gesture_tap_count gesture_get_drag_time gesture_get_drag_distance gesture_get_flick_speed gesture_get_double_tap_time gesture_get_double_tap_distance gesture_get_pinch_distance gesture_get_pinch_angle_towards gesture_get_pinch_angle_away gesture_get_rotate_time gesture_get_rotate_angle gesture_get_tap_count keyboard_virtual_show keyboard_virtual_hide keyboard_virtual_status keyboard_virtual_height", + literal: + "self other all noone global local undefined pointer_invalid pointer_null path_action_stop path_action_restart path_action_continue path_action_reverse true false pi GM_build_date GM_version GM_runtime_version timezone_local timezone_utc gamespeed_fps gamespeed_microseconds ev_create ev_destroy ev_step ev_alarm ev_keyboard ev_mouse ev_collision ev_other ev_draw ev_draw_begin ev_draw_end ev_draw_pre ev_draw_post ev_keypress ev_keyrelease ev_trigger ev_left_button ev_right_button ev_middle_button ev_no_button ev_left_press ev_right_press ev_middle_press ev_left_release ev_right_release ev_middle_release ev_mouse_enter ev_mouse_leave ev_mouse_wheel_up ev_mouse_wheel_down ev_global_left_button ev_global_right_button ev_global_middle_button ev_global_left_press ev_global_right_press ev_global_middle_press ev_global_left_release ev_global_right_release ev_global_middle_release ev_joystick1_left ev_joystick1_right ev_joystick1_up ev_joystick1_down ev_joystick1_button1 ev_joystick1_button2 ev_joystick1_button3 ev_joystick1_button4 ev_joystick1_button5 ev_joystick1_button6 ev_joystick1_button7 ev_joystick1_button8 ev_joystick2_left ev_joystick2_right ev_joystick2_up ev_joystick2_down ev_joystick2_button1 ev_joystick2_button2 ev_joystick2_button3 ev_joystick2_button4 ev_joystick2_button5 ev_joystick2_button6 ev_joystick2_button7 ev_joystick2_button8 ev_outside ev_boundary ev_game_start ev_game_end ev_room_start ev_room_end ev_no_more_lives ev_animation_end ev_end_of_path ev_no_more_health ev_close_button ev_user0 ev_user1 ev_user2 ev_user3 ev_user4 ev_user5 ev_user6 ev_user7 ev_user8 ev_user9 ev_user10 ev_user11 ev_user12 ev_user13 ev_user14 ev_user15 ev_step_normal ev_step_begin ev_step_end ev_gui ev_gui_begin ev_gui_end ev_cleanup ev_gesture ev_gesture_tap ev_gesture_double_tap ev_gesture_drag_start ev_gesture_dragging ev_gesture_drag_end ev_gesture_flick ev_gesture_pinch_start ev_gesture_pinch_in ev_gesture_pinch_out ev_gesture_pinch_end ev_gesture_rotate_start ev_gesture_rotating ev_gesture_rotate_end ev_global_gesture_tap ev_global_gesture_double_tap ev_global_gesture_drag_start ev_global_gesture_dragging ev_global_gesture_drag_end ev_global_gesture_flick ev_global_gesture_pinch_start ev_global_gesture_pinch_in ev_global_gesture_pinch_out ev_global_gesture_pinch_end ev_global_gesture_rotate_start ev_global_gesture_rotating ev_global_gesture_rotate_end vk_nokey vk_anykey vk_enter vk_return vk_shift vk_control vk_alt vk_escape vk_space vk_backspace vk_tab vk_pause vk_printscreen vk_left vk_right vk_up vk_down vk_home vk_end vk_delete vk_insert vk_pageup vk_pagedown vk_f1 vk_f2 vk_f3 vk_f4 vk_f5 vk_f6 vk_f7 vk_f8 vk_f9 vk_f10 vk_f11 vk_f12 vk_numpad0 vk_numpad1 vk_numpad2 vk_numpad3 vk_numpad4 vk_numpad5 vk_numpad6 vk_numpad7 vk_numpad8 vk_numpad9 vk_divide vk_multiply vk_subtract vk_add vk_decimal vk_lshift vk_lcontrol vk_lalt vk_rshift vk_rcontrol vk_ralt mb_any mb_none mb_left mb_right mb_middle c_aqua c_black c_blue c_dkgray c_fuchsia c_gray c_green c_lime c_ltgray c_maroon c_navy c_olive c_purple c_red c_silver c_teal c_white c_yellow c_orange fa_left fa_center fa_right fa_top fa_middle fa_bottom pr_pointlist pr_linelist pr_linestrip pr_trianglelist pr_trianglestrip pr_trianglefan bm_complex bm_normal bm_add bm_max bm_subtract bm_zero bm_one bm_src_colour bm_inv_src_colour bm_src_color bm_inv_src_color bm_src_alpha bm_inv_src_alpha bm_dest_alpha bm_inv_dest_alpha bm_dest_colour bm_inv_dest_colour bm_dest_color bm_inv_dest_color bm_src_alpha_sat tf_point tf_linear tf_anisotropic mip_off mip_on mip_markedonly audio_falloff_none audio_falloff_inverse_distance audio_falloff_inverse_distance_clamped audio_falloff_linear_distance audio_falloff_linear_distance_clamped audio_falloff_exponent_distance audio_falloff_exponent_distance_clamped audio_old_system audio_new_system audio_mono audio_stereo audio_3d cr_default cr_none cr_arrow cr_cross cr_beam cr_size_nesw cr_size_ns cr_size_nwse cr_size_we cr_uparrow cr_hourglass cr_drag cr_appstart 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GB2312_CHARSET CHINESEBIG5_CHARSET JOHAB_CHARSET HEBREW_CHARSET ARABIC_CHARSET GREEK_CHARSET TURKISH_CHARSET VIETNAMESE_CHARSET THAI_CHARSET MAC_CHARSET BALTIC_CHARSET OEM_CHARSET gp_face1 gp_face2 gp_face3 gp_face4 gp_shoulderl gp_shoulderr gp_shoulderlb gp_shoulderrb gp_select gp_start gp_stickl gp_stickr gp_padu gp_padd gp_padl gp_padr gp_axislh gp_axislv gp_axisrh gp_axisrv ov_friends ov_community ov_players ov_settings ov_gamegroup ov_achievements lb_sort_none lb_sort_ascending lb_sort_descending lb_disp_none lb_disp_numeric lb_disp_time_sec lb_disp_time_ms ugc_result_success ugc_filetype_community ugc_filetype_microtrans ugc_visibility_public ugc_visibility_friends_only ugc_visibility_private ugc_query_RankedByVote ugc_query_RankedByPublicationDate ugc_query_AcceptedForGameRankedByAcceptanceDate ugc_query_RankedByTrend ugc_query_FavoritedByFriendsRankedByPublicationDate ugc_query_CreatedByFriendsRankedByPublicationDate ugc_query_RankedByNumTimesReported 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"argument_relative argument argument0 argument1 argument2 argument3 argument4 argument5 argument6 argument7 argument8 argument9 argument10 argument11 argument12 argument13 argument14 argument15 argument_count x|0 y|0 xprevious yprevious xstart ystart hspeed vspeed direction speed friction gravity gravity_direction path_index path_position path_positionprevious path_speed path_scale path_orientation path_endaction object_index id solid persistent mask_index instance_count instance_id room_speed fps fps_real current_time current_year current_month current_day current_weekday current_hour current_minute current_second alarm timeline_index timeline_position timeline_speed timeline_running timeline_loop room room_first room_last room_width room_height room_caption room_persistent score lives health show_score show_lives show_health caption_score caption_lives caption_health event_type event_number event_object event_action application_surface gamemaker_pro gamemaker_registered gamemaker_version error_occurred error_last debug_mode keyboard_key keyboard_lastkey keyboard_lastchar keyboard_string mouse_x mouse_y mouse_button mouse_lastbutton cursor_sprite visible sprite_index sprite_width sprite_height sprite_xoffset sprite_yoffset image_number image_index image_speed depth image_xscale image_yscale image_angle image_alpha image_blend bbox_left bbox_right bbox_top bbox_bottom layer background_colour background_showcolour background_color background_showcolor view_enabled view_current view_visible view_xview view_yview view_wview view_hview view_xport view_yport view_wport view_hport view_angle view_hborder view_vborder view_hspeed view_vspeed view_object view_surface_id view_camera game_id game_display_name game_project_name game_save_id working_directory temp_directory program_directory browser_width browser_height os_type os_device os_browser os_version display_aa async_load delta_time webgl_enabled event_data iap_data phy_rotation phy_position_x phy_position_y phy_angular_velocity phy_linear_velocity_x phy_linear_velocity_y phy_speed_x phy_speed_y phy_speed phy_angular_damping phy_linear_damping phy_bullet phy_fixed_rotation phy_active phy_mass phy_inertia phy_com_x phy_com_y phy_dynamic phy_kinematic phy_sleeping phy_collision_points phy_collision_x phy_collision_y phy_col_normal_x phy_col_normal_y phy_position_xprevious phy_position_yprevious", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + ], + }; +}; +var jg = function (e) { + var t = { + keyword: + "break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune", + literal: "true false iota nil", + built_in: + "append cap close complex copy imag len make new panic print println real recover delete", + }; + return { + name: "Go", + aliases: ["golang"], + keywords: t, + illegal: " 1 && void 0 !== arguments[1] ? arguments[1] : {}; + return (t.variants = e), t; +} +var nE = function (e) { + var t = "[A-Za-z0-9_$]+", + n = tE([ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT("/\\*\\*", "\\*/", { + relevance: 0, + contains: [ + { begin: /\w+@/, relevance: 0 }, + { className: "doctag", begin: "@[A-Za-z]+" }, + ], + }), + ]), + a = { + className: "regexp", + begin: /~?\/[^\/\n]+\//, + contains: [e.BACKSLASH_ESCAPE], + }, + r = tE([e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE]), + i = tE( + [ + { begin: /"""/, end: /"""/ }, + { begin: /'''/, end: /'''/ }, + { begin: "\\$/", end: "/\\$", relevance: 10 }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + { className: "string" }, + ); + return { + name: "Groovy", + keywords: { + built_in: "this super", + literal: "true false null", + keyword: + "byte short char int long boolean float double void def as in assert trait abstract static volatile transient public private protected synchronized final class interface enum if else for while switch case break default continue throw throws try catch finally implements extends new import package return instanceof", + }, + contains: [ + e.SHEBANG({ binary: "groovy", relevance: 10 }), + n, + i, + a, + r, + { + className: "class", + beginKeywords: "class interface trait enum", + end: /\{/, + illegal: ":", + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { className: "meta", begin: "@[A-Za-z]+", relevance: 0 }, + { className: "attr", begin: t + "[ \t]*:", relevance: 0 }, + { begin: /\?/, end: /:/, relevance: 0, contains: [n, i, a, r, "self"] }, + { + className: "symbol", + begin: "^[ \t]*" + eE(t + ":"), + excludeBegin: !0, + end: t + ":", + relevance: 0, + }, + ], + illegal: /#|<\//, + }; +}; +var aE = function (e) { + return { + name: "HAML", + case_insensitive: !0, + contains: [ + { + className: "meta", + begin: "^!!!( (5|1\\.1|Strict|Frameset|Basic|Mobile|RDFa|XML\\b.*))?$", + relevance: 10, + }, + e.COMMENT("^\\s*(!=#|=#|-#|/).*$", !1, { relevance: 0 }), + { + begin: "^\\s*(-|=|!=)(?!#)", + starts: { end: "\\n", subLanguage: "ruby" }, + }, + { + className: "tag", + begin: "^\\s*%", + contains: [ + { className: "selector-tag", begin: "\\w+" }, + { className: "selector-id", begin: "#[\\w-]+" }, + { className: "selector-class", begin: "\\.[\\w-]+" }, + { + begin: /\{\s*/, + end: /\s*\}/, + contains: [ + { + begin: ":\\w+\\s*=>", + end: ",\\s+", + returnBegin: !0, + endsWithParent: !0, + contains: [ + { className: "attr", begin: ":\\w+" }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: "\\w+", relevance: 0 }, + ], + }, + ], + }, + { + begin: "\\(\\s*", + end: "\\s*\\)", + excludeEnd: !0, + contains: [ + { + begin: "\\w+\\s*=", + end: "\\s+", + returnBegin: !0, + endsWithParent: !0, + contains: [ + { className: "attr", begin: "\\w+", relevance: 0 }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { begin: "\\w+", relevance: 0 }, + ], + }, + ], + }, + ], + }, + { begin: "^\\s*[=~]\\s*" }, + { begin: /#\{/, starts: { end: /\}/, subLanguage: "ruby" } }, + ], + }; +}; +function rE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function iE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return rE(e); + }) + .join(""); + return a; +} +var oE = function (e) { + var t = { + "builtin-name": [ + "action", + "bindattr", + "collection", + "component", + "concat", + "debugger", + "each", + "each-in", + "get", + "hash", + "if", + "in", + "input", + "link-to", + "loc", + "log", + "lookup", + "mut", + "outlet", + "partial", + "query-params", + "render", + "template", + "textarea", + "unbound", + "unless", + "view", + "with", + "yield", + ], + }, + n = /\[\]|\[[^\]]+\]/, + a = /[^\s!"#%&'()*+,.\/;<=>@\[\\\]^`{|}~]+/, + r = (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return ( + "(" + + t + .map(function (e) { + return rE(e); + }) + .join("|") + + ")" + ); + })(/""|"[^"]+"/, /''|'[^']+'/, n, a), + i = iE( + iE("(", /\.|\.\/|\//, ")?"), + r, + (function (e) { + return iE("(", e, ")*"); + })(iE(/(\.|\/)/, r)), + ), + o = iE("(", n, "|", a, ")(?==)"), + s = { begin: i, lexemes: /[\w.\/]+/ }, + l = e.inherit(s, { + keywords: { literal: ["true", "false", "undefined", "null"] }, + }), + c = { begin: /\(/, end: /\)/ }, + _ = { + className: "attr", + begin: o, + relevance: 0, + starts: { + begin: /=/, + end: /=/, + starts: { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + l, + c, + ], + }, + }, + }, + d = { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + begin: /as\s+\|/, + keywords: { keyword: "as" }, + end: /\|/, + contains: [{ begin: /\w+/ }], + }, + _, + l, + c, + ], + returnEnd: !0, + }, + u = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\)/ }), + }); + c.contains = [u]; + var m = e.inherit(s, { + keywords: t, + className: "name", + starts: e.inherit(d, { end: /\}\}/ }), + }), + p = e.inherit(s, { keywords: t, className: "name" }), + g = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\}\}/ }), + }); + return { + name: "Handlebars", + aliases: ["hbs", "html.hbs", "html.handlebars", "htmlbars"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + { begin: /\\\{\{/, skip: !0 }, + { begin: /\\\\(?=\{\{)/, skip: !0 }, + e.COMMENT(/\{\{!--/, /--\}\}/), + e.COMMENT(/\{\{!/, /\}\}/), + { + className: "template-tag", + begin: /\{\{\{\{(?!\/)/, + end: /\}\}\}\}/, + contains: [m], + starts: { end: /\{\{\{\{\//, returnEnd: !0, subLanguage: "xml" }, + }, + { + className: "template-tag", + begin: /\{\{\{\{\//, + end: /\}\}\}\}/, + contains: [p], + }, + { className: "template-tag", begin: /\{\{#/, end: /\}\}/, contains: [m] }, + { + className: "template-tag", + begin: /\{\{(?=else\}\})/, + end: /\}\}/, + keywords: "else", + }, + { + className: "template-tag", + begin: /\{\{(?=else if)/, + end: /\}\}/, + keywords: "else if", + }, + { + className: "template-tag", + begin: /\{\{\//, + end: /\}\}/, + contains: [p], + }, + { + className: "template-variable", + begin: /\{\{\{/, + end: /\}\}\}/, + contains: [g], + }, + { + className: "template-variable", + begin: /\{\{/, + end: /\}\}/, + contains: [g], + }, + ], + }; +}; +var sE = function (e) { + var t = { + variants: [ + e.COMMENT("--", "$"), + e.COMMENT(/\{-/, /-\}/, { contains: ["self"] }), + ], + }, + n = { className: "meta", begin: /\{-#/, end: /#-\}/ }, + a = { className: "meta", begin: "^#", end: "$" }, + r = { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + i = { + begin: "\\(", + end: "\\)", + illegal: '"', + contains: [ + n, + a, + { className: "type", begin: "\\b[A-Z][\\w]*(\\((\\.\\.|,|\\w+)\\))?" }, + e.inherit(e.TITLE_MODE, { begin: "[_a-z][\\w']*" }), + t, + ], + }; + return { + name: "Haskell", + aliases: ["hs"], + keywords: + "let in if then else case of where do module import hiding qualified type data newtype deriving class instance as default infix infixl infixr foreign export ccall stdcall cplusplus jvm dotnet safe unsafe family forall mdo proc rec", + contains: [ + { + beginKeywords: "module", + end: "where", + keywords: "module where", + contains: [i, t], + illegal: "\\W\\.|;", + }, + { + begin: "\\bimport\\b", + end: "$", + keywords: "import qualified as hiding", + contains: [i, t], + illegal: "\\W\\.|;", + }, + { + className: "class", + begin: "^(\\s*)?(class|instance)\\b", + end: "where", + keywords: "class family instance where", + contains: [r, i, t], + }, + { + className: "class", + begin: "\\b(data|(new)?type)\\b", + end: "$", + keywords: "data family type newtype deriving", + contains: [ + n, + r, + i, + { begin: /\{/, end: /\}/, contains: i.contains }, + t, + ], + }, + { beginKeywords: "default", end: "$", contains: [r, i, t] }, + { + beginKeywords: "infix infixl infixr", + end: "$", + contains: [e.C_NUMBER_MODE, t], + }, + { + begin: "\\bforeign\\b", + end: "$", + keywords: + "foreign import export ccall stdcall cplusplus jvm dotnet safe unsafe", + contains: [r, e.QUOTE_STRING_MODE, t], + }, + { className: "meta", begin: "#!\\/usr\\/bin\\/env runhaskell", end: "$" }, + n, + a, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + r, + e.inherit(e.TITLE_MODE, { begin: "^[_a-z][\\w']*" }), + t, + { begin: "->|<-" }, + ], + }; +}; +var lE = function (e) { + return { + name: "Haxe", + aliases: ["hx"], + keywords: { + keyword: + "break case cast catch continue default do dynamic else enum extern for function here if import in inline never new override package private get set public return static super switch this throw trace try typedef untyped using var while Int Float String Bool Dynamic Void Array ", + built_in: "trace this", + literal: "true false null _", + }, + contains: [ + { + className: "string", + begin: "'", + end: "'", + contains: [ + e.BACKSLASH_ESCAPE, + { className: "subst", begin: "\\$\\{", end: "\\}" }, + { className: "subst", begin: "\\$", end: /\W\}/ }, + ], + }, + e.QUOTE_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.C_NUMBER_MODE, + { className: "meta", begin: "@:", end: "$" }, + { + className: "meta", + begin: "#", + end: "$", + keywords: { "meta-keyword": "if else elseif end error" }, + }, + { + className: "type", + begin: ":[ \t]*", + end: "[^A-Za-z0-9_ \t\\->]", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + { + className: "type", + begin: ":[ \t]*", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "type", + begin: "new *", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "class", + beginKeywords: "enum", + end: "\\{", + contains: [e.TITLE_MODE], + }, + { + className: "class", + beginKeywords: "abstract", + end: "[\\{$]", + contains: [ + { + className: "type", + begin: "\\(", + end: "\\)", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "type", + begin: "from +", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + { + className: "type", + begin: "to +", + end: "\\W", + excludeBegin: !0, + excludeEnd: !0, + }, + e.TITLE_MODE, + ], + keywords: { keyword: "abstract from to" }, + }, + { + className: "class", + begin: "\\b(class|interface) +", + end: "[\\{$]", + excludeEnd: !0, + keywords: "class interface", + contains: [ + { + className: "keyword", + begin: "\\b(extends|implements) +", + keywords: "extends implements", + contains: [{ className: "type", begin: e.IDENT_RE, relevance: 0 }], + }, + e.TITLE_MODE, + ], + }, + { + className: "function", + beginKeywords: "function", + end: "\\(", + excludeEnd: !0, + illegal: "\\S", + contains: [e.TITLE_MODE], + }, + ], + illegal: /<\//, + }; +}; +var cE = function (e) { + return { + name: "HSP", + case_insensitive: !0, + keywords: { + $pattern: /[\w._]+/, + keyword: + "goto gosub return break repeat loop continue wait await dim sdim foreach dimtype dup dupptr end stop newmod delmod mref run exgoto on mcall assert logmes newlab resume yield onexit onerror onkey onclick oncmd exist delete mkdir chdir dirlist bload bsave bcopy memfile if else poke wpoke lpoke getstr chdpm memexpand memcpy memset notesel noteadd notedel noteload notesave randomize noteunsel noteget split strrep setease button chgdisp exec dialog mmload mmplay mmstop mci pset pget syscolor mes print title pos circle cls font sysfont objsize picload color palcolor palette redraw width gsel gcopy gzoom gmode bmpsave hsvcolor getkey listbox chkbox combox input mesbox buffer screen bgscr mouse objsel groll line clrobj boxf objprm objmode stick grect grotate gsquare gradf objimage objskip objenable celload celdiv celput newcom querycom delcom cnvstow comres axobj winobj sendmsg comevent comevarg sarrayconv callfunc cnvwtos comevdisp libptr system hspstat hspver stat cnt err strsize looplev sublev iparam wparam lparam refstr refdval int rnd strlen length length2 length3 length4 vartype gettime peek wpeek lpeek varptr varuse noteinfo instr abs limit getease str strmid strf getpath strtrim sin cos tan atan sqrt double absf expf logf limitf powf geteasef mousex mousey mousew hwnd hinstance hdc ginfo objinfo dirinfo sysinfo thismod __hspver__ __hsp30__ __date__ __time__ __line__ __file__ _debug __hspdef__ and or xor not screen_normal screen_palette screen_hide screen_fixedsize screen_tool screen_frame gmode_gdi gmode_mem gmode_rgb0 gmode_alpha gmode_rgb0alpha gmode_add gmode_sub gmode_pixela ginfo_mx ginfo_my ginfo_act ginfo_sel ginfo_wx1 ginfo_wy1 ginfo_wx2 ginfo_wy2 ginfo_vx ginfo_vy ginfo_sizex ginfo_sizey ginfo_winx ginfo_winy ginfo_mesx ginfo_mesy ginfo_r ginfo_g ginfo_b ginfo_paluse ginfo_dispx ginfo_dispy ginfo_cx ginfo_cy ginfo_intid ginfo_newid ginfo_sx ginfo_sy objinfo_mode objinfo_bmscr objinfo_hwnd notemax notesize dir_cur dir_exe dir_win dir_sys dir_cmdline dir_desktop dir_mydoc dir_tv font_normal font_bold font_italic font_underline font_strikeout font_antialias objmode_normal objmode_guifont objmode_usefont gsquare_grad msgothic msmincho do until while wend for next _break _continue switch case default swbreak swend ddim ldim alloc m_pi rad2deg deg2rad ease_linear ease_quad_in ease_quad_out ease_quad_inout ease_cubic_in ease_cubic_out ease_cubic_inout ease_quartic_in ease_quartic_out ease_quartic_inout ease_bounce_in ease_bounce_out ease_bounce_inout ease_shake_in ease_shake_out ease_shake_inout ease_loop", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + className: "string", + begin: /\{"/, + end: /"\}/, + contains: [e.BACKSLASH_ESCAPE], + }, + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "meta", + begin: "#", + end: "$", + keywords: { + "meta-keyword": + "addion cfunc cmd cmpopt comfunc const defcfunc deffunc define else endif enum epack func global if ifdef ifndef include modcfunc modfunc modinit modterm module pack packopt regcmd runtime undef usecom uselib", + }, + contains: [ + e.inherit(e.QUOTE_STRING_MODE, { className: "meta-string" }), + e.NUMBER_MODE, + e.C_NUMBER_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + { className: "symbol", begin: "^\\*(\\w+|@)" }, + e.NUMBER_MODE, + e.C_NUMBER_MODE, + ], + }; +}; +function _E(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function dE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return _E(e); + }) + .join(""); + return a; +} +function uE(e) { + var t = { + "builtin-name": [ + "action", + "bindattr", + "collection", + "component", + "concat", + "debugger", + "each", + "each-in", + "get", + "hash", + "if", + "in", + "input", + "link-to", + "loc", + "log", + "lookup", + "mut", + "outlet", + "partial", + "query-params", + "render", + "template", + "textarea", + "unbound", + "unless", + "view", + "with", + "yield", + ], + }, + n = /\[\]|\[[^\]]+\]/, + a = /[^\s!"#%&'()*+,.\/;<=>@\[\\\]^`{|}~]+/, + r = (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return ( + "(" + + t + .map(function (e) { + return _E(e); + }) + .join("|") + + ")" + ); + })(/""|"[^"]+"/, /''|'[^']+'/, n, a), + i = dE( + dE("(", /\.|\.\/|\//, ")?"), + r, + (function (e) { + return dE("(", e, ")*"); + })(dE(/(\.|\/)/, r)), + ), + o = dE("(", n, "|", a, ")(?==)"), + s = { begin: i, lexemes: /[\w.\/]+/ }, + l = e.inherit(s, { + keywords: { literal: ["true", "false", "undefined", "null"] }, + }), + c = { begin: /\(/, end: /\)/ }, + _ = { + className: "attr", + begin: o, + relevance: 0, + starts: { + begin: /=/, + end: /=/, + starts: { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + l, + c, + ], + }, + }, + }, + d = { + contains: [ + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + { + begin: /as\s+\|/, + keywords: { keyword: "as" }, + end: /\|/, + contains: [{ begin: /\w+/ }], + }, + _, + l, + c, + ], + returnEnd: !0, + }, + u = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\)/ }), + }); + c.contains = [u]; + var m = e.inherit(s, { + keywords: t, + className: "name", + starts: e.inherit(d, { end: /\}\}/ }), + }), + p = e.inherit(s, { keywords: t, className: "name" }), + g = e.inherit(s, { + className: "name", + keywords: t, + starts: e.inherit(d, { end: /\}\}/ }), + }); + return { + name: "Handlebars", + aliases: ["hbs", "html.hbs", "html.handlebars", "htmlbars"], + case_insensitive: !0, + subLanguage: "xml", + contains: [ + { begin: /\\\{\{/, skip: !0 }, + { begin: /\\\\(?=\{\{)/, skip: !0 }, + e.COMMENT(/\{\{!--/, /--\}\}/), + e.COMMENT(/\{\{!/, /\}\}/), + { + className: "template-tag", + begin: /\{\{\{\{(?!\/)/, + end: /\}\}\}\}/, + contains: [m], + starts: { end: /\{\{\{\{\//, returnEnd: !0, subLanguage: "xml" }, + }, + { + className: "template-tag", + begin: /\{\{\{\{\//, + end: /\}\}\}\}/, + contains: [p], + }, + { className: "template-tag", begin: /\{\{#/, end: /\}\}/, contains: [m] }, + { + className: "template-tag", + begin: /\{\{(?=else\}\})/, + end: /\}\}/, + keywords: "else", + }, + { + className: "template-tag", + begin: /\{\{(?=else if)/, + end: /\}\}/, + keywords: "else if", + }, + { + className: "template-tag", + begin: /\{\{\//, + end: /\}\}/, + contains: [p], + }, + { + className: "template-variable", + begin: /\{\{\{/, + end: /\}\}\}/, + contains: [g], + }, + { + className: "template-variable", + begin: /\{\{/, + end: /\}\}/, + contains: [g], + }, + ], + }; +} +var mE = function (e) { + var t = uE(e); + return ( + (t.name = "HTMLbars"), + e.getLanguage("handlebars") && (t.disableAutodetect = !0), + t + ); +}; +function pE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function gE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return pE(e); + }) + .join(""); + return a; +} +var EE = function (e) { + var t = "HTTP/(2|1\\.[01])", + n = { + className: "attribute", + begin: gE("^", /[A-Za-z][A-Za-z0-9-]*/, "(?=\\:\\s)"), + starts: { + contains: [ + { + className: "punctuation", + begin: /: /, + relevance: 0, + starts: { end: "$", relevance: 0 }, + }, + ], + }, + }, + a = [ + n, + { begin: "\\n\\n", starts: { subLanguage: [], endsWithParent: !0 } }, + ]; + return { + name: "HTTP", + aliases: ["https"], + illegal: /\S/, + contains: [ + { + begin: "^(?=" + t + " \\d{3})", + end: /$/, + contains: [ + { className: "meta", begin: t }, + { className: "number", begin: "\\b\\d{3}\\b" }, + ], + starts: { end: /\b\B/, illegal: /\S/, contains: a }, + }, + { + begin: "(?=^[A-Z]+ (.*?) " + t + "$)", + end: /$/, + contains: [ + { + className: "string", + begin: " ", + end: " ", + excludeBegin: !0, + excludeEnd: !0, + }, + { className: "meta", begin: t }, + { className: "keyword", begin: "[A-Z]+" }, + ], + starts: { end: /\b\B/, illegal: /\S/, contains: a }, + }, + e.inherit(n, { relevance: 0 }), + ], + }; +}; +var SE = function (e) { + var t = "a-zA-Z_\\-!.?+*=<>&#'", + n = "[" + t + "][" + t + "0-9/;:]*", + a = { + $pattern: n, + "builtin-name": + "!= % %= & &= * ** **= *= *map + += , --build-class-- --import-- -= . / // //= /= < << <<= <= = > >= >> >>= @ @= ^ ^= abs accumulate all and any ap-compose ap-dotimes ap-each ap-each-while ap-filter ap-first ap-if ap-last ap-map ap-map-when ap-pipe ap-reduce ap-reject apply as-> ascii assert assoc bin break butlast callable calling-module-name car case cdr chain chr coll? combinations compile compress cond cons cons? continue count curry cut cycle dec def default-method defclass defmacro defmacro-alias defmacro/g! defmain defmethod defmulti defn defn-alias defnc defnr defreader defseq del delattr delete-route dict-comp dir disassemble dispatch-reader-macro distinct divmod do doto drop drop-last drop-while empty? end-sequence eval eval-and-compile eval-when-compile even? every? except exec filter first flatten float? fn fnc fnr for for* format fraction genexpr gensym get getattr global globals group-by hasattr hash hex id identity if if* if-not if-python2 import in inc input instance? integer integer-char? integer? interleave interpose is is-coll is-cons is-empty is-even is-every is-float is-instance is-integer is-integer-char is-iterable is-iterator is-keyword is-neg is-none is-not is-numeric is-odd is-pos is-string is-symbol is-zero isinstance islice issubclass iter iterable? iterate iterator? keyword keyword? lambda last len let lif lif-not list* list-comp locals loop macro-error macroexpand macroexpand-1 macroexpand-all map max merge-with method-decorator min multi-decorator multicombinations name neg? next none? nonlocal not not-in not? nth numeric? oct odd? open or ord partition permutations pos? post-route postwalk pow prewalk print product profile/calls profile/cpu put-route quasiquote quote raise range read read-str recursive-replace reduce remove repeat repeatedly repr require rest round route route-with-methods rwm second seq set-comp setattr setv some sorted string string? sum switch symbol? take take-nth take-while tee try unless unquote unquote-splicing vars walk when while with with* with-decorator with-gensyms xi xor yield yield-from zero? zip zip-longest | |= ~", + }, + r = { begin: n, relevance: 0 }, + i = { className: "number", begin: "[-+]?\\d+(\\.\\d+)?", relevance: 0 }, + o = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + s = e.COMMENT(";", "$", { relevance: 0 }), + l = { className: "literal", begin: /\b([Tt]rue|[Ff]alse|nil|None)\b/ }, + c = { begin: "[\\[\\{]", end: "[\\]\\}]" }, + _ = { className: "comment", begin: "\\^" + n }, + d = e.COMMENT("\\^\\{", "\\}"), + u = { className: "symbol", begin: "[:]{1,2}" + n }, + m = { begin: "\\(", end: "\\)" }, + p = { endsWithParent: !0, relevance: 0 }, + g = { className: "name", relevance: 0, keywords: a, begin: n, starts: p }, + E = [m, o, _, d, s, u, c, i, l, r]; + return ( + (m.contains = [e.COMMENT("comment", ""), g, p]), + (p.contains = E), + (c.contains = E), + { + name: "Hy", + aliases: ["hylang"], + illegal: /\S/, + contains: [e.SHEBANG(), m, o, _, d, s, u, c, i, l], + } + ); +}; +var bE = function (e) { + return { + name: "Inform 7", + aliases: ["i7"], + case_insensitive: !0, + keywords: { + keyword: + "thing room person man woman animal container supporter backdrop door scenery open closed locked inside gender is are say understand kind of rule", + }, + contains: [ + { + className: "string", + begin: '"', + end: '"', + relevance: 0, + contains: [{ className: "subst", begin: "\\[", end: "\\]" }], + }, + { + className: "section", + begin: /^(Volume|Book|Part|Chapter|Section|Table)\b/, + end: "$", + }, + { + begin: + /^(Check|Carry out|Report|Instead of|To|Rule|When|Before|After)\b/, + end: ":", + contains: [{ begin: "\\(This", end: "\\)" }], + }, + { className: "comment", begin: "\\[", end: "\\]", contains: ["self"] }, + ], + }; +}; +function TE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function fE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return TE(e); + }) + .join(""); + return a; +} +var CE = function (e) { + var t = { + className: "number", + relevance: 0, + variants: [{ begin: /([+-]+)?[\d]+_[\d_]+/ }, { begin: e.NUMBER_RE }], + }, + n = e.COMMENT(); + n.variants = [ + { begin: /;/, end: /$/ }, + { begin: /#/, end: /$/ }, + ]; + var a = { + className: "variable", + variants: [{ begin: /\$[\w\d"][\w\d_]*/ }, { begin: /\$\{(.*?)\}/ }], + }, + r = { className: "literal", begin: /\bon|off|true|false|yes|no\b/ }, + i = { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { begin: "'''", end: "'''", relevance: 10 }, + { begin: '"""', end: '"""', relevance: 10 }, + { begin: '"', end: '"' }, + { begin: "'", end: "'" }, + ], + }, + o = { + begin: /\[/, + end: /\]/, + contains: [n, r, a, i, t, "self"], + relevance: 0, + }, + s = (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return ( + "(" + + t + .map(function (e) { + return TE(e); + }) + .join("|") + + ")" + ); + })(/[A-Za-z0-9_-]+/, /"(\\"|[^"])*"/, /'[^']*'/); + return { + name: "TOML, also INI", + aliases: ["toml"], + case_insensitive: !0, + illegal: /\S/, + contains: [ + n, + { className: "section", begin: /\[+/, end: /\]+/ }, + { + begin: fE(s, "(\\s*\\.\\s*", s, ")*", fE("(?=", /\s*=\s*[^#\s]/, ")")), + className: "attr", + starts: { end: /$/, contains: [n, o, r, a, i, t] }, + }, + ], + }; +}; +function NE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function RE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return NE(e); + }) + .join(""); + return a; +} +var vE = function (e) { + var t = /(_[a-z_\d]+)?/, + n = /([de][+-]?\d+)?/, + a = { + className: "number", + variants: [ + { begin: RE(/\b\d+/, /\.(\d*)/, n, t) }, + { begin: RE(/\b\d+/, n, t) }, + { begin: RE(/\.\d+/, n, t) }, + ], + relevance: 0, + }; + return { + name: "IRPF90", + case_insensitive: !0, + keywords: { + literal: ".False. .True.", + keyword: + "kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data begin_provider &begin_provider end_provider begin_shell end_shell begin_template end_template subst assert touch soft_touch provide no_dep free irp_if irp_else irp_endif irp_write irp_read", + built_in: + "alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_of acosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image IRP_ALIGN irp_here", + }, + illegal: /\/\*/, + contains: [ + e.inherit(e.APOS_STRING_MODE, { className: "string", relevance: 0 }), + e.inherit(e.QUOTE_STRING_MODE, { className: "string", relevance: 0 }), + { + className: "function", + beginKeywords: "subroutine function program", + illegal: "[${=\\n]", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }, + e.COMMENT("!", "$", { relevance: 0 }), + e.COMMENT("begin_doc", "end_doc", { relevance: 10 }), + a, + ], + }; +}; +var OE = function (e) { + var t = "[A-Za-zА-Яа-яёЁ_!][A-Za-zА-Яа-яёЁ_0-9]*", + n = { className: "number", begin: e.NUMBER_RE, relevance: 0 }, + a = { + className: "string", + variants: [ + { begin: '"', end: '"' }, + { begin: "'", end: "'" }, + ], + }, + r = { + className: "doctag", + begin: "\\b(?:TODO|DONE|BEGIN|END|STUB|CHG|FIXME|NOTE|BUG|XXX)\\b", + relevance: 0, + }, + i = { + variants: [ + { + className: "comment", + begin: "//", + end: "$", + relevance: 0, + contains: [e.PHRASAL_WORDS_MODE, r], + }, + { + className: "comment", + begin: "/\\*", + end: "\\*/", + relevance: 0, + contains: [e.PHRASAL_WORDS_MODE, r], + }, + ], + }, + o = { + $pattern: t, + keyword: + "and и else иначе endexcept endfinally endforeach конецвсе endif конецесли endwhile конецпока except exitfor finally foreach все if если in в not не or или try while пока ", + built_in: + "SYSRES_CONST_ACCES_RIGHT_TYPE_EDIT SYSRES_CONST_ACCES_RIGHT_TYPE_FULL SYSRES_CONST_ACCES_RIGHT_TYPE_VIEW SYSRES_CONST_ACCESS_MODE_REQUISITE_CODE SYSRES_CONST_ACCESS_NO_ACCESS_VIEW SYSRES_CONST_ACCESS_NO_ACCESS_VIEW_CODE SYSRES_CONST_ACCESS_RIGHTS_ADD_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_ADD_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_CHANGE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_CHANGE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_DELETE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_DELETE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_EXECUTE_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_EXECUTE_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_NO_ACCESS_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_RATIFY_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW SYSRES_CONST_ACCESS_RIGHTS_VIEW_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_CODE SYSRES_CONST_ACCESS_RIGHTS_VIEW_REQUISITE_YES_CODE SYSRES_CONST_ACCESS_TYPE_CHANGE SYSRES_CONST_ACCESS_TYPE_CHANGE_CODE SYSRES_CONST_ACCESS_TYPE_EXISTS SYSRES_CONST_ACCESS_TYPE_EXISTS_CODE SYSRES_CONST_ACCESS_TYPE_FULL SYSRES_CONST_ACCESS_TYPE_FULL_CODE SYSRES_CONST_ACCESS_TYPE_VIEW SYSRES_CONST_ACCESS_TYPE_VIEW_CODE SYSRES_CONST_ACTION_TYPE_ABORT SYSRES_CONST_ACTION_TYPE_ACCEPT SYSRES_CONST_ACTION_TYPE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ADD_ATTACHMENT SYSRES_CONST_ACTION_TYPE_CHANGE_CARD SYSRES_CONST_ACTION_TYPE_CHANGE_KIND SYSRES_CONST_ACTION_TYPE_CHANGE_STORAGE SYSRES_CONST_ACTION_TYPE_CONTINUE SYSRES_CONST_ACTION_TYPE_COPY SYSRES_CONST_ACTION_TYPE_CREATE SYSRES_CONST_ACTION_TYPE_CREATE_VERSION SYSRES_CONST_ACTION_TYPE_DELETE SYSRES_CONST_ACTION_TYPE_DELETE_ATTACHMENT SYSRES_CONST_ACTION_TYPE_DELETE_VERSION SYSRES_CONST_ACTION_TYPE_DISABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENABLE_DELEGATE_ACCESS_RIGHTS SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_CERTIFICATE_AND_PASSWORD SYSRES_CONST_ACTION_TYPE_ENCRYPTION_BY_PASSWORD SYSRES_CONST_ACTION_TYPE_EXPORT_WITH_LOCK SYSRES_CONST_ACTION_TYPE_EXPORT_WITHOUT_LOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITH_UNLOCK SYSRES_CONST_ACTION_TYPE_IMPORT_WITHOUT_UNLOCK SYSRES_CONST_ACTION_TYPE_LIFE_CYCLE_STAGE SYSRES_CONST_ACTION_TYPE_LOCK SYSRES_CONST_ACTION_TYPE_LOCK_FOR_SERVER SYSRES_CONST_ACTION_TYPE_LOCK_MODIFY SYSRES_CONST_ACTION_TYPE_MARK_AS_READED SYSRES_CONST_ACTION_TYPE_MARK_AS_UNREADED SYSRES_CONST_ACTION_TYPE_MODIFY SYSRES_CONST_ACTION_TYPE_MODIFY_CARD SYSRES_CONST_ACTION_TYPE_MOVE_TO_ARCHIVE SYSRES_CONST_ACTION_TYPE_OFF_ENCRYPTION SYSRES_CONST_ACTION_TYPE_PASSWORD_CHANGE SYSRES_CONST_ACTION_TYPE_PERFORM SYSRES_CONST_ACTION_TYPE_RECOVER_FROM_LOCAL_COPY SYSRES_CONST_ACTION_TYPE_RESTART SYSRES_CONST_ACTION_TYPE_RESTORE_FROM_ARCHIVE SYSRES_CONST_ACTION_TYPE_REVISION SYSRES_CONST_ACTION_TYPE_SEND_BY_MAIL SYSRES_CONST_ACTION_TYPE_SIGN SYSRES_CONST_ACTION_TYPE_START SYSRES_CONST_ACTION_TYPE_UNLOCK SYSRES_CONST_ACTION_TYPE_UNLOCK_FROM_SERVER SYSRES_CONST_ACTION_TYPE_VERSION_STATE SYSRES_CONST_ACTION_TYPE_VERSION_VISIBILITY SYSRES_CONST_ACTION_TYPE_VIEW SYSRES_CONST_ACTION_TYPE_VIEW_SHADOW_COPY SYSRES_CONST_ACTION_TYPE_WORKFLOW_DESCRIPTION_MODIFY SYSRES_CONST_ACTION_TYPE_WRITE_HISTORY SYSRES_CONST_ACTIVE_VERSION_STATE_PICK_VALUE SYSRES_CONST_ADD_REFERENCE_MODE_NAME SYSRES_CONST_ADDITION_REQUISITE_CODE SYSRES_CONST_ADDITIONAL_PARAMS_REQUISITE_CODE SYSRES_CONST_ADITIONAL_JOB_END_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_READ_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_START_DATE_REQUISITE_NAME SYSRES_CONST_ADITIONAL_JOB_STATE_REQUISITE_NAME SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_ADDING_USER_TO_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC SYSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_CODE SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONENT_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_DATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CONST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENCE_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SYSRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FOLDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSRES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_CONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_KIND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CONST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYSRES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONST_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_REQUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONST_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYSRES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 SYSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TYPE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRING_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_REFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_TEXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR SYSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAITING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE SYSRES_CONST_YES_VALUE CR FALSE nil NO_VALUE NULL TAB TRUE YES_VALUE ADMINISTRATORS_GROUP_NAME CUSTOMIZERS_GROUP_NAME DEVELOPERS_GROUP_NAME SERVICE_USERS_GROUP_NAME DECISION_BLOCK_FIRST_OPERAND_PROPERTY DECISION_BLOCK_NAME_PROPERTY DECISION_BLOCK_OPERATION_PROPERTY DECISION_BLOCK_RESULT_TYPE_PROPERTY DECISION_BLOCK_SECOND_OPERAND_PROPERTY ANY_FILE_EXTENTION COMPRESSED_DOCUMENT_EXTENSION EXTENDED_DOCUMENT_EXTENSION SHORT_COMPRESSED_DOCUMENT_EXTENSION SHORT_EXTENDED_DOCUMENT_EXTENSION JOB_BLOCK_ABORT_DEADLINE_PROPERTY JOB_BLOCK_AFTER_FINISH_EVENT JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT JOB_BLOCK_ATTACHMENT_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT JOB_BLOCK_BEFORE_START_EVENT JOB_BLOCK_CREATED_JOBS_PROPERTY JOB_BLOCK_DEADLINE_PROPERTY JOB_BLOCK_EXECUTION_RESULTS_PROPERTY JOB_BLOCK_IS_PARALLEL_PROPERTY JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_CODE smHidden smMaximized smMinimized smNormal wmNo wmYes COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY NOTICE_BLOCK_BEFORE_START_EVENT NOTICE_BLOCK_CREATED_NOTICES_PROPERTY NOTICE_BLOCK_DEADLINE_PROPERTY NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY NOTICE_BLOCK_NAME_PROPERTY NOTICE_BLOCK_NOTICE_TEXT_PROPERTY NOTICE_BLOCK_PERFORMER_PROPERTY NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY NOTICE_BLOCK_SUBJECT_PROPERTY dseAfterCancel dseAfterClose dseAfterDelete dseAfterDeleteOutOfTransaction dseAfterInsert dseAfterOpen dseAfterScroll dseAfterUpdate dseAfterUpdateOutOfTransaction dseBeforeCancel dseBeforeClose dseBeforeDelete dseBeforeDetailUpdate dseBeforeInsert dseBeforeOpen dseBeforeUpdate dseOnAnyRequisiteChange dseOnCloseRecord dseOnDeleteError dseOnOpenRecord dseOnPrepareUpdate dseOnUpdateError dseOnUpdateRatifiedRecord dseOnValidDelete dseOnValidUpdate reOnChange reOnChangeValues SELECTION_BEGIN_ROUTE_EVENT SELECTION_END_ROUTE_EVENT CURRENT_PERIOD_IS_REQUIRED PREVIOUS_CARD_TYPE_NAME SHOW_RECORD_PROPERTIES_FORM ACCESS_RIGHTS_SETTING_DIALOG_CODE ADMINISTRATOR_USER_CODE ANALYTIC_REPORT_TYPE asrtHideLocal asrtHideRemote CALCULATED_ROLE_TYPE_CODE COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE DCTS_TEST_PROTOCOLS_FOLDER_PATH E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER E_EDOC_VERSION_ALREDY_SIGNED E_EDOC_VERSION_ALREDY_SIGNED_BY_USER EDOC_TYPES_CODE_REQUISITE_FIELD_NAME EDOCUMENTS_ALIAS_NAME FILES_FOLDER_PATH FILTER_OPERANDS_DELIMITER FILTER_OPERATIONS_DELIMITER FORMCARD_NAME FORMLIST_NAME GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE INTEGRATED_REPORT_TYPE IS_BUILDER_APPLICATION_ROLE IS_BUILDER_APPLICATION_ROLE2 IS_BUILDER_USERS ISBSYSDEV LOG_FOLDER_PATH mbCancel mbNo mbNoToAll mbOK mbYes mbYesToAll MEMORY_DATASET_DESRIPTIONS_FILENAME mrNo mrNoToAll mrYes mrYesToAll MULTIPLE_SELECT_DIALOG_CODE NONOPERATING_RECORD_FLAG_FEMININE NONOPERATING_RECORD_FLAG_MASCULINE OPERATING_RECORD_FLAG_FEMININE OPERATING_RECORD_FLAG_MASCULINE PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE PROGRAM_INITIATED_LOOKUP_ACTION ratDelete ratEdit ratInsert REPORT_TYPE REQUIRED_PICK_VALUES_VARIABLE rmCard rmList SBRTE_PROGID_DEV SBRTE_PROGID_RELEASE STATIC_ROLE_TYPE_CODE SUPPRESS_EMPTY_TEMPLATE_CREATION SYSTEM_USER_CODE UPDATE_DIALOG_DATASET USED_IN_OBJECT_HINT_PARAM USER_INITIATED_LOOKUP_ACTION USER_NAME_FORMAT USER_SELECTION_RESTRICTIONS WORKFLOW_TEST_PROTOCOLS_FOLDER_PATH ELS_SUBTYPE_CONTROL_NAME ELS_FOLDER_KIND_CONTROL_NAME REPEAT_PROCESS_CURRENT_OBJECT_EXCEPTION_NAME PRIVILEGE_COMPONENT_FULL_ACCESS PRIVILEGE_DEVELOPMENT_EXPORT PRIVILEGE_DEVELOPMENT_IMPORT PRIVILEGE_DOCUMENT_DELETE PRIVILEGE_ESD PRIVILEGE_FOLDER_DELETE PRIVILEGE_MANAGE_ACCESS_RIGHTS PRIVILEGE_MANAGE_REPLICATION PRIVILEGE_MANAGE_SESSION_SERVER PRIVILEGE_OBJECT_FULL_ACCESS PRIVILEGE_OBJECT_VIEW PRIVILEGE_RESERVE_LICENSE PRIVILEGE_SYSTEM_CUSTOMIZE PRIVILEGE_SYSTEM_DEVELOP PRIVILEGE_SYSTEM_INSTALL PRIVILEGE_TASK_DELETE PRIVILEGE_USER_PLUGIN_SETTINGS_CUSTOMIZE PRIVILEGES_PSEUDOREFERENCE_CODE ACCESS_TYPES_PSEUDOREFERENCE_CODE ALL_AVAILABLE_COMPONENTS_PSEUDOREFERENCE_CODE ALL_AVAILABLE_PRIVILEGES_PSEUDOREFERENCE_CODE ALL_REPLICATE_COMPONENTS_PSEUDOREFERENCE_CODE AVAILABLE_DEVELOPERS_COMPONENTS_PSEUDOREFERENCE_CODE COMPONENTS_PSEUDOREFERENCE_CODE FILTRATER_SETTINGS_CONFLICTS_PSEUDOREFERENCE_CODE GROUPS_PSEUDOREFERENCE_CODE RECEIVE_PROTOCOL_PSEUDOREFERENCE_CODE REFERENCE_REQUISITE_PSEUDOREFERENCE_CODE REFERENCE_REQUISITES_PSEUDOREFERENCE_CODE REFTYPES_PSEUDOREFERENCE_CODE REPLICATION_SEANCES_DIARY_PSEUDOREFERENCE_CODE SEND_PROTOCOL_PSEUDOREFERENCE_CODE SUBSTITUTES_PSEUDOREFERENCE_CODE SYSTEM_SETTINGS_PSEUDOREFERENCE_CODE UNITS_PSEUDOREFERENCE_CODE USERS_PSEUDOREFERENCE_CODE VIEWERS_PSEUDOREFERENCE_CODE CERTIFICATE_TYPE_ENCRYPT CERTIFICATE_TYPE_SIGN CERTIFICATE_TYPE_SIGN_AND_ENCRYPT STORAGE_TYPE_FILE STORAGE_TYPE_NAS_CIFS STORAGE_TYPE_SAPERION STORAGE_TYPE_SQL_SERVER COMPTYPE2_REQUISITE_DOCUMENTS_VALUE COMPTYPE2_REQUISITE_TASKS_VALUE COMPTYPE2_REQUISITE_FOLDERS_VALUE COMPTYPE2_REQUISITE_REFERENCES_VALUE SYSREQ_CODE SYSREQ_COMPTYPE2 SYSREQ_CONST_AVAILABLE_FOR_WEB SYSREQ_CONST_COMMON_CODE SYSREQ_CONST_COMMON_VALUE SYSREQ_CONST_FIRM_CODE SYSREQ_CONST_FIRM_STATUS SYSREQ_CONST_FIRM_VALUE SYSREQ_CONST_SERVER_STATUS SYSREQ_CONTENTS SYSREQ_DATE_OPEN SYSREQ_DATE_CLOSE SYSREQ_DESCRIPTION SYSREQ_DESCRIPTION_LOCALIZE_ID SYSREQ_DOUBLE SYSREQ_EDOC_ACCESS_TYPE SYSREQ_EDOC_AUTHOR SYSREQ_EDOC_CREATED SYSREQ_EDOC_DELEGATE_RIGHTS_REQUISITE_CODE SYSREQ_EDOC_EDITOR SYSREQ_EDOC_ENCODE_TYPE SYSREQ_EDOC_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_EXPORT_DATE SYSREQ_EDOC_EXPORTER SYSREQ_EDOC_KIND SYSREQ_EDOC_LIFE_STAGE_NAME SYSREQ_EDOC_LOCKED_FOR_SERVER_CODE SYSREQ_EDOC_MODIFIED SYSREQ_EDOC_NAME SYSREQ_EDOC_NOTE SYSREQ_EDOC_QUALIFIED_ID SYSREQ_EDOC_SESSION_KEY SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_SIGNATURE_TYPE SYSREQ_EDOC_SIGNED SYSREQ_EDOC_STORAGE SYSREQ_EDOC_STORAGES_ARCHIVE_STORAGE SYSREQ_EDOC_STORAGES_CHECK_RIGHTS SYSREQ_EDOC_STORAGES_COMPUTER_NAME SYSREQ_EDOC_STORAGES_EDIT_IN_STORAGE SYSREQ_EDOC_STORAGES_EXECUTIVE_STORAGE SYSREQ_EDOC_STORAGES_FUNCTION SYSREQ_EDOC_STORAGES_INITIALIZED SYSREQ_EDOC_STORAGES_LOCAL_PATH SYSREQ_EDOC_STORAGES_SAPERION_DATABASE_NAME SYSREQ_EDOC_STORAGES_SEARCH_BY_TEXT SYSREQ_EDOC_STORAGES_SERVER_NAME SYSREQ_EDOC_STORAGES_SHARED_SOURCE_NAME SYSREQ_EDOC_STORAGES_TYPE SYSREQ_EDOC_TEXT_MODIFIED SYSREQ_EDOC_TYPE_ACT_CODE SYSREQ_EDOC_TYPE_ACT_DESCRIPTION SYSREQ_EDOC_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_EDOC_TYPE_ACT_SECTION SYSREQ_EDOC_TYPE_ADD_PARAMS SYSREQ_EDOC_TYPE_COMMENT SYSREQ_EDOC_TYPE_EVENT_TEXT SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_EDOC_TYPE_NAME_LOCALIZE_ID SYSREQ_EDOC_TYPE_NUMERATION_METHOD SYSREQ_EDOC_TYPE_PSEUDO_REQUISITE_CODE SYSREQ_EDOC_TYPE_REQ_CODE SYSREQ_EDOC_TYPE_REQ_DESCRIPTION SYSREQ_EDOC_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_REQ_IS_LEADING SYSREQ_EDOC_TYPE_REQ_IS_REQUIRED SYSREQ_EDOC_TYPE_REQ_NUMBER SYSREQ_EDOC_TYPE_REQ_ON_CHANGE SYSREQ_EDOC_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_EDOC_TYPE_REQ_ON_SELECT SYSREQ_EDOC_TYPE_REQ_ON_SELECT_KIND SYSREQ_EDOC_TYPE_REQ_SECTION SYSREQ_EDOC_TYPE_VIEW_CARD SYSREQ_EDOC_TYPE_VIEW_CODE SYSREQ_EDOC_TYPE_VIEW_COMMENT SYSREQ_EDOC_TYPE_VIEW_IS_MAIN SYSREQ_EDOC_TYPE_VIEW_NAME SYSREQ_EDOC_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_EDOC_VERSION_AUTHOR SYSREQ_EDOC_VERSION_CRC SYSREQ_EDOC_VERSION_DATA SYSREQ_EDOC_VERSION_EDITOR SYSREQ_EDOC_VERSION_EXPORT_DATE SYSREQ_EDOC_VERSION_EXPORTER SYSREQ_EDOC_VERSION_HIDDEN SYSREQ_EDOC_VERSION_LIFE_STAGE SYSREQ_EDOC_VERSION_MODIFIED SYSREQ_EDOC_VERSION_NOTE SYSREQ_EDOC_VERSION_SIGNATURE_TYPE SYSREQ_EDOC_VERSION_SIGNED SYSREQ_EDOC_VERSION_SIZE SYSREQ_EDOC_VERSION_SOURCE SYSREQ_EDOC_VERSION_TEXT_MODIFIED SYSREQ_EDOCKIND_DEFAULT_VERSION_STATE_CODE SYSREQ_FOLDER_KIND SYSREQ_FUNC_CATEGORY SYSREQ_FUNC_COMMENT SYSREQ_FUNC_GROUP SYSREQ_FUNC_GROUP_COMMENT SYSREQ_FUNC_GROUP_NUMBER SYSREQ_FUNC_HELP SYSREQ_FUNC_PARAM_DEF_VALUE SYSREQ_FUNC_PARAM_IDENT SYSREQ_FUNC_PARAM_NUMBER SYSREQ_FUNC_PARAM_TYPE SYSREQ_FUNC_TEXT SYSREQ_GROUP_CATEGORY SYSREQ_ID SYSREQ_LAST_UPDATE SYSREQ_LEADER_REFERENCE SYSREQ_LINE_NUMBER SYSREQ_MAIN_RECORD_ID SYSREQ_NAME SYSREQ_NAME_LOCALIZE_ID SYSREQ_NOTE SYSREQ_ORIGINAL_RECORD SYSREQ_OUR_FIRM SYSREQ_PROFILING_SETTINGS_BATCH_LOGING SYSREQ_PROFILING_SETTINGS_BATCH_SIZE SYSREQ_PROFILING_SETTINGS_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_SQL_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_START_LOGGED SYSREQ_RECORD_STATUS SYSREQ_REF_REQ_FIELD_NAME SYSREQ_REF_REQ_FORMAT SYSREQ_REF_REQ_GENERATED SYSREQ_REF_REQ_LENGTH SYSREQ_REF_REQ_PRECISION SYSREQ_REF_REQ_REFERENCE SYSREQ_REF_REQ_SECTION SYSREQ_REF_REQ_STORED SYSREQ_REF_REQ_TOKENS SYSREQ_REF_REQ_TYPE SYSREQ_REF_REQ_VIEW SYSREQ_REF_TYPE_ACT_CODE SYSREQ_REF_TYPE_ACT_DESCRIPTION SYSREQ_REF_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_ACT_ON_EXECUTE SYSREQ_REF_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_REF_TYPE_ACT_SECTION SYSREQ_REF_TYPE_ADD_PARAMS SYSREQ_REF_TYPE_COMMENT SYSREQ_REF_TYPE_COMMON_SETTINGS SYSREQ_REF_TYPE_DISPLAY_REQUISITE_NAME SYSREQ_REF_TYPE_EVENT_TEXT SYSREQ_REF_TYPE_MAIN_LEADING_REF SYSREQ_REF_TYPE_NAME_IN_SINGULAR SYSREQ_REF_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_REF_TYPE_NAME_LOCALIZE_ID SYSREQ_REF_TYPE_NUMERATION_METHOD SYSREQ_REF_TYPE_REQ_CODE SYSREQ_REF_TYPE_REQ_DESCRIPTION SYSREQ_REF_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_REQ_IS_CONTROL SYSREQ_REF_TYPE_REQ_IS_FILTER SYSREQ_REF_TYPE_REQ_IS_LEADING SYSREQ_REF_TYPE_REQ_IS_REQUIRED SYSREQ_REF_TYPE_REQ_NUMBER SYSREQ_REF_TYPE_REQ_ON_CHANGE SYSREQ_REF_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_REF_TYPE_REQ_ON_SELECT SYSREQ_REF_TYPE_REQ_ON_SELECT_KIND SYSREQ_REF_TYPE_REQ_SECTION SYSREQ_REF_TYPE_VIEW_CARD SYSREQ_REF_TYPE_VIEW_CODE SYSREQ_REF_TYPE_VIEW_COMMENT SYSREQ_REF_TYPE_VIEW_IS_MAIN SYSREQ_REF_TYPE_VIEW_NAME SYSREQ_REF_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_REFERENCE_TYPE_ID SYSREQ_STATE SYSREQ_STATЕ SYSREQ_SYSTEM_SETTINGS_VALUE SYSREQ_TYPE SYSREQ_UNIT SYSREQ_UNIT_ID SYSREQ_USER_GROUPS_GROUP_FULL_NAME SYSREQ_USER_GROUPS_GROUP_NAME SYSREQ_USER_GROUPS_GROUP_SERVER_NAME SYSREQ_USERS_ACCESS_RIGHTS SYSREQ_USERS_AUTHENTICATION SYSREQ_USERS_CATEGORY SYSREQ_USERS_COMPONENT SYSREQ_USERS_COMPONENT_USER_IS_PUBLIC SYSREQ_USERS_DOMAIN SYSREQ_USERS_FULL_USER_NAME SYSREQ_USERS_GROUP SYSREQ_USERS_IS_MAIN_SERVER SYSREQ_USERS_LOGIN SYSREQ_USERS_REFERENCE_USER_IS_PUBLIC SYSREQ_USERS_STATUS SYSREQ_USERS_USER_CERTIFICATE SYSREQ_USERS_USER_CERTIFICATE_INFO SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_NAME SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_VERSION SYSREQ_USERS_USER_CERTIFICATE_STATE SYSREQ_USERS_USER_CERTIFICATE_SUBJECT_NAME SYSREQ_USERS_USER_CERTIFICATE_THUMBPRINT SYSREQ_USERS_USER_DEFAULT_CERTIFICATE SYSREQ_USERS_USER_DESCRIPTION SYSREQ_USERS_USER_GLOBAL_NAME SYSREQ_USERS_USER_LOGIN SYSREQ_USERS_USER_MAIN_SERVER SYSREQ_USERS_USER_TYPE SYSREQ_WORK_RULES_FOLDER_ID RESULT_VAR_NAME RESULT_VAR_NAME_ENG AUTO_NUMERATION_RULE_ID CANT_CHANGE_ID_REQUISITE_RULE_ID CANT_CHANGE_OURFIRM_REQUISITE_RULE_ID CHECK_CHANGING_REFERENCE_RECORD_USE_RULE_ID CHECK_CODE_REQUISITE_RULE_ID CHECK_DELETING_REFERENCE_RECORD_USE_RULE_ID CHECK_FILTRATER_CHANGES_RULE_ID CHECK_RECORD_INTERVAL_RULE_ID CHECK_REFERENCE_INTERVAL_RULE_ID CHECK_REQUIRED_DATA_FULLNESS_RULE_ID CHECK_REQUIRED_REQUISITES_FULLNESS_RULE_ID MAKE_RECORD_UNRATIFIED_RULE_ID RESTORE_AUTO_NUMERATION_RULE_ID SET_FIRM_CONTEXT_FROM_RECORD_RULE_ID SET_FIRST_RECORD_IN_LIST_FORM_RULE_ID SET_IDSPS_VALUE_RULE_ID SET_NEXT_CODE_VALUE_RULE_ID SET_OURFIRM_BOUNDS_RULE_ID SET_OURFIRM_REQUISITE_RULE_ID SCRIPT_BLOCK_AFTER_FINISH_EVENT SCRIPT_BLOCK_BEFORE_START_EVENT SCRIPT_BLOCK_EXECUTION_RESULTS_PROPERTY SCRIPT_BLOCK_NAME_PROPERTY SCRIPT_BLOCK_SCRIPT_PROPERTY SUBTASK_BLOCK_ABORT_DEADLINE_PROPERTY SUBTASK_BLOCK_AFTER_FINISH_EVENT SUBTASK_BLOCK_ASSIGN_PARAMS_EVENT SUBTASK_BLOCK_ATTACHMENTS_PROPERTY SUBTASK_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY SUBTASK_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY SUBTASK_BLOCK_BEFORE_START_EVENT SUBTASK_BLOCK_CREATED_TASK_PROPERTY SUBTASK_BLOCK_CREATION_EVENT SUBTASK_BLOCK_DEADLINE_PROPERTY SUBTASK_BLOCK_IMPORTANCE_PROPERTY SUBTASK_BLOCK_INITIATOR_PROPERTY SUBTASK_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY SUBTASK_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY SUBTASK_BLOCK_JOBS_TYPE_PROPERTY SUBTASK_BLOCK_NAME_PROPERTY SUBTASK_BLOCK_PARALLEL_ROUTE_PROPERTY SUBTASK_BLOCK_PERFORMERS_PROPERTY SUBTASK_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY SUBTASK_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY SUBTASK_BLOCK_REQUIRE_SIGN_PROPERTY SUBTASK_BLOCK_STANDARD_ROUTE_PROPERTY SUBTASK_BLOCK_START_EVENT SUBTASK_BLOCK_STEP_CONTROL_PROPERTY SUBTASK_BLOCK_SUBJECT_PROPERTY SUBTASK_BLOCK_TASK_CONTROL_PROPERTY SUBTASK_BLOCK_TEXT_PROPERTY SUBTASK_BLOCK_UNLOCK_ATTACHMENTS_ON_STOP_PROPERTY SUBTASK_BLOCK_USE_STANDARD_ROUTE_PROPERTY SUBTASK_BLOCK_WAIT_FOR_TASK_COMPLETE_PROPERTY SYSCOMP_CONTROL_JOBS SYSCOMP_FOLDERS SYSCOMP_JOBS SYSCOMP_NOTICES SYSCOMP_TASKS SYSDLG_CREATE_EDOCUMENT SYSDLG_CREATE_EDOCUMENT_VERSION SYSDLG_CURRENT_PERIOD SYSDLG_EDIT_FUNCTION_HELP SYSDLG_EDOCUMENT_KINDS_FOR_TEMPLATE SYSDLG_EXPORT_MULTIPLE_EDOCUMENTS SYSDLG_EXPORT_SINGLE_EDOCUMENT SYSDLG_IMPORT_EDOCUMENT SYSDLG_MULTIPLE_SELECT SYSDLG_SETUP_ACCESS_RIGHTS SYSDLG_SETUP_DEFAULT_RIGHTS SYSDLG_SETUP_FILTER_CONDITION SYSDLG_SETUP_SIGN_RIGHTS SYSDLG_SETUP_TASK_OBSERVERS SYSDLG_SETUP_TASK_ROUTE SYSDLG_SETUP_USERS_LIST SYSDLG_SIGN_EDOCUMENT SYSDLG_SIGN_MULTIPLE_EDOCUMENTS SYSREF_ACCESS_RIGHTS_TYPES SYSREF_ADMINISTRATION_HISTORY SYSREF_ALL_AVAILABLE_COMPONENTS SYSREF_ALL_AVAILABLE_PRIVILEGES SYSREF_ALL_REPLICATING_COMPONENTS SYSREF_AVAILABLE_DEVELOPERS_COMPONENTS SYSREF_CALENDAR_EVENTS SYSREF_COMPONENT_TOKEN_HISTORY SYSREF_COMPONENT_TOKENS SYSREF_COMPONENTS SYSREF_CONSTANTS SYSREF_DATA_RECEIVE_PROTOCOL SYSREF_DATA_SEND_PROTOCOL SYSREF_DIALOGS SYSREF_DIALOGS_REQUISITES SYSREF_EDITORS SYSREF_EDOC_CARDS SYSREF_EDOC_TYPES SYSREF_EDOCUMENT_CARD_REQUISITES SYSREF_EDOCUMENT_CARD_TYPES SYSREF_EDOCUMENT_CARD_TYPES_REFERENCE SYSREF_EDOCUMENT_CARDS SYSREF_EDOCUMENT_HISTORY SYSREF_EDOCUMENT_KINDS SYSREF_EDOCUMENT_REQUISITES SYSREF_EDOCUMENT_SIGNATURES SYSREF_EDOCUMENT_TEMPLATES SYSREF_EDOCUMENT_TEXT_STORAGES SYSREF_EDOCUMENT_VIEWS SYSREF_FILTERER_SETUP_CONFLICTS SYSREF_FILTRATER_SETTING_CONFLICTS SYSREF_FOLDER_HISTORY SYSREF_FOLDERS SYSREF_FUNCTION_GROUPS SYSREF_FUNCTION_PARAMS SYSREF_FUNCTIONS SYSREF_JOB_HISTORY SYSREF_LINKS SYSREF_LOCALIZATION_DICTIONARY SYSREF_LOCALIZATION_LANGUAGES SYSREF_MODULES SYSREF_PRIVILEGES SYSREF_RECORD_HISTORY SYSREF_REFERENCE_REQUISITES SYSREF_REFERENCE_TYPE_VIEWS SYSREF_REFERENCE_TYPES SYSREF_REFERENCES SYSREF_REFERENCES_REQUISITES SYSREF_REMOTE_SERVERS SYSREF_REPLICATION_SESSIONS_LOG SYSREF_REPLICATION_SESSIONS_PROTOCOL SYSREF_REPORTS SYSREF_ROLES SYSREF_ROUTE_BLOCK_GROUPS SYSREF_ROUTE_BLOCKS SYSREF_SCRIPTS SYSREF_SEARCHES SYSREF_SERVER_EVENTS SYSREF_SERVER_EVENTS_HISTORY SYSREF_STANDARD_ROUTE_GROUPS SYSREF_STANDARD_ROUTES SYSREF_STATUSES SYSREF_SYSTEM_SETTINGS SYSREF_TASK_HISTORY SYSREF_TASK_KIND_GROUPS SYSREF_TASK_KINDS SYSREF_TASK_RIGHTS SYSREF_TASK_SIGNATURES SYSREF_TASKS SYSREF_UNITS SYSREF_USER_GROUPS SYSREF_USER_GROUPS_REFERENCE SYSREF_USER_SUBSTITUTION SYSREF_USERS SYSREF_USERS_REFERENCE SYSREF_VIEWERS SYSREF_WORKING_TIME_CALENDARS ACCESS_RIGHTS_TABLE_NAME EDMS_ACCESS_TABLE_NAME EDOC_TYPES_TABLE_NAME TEST_DEV_DB_NAME TEST_DEV_SYSTEM_CODE TEST_EDMS_DB_NAME TEST_EDMS_MAIN_CODE TEST_EDMS_MAIN_DB_NAME TEST_EDMS_SECOND_CODE TEST_EDMS_SECOND_DB_NAME TEST_EDMS_SYSTEM_CODE TEST_ISB5_MAIN_CODE TEST_ISB5_SECOND_CODE TEST_SQL_SERVER_2005_NAME TEST_SQL_SERVER_NAME ATTENTION_CAPTION cbsCommandLinks cbsDefault CONFIRMATION_CAPTION ERROR_CAPTION INFORMATION_CAPTION mrCancel mrOk EDOC_VERSION_ACTIVE_STAGE_CODE EDOC_VERSION_DESIGN_STAGE_CODE EDOC_VERSION_OBSOLETE_STAGE_CODE cpDataEnciphermentEnabled cpDigitalSignatureEnabled cpID cpIssuer cpPluginVersion cpSerial cpSubjectName cpSubjSimpleName cpValidFromDate cpValidToDate ISBL_SYNTAX NO_SYNTAX XML_SYNTAX WAIT_BLOCK_AFTER_FINISH_EVENT WAIT_BLOCK_BEFORE_START_EVENT WAIT_BLOCK_DEADLINE_PROPERTY WAIT_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY WAIT_BLOCK_NAME_PROPERTY WAIT_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY SYSRES_COMMON SYSRES_CONST SYSRES_MBFUNC SYSRES_SBDATA SYSRES_SBGUI SYSRES_SBINTF SYSRES_SBREFDSC SYSRES_SQLERRORS SYSRES_SYSCOMP atUser atGroup atRole aemEnabledAlways aemDisabledAlways aemEnabledOnBrowse aemEnabledOnEdit aemDisabledOnBrowseEmpty apBegin apEnd alLeft alRight asmNever asmNoButCustomize asmAsLastTime asmYesButCustomize asmAlways cirCommon cirRevoked ctSignature ctEncode ctSignatureEncode clbUnchecked clbChecked clbGrayed ceISB ceAlways ceNever ctDocument ctReference ctScript ctUnknown ctReport ctDialog ctFunction ctFolder ctEDocument ctTask ctJob ctNotice ctControlJob cfInternal cfDisplay ciUnspecified ciWrite ciRead ckFolder ckEDocument ckTask ckJob ckComponentToken ckAny ckReference ckScript ckReport ckDialog ctISBLEditor ctBevel ctButton ctCheckListBox ctComboBox ctComboEdit ctGrid ctDBCheckBox ctDBComboBox ctDBEdit ctDBEllipsis ctDBMemo ctDBNavigator ctDBRadioGroup ctDBStatusLabel ctEdit ctGroupBox ctInplaceHint ctMemo ctPanel ctListBox ctRadioButton ctRichEdit ctTabSheet ctWebBrowser ctImage ctHyperLink ctLabel ctDBMultiEllipsis ctRibbon ctRichView ctInnerPanel ctPanelGroup ctBitButton cctDate cctInteger cctNumeric cctPick cctReference cctString cctText cltInternal cltPrimary cltGUI dseBeforeOpen dseAfterOpen dseBeforeClose dseAfterClose dseOnValidDelete dseBeforeDelete dseAfterDelete dseAfterDeleteOutOfTransaction dseOnDeleteError dseBeforeInsert dseAfterInsert dseOnValidUpdate dseBeforeUpdate dseOnUpdateRatifiedRecord dseAfterUpdate dseAfterUpdateOutOfTransaction dseOnUpdateError dseAfterScroll dseOnOpenRecord dseOnCloseRecord dseBeforeCancel dseAfterCancel dseOnUpdateDeadlockError dseBeforeDetailUpdate dseOnPrepareUpdate dseOnAnyRequisiteChange dssEdit dssInsert dssBrowse dssInActive dftDate dftShortDate dftDateTime dftTimeStamp dotDays dotHours dotMinutes dotSeconds dtkndLocal dtkndUTC arNone arView arEdit arFull ddaView ddaEdit emLock emEdit emSign emExportWithLock emImportWithUnlock emChangeVersionNote emOpenForModify emChangeLifeStage emDelete emCreateVersion emImport emUnlockExportedWithLock emStart emAbort emReInit emMarkAsReaded emMarkAsUnreaded emPerform emAccept emResume emChangeRights emEditRoute emEditObserver emRecoveryFromLocalCopy emChangeWorkAccessType emChangeEncodeTypeToCertificate emChangeEncodeTypeToPassword emChangeEncodeTypeToNone emChangeEncodeTypeToCertificatePassword emChangeStandardRoute emGetText emOpenForView emMoveToStorage emCreateObject emChangeVersionHidden emDeleteVersion emChangeLifeCycleStage emApprovingSign emExport emContinue emLockFromEdit emUnLockForEdit emLockForServer emUnlockFromServer emDelegateAccessRights emReEncode ecotFile ecotProcess eaGet eaCopy eaCreate eaCreateStandardRoute edltAll edltNothing edltQuery essmText essmCard esvtLast esvtLastActive esvtSpecified edsfExecutive edsfArchive edstSQLServer edstFile edvstNone edvstEDocumentVersionCopy edvstFile edvstTemplate edvstScannedFile vsDefault vsDesign vsActive vsObsolete etNone etCertificate etPassword etCertificatePassword ecException ecWarning ecInformation estAll estApprovingOnly evtLast evtLastActive evtQuery fdtString fdtNumeric fdtInteger fdtDate fdtText fdtUnknown fdtWideString fdtLargeInteger ftInbox ftOutbox ftFavorites ftCommonFolder ftUserFolder ftComponents ftQuickLaunch ftShortcuts ftSearch grhAuto grhX1 grhX2 grhX3 hltText hltRTF hltHTML iffBMP iffJPEG iffMultiPageTIFF iffSinglePageTIFF iffTIFF iffPNG im8bGrayscale im24bRGB im1bMonochrome itBMP itJPEG itWMF itPNG ikhInformation ikhWarning ikhError ikhNoIcon icUnknown icScript icFunction icIntegratedReport icAnalyticReport icDataSetEventHandler icActionHandler icFormEventHandler icLookUpEventHandler icRequisiteChangeEventHandler icBeforeSearchEventHandler icRoleCalculation icSelectRouteEventHandler icBlockPropertyCalculation icBlockQueryParamsEventHandler icChangeSearchResultEventHandler icBlockEventHandler icSubTaskInitEventHandler icEDocDataSetEventHandler icEDocLookUpEventHandler icEDocActionHandler icEDocFormEventHandler icEDocRequisiteChangeEventHandler icStructuredConversionRule icStructuredConversionEventBefore icStructuredConversionEventAfter icWizardEventHandler icWizardFinishEventHandler icWizardStepEventHandler icWizardStepFinishEventHandler icWizardActionEnableEventHandler icWizardActionExecuteEventHandler icCreateJobsHandler icCreateNoticesHandler icBeforeLookUpEventHandler icAfterLookUpEventHandler icTaskAbortEventHandler icWorkflowBlockActionHandler icDialogDataSetEventHandler icDialogActionHandler icDialogLookUpEventHandler icDialogRequisiteChangeEventHandler icDialogFormEventHandler icDialogValidCloseEventHandler icBlockFormEventHandler icTaskFormEventHandler icReferenceMethod icEDocMethod icDialogMethod icProcessMessageHandler isShow isHide isByUserSettings jkJob jkNotice jkControlJob jtInner jtLeft jtRight jtFull jtCross lbpAbove lbpBelow lbpLeft lbpRight eltPerConnection eltPerUser sfcUndefined sfcBlack sfcGreen sfcRed sfcBlue sfcOrange sfcLilac sfsItalic sfsStrikeout sfsNormal ldctStandardRoute ldctWizard ldctScript ldctFunction ldctRouteBlock ldctIntegratedReport ldctAnalyticReport ldctReferenceType ldctEDocumentType ldctDialog ldctServerEvents mrcrtNone mrcrtUser mrcrtMaximal mrcrtCustom vtEqual vtGreaterOrEqual vtLessOrEqual vtRange rdYesterday rdToday rdTomorrow rdThisWeek rdThisMonth rdThisYear rdNextMonth rdNextWeek rdLastWeek rdLastMonth rdWindow rdFile rdPrinter rdtString rdtNumeric rdtInteger rdtDate rdtReference rdtAccount rdtText rdtPick rdtUnknown rdtLargeInteger rdtDocument reOnChange reOnChangeValues ttGlobal ttLocal ttUser ttSystem ssmBrowse ssmSelect ssmMultiSelect ssmBrowseModal smSelect smLike smCard stNone stAuthenticating stApproving sctString sctStream sstAnsiSort sstNaturalSort svtEqual svtContain soatString soatNumeric soatInteger soatDatetime soatReferenceRecord soatText soatPick soatBoolean soatEDocument soatAccount soatIntegerCollection soatNumericCollection soatStringCollection soatPickCollection soatDatetimeCollection soatBooleanCollection soatReferenceRecordCollection soatEDocumentCollection soatAccountCollection soatContents soatUnknown tarAbortByUser tarAbortByWorkflowException tvtAllWords tvtExactPhrase tvtAnyWord usNone usCompleted usRedSquare usBlueSquare usYellowSquare usGreenSquare usOrangeSquare usPurpleSquare usFollowUp utUnknown utUser utDeveloper utAdministrator utSystemDeveloper utDisconnected btAnd btDetailAnd btOr btNotOr btOnly vmView vmSelect vmNavigation vsmSingle vsmMultiple vsmMultipleCheck vsmNoSelection wfatPrevious wfatNext wfatCancel wfatFinish wfepUndefined wfepText3 wfepText6 wfepText9 wfepSpinEdit wfepDropDown wfepRadioGroup wfepFlag wfepText12 wfepText15 wfepText18 wfepText21 wfepText24 wfepText27 wfepText30 wfepRadioGroupColumn1 wfepRadioGroupColumn2 wfepRadioGroupColumn3 wfetQueryParameter wfetText wfetDelimiter wfetLabel wptString wptInteger wptNumeric wptBoolean wptDateTime wptPick wptText wptUser wptUserList wptEDocumentInfo wptEDocumentInfoList wptReferenceRecordInfo wptReferenceRecordInfoList wptFolderInfo wptTaskInfo wptContents wptFileName wptDate wsrComplete wsrGoNext wsrGoPrevious wsrCustom wsrCancel wsrGoFinal wstForm wstEDocument wstTaskCard wstReferenceRecordCard wstFinal waAll waPerformers waManual wsbStart wsbFinish wsbNotice wsbStep wsbDecision wsbWait wsbMonitor wsbScript wsbConnector wsbSubTask wsbLifeCycleStage wsbPause wdtInteger wdtFloat wdtString wdtPick wdtDateTime wdtBoolean wdtTask wdtJob wdtFolder wdtEDocument wdtReferenceRecord wdtUser wdtGroup wdtRole wdtIntegerCollection wdtFloatCollection wdtStringCollection wdtPickCollection wdtDateTimeCollection wdtBooleanCollection wdtTaskCollection wdtJobCollection wdtFolderCollection wdtEDocumentCollection wdtReferenceRecordCollection wdtUserCollection wdtGroupCollection wdtRoleCollection wdtContents wdtUserList wdtSearchDescription wdtDeadLine wdtPickSet wdtAccountCollection wiLow wiNormal wiHigh wrtSoft wrtHard wsInit wsRunning wsDone wsControlled wsAborted wsContinued wtmFull wtmFromCurrent wtmOnlyCurrent ", + class: + "AltState Application CallType ComponentTokens CreatedJobs CreatedNotices ControlState DialogResult Dialogs EDocuments EDocumentVersionSource Folders GlobalIDs Job Jobs InputValue LookUpReference LookUpRequisiteNames LookUpSearch Object ParentComponent Processes References Requisite ReportName Reports Result Scripts Searches SelectedAttachments SelectedItems SelectMode Sender ServerEvents ServiceFactory ShiftState SubTask SystemDialogs Tasks Wizard Wizards Work ВызовСпособ ИмяОтчета РеквЗнач ", + literal: "null true false nil ", + }, + s = { + begin: "\\.\\s*" + e.UNDERSCORE_IDENT_RE, + keywords: o, + relevance: 0, + }, + l = { + className: "type", + begin: + ":[ \\t]*(" + + "IApplication IAccessRights IAccountRepository IAccountSelectionRestrictions IAction IActionList IAdministrationHistoryDescription IAnchors IApplication IArchiveInfo IAttachment IAttachmentList ICheckListBox ICheckPointedList IColumn IComponent IComponentDescription IComponentToken IComponentTokenFactory IComponentTokenInfo ICompRecordInfo IConnection IContents IControl IControlJob IControlJobInfo IControlList ICrypto ICrypto2 ICustomJob ICustomJobInfo ICustomListBox ICustomObjectWizardStep ICustomWork ICustomWorkInfo IDataSet IDataSetAccessInfo IDataSigner IDateCriterion IDateRequisite IDateRequisiteDescription IDateValue IDeaAccessRights IDeaObjectInfo IDevelopmentComponentLock IDialog IDialogFactory IDialogPickRequisiteItems IDialogsFactory IDICSFactory IDocRequisite IDocumentInfo IDualListDialog IECertificate IECertificateInfo IECertificates IEditControl IEditorForm IEdmsExplorer IEdmsObject IEdmsObjectDescription IEdmsObjectFactory IEdmsObjectInfo IEDocument IEDocumentAccessRights IEDocumentDescription IEDocumentEditor IEDocumentFactory IEDocumentInfo IEDocumentStorage IEDocumentVersion IEDocumentVersionListDialog IEDocumentVersionSource IEDocumentWizardStep IEDocVerSignature IEDocVersionState IEnabledMode IEncodeProvider IEncrypter IEvent IEventList IException IExternalEvents IExternalHandler IFactory IField IFileDialog IFolder IFolderDescription IFolderDialog IFolderFactory IFolderInfo IForEach IForm IFormTitle IFormWizardStep IGlobalIDFactory IGlobalIDInfo IGrid IHasher IHistoryDescription IHyperLinkControl IImageButton IImageControl IInnerPanel IInplaceHint IIntegerCriterion IIntegerList IIntegerRequisite IIntegerValue IISBLEditorForm IJob IJobDescription IJobFactory IJobForm IJobInfo ILabelControl ILargeIntegerCriterion ILargeIntegerRequisite ILargeIntegerValue ILicenseInfo ILifeCycleStage IList IListBox ILocalIDInfo ILocalization ILock IMemoryDataSet IMessagingFactory IMetadataRepository INotice INoticeInfo INumericCriterion INumericRequisite INumericValue IObject IObjectDescription IObjectImporter IObjectInfo IObserver IPanelGroup IPickCriterion IPickProperty IPickRequisite IPickRequisiteDescription IPickRequisiteItem IPickRequisiteItems IPickValue IPrivilege IPrivilegeList IProcess IProcessFactory IProcessMessage IProgress IProperty IPropertyChangeEvent IQuery IReference IReferenceCriterion IReferenceEnabledMode IReferenceFactory IReferenceHistoryDescription IReferenceInfo IReferenceRecordCardWizardStep IReferenceRequisiteDescription IReferencesFactory IReferenceValue IRefRequisite IReport IReportFactory IRequisite IRequisiteDescription IRequisiteDescriptionList IRequisiteFactory IRichEdit IRouteStep IRule IRuleList ISchemeBlock IScript IScriptFactory ISearchCriteria ISearchCriterion ISearchDescription ISearchFactory ISearchFolderInfo ISearchForObjectDescription ISearchResultRestrictions ISecuredContext ISelectDialog IServerEvent IServerEventFactory IServiceDialog IServiceFactory ISignature ISignProvider ISignProvider2 ISignProvider3 ISimpleCriterion IStringCriterion IStringList IStringRequisite IStringRequisiteDescription IStringValue ISystemDialogsFactory ISystemInfo ITabSheet ITask ITaskAbortReasonInfo ITaskCardWizardStep ITaskDescription ITaskFactory ITaskInfo ITaskRoute ITextCriterion ITextRequisite ITextValue ITreeListSelectDialog IUser IUserList IValue IView IWebBrowserControl IWizard IWizardAction IWizardFactory IWizardFormElement IWizardParam IWizardPickParam IWizardReferenceParam IWizardStep IWorkAccessRights IWorkDescription IWorkflowAskableParam IWorkflowAskableParams IWorkflowBlock IWorkflowBlockResult IWorkflowEnabledMode IWorkflowParam IWorkflowPickParam IWorkflowReferenceParam IWorkState IWorkTreeCustomNode IWorkTreeJobNode IWorkTreeTaskNode IXMLEditorForm SBCrypto " + .trim() + .replace(/\s/g, "|") + + ")", + end: "[ \\t]*=", + excludeEnd: !0, + }, + c = { + className: "variable", + keywords: o, + begin: t, + relevance: 0, + contains: [l, s], + }, + _ = "[A-Za-zА-Яа-яёЁ_][A-Za-zА-Яа-яёЁ_0-9]*\\("; + return { + name: "ISBL", + case_insensitive: !0, + keywords: o, + illegal: "\\$|\\?|%|,|;$|~|#|@|)?\\s+)+" + + e.UNDERSCORE_IDENT_RE + + "\\s*\\(", + returnBegin: !0, + end: /[{;=]/, + excludeEnd: !0, + keywords: n, + contains: [ + { + begin: e.UNDERSCORE_IDENT_RE + "\\s*\\(", + returnBegin: !0, + relevance: 0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: n, + relevance: 0, + contains: [ + a, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + r, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + r, + a, + ], + }; + }, + DE = [ + "as", + "in", + "of", + "if", + "for", + "while", + "finally", + "var", + "new", + "function", + "do", + "return", + "void", + "else", + "break", + "catch", + "instanceof", + "with", + "throw", + "case", + "default", + "try", + "switch", + "continue", + "typeof", + "delete", + "let", + "yield", + "const", + "class", + "debugger", + "async", + "await", + "static", + "import", + "from", + "export", + "extends", + ], + ME = ["true", "false", "null", "undefined", "NaN", "Infinity"], + LE = [].concat( + [ + "setInterval", + "setTimeout", + "clearInterval", + "clearTimeout", + "require", + "exports", + "eval", + "isFinite", + "isNaN", + "parseFloat", + "parseInt", + "decodeURI", + "decodeURIComponent", + "encodeURI", + "encodeURIComponent", + "escape", + "unescape", + ], + [ + "arguments", + "this", + "super", + "console", + "window", + "document", + "localStorage", + "module", + "global", + ], + [ + "Intl", + "DataView", + "Number", + "Math", + "Date", + "String", + "RegExp", + "Object", + "Function", + "Boolean", + "Error", + "Symbol", + "Set", + "Map", + "WeakSet", + "WeakMap", + "Proxy", + "Reflect", + "JSON", + "Promise", + "Float64Array", + "Int16Array", + "Int32Array", + "Int8Array", + "Uint16Array", + "Uint32Array", + "Float32Array", + "Array", + "Uint8Array", + "Uint8ClampedArray", + "ArrayBuffer", + "BigInt64Array", + "BigUint64Array", + "BigInt", + ], + [ + "EvalError", + "InternalError", + "RangeError", + "ReferenceError", + "SyntaxError", + "TypeError", + "URIError", + ], + ); +function wE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function xE(e) { + return PE("(?=", e, ")"); +} +function PE() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return wE(e); + }) + .join(""); + return a; +} +var kE = function (e) { + var t = "[A-Za-z$_][0-9A-Za-z$_]*", + n = "<>", + a = "", + r = { + begin: /<[A-Za-z0-9\\._:-]+/, + end: /\/[A-Za-z0-9\\._:-]+>|\/>/, + isTrulyOpeningTag: function (e, t) { + var n = e[0].length + e.index, + a = e.input[n]; + "<" !== a + ? ">" === a && + ((function (e, t) { + var n = t.after, + a = "", + returnBegin: !0, + end: "\\s*=>", + contains: [ + { + className: "params", + variants: [ + { begin: e.UNDERSCORE_IDENT_RE, relevance: 0 }, + { className: null, begin: /\(\s*\)/, skip: !0 }, + { + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: i, + contains: S, + }, + ], + }, + ], + }, + { begin: /,/, relevance: 0 }, + { className: "", begin: /\s/, end: /\s*/, skip: !0 }, + { + variants: [ + { begin: n, end: a }, + { begin: r.begin, "on:begin": r.isTrulyOpeningTag, end: r.end }, + ], + subLanguage: "xml", + contains: [ + { begin: r.begin, end: r.end, skip: !0, contains: ["self"] }, + ], + }, + ], + relevance: 0, + }, + { + className: "function", + beginKeywords: "function", + end: /[{;]/, + excludeEnd: !0, + keywords: i, + contains: ["self", e.inherit(e.TITLE_MODE, { begin: t }), b], + illegal: /%/, + }, + { beginKeywords: "while if switch catch for" }, + { + className: "function", + begin: + e.UNDERSCORE_IDENT_RE + + "\\([^()]*(\\([^()]*(\\([^()]*\\)[^()]*)*\\)[^()]*)*\\)\\s*\\{", + returnBegin: !0, + contains: [b, e.inherit(e.TITLE_MODE, { begin: t })], + }, + { variants: [{ begin: "\\." + t }, { begin: "\\$" + t }], relevance: 0 }, + { + className: "class", + beginKeywords: "class", + end: /[{;=]/, + excludeEnd: !0, + illegal: /[:"[\]]/, + contains: [{ beginKeywords: "extends" }, e.UNDERSCORE_TITLE_MODE], + }, + { + begin: /\b(?=constructor)/, + end: /[{;]/, + excludeEnd: !0, + contains: [e.inherit(e.TITLE_MODE, { begin: t }), "self", b], + }, + { + begin: "(get|set)\\s+(?=" + t + "\\()", + end: /\{/, + keywords: "get set", + contains: [e.inherit(e.TITLE_MODE, { begin: t }), { begin: /\(\)/ }, b], + }, + { begin: /\$[(.]/ }, + ], + }; +}; +var UE = function (e) { + var t = { + className: "params", + begin: /\(/, + end: /\)/, + contains: [ + { + begin: /[\w-]+ *=/, + returnBegin: !0, + relevance: 0, + contains: [{ className: "attr", begin: /[\w-]+/ }], + }, + ], + relevance: 0, + }; + return { + name: "JBoss CLI", + aliases: ["wildfly-cli"], + keywords: { + $pattern: "[a-z-]+", + keyword: + "alias batch cd clear command connect connection-factory connection-info data-source deploy deployment-info deployment-overlay echo echo-dmr help history if jdbc-driver-info jms-queue|20 jms-topic|20 ls patch pwd quit read-attribute read-operation reload rollout-plan run-batch set shutdown try unalias undeploy unset version xa-data-source", + literal: "true false", + }, + contains: [ + e.HASH_COMMENT_MODE, + e.QUOTE_STRING_MODE, + { className: "params", begin: /--[\w\-=\/]+/ }, + { className: "function", begin: /:[\w\-.]+/, relevance: 0 }, + { className: "string", begin: /\B([\/.])[\w\-.\/=]+/ }, + t, + ], + }; +}; +var FE = function (e) { + var t = { literal: "true false null" }, + n = [e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + a = [e.QUOTE_STRING_MODE, e.C_NUMBER_MODE], + r = { + end: ",", + endsWithParent: !0, + excludeEnd: !0, + contains: a, + keywords: t, + }, + i = { + begin: /\{/, + end: /\}/, + contains: [ + { + className: "attr", + begin: /"/, + end: /"/, + contains: [e.BACKSLASH_ESCAPE], + illegal: "\\n", + }, + e.inherit(r, { begin: /:/ }), + ].concat(n), + illegal: "\\S", + }, + o = { begin: "\\[", end: "\\]", contains: [e.inherit(r)], illegal: "\\S" }; + return ( + a.push(i, o), + n.forEach(function (e) { + a.push(e); + }), + { name: "JSON", contains: a, keywords: t, illegal: "\\S" } + ); +}; +var BE = function (e) { + var t = "[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*", + n = { + $pattern: t, + keyword: [ + "baremodule", + "begin", + "break", + "catch", + "ccall", + "const", + "continue", + "do", + "else", + "elseif", + "end", + "export", + "false", + "finally", + "for", + "function", + "global", + "if", + "import", + "in", + "isa", + "let", + "local", + "macro", + "module", + "quote", + "return", + "true", + "try", + "using", + "where", + "while", + ], + literal: [ + "ARGS", + "C_NULL", + "DEPOT_PATH", + "ENDIAN_BOM", + "ENV", + "Inf", + "Inf16", + "Inf32", + "Inf64", + "InsertionSort", + "LOAD_PATH", + "MergeSort", + "NaN", + "NaN16", + "NaN32", + "NaN64", + "PROGRAM_FILE", + "QuickSort", + "RoundDown", + "RoundFromZero", + "RoundNearest", + "RoundNearestTiesAway", + "RoundNearestTiesUp", + "RoundToZero", + "RoundUp", + "VERSION|0", + "devnull", + "false", + "im", + "missing", + "nothing", + "pi", + "stderr", + "stdin", + "stdout", + "true", + "undef", + "π", + "ℯ", + ], + built_in: [ + "AbstractArray", + "AbstractChannel", + "AbstractChar", + "AbstractDict", + "AbstractDisplay", + "AbstractFloat", + "AbstractIrrational", + "AbstractMatrix", + "AbstractRange", + "AbstractSet", + "AbstractString", + "AbstractUnitRange", + "AbstractVecOrMat", + "AbstractVector", + "Any", + "ArgumentError", + "Array", + "AssertionError", + "BigFloat", + "BigInt", + "BitArray", + "BitMatrix", + "BitSet", + "BitVector", + "Bool", + "BoundsError", + "CapturedException", + "CartesianIndex", + "CartesianIndices", + "Cchar", + "Cdouble", + "Cfloat", + "Channel", + "Char", + "Cint", + "Cintmax_t", + "Clong", + "Clonglong", + "Cmd", + "Colon", + "Complex", + "ComplexF16", + "ComplexF32", + "ComplexF64", + "CompositeException", + "Condition", + "Cptrdiff_t", + "Cshort", + "Csize_t", + "Cssize_t", + "Cstring", + "Cuchar", + "Cuint", + "Cuintmax_t", + "Culong", + "Culonglong", + "Cushort", + "Cvoid", + "Cwchar_t", + "Cwstring", + "DataType", + "DenseArray", + "DenseMatrix", + "DenseVecOrMat", + "DenseVector", + "Dict", + "DimensionMismatch", + "Dims", + "DivideError", + "DomainError", + "EOFError", + "Enum", + "ErrorException", + "Exception", + "ExponentialBackOff", + "Expr", + "Float16", + "Float32", + "Float64", + "Function", + "GlobalRef", + "HTML", + "IO", + "IOBuffer", + "IOContext", + "IOStream", + "IdDict", + "IndexCartesian", + "IndexLinear", + "IndexStyle", + "InexactError", + "InitError", + "Int", + "Int128", + "Int16", + "Int32", + "Int64", + "Int8", + "Integer", + "InterruptException", + "InvalidStateException", + "Irrational", + "KeyError", + "LinRange", + "LineNumberNode", + "LinearIndices", + "LoadError", + "MIME", + "Matrix", + "Method", + "MethodError", + "Missing", + "MissingException", + "Module", + "NTuple", + "NamedTuple", + "Nothing", + "Number", + "OrdinalRange", + "OutOfMemoryError", + "OverflowError", + "Pair", + "PartialQuickSort", + "PermutedDimsArray", + "Pipe", + "ProcessFailedException", + "Ptr", + "QuoteNode", + "Rational", + "RawFD", + "ReadOnlyMemoryError", + "Real", + "ReentrantLock", + "Ref", + "Regex", + "RegexMatch", + "RoundingMode", + "SegmentationFault", + "Set", + "Signed", + "Some", + "StackOverflowError", + "StepRange", + "StepRangeLen", + "StridedArray", + "StridedMatrix", + "StridedVecOrMat", + "StridedVector", + "String", + "StringIndexError", + "SubArray", + "SubString", + "SubstitutionString", + "Symbol", + "SystemError", + "Task", + "TaskFailedException", + "Text", + "TextDisplay", + "Timer", + "Tuple", + "Type", + "TypeError", + "TypeVar", + "UInt", + "UInt128", + "UInt16", + "UInt32", + "UInt64", + "UInt8", + "UndefInitializer", + "UndefKeywordError", + "UndefRefError", + "UndefVarError", + "Union", + "UnionAll", + "UnitRange", + "Unsigned", + "Val", + "Vararg", + "VecElement", + "VecOrMat", + "Vector", + "VersionNumber", + "WeakKeyDict", + "WeakRef", + ], + }, + a = { keywords: n, illegal: /<\// }, + r = { className: "subst", begin: /\$\(/, end: /\)/, keywords: n }, + i = { className: "variable", begin: "\\$" + t }, + o = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, r, i], + variants: [ + { begin: /\w*"""/, end: /"""\w*/, relevance: 10 }, + { begin: /\w*"/, end: /"\w*/ }, + ], + }, + s = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, r, i], + begin: "`", + end: "`", + }, + l = { className: "meta", begin: "@" + t }; + return ( + (a.name = "Julia"), + (a.contains = [ + { + className: "number", + begin: + /(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/, + relevance: 0, + }, + { className: "string", begin: /'(.|\\[xXuU][a-zA-Z0-9]+)'/ }, + o, + s, + l, + { + className: "comment", + variants: [ + { begin: "#=", end: "=#", relevance: 10 }, + { begin: "#", end: "$" }, + ], + }, + e.HASH_COMMENT_MODE, + { + className: "keyword", + begin: "\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b", + }, + { begin: /<:/ }, + ]), + (r.contains = a.contains), + a + ); +}; +var GE = function (e) { + return { + name: "Julia REPL", + contains: [ + { + className: "meta", + begin: /^julia>/, + relevance: 10, + starts: { end: /^(?![ ]{6})/, subLanguage: "julia" }, + aliases: ["jldoctest"], + }, + ], + }; + }, + YE = "\\.(".concat("[0-9](_*[0-9])*", ")"), + HE = "[0-9a-fA-F](_*[0-9a-fA-F])*", + VE = { + className: "number", + variants: [ + { + begin: + "(\\b(" + .concat("[0-9](_*[0-9])*", ")((") + .concat(YE, ")|\\.)?|(") + .concat(YE, "))") + + "[eE][+-]?(".concat("[0-9](_*[0-9])*", ")[fFdD]?\\b"), + }, + { + begin: "\\b(" + .concat("[0-9](_*[0-9])*", ")((") + .concat(YE, ")[fFdD]?\\b|\\.([fFdD]\\b)?)"), + }, + { begin: "(".concat(YE, ")[fFdD]?\\b") }, + { begin: "\\b(".concat("[0-9](_*[0-9])*", ")[fFdD]\\b") }, + { + begin: + "\\b0[xX]((" + .concat(HE, ")\\.?|(") + .concat(HE, ")?\\.(") + .concat(HE, "))") + + "[pP][+-]?(".concat("[0-9](_*[0-9])*", ")[fFdD]?\\b"), + }, + { begin: "\\b(0|[1-9](_*[0-9])*)[lL]?\\b" }, + { begin: "\\b0[xX](".concat(HE, ")[lL]?\\b") }, + { begin: "\\b0(_*[0-7])*[lL]?\\b" }, + { begin: "\\b0[bB][01](_*[01])*[lL]?\\b" }, + ], + relevance: 0, + }; +var qE = function (e) { + var t = { + keyword: + "abstract as val var vararg get set class object open private protected public noinline crossinline dynamic final enum if else do while for when throw try catch finally import package is in fun override companion reified inline lateinit init interface annotation data sealed internal infix operator out by constructor super tailrec where const inner suspend typealias external expect actual", + built_in: + "Byte Short Char Int Long Boolean Float Double Void Unit Nothing", + literal: "true false null", + }, + n = { className: "symbol", begin: e.UNDERSCORE_IDENT_RE + "@" }, + a = { + className: "subst", + begin: /\$\{/, + end: /\}/, + contains: [e.C_NUMBER_MODE], + }, + r = { className: "variable", begin: "\\$" + e.UNDERSCORE_IDENT_RE }, + i = { + className: "string", + variants: [ + { begin: '"""', end: '"""(?=[^"])', contains: [r, a] }, + { begin: "'", end: "'", illegal: /\n/, contains: [e.BACKSLASH_ESCAPE] }, + { + begin: '"', + end: '"', + illegal: /\n/, + contains: [e.BACKSLASH_ESCAPE, r, a], + }, + ], + }; + a.contains.push(i); + var o = { + className: "meta", + begin: + "@(?:file|property|field|get|set|receiver|param|setparam|delegate)\\s*:(?:\\s*" + + e.UNDERSCORE_IDENT_RE + + ")?", + }, + s = { + className: "meta", + begin: "@" + e.UNDERSCORE_IDENT_RE, + contains: [ + { + begin: /\(/, + end: /\)/, + contains: [e.inherit(i, { className: "meta-string" })], + }, + ], + }, + l = VE, + c = e.COMMENT("/\\*", "\\*/", { contains: [e.C_BLOCK_COMMENT_MODE] }), + _ = { + variants: [ + { className: "type", begin: e.UNDERSCORE_IDENT_RE }, + { begin: /\(/, end: /\)/, contains: [] }, + ], + }, + d = _; + return ( + (d.variants[1].contains = [_]), + (_.variants[1].contains = [d]), + { + name: "Kotlin", + aliases: ["kt", "kts"], + keywords: t, + contains: [ + e.COMMENT("/\\*\\*", "\\*/", { + relevance: 0, + contains: [{ className: "doctag", begin: "@[A-Za-z]+" }], + }), + e.C_LINE_COMMENT_MODE, + c, + { + className: "keyword", + begin: /\b(break|continue|return|this)\b/, + starts: { contains: [{ className: "symbol", begin: /@\w+/ }] }, + }, + n, + o, + s, + { + className: "function", + beginKeywords: "fun", + end: "[(]|$", + returnBegin: !0, + excludeEnd: !0, + keywords: t, + relevance: 5, + contains: [ + { + begin: e.UNDERSCORE_IDENT_RE + "\\s*\\(", + returnBegin: !0, + relevance: 0, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "type", + begin: //, + keywords: "reified", + relevance: 0, + }, + { + className: "params", + begin: /\(/, + end: /\)/, + endsParent: !0, + keywords: t, + relevance: 0, + contains: [ + { + begin: /:/, + end: /[=,\/]/, + endsWithParent: !0, + contains: [_, e.C_LINE_COMMENT_MODE, c], + relevance: 0, + }, + e.C_LINE_COMMENT_MODE, + c, + o, + s, + i, + e.C_NUMBER_MODE, + ], + }, + c, + ], + }, + { + className: "class", + beginKeywords: "class interface trait", + end: /[:\{(]|$/, + excludeEnd: !0, + illegal: "extends implements", + contains: [ + { beginKeywords: "public protected internal private constructor" }, + e.UNDERSCORE_TITLE_MODE, + { + className: "type", + begin: //, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + { + className: "type", + begin: /[,:]\s*/, + end: /[<\(,]|$/, + excludeBegin: !0, + returnEnd: !0, + }, + o, + s, + ], + }, + i, + { + className: "meta", + begin: "^#!/usr/bin/env", + end: "$", + illegal: "\n", + }, + l, + ], + } + ); +}; +var zE = function (e) { + var t = "[a-zA-Z_][\\w.]*", + n = "<\\?(lasso(script)?|=)", + a = "\\]|\\?>", + r = { + $pattern: "[a-zA-Z_][\\w.]*|&[lg]t;", + literal: + "true false none minimal full all void and or not bw nbw ew new cn ncn lt lte gt gte eq neq rx nrx ft", + built_in: + "array date decimal duration integer map pair string tag xml null boolean bytes keyword list locale queue set stack staticarray local var variable global data self inherited currentcapture givenblock", + keyword: + "cache database_names database_schemanames database_tablenames define_tag define_type email_batch encode_set html_comment handle handle_error header if inline iterate ljax_target link link_currentaction link_currentgroup link_currentrecord link_detail link_firstgroup link_firstrecord link_lastgroup link_lastrecord link_nextgroup link_nextrecord link_prevgroup link_prevrecord log loop namespace_using output_none portal private protect records referer referrer repeating resultset rows search_args search_arguments select sort_args sort_arguments thread_atomic value_list while abort case else fail_if fail_ifnot fail if_empty if_false if_null if_true loop_abort loop_continue loop_count params params_up return return_value run_children soap_definetag soap_lastrequest soap_lastresponse tag_name ascending average by define descending do equals frozen group handle_failure import in into join let match max min on order parent protected provide public require returnhome skip split_thread sum take thread to trait type where with yield yieldhome", + }, + i = e.COMMENT("\x3c!--", "--\x3e", { relevance: 0 }), + o = { + className: "meta", + begin: "\\[noprocess\\]", + starts: { end: "\\[/noprocess\\]", returnEnd: !0, contains: [i] }, + }, + s = { className: "meta", begin: "\\[/noprocess|" + n }, + l = { className: "symbol", begin: "'[a-zA-Z_][\\w.]*'" }, + c = [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.inherit(e.C_NUMBER_MODE, { + begin: e.C_NUMBER_RE + "|(-?infinity|NaN)\\b", + }), + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { className: "string", begin: "`", end: "`" }, + { + variants: [ + { begin: "[#$][a-zA-Z_][\\w.]*" }, + { begin: "#", end: "\\d+", illegal: "\\W" }, + ], + }, + { className: "type", begin: "::\\s*", end: t, illegal: "\\W" }, + { + className: "params", + variants: [ + { begin: "-(?!infinity)[a-zA-Z_][\\w.]*", relevance: 0 }, + { begin: "(\\.\\.\\.)" }, + ], + }, + { begin: /(->|\.)\s*/, relevance: 0, contains: [l] }, + { + className: "class", + beginKeywords: "define", + returnEnd: !0, + end: "\\(|=>", + contains: [ + e.inherit(e.TITLE_MODE, { + begin: "[a-zA-Z_][\\w.]*(=(?!>))?|[-+*/%](?!>)", + }), + ], + }, + ]; + return { + name: "Lasso", + aliases: ["ls", "lassoscript"], + case_insensitive: !0, + keywords: r, + contains: [ + { + className: "meta", + begin: a, + relevance: 0, + starts: { end: "\\[|" + n, returnEnd: !0, relevance: 0, contains: [i] }, + }, + o, + s, + { + className: "meta", + begin: "\\[no_square_brackets", + starts: { + end: "\\[/no_square_brackets\\]", + keywords: r, + contains: [ + { + className: "meta", + begin: a, + relevance: 0, + starts: { + end: "\\[noprocess\\]|" + n, + returnEnd: !0, + contains: [i], + }, + }, + o, + s, + ].concat(c), + }, + }, + { className: "meta", begin: "\\[", relevance: 0 }, + { className: "meta", begin: "^#!", end: "lasso9$", relevance: 10 }, + ].concat(c), + }; +}; +function WE(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function $E() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return WE(e); + }) + .join("|") + + ")"; + return a; +} +var QE = function (e) { + var t, + n = [ + { begin: /\^{6}[0-9a-f]{6}/ }, + { begin: /\^{5}[0-9a-f]{5}/ }, + { begin: /\^{4}[0-9a-f]{4}/ }, + { begin: /\^{3}[0-9a-f]{3}/ }, + { begin: /\^{2}[0-9a-f]{2}/ }, + { begin: /\^{2}[\u0000-\u007f]/ }, + ], + a = [ + { + className: "keyword", + begin: /\\/, + relevance: 0, + contains: [ + { + endsParent: !0, + begin: $E.apply( + void 0, + c( + [ + "(?:NeedsTeXFormat|RequirePackage|GetIdInfo)", + "Provides(?:Expl)?(?:Package|Class|File)", + "(?:DeclareOption|ProcessOptions)", + "(?:documentclass|usepackage|input|include)", + "makeat(?:letter|other)", + "ExplSyntax(?:On|Off)", + "(?:new|renew|provide)?command", + "(?:re)newenvironment", + "(?:New|Renew|Provide|Declare)(?:Expandable)?DocumentCommand", + "(?:New|Renew|Provide|Declare)DocumentEnvironment", + "(?:(?:e|g|x)?def|let)", + "(?:begin|end)", + "(?:part|chapter|(?:sub){0,2}section|(?:sub)?paragraph)", + "caption", + "(?:label|(?:eq|page|name)?ref|(?:paren|foot|super)?cite)", + "(?:alpha|beta|[Gg]amma|[Dd]elta|(?:var)?epsilon|zeta|eta|[Tt]heta|vartheta)", + "(?:iota|(?:var)?kappa|[Ll]ambda|mu|nu|[Xx]i|[Pp]i|varpi|(?:var)rho)", + "(?:[Ss]igma|varsigma|tau|[Uu]psilon|[Pp]hi|varphi|chi|[Pp]si|[Oo]mega)", + "(?:frac|sum|prod|lim|infty|times|sqrt|leq|geq|left|right|middle|[bB]igg?)", + "(?:[lr]angle|q?quad|[lcvdi]?dots|d?dot|hat|tilde|bar)", + ].map(function (e) { + return e + "(?![a-zA-Z@:_])"; + }), + ), + ), + }, + { + endsParent: !0, + begin: new RegExp( + [ + "(?:__)?[a-zA-Z]{2,}_[a-zA-Z](?:_?[a-zA-Z])+:[a-zA-Z]*", + "[lgc]__?[a-zA-Z](?:_?[a-zA-Z])*_[a-zA-Z]{2,}", + "[qs]__?[a-zA-Z](?:_?[a-zA-Z])+", + "use(?:_i)?:[a-zA-Z]*", + "(?:else|fi|or):", + "(?:if|cs|exp):w", + "(?:hbox|vbox):n", + "::[a-zA-Z]_unbraced", + "::[a-zA-Z:]", + ] + .map(function (e) { + return e + "(?![a-zA-Z:_])"; + }) + .join("|"), + ), + }, + { endsParent: !0, variants: n }, + { + endsParent: !0, + relevance: 0, + variants: [{ begin: /[a-zA-Z@]+/ }, { begin: /[^a-zA-Z@]?/ }], + }, + ], + }, + { className: "params", relevance: 0, begin: /#+\d?/ }, + { variants: n }, + { className: "built_in", relevance: 0, begin: /[$&^_]/ }, + { className: "meta", begin: "% !TeX", end: "$", relevance: 10 }, + e.COMMENT("%", "$", { relevance: 0 }), + ], + r = { begin: /\{/, end: /\}/, relevance: 0, contains: ["self"].concat(a) }, + i = e.inherit(r, { relevance: 0, endsParent: !0, contains: [r].concat(a) }), + o = { + begin: /\[/, + end: /\]/, + endsParent: !0, + relevance: 0, + contains: [r].concat(a), + }, + s = { begin: /\s+/, relevance: 0 }, + l = [i], + _ = [o], + d = function (e, t) { + return { + contains: [s], + starts: { relevance: 0, contains: e, starts: t }, + }; + }, + u = function (e, t) { + return { + begin: "\\\\" + e + "(?![a-zA-Z@:_])", + keywords: { $pattern: /\\[a-zA-Z]+/, keyword: "\\" + e }, + relevance: 0, + contains: [s], + starts: t, + }; + }, + m = function (t, n) { + return e.inherit( + { + begin: "\\\\begin(?=[ \t]*(\\r?\\n[ \t]*)?\\{" + t + "\\})", + keywords: { $pattern: /\\[a-zA-Z]+/, keyword: "\\begin" }, + relevance: 0, + }, + d(l, n), + ); + }, + p = function () { + var t = + arguments.length > 0 && void 0 !== arguments[0] + ? arguments[0] + : "string"; + return e.END_SAME_AS_BEGIN({ + className: t, + begin: /(.|\r?\n)/, + end: /(.|\r?\n)/, + excludeBegin: !0, + excludeEnd: !0, + endsParent: !0, + }); + }, + g = function (e) { + return { className: "string", end: "(?=\\\\end\\{" + e + "\\})" }; + }, + E = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] + ? arguments[0] + : "string"; + return { + relevance: 0, + begin: /\{/, + starts: { + endsParent: !0, + contains: [ + { + className: e, + end: /(?=\})/, + endsParent: !0, + contains: [ + { begin: /\{/, end: /\}/, relevance: 0, contains: ["self"] }, + ], + }, + ], + }, + }; + }, + S = [].concat( + c( + ["verb", "lstinline"].map(function (e) { + return u(e, { contains: [p()] }); + }), + ), + [ + u("mint", d(l, { contains: [p()] })), + u("mintinline", d(l, { contains: [E(), p()] })), + u("url", { contains: [E("link"), E("link")] }), + u("hyperref", { contains: [E("link")] }), + u("href", d(_, { contains: [E("link")] })), + ], + c( + (t = []).concat.apply( + t, + c( + ["", "\\*"].map(function (e) { + return [ + m("verbatim" + e, g("verbatim" + e)), + m("filecontents" + e, d(l, g("filecontents" + e))), + ].concat( + c( + ["", "B", "L"].map(function (t) { + return m(t + "Verbatim" + e, d(_, g(t + "Verbatim" + e))); + }), + ), + ); + }), + ), + ), + ), + [m("minted", d(_, d(l, g("minted"))))], + ); + return { name: "LaTeX", aliases: ["tex"], contains: [].concat(c(S), a) }; +}; +var KE = function (e) { + return { + name: "LDIF", + contains: [ + { + className: "attribute", + begin: "^dn", + end: ": ", + excludeEnd: !0, + starts: { end: "$", relevance: 0 }, + relevance: 10, + }, + { + className: "attribute", + begin: "^\\w", + end: ": ", + excludeEnd: !0, + starts: { end: "$", relevance: 0 }, + }, + { className: "literal", begin: "^-", end: "$" }, + e.HASH_COMMENT_MODE, + ], + }; +}; +var jE = function (e) { + return { + name: "Leaf", + contains: [ + { + className: "function", + begin: "#+[A-Za-z_0-9]*\\(", + end: / \{/, + returnBegin: !0, + excludeEnd: !0, + contains: [ + { className: "keyword", begin: "#+" }, + { className: "title", begin: "[A-Za-z_][A-Za-z_0-9]*" }, + { + className: "params", + begin: "\\(", + end: "\\)", + endsParent: !0, + contains: [ + { className: "string", begin: '"', end: '"' }, + { className: "variable", begin: "[A-Za-z_][A-Za-z_0-9]*" }, + ], + }, + ], + }, + ], + }; + }, + XE = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + ZE = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + JE = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + eS = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + tS = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(), + nS = JE.concat(eS); +var aS = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = nS, + a = "([\\w-]+|@\\{[\\w-]+\\})", + r = [], + i = [], + o = function (e) { + return { className: "string", begin: "~?" + e + ".*?" + e }; + }, + s = function (e, t, n) { + return { className: e, begin: t, relevance: n }; + }, + l = { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: ZE.join(" "), + }, + c = { begin: "\\(", end: "\\)", contains: i, keywords: l, relevance: 0 }; + i.push( + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + o("'"), + o('"'), + e.CSS_NUMBER_MODE, + { + begin: "(url|data-uri)\\(", + starts: { className: "string", end: "[\\)\\n]", excludeEnd: !0 }, + }, + t.HEXCOLOR, + c, + s("variable", "@@?[\\w-]+", 10), + s("variable", "@\\{[\\w-]+\\}"), + s("built_in", "~?`[^`]*?`"), + { + className: "attribute", + begin: "[\\w-]+\\s*:", + end: ":", + returnBegin: !0, + excludeEnd: !0, + }, + t.IMPORTANT, + ); + var _ = i.concat({ begin: /\{/, end: /\}/, contains: r }), + d = { + beginKeywords: "when", + endsWithParent: !0, + contains: [{ beginKeywords: "and not" }].concat(i), + }, + u = { + begin: a + "\\s*:", + returnBegin: !0, + end: /[;}]/, + relevance: 0, + contains: [ + { begin: /-(webkit|moz|ms|o)-/ }, + { + className: "attribute", + begin: "\\b(" + tS.join("|") + ")\\b", + end: /(?=:)/, + starts: { + endsWithParent: !0, + illegal: "[<=$]", + relevance: 0, + contains: i, + }, + }, + ], + }, + m = { + className: "keyword", + begin: + "@(import|media|charset|font-face|(-[a-z]+-)?keyframes|supports|document|namespace|page|viewport|host)\\b", + starts: { + end: "[;{}]", + keywords: l, + returnEnd: !0, + contains: i, + relevance: 0, + }, + }, + p = { + className: "variable", + variants: [ + { begin: "@[\\w-]+\\s*:", relevance: 15 }, + { begin: "@[\\w-]+" }, + ], + starts: { end: "[;}]", returnEnd: !0, contains: _ }, + }, + g = { + variants: [ + { begin: "[\\.#:&\\[>]", end: "[;{}]" }, + { begin: a, end: /\{/ }, + ], + returnBegin: !0, + returnEnd: !0, + illegal: "[<='$\"]", + relevance: 0, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + d, + s("keyword", "all\\b"), + s("variable", "@\\{[\\w-]+\\}"), + { begin: "\\b(" + XE.join("|") + ")\\b", className: "selector-tag" }, + s("selector-tag", a + "%?", 0), + s("selector-id", "#" + a), + s("selector-class", "\\." + a, 0), + s("selector-tag", "&", 0), + t.ATTRIBUTE_SELECTOR_MODE, + { className: "selector-pseudo", begin: ":(" + JE.join("|") + ")" }, + { className: "selector-pseudo", begin: "::(" + eS.join("|") + ")" }, + { begin: "\\(", end: "\\)", contains: _ }, + { begin: "!important" }, + ], + }, + E = { + begin: "[\\w-]+:(:)?" + "(".concat(n.join("|"), ")"), + returnBegin: !0, + contains: [g], + }; + return ( + r.push(e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE, m, p, E, u, g), + { name: "Less", case_insensitive: !0, illegal: "[=>'/<($\"]", contains: r } + ); +}; +var rS = function (e) { + var t = "[a-zA-Z_\\-+\\*\\/<=>&#][a-zA-Z0-9_\\-+*\\/<=>&#!]*", + n = "\\|[^]*?\\|", + a = "(-|\\+)?\\d+(\\.\\d+|\\/\\d+)?((d|e|f|l|s|D|E|F|L|S)(\\+|-)?\\d+)?", + r = { className: "literal", begin: "\\b(t{1}|nil)\\b" }, + i = { + className: "number", + variants: [ + { begin: a, relevance: 0 }, + { begin: "#(b|B)[0-1]+(/[0-1]+)?" }, + { begin: "#(o|O)[0-7]+(/[0-7]+)?" }, + { begin: "#(x|X)[0-9a-fA-F]+(/[0-9a-fA-F]+)?" }, + { begin: "#(c|C)\\(" + a + " +" + a, end: "\\)" }, + ], + }, + o = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + s = e.COMMENT(";", "$", { relevance: 0 }), + l = { begin: "\\*", end: "\\*" }, + c = { className: "symbol", begin: "[:&]" + t }, + _ = { begin: t, relevance: 0 }, + d = { begin: n }, + u = { + contains: [ + i, + o, + l, + c, + { begin: "\\(", end: "\\)", contains: ["self", r, o, i, _] }, + _, + ], + variants: [ + { begin: "['`]\\(", end: "\\)" }, + { begin: "\\(quote ", end: "\\)", keywords: { name: "quote" } }, + { begin: "'" + n }, + ], + }, + m = { + variants: [{ begin: "'" + t }, { begin: "#'" + t + "(::" + t + ")*" }], + }, + p = { begin: "\\(\\s*", end: "\\)" }, + g = { endsWithParent: !0, relevance: 0 }; + return ( + (p.contains = [ + { + className: "name", + variants: [{ begin: t, relevance: 0 }, { begin: n }], + }, + g, + ]), + (g.contains = [u, m, p, r, i, o, s, l, c, d, _]), + { + name: "Lisp", + illegal: /\S/, + contains: [i, e.SHEBANG(), r, o, s, u, m, p, _], + } + ); +}; +var iS = function (e) { + var t = { + className: "variable", + variants: [ + { begin: "\\b([gtps][A-Z]{1}[a-zA-Z0-9]*)(\\[.+\\])?(?:\\s*?)" }, + { begin: "\\$_[A-Z]+" }, + ], + relevance: 0, + }, + n = [ + e.C_BLOCK_COMMENT_MODE, + e.HASH_COMMENT_MODE, + e.COMMENT("--", "$"), + e.COMMENT("[^:]//", "$"), + ], + a = e.inherit(e.TITLE_MODE, { + variants: [ + { begin: "\\b_*rig[A-Z][A-Za-z0-9_\\-]*" }, + { begin: "\\b_[a-z0-9\\-]+" }, + ], + }), + r = e.inherit(e.TITLE_MODE, { begin: "\\b([A-Za-z0-9_\\-]+)\\b" }); + return { + name: "LiveCode", + case_insensitive: !1, + keywords: { + keyword: + "$_COOKIE $_FILES $_GET $_GET_BINARY $_GET_RAW $_POST $_POST_BINARY $_POST_RAW $_SESSION $_SERVER codepoint codepoints segment segments codeunit codeunits sentence sentences trueWord trueWords paragraph after byte bytes english the until http forever descending using line real8 with seventh for stdout finally element word words fourth before black ninth sixth characters chars stderr uInt1 uInt1s uInt2 uInt2s stdin string lines relative rel any fifth items from middle mid at else of catch then third it file milliseconds seconds second secs sec int1 int1s int4 int4s internet int2 int2s normal text item last long detailed effective uInt4 uInt4s repeat end repeat URL in try into switch to words https token binfile each tenth as ticks tick system real4 by dateItems without char character ascending eighth whole dateTime numeric short first ftp integer abbreviated abbr abbrev private case while if div mod wrap and or bitAnd bitNot bitOr bitXor among not in a an within contains ends with begins the keys of keys", + literal: + "SIX TEN FORMFEED NINE ZERO NONE SPACE FOUR FALSE COLON CRLF PI COMMA ENDOFFILE EOF EIGHT FIVE QUOTE EMPTY ONE TRUE RETURN CR LINEFEED RIGHT BACKSLASH NULL SEVEN TAB THREE TWO six ten formfeed nine zero none space four false colon crlf pi comma endoffile eof eight five quote empty one true return cr linefeed right backslash null seven tab three two RIVERSION RISTATE FILE_READ_MODE FILE_WRITE_MODE FILE_WRITE_MODE DIR_WRITE_MODE FILE_READ_UMASK FILE_WRITE_UMASK DIR_READ_UMASK DIR_WRITE_UMASK", + built_in: + "put abs acos aliasReference annuity arrayDecode arrayEncode asin atan atan2 average avg avgDev base64Decode base64Encode baseConvert binaryDecode binaryEncode byteOffset byteToNum cachedURL cachedURLs charToNum cipherNames codepointOffset codepointProperty codepointToNum codeunitOffset commandNames compound compress constantNames cos date dateFormat decompress difference directories diskSpace DNSServers exp exp1 exp2 exp10 extents files flushEvents folders format functionNames geometricMean global globals hasMemory harmonicMean hostAddress hostAddressToName hostName hostNameToAddress isNumber ISOToMac itemOffset keys len length libURLErrorData libUrlFormData libURLftpCommand libURLLastHTTPHeaders libURLLastRHHeaders libUrlMultipartFormAddPart libUrlMultipartFormData libURLVersion lineOffset ln ln1 localNames log log2 log10 longFilePath lower macToISO matchChunk matchText matrixMultiply max md5Digest median merge messageAuthenticationCode messageDigest millisec millisecs millisecond milliseconds min monthNames nativeCharToNum normalizeText num number numToByte numToChar numToCodepoint numToNativeChar offset open openfiles openProcesses openProcessIDs openSockets paragraphOffset paramCount param params peerAddress pendingMessages platform popStdDev populationStandardDeviation populationVariance popVariance processID random randomBytes replaceText result revCreateXMLTree revCreateXMLTreeFromFile revCurrentRecord revCurrentRecordIsFirst revCurrentRecordIsLast revDatabaseColumnCount revDatabaseColumnIsNull revDatabaseColumnLengths revDatabaseColumnNames revDatabaseColumnNamed revDatabaseColumnNumbered revDatabaseColumnTypes revDatabaseConnectResult revDatabaseCursors revDatabaseID revDatabaseTableNames revDatabaseType revDataFromQuery revdb_closeCursor revdb_columnbynumber revdb_columncount revdb_columnisnull revdb_columnlengths revdb_columnnames revdb_columntypes revdb_commit revdb_connect revdb_connections revdb_connectionerr revdb_currentrecord revdb_cursorconnection revdb_cursorerr revdb_cursors revdb_dbtype revdb_disconnect revdb_execute revdb_iseof revdb_isbof revdb_movefirst revdb_movelast revdb_movenext revdb_moveprev revdb_query revdb_querylist revdb_recordcount revdb_rollback revdb_tablenames revGetDatabaseDriverPath revNumberOfRecords revOpenDatabase revOpenDatabases revQueryDatabase revQueryDatabaseBlob revQueryResult revQueryIsAtStart revQueryIsAtEnd revUnixFromMacPath revXMLAttribute revXMLAttributes revXMLAttributeValues revXMLChildContents revXMLChildNames revXMLCreateTreeFromFileWithNamespaces revXMLCreateTreeWithNamespaces revXMLDataFromXPathQuery revXMLEvaluateXPath revXMLFirstChild revXMLMatchingNode revXMLNextSibling revXMLNodeContents revXMLNumberOfChildren revXMLParent revXMLPreviousSibling revXMLRootNode revXMLRPC_CreateRequest revXMLRPC_Documents revXMLRPC_Error revXMLRPC_GetHost revXMLRPC_GetMethod revXMLRPC_GetParam revXMLText revXMLRPC_Execute revXMLRPC_GetParamCount revXMLRPC_GetParamNode revXMLRPC_GetParamType revXMLRPC_GetPath revXMLRPC_GetPort revXMLRPC_GetProtocol revXMLRPC_GetRequest revXMLRPC_GetResponse revXMLRPC_GetSocket revXMLTree revXMLTrees revXMLValidateDTD revZipDescribeItem revZipEnumerateItems revZipOpenArchives round sampVariance sec secs seconds sentenceOffset sha1Digest shell shortFilePath sin specialFolderPath sqrt standardDeviation statRound stdDev sum sysError systemVersion tan tempName textDecode textEncode tick ticks time to tokenOffset toLower toUpper transpose truewordOffset trunc uniDecode uniEncode upper URLDecode URLEncode URLStatus uuid value variableNames variance version waitDepth weekdayNames wordOffset xsltApplyStylesheet xsltApplyStylesheetFromFile xsltLoadStylesheet xsltLoadStylesheetFromFile add breakpoint cancel clear local variable file word line folder directory URL close socket process combine constant convert create new alias folder directory decrypt delete variable word line folder directory URL dispatch divide do encrypt filter get include intersect kill libURLDownloadToFile libURLFollowHttpRedirects libURLftpUpload libURLftpUploadFile libURLresetAll libUrlSetAuthCallback libURLSetDriver libURLSetCustomHTTPHeaders libUrlSetExpect100 libURLSetFTPListCommand libURLSetFTPMode libURLSetFTPStopTime libURLSetStatusCallback load extension loadedExtensions multiply socket prepare process post seek rel relative read from process rename replace require resetAll resolve revAddXMLNode revAppendXML revCloseCursor revCloseDatabase revCommitDatabase revCopyFile revCopyFolder revCopyXMLNode revDeleteFolder revDeleteXMLNode revDeleteAllXMLTrees revDeleteXMLTree revExecuteSQL revGoURL revInsertXMLNode revMoveFolder revMoveToFirstRecord revMoveToLastRecord revMoveToNextRecord revMoveToPreviousRecord revMoveToRecord revMoveXMLNode revPutIntoXMLNode revRollBackDatabase revSetDatabaseDriverPath revSetXMLAttribute revXMLRPC_AddParam revXMLRPC_DeleteAllDocuments revXMLAddDTD revXMLRPC_Free revXMLRPC_FreeAll revXMLRPC_DeleteDocument revXMLRPC_DeleteParam revXMLRPC_SetHost revXMLRPC_SetMethod revXMLRPC_SetPort revXMLRPC_SetProtocol revXMLRPC_SetSocket revZipAddItemWithData revZipAddItemWithFile revZipAddUncompressedItemWithData revZipAddUncompressedItemWithFile revZipCancel revZipCloseArchive revZipDeleteItem revZipExtractItemToFile revZipExtractItemToVariable revZipSetProgressCallback revZipRenameItem revZipReplaceItemWithData revZipReplaceItemWithFile revZipOpenArchive send set sort split start stop subtract symmetric union unload vectorDotProduct wait write", + }, + contains: [ + t, + { className: "keyword", begin: "\\bend\\sif\\b" }, + { + className: "function", + beginKeywords: "function", + end: "$", + contains: [ + t, + r, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.BINARY_NUMBER_MODE, + e.C_NUMBER_MODE, + a, + ], + }, + { + className: "function", + begin: "\\bend\\s+", + end: "$", + keywords: "end", + contains: [r, a], + relevance: 0, + }, + { + beginKeywords: "command on", + end: "$", + contains: [ + t, + r, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.BINARY_NUMBER_MODE, + e.C_NUMBER_MODE, + a, + ], + }, + { + className: "meta", + variants: [ + { begin: "<\\?(rev|lc|livecode)", relevance: 10 }, + { begin: "<\\?" }, + { begin: "\\?>" }, + ], + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.BINARY_NUMBER_MODE, + e.C_NUMBER_MODE, + a, + ].concat(n), + illegal: ";$|^\\[|^=|&|\\{", + }; + }, + oS = [ + "as", + "in", + "of", + "if", + "for", + "while", + "finally", + "var", + "new", + "function", + "do", + "return", + "void", + "else", + "break", + "catch", + "instanceof", + "with", + "throw", + "case", + "default", + "try", + "switch", + "continue", + "typeof", + "delete", + "let", + "yield", + "const", + "class", + "debugger", + "async", + "await", + "static", + "import", + "from", + "export", + "extends", + ], + sS = ["true", "false", "null", "undefined", "NaN", "Infinity"], + lS = [].concat( + [ + "setInterval", + "setTimeout", + "clearInterval", + "clearTimeout", + "require", + "exports", + "eval", + "isFinite", + "isNaN", + "parseFloat", + "parseInt", + "decodeURI", + "decodeURIComponent", + "encodeURI", + "encodeURIComponent", + "escape", + "unescape", + ], + [ + "arguments", + "this", + "super", + "console", + "window", + "document", + "localStorage", + "module", + "global", + ], + [ + "Intl", + "DataView", + "Number", + "Math", + "Date", + "String", + "RegExp", + "Object", + "Function", + "Boolean", + "Error", + "Symbol", + "Set", + "Map", + "WeakSet", + "WeakMap", + "Proxy", + "Reflect", + "JSON", + "Promise", + "Float64Array", + "Int16Array", + "Int32Array", + "Int8Array", + "Uint16Array", + "Uint32Array", + "Float32Array", + "Array", + "Uint8Array", + "Uint8ClampedArray", + "ArrayBuffer", + "BigInt64Array", + "BigUint64Array", + "BigInt", + ], + [ + "EvalError", + "InternalError", + "RangeError", + "ReferenceError", + "SyntaxError", + "TypeError", + "URIError", + ], + ); +var cS = function (e) { + var t = { + keyword: oS.concat([ + "then", + "unless", + "until", + "loop", + "of", + "by", + "when", + "and", + "or", + "is", + "isnt", + "not", + "it", + "that", + "otherwise", + "from", + "to", + "til", + "fallthrough", + "case", + "enum", + "native", + "list", + "map", + "__hasProp", + "__extends", + "__slice", + "__bind", + "__indexOf", + ]), + literal: sS.concat(["yes", "no", "on", "off", "it", "that", "void"]), + built_in: lS.concat(["npm", "print"]), + }, + n = "[A-Za-z$_](?:-[0-9A-Za-z$_]|[0-9A-Za-z$_])*", + a = e.inherit(e.TITLE_MODE, { begin: n }), + r = { className: "subst", begin: /#\{/, end: /\}/, keywords: t }, + i = { + className: "subst", + begin: /#[A-Za-z$_]/, + end: /(?:-[0-9A-Za-z$_]|[0-9A-Za-z$_])*/, + keywords: t, + }, + o = [ + e.BINARY_NUMBER_MODE, + { + className: "number", + begin: + "(\\b0[xX][a-fA-F0-9_]+)|(\\b\\d(\\d|_\\d)*(\\.(\\d(\\d|_\\d)*)?)?(_*[eE]([-+]\\d(_\\d|\\d)*)?)?[_a-z]*)", + relevance: 0, + starts: { end: "(\\s*/)?", relevance: 0 }, + }, + { + className: "string", + variants: [ + { begin: /'''/, end: /'''/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /'/, end: /'/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /"""/, end: /"""/, contains: [e.BACKSLASH_ESCAPE, r, i] }, + { begin: /"/, end: /"/, contains: [e.BACKSLASH_ESCAPE, r, i] }, + { begin: /\\/, end: /(\s|$)/, excludeEnd: !0 }, + ], + }, + { + className: "regexp", + variants: [ + { begin: "//", end: "//[gim]*", contains: [r, e.HASH_COMMENT_MODE] }, + { begin: /\/(?![ *])(\\.|[^\\\n])*?\/[gim]*(?=\W)/ }, + ], + }, + { begin: "@" + n }, + { + begin: "``", + end: "``", + excludeBegin: !0, + excludeEnd: !0, + subLanguage: "javascript", + }, + ]; + r.contains = o; + var s = { + className: "params", + begin: "\\(", + returnBegin: !0, + contains: [ + { begin: /\(/, end: /\)/, keywords: t, contains: ["self"].concat(o) }, + ], + }; + return { + name: "LiveScript", + aliases: ["ls"], + keywords: t, + illegal: /\/\*/, + contains: o.concat([ + e.COMMENT("\\/\\*", "\\*\\/"), + e.HASH_COMMENT_MODE, + { begin: "(#=>|=>|\\|>>|-?->|!->)" }, + { + className: "function", + contains: [a, s], + returnBegin: !0, + variants: [ + { + begin: "(" + n + "\\s*(?:=|:=)\\s*)?(\\(.*\\)\\s*)?\\B->\\*?", + end: "->\\*?", + }, + { + begin: + "(" + n + "\\s*(?:=|:=)\\s*)?!?(\\(.*\\)\\s*)?\\B[-~]{1,2}>\\*?", + end: "[-~]{1,2}>\\*?", + }, + { + begin: + "(" + n + "\\s*(?:=|:=)\\s*)?(\\(.*\\)\\s*)?\\B!?[-~]{1,2}>\\*?", + end: "!?[-~]{1,2}>\\*?", + }, + ], + }, + { + className: "class", + beginKeywords: "class", + end: "$", + illegal: /[:="\[\]]/, + contains: [ + { + beginKeywords: "extends", + endsWithParent: !0, + illegal: /[:="\[\]]/, + contains: [a], + }, + a, + ], + }, + { + begin: n + ":", + end: ":", + returnBegin: !0, + returnEnd: !0, + relevance: 0, + }, + ]), + }; +}; +function _S(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function dS() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return _S(e); + }) + .join(""); + return a; +} +var uS = function (e) { + var t = /([-a-zA-Z$._][\w$.-]*)/, + n = { + className: "variable", + variants: [{ begin: dS(/%/, t) }, { begin: /%\d+/ }, { begin: /#\d+/ }], + }, + a = { + className: "title", + variants: [ + { begin: dS(/@/, t) }, + { begin: /@\d+/ }, + { begin: dS(/!/, t) }, + { begin: dS(/!\d+/, t) }, + { begin: /!\d+/ }, + ], + }; + return { + name: "LLVM IR", + keywords: + "begin end true false declare define global constant private linker_private internal available_externally linkonce linkonce_odr weak weak_odr appending dllimport dllexport common default hidden protected extern_weak external thread_local zeroinitializer undef null to tail target triple datalayout volatile nuw nsw nnan ninf nsz arcp fast exact inbounds align addrspace section alias module asm sideeffect gc dbg linker_private_weak attributes blockaddress initialexec localdynamic localexec prefix unnamed_addr ccc fastcc coldcc x86_stdcallcc x86_fastcallcc arm_apcscc arm_aapcscc arm_aapcs_vfpcc ptx_device ptx_kernel intel_ocl_bicc msp430_intrcc spir_func spir_kernel x86_64_sysvcc x86_64_win64cc x86_thiscallcc cc c signext zeroext inreg sret nounwind noreturn noalias nocapture byval nest readnone readonly inlinehint noinline alwaysinline optsize ssp sspreq noredzone noimplicitfloat naked builtin cold nobuiltin noduplicate nonlazybind optnone returns_twice sanitize_address sanitize_memory sanitize_thread sspstrong uwtable returned type opaque eq ne slt sgt sle sge ult ugt ule uge oeq one olt ogt ole oge ord uno ueq une x acq_rel acquire alignstack atomic catch cleanup filter inteldialect max min monotonic nand personality release seq_cst singlethread umax umin unordered xchg add fadd sub fsub mul fmul udiv sdiv fdiv urem srem frem shl lshr ashr and or xor icmp fcmp phi call trunc zext sext fptrunc fpext uitofp sitofp fptoui fptosi inttoptr ptrtoint bitcast addrspacecast select va_arg ret br switch invoke unwind unreachable indirectbr landingpad resume malloc alloca free load store getelementptr extractelement insertelement shufflevector getresult extractvalue insertvalue atomicrmw cmpxchg fence argmemonly double", + contains: [ + { className: "type", begin: /\bi\d+(?=\s|\b)/ }, + e.COMMENT(/;\s*$/, null, { relevance: 0 }), + e.COMMENT(/;/, /$/), + e.QUOTE_STRING_MODE, + { className: "string", variants: [{ begin: /"/, end: /[^\\]"/ }] }, + a, + { className: "punctuation", relevance: 0, begin: /,/ }, + { className: "operator", relevance: 0, begin: /=/ }, + n, + { + className: "symbol", + variants: [{ begin: /^\s*[a-z]+:/ }], + relevance: 0, + }, + { + className: "number", + variants: [ + { begin: /0[xX][a-fA-F0-9]+/ }, + { begin: /-?\d+(?:[.]\d+)?(?:[eE][-+]?\d+(?:[.]\d+)?)?/ }, + ], + relevance: 0, + }, + ], + }; +}; +var mS = function (e) { + var t = { + className: "string", + begin: '"', + end: '"', + contains: [{ className: "subst", begin: /\\[tn"\\]/ }], + }, + n = { className: "number", relevance: 0, begin: e.C_NUMBER_RE }; + return { + name: "LSL (Linden Scripting Language)", + illegal: ":", + contains: [ + t, + { + className: "comment", + variants: [e.COMMENT("//", "$"), e.COMMENT("/\\*", "\\*/")], + relevance: 0, + }, + n, + { + className: "section", + variants: [ + { begin: "\\b(state|default)\\b" }, + { + begin: + "\\b(state_(entry|exit)|touch(_(start|end))?|(land_)?collision(_(start|end))?|timer|listen|(no_)?sensor|control|(not_)?at_(rot_)?target|money|email|experience_permissions(_denied)?|run_time_permissions|changed|attach|dataserver|moving_(start|end)|link_message|(on|object)_rez|remote_data|http_re(sponse|quest)|path_update|transaction_result)\\b", + }, + ], + }, + { + className: "built_in", + begin: + "\\b(ll(AgentInExperience|(Create|DataSize|Delete|KeyCount|Keys|Read|Update)KeyValue|GetExperience(Details|ErrorMessage)|ReturnObjectsBy(ID|Owner)|Json(2List|[GS]etValue|ValueType)|Sin|Cos|Tan|Atan2|Sqrt|Pow|Abs|Fabs|Frand|Floor|Ceil|Round|Vec(Mag|Norm|Dist)|Rot(Between|2(Euler|Fwd|Left|Up))|(Euler|Axes)2Rot|Whisper|(Region|Owner)?Say|Shout|Listen(Control|Remove)?|Sensor(Repeat|Remove)?|Detected(Name|Key|Owner|Type|Pos|Vel|Grab|Rot|Group|LinkNumber)|Die|Ground|Wind|([GS]et)(AnimationOverride|MemoryLimit|PrimMediaParams|ParcelMusicURL|Object(Desc|Name)|PhysicsMaterial|Status|Scale|Color|Alpha|Texture|Pos|Rot|Force|Torque)|ResetAnimationOverride|(Scale|Offset|Rotate)Texture|(Rot)?Target(Remove)?|(Stop)?MoveToTarget|Apply(Rotational)?Impulse|Set(KeyframedMotion|ContentType|RegionPos|(Angular)?Velocity|Buoyancy|HoverHeight|ForceAndTorque|TimerEvent|ScriptState|Damage|TextureAnim|Sound(Queueing|Radius)|Vehicle(Type|(Float|Vector|Rotation)Param)|(Touch|Sit)?Text|Camera(Eye|At)Offset|PrimitiveParams|ClickAction|Link(Alpha|Color|PrimitiveParams(Fast)?|Texture(Anim)?|Camera|Media)|RemoteScriptAccessPin|PayPrice|LocalRot)|ScaleByFactor|Get((Max|Min)ScaleFactor|ClosestNavPoint|StaticPath|SimStats|Env|PrimitiveParams|Link(PrimitiveParams|Number(OfSides)?|Key|Name|Media)|HTTPHeader|FreeURLs|Object(Details|PermMask|PrimCount)|Parcel(MaxPrims|Details|Prim(Count|Owners))|Attached(List)?|(SPMax|Free|Used)Memory|Region(Name|TimeDilation|FPS|Corner|AgentCount)|Root(Position|Rotation)|UnixTime|(Parcel|Region)Flags|(Wall|GMT)clock|SimulatorHostname|BoundingBox|GeometricCenter|Creator|NumberOf(Prims|NotecardLines|Sides)|Animation(List)?|(Camera|Local)(Pos|Rot)|Vel|Accel|Omega|Time(stamp|OfDay)|(Object|CenterOf)?Mass|MassMKS|Energy|Owner|(Owner)?Key|SunDirection|Texture(Offset|Scale|Rot)|Inventory(Number|Name|Key|Type|Creator|PermMask)|Permissions(Key)?|StartParameter|List(Length|EntryType)|Date|Agent(Size|Info|Language|List)|LandOwnerAt|NotecardLine|Script(Name|State))|(Get|Reset|GetAndReset)Time|PlaySound(Slave)?|LoopSound(Master|Slave)?|(Trigger|Stop|Preload)Sound|((Get|Delete)Sub|Insert)String|To(Upper|Lower)|Give(InventoryList|Money)|RezObject|(Stop)?LookAt|Sleep|CollisionFilter|(Take|Release)Controls|DetachFromAvatar|AttachToAvatar(Temp)?|InstantMessage|(GetNext)?Email|StopHover|MinEventDelay|RotLookAt|String(Length|Trim)|(Start|Stop)Animation|TargetOmega|Request(Experience)?Permissions|(Create|Break)Link|BreakAllLinks|(Give|Remove)Inventory|Water|PassTouches|Request(Agent|Inventory)Data|TeleportAgent(Home|GlobalCoords)?|ModifyLand|CollisionSound|ResetScript|MessageLinked|PushObject|PassCollisions|AxisAngle2Rot|Rot2(Axis|Angle)|A(cos|sin)|AngleBetween|AllowInventoryDrop|SubStringIndex|List2(CSV|Integer|Json|Float|String|Key|Vector|Rot|List(Strided)?)|DeleteSubList|List(Statistics|Sort|Randomize|(Insert|Find|Replace)List)|EdgeOfWorld|AdjustSoundVolume|Key2Name|TriggerSoundLimited|EjectFromLand|(CSV|ParseString)2List|OverMyLand|SameGroup|UnSit|Ground(Slope|Normal|Contour)|GroundRepel|(Set|Remove)VehicleFlags|SitOnLink|(AvatarOn)?(Link)?SitTarget|Script(Danger|Profiler)|Dialog|VolumeDetect|ResetOtherScript|RemoteLoadScriptPin|(Open|Close)RemoteDataChannel|SendRemoteData|RemoteDataReply|(Integer|String)ToBase64|XorBase64|Log(10)?|Base64To(String|Integer)|ParseStringKeepNulls|RezAtRoot|RequestSimulatorData|ForceMouselook|(Load|Release|(E|Une)scape)URL|ParcelMedia(CommandList|Query)|ModPow|MapDestination|(RemoveFrom|AddTo|Reset)Land(Pass|Ban)List|(Set|Clear)CameraParams|HTTP(Request|Response)|TextBox|DetectedTouch(UV|Face|Pos|(N|Bin)ormal|ST)|(MD5|SHA1|DumpList2)String|Request(Secure)?URL|Clear(Prim|Link)Media|(Link)?ParticleSystem|(Get|Request)(Username|DisplayName)|RegionSayTo|CastRay|GenerateKey|TransferLindenDollars|ManageEstateAccess|(Create|Delete)Character|ExecCharacterCmd|Evade|FleeFrom|NavigateTo|PatrolPoints|Pursue|UpdateCharacter|WanderWithin))\\b", + }, + { + className: "literal", + variants: [ + { begin: "\\b(PI|TWO_PI|PI_BY_TWO|DEG_TO_RAD|RAD_TO_DEG|SQRT2)\\b" }, + { + begin: + "\\b(XP_ERROR_(EXPERIENCES_DISABLED|EXPERIENCE_(DISABLED|SUSPENDED)|INVALID_(EXPERIENCE|PARAMETERS)|KEY_NOT_FOUND|MATURITY_EXCEEDED|NONE|NOT_(FOUND|PERMITTED(_LAND)?)|NO_EXPERIENCE|QUOTA_EXCEEDED|RETRY_UPDATE|STORAGE_EXCEPTION|STORE_DISABLED|THROTTLED|UNKNOWN_ERROR)|JSON_APPEND|STATUS_(PHYSICS|ROTATE_[XYZ]|PHANTOM|SANDBOX|BLOCK_GRAB(_OBJECT)?|(DIE|RETURN)_AT_EDGE|CAST_SHADOWS|OK|MALFORMED_PARAMS|TYPE_MISMATCH|BOUNDS_ERROR|NOT_(FOUND|SUPPORTED)|INTERNAL_ERROR|WHITELIST_FAILED)|AGENT(_(BY_(LEGACY_|USER)NAME|FLYING|ATTACHMENTS|SCRIPTED|MOUSELOOK|SITTING|ON_OBJECT|AWAY|WALKING|IN_AIR|TYPING|CROUCHING|BUSY|ALWAYS_RUN|AUTOPILOT|LIST_(PARCEL(_OWNER)?|REGION)))?|CAMERA_(PITCH|DISTANCE|BEHINDNESS_(ANGLE|LAG)|(FOCUS|POSITION)(_(THRESHOLD|LOCKED|LAG))?|FOCUS_OFFSET|ACTIVE)|ANIM_ON|LOOP|REVERSE|PING_PONG|SMOOTH|ROTATE|SCALE|ALL_SIDES|LINK_(ROOT|SET|ALL_(OTHERS|CHILDREN)|THIS)|ACTIVE|PASS(IVE|_(ALWAYS|IF_NOT_HANDLED|NEVER))|SCRIPTED|CONTROL_(FWD|BACK|(ROT_)?(LEFT|RIGHT)|UP|DOWN|(ML_)?LBUTTON)|PERMISSION_(RETURN_OBJECTS|DEBIT|OVERRIDE_ANIMATIONS|SILENT_ESTATE_MANAGEMENT|TAKE_CONTROLS|TRIGGER_ANIMATION|ATTACH|CHANGE_LINKS|(CONTROL|TRACK)_CAMERA|TELEPORT)|INVENTORY_(TEXTURE|SOUND|OBJECT|SCRIPT|LANDMARK|CLOTHING|NOTECARD|BODYPART|ANIMATION|GESTURE|ALL|NONE)|CHANGED_(INVENTORY|COLOR|SHAPE|SCALE|TEXTURE|LINK|ALLOWED_DROP|OWNER|REGION(_START)?|TELEPORT|MEDIA)|OBJECT_(CLICK_ACTION|HOVER_HEIGHT|LAST_OWNER_ID|(PHYSICS|SERVER|STREAMING)_COST|UNKNOWN_DETAIL|CHARACTER_TIME|PHANTOM|PHYSICS|TEMP_(ATTACHED|ON_REZ)|NAME|DESC|POS|PRIM_(COUNT|EQUIVALENCE)|RETURN_(PARCEL(_OWNER)?|REGION)|REZZER_KEY|ROO?T|VELOCITY|OMEGA|OWNER|GROUP(_TAG)?|CREATOR|ATTACHED_(POINT|SLOTS_AVAILABLE)|RENDER_WEIGHT|(BODY_SHAPE|PATHFINDING)_TYPE|(RUNNING|TOTAL)_SCRIPT_COUNT|TOTAL_INVENTORY_COUNT|SCRIPT_(MEMORY|TIME))|TYPE_(INTEGER|FLOAT|STRING|KEY|VECTOR|ROTATION|INVALID)|(DEBUG|PUBLIC)_CHANNEL|ATTACH_(AVATAR_CENTER|CHEST|HEAD|BACK|PELVIS|MOUTH|CHIN|NECK|NOSE|BELLY|[LR](SHOULDER|HAND|FOOT|EAR|EYE|[UL](ARM|LEG)|HIP)|(LEFT|RIGHT)_PEC|HUD_(CENTER_[12]|TOP_(RIGHT|CENTER|LEFT)|BOTTOM(_(RIGHT|LEFT))?)|[LR]HAND_RING1|TAIL_(BASE|TIP)|[LR]WING|FACE_(JAW|[LR]EAR|[LR]EYE|TOUNGE)|GROIN|HIND_[LR]FOOT)|LAND_(LEVEL|RAISE|LOWER|SMOOTH|NOISE|REVERT)|DATA_(ONLINE|NAME|BORN|SIM_(POS|STATUS|RATING)|PAYINFO)|PAYMENT_INFO_(ON_FILE|USED)|REMOTE_DATA_(CHANNEL|REQUEST|REPLY)|PSYS_(PART_(BF_(ZERO|ONE(_MINUS_(DEST_COLOR|SOURCE_(ALPHA|COLOR)))?|DEST_COLOR|SOURCE_(ALPHA|COLOR))|BLEND_FUNC_(DEST|SOURCE)|FLAGS|(START|END)_(COLOR|ALPHA|SCALE|GLOW)|MAX_AGE|(RIBBON|WIND|INTERP_(COLOR|SCALE)|BOUNCE|FOLLOW_(SRC|VELOCITY)|TARGET_(POS|LINEAR)|EMISSIVE)_MASK)|SRC_(MAX_AGE|PATTERN|ANGLE_(BEGIN|END)|BURST_(RATE|PART_COUNT|RADIUS|SPEED_(MIN|MAX))|ACCEL|TEXTURE|TARGET_KEY|OMEGA|PATTERN_(DROP|EXPLODE|ANGLE(_CONE(_EMPTY)?)?)))|VEHICLE_(REFERENCE_FRAME|TYPE_(NONE|SLED|CAR|BOAT|AIRPLANE|BALLOON)|(LINEAR|ANGULAR)_(FRICTION_TIMESCALE|MOTOR_DIRECTION)|LINEAR_MOTOR_OFFSET|HOVER_(HEIGHT|EFFICIENCY|TIMESCALE)|BUOYANCY|(LINEAR|ANGULAR)_(DEFLECTION_(EFFICIENCY|TIMESCALE)|MOTOR_(DECAY_)?TIMESCALE)|VERTICAL_ATTRACTION_(EFFICIENCY|TIMESCALE)|BANKING_(EFFICIENCY|MIX|TIMESCALE)|FLAG_(NO_DEFLECTION_UP|LIMIT_(ROLL_ONLY|MOTOR_UP)|HOVER_((WATER|TERRAIN|UP)_ONLY|GLOBAL_HEIGHT)|MOUSELOOK_(STEER|BANK)|CAMERA_DECOUPLED))|PRIM_(ALLOW_UNSIT|ALPHA_MODE(_(BLEND|EMISSIVE|MASK|NONE))?|NORMAL|SPECULAR|TYPE(_(BOX|CYLINDER|PRISM|SPHERE|TORUS|TUBE|RING|SCULPT))?|HOLE_(DEFAULT|CIRCLE|SQUARE|TRIANGLE)|MATERIAL(_(STONE|METAL|GLASS|WOOD|FLESH|PLASTIC|RUBBER))?|SHINY_(NONE|LOW|MEDIUM|HIGH)|BUMP_(NONE|BRIGHT|DARK|WOOD|BARK|BRICKS|CHECKER|CONCRETE|TILE|STONE|DISKS|GRAVEL|BLOBS|SIDING|LARGETILE|STUCCO|SUCTION|WEAVE)|TEXGEN_(DEFAULT|PLANAR)|SCRIPTED_SIT_ONLY|SCULPT_(TYPE_(SPHERE|TORUS|PLANE|CYLINDER|MASK)|FLAG_(MIRROR|INVERT))|PHYSICS(_(SHAPE_(CONVEX|NONE|PRIM|TYPE)))?|(POS|ROT)_LOCAL|SLICE|TEXT|FLEXIBLE|POINT_LIGHT|TEMP_ON_REZ|PHANTOM|POSITION|SIT_TARGET|SIZE|ROTATION|TEXTURE|NAME|OMEGA|DESC|LINK_TARGET|COLOR|BUMP_SHINY|FULLBRIGHT|TEXGEN|GLOW|MEDIA_(ALT_IMAGE_ENABLE|CONTROLS|(CURRENT|HOME)_URL|AUTO_(LOOP|PLAY|SCALE|ZOOM)|FIRST_CLICK_INTERACT|(WIDTH|HEIGHT)_PIXELS|WHITELIST(_ENABLE)?|PERMS_(INTERACT|CONTROL)|PARAM_MAX|CONTROLS_(STANDARD|MINI)|PERM_(NONE|OWNER|GROUP|ANYONE)|MAX_(URL_LENGTH|WHITELIST_(SIZE|COUNT)|(WIDTH|HEIGHT)_PIXELS)))|MASK_(BASE|OWNER|GROUP|EVERYONE|NEXT)|PERM_(TRANSFER|MODIFY|COPY|MOVE|ALL)|PARCEL_(MEDIA_COMMAND_(STOP|PAUSE|PLAY|LOOP|TEXTURE|URL|TIME|AGENT|UNLOAD|AUTO_ALIGN|TYPE|SIZE|DESC|LOOP_SET)|FLAG_(ALLOW_(FLY|(GROUP_)?SCRIPTS|LANDMARK|TERRAFORM|DAMAGE|CREATE_(GROUP_)?OBJECTS)|USE_(ACCESS_(GROUP|LIST)|BAN_LIST|LAND_PASS_LIST)|LOCAL_SOUND_ONLY|RESTRICT_PUSHOBJECT|ALLOW_(GROUP|ALL)_OBJECT_ENTRY)|COUNT_(TOTAL|OWNER|GROUP|OTHER|SELECTED|TEMP)|DETAILS_(NAME|DESC|OWNER|GROUP|AREA|ID|SEE_AVATARS))|LIST_STAT_(MAX|MIN|MEAN|MEDIAN|STD_DEV|SUM(_SQUARES)?|NUM_COUNT|GEOMETRIC_MEAN|RANGE)|PAY_(HIDE|DEFAULT)|REGION_FLAG_(ALLOW_DAMAGE|FIXED_SUN|BLOCK_TERRAFORM|SANDBOX|DISABLE_(COLLISIONS|PHYSICS)|BLOCK_FLY|ALLOW_DIRECT_TELEPORT|RESTRICT_PUSH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+ }, + { begin: "\\b(FALSE|TRUE)\\b" }, + { begin: "\\b(ZERO_ROTATION)\\b" }, + { + begin: + "\\b(EOF|JSON_(ARRAY|DELETE|FALSE|INVALID|NULL|NUMBER|OBJECT|STRING|TRUE)|NULL_KEY|TEXTURE_(BLANK|DEFAULT|MEDIA|PLYWOOD|TRANSPARENT)|URL_REQUEST_(GRANTED|DENIED))\\b", + }, + { begin: "\\b(ZERO_VECTOR|TOUCH_INVALID_(TEXCOORD|VECTOR))\\b" }, + ], + }, + { + className: "type", + begin: + "\\b(integer|float|string|key|vector|quaternion|rotation|list)\\b", + }, + ], + }; +}; +var pS = function (e) { + var t = "\\[=*\\[", + n = "\\]=*\\]", + a = { begin: t, end: n, contains: ["self"] }, + r = [ + e.COMMENT("--(?!\\[=*\\[)", "$"), + e.COMMENT("--\\[=*\\[", n, { contains: [a], relevance: 10 }), + ]; + return { + name: "Lua", + keywords: { + $pattern: e.UNDERSCORE_IDENT_RE, + literal: "true false nil", + keyword: + "and break do else elseif end for goto if in local not or repeat return then until while", + built_in: + "_G _ENV _VERSION __index __newindex __mode __call __metatable __tostring __len __gc 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"0'.\\|0[box][0-9a-fA-F]*" }, + e.NUMBER_MODE, + n, + a, + { begin: /:-/ }, + { begin: /\.$/ }, + ], + } + ); +}; +var hS = function (e) { + return { + name: "MIPS Assembly", + case_insensitive: !0, + aliases: ["mips"], + keywords: { + $pattern: "\\.?" + e.IDENT_RE, + meta: ".2byte .4byte .align .ascii .asciz .balign .byte .code .data .else .end .endif .endm .endr .equ .err .exitm .extern .global .hword .if .ifdef .ifndef .include .irp .long .macro .rept .req .section .set .skip .space .text .word .ltorg ", + built_in: + "$0 $1 $2 $3 $4 $5 $6 $7 $8 $9 $10 $11 $12 $13 $14 $15 $16 $17 $18 $19 $20 $21 $22 $23 $24 $25 $26 $27 $28 $29 $30 $31 zero at v0 v1 a0 a1 a2 a3 a4 a5 a6 a7 t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 s0 s1 s2 s3 s4 s5 s6 s7 s8 k0 k1 gp sp fp ra $f0 $f1 $f2 $f2 $f4 $f5 $f6 $f7 $f8 $f9 $f10 $f11 $f12 $f13 $f14 $f15 $f16 $f17 $f18 $f19 $f20 $f21 $f22 $f23 $f24 $f25 $f26 $f27 $f28 $f29 $f30 $f31 Context Random EntryLo0 EntryLo1 Context PageMask Wired EntryHi HWREna BadVAddr Count Compare SR IntCtl SRSCtl SRSMap Cause EPC PRId EBase Config Config1 Config2 Config3 LLAddr Debug DEPC DESAVE CacheErr ECC ErrorEPC TagLo DataLo TagHi DataHi WatchLo WatchHi PerfCtl PerfCnt ", + }, + contains: [ + { + className: "keyword", + begin: + "\\b(addi?u?|andi?|b(al)?|beql?|bgez(al)?l?|bgtzl?|blezl?|bltz(al)?l?|bnel?|cl[oz]|divu?|ext|ins|j(al)?|jalr(\\.hb)?|jr(\\.hb)?|lbu?|lhu?|ll|lui|lw[lr]?|maddu?|mfhi|mflo|movn|movz|move|msubu?|mthi|mtlo|mul|multu?|nop|nor|ori?|rotrv?|sb|sc|se[bh]|sh|sllv?|slti?u?|srav?|srlv?|subu?|sw[lr]?|xori?|wsbh|abs\\.[sd]|add\\.[sd]|alnv.ps|bc1[ft]l?|c\\.(s?f|un|u?eq|[ou]lt|[ou]le|ngle?|seq|l[et]|ng[et])\\.[sd]|(ceil|floor|round|trunc)\\.[lw]\\.[sd]|cfc1|cvt\\.d\\.[lsw]|cvt\\.l\\.[dsw]|cvt\\.ps\\.s|cvt\\.s\\.[dlw]|cvt\\.s\\.p[lu]|cvt\\.w\\.[dls]|div\\.[ds]|ldx?c1|luxc1|lwx?c1|madd\\.[sd]|mfc1|mov[fntz]?\\.[ds]|msub\\.[sd]|mth?c1|mul\\.[ds]|neg\\.[ds]|nmadd\\.[ds]|nmsub\\.[ds]|p[lu][lu]\\.ps|recip\\.fmt|r?sqrt\\.[ds]|sdx?c1|sub\\.[ds]|suxc1|swx?c1|break|cache|d?eret|[de]i|ehb|mfc0|mtc0|pause|prefx?|rdhwr|rdpgpr|sdbbp|ssnop|synci?|syscall|teqi?|tgei?u?|tlb(p|r|w[ir])|tlti?u?|tnei?|wait|wrpgpr)", + end: "\\s", + }, + e.COMMENT("[;#](?!\\s*$)", "$"), + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + { className: "string", begin: "'", end: "[^\\\\]'", relevance: 0 }, + { + className: "title", + begin: "\\|", + end: "\\|", + illegal: "\\n", + relevance: 0, + }, + { + className: "number", + variants: [{ begin: "0x[0-9a-f]+" }, { begin: "\\b-?\\d+" }], + relevance: 0, + }, + { + className: "symbol", + variants: [ + { begin: "^\\s*[a-z_\\.\\$][a-z0-9_\\.\\$]+:" }, + { begin: "^\\s*[0-9]+:" }, + { begin: "[0-9]+[bf]" }, + ], + relevance: 0, + }, + ], + illegal: /\//, + }; +}; +var yS = function (e) { + return { + name: "Mizar", + keywords: + "environ vocabularies notations constructors definitions registrations theorems schemes requirements begin end definition registration cluster existence pred func defpred deffunc theorem proof let take assume then thus hence ex for st holds consider reconsider such that and in provided of as from be being by means equals implies iff redefine define now not or attr is mode suppose per cases set thesis contradiction scheme reserve struct correctness compatibility coherence symmetry assymetry reflexivity irreflexivity connectedness uniqueness commutativity idempotence involutiveness projectivity", + contains: [e.COMMENT("::", "$")], + }; +}; +function IS(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function AS() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return IS(e); + }) + .join(""); + return a; +} +function DS() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return IS(e); + }) + .join("|") + + ")"; + return a; +} +var MS = function (e) { + var t = /[dualxmsipngr]{0,12}/, + n = { + $pattern: /[\w.]+/, + keyword: [ + "abs", + "accept", + "alarm", + "and", + "atan2", + "bind", + "binmode", + "bless", + "break", + "caller", + "chdir", + "chmod", + "chomp", + "chop", + "chown", + "chr", + "chroot", + "close", + "closedir", + "connect", + "continue", + "cos", + "crypt", + "dbmclose", + "dbmopen", + "defined", + "delete", + "die", + "do", + "dump", + "each", + "else", + "elsif", + "endgrent", + "endhostent", + "endnetent", + "endprotoent", + "endpwent", + "endservent", + "eof", + "eval", + "exec", + "exists", + "exit", + "exp", + "fcntl", + "fileno", + "flock", + "for", + "foreach", + "fork", + "format", + "formline", + "getc", + "getgrent", + "getgrgid", + "getgrnam", + "gethostbyaddr", + "gethostbyname", + "gethostent", + "getlogin", + "getnetbyaddr", + "getnetbyname", + "getnetent", + "getpeername", + "getpgrp", + "getpriority", + "getprotobyname", + "getprotobynumber", + "getprotoent", + "getpwent", + "getpwnam", + "getpwuid", + "getservbyname", + "getservbyport", + "getservent", + "getsockname", + "getsockopt", + "given", + "glob", + "gmtime", + "goto", + "grep", + "gt", + "hex", + "if", + "index", + "int", + "ioctl", + "join", + "keys", + "kill", + "last", + "lc", + "lcfirst", + "length", + "link", + "listen", + "local", + "localtime", + "log", + "lstat", + "lt", + "ma", + "map", + "mkdir", + "msgctl", + "msgget", + "msgrcv", + "msgsnd", + "my", + "ne", + "next", + "no", + "not", + "oct", + "open", + "opendir", + "or", + "ord", + "our", + "pack", + "package", + "pipe", + "pop", + "pos", + "print", + "printf", + "prototype", + "push", + "q|0", + "qq", + "quotemeta", + "qw", + "qx", + "rand", + "read", + "readdir", + "readline", + "readlink", + "readpipe", + "recv", + "redo", + "ref", + "rename", + "require", + "reset", + "return", + "reverse", + "rewinddir", + "rindex", + "rmdir", + "say", + "scalar", + "seek", + "seekdir", + "select", + "semctl", + "semget", + "semop", + "send", + "setgrent", + "sethostent", + "setnetent", + "setpgrp", + "setpriority", + "setprotoent", + "setpwent", + "setservent", + "setsockopt", + "shift", + "shmctl", + "shmget", + "shmread", + "shmwrite", + "shutdown", + "sin", + "sleep", + "socket", + "socketpair", + "sort", + "splice", + "split", + "sprintf", + "sqrt", + "srand", + "stat", + "state", + "study", + "sub", + "substr", + "symlink", + "syscall", + "sysopen", + "sysread", + "sysseek", + "system", + "syswrite", + "tell", + "telldir", + "tie", + "tied", + "time", + "times", + "tr", + "truncate", + "uc", + "ucfirst", + "umask", + "undef", + "unless", + "unlink", + "unpack", + "unshift", + "untie", + "until", + "use", + "utime", + "values", + "vec", + "wait", + "waitpid", + "wantarray", + "warn", + "when", + "while", + "write", + "x|0", + "xor", + "y|0", + ].join(" "), + }, + a = { className: "subst", begin: "[$@]\\{", end: "\\}", keywords: n }, + r = { begin: /->\{/, end: /\}/ }, + i = { + variants: [ + { begin: /\$\d/ }, + { + begin: AS( + /[$%@](\^\w\b|#\w+(::\w+)*|\{\w+\}|\w+(::\w*)*)/, + "(?![A-Za-z])(?![@$%])", + ), + }, + { begin: /[$%@][^\s\w{]/, relevance: 0 }, + ], + }, + o = [e.BACKSLASH_ESCAPE, a, i], + s = [/!/, /\//, /\|/, /\?/, /'/, /"/, /#/], + l = function (e, n) { + var a = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "\\1", + r = "\\1" === a ? a : AS(a, n); + return AS( + AS("(?:", e, ")"), + n, + /(?:\\.|[^\\\/])*?/, + r, + /(?:\\.|[^\\\/])*?/, + a, + t, + ); + }, + c = function (e, n, a) { + return AS(AS("(?:", e, ")"), n, /(?:\\.|[^\\\/])*?/, a, t); + }, + _ = [ + i, + e.HASH_COMMENT_MODE, + e.COMMENT(/^=\w/, /=cut/, { endsWithParent: !0 }), + r, + { + className: "string", + contains: o, + variants: [ + { begin: "q[qwxr]?\\s*\\(", end: "\\)", relevance: 5 }, + { begin: "q[qwxr]?\\s*\\[", end: "\\]", relevance: 5 }, + { begin: "q[qwxr]?\\s*\\{", end: "\\}", relevance: 5 }, + { begin: "q[qwxr]?\\s*\\|", end: "\\|", relevance: 5 }, + { begin: "q[qwxr]?\\s*<", end: ">", relevance: 5 }, + { begin: "qw\\s+q", end: "q", relevance: 5 }, + { begin: "'", end: "'", contains: [e.BACKSLASH_ESCAPE] }, + { begin: '"', end: '"' }, + { begin: "`", end: "`", contains: [e.BACKSLASH_ESCAPE] }, + { begin: /\{\w+\}/, relevance: 0 }, + { begin: "-?\\w+\\s*=>", relevance: 0 }, + ], + }, + { + className: "number", + begin: + "(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b", + relevance: 0, + }, + { + begin: + "(\\/\\/|" + + e.RE_STARTERS_RE + + "|\\b(split|return|print|reverse|grep)\\b)\\s*", + keywords: "split return print reverse grep", + relevance: 0, + contains: [ + e.HASH_COMMENT_MODE, + { + className: "regexp", + variants: [ + { begin: l("s|tr|y", DS.apply(void 0, s)) }, + { begin: l("s|tr|y", "\\(", "\\)") }, + { begin: l("s|tr|y", "\\[", "\\]") }, + { begin: l("s|tr|y", "\\{", "\\}") }, + ], + relevance: 2, + }, + { + className: "regexp", + variants: [ + { begin: /(m|qr)\/\//, relevance: 0 }, + { begin: c("(?:m|qr)?", /\//, /\//) }, + { begin: c("m|qr", DS.apply(void 0, s), /\1/) }, + { begin: c("m|qr", /\(/, /\)/) }, + { begin: c("m|qr", /\[/, /\]/) }, + { begin: c("m|qr", /\{/, /\}/) }, + ], + }, + ], + }, + { + className: "function", + beginKeywords: "sub", + end: "(\\s*\\(.*?\\))?[;{]", + excludeEnd: !0, + relevance: 5, + contains: [e.TITLE_MODE], + }, + { begin: "-\\w\\b", relevance: 0 }, + { + begin: "^__DATA__$", + end: "^__END__$", + subLanguage: "mojolicious", + contains: [{ begin: "^@@.*", end: "$", className: "comment" }], + }, + ]; + return ( + (a.contains = _), + (r.contains = _), + { name: "Perl", aliases: ["pl", "pm"], keywords: n, contains: _ } + ); +}; +var LS = function (e) { + return { + name: "Mojolicious", + subLanguage: "xml", + contains: [ + { className: "meta", begin: "^__(END|DATA)__$" }, + { begin: "^\\s*%{1,2}={0,2}", end: "$", subLanguage: "perl" }, + { + begin: "<%{1,2}={0,2}", + end: "={0,1}%>", + subLanguage: "perl", + excludeBegin: !0, + excludeEnd: !0, + }, + ], + }; +}; +var wS = function (e) { + var t = { + className: "number", + relevance: 0, + variants: [{ begin: "[$][a-fA-F0-9]+" }, e.NUMBER_MODE], + }; + return { + name: "Monkey", + case_insensitive: !0, + keywords: { + keyword: + "public private property continue exit extern new try catch eachin not abstract final select case default const local global field end if then else elseif endif while wend repeat until forever for to step next return module inline throw import", + built_in: + "DebugLog DebugStop Error Print ACos ACosr ASin ASinr ATan ATan2 ATan2r ATanr Abs Abs Ceil Clamp Clamp Cos Cosr Exp Floor Log Max Max Min Min Pow Sgn Sgn Sin Sinr Sqrt Tan Tanr Seed PI HALFPI TWOPI", + literal: "true false null and or shl shr mod", + }, + illegal: /\/\*/, + contains: [ + e.COMMENT("#rem", "#end"), + e.COMMENT("'", "$", { relevance: 0 }), + { + className: "function", + beginKeywords: "function method", + end: "[(=:]|$", + illegal: /\n/, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + className: "class", + beginKeywords: "class interface", + end: "$", + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { className: "built_in", begin: "\\b(self|super)\\b" }, + { + className: "meta", + begin: "\\s*#", + end: "$", + keywords: { "meta-keyword": "if else elseif endif end then" }, + }, + { className: "meta", begin: "^\\s*strict\\b" }, + { beginKeywords: "alias", end: "=", contains: [e.UNDERSCORE_TITLE_MODE] }, + e.QUOTE_STRING_MODE, + t, + ], + }; +}; +var xS = function (e) { + var t = { + keyword: + "if then not for in while do return else elseif break continue switch and or unless when class extends super local import export from using", + literal: "true false nil", + built_in: + "_G _VERSION assert collectgarbage dofile error getfenv getmetatable ipairs load loadfile loadstring module next pairs pcall print rawequal rawget rawset require select setfenv setmetatable tonumber tostring type unpack xpcall coroutine debug io math os package string table", + }, + n = "[A-Za-z$_][0-9A-Za-z$_]*", + a = { className: "subst", begin: /#\{/, end: /\}/, keywords: t }, + r = [ + e.inherit(e.C_NUMBER_MODE, { starts: { end: "(\\s*/)?", relevance: 0 } }), + { + className: "string", + variants: [ + { begin: /'/, end: /'/, contains: [e.BACKSLASH_ESCAPE] }, + { begin: /"/, end: /"/, contains: [e.BACKSLASH_ESCAPE, a] }, + ], + }, + { className: "built_in", begin: "@__" + e.IDENT_RE }, + { begin: "@" + e.IDENT_RE }, + { begin: e.IDENT_RE + "\\\\" + e.IDENT_RE }, + ]; + a.contains = r; + var i = e.inherit(e.TITLE_MODE, { begin: n }), + o = "(\\(.*\\)\\s*)?\\B[-=]>", + s = { + className: "params", + begin: "\\([^\\(]", + returnBegin: !0, + contains: [ + { begin: /\(/, end: /\)/, keywords: t, contains: ["self"].concat(r) }, + ], + }; + return { + name: "MoonScript", + aliases: ["moon"], + keywords: t, + illegal: /\/\*/, + contains: r.concat([ + e.COMMENT("--", "$"), + { + className: "function", + begin: "^\\s*" + n + "\\s*=\\s*" + o, + end: "[-=]>", + returnBegin: !0, + contains: [i, s], + }, + { + begin: /[\(,:=]\s*/, + relevance: 0, + contains: [ + { + className: "function", + begin: o, + end: "[-=]>", + returnBegin: !0, + contains: [s], + }, + ], + }, + { + className: "class", + beginKeywords: "class", + end: "$", + illegal: /[:="\[\]]/, + contains: [ + { + beginKeywords: "extends", + endsWithParent: !0, + illegal: /[:="\[\]]/, + contains: [i], + }, + i, + ], + }, + { + className: "name", + begin: n + ":", + end: ":", + returnBegin: !0, + returnEnd: !0, + relevance: 0, + }, + ]), + }; +}; +var PS = function (e) { + return { + name: "N1QL", + case_insensitive: !0, + contains: [ + { + beginKeywords: + "build create index delete drop explain infer|10 insert merge prepare select update upsert|10", + end: /;/, + endsWithParent: !0, + keywords: { + keyword: + "all alter analyze and any array as asc begin between binary boolean break bucket build by call case cast cluster collate collection commit connect continue correlate cover create database dataset datastore declare decrement delete derived desc describe distinct do drop each element else end every except exclude execute exists explain fetch first flatten for force from function grant group gsi having if ignore ilike in include increment index infer inline inner insert intersect into is join key keys keyspace known last left let letting like limit lsm map mapping matched materialized merge minus namespace nest not number object offset on option or order outer over parse partition password path pool prepare primary private privilege procedure public raw realm reduce rename return returning revoke right role rollback satisfies schema select self semi set show some start statistics string system then to transaction trigger truncate under union unique unknown unnest unset update upsert use user using validate value valued values via view when where while with within work xor", + literal: "true false null missing|5", + built_in: + "array_agg array_append array_concat array_contains array_count array_distinct array_ifnull array_length array_max array_min array_position array_prepend array_put array_range array_remove array_repeat array_replace array_reverse array_sort array_sum avg count max min sum greatest least ifmissing ifmissingornull ifnull missingif nullif ifinf ifnan ifnanorinf naninf neginfif posinfif clock_millis clock_str date_add_millis date_add_str date_diff_millis date_diff_str date_part_millis date_part_str date_trunc_millis date_trunc_str duration_to_str millis str_to_millis millis_to_str millis_to_utc millis_to_zone_name now_millis now_str str_to_duration str_to_utc str_to_zone_name decode_json encode_json encoded_size poly_length base64 base64_encode base64_decode meta uuid abs acos asin atan atan2 ceil cos degrees e exp ln log floor pi power radians random round sign sin sqrt tan trunc object_length object_names object_pairs object_inner_pairs object_values object_inner_values object_add object_put object_remove object_unwrap regexp_contains regexp_like regexp_position regexp_replace contains initcap length lower ltrim position repeat replace rtrim split substr title trim upper isarray isatom isboolean isnumber isobject isstring type toarray toatom toboolean tonumber toobject tostring", + }, + contains: [ + { + className: "string", + begin: "'", + end: "'", + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "string", + begin: '"', + end: '"', + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "symbol", + begin: "`", + end: "`", + contains: [e.BACKSLASH_ESCAPE], + relevance: 2, + }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var kS = function (e) { + var t = { + className: "variable", + variants: [ + { begin: /\$\d+/ }, + { begin: /\$\{/, end: /\}/ }, + { begin: /[$@]/ + e.UNDERSCORE_IDENT_RE }, + ], + }, + n = { + endsWithParent: !0, + keywords: { + $pattern: "[a-z/_]+", + literal: + "on off yes no true false none blocked debug info notice warn error crit select break last permanent redirect kqueue rtsig epoll poll /dev/poll", + }, + relevance: 0, + illegal: "=>", + contains: [ + e.HASH_COMMENT_MODE, + { + className: "string", + contains: [e.BACKSLASH_ESCAPE, t], + variants: [ + { begin: /"/, end: /"/ }, + { begin: /'/, end: /'/ }, + ], + }, + { + begin: "([a-z]+):/", + end: "\\s", + endsWithParent: !0, + excludeEnd: !0, + contains: [t], + }, + { + className: "regexp", + contains: [e.BACKSLASH_ESCAPE, t], + variants: [ + { begin: "\\s\\^", end: "\\s|\\{|;", returnEnd: !0 }, + { begin: "~\\*?\\s+", end: "\\s|\\{|;", returnEnd: !0 }, + { begin: "\\*(\\.[a-z\\-]+)+" }, + { begin: "([a-z\\-]+\\.)+\\*" }, + ], + }, + { + className: "number", + begin: "\\b\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}(:\\d{1,5})?\\b", + }, + { + className: "number", + begin: "\\b\\d+[kKmMgGdshdwy]*\\b", + relevance: 0, + }, + t, + ], + }; + return { + name: "Nginx config", + aliases: ["nginxconf"], + contains: [ + e.HASH_COMMENT_MODE, + { + begin: e.UNDERSCORE_IDENT_RE + "\\s+\\{", + returnBegin: !0, + end: /\{/, + contains: [{ className: "section", begin: e.UNDERSCORE_IDENT_RE }], + relevance: 0, + }, + { + begin: e.UNDERSCORE_IDENT_RE + "\\s", + end: ";|\\{", + returnBegin: !0, + contains: [ + { className: "attribute", begin: e.UNDERSCORE_IDENT_RE, starts: n }, + ], + relevance: 0, + }, + ], + illegal: "[^\\s\\}]", + }; +}; +var US = function (e) { + return { + name: "Nim", + keywords: { + keyword: + "addr and as asm bind block break case cast const continue converter discard distinct div do elif else end enum except export finally for from func generic if import in include interface is isnot iterator let macro method mixin mod nil not notin object of or out proc ptr raise ref return shl shr static template try tuple type using var when while with without xor yield", + literal: "shared guarded stdin stdout stderr result true false", + built_in: + "int int8 int16 int32 int64 uint uint8 uint16 uint32 uint64 float float32 float64 bool char string cstring pointer expr stmt void auto any range array openarray varargs seq set clong culong cchar cschar cshort cint csize clonglong cfloat cdouble clongdouble cuchar cushort cuint culonglong cstringarray semistatic", + }, + contains: [ + { className: "meta", begin: /\{\./, end: /\.\}/, relevance: 10 }, + { + className: "string", + begin: /[a-zA-Z]\w*"/, + end: /"/, + contains: [{ begin: /""/ }], + }, + { className: "string", begin: /([a-zA-Z]\w*)?"""/, end: /"""/ }, + e.QUOTE_STRING_MODE, + { className: "type", begin: /\b[A-Z]\w+\b/, relevance: 0 }, + { + className: "number", + relevance: 0, + variants: [ + { begin: /\b(0[xX][0-9a-fA-F][_0-9a-fA-F]*)('?[iIuU](8|16|32|64))?/ }, + { begin: /\b(0o[0-7][_0-7]*)('?[iIuUfF](8|16|32|64))?/ }, + { begin: /\b(0(b|B)[01][_01]*)('?[iIuUfF](8|16|32|64))?/ }, + { begin: /\b(\d[_\d]*)('?[iIuUfF](8|16|32|64))?/ }, + ], + }, + e.HASH_COMMENT_MODE, + ], + }; +}; +var FS = function (e) { + var t = { + keyword: "rec with let in inherit assert if else then", + literal: "true false or and null", + built_in: + "import abort baseNameOf dirOf isNull builtins map removeAttrs throw toString derivation", + }, + n = { className: "subst", begin: /\$\{/, end: /\}/, keywords: t }, + a = { + className: "string", + contains: [n], + variants: [ + { begin: "''", end: "''" }, + { begin: '"', end: '"' }, + ], + }, + r = [ + e.NUMBER_MODE, + e.HASH_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + a, + { + begin: /[a-zA-Z0-9-_]+(\s*=)/, + returnBegin: !0, + relevance: 0, + contains: [{ className: "attr", begin: /\S+/ }], + }, + ]; + return ( + (n.contains = r), + { name: "Nix", aliases: ["nixos"], keywords: t, contains: r } + ); +}; +var BS = function (e) { + return { + name: "Node REPL", + contains: [ + { + className: "meta", + starts: { end: / |$/, starts: { end: "$", subLanguage: "javascript" } }, + variants: [{ begin: /^>(?=[ ]|$)/ }, { begin: /^\.\.\.(?=[ ]|$)/ }], + }, + ], + }; +}; +var GS = function (e) { + var t = { className: "variable", begin: /\$+\{[\w.:-]+\}/ }, + n = { className: "variable", begin: /\$+\w+/, illegal: /\(\)\{\}/ }, + a = { className: "variable", begin: /\$+\([\w^.:-]+\)/ }, + r = { + className: "string", + variants: [ + { begin: '"', end: '"' }, + { begin: "'", end: "'" }, + { begin: "`", end: "`" }, + ], + illegal: /\n/, + contains: [ + { className: "meta", begin: /\$(\\[nrt]|\$)/ }, + { + className: "variable", + begin: + /\$(ADMINTOOLS|APPDATA|CDBURN_AREA|CMDLINE|COMMONFILES32|COMMONFILES64|COMMONFILES|COOKIES|DESKTOP|DOCUMENTS|EXEDIR|EXEFILE|EXEPATH|FAVORITES|FONTS|HISTORY|HWNDPARENT|INSTDIR|INTERNET_CACHE|LANGUAGE|LOCALAPPDATA|MUSIC|NETHOOD|OUTDIR|PICTURES|PLUGINSDIR|PRINTHOOD|PROFILE|PROGRAMFILES32|PROGRAMFILES64|PROGRAMFILES|QUICKLAUNCH|RECENT|RESOURCES_LOCALIZED|RESOURCES|SENDTO|SMPROGRAMS|SMSTARTUP|STARTMENU|SYSDIR|TEMP|TEMPLATES|VIDEOS|WINDIR)/, + }, + t, + n, + a, + ], + }; + return { + name: "NSIS", + case_insensitive: !1, + keywords: { + keyword: + "Abort AddBrandingImage AddSize AllowRootDirInstall AllowSkipFiles AutoCloseWindow BGFont BGGradient BrandingText BringToFront Call CallInstDLL Caption ChangeUI CheckBitmap ClearErrors CompletedText ComponentText CopyFiles CRCCheck CreateDirectory CreateFont CreateShortCut Delete DeleteINISec DeleteINIStr DeleteRegKey DeleteRegValue DetailPrint DetailsButtonText DirText DirVar DirVerify EnableWindow EnumRegKey EnumRegValue Exch Exec ExecShell ExecShellWait ExecWait ExpandEnvStrings File FileBufSize FileClose FileErrorText FileOpen FileRead FileReadByte FileReadUTF16LE FileReadWord FileWriteUTF16LE FileSeek FileWrite FileWriteByte FileWriteWord FindClose FindFirst FindNext FindWindow FlushINI GetCurInstType GetCurrentAddress GetDlgItem GetDLLVersion GetDLLVersionLocal GetErrorLevel GetFileTime GetFileTimeLocal GetFullPathName GetFunctionAddress GetInstDirError GetKnownFolderPath GetLabelAddress GetTempFileName Goto HideWindow Icon IfAbort IfErrors IfFileExists IfRebootFlag IfRtlLanguage IfShellVarContextAll IfSilent InitPluginsDir InstallButtonText InstallColors InstallDir InstallDirRegKey InstProgressFlags InstType InstTypeGetText InstTypeSetText Int64Cmp Int64CmpU Int64Fmt IntCmp IntCmpU IntFmt IntOp IntPtrCmp IntPtrCmpU IntPtrOp IsWindow LangString LicenseBkColor LicenseData LicenseForceSelection LicenseLangString LicenseText LoadAndSetImage LoadLanguageFile LockWindow LogSet LogText ManifestDPIAware ManifestLongPathAware ManifestMaxVersionTested ManifestSupportedOS MessageBox MiscButtonText Name Nop OutFile Page PageCallbacks PEAddResource PEDllCharacteristics PERemoveResource PESubsysVer Pop Push Quit ReadEnvStr ReadINIStr ReadRegDWORD ReadRegStr Reboot RegDLL Rename RequestExecutionLevel ReserveFile Return RMDir SearchPath SectionGetFlags SectionGetInstTypes SectionGetSize SectionGetText SectionIn SectionSetFlags SectionSetInstTypes SectionSetSize SectionSetText SendMessage SetAutoClose SetBrandingImage SetCompress SetCompressor SetCompressorDictSize SetCtlColors SetCurInstType SetDatablockOptimize SetDateSave SetDetailsPrint SetDetailsView SetErrorLevel SetErrors SetFileAttributes SetFont SetOutPath SetOverwrite SetRebootFlag SetRegView SetShellVarContext SetSilent ShowInstDetails ShowUninstDetails ShowWindow SilentInstall SilentUnInstall Sleep SpaceTexts StrCmp StrCmpS StrCpy StrLen SubCaption Unicode UninstallButtonText UninstallCaption UninstallIcon UninstallSubCaption UninstallText UninstPage UnRegDLL Var VIAddVersionKey VIFileVersion VIProductVersion WindowIcon WriteINIStr WriteRegBin WriteRegDWORD WriteRegExpandStr WriteRegMultiStr WriteRegNone WriteRegStr WriteUninstaller XPStyle", + literal: + "admin all auto both bottom bzip2 colored components current custom directory false force hide highest ifdiff ifnewer instfiles lastused leave left license listonly lzma nevershow none normal notset off on open print right show silent silentlog smooth textonly top true try un.components un.custom un.directory un.instfiles un.license uninstConfirm user Win10 Win7 Win8 WinVista zlib", + }, + contains: [ + e.HASH_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "function", + beginKeywords: "Function PageEx Section SectionGroup", + end: "$", + }, + r, + { + className: "keyword", + begin: + /!(addincludedir|addplugindir|appendfile|cd|define|delfile|echo|else|endif|error|execute|finalize|getdllversion|gettlbversion|if|ifdef|ifmacrodef|ifmacrondef|ifndef|include|insertmacro|macro|macroend|makensis|packhdr|searchparse|searchreplace|system|tempfile|undef|verbose|warning)/, + }, + t, + n, + a, + { + className: "params", + begin: + "(ARCHIVE|FILE_ATTRIBUTE_ARCHIVE|FILE_ATTRIBUTE_NORMAL|FILE_ATTRIBUTE_OFFLINE|FILE_ATTRIBUTE_READONLY|FILE_ATTRIBUTE_SYSTEM|FILE_ATTRIBUTE_TEMPORARY|HKCR|HKCU|HKDD|HKEY_CLASSES_ROOT|HKEY_CURRENT_CONFIG|HKEY_CURRENT_USER|HKEY_DYN_DATA|HKEY_LOCAL_MACHINE|HKEY_PERFORMANCE_DATA|HKEY_USERS|HKLM|HKPD|HKU|IDABORT|IDCANCEL|IDIGNORE|IDNO|IDOK|IDRETRY|IDYES|MB_ABORTRETRYIGNORE|MB_DEFBUTTON1|MB_DEFBUTTON2|MB_DEFBUTTON3|MB_DEFBUTTON4|MB_ICONEXCLAMATION|MB_ICONINFORMATION|MB_ICONQUESTION|MB_ICONSTOP|MB_OK|MB_OKCANCEL|MB_RETRYCANCEL|MB_RIGHT|MB_RTLREADING|MB_SETFOREGROUND|MB_TOPMOST|MB_USERICON|MB_YESNO|NORMAL|OFFLINE|READONLY|SHCTX|SHELL_CONTEXT|SYSTEM|TEMPORARY)", + }, + { className: "class", begin: /\w+::\w+/ }, + e.NUMBER_MODE, + ], + }; +}; +var YS = function (e) { + var t = /[a-zA-Z@][a-zA-Z0-9_]*/, + n = { $pattern: t, keyword: "@interface @class @protocol @implementation" }; + return { + name: "Objective-C", + aliases: ["mm", "objc", "obj-c", "obj-c++", "objective-c++"], + keywords: { + $pattern: t, + keyword: + "int float while char export sizeof typedef const struct for union unsigned long volatile static bool mutable if do return goto void enum else break extern asm case short default double register explicit signed typename this switch continue wchar_t inline readonly assign readwrite self @synchronized id typeof nonatomic super unichar IBOutlet IBAction strong weak copy in out inout bycopy byref oneway __strong __weak __block __autoreleasing @private @protected @public @try @property @end @throw @catch @finally @autoreleasepool @synthesize @dynamic @selector @optional @required @encode @package @import @defs @compatibility_alias __bridge __bridge_transfer __bridge_retained __bridge_retain __covariant __contravariant __kindof _Nonnull _Nullable _Null_unspecified __FUNCTION__ __PRETTY_FUNCTION__ __attribute__ getter setter retain unsafe_unretained nonnull nullable null_unspecified null_resettable class instancetype NS_DESIGNATED_INITIALIZER NS_UNAVAILABLE NS_REQUIRES_SUPER NS_RETURNS_INNER_POINTER NS_INLINE NS_AVAILABLE NS_DEPRECATED NS_ENUM NS_OPTIONS NS_SWIFT_UNAVAILABLE NS_ASSUME_NONNULL_BEGIN NS_ASSUME_NONNULL_END NS_REFINED_FOR_SWIFT NS_SWIFT_NAME NS_SWIFT_NOTHROW NS_DURING NS_HANDLER NS_ENDHANDLER NS_VALUERETURN NS_VOIDRETURN", + literal: "false true FALSE TRUE nil YES NO NULL", + built_in: + "BOOL dispatch_once_t dispatch_queue_t dispatch_sync dispatch_async dispatch_once", + }, + illegal: "/, + end: /$/, + illegal: "\\n", + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }, + { + className: "class", + begin: "(" + n.keyword.split(" ").join("|") + ")\\b", + end: /(\{|$)/, + excludeEnd: !0, + keywords: n, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { begin: "\\." + e.UNDERSCORE_IDENT_RE, relevance: 0 }, + ], + }; +}; +var HS = function (e) { + return { + name: "OCaml", + aliases: ["ml"], + keywords: { + $pattern: "[a-z_]\\w*!?", + keyword: + "and as assert asr begin class constraint do done downto else end exception external for fun function functor if in include inherit! inherit initializer land lazy let lor lsl lsr lxor match method!|10 method mod module mutable new object of open! open or private rec sig struct then to try type val! val virtual when while with parser value", + built_in: + "array bool bytes char exn|5 float int int32 int64 list lazy_t|5 nativeint|5 string unit in_channel out_channel ref", + literal: "true false", + }, + illegal: /\/\/|>>/, + contains: [ + { className: "literal", begin: "\\[(\\|\\|)?\\]|\\(\\)", relevance: 0 }, + e.COMMENT("\\(\\*", "\\*\\)", { contains: ["self"] }), + { className: "symbol", begin: "'[A-Za-z_](?!')[\\w']*" }, + { className: "type", begin: "`[A-Z][\\w']*" }, + { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + { begin: "[a-z_]\\w*'[\\w']*", relevance: 0 }, + e.inherit(e.APOS_STRING_MODE, { className: "string", relevance: 0 }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { + className: "number", + begin: + "\\b(0[xX][a-fA-F0-9_]+[Lln]?|0[oO][0-7_]+[Lln]?|0[bB][01_]+[Lln]?|[0-9][0-9_]*([Lln]|(\\.[0-9_]*)?([eE][-+]?[0-9_]+)?)?)", + relevance: 0, + }, + { begin: /->/ }, + ], + }; +}; +var VS = function (e) { + var t = { className: "keyword", begin: "\\$(f[asn]|t|vp[rtd]|children)" }, + n = { + className: "number", + begin: "\\b\\d+(\\.\\d+)?(e-?\\d+)?", + relevance: 0, + }, + a = e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + r = { + className: "function", + beginKeywords: "module function", + end: /=|\{/, + contains: [ + { + className: "params", + begin: "\\(", + end: "\\)", + contains: [ + "self", + n, + a, + t, + { className: "literal", begin: "false|true|PI|undef" }, + ], + }, + e.UNDERSCORE_TITLE_MODE, + ], + }; + return { + name: "OpenSCAD", + aliases: ["scad"], + keywords: { + keyword: "function module include use for intersection_for if else \\%", + literal: "false true PI undef", + built_in: + "circle square polygon text sphere cube cylinder polyhedron translate rotate scale resize mirror multmatrix color offset hull minkowski union difference intersection abs sign sin cos tan acos asin atan atan2 floor round ceil ln log pow sqrt exp rands min max concat lookup str chr search version version_num norm cross parent_module echo import import_dxf dxf_linear_extrude linear_extrude rotate_extrude surface projection render children dxf_cross dxf_dim let assign", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + { + className: "meta", + keywords: { "meta-keyword": "include use" }, + begin: "include|use <", + end: ">", + }, + a, + t, + { begin: "[*!#%]", relevance: 0 }, + r, + ], + }; +}; +var qS = function (e) { + var t = { + $pattern: /\.?\w+/, + keyword: + "abstract add and array as asc aspect assembly async begin break block by case class concat const copy constructor continue create default delegate desc distinct div do downto dynamic each else empty end ensure enum equals event except exit extension external false final finalize finalizer finally flags for forward from function future global group has if implementation implements implies in index inherited inline interface into invariants is iterator join locked locking loop matching method mod module namespace nested new nil not notify nullable of old on operator or order out override parallel params partial pinned private procedure property protected public queryable raise read readonly record reintroduce remove repeat require result reverse sealed select self sequence set shl shr skip static step soft take then to true try tuple type union unit unsafe until uses using var virtual raises volatile where while with write xor yield await mapped deprecated stdcall cdecl pascal register safecall overload library platform reference packed strict published autoreleasepool selector strong weak unretained", + }, + n = e.COMMENT(/\{/, /\}/, { relevance: 0 }), + a = e.COMMENT("\\(\\*", "\\*\\)", { relevance: 10 }), + r = { + className: "string", + begin: "'", + end: "'", + contains: [{ begin: "''" }], + }, + i = { className: "string", begin: "(#\\d+)+" }, + o = { + className: "function", + beginKeywords: "function constructor destructor procedure method", + end: "[:;]", + keywords: "function constructor|10 destructor|10 procedure|10 method|10", + contains: [ + e.TITLE_MODE, + { + className: "params", + begin: "\\(", + end: "\\)", + keywords: t, + contains: [r, i], + }, + n, + a, + ], + }; + return { + name: "Oxygene", + case_insensitive: !0, + keywords: t, + illegal: '("|\\$[G-Zg-z]|\\/\\*||->)', + contains: [ + n, + a, + e.C_LINE_COMMENT_MODE, + r, + i, + e.NUMBER_MODE, + o, + { + className: "class", + begin: "=\\bclass\\b", + end: "end;", + keywords: t, + contains: [r, i, n, a, e.C_LINE_COMMENT_MODE, o], + }, + ], + }; +}; +var zS = function (e) { + var t = e.COMMENT(/\{/, /\}/, { contains: ["self"] }); + return { + name: "Parser3", + subLanguage: "xml", + relevance: 0, + contains: [ + e.COMMENT("^#", "$"), + e.COMMENT(/\^rem\{/, /\}/, { relevance: 10, contains: [t] }), + { + className: "meta", + begin: "^@(?:BASE|USE|CLASS|OPTIONS)$", + relevance: 10, + }, + { + className: "title", + begin: "@[\\w\\-]+\\[[\\w^;\\-]*\\](?:\\[[\\w^;\\-]*\\])?(?:.*)$", + }, + { className: "variable", begin: /\$\{?[\w\-.:]+\}?/ }, + { className: "keyword", begin: /\^[\w\-.:]+/ }, + { className: "number", begin: "\\^#[0-9a-fA-F]+" }, + e.C_NUMBER_MODE, + ], + }; +}; +var WS = function (e) { + return { + name: "Packet Filter config", + aliases: ["pf.conf"], + keywords: { + $pattern: /[a-z0-9_<>-]+/, + built_in: "block match pass load anchor|5 antispoof|10 set table", + keyword: + "in out log quick on rdomain inet inet6 proto from port os to route allow-opts divert-packet divert-reply divert-to flags group icmp-type icmp6-type label once probability recieved-on rtable prio queue tos tag tagged user keep fragment for os drop af-to|10 binat-to|10 nat-to|10 rdr-to|10 bitmask least-stats random round-robin source-hash static-port dup-to reply-to route-to parent bandwidth default min max qlimit block-policy debug fingerprints hostid limit loginterface optimization reassemble ruleset-optimization basic none profile skip state-defaults state-policy timeout const counters persist no modulate synproxy state|5 floating if-bound no-sync pflow|10 sloppy source-track global rule max-src-nodes max-src-states max-src-conn max-src-conn-rate overload flush scrub|5 max-mss min-ttl no-df|10 random-id", + literal: "all any no-route self urpf-failed egress|5 unknown", + }, + contains: [ + e.HASH_COMMENT_MODE, + e.NUMBER_MODE, + e.QUOTE_STRING_MODE, + { className: "variable", begin: /\$[\w\d#@][\w\d_]*/ }, + { className: "variable", begin: /<(?!\/)/, end: />/ }, + ], + }; +}; +var $S = function (e) { + var t = e.COMMENT("--", "$"), + n = "\\$([a-zA-Z_]?|[a-zA-Z_][a-zA-Z_0-9]*)\\$", + a = + "BIGINT INT8 BIGSERIAL SERIAL8 BIT VARYING VARBIT BOOLEAN BOOL BOX BYTEA CHARACTER CHAR VARCHAR CIDR CIRCLE DATE DOUBLE PRECISION FLOAT8 FLOAT INET INTEGER INT INT4 INTERVAL JSON JSONB LINE LSEG|10 MACADDR MACADDR8 MONEY NUMERIC DEC DECIMAL PATH POINT POLYGON REAL FLOAT4 SMALLINT INT2 SMALLSERIAL|10 SERIAL2|10 SERIAL|10 SERIAL4|10 TEXT TIME ZONE TIMETZ|10 TIMESTAMP TIMESTAMPTZ|10 TSQUERY|10 TSVECTOR|10 TXID_SNAPSHOT|10 UUID XML NATIONAL NCHAR INT4RANGE|10 INT8RANGE|10 NUMRANGE|10 TSRANGE|10 TSTZRANGE|10 DATERANGE|10 ANYELEMENT ANYARRAY ANYNONARRAY ANYENUM ANYRANGE CSTRING INTERNAL RECORD PG_DDL_COMMAND VOID UNKNOWN OPAQUE REFCURSOR NAME OID REGPROC|10 REGPROCEDURE|10 REGOPER|10 REGOPERATOR|10 REGCLASS|10 REGTYPE|10 REGROLE|10 REGNAMESPACE|10 REGCONFIG|10 REGDICTIONARY|10 ", + r = a + .trim() + .split(" ") + .map(function (e) { + return e.split("|")[0]; + }) + .join("|"), + i = + "ARRAY_AGG AVG BIT_AND BIT_OR BOOL_AND BOOL_OR COUNT EVERY JSON_AGG JSONB_AGG JSON_OBJECT_AGG JSONB_OBJECT_AGG MAX MIN MODE STRING_AGG SUM XMLAGG CORR COVAR_POP COVAR_SAMP REGR_AVGX REGR_AVGY REGR_COUNT REGR_INTERCEPT REGR_R2 REGR_SLOPE REGR_SXX REGR_SXY REGR_SYY STDDEV STDDEV_POP STDDEV_SAMP VARIANCE VAR_POP VAR_SAMP PERCENTILE_CONT PERCENTILE_DISC ROW_NUMBER RANK DENSE_RANK PERCENT_RANK CUME_DIST NTILE LAG LEAD FIRST_VALUE LAST_VALUE NTH_VALUE NUM_NONNULLS NUM_NULLS ABS CBRT CEIL CEILING DEGREES DIV EXP FLOOR LN LOG MOD PI POWER RADIANS ROUND SCALE SIGN SQRT TRUNC WIDTH_BUCKET RANDOM SETSEED ACOS ACOSD ASIN ASIND ATAN ATAND ATAN2 ATAN2D COS COSD COT COTD SIN SIND TAN TAND BIT_LENGTH CHAR_LENGTH CHARACTER_LENGTH LOWER OCTET_LENGTH OVERLAY POSITION SUBSTRING TREAT TRIM UPPER ASCII BTRIM CHR CONCAT CONCAT_WS CONVERT CONVERT_FROM CONVERT_TO DECODE ENCODE INITCAP LEFT LENGTH LPAD LTRIM MD5 PARSE_IDENT PG_CLIENT_ENCODING QUOTE_IDENT|10 QUOTE_LITERAL|10 QUOTE_NULLABLE|10 REGEXP_MATCH REGEXP_MATCHES REGEXP_REPLACE REGEXP_SPLIT_TO_ARRAY REGEXP_SPLIT_TO_TABLE REPEAT REPLACE REVERSE RIGHT RPAD RTRIM SPLIT_PART STRPOS SUBSTR TO_ASCII TO_HEX TRANSLATE OCTET_LENGTH GET_BIT GET_BYTE SET_BIT SET_BYTE TO_CHAR TO_DATE TO_NUMBER TO_TIMESTAMP AGE CLOCK_TIMESTAMP|10 DATE_PART DATE_TRUNC ISFINITE JUSTIFY_DAYS JUSTIFY_HOURS JUSTIFY_INTERVAL MAKE_DATE MAKE_INTERVAL|10 MAKE_TIME MAKE_TIMESTAMP|10 MAKE_TIMESTAMPTZ|10 NOW STATEMENT_TIMESTAMP|10 TIMEOFDAY TRANSACTION_TIMESTAMP|10 ENUM_FIRST ENUM_LAST ENUM_RANGE AREA CENTER DIAMETER HEIGHT ISCLOSED ISOPEN NPOINTS PCLOSE POPEN RADIUS WIDTH BOX BOUND_BOX CIRCLE LINE LSEG PATH POLYGON ABBREV BROADCAST HOST HOSTMASK MASKLEN NETMASK NETWORK SET_MASKLEN TEXT INET_SAME_FAMILY INET_MERGE MACADDR8_SET7BIT ARRAY_TO_TSVECTOR GET_CURRENT_TS_CONFIG NUMNODE PLAINTO_TSQUERY PHRASETO_TSQUERY WEBSEARCH_TO_TSQUERY QUERYTREE SETWEIGHT STRIP TO_TSQUERY TO_TSVECTOR JSON_TO_TSVECTOR JSONB_TO_TSVECTOR TS_DELETE TS_FILTER TS_HEADLINE TS_RANK TS_RANK_CD TS_REWRITE TSQUERY_PHRASE TSVECTOR_TO_ARRAY TSVECTOR_UPDATE_TRIGGER TSVECTOR_UPDATE_TRIGGER_COLUMN XMLCOMMENT XMLCONCAT XMLELEMENT XMLFOREST XMLPI XMLROOT XMLEXISTS XML_IS_WELL_FORMED XML_IS_WELL_FORMED_DOCUMENT XML_IS_WELL_FORMED_CONTENT XPATH XPATH_EXISTS XMLTABLE XMLNAMESPACES TABLE_TO_XML TABLE_TO_XMLSCHEMA TABLE_TO_XML_AND_XMLSCHEMA QUERY_TO_XML QUERY_TO_XMLSCHEMA QUERY_TO_XML_AND_XMLSCHEMA CURSOR_TO_XML CURSOR_TO_XMLSCHEMA SCHEMA_TO_XML SCHEMA_TO_XMLSCHEMA SCHEMA_TO_XML_AND_XMLSCHEMA DATABASE_TO_XML DATABASE_TO_XMLSCHEMA DATABASE_TO_XML_AND_XMLSCHEMA XMLATTRIBUTES TO_JSON TO_JSONB ARRAY_TO_JSON ROW_TO_JSON JSON_BUILD_ARRAY JSONB_BUILD_ARRAY JSON_BUILD_OBJECT JSONB_BUILD_OBJECT JSON_OBJECT JSONB_OBJECT JSON_ARRAY_LENGTH JSONB_ARRAY_LENGTH JSON_EACH JSONB_EACH JSON_EACH_TEXT JSONB_EACH_TEXT JSON_EXTRACT_PATH JSONB_EXTRACT_PATH JSON_OBJECT_KEYS JSONB_OBJECT_KEYS JSON_POPULATE_RECORD JSONB_POPULATE_RECORD JSON_POPULATE_RECORDSET JSONB_POPULATE_RECORDSET JSON_ARRAY_ELEMENTS JSONB_ARRAY_ELEMENTS JSON_ARRAY_ELEMENTS_TEXT JSONB_ARRAY_ELEMENTS_TEXT JSON_TYPEOF JSONB_TYPEOF JSON_TO_RECORD JSONB_TO_RECORD JSON_TO_RECORDSET JSONB_TO_RECORDSET JSON_STRIP_NULLS JSONB_STRIP_NULLS JSONB_SET JSONB_INSERT JSONB_PRETTY CURRVAL LASTVAL NEXTVAL SETVAL COALESCE NULLIF GREATEST LEAST ARRAY_APPEND ARRAY_CAT ARRAY_NDIMS ARRAY_DIMS ARRAY_FILL ARRAY_LENGTH ARRAY_LOWER ARRAY_POSITION ARRAY_POSITIONS ARRAY_PREPEND ARRAY_REMOVE ARRAY_REPLACE ARRAY_TO_STRING ARRAY_UPPER CARDINALITY STRING_TO_ARRAY UNNEST ISEMPTY LOWER_INC UPPER_INC LOWER_INF UPPER_INF RANGE_MERGE GENERATE_SERIES GENERATE_SUBSCRIPTS CURRENT_DATABASE CURRENT_QUERY CURRENT_SCHEMA|10 CURRENT_SCHEMAS|10 INET_CLIENT_ADDR INET_CLIENT_PORT INET_SERVER_ADDR INET_SERVER_PORT ROW_SECURITY_ACTIVE FORMAT_TYPE TO_REGCLASS TO_REGPROC TO_REGPROCEDURE TO_REGOPER TO_REGOPERATOR TO_REGTYPE TO_REGNAMESPACE TO_REGROLE COL_DESCRIPTION OBJ_DESCRIPTION SHOBJ_DESCRIPTION TXID_CURRENT TXID_CURRENT_IF_ASSIGNED TXID_CURRENT_SNAPSHOT TXID_SNAPSHOT_XIP TXID_SNAPSHOT_XMAX TXID_SNAPSHOT_XMIN TXID_VISIBLE_IN_SNAPSHOT TXID_STATUS CURRENT_SETTING SET_CONFIG BRIN_SUMMARIZE_NEW_VALUES BRIN_SUMMARIZE_RANGE BRIN_DESUMMARIZE_RANGE GIN_CLEAN_PENDING_LIST SUPPRESS_REDUNDANT_UPDATES_TRIGGER LO_FROM_BYTEA LO_PUT LO_GET LO_CREAT LO_CREATE LO_UNLINK LO_IMPORT LO_EXPORT LOREAD LOWRITE GROUPING CAST " + .trim() + .split(" ") + .map(function (e) { + return e.split("|")[0]; + }) + .join("|"); + return { + name: "PostgreSQL", + aliases: ["postgres", "postgresql"], + case_insensitive: !0, + keywords: { + keyword: + "ABORT ALTER ANALYZE BEGIN CALL CHECKPOINT|10 CLOSE CLUSTER COMMENT COMMIT COPY CREATE DEALLOCATE DECLARE DELETE DISCARD DO DROP END EXECUTE EXPLAIN FETCH GRANT IMPORT INSERT LISTEN LOAD LOCK MOVE NOTIFY PREPARE REASSIGN|10 REFRESH REINDEX RELEASE RESET REVOKE ROLLBACK SAVEPOINT SECURITY SELECT SET SHOW START TRUNCATE UNLISTEN|10 UPDATE VACUUM|10 VALUES AGGREGATE COLLATION CONVERSION|10 DATABASE DEFAULT PRIVILEGES DOMAIN TRIGGER EXTENSION FOREIGN WRAPPER|10 TABLE FUNCTION GROUP LANGUAGE LARGE OBJECT MATERIALIZED VIEW OPERATOR CLASS FAMILY POLICY PUBLICATION|10 ROLE RULE SCHEMA SEQUENCE SERVER STATISTICS SUBSCRIPTION SYSTEM TABLESPACE CONFIGURATION DICTIONARY PARSER TEMPLATE TYPE USER MAPPING PREPARED ACCESS METHOD CAST AS TRANSFORM TRANSACTION OWNED TO INTO SESSION AUTHORIZATION INDEX PROCEDURE ASSERTION ALL ANALYSE AND ANY ARRAY ASC ASYMMETRIC|10 BOTH CASE CHECK COLLATE COLUMN CONCURRENTLY|10 CONSTRAINT CROSS DEFERRABLE RANGE DESC DISTINCT ELSE EXCEPT FOR FREEZE|10 FROM FULL HAVING ILIKE IN INITIALLY INNER INTERSECT IS ISNULL JOIN LATERAL LEADING LIKE LIMIT NATURAL NOT NOTNULL NULL OFFSET ON ONLY OR ORDER OUTER OVERLAPS PLACING PRIMARY REFERENCES RETURNING SIMILAR SOME SYMMETRIC TABLESAMPLE THEN TRAILING UNION UNIQUE USING VARIADIC|10 VERBOSE WHEN WHERE WINDOW WITH BY RETURNS INOUT OUT SETOF|10 IF STRICT CURRENT CONTINUE OWNER LOCATION OVER PARTITION WITHIN BETWEEN ESCAPE EXTERNAL INVOKER DEFINER WORK RENAME VERSION CONNECTION CONNECT TABLES TEMP TEMPORARY FUNCTIONS SEQUENCES TYPES SCHEMAS OPTION CASCADE RESTRICT ADD ADMIN EXISTS VALID VALIDATE ENABLE DISABLE REPLICA|10 ALWAYS PASSING COLUMNS PATH REF VALUE OVERRIDING IMMUTABLE STABLE VOLATILE BEFORE AFTER EACH ROW PROCEDURAL ROUTINE NO HANDLER VALIDATOR OPTIONS STORAGE OIDS|10 WITHOUT INHERIT DEPENDS CALLED INPUT LEAKPROOF|10 COST ROWS NOWAIT SEARCH UNTIL ENCRYPTED|10 PASSWORD CONFLICT|10 INSTEAD INHERITS CHARACTERISTICS WRITE CURSOR ALSO STATEMENT SHARE EXCLUSIVE INLINE ISOLATION REPEATABLE READ COMMITTED SERIALIZABLE UNCOMMITTED LOCAL GLOBAL SQL PROCEDURES RECURSIVE SNAPSHOT ROLLUP CUBE TRUSTED|10 INCLUDE FOLLOWING PRECEDING UNBOUNDED RANGE GROUPS UNENCRYPTED|10 SYSID FORMAT DELIMITER HEADER QUOTE ENCODING FILTER OFF FORCE_QUOTE FORCE_NOT_NULL FORCE_NULL COSTS BUFFERS TIMING SUMMARY DISABLE_PAGE_SKIPPING RESTART CYCLE GENERATED IDENTITY DEFERRED IMMEDIATE LEVEL LOGGED UNLOGGED OF NOTHING NONE EXCLUDE ATTRIBUTE USAGE ROUTINES TRUE FALSE NAN INFINITY ALIAS BEGIN CONSTANT DECLARE END EXCEPTION RETURN PERFORM|10 RAISE GET DIAGNOSTICS STACKED|10 FOREACH LOOP ELSIF EXIT WHILE REVERSE SLICE DEBUG LOG INFO NOTICE WARNING ASSERT OPEN SUPERUSER NOSUPERUSER CREATEDB NOCREATEDB CREATEROLE NOCREATEROLE INHERIT NOINHERIT LOGIN NOLOGIN REPLICATION NOREPLICATION BYPASSRLS NOBYPASSRLS ", + built_in: + "CURRENT_TIME CURRENT_TIMESTAMP CURRENT_USER CURRENT_CATALOG|10 CURRENT_DATE LOCALTIME LOCALTIMESTAMP CURRENT_ROLE|10 CURRENT_SCHEMA|10 SESSION_USER PUBLIC FOUND NEW OLD TG_NAME|10 TG_WHEN|10 TG_LEVEL|10 TG_OP|10 TG_RELID|10 TG_RELNAME|10 TG_TABLE_NAME|10 TG_TABLE_SCHEMA|10 TG_NARGS|10 TG_ARGV|10 TG_EVENT|10 TG_TAG|10 ROW_COUNT RESULT_OID|10 PG_CONTEXT|10 RETURNED_SQLSTATE COLUMN_NAME CONSTRAINT_NAME PG_DATATYPE_NAME|10 MESSAGE_TEXT TABLE_NAME SCHEMA_NAME PG_EXCEPTION_DETAIL|10 PG_EXCEPTION_HINT|10 PG_EXCEPTION_CONTEXT|10 SQLSTATE SQLERRM|10 SUCCESSFUL_COMPLETION WARNING DYNAMIC_RESULT_SETS_RETURNED IMPLICIT_ZERO_BIT_PADDING NULL_VALUE_ELIMINATED_IN_SET_FUNCTION PRIVILEGE_NOT_GRANTED PRIVILEGE_NOT_REVOKED STRING_DATA_RIGHT_TRUNCATION DEPRECATED_FEATURE NO_DATA NO_ADDITIONAL_DYNAMIC_RESULT_SETS_RETURNED SQL_STATEMENT_NOT_YET_COMPLETE CONNECTION_EXCEPTION CONNECTION_DOES_NOT_EXIST CONNECTION_FAILURE SQLCLIENT_UNABLE_TO_ESTABLISH_SQLCONNECTION SQLSERVER_REJECTED_ESTABLISHMENT_OF_SQLCONNECTION TRANSACTION_RESOLUTION_UNKNOWN PROTOCOL_VIOLATION TRIGGERED_ACTION_EXCEPTION FEATURE_NOT_SUPPORTED INVALID_TRANSACTION_INITIATION LOCATOR_EXCEPTION INVALID_LOCATOR_SPECIFICATION INVALID_GRANTOR INVALID_GRANT_OPERATION INVALID_ROLE_SPECIFICATION DIAGNOSTICS_EXCEPTION STACKED_DIAGNOSTICS_ACCESSED_WITHOUT_ACTIVE_HANDLER CASE_NOT_FOUND CARDINALITY_VIOLATION DATA_EXCEPTION ARRAY_SUBSCRIPT_ERROR CHARACTER_NOT_IN_REPERTOIRE DATETIME_FIELD_OVERFLOW DIVISION_BY_ZERO ERROR_IN_ASSIGNMENT ESCAPE_CHARACTER_CONFLICT INDICATOR_OVERFLOW INTERVAL_FIELD_OVERFLOW INVALID_ARGUMENT_FOR_LOGARITHM INVALID_ARGUMENT_FOR_NTILE_FUNCTION INVALID_ARGUMENT_FOR_NTH_VALUE_FUNCTION INVALID_ARGUMENT_FOR_POWER_FUNCTION INVALID_ARGUMENT_FOR_WIDTH_BUCKET_FUNCTION INVALID_CHARACTER_VALUE_FOR_CAST INVALID_DATETIME_FORMAT INVALID_ESCAPE_CHARACTER INVALID_ESCAPE_OCTET INVALID_ESCAPE_SEQUENCE NONSTANDARD_USE_OF_ESCAPE_CHARACTER INVALID_INDICATOR_PARAMETER_VALUE INVALID_PARAMETER_VALUE INVALID_REGULAR_EXPRESSION INVALID_ROW_COUNT_IN_LIMIT_CLAUSE INVALID_ROW_COUNT_IN_RESULT_OFFSET_CLAUSE INVALID_TABLESAMPLE_ARGUMENT INVALID_TABLESAMPLE_REPEAT INVALID_TIME_ZONE_DISPLACEMENT_VALUE INVALID_USE_OF_ESCAPE_CHARACTER MOST_SPECIFIC_TYPE_MISMATCH NULL_VALUE_NOT_ALLOWED NULL_VALUE_NO_INDICATOR_PARAMETER NUMERIC_VALUE_OUT_OF_RANGE SEQUENCE_GENERATOR_LIMIT_EXCEEDED STRING_DATA_LENGTH_MISMATCH STRING_DATA_RIGHT_TRUNCATION SUBSTRING_ERROR TRIM_ERROR UNTERMINATED_C_STRING ZERO_LENGTH_CHARACTER_STRING FLOATING_POINT_EXCEPTION INVALID_TEXT_REPRESENTATION INVALID_BINARY_REPRESENTATION BAD_COPY_FILE_FORMAT UNTRANSLATABLE_CHARACTER NOT_AN_XML_DOCUMENT INVALID_XML_DOCUMENT INVALID_XML_CONTENT INVALID_XML_COMMENT INVALID_XML_PROCESSING_INSTRUCTION INTEGRITY_CONSTRAINT_VIOLATION RESTRICT_VIOLATION NOT_NULL_VIOLATION FOREIGN_KEY_VIOLATION UNIQUE_VIOLATION CHECK_VIOLATION EXCLUSION_VIOLATION INVALID_CURSOR_STATE INVALID_TRANSACTION_STATE ACTIVE_SQL_TRANSACTION BRANCH_TRANSACTION_ALREADY_ACTIVE HELD_CURSOR_REQUIRES_SAME_ISOLATION_LEVEL INAPPROPRIATE_ACCESS_MODE_FOR_BRANCH_TRANSACTION INAPPROPRIATE_ISOLATION_LEVEL_FOR_BRANCH_TRANSACTION NO_ACTIVE_SQL_TRANSACTION_FOR_BRANCH_TRANSACTION READ_ONLY_SQL_TRANSACTION SCHEMA_AND_DATA_STATEMENT_MIXING_NOT_SUPPORTED NO_ACTIVE_SQL_TRANSACTION IN_FAILED_SQL_TRANSACTION IDLE_IN_TRANSACTION_SESSION_TIMEOUT INVALID_SQL_STATEMENT_NAME TRIGGERED_DATA_CHANGE_VIOLATION INVALID_AUTHORIZATION_SPECIFICATION INVALID_PASSWORD DEPENDENT_PRIVILEGE_DESCRIPTORS_STILL_EXIST DEPENDENT_OBJECTS_STILL_EXIST INVALID_TRANSACTION_TERMINATION SQL_ROUTINE_EXCEPTION FUNCTION_EXECUTED_NO_RETURN_STATEMENT MODIFYING_SQL_DATA_NOT_PERMITTED PROHIBITED_SQL_STATEMENT_ATTEMPTED READING_SQL_DATA_NOT_PERMITTED INVALID_CURSOR_NAME EXTERNAL_ROUTINE_EXCEPTION CONTAINING_SQL_NOT_PERMITTED MODIFYING_SQL_DATA_NOT_PERMITTED PROHIBITED_SQL_STATEMENT_ATTEMPTED READING_SQL_DATA_NOT_PERMITTED EXTERNAL_ROUTINE_INVOCATION_EXCEPTION INVALID_SQLSTATE_RETURNED NULL_VALUE_NOT_ALLOWED TRIGGER_PROTOCOL_VIOLATED SRF_PROTOCOL_VIOLATED EVENT_TRIGGER_PROTOCOL_VIOLATED SAVEPOINT_EXCEPTION INVALID_SAVEPOINT_SPECIFICATION INVALID_CATALOG_NAME INVALID_SCHEMA_NAME TRANSACTION_ROLLBACK TRANSACTION_INTEGRITY_CONSTRAINT_VIOLATION SERIALIZATION_FAILURE STATEMENT_COMPLETION_UNKNOWN DEADLOCK_DETECTED SYNTAX_ERROR_OR_ACCESS_RULE_VIOLATION SYNTAX_ERROR INSUFFICIENT_PRIVILEGE CANNOT_COERCE GROUPING_ERROR WINDOWING_ERROR INVALID_RECURSION INVALID_FOREIGN_KEY INVALID_NAME NAME_TOO_LONG RESERVED_NAME DATATYPE_MISMATCH INDETERMINATE_DATATYPE COLLATION_MISMATCH INDETERMINATE_COLLATION WRONG_OBJECT_TYPE GENERATED_ALWAYS UNDEFINED_COLUMN UNDEFINED_FUNCTION UNDEFINED_TABLE UNDEFINED_PARAMETER UNDEFINED_OBJECT DUPLICATE_COLUMN DUPLICATE_CURSOR DUPLICATE_DATABASE DUPLICATE_FUNCTION DUPLICATE_PREPARED_STATEMENT DUPLICATE_SCHEMA DUPLICATE_TABLE DUPLICATE_ALIAS DUPLICATE_OBJECT AMBIGUOUS_COLUMN AMBIGUOUS_FUNCTION AMBIGUOUS_PARAMETER AMBIGUOUS_ALIAS INVALID_COLUMN_REFERENCE INVALID_COLUMN_DEFINITION INVALID_CURSOR_DEFINITION INVALID_DATABASE_DEFINITION INVALID_FUNCTION_DEFINITION INVALID_PREPARED_STATEMENT_DEFINITION INVALID_SCHEMA_DEFINITION INVALID_TABLE_DEFINITION INVALID_OBJECT_DEFINITION WITH_CHECK_OPTION_VIOLATION INSUFFICIENT_RESOURCES DISK_FULL OUT_OF_MEMORY TOO_MANY_CONNECTIONS CONFIGURATION_LIMIT_EXCEEDED PROGRAM_LIMIT_EXCEEDED STATEMENT_TOO_COMPLEX TOO_MANY_COLUMNS TOO_MANY_ARGUMENTS OBJECT_NOT_IN_PREREQUISITE_STATE OBJECT_IN_USE CANT_CHANGE_RUNTIME_PARAM LOCK_NOT_AVAILABLE OPERATOR_INTERVENTION QUERY_CANCELED ADMIN_SHUTDOWN CRASH_SHUTDOWN CANNOT_CONNECT_NOW DATABASE_DROPPED SYSTEM_ERROR IO_ERROR UNDEFINED_FILE DUPLICATE_FILE SNAPSHOT_TOO_OLD CONFIG_FILE_ERROR LOCK_FILE_EXISTS FDW_ERROR FDW_COLUMN_NAME_NOT_FOUND FDW_DYNAMIC_PARAMETER_VALUE_NEEDED FDW_FUNCTION_SEQUENCE_ERROR FDW_INCONSISTENT_DESCRIPTOR_INFORMATION FDW_INVALID_ATTRIBUTE_VALUE FDW_INVALID_COLUMN_NAME FDW_INVALID_COLUMN_NUMBER FDW_INVALID_DATA_TYPE FDW_INVALID_DATA_TYPE_DESCRIPTORS FDW_INVALID_DESCRIPTOR_FIELD_IDENTIFIER FDW_INVALID_HANDLE FDW_INVALID_OPTION_INDEX FDW_INVALID_OPTION_NAME FDW_INVALID_STRING_LENGTH_OR_BUFFER_LENGTH FDW_INVALID_STRING_FORMAT FDW_INVALID_USE_OF_NULL_POINTER FDW_TOO_MANY_HANDLES FDW_OUT_OF_MEMORY FDW_NO_SCHEMAS FDW_OPTION_NAME_NOT_FOUND FDW_REPLY_HANDLE FDW_SCHEMA_NOT_FOUND FDW_TABLE_NOT_FOUND FDW_UNABLE_TO_CREATE_EXECUTION FDW_UNABLE_TO_CREATE_REPLY FDW_UNABLE_TO_ESTABLISH_CONNECTION PLPGSQL_ERROR RAISE_EXCEPTION NO_DATA_FOUND TOO_MANY_ROWS ASSERT_FAILURE INTERNAL_ERROR DATA_CORRUPTED INDEX_CORRUPTED ", + }, + illegal: /:==|\W\s*\(\*|(^|\s)\$[a-z]|\{\{|[a-z]:\s*$|\.\.\.|TO:|DO:/, + contains: [ + { + className: "keyword", + variants: [ + { begin: /\bTEXT\s*SEARCH\b/ }, + { begin: /\b(PRIMARY|FOREIGN|FOR(\s+NO)?)\s+KEY\b/ }, + { begin: /\bPARALLEL\s+(UNSAFE|RESTRICTED|SAFE)\b/ }, + { begin: /\bSTORAGE\s+(PLAIN|EXTERNAL|EXTENDED|MAIN)\b/ }, + { begin: /\bMATCH\s+(FULL|PARTIAL|SIMPLE)\b/ }, + { begin: /\bNULLS\s+(FIRST|LAST)\b/ }, + { begin: /\bEVENT\s+TRIGGER\b/ }, + { begin: /\b(MAPPING|OR)\s+REPLACE\b/ }, + { begin: /\b(FROM|TO)\s+(PROGRAM|STDIN|STDOUT)\b/ }, + { begin: /\b(SHARE|EXCLUSIVE)\s+MODE\b/ }, + { begin: /\b(LEFT|RIGHT)\s+(OUTER\s+)?JOIN\b/ }, + { + begin: + /\b(FETCH|MOVE)\s+(NEXT|PRIOR|FIRST|LAST|ABSOLUTE|RELATIVE|FORWARD|BACKWARD)\b/, + }, + { begin: /\bPRESERVE\s+ROWS\b/ }, + { begin: /\bDISCARD\s+PLANS\b/ }, + { begin: /\bREFERENCING\s+(OLD|NEW)\b/ }, + { begin: /\bSKIP\s+LOCKED\b/ }, + { begin: /\bGROUPING\s+SETS\b/ }, + { + begin: /\b(BINARY|INSENSITIVE|SCROLL|NO\s+SCROLL)\s+(CURSOR|FOR)\b/, + }, + { begin: /\b(WITH|WITHOUT)\s+HOLD\b/ }, + { begin: /\bWITH\s+(CASCADED|LOCAL)\s+CHECK\s+OPTION\b/ }, + { begin: /\bEXCLUDE\s+(TIES|NO\s+OTHERS)\b/ }, + { begin: /\bFORMAT\s+(TEXT|XML|JSON|YAML)\b/ }, + { begin: /\bSET\s+((SESSION|LOCAL)\s+)?NAMES\b/ }, + { begin: /\bIS\s+(NOT\s+)?UNKNOWN\b/ }, + { begin: /\bSECURITY\s+LABEL\b/ }, + { begin: /\bSTANDALONE\s+(YES|NO|NO\s+VALUE)\b/ }, + { begin: /\bWITH\s+(NO\s+)?DATA\b/ }, + { begin: /\b(FOREIGN|SET)\s+DATA\b/ }, + { begin: /\bSET\s+(CATALOG|CONSTRAINTS)\b/ }, + { begin: /\b(WITH|FOR)\s+ORDINALITY\b/ }, + { begin: /\bIS\s+(NOT\s+)?DOCUMENT\b/ }, + { begin: /\bXML\s+OPTION\s+(DOCUMENT|CONTENT)\b/ }, + { begin: /\b(STRIP|PRESERVE)\s+WHITESPACE\b/ }, + { begin: /\bNO\s+(ACTION|MAXVALUE|MINVALUE)\b/ }, + { begin: /\bPARTITION\s+BY\s+(RANGE|LIST|HASH)\b/ }, + { begin: /\bAT\s+TIME\s+ZONE\b/ }, + { begin: /\bGRANTED\s+BY\b/ }, + { begin: /\bRETURN\s+(QUERY|NEXT)\b/ }, + { begin: /\b(ATTACH|DETACH)\s+PARTITION\b/ }, + { begin: /\bFORCE\s+ROW\s+LEVEL\s+SECURITY\b/ }, + { + begin: + /\b(INCLUDING|EXCLUDING)\s+(COMMENTS|CONSTRAINTS|DEFAULTS|IDENTITY|INDEXES|STATISTICS|STORAGE|ALL)\b/, + }, + { + begin: + /\bAS\s+(ASSIGNMENT|IMPLICIT|PERMISSIVE|RESTRICTIVE|ENUM|RANGE)\b/, + }, + ], + }, + { begin: /\b(FORMAT|FAMILY|VERSION)\s*\(/ }, + { begin: /\bINCLUDE\s*\(/, keywords: "INCLUDE" }, + { begin: /\bRANGE(?!\s*(BETWEEN|UNBOUNDED|CURRENT|[-0-9]+))/ }, + { + begin: + /\b(VERSION|OWNER|TEMPLATE|TABLESPACE|CONNECTION\s+LIMIT|PROCEDURE|RESTRICT|JOIN|PARSER|COPY|START|END|COLLATION|INPUT|ANALYZE|STORAGE|LIKE|DEFAULT|DELIMITER|ENCODING|COLUMN|CONSTRAINT|TABLE|SCHEMA)\s*=/, + }, + { begin: /\b(PG_\w+?|HAS_[A-Z_]+_PRIVILEGE)\b/, relevance: 10 }, + { + begin: /\bEXTRACT\s*\(/, + end: /\bFROM\b/, + returnEnd: !0, + keywords: { + type: "CENTURY DAY DECADE DOW DOY EPOCH HOUR ISODOW ISOYEAR MICROSECONDS MILLENNIUM MILLISECONDS MINUTE MONTH QUARTER SECOND TIMEZONE TIMEZONE_HOUR TIMEZONE_MINUTE WEEK YEAR", + }, + }, + { + begin: /\b(XMLELEMENT|XMLPI)\s*\(\s*NAME/, + keywords: { keyword: "NAME" }, + }, + { + begin: /\b(XMLPARSE|XMLSERIALIZE)\s*\(\s*(DOCUMENT|CONTENT)/, + keywords: { keyword: "DOCUMENT CONTENT" }, + }, + { + beginKeywords: "CACHE INCREMENT MAXVALUE MINVALUE", + end: e.C_NUMBER_RE, + returnEnd: !0, + keywords: "BY CACHE INCREMENT MAXVALUE MINVALUE", + }, + { className: "type", begin: /\b(WITH|WITHOUT)\s+TIME\s+ZONE\b/ }, + { + className: "type", + begin: + /\bINTERVAL\s+(YEAR|MONTH|DAY|HOUR|MINUTE|SECOND)(\s+TO\s+(MONTH|HOUR|MINUTE|SECOND))?\b/, + }, + { + begin: + /\bRETURNS\s+(LANGUAGE_HANDLER|TRIGGER|EVENT_TRIGGER|FDW_HANDLER|INDEX_AM_HANDLER|TSM_HANDLER)\b/, + keywords: { + keyword: "RETURNS", + type: "LANGUAGE_HANDLER TRIGGER EVENT_TRIGGER FDW_HANDLER INDEX_AM_HANDLER TSM_HANDLER", + }, + }, + { begin: "\\b(" + i + ")\\s*\\(" }, + { begin: "\\.(" + r + ")\\b" }, + { + begin: "\\b(" + r + ")\\s+PATH\\b", + keywords: { keyword: "PATH", type: a.replace("PATH ", "") }, + }, + { className: "type", begin: "\\b(" + r + ")\\b" }, + { + className: "string", + begin: "'", + end: "'", + contains: [{ begin: "''" }], + }, + { + className: "string", + begin: "(e|E|u&|U&)'", + end: "'", + contains: [{ begin: "\\\\." }], + relevance: 10, + }, + e.END_SAME_AS_BEGIN({ + begin: n, + end: n, + contains: [ + { + subLanguage: [ + "pgsql", + "perl", + "python", + "tcl", + "r", + "lua", + "java", + "php", + "ruby", + "bash", + "scheme", + "xml", + "json", + ], + endsWithParent: !0, + }, + ], + }), + { begin: '"', end: '"', contains: [{ begin: '""' }] }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + { + className: "meta", + variants: [ + { begin: "%(ROW)?TYPE", relevance: 10 }, + { begin: "\\$\\d+" }, + { begin: "^#\\w", end: "$" }, + ], + }, + { + className: "symbol", + begin: "<<\\s*[a-zA-Z_][a-zA-Z_0-9$]*\\s*>>", + relevance: 10, + }, + ], + }; +}; +var QS = function (e) { + var t = { + className: "variable", + begin: "\\$+[a-zA-Z_-ÿ][a-zA-Z0-9_-ÿ]*(?![A-Za-z0-9])(?![$])", + }, + n = { + className: "meta", + variants: [ + { begin: /<\?php/, relevance: 10 }, + { begin: /<\?[=]?/ }, + { begin: /\?>/ }, + ], + }, + a = { + className: "subst", + variants: [{ begin: /\$\w+/ }, { begin: /\{\$/, end: /\}/ }], + }, + r = e.inherit(e.APOS_STRING_MODE, { illegal: null }), + i = e.inherit(e.QUOTE_STRING_MODE, { + illegal: null, + contains: e.QUOTE_STRING_MODE.contains.concat(a), + }), + o = e.END_SAME_AS_BEGIN({ + begin: /<<<[ \t]*(\w+)\n/, + end: /[ \t]*(\w+)\b/, + contains: e.QUOTE_STRING_MODE.contains.concat(a), + }), + s = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, n], + variants: [ + e.inherit(r, { begin: "b'", end: "'" }), + e.inherit(i, { begin: 'b"', end: '"' }), + i, + r, + o, + ], + }, + l = { + className: "number", + variants: [ + { begin: "\\b0b[01]+(?:_[01]+)*\\b" }, + { begin: "\\b0o[0-7]+(?:_[0-7]+)*\\b" }, + { begin: "\\b0x[\\da-f]+(?:_[\\da-f]+)*\\b" }, + { + begin: + "(?:\\b\\d+(?:_\\d+)*(\\.(?:\\d+(?:_\\d+)*))?|\\B\\.\\d+)(?:e[+-]?\\d+)?", + }, + ], + relevance: 0, + }, + c = { + keyword: + "__CLASS__ __DIR__ __FILE__ __FUNCTION__ __LINE__ __METHOD__ __NAMESPACE__ __TRAIT__ die echo exit include include_once print require require_once array abstract and as binary bool boolean break callable case catch class clone const continue declare default do double else elseif empty enddeclare endfor endforeach endif endswitch endwhile enum eval extends final finally float for foreach from global goto if implements instanceof insteadof int integer interface isset iterable list match|0 mixed new object or private protected public real return string switch throw trait try unset use var void while xor yield", + literal: "false null true", + built_in: + "Error|0 AppendIterator ArgumentCountError ArithmeticError ArrayIterator ArrayObject AssertionError BadFunctionCallException BadMethodCallException CachingIterator CallbackFilterIterator CompileError Countable DirectoryIterator DivisionByZeroError DomainException EmptyIterator ErrorException Exception FilesystemIterator FilterIterator GlobIterator InfiniteIterator InvalidArgumentException IteratorIterator LengthException LimitIterator LogicException MultipleIterator NoRewindIterator OutOfBoundsException OutOfRangeException OuterIterator OverflowException ParentIterator ParseError RangeException RecursiveArrayIterator RecursiveCachingIterator RecursiveCallbackFilterIterator RecursiveDirectoryIterator RecursiveFilterIterator RecursiveIterator RecursiveIteratorIterator RecursiveRegexIterator RecursiveTreeIterator RegexIterator RuntimeException SeekableIterator SplDoublyLinkedList SplFileInfo SplFileObject SplFixedArray SplHeap SplMaxHeap SplMinHeap SplObjectStorage SplObserver SplObserver SplPriorityQueue SplQueue SplStack SplSubject SplSubject SplTempFileObject TypeError UnderflowException UnexpectedValueException UnhandledMatchError ArrayAccess Closure Generator Iterator IteratorAggregate Serializable Stringable Throwable Traversable WeakReference WeakMap Directory __PHP_Incomplete_Class parent php_user_filter self static stdClass", + }; + return { + aliases: ["php3", "php4", "php5", "php6", "php7", "php8"], + case_insensitive: !0, + keywords: c, + contains: [ + e.HASH_COMMENT_MODE, + e.COMMENT("//", "$", { contains: [n] }), + e.COMMENT("/\\*", "\\*/", { + contains: [{ className: "doctag", begin: "@[A-Za-z]+" }], + }), + e.COMMENT("__halt_compiler.+?;", !1, { + endsWithParent: !0, + keywords: "__halt_compiler", + }), + n, + { className: "keyword", begin: /\$this\b/ }, + t, + { begin: /(::|->)+[a-zA-Z_\x7f-\xff][a-zA-Z0-9_\x7f-\xff]*/ }, + { + className: "function", + relevance: 0, + beginKeywords: "fn function", + end: /[;{]/, + excludeEnd: !0, + illegal: "[$%\\[]", + contains: [ + { beginKeywords: "use" }, + e.UNDERSCORE_TITLE_MODE, + { begin: "=>", endsParent: !0 }, + { + className: "params", + begin: "\\(", + end: "\\)", + excludeBegin: !0, + excludeEnd: !0, + keywords: c, + contains: ["self", t, e.C_BLOCK_COMMENT_MODE, s, l], + }, + ], + }, + { + className: "class", + variants: [ + { beginKeywords: "enum", illegal: /[($"]/ }, + { beginKeywords: "class interface trait", illegal: /[:($"]/ }, + ], + relevance: 0, + end: /\{/, + excludeEnd: !0, + contains: [ + { beginKeywords: "extends implements" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { + beginKeywords: "namespace", + relevance: 0, + end: ";", + illegal: /[.']/, + contains: [e.UNDERSCORE_TITLE_MODE], + }, + { + beginKeywords: "use", + relevance: 0, + end: ";", + contains: [e.UNDERSCORE_TITLE_MODE], + }, + s, + l, + ], + }; +}; +var KS = function (e) { + return { + name: "PHP template", + subLanguage: "xml", + contains: [ + { + begin: /<\?(php|=)?/, + end: /\?>/, + subLanguage: "php", + contains: [ + { begin: "/\\*", end: "\\*/", skip: !0 }, + { begin: 'b"', end: '"', skip: !0 }, + { begin: "b'", end: "'", skip: !0 }, + e.inherit(e.APOS_STRING_MODE, { + illegal: null, + className: null, + contains: null, + skip: !0, + }), + e.inherit(e.QUOTE_STRING_MODE, { + illegal: null, + className: null, + contains: null, + skip: !0, + }), + ], + }, + ], + }; +}; +var jS = function (e) { + return { + name: "Plain text", + aliases: ["text", "txt"], + disableAutodetect: !0, + }; +}; +var XS = function (e) { + return { + name: "Pony", + keywords: { + keyword: + "actor addressof and as be break class compile_error compile_intrinsic consume continue delegate digestof do else elseif embed end error for fun if ifdef in interface is isnt lambda let match new not object or primitive recover repeat return struct then trait try type until use var where while with xor", + meta: "iso val tag trn box ref", + literal: "this false true", + }, + contains: [ + { className: "type", begin: "\\b_?[A-Z][\\w]*", relevance: 0 }, + { className: "string", begin: '"""', end: '"""', relevance: 10 }, + { + className: "string", + begin: '"', + end: '"', + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "string", + begin: "'", + end: "'", + contains: [e.BACKSLASH_ESCAPE], + relevance: 0, + }, + { begin: e.IDENT_RE + "'", relevance: 0 }, + { + className: "number", + begin: + "(-?)(\\b0[xX][a-fA-F0-9]+|\\b0[bB][01]+|(\\b\\d+(_\\d+)?(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)", + relevance: 0, + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var ZS = function (e) { + var t = { + $pattern: /-?[A-z\.\-]+\b/, + keyword: + "if else foreach return do while until elseif begin for trap data dynamicparam end break throw param continue finally in switch exit filter try process catch hidden static parameter", + built_in: + "ac asnp cat cd CFS chdir clc clear clhy cli clp cls clv cnsn compare copy cp cpi cpp curl cvpa dbp del diff dir dnsn ebp echo|0 epal epcsv epsn erase etsn exsn fc fhx fl ft fw gal gbp gc gcb gci gcm gcs gdr gerr ghy gi gin gjb gl gm gmo gp gps gpv group gsn gsnp gsv gtz gu gv gwmi h history icm iex ihy ii ipal ipcsv ipmo ipsn irm ise iwmi iwr kill lp ls man md measure mi mount move mp mv nal ndr ni nmo npssc nsn nv ogv oh popd ps pushd pwd r rbp rcjb rcsn rd rdr ren ri rjb rm rmdir rmo rni rnp rp rsn rsnp rujb rv rvpa rwmi sajb sal saps sasv sbp sc scb select set shcm si sl sleep sls sort sp spjb spps spsv start stz sujb sv swmi tee trcm type wget where wjb write", + }, + n = { begin: "`[\\s\\S]", relevance: 0 }, + a = { + className: "variable", + variants: [ + { begin: /\$\B/ }, + { className: "keyword", begin: /\$this/ }, + { begin: /\$[\w\d][\w\d_:]*/ }, + ], + }, + r = { + className: "string", + variants: [ + { begin: /"/, end: /"/ }, + { begin: /@"/, end: /^"@/ }, + ], + contains: [ + n, + a, + { className: "variable", begin: /\$[A-z]/, end: /[^A-z]/ }, + ], + }, + i = { + className: "string", + variants: [ + { begin: /'/, end: /'/ }, + { begin: /@'/, end: /^'@/ }, + ], + }, + o = e.inherit(e.COMMENT(null, null), { + variants: [ + { begin: /#/, end: /$/ }, + { begin: /<#/, end: /#>/ }, + ], + contains: [ + { + className: "doctag", + variants: [ + { + begin: + /\.(synopsis|description|example|inputs|outputs|notes|link|component|role|functionality)/, + }, + { + begin: + /\.(parameter|forwardhelptargetname|forwardhelpcategory|remotehelprunspace|externalhelp)\s+\S+/, + }, + ], + }, + ], + }), + s = { + className: "built_in", + variants: [ + { + begin: "(".concat( + "Add|Clear|Close|Copy|Enter|Exit|Find|Format|Get|Hide|Join|Lock|Move|New|Open|Optimize|Pop|Push|Redo|Remove|Rename|Reset|Resize|Search|Select|Set|Show|Skip|Split|Step|Switch|Undo|Unlock|Watch|Backup|Checkpoint|Compare|Compress|Convert|ConvertFrom|ConvertTo|Dismount|Edit|Expand|Export|Group|Import|Initialize|Limit|Merge|Mount|Out|Publish|Restore|Save|Sync|Unpublish|Update|Approve|Assert|Build|Complete|Confirm|Deny|Deploy|Disable|Enable|Install|Invoke|Register|Request|Restart|Resume|Start|Stop|Submit|Suspend|Uninstall|Unregister|Wait|Debug|Measure|Ping|Repair|Resolve|Test|Trace|Connect|Disconnect|Read|Receive|Send|Write|Block|Grant|Protect|Revoke|Unblock|Unprotect|Use|ForEach|Sort|Tee|Where", + ")+(-)[\\w\\d]+", + ), + }, + ], + }, + l = { + className: "class", + beginKeywords: "class enum", + end: /\s*[{]/, + excludeEnd: !0, + relevance: 0, + contains: [e.TITLE_MODE], + }, + c = { + className: "function", + begin: /function\s+/, + end: /\s*\{|$/, + excludeEnd: !0, + returnBegin: !0, + relevance: 0, + contains: [ + { begin: "function", relevance: 0, className: "keyword" }, + { className: "title", begin: /\w[\w\d]*((-)[\w\d]+)*/, relevance: 0 }, + { + begin: /\(/, + end: /\)/, + className: "params", + relevance: 0, + contains: [a], + }, + ], + }, + _ = { + begin: /using\s/, + end: /$/, + returnBegin: !0, + contains: [ + r, + i, + { + className: "keyword", + begin: /(using|assembly|command|module|namespace|type)/, + }, + ], + }, + d = { + variants: [ + { + className: "operator", + begin: "(".concat( + "-and|-as|-band|-bnot|-bor|-bxor|-casesensitive|-ccontains|-ceq|-cge|-cgt|-cle|-clike|-clt|-cmatch|-cne|-cnotcontains|-cnotlike|-cnotmatch|-contains|-creplace|-csplit|-eq|-exact|-f|-file|-ge|-gt|-icontains|-ieq|-ige|-igt|-ile|-ilike|-ilt|-imatch|-in|-ine|-inotcontains|-inotlike|-inotmatch|-ireplace|-is|-isnot|-isplit|-join|-le|-like|-lt|-match|-ne|-not|-notcontains|-notin|-notlike|-notmatch|-or|-regex|-replace|-shl|-shr|-split|-wildcard|-xor", + ")\\b", + ), + }, + { className: "literal", begin: /(-)[\w\d]+/, relevance: 0 }, + ], + }, + u = { + className: "function", + begin: /\[.*\]\s*[\w]+[ ]??\(/, + end: /$/, + returnBegin: !0, + relevance: 0, + contains: [ + { + className: "keyword", + begin: "(".concat(t.keyword.toString().replace(/\s/g, "|"), ")\\b"), + endsParent: !0, + relevance: 0, + }, + e.inherit(e.TITLE_MODE, { endsParent: !0 }), + ], + }, + m = [ + u, + o, + n, + e.NUMBER_MODE, + r, + i, + s, + a, + { className: "literal", begin: /\$(null|true|false)\b/ }, + { className: "selector-tag", begin: /@\B/, relevance: 0 }, + ], + p = { + begin: /\[/, + end: /\]/, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + contains: [].concat( + "self", + m, + { + begin: + "(" + + [ + "string", + "char", + "byte", + "int", + "long", + "bool", + "decimal", + "single", + "double", + "DateTime", + "xml", + "array", + "hashtable", + "void", + ].join("|") + + ")", + className: "built_in", + relevance: 0, + }, + { className: "type", begin: /[\.\w\d]+/, relevance: 0 }, + ), + }; + return ( + u.contains.unshift(p), + { + name: "PowerShell", + aliases: ["ps", "ps1"], + case_insensitive: !0, + keywords: t, + contains: m.concat(l, c, _, d, p), + } + ); +}; +var JS = function (e) { + return { + name: "Processing", + keywords: { + keyword: + "BufferedReader PVector PFont PImage PGraphics HashMap boolean byte char color double float int long String Array FloatDict FloatList IntDict IntList JSONArray JSONObject Object StringDict StringList Table TableRow XML false synchronized int abstract float private char boolean static null if const for true while long throw strictfp finally protected import native final return void enum else break transient new catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private", + literal: "P2D P3D HALF_PI PI QUARTER_PI TAU TWO_PI", + title: "setup draw", + built_in: + "displayHeight displayWidth mouseY mouseX mousePressed pmouseX pmouseY key keyCode pixels focused frameCount frameRate height width size createGraphics beginDraw createShape loadShape PShape arc ellipse line point quad rect triangle bezier bezierDetail bezierPoint bezierTangent curve curveDetail curvePoint curveTangent curveTightness shape shapeMode beginContour beginShape bezierVertex curveVertex endContour endShape quadraticVertex vertex ellipseMode noSmooth rectMode smooth strokeCap strokeJoin strokeWeight mouseClicked mouseDragged mouseMoved mousePressed mouseReleased mouseWheel keyPressed keyPressedkeyReleased keyTyped print println save saveFrame day hour millis minute month second year background clear colorMode fill noFill noStroke stroke alpha blue brightness color green hue lerpColor red saturation modelX modelY modelZ screenX screenY screenZ ambient emissive shininess specular add createImage beginCamera camera endCamera frustum ortho perspective printCamera printProjection cursor frameRate noCursor exit loop noLoop popStyle pushStyle redraw binary boolean byte char float hex int str unbinary unhex join match matchAll nf nfc nfp nfs split splitTokens trim append arrayCopy concat expand reverse shorten sort splice subset box sphere sphereDetail createInput createReader loadBytes loadJSONArray loadJSONObject loadStrings loadTable loadXML open parseXML saveTable selectFolder selectInput beginRaw beginRecord createOutput createWriter endRaw endRecord PrintWritersaveBytes saveJSONArray saveJSONObject saveStream saveStrings saveXML selectOutput popMatrix printMatrix pushMatrix resetMatrix rotate rotateX rotateY rotateZ scale shearX shearY translate ambientLight directionalLight lightFalloff lights lightSpecular noLights normal pointLight spotLight image imageMode loadImage noTint requestImage tint texture textureMode textureWrap blend copy filter get loadPixels set updatePixels blendMode loadShader PShaderresetShader shader createFont loadFont text textFont textAlign textLeading textMode textSize textWidth textAscent textDescent abs ceil constrain dist exp floor lerp log mag map max min norm pow round sq sqrt acos asin atan atan2 cos degrees radians sin tan noise noiseDetail noiseSeed random randomGaussian randomSeed", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.C_NUMBER_MODE, + ], + }; +}; +var eb = function (e) { + return { + name: "Python profiler", + contains: [ + e.C_NUMBER_MODE, + { + begin: "[a-zA-Z_][\\da-zA-Z_]+\\.[\\da-zA-Z_]{1,3}", + end: ":", + excludeEnd: !0, + }, + { + begin: "(ncalls|tottime|cumtime)", + end: "$", + keywords: "ncalls tottime|10 cumtime|10 filename", + relevance: 10, + }, + { + begin: "function calls", + end: "$", + contains: [e.C_NUMBER_MODE], + relevance: 10, + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { + className: "string", + begin: "\\(", + end: "\\)$", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + ], + }; +}; +var tb = function (e) { + var t = { begin: /\(/, end: /\)/, relevance: 0 }, + n = { begin: /\[/, end: /\]/ }, + a = { + className: "comment", + begin: /%/, + end: /$/, + contains: [e.PHRASAL_WORDS_MODE], + }, + r = { + className: "string", + begin: /`/, + end: /`/, + contains: [e.BACKSLASH_ESCAPE], + }, + i = [ + { begin: /[a-z][A-Za-z0-9_]*/, relevance: 0 }, + { + className: "symbol", + variants: [ + { begin: /[A-Z][a-zA-Z0-9_]*/ }, + { begin: /_[A-Za-z0-9_]*/ }, + ], + relevance: 0, + }, + t, + { begin: /:-/ }, + n, + a, + e.C_BLOCK_COMMENT_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + r, + { className: "string", begin: /0'(\\'|.)/ }, + { className: "string", begin: /0'\\s/ }, + e.C_NUMBER_MODE, + ]; + return ( + (t.contains = i), + (n.contains = i), + { name: "Prolog", contains: i.concat([{ begin: /\.$/ }]) } + ); +}; +var nb = function (e) { + var t = "[ \\t\\f]*", + n = t + "[:=]" + t, + a = "[ \\t\\f]+", + r = "(" + n + "|" + "[ \\t\\f]+)", + i = "([^\\\\\\W:= \\t\\f\\n]|\\\\.)+", + o = "([^\\\\:= \\t\\f\\n]|\\\\.)+", + s = { + end: r, + relevance: 0, + starts: { + className: "string", + end: /$/, + relevance: 0, + contains: [{ begin: "\\\\\\\\" }, { begin: "\\\\\\n" }], + }, + }; + return { + name: ".properties", + case_insensitive: !0, + illegal: /\S/, + contains: [ + e.COMMENT("^\\s*[!#]", "$"), + { + returnBegin: !0, + variants: [ + { begin: i + n, relevance: 1 }, + { begin: i + a, relevance: 0 }, + ], + contains: [ + { className: "attr", begin: i, endsParent: !0, relevance: 0 }, + ], + starts: s, + }, + { + begin: o + r, + returnBegin: !0, + relevance: 0, + contains: [ + { className: "meta", begin: o, endsParent: !0, relevance: 0 }, + ], + starts: s, + }, + { className: "attr", relevance: 0, begin: o + t + "$" }, + ], + }; +}; +var ab = function (e) { + return { + name: "Protocol Buffers", + keywords: { + keyword: "package import option optional required repeated group oneof", + built_in: + "double float int32 int64 uint32 uint64 sint32 sint64 fixed32 fixed64 sfixed32 sfixed64 bool string bytes", + literal: "true false", + }, + contains: [ + e.QUOTE_STRING_MODE, + e.NUMBER_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "class", + beginKeywords: "message enum service", + end: /\{/, + illegal: /\n/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, excludeEnd: !0 }, + }), + ], + }, + { + className: "function", + beginKeywords: "rpc", + end: /[{;]/, + excludeEnd: !0, + keywords: "rpc returns", + }, + { begin: /^\s*[A-Z_]+(?=\s*=[^\n]+;$)/ }, + ], + }; +}; +var rb = function (e) { + var t = e.COMMENT("#", "$"), + n = "([A-Za-z_]|::)(\\w|::)*", + a = e.inherit(e.TITLE_MODE, { begin: n }), + r = { className: "variable", begin: "\\$" + n }, + i = { + className: "string", + contains: [e.BACKSLASH_ESCAPE, r], + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + ], + }; + return { + name: "Puppet", + aliases: ["pp"], + contains: [ + t, + r, + i, + { beginKeywords: "class", end: "\\{|;", illegal: /=/, contains: [a, t] }, + { + beginKeywords: "define", + end: /\{/, + contains: [{ className: "section", begin: e.IDENT_RE, endsParent: !0 }], + }, + { + begin: e.IDENT_RE + "\\s+\\{", + returnBegin: !0, + end: /\S/, + contains: [ + { className: "keyword", begin: e.IDENT_RE }, + { + begin: /\{/, + end: /\}/, + keywords: { + keyword: + "and case default else elsif false if in import enherits node or true undef unless main settings $string ", + literal: + "alias audit before loglevel noop require subscribe tag owner ensure group mode name|0 changes context force incl lens load_path onlyif provider returns root show_diff type_check en_address ip_address realname command environment hour monute month monthday special target weekday creates cwd ogoutput refresh refreshonly tries try_sleep umask backup checksum content ctime force ignore links mtime purge recurse recurselimit replace selinux_ignore_defaults selrange selrole seltype seluser source souirce_permissions sourceselect validate_cmd validate_replacement allowdupe attribute_membership auth_membership forcelocal gid ia_load_module members system host_aliases ip allowed_trunk_vlans description device_url duplex encapsulation etherchannel native_vlan speed principals allow_root auth_class auth_type authenticate_user k_of_n mechanisms rule session_owner shared options device fstype enable hasrestart directory present absent link atboot blockdevice device dump pass remounts poller_tag use message withpath adminfile allow_virtual allowcdrom category configfiles flavor install_options instance package_settings platform responsefile status uninstall_options vendor unless_system_user unless_uid binary control flags hasstatus manifest pattern restart running start stop allowdupe auths expiry gid groups home iterations key_membership keys managehome membership password password_max_age password_min_age profile_membership profiles project purge_ssh_keys role_membership roles salt shell uid baseurl cost descr enabled enablegroups exclude failovermethod gpgcheck gpgkey http_caching include includepkgs keepalive metadata_expire metalink mirrorlist priority protect proxy proxy_password proxy_username repo_gpgcheck s3_enabled skip_if_unavailable sslcacert sslclientcert sslclientkey sslverify mounted", + built_in: + "architecture augeasversion blockdevices boardmanufacturer boardproductname boardserialnumber cfkey dhcp_servers domain ec2_ ec2_userdata facterversion filesystems ldom fqdn gid hardwareisa hardwaremodel hostname id|0 interfaces ipaddress ipaddress_ ipaddress6 ipaddress6_ iphostnumber is_virtual kernel kernelmajversion kernelrelease kernelversion kernelrelease kernelversion lsbdistcodename lsbdistdescription lsbdistid lsbdistrelease lsbmajdistrelease lsbminordistrelease lsbrelease macaddress macaddress_ macosx_buildversion macosx_productname macosx_productversion macosx_productverson_major macosx_productversion_minor manufacturer memoryfree memorysize netmask metmask_ network_ operatingsystem operatingsystemmajrelease operatingsystemrelease osfamily partitions path physicalprocessorcount processor processorcount productname ps puppetversion rubysitedir rubyversion selinux selinux_config_mode selinux_config_policy selinux_current_mode selinux_current_mode selinux_enforced selinux_policyversion serialnumber sp_ sshdsakey sshecdsakey sshrsakey swapencrypted swapfree swapsize timezone type uniqueid uptime uptime_days uptime_hours uptime_seconds uuid virtual vlans xendomains zfs_version zonenae zones zpool_version", + }, + relevance: 0, + contains: [ + i, + t, + { + begin: "[a-zA-Z_]+\\s*=>", + returnBegin: !0, + end: "=>", + contains: [{ className: "attr", begin: e.IDENT_RE }], + }, + { + className: "number", + begin: + "(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b", + relevance: 0, + }, + r, + ], + }, + ], + relevance: 0, + }, + ], + }; +}; +var ib = function (e) { + return { + name: "PureBASIC", + aliases: ["pb", "pbi"], + keywords: + "Align And Array As Break CallDebugger Case CompilerCase CompilerDefault CompilerElse CompilerElseIf CompilerEndIf CompilerEndSelect CompilerError CompilerIf CompilerSelect CompilerWarning Continue Data DataSection Debug DebugLevel Declare DeclareC DeclareCDLL DeclareDLL DeclareModule Default Define Dim DisableASM DisableDebugger DisableExplicit Else ElseIf EnableASM EnableDebugger EnableExplicit End EndDataSection EndDeclareModule EndEnumeration EndIf EndImport EndInterface EndMacro EndModule EndProcedure EndSelect EndStructure EndStructureUnion EndWith Enumeration EnumerationBinary Extends FakeReturn For ForEach ForEver Global Gosub Goto If Import ImportC IncludeBinary IncludeFile IncludePath Interface List Macro MacroExpandedCount Map Module NewList NewMap Next Not Or Procedure ProcedureC ProcedureCDLL ProcedureDLL ProcedureReturn Protected Prototype PrototypeC ReDim Read Repeat Restore Return Runtime Select Shared Static Step Structure StructureUnion Swap Threaded To UndefineMacro Until Until UnuseModule UseModule Wend While With XIncludeFile XOr", + contains: [ + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "function", + begin: "\\b(Procedure|Declare)(C|CDLL|DLL)?\\b", + end: "\\(", + excludeEnd: !0, + returnBegin: !0, + contains: [ + { + className: "keyword", + begin: "(Procedure|Declare)(C|CDLL|DLL)?", + excludeEnd: !0, + }, + { className: "type", begin: "\\.\\w*" }, + e.UNDERSCORE_TITLE_MODE, + ], + }, + { className: "string", begin: '(~)?"', end: '"', illegal: "\\n" }, + { className: "symbol", begin: "#[a-zA-Z_]\\w*\\$?" }, + ], + }; +}; +function ob(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function sb(e) { + return (function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + return t + .map(function (e) { + return ob(e); + }) + .join(""); + })("(?=", e, ")"); +} +var lb = function (e) { + var t = { + $pattern: /[A-Za-z]\w+|__\w+__/, + keyword: [ + "and", + "as", + "assert", + "async", + "await", + "break", + "class", + "continue", + "def", + "del", + "elif", + "else", + "except", + "finally", + "for", + "from", + "global", + "if", + "import", + "in", + "is", + "lambda", + "nonlocal|10", + "not", + "or", + "pass", + "raise", + "return", + "try", + "while", + "with", + "yield", + ], + built_in: [ + "__import__", + "abs", + "all", + "any", + "ascii", + "bin", + "bool", + "breakpoint", + "bytearray", + "bytes", + "callable", + "chr", + "classmethod", + "compile", + "complex", + "delattr", + "dict", + "dir", + "divmod", + "enumerate", + "eval", + "exec", + "filter", + "float", + "format", + "frozenset", + "getattr", + "globals", + "hasattr", + "hash", + "help", + "hex", + "id", + "input", + "int", + "isinstance", + "issubclass", + "iter", + "len", + "list", + "locals", + "map", + "max", + "memoryview", + "min", + "next", + "object", + "oct", + "open", + "ord", + "pow", + "print", + "property", + "range", + "repr", + "reversed", + "round", + "set", + "setattr", + "slice", + "sorted", + "staticmethod", + "str", + "sum", + "super", + "tuple", + "type", + "vars", + "zip", + ], + literal: [ + "__debug__", + "Ellipsis", + "False", + "None", + "NotImplemented", + "True", + ], + type: [ + "Any", + "Callable", + "Coroutine", + "Dict", + "List", + "Literal", + "Generic", + "Optional", + "Sequence", + "Set", + "Tuple", + "Type", + "Union", + ], + }, + n = { className: "meta", begin: /^(>>>|\.\.\.) / }, + a = { + className: "subst", + begin: /\{/, + end: /\}/, + keywords: t, + illegal: /#/, + }, + r = { begin: /\{\{/, relevance: 0 }, + i = { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { + begin: /([uU]|[bB]|[rR]|[bB][rR]|[rR][bB])?'''/, + end: /'''/, + contains: [e.BACKSLASH_ESCAPE, n], + relevance: 10, + }, + { + begin: /([uU]|[bB]|[rR]|[bB][rR]|[rR][bB])?"""/, + end: /"""/, + contains: [e.BACKSLASH_ESCAPE, n], + relevance: 10, + }, + { + begin: /([fF][rR]|[rR][fF]|[fF])'''/, + end: /'''/, + contains: [e.BACKSLASH_ESCAPE, n, r, a], + }, + { + begin: /([fF][rR]|[rR][fF]|[fF])"""/, + end: /"""/, + contains: [e.BACKSLASH_ESCAPE, n, r, a], + }, + { begin: /([uU]|[rR])'/, end: /'/, relevance: 10 }, + { begin: /([uU]|[rR])"/, end: /"/, relevance: 10 }, + { begin: /([bB]|[bB][rR]|[rR][bB])'/, end: /'/ }, + { begin: /([bB]|[bB][rR]|[rR][bB])"/, end: /"/ }, + { + begin: /([fF][rR]|[rR][fF]|[fF])'/, + end: /'/, + contains: [e.BACKSLASH_ESCAPE, r, a], + }, + { + begin: /([fF][rR]|[rR][fF]|[fF])"/, + end: /"/, + contains: [e.BACKSLASH_ESCAPE, r, a], + }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + ], + }, + o = "[0-9](_?[0-9])*", + s = "(\\b(".concat(o, "))?\\.(").concat(o, ")|\\b(").concat(o, ")\\."), + l = { + className: "number", + relevance: 0, + variants: [ + { + begin: "(\\b(" + .concat(o, ")|(") + .concat(s, "))[eE][+-]?(") + .concat(o, ")[jJ]?\\b"), + }, + { begin: "(".concat(s, ")[jJ]?") }, + { begin: "\\b([1-9](_?[0-9])*|0+(_?0)*)[lLjJ]?\\b" }, + { begin: "\\b0[bB](_?[01])+[lL]?\\b" }, + { begin: "\\b0[oO](_?[0-7])+[lL]?\\b" }, + { begin: "\\b0[xX](_?[0-9a-fA-F])+[lL]?\\b" }, + { begin: "\\b(".concat(o, ")[jJ]\\b") }, + ], + }, + c = { + className: "comment", + begin: sb(/# type:/), + end: /$/, + keywords: t, + contains: [ + { begin: /# type:/ }, + { begin: /#/, end: /\b\B/, endsWithParent: !0 }, + ], + }, + _ = { + className: "params", + variants: [ + { className: "", begin: /\(\s*\)/, skip: !0 }, + { + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + keywords: t, + contains: ["self", n, l, i, e.HASH_COMMENT_MODE], + }, + ], + }; + return ( + (a.contains = [i, l, n]), + { + name: "Python", + aliases: ["py", "gyp", "ipython"], + keywords: t, + illegal: /(<\/|->|\?)|=>/, + contains: [ + n, + l, + { begin: /\bself\b/ }, + { beginKeywords: "if", relevance: 0 }, + i, + c, + e.HASH_COMMENT_MODE, + { + variants: [ + { className: "function", beginKeywords: "def" }, + { className: "class", beginKeywords: "class" }, + ], + end: /:/, + illegal: /[${=;\n,]/, + contains: [ + e.UNDERSCORE_TITLE_MODE, + _, + { begin: /->/, endsWithParent: !0, keywords: t }, + ], + }, + { + className: "meta", + begin: /^[\t ]*@/, + end: /(?=#)|$/, + contains: [l, _, i], + }, + ], + } + ); +}; +var cb = function (e) { + return { + aliases: ["pycon"], + contains: [ + { + className: "meta", + starts: { end: / |$/, starts: { end: "$", subLanguage: "python" } }, + variants: [{ begin: /^>>>(?=[ ]|$)/ }, { begin: /^\.\.\.(?=[ ]|$)/ }], + }, + ], + }; +}; +var _b = function (e) { + return { + name: "Q", + aliases: ["k", "kdb"], + keywords: { + $pattern: /(`?)[A-Za-z0-9_]+\b/, + keyword: "do while select delete by update from", + literal: "0b 1b", + built_in: + "neg not null string reciprocal floor ceiling signum mod xbar xlog and or each scan over prior mmu lsq inv md5 ltime gtime count first var dev med cov cor all any rand sums prds mins maxs fills deltas ratios avgs differ prev next rank reverse iasc idesc asc desc msum mcount mavg mdev xrank mmin mmax xprev rotate distinct group where flip type key til get value attr cut set upsert raze union inter except cross sv vs sublist enlist read0 read1 hopen hclose hdel hsym hcount peach system ltrim rtrim trim lower upper ssr view tables views cols xcols keys xkey xcol xasc xdesc fkeys meta lj aj aj0 ij pj asof uj ww wj wj1 fby xgroup ungroup ej save load rsave rload show csv parse eval min max avg wavg wsum sin cos tan sum", + type: "`float `double int `timestamp `timespan `datetime `time `boolean `symbol `char `byte `short `long `real `month `date `minute `second `guid", + }, + contains: [e.C_LINE_COMMENT_MODE, e.QUOTE_STRING_MODE, e.C_NUMBER_MODE], + }; +}; +function db(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function ub() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return db(e); + }) + .join(""); + return a; +} +var mb = function (e) { + var t = "[a-zA-Z_][a-zA-Z0-9\\._]*", + n = { + className: "attribute", + begin: "\\bid\\s*:", + starts: { className: "string", end: t, returnEnd: !1 }, + }, + a = { + begin: t + "\\s*:", + returnBegin: !0, + contains: [ + { + className: "attribute", + begin: t, + end: "\\s*:", + excludeEnd: !0, + relevance: 0, + }, + ], + relevance: 0, + }, + r = { + begin: ub(t, /\s*\{/), + end: /\{/, + returnBegin: !0, + relevance: 0, + contains: [e.inherit(e.TITLE_MODE, { begin: t })], + }; + return { + name: "QML", + aliases: ["qt"], + case_insensitive: !1, + keywords: { + keyword: + "in of on if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await import", + literal: "true false null undefined NaN Infinity", + built_in: + "eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Behavior bool color coordinate date double enumeration font geocircle georectangle geoshape int list matrix4x4 parent point quaternion real rect size string url variant vector2d vector3d vector4d Promise", + }, + contains: [ + { className: "meta", begin: /^\s*['"]use (strict|asm)['"]/ }, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + { + className: "string", + begin: "`", + end: "`", + contains: [ + e.BACKSLASH_ESCAPE, + { className: "subst", begin: "\\$\\{", end: "\\}" }, + ], + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { + className: "number", + variants: [ + { begin: "\\b(0[bB][01]+)" }, + { begin: "\\b(0[oO][0-7]+)" }, + { begin: e.C_NUMBER_RE }, + ], + relevance: 0, + }, + { + begin: "(" + e.RE_STARTERS_RE + "|\\b(case|return|throw)\\b)\\s*", + keywords: "return throw case", + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.REGEXP_MODE, + { begin: /\s*[);\]]/, relevance: 0, subLanguage: "xml" }, + ], + relevance: 0, + }, + { + className: "keyword", + begin: "\\bsignal\\b", + starts: { + className: "string", + end: "(\\(|:|=|;|,|//|/\\*|$)", + returnEnd: !0, + }, + }, + { + className: "keyword", + begin: "\\bproperty\\b", + starts: { + className: "string", + end: "(:|=|;|,|//|/\\*|$)", + returnEnd: !0, + }, + }, + { + className: "function", + beginKeywords: "function", + end: /\{/, + excludeEnd: !0, + contains: [ + e.inherit(e.TITLE_MODE, { begin: /[A-Za-z$_][0-9A-Za-z$_]*/ }), + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + contains: [e.C_LINE_COMMENT_MODE, e.C_BLOCK_COMMENT_MODE], + }, + ], + illegal: /\[|%/, + }, + { begin: "\\." + e.IDENT_RE, relevance: 0 }, + n, + a, + r, + ], + illegal: /#/, + }; +}; +function pb(e) { + return e ? 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connection console customer default dhcp-client dhcp-server discovery dns e-mail ethernet filter firmware gps graphing group hardware health hotspot identity igmp-proxy incoming instance interface ip ipsec ipv6 irq l2tp-server lcd ldp logging mac-server mac-winbox mangle manual mirror mme mpls nat nd neighbor network note ntp ospf ospf-v3 ovpn-server page peer pim ping policy pool port ppp pppoe-client pptp-server prefix profile proposal proxy queue radius resource rip ripng route routing screen script security-profiles server service service-port settings shares smb sms sniffer snmp snooper socks sstp-server system tool tracking type upgrade upnp user-manager users user vlan secret vrrp watchdog web-access wireless pptp pppoe lan wan layer7-protocol lease simple raw" + .split(" ") + .join("|") + + ");?\\s)+", + }, + { begin: /\.\./, relevance: 0 }, + ], + }, + ], + }; +}; +var Cb = function (e) { + return { + name: "RenderMan RSL", + keywords: { + keyword: + "float color point normal vector matrix while for if do return else break extern continue", + built_in: + "abs acos ambient area asin atan atmosphere attribute calculatenormal ceil cellnoise clamp comp concat cos degrees depth Deriv diffuse distance Du Dv environment exp faceforward filterstep floor format fresnel incident length lightsource log match max min mod noise normalize ntransform opposite option phong pnoise pow printf ptlined radians random reflect refract renderinfo round setcomp setxcomp setycomp setzcomp shadow sign sin smoothstep specular specularbrdf spline sqrt step tan texture textureinfo trace transform vtransform xcomp ycomp zcomp", + }, + illegal: "" }, + ], + }; +}; +var vb = function (e) { + return { + name: "SAS", + case_insensitive: !0, + keywords: { + literal: + "null missing _all_ _automatic_ _character_ _infile_ _n_ _name_ _null_ _numeric_ _user_ _webout_", + meta: "do if then else end until while abort array attrib by call cards cards4 catname continue datalines datalines4 delete delim delimiter display dm drop endsas error file filename footnote format goto in infile informat input keep label leave length libname link list lostcard merge missing modify options output out page put redirect remove rename replace retain return select set skip startsas stop title update waitsas where window x systask add and alter as cascade check create delete describe distinct drop foreign from group having index insert into in key like message modify msgtype not null on or order primary references reset restrict select set table unique update validate view where", + }, + contains: [ + { className: "keyword", begin: /^\s*(proc [\w\d_]+|data|run|quit)[\s;]/ }, + { className: "variable", begin: /&[a-zA-Z_&][a-zA-Z0-9_]*\.?/ }, + { + className: "emphasis", + begin: /^\s*datalines|cards.*;/, + end: /^\s*;\s*$/, + }, + { + className: "built_in", + begin: + "%(bquote|nrbquote|cmpres|qcmpres|compstor|datatyp|display|do|else|end|eval|global|goto|if|index|input|keydef|label|left|length|let|local|lowcase|macro|mend|nrbquote|nrquote|nrstr|put|qcmpres|qleft|qlowcase|qscan|qsubstr|qsysfunc|qtrim|quote|qupcase|scan|str|substr|superq|syscall|sysevalf|sysexec|sysfunc|sysget|syslput|sysprod|sysrc|sysrput|then|to|trim|unquote|until|upcase|verify|while|window)", + }, + { className: "name", begin: /%[a-zA-Z_][a-zA-Z_0-9]*/ }, + { + className: "meta", + begin: + "[^%](abs|addr|airy|arcos|arsin|atan|attrc|attrn|band|betainv|blshift|bnot|bor|brshift|bxor|byte|cdf|ceil|cexist|cinv|close|cnonct|collate|compbl|compound|compress|cos|cosh|css|curobs|cv|daccdb|daccdbsl|daccsl|daccsyd|dacctab|dairy|date|datejul|datepart|datetime|day|dclose|depdb|depdbsl|depdbsl|depsl|depsl|depsyd|depsyd|deptab|deptab|dequote|dhms|dif|digamma|dim|dinfo|dnum|dopen|doptname|doptnum|dread|dropnote|dsname|erf|erfc|exist|exp|fappend|fclose|fcol|fdelete|fetch|fetchobs|fexist|fget|fileexist|filename|fileref|finfo|finv|fipname|fipnamel|fipstate|floor|fnonct|fnote|fopen|foptname|foptnum|fpoint|fpos|fput|fread|frewind|frlen|fsep|fuzz|fwrite|gaminv|gamma|getoption|getvarc|getvarn|hbound|hms|hosthelp|hour|ibessel|index|indexc|indexw|input|inputc|inputn|int|intck|intnx|intrr|irr|jbessel|juldate|kurtosis|lag|lbound|left|length|lgamma|libname|libref|log|log10|log2|logpdf|logpmf|logsdf|lowcase|max|mdy|mean|min|minute|mod|month|mopen|mort|n|netpv|nmiss|normal|note|npv|open|ordinal|pathname|pdf|peek|peekc|pmf|point|poisson|poke|probbeta|probbnml|probchi|probf|probgam|probhypr|probit|probnegb|probnorm|probt|put|putc|putn|qtr|quote|ranbin|rancau|ranexp|rangam|range|rank|rannor|ranpoi|rantbl|rantri|ranuni|repeat|resolve|reverse|rewind|right|round|saving|scan|sdf|second|sign|sin|sinh|skewness|soundex|spedis|sqrt|std|stderr|stfips|stname|stnamel|substr|sum|symget|sysget|sysmsg|sysprod|sysrc|system|tan|tanh|time|timepart|tinv|tnonct|today|translate|tranwrd|trigamma|trim|trimn|trunc|uniform|upcase|uss|var|varfmt|varinfmt|varlabel|varlen|varname|varnum|varray|varrayx|vartype|verify|vformat|vformatd|vformatdx|vformatn|vformatnx|vformatw|vformatwx|vformatx|vinarray|vinarrayx|vinformat|vinformatd|vinformatdx|vinformatn|vinformatnx|vinformatw|vinformatwx|vinformatx|vlabel|vlabelx|vlength|vlengthx|vname|vnamex|vtype|vtypex|weekday|year|yyq|zipfips|zipname|zipnamel|zipstate)[(]", + }, + { + className: "string", + variants: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + e.COMMENT("\\*", ";"), + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var Ob = function (e) { + var t = { + className: "subst", + variants: [{ begin: "\\$[A-Za-z0-9_]+" }, { begin: /\$\{/, end: /\}/ }], + }, + n = { + className: "string", + variants: [ + { begin: '"""', end: '"""' }, + { + begin: '"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE], + }, + { + begin: '[a-z]+"', + end: '"', + illegal: "\\n", + contains: [e.BACKSLASH_ESCAPE, t], + }, + { + className: "string", + begin: '[a-z]+"""', + end: '"""', + contains: [t], + relevance: 10, + }, + ], + }, + a = { className: "type", begin: "\\b[A-Z][A-Za-z0-9_]*", relevance: 0 }, + r = { + className: "title", + begin: + /[^0-9\n\t "'(),.`{}\[\]:;][^\n\t "'(),.`{}\[\]:;]+|[^0-9\n\t "'(),.`{}\[\]:;=]/, + relevance: 0, + }, + i = { + className: "class", + beginKeywords: "class object trait type", + end: /[:={\[\n;]/, + excludeEnd: !0, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { beginKeywords: "extends with", relevance: 10 }, + { + begin: /\[/, + end: /\]/, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + contains: [a], + }, + { + className: "params", + begin: /\(/, + end: /\)/, + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + contains: [a], + }, + r, + ], + }, + o = { + className: "function", + beginKeywords: "def", + end: /[:={\[(\n;]/, + excludeEnd: !0, + contains: [r], + }; + return { + name: "Scala", + keywords: { + literal: "true false null", + keyword: + "type yield lazy override def with val var sealed abstract private trait object if forSome for while throw finally protected extends import final return else break new catch super class case package default try this match continue throws implicit", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + { className: "symbol", begin: "'\\w[\\w\\d_]*(?!')" }, + a, + o, + i, + e.C_NUMBER_MODE, + { className: "meta", begin: "@[A-Za-z]+" }, + ], + }; +}; +var hb = function (e) { + var t = "[^\\(\\)\\[\\]\\{\\}\",'`;#|\\\\\\s]+", + n = "(-|\\+)?\\d+([./]\\d+)?", + a = { + $pattern: t, + "builtin-name": + "case-lambda call/cc class define-class exit-handler field import inherit init-field interface let*-values let-values let/ec mixin opt-lambda override protect provide public rename require require-for-syntax syntax syntax-case syntax-error unit/sig unless when with-syntax and begin call-with-current-continuation call-with-input-file call-with-output-file case cond define define-syntax delay do dynamic-wind else for-each if lambda let let* let-syntax letrec letrec-syntax map or syntax-rules ' * + , ,@ - ... / ; < <= = => > >= ` abs acos angle append apply asin assoc assq assv atan boolean? caar cadr call-with-input-file call-with-output-file call-with-values car cdddar cddddr cdr ceiling char->integer char-alphabetic? char-ci<=? char-ci=? char-ci>? char-downcase char-lower-case? char-numeric? char-ready? char-upcase char-upper-case? char-whitespace? char<=? char=? char>? char? close-input-port close-output-port complex? cons cos current-input-port current-output-port denominator display eof-object? eq? equal? eqv? eval even? exact->inexact exact? exp expt floor force gcd imag-part inexact->exact inexact? input-port? integer->char integer? interaction-environment lcm length list list->string list->vector list-ref list-tail list? load log magnitude make-polar make-rectangular make-string make-vector max member memq memv min modulo negative? newline not null-environment null? number->string number? numerator odd? open-input-file open-output-file output-port? pair? peek-char port? positive? procedure? quasiquote quote quotient rational? rationalize read read-char real-part real? remainder reverse round scheme-report-environment set! set-car! set-cdr! sin sqrt string string->list string->number string->symbol string-append string-ci<=? string-ci=? string-ci>? string-copy string-fill! string-length string-ref string-set! string<=? string=? string>? string? substring symbol->string symbol? tan transcript-off transcript-on truncate values vector vector->list vector-fill! vector-length vector-ref vector-set! with-input-from-file with-output-to-file write write-char zero?", + }, + r = { className: "literal", begin: "(#t|#f|#\\\\" + t + "|#\\\\.)" }, + i = { + className: "number", + variants: [ + { begin: n, relevance: 0 }, + { + begin: "(-|\\+)?\\d+([./]\\d+)?[+\\-](-|\\+)?\\d+([./]\\d+)?i", + relevance: 0, + }, + { begin: "#b[0-1]+(/[0-1]+)?" }, + { begin: "#o[0-7]+(/[0-7]+)?" }, + { begin: "#x[0-9a-f]+(/[0-9a-f]+)?" }, + ], + }, + o = e.QUOTE_STRING_MODE, + s = [e.COMMENT(";", "$", { relevance: 0 }), e.COMMENT("#\\|", "\\|#")], + l = { begin: t, relevance: 0 }, + c = { className: "symbol", begin: "'" + t }, + _ = { endsWithParent: !0, relevance: 0 }, + d = { + variants: [{ begin: /'/ }, { begin: "`" }], + contains: [ + { begin: "\\(", end: "\\)", contains: ["self", r, o, i, l, c] }, + ], + }, + u = { className: "name", relevance: 0, begin: t, keywords: a }, + m = { + variants: [ + { begin: "\\(", end: "\\)" }, + { begin: "\\[", end: "\\]" }, + ], + contains: [ + { + begin: /lambda/, + endsWithParent: !0, + returnBegin: !0, + contains: [ + u, + { + endsParent: !0, + variants: [ + { begin: /\(/, end: /\)/ }, + { begin: /\[/, end: /\]/ }, + ], + contains: [l], + }, + ], + }, + u, + _, + ], + }; + return ( + (_.contains = [r, i, o, l, c, d, m].concat(s)), + { + name: "Scheme", + illegal: /\S/, + contains: [e.SHEBANG(), i, o, c, d, m].concat(s), + } + ); +}; +var yb = function (e) { + var t = [ + e.C_NUMBER_MODE, + { + className: "string", + begin: "'|\"", + end: "'|\"", + contains: [e.BACKSLASH_ESCAPE, { begin: "''" }], + }, + ]; + return { + name: "Scilab", + aliases: ["sci"], + keywords: { + $pattern: /%?\w+/, + keyword: + "abort break case clear catch continue do elseif else endfunction end for function global if pause return resume select try then while", + literal: "%f %F %t %T %pi %eps %inf %nan %e %i %z %s", + built_in: + "abs and acos asin atan ceil cd chdir clearglobal cosh cos cumprod deff disp error exec execstr exists exp eye gettext floor fprintf fread fsolve imag isdef isempty isinfisnan isvector lasterror length load linspace list listfiles log10 log2 log max min msprintf mclose mopen ones or pathconvert poly printf prod pwd rand real round sinh sin size gsort sprintf sqrt strcat strcmps tring sum system tanh tan type typename warning zeros matrix", + }, + illegal: '("|#|/\\*|\\s+/\\w+)', + contains: [ + { + className: "function", + beginKeywords: "function", + end: "$", + contains: [ + e.UNDERSCORE_TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }, + { begin: "[a-zA-Z_][a-zA-Z_0-9]*[\\.']+", relevance: 0 }, + { begin: "\\[", end: "\\][\\.']*", relevance: 0, contains: t }, + e.COMMENT("//", "$"), + ].concat(t), + }; + }, + Ib = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + Ab = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + Db = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + Mb = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + Lb = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(); +var wb = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = Mb, + a = Db, + r = "@[a-z-]+", + i = { className: "variable", begin: "(\\$[a-zA-Z-][a-zA-Z0-9_-]*)\\b" }; + return { + name: "SCSS", + case_insensitive: !0, + illegal: "[=/|']", + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + { className: "selector-id", begin: "#[A-Za-z0-9_-]+", relevance: 0 }, + { className: "selector-class", begin: "\\.[A-Za-z0-9_-]+", relevance: 0 }, + t.ATTRIBUTE_SELECTOR_MODE, + { + className: "selector-tag", + begin: "\\b(" + Ib.join("|") + ")\\b", + relevance: 0, + }, + { className: "selector-pseudo", begin: ":(" + a.join("|") + ")" }, + { className: "selector-pseudo", begin: "::(" + n.join("|") + ")" }, + i, + { begin: /\(/, end: /\)/, contains: [e.CSS_NUMBER_MODE] }, + { className: "attribute", begin: "\\b(" + Lb.join("|") + ")\\b" }, + { + begin: + "\\b(whitespace|wait|w-resize|visible|vertical-text|vertical-ideographic|uppercase|upper-roman|upper-alpha|underline|transparent|top|thin|thick|text|text-top|text-bottom|tb-rl|table-header-group|table-footer-group|sw-resize|super|strict|static|square|solid|small-caps|separate|se-resize|scroll|s-resize|rtl|row-resize|ridge|right|repeat|repeat-y|repeat-x|relative|progress|pointer|overline|outside|outset|oblique|nowrap|not-allowed|normal|none|nw-resize|no-repeat|no-drop|newspaper|ne-resize|n-resize|move|middle|medium|ltr|lr-tb|lowercase|lower-roman|lower-alpha|loose|list-item|line|line-through|line-edge|lighter|left|keep-all|justify|italic|inter-word|inter-ideograph|inside|inset|inline|inline-block|inherit|inactive|ideograph-space|ideograph-parenthesis|ideograph-numeric|ideograph-alpha|horizontal|hidden|help|hand|groove|fixed|ellipsis|e-resize|double|dotted|distribute|distribute-space|distribute-letter|distribute-all-lines|disc|disabled|default|decimal|dashed|crosshair|collapse|col-resize|circle|char|center|capitalize|break-word|break-all|bottom|both|bolder|bold|block|bidi-override|below|baseline|auto|always|all-scroll|absolute|table|table-cell)\\b", + }, + { + begin: ":", + end: ";", + contains: [ + i, + t.HEXCOLOR, + e.CSS_NUMBER_MODE, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + t.IMPORTANT, + ], + }, + { begin: "@(page|font-face)", lexemes: r, keywords: "@page @font-face" }, + { + begin: "@", + end: "[{;]", + returnBegin: !0, + keywords: { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: Ab.join(" "), + }, + contains: [ + { begin: r, className: "keyword" }, + { begin: /[a-z-]+(?=:)/, className: "attribute" }, + i, + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + t.HEXCOLOR, + e.CSS_NUMBER_MODE, + ], + }, + ], + }; +}; +var xb = function (e) { + return { + name: "Shell Session", + aliases: ["console"], + contains: [ + { + className: "meta", + begin: /^\s{0,3}[/~\w\d[\]()@-]*[>%$#]/, + starts: { end: /[^\\](?=\s*$)/, subLanguage: "bash" }, + }, + ], + }; +}; +var Pb = function (e) { + var t = [ + "add", + "and", + "cmp", + "cmpg", + "cmpl", + "const", + "div", + "double", + "float", + "goto", + "if", + "int", + "long", + "move", + "mul", + "neg", + "new", + "nop", + "not", + "or", + "rem", + "return", + "shl", + "shr", + "sput", + "sub", + "throw", + "ushr", + "xor", + ]; + return { + name: "Smali", + contains: [ + { className: "string", begin: '"', end: '"', relevance: 0 }, + e.COMMENT("#", "$", { relevance: 0 }), + { + className: "keyword", + variants: [ + { begin: "\\s*\\.end\\s[a-zA-Z0-9]*" }, + { begin: "^[ ]*\\.[a-zA-Z]*", relevance: 0 }, + { begin: "\\s:[a-zA-Z_0-9]*", relevance: 0 }, + { + begin: + "\\s(" + + [ + "transient", + "constructor", + "abstract", + "final", + "synthetic", + "public", + "private", + "protected", + "static", + "bridge", + "system", + ].join("|") + + ")", + }, + ], + }, + { + className: "built_in", + variants: [ + { begin: "\\s(" + t.join("|") + ")\\s" }, + { + begin: "\\s(" + t.join("|") + ")((-|/)[a-zA-Z0-9]+)+\\s", + relevance: 10, + }, + { + begin: + "\\s(" + + [ + "aget", + "aput", + "array", + "check", + "execute", + "fill", + "filled", + "goto/16", + "goto/32", + "iget", + "instance", + "invoke", + "iput", + "monitor", + "packed", + "sget", + "sparse", + ].join("|") + + ")((-|/)[a-zA-Z0-9]+)*\\s", + relevance: 10, + }, + ], + }, + { className: "class", begin: "L[^(;:\n]*;", relevance: 0 }, + { begin: "[vp][0-9]+" }, + ], + }; +}; +var kb = function (e) { + var t = "[a-z][a-zA-Z0-9_]*", + n = { className: "string", begin: "\\$.{1}" }, + a = { className: "symbol", begin: "#" + e.UNDERSCORE_IDENT_RE }; + return { + name: "Smalltalk", + aliases: ["st"], + keywords: "self super nil true false thisContext", + contains: [ + e.COMMENT('"', '"'), + e.APOS_STRING_MODE, + { className: "type", begin: "\\b[A-Z][A-Za-z0-9_]*", relevance: 0 }, + { begin: t + ":", relevance: 0 }, + e.C_NUMBER_MODE, + a, + n, + { + begin: "\\|[ ]*" + t + "([ ]+" + t + ")*[ ]*\\|", + returnBegin: !0, + end: /\|/, + illegal: /\S/, + contains: [{ begin: "(\\|[ ]*)?" + t }], + }, + { + begin: "#\\(", + end: "\\)", + contains: [e.APOS_STRING_MODE, n, e.C_NUMBER_MODE, a], + }, + ], + }; +}; +var Ub = function (e) { + return { + name: "SML (Standard ML)", + aliases: ["ml"], + keywords: { + $pattern: "[a-z_]\\w*!?", + keyword: + "abstype and andalso as case datatype do else end eqtype exception fn fun functor handle if in include infix infixr let local nonfix of op open orelse raise rec sharing sig signature struct structure then type val with withtype where while", + built_in: + "array bool char exn int list option order real ref string substring vector unit word", + literal: "true false NONE SOME LESS EQUAL GREATER nil", + }, + illegal: /\/\/|>>/, + contains: [ + { className: "literal", begin: /\[(\|\|)?\]|\(\)/, relevance: 0 }, + e.COMMENT("\\(\\*", "\\*\\)", { contains: ["self"] }), + { className: "symbol", begin: "'[A-Za-z_](?!')[\\w']*" }, + { className: "type", begin: "`[A-Z][\\w']*" }, + { className: "type", begin: "\\b[A-Z][\\w']*", relevance: 0 }, + { begin: "[a-z_]\\w*'[\\w']*" }, + e.inherit(e.APOS_STRING_MODE, { className: "string", relevance: 0 }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { + className: "number", + begin: + "\\b(0[xX][a-fA-F0-9_]+[Lln]?|0[oO][0-7_]+[Lln]?|0[bB][01_]+[Lln]?|[0-9][0-9_]*([Lln]|(\\.[0-9_]*)?([eE][-+]?[0-9_]+)?)?)", + relevance: 0, + }, + { begin: /[-=]>/ }, + ], + }; +}; +var Fb = function (e) { + var t = { + className: "string", + variants: [ + { begin: '"', end: '"', contains: [{ begin: '""', relevance: 0 }] }, + { begin: "'", end: "'", contains: [{ begin: "''", relevance: 0 }] }, + ], + }, + n = { + className: "meta", + begin: /#\s*[a-z]+\b/, + end: /$/, + keywords: { + "meta-keyword": "define undef ifdef ifndef else endif include", + }, + contains: [ + { begin: /\\\n/, relevance: 0 }, + e.inherit(t, { className: "meta-string" }), + { + className: "meta-string", + begin: /<[^\n>]*>/, + end: /$/, + illegal: "\\n", + }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; + return { + name: "SQF", + case_insensitive: !0, + keywords: { + keyword: + "case catch default do else exit exitWith for forEach from if private switch then throw to try waitUntil while with", + built_in: + "abs accTime acos action actionIDs actionKeys actionKeysImages actionKeysNames actionKeysNamesArray actionName actionParams activateAddons activatedAddons activateKey add3DENConnection add3DENEventHandler add3DENLayer addAction addBackpack addBackpackCargo addBackpackCargoGlobal addBackpackGlobal addCamShake addCuratorAddons addCuratorCameraArea addCuratorEditableObjects addCuratorEditingArea addCuratorPoints addEditorObject addEventHandler addForce addGoggles addGroupIcon addHandgunItem addHeadgear addItem addItemCargo addItemCargoGlobal addItemPool addItemToBackpack addItemToUniform addItemToVest addLiveStats addMagazine addMagazineAmmoCargo addMagazineCargo addMagazineCargoGlobal addMagazineGlobal addMagazinePool addMagazines addMagazineTurret addMenu addMenuItem addMissionEventHandler addMPEventHandler addMusicEventHandler addOwnedMine addPlayerScores addPrimaryWeaponItem addPublicVariableEventHandler addRating addResources addScore addScoreSide addSecondaryWeaponItem addSwitchableUnit addTeamMember addToRemainsCollector addTorque addUniform addVehicle addVest addWaypoint addWeapon addWeaponCargo addWeaponCargoGlobal addWeaponGlobal addWeaponItem addWeaponPool addWeaponTurret admin agent agents AGLToASL aimedAtTarget aimPos airDensityRTD airplaneThrottle airportSide AISFinishHeal alive all3DENEntities allAirports allControls allCurators allCutLayers allDead allDeadMen allDisplays allGroups allMapMarkers allMines allMissionObjects allow3DMode allowCrewInImmobile allowCuratorLogicIgnoreAreas allowDamage allowDammage allowFileOperations allowFleeing allowGetIn allowSprint allPlayers allSimpleObjects allSites allTurrets allUnits allUnitsUAV allVariables ammo ammoOnPylon and animate animateBay animateDoor animatePylon animateSource animationNames animationPhase animationSourcePhase animationState append apply armoryPoints arrayIntersect asin ASLToAGL ASLToATL assert assignAsCargo assignAsCargoIndex assignAsCommander assignAsDriver assignAsGunner assignAsTurret assignCurator assignedCargo assignedCommander assignedDriver assignedGunner assignedItems assignedTarget assignedTeam assignedVehicle assignedVehicleRole assignItem assignTeam assignToAirport atan atan2 atg ATLToASL attachedObject attachedObjects attachedTo attachObject attachTo attackEnabled backpack backpackCargo backpackContainer backpackItems backpackMagazines backpackSpaceFor behaviour benchmark binocular boundingBox boundingBoxReal boundingCenter breakOut breakTo briefingName buildingExit buildingPos buttonAction buttonSetAction cadetMode call callExtension camCommand camCommit camCommitPrepared camCommitted camConstuctionSetParams camCreate camDestroy cameraEffect cameraEffectEnableHUD cameraInterest cameraOn cameraView campaignConfigFile camPreload camPreloaded camPrepareBank camPrepareDir camPrepareDive camPrepareFocus camPrepareFov camPrepareFovRange camPreparePos camPrepareRelPos camPrepareTarget camSetBank camSetDir camSetDive camSetFocus camSetFov camSetFovRange camSetPos camSetRelPos camSetTarget camTarget camUseNVG canAdd canAddItemToBackpack canAddItemToUniform canAddItemToVest cancelSimpleTaskDestination canFire canMove canSlingLoad canStand canSuspend canTriggerDynamicSimulation canUnloadInCombat canVehicleCargo captive captiveNum cbChecked cbSetChecked ceil channelEnabled cheatsEnabled checkAIFeature checkVisibility className clearAllItemsFromBackpack clearBackpackCargo clearBackpackCargoGlobal clearGroupIcons clearItemCargo clearItemCargoGlobal clearItemPool clearMagazineCargo clearMagazineCargoGlobal clearMagazinePool clearOverlay clearRadio clearWeaponCargo clearWeaponCargoGlobal clearWeaponPool clientOwner closeDialog closeDisplay closeOverlay collapseObjectTree collect3DENHistory collectiveRTD combatMode commandArtilleryFire commandChat commander commandFire commandFollow commandFSM commandGetOut commandingMenu commandMove commandRadio commandStop commandSuppressiveFire commandTarget commandWatch comment commitOverlay compile compileFinal completedFSM composeText configClasses configFile configHierarchy configName configProperties configSourceAddonList configSourceMod configSourceModList confirmSensorTarget connectTerminalToUAV controlsGroupCtrl copyFromClipboard copyToClipboard copyWaypoints cos count countEnemy countFriendly countSide countType countUnknown create3DENComposition create3DENEntity createAgent createCenter createDialog createDiaryLink createDiaryRecord createDiarySubject createDisplay createGearDialog createGroup createGuardedPoint createLocation createMarker createMarkerLocal createMenu createMine createMissionDisplay createMPCampaignDisplay createSimpleObject createSimpleTask createSite createSoundSource createTask createTeam createTrigger createUnit createVehicle createVehicleCrew createVehicleLocal crew ctAddHeader ctAddRow ctClear ctCurSel ctData ctFindHeaderRows ctFindRowHeader ctHeaderControls ctHeaderCount ctRemoveHeaders ctRemoveRows ctrlActivate ctrlAddEventHandler ctrlAngle ctrlAutoScrollDelay ctrlAutoScrollRewind ctrlAutoScrollSpeed ctrlChecked ctrlClassName ctrlCommit ctrlCommitted ctrlCreate ctrlDelete ctrlEnable ctrlEnabled ctrlFade ctrlHTMLLoaded ctrlIDC ctrlIDD ctrlMapAnimAdd ctrlMapAnimClear ctrlMapAnimCommit ctrlMapAnimDone ctrlMapCursor ctrlMapMouseOver ctrlMapScale ctrlMapScreenToWorld ctrlMapWorldToScreen ctrlModel ctrlModelDirAndUp ctrlModelScale ctrlParent ctrlParentControlsGroup ctrlPosition ctrlRemoveAllEventHandlers ctrlRemoveEventHandler ctrlScale ctrlSetActiveColor ctrlSetAngle ctrlSetAutoScrollDelay ctrlSetAutoScrollRewind ctrlSetAutoScrollSpeed ctrlSetBackgroundColor ctrlSetChecked ctrlSetEventHandler ctrlSetFade ctrlSetFocus ctrlSetFont ctrlSetFontH1 ctrlSetFontH1B ctrlSetFontH2 ctrlSetFontH2B ctrlSetFontH3 ctrlSetFontH3B ctrlSetFontH4 ctrlSetFontH4B ctrlSetFontH5 ctrlSetFontH5B ctrlSetFontH6 ctrlSetFontH6B ctrlSetFontHeight ctrlSetFontHeightH1 ctrlSetFontHeightH2 ctrlSetFontHeightH3 ctrlSetFontHeightH4 ctrlSetFontHeightH5 ctrlSetFontHeightH6 ctrlSetFontHeightSecondary ctrlSetFontP ctrlSetFontPB ctrlSetFontSecondary ctrlSetForegroundColor ctrlSetModel ctrlSetModelDirAndUp ctrlSetModelScale ctrlSetPixelPrecision ctrlSetPosition ctrlSetScale ctrlSetStructuredText ctrlSetText ctrlSetTextColor ctrlSetTooltip ctrlSetTooltipColorBox ctrlSetTooltipColorShade ctrlSetTooltipColorText ctrlShow ctrlShown ctrlText ctrlTextHeight ctrlTextWidth ctrlType ctrlVisible ctRowControls ctRowCount ctSetCurSel ctSetData ctSetHeaderTemplate ctSetRowTemplate ctSetValue ctValue curatorAddons curatorCamera curatorCameraArea curatorCameraAreaCeiling curatorCoef curatorEditableObjects curatorEditingArea curatorEditingAreaType curatorMouseOver curatorPoints curatorRegisteredObjects curatorSelected curatorWaypointCost current3DENOperation currentChannel currentCommand currentMagazine currentMagazineDetail currentMagazineDetailTurret currentMagazineTurret currentMuzzle currentNamespace currentTask currentTasks currentThrowable currentVisionMode currentWaypoint currentWeapon currentWeaponMode currentWeaponTurret currentZeroing cursorObject cursorTarget customChat customRadio cutFadeOut cutObj cutRsc cutText damage date dateToNumber daytime deActivateKey debriefingText debugFSM debugLog deg delete3DENEntities deleteAt deleteCenter deleteCollection deleteEditorObject deleteGroup deleteGroupWhenEmpty deleteIdentity deleteLocation deleteMarker deleteMarkerLocal deleteRange deleteResources deleteSite deleteStatus deleteTeam deleteVehicle deleteVehicleCrew deleteWaypoint detach detectedMines diag_activeMissionFSMs diag_activeScripts diag_activeSQFScripts diag_activeSQSScripts diag_captureFrame diag_captureFrameToFile diag_captureSlowFrame diag_codePerformance diag_drawMode diag_enable diag_enabled diag_fps diag_fpsMin diag_frameNo diag_lightNewLoad diag_list diag_log diag_logSlowFrame diag_mergeConfigFile diag_recordTurretLimits diag_setLightNew diag_tickTime diag_toggle dialog diarySubjectExists didJIP didJIPOwner difficulty difficultyEnabled difficultyEnabledRTD difficultyOption direction directSay disableAI disableCollisionWith disableConversation disableDebriefingStats disableMapIndicators disableNVGEquipment disableRemoteSensors disableSerialization disableTIEquipment disableUAVConnectability disableUserInput displayAddEventHandler displayCtrl displayParent displayRemoveAllEventHandlers displayRemoveEventHandler displaySetEventHandler dissolveTeam distance distance2D distanceSqr distributionRegion do3DENAction doArtilleryFire doFire doFollow doFSM doGetOut doMove doorPhase doStop doSuppressiveFire doTarget doWatch drawArrow drawEllipse drawIcon drawIcon3D drawLine drawLine3D drawLink drawLocation drawPolygon drawRectangle drawTriangle driver drop dynamicSimulationDistance dynamicSimulationDistanceCoef dynamicSimulationEnabled dynamicSimulationSystemEnabled echo edit3DENMissionAttributes editObject editorSetEventHandler effectiveCommander emptyPositions enableAI enableAIFeature enableAimPrecision enableAttack enableAudioFeature enableAutoStartUpRTD enableAutoTrimRTD enableCamShake enableCaustics enableChannel enableCollisionWith enableCopilot enableDebriefingStats enableDiagLegend enableDynamicSimulation enableDynamicSimulationSystem enableEndDialog enableEngineArtillery enableEnvironment enableFatigue enableGunLights enableInfoPanelComponent enableIRLasers enableMimics enablePersonTurret enableRadio enableReload enableRopeAttach enableSatNormalOnDetail enableSaving enableSentences enableSimulation enableSimulationGlobal enableStamina enableTeamSwitch enableTraffic enableUAVConnectability enableUAVWaypoints enableVehicleCargo enableVehicleSensor enableWeaponDisassembly endLoadingScreen endMission engineOn enginesIsOnRTD enginesRpmRTD enginesTorqueRTD entities environmentEnabled estimatedEndServerTime estimatedTimeLeft evalObjectArgument everyBackpack everyContainer exec execEditorScript execFSM execVM exp expectedDestination exportJIPMessages eyeDirection eyePos face faction fadeMusic fadeRadio fadeSound fadeSpeech failMission fillWeaponsFromPool find findCover findDisplay findEditorObject findEmptyPosition findEmptyPositionReady findIf findNearestEnemy finishMissionInit finite fire fireAtTarget firstBackpack flag flagAnimationPhase flagOwner flagSide flagTexture fleeing floor flyInHeight flyInHeightASL fog fogForecast fogParams forceAddUniform forcedMap forceEnd forceFlagTexture forceFollowRoad forceMap forceRespawn forceSpeed forceWalk forceWeaponFire forceWeatherChange forEachMember forEachMemberAgent forEachMemberTeam forgetTarget format formation formationDirection formationLeader formationMembers formationPosition formationTask formatText formLeader freeLook fromEditor fuel fullCrew gearIDCAmmoCount gearSlotAmmoCount gearSlotData get3DENActionState get3DENAttribute get3DENCamera get3DENConnections get3DENEntity get3DENEntityID get3DENGrid get3DENIconsVisible get3DENLayerEntities get3DENLinesVisible get3DENMissionAttribute get3DENMouseOver get3DENSelected getAimingCoef getAllEnvSoundControllers getAllHitPointsDamage getAllOwnedMines getAllSoundControllers getAmmoCargo getAnimAimPrecision getAnimSpeedCoef getArray getArtilleryAmmo getArtilleryComputerSettings getArtilleryETA getAssignedCuratorLogic getAssignedCuratorUnit getBackpackCargo getBleedingRemaining getBurningValue getCameraViewDirection getCargoIndex getCenterOfMass getClientState getClientStateNumber getCompatiblePylonMagazines getConnectedUAV getContainerMaxLoad getCursorObjectParams getCustomAimCoef getDammage getDescription getDir getDirVisual getDLCAssetsUsage getDLCAssetsUsageByName getDLCs getEditorCamera getEditorMode getEditorObjectScope getElevationOffset getEnvSoundController getFatigue getForcedFlagTexture getFriend getFSMVariable getFuelCargo getGroupIcon getGroupIconParams getGroupIcons getHideFrom getHit getHitIndex getHitPointDamage getItemCargo getMagazineCargo getMarkerColor getMarkerPos getMarkerSize getMarkerType getMass getMissionConfig getMissionConfigValue getMissionDLCs getMissionLayerEntities getModelInfo getMousePosition getMusicPlayedTime getNumber getObjectArgument getObjectChildren getObjectDLC getObjectMaterials getObjectProxy getObjectTextures getObjectType getObjectViewDistance getOxygenRemaining getPersonUsedDLCs getPilotCameraDirection getPilotCameraPosition getPilotCameraRotation getPilotCameraTarget getPlateNumber getPlayerChannel getPlayerScores getPlayerUID getPos getPosASL getPosASLVisual getPosASLW getPosATL getPosATLVisual getPosVisual getPosWorld getPylonMagazines getRelDir getRelPos getRemoteSensorsDisabled getRepairCargo getResolution getShadowDistance getShotParents getSlingLoad getSoundController getSoundControllerResult getSpeed getStamina getStatValue getSuppression getTerrainGrid getTerrainHeightASL getText getTotalDLCUsageTime getUnitLoadout getUnitTrait getUserMFDText getUserMFDvalue getVariable getVehicleCargo getWeaponCargo getWeaponSway getWingsOrientationRTD getWingsPositionRTD getWPPos glanceAt globalChat globalRadio goggles goto group groupChat groupFromNetId groupIconSelectable groupIconsVisible groupId groupOwner groupRadio groupSelectedUnits groupSelectUnit gunner gusts halt handgunItems handgunMagazine handgunWeapon handsHit hasInterface hasPilotCamera hasWeapon hcAllGroups hcGroupParams hcLeader hcRemoveAllGroups hcRemoveGroup hcSelected hcSelectGroup hcSetGroup hcShowBar hcShownBar headgear hideBody hideObject hideObjectGlobal hideSelection hint hintC hintCadet hintSilent hmd hostMission htmlLoad HUDMovementLevels humidity image importAllGroups importance in inArea inAreaArray incapacitatedState inflame inflamed infoPanel infoPanelComponentEnabled infoPanelComponents infoPanels inGameUISetEventHandler inheritsFrom initAmbientLife inPolygon inputAction inRangeOfArtillery insertEditorObject intersect is3DEN is3DENMultiplayer isAbleToBreathe isAgent isArray isAutoHoverOn isAutonomous isAutotest isBleeding isBurning isClass isCollisionLightOn isCopilotEnabled isDamageAllowed isDedicated isDLCAvailable isEngineOn isEqualTo isEqualType isEqualTypeAll isEqualTypeAny isEqualTypeArray isEqualTypeParams isFilePatchingEnabled isFlashlightOn isFlatEmpty isForcedWalk isFormationLeader isGroupDeletedWhenEmpty isHidden isInRemainsCollector isInstructorFigureEnabled isIRLaserOn isKeyActive isKindOf isLaserOn isLightOn isLocalized isManualFire isMarkedForCollection isMultiplayer isMultiplayerSolo isNil isNull isNumber isObjectHidden isObjectRTD isOnRoad isPipEnabled isPlayer isRealTime isRemoteExecuted isRemoteExecutedJIP isServer isShowing3DIcons isSimpleObject isSprintAllowed isStaminaEnabled isSteamMission isStreamFriendlyUIEnabled isText isTouchingGround isTurnedOut isTutHintsEnabled isUAVConnectable isUAVConnected isUIContext isUniformAllowed isVehicleCargo isVehicleRadarOn isVehicleSensorEnabled isWalking isWeaponDeployed isWeaponRested itemCargo items itemsWithMagazines join joinAs joinAsSilent joinSilent joinString kbAddDatabase kbAddDatabaseTargets kbAddTopic kbHasTopic kbReact kbRemoveTopic kbTell kbWasSaid keyImage keyName knowsAbout land landAt landResult language laserTarget lbAdd lbClear lbColor lbColorRight lbCurSel lbData lbDelete lbIsSelected lbPicture lbPictureRight lbSelection lbSetColor lbSetColorRight lbSetCurSel lbSetData lbSetPicture lbSetPictureColor lbSetPictureColorDisabled lbSetPictureColorSelected lbSetPictureRight lbSetPictureRightColor lbSetPictureRightColorDisabled lbSetPictureRightColorSelected lbSetSelectColor lbSetSelectColorRight lbSetSelected lbSetText lbSetTextRight lbSetTooltip lbSetValue lbSize lbSort lbSortByValue lbText lbTextRight lbValue leader leaderboardDeInit leaderboardGetRows leaderboardInit leaderboardRequestRowsFriends leaderboardsRequestUploadScore leaderboardsRequestUploadScoreKeepBest leaderboardState leaveVehicle libraryCredits libraryDisclaimers lifeState lightAttachObject lightDetachObject lightIsOn lightnings limitSpeed linearConversion lineIntersects lineIntersectsObjs lineIntersectsSurfaces lineIntersectsWith linkItem list listObjects listRemoteTargets listVehicleSensors ln lnbAddArray lnbAddColumn lnbAddRow lnbClear lnbColor lnbCurSelRow lnbData lnbDeleteColumn lnbDeleteRow lnbGetColumnsPosition lnbPicture lnbSetColor lnbSetColumnsPos lnbSetCurSelRow lnbSetData lnbSetPicture lnbSetText lnbSetValue lnbSize lnbSort lnbSortByValue lnbText lnbValue load loadAbs loadBackpack loadFile loadGame loadIdentity loadMagazine loadOverlay loadStatus loadUniform loadVest local localize locationPosition lock lockCameraTo lockCargo lockDriver locked lockedCargo lockedDriver lockedTurret lockIdentity lockTurret lockWP log logEntities logNetwork logNetworkTerminate lookAt lookAtPos magazineCargo magazines magazinesAllTurrets magazinesAmmo magazinesAmmoCargo magazinesAmmoFull magazinesDetail magazinesDetailBackpack magazinesDetailUniform magazinesDetailVest magazinesTurret magazineTurretAmmo mapAnimAdd mapAnimClear mapAnimCommit mapAnimDone mapCenterOnCamera mapGridPosition markAsFinishedOnSteam markerAlpha markerBrush markerColor markerDir markerPos markerShape markerSize markerText markerType max members menuAction menuAdd menuChecked menuClear menuCollapse menuData menuDelete menuEnable menuEnabled menuExpand menuHover menuPicture menuSetAction menuSetCheck menuSetData menuSetPicture menuSetValue menuShortcut menuShortcutText menuSize menuSort menuText menuURL menuValue min mineActive mineDetectedBy missionConfigFile missionDifficulty missionName missionNamespace missionStart missionVersion mod modelToWorld modelToWorldVisual modelToWorldVisualWorld modelToWorldWorld modParams moonIntensity moonPhase morale move move3DENCamera moveInAny moveInCargo moveInCommander moveInDriver moveInGunner moveInTurret moveObjectToEnd moveOut moveTime moveTo moveToCompleted moveToFailed musicVolume name nameSound nearEntities nearestBuilding nearestLocation nearestLocations nearestLocationWithDubbing nearestObject nearestObjects nearestTerrainObjects nearObjects nearObjectsReady nearRoads nearSupplies nearTargets needReload netId netObjNull newOverlay nextMenuItemIndex nextWeatherChange nMenuItems not numberOfEnginesRTD numberToDate objectCurators objectFromNetId objectParent objStatus onBriefingGroup onBriefingNotes onBriefingPlan onBriefingTeamSwitch onCommandModeChanged onDoubleClick onEachFrame onGroupIconClick onGroupIconOverEnter onGroupIconOverLeave onHCGroupSelectionChanged onMapSingleClick onPlayerConnected onPlayerDisconnected onPreloadFinished onPreloadStarted onShowNewObject onTeamSwitch openCuratorInterface openDLCPage openMap openSteamApp openYoutubeVideo or orderGetIn overcast overcastForecast owner param params parseNumber parseSimpleArray parseText parsingNamespace particlesQuality pickWeaponPool pitch pixelGrid pixelGridBase pixelGridNoUIScale pixelH pixelW playableSlotsNumber playableUnits playAction playActionNow player playerRespawnTime playerSide playersNumber playGesture playMission playMove playMoveNow playMusic playScriptedMission playSound playSound3D position positionCameraToWorld posScreenToWorld posWorldToScreen ppEffectAdjust ppEffectCommit ppEffectCommitted ppEffectCreate ppEffectDestroy ppEffectEnable ppEffectEnabled ppEffectForceInNVG precision preloadCamera preloadObject preloadSound preloadTitleObj preloadTitleRsc preprocessFile preprocessFileLineNumbers primaryWeapon primaryWeaponItems primaryWeaponMagazine priority processDiaryLink productVersion profileName profileNamespace profileNameSteam progressLoadingScreen progressPosition progressSetPosition publicVariable publicVariableClient publicVariableServer pushBack pushBackUnique putWeaponPool queryItemsPool queryMagazinePool queryWeaponPool rad radioChannelAdd radioChannelCreate radioChannelRemove radioChannelSetCallSign radioChannelSetLabel radioVolume rain rainbow random rank rankId rating rectangular registeredTasks registerTask reload reloadEnabled remoteControl remoteExec remoteExecCall remoteExecutedOwner remove3DENConnection remove3DENEventHandler remove3DENLayer removeAction removeAll3DENEventHandlers removeAllActions removeAllAssignedItems removeAllContainers removeAllCuratorAddons removeAllCuratorCameraAreas removeAllCuratorEditingAreas removeAllEventHandlers removeAllHandgunItems removeAllItems removeAllItemsWithMagazines removeAllMissionEventHandlers removeAllMPEventHandlers removeAllMusicEventHandlers removeAllOwnedMines removeAllPrimaryWeaponItems removeAllWeapons removeBackpack removeBackpackGlobal removeCuratorAddons removeCuratorCameraArea removeCuratorEditableObjects removeCuratorEditingArea removeDrawIcon removeDrawLinks removeEventHandler removeFromRemainsCollector removeGoggles removeGroupIcon removeHandgunItem removeHeadgear removeItem removeItemFromBackpack removeItemFromUniform removeItemFromVest removeItems removeMagazine removeMagazineGlobal removeMagazines removeMagazinesTurret removeMagazineTurret removeMenuItem removeMissionEventHandler removeMPEventHandler removeMusicEventHandler removeOwnedMine removePrimaryWeaponItem removeSecondaryWeaponItem removeSimpleTask removeSwitchableUnit removeTeamMember removeUniform removeVest removeWeapon removeWeaponAttachmentCargo removeWeaponCargo removeWeaponGlobal removeWeaponTurret reportRemoteTarget requiredVersion resetCamShake resetSubgroupDirection resize resources respawnVehicle restartEditorCamera reveal revealMine reverse reversedMouseY roadAt roadsConnectedTo roleDescription ropeAttachedObjects ropeAttachedTo ropeAttachEnabled ropeAttachTo ropeCreate ropeCut ropeDestroy ropeDetach ropeEndPosition ropeLength ropes ropeUnwind ropeUnwound rotorsForcesRTD rotorsRpmRTD round runInitScript safeZoneH safeZoneW safeZoneWAbs safeZoneX safeZoneXAbs safeZoneY save3DENInventory saveGame saveIdentity saveJoysticks saveOverlay saveProfileNamespace saveStatus saveVar savingEnabled say say2D say3D scopeName score scoreSide screenshot screenToWorld scriptDone scriptName scudState secondaryWeapon secondaryWeaponItems secondaryWeaponMagazine select selectBestPlaces selectDiarySubject selectedEditorObjects selectEditorObject selectionNames selectionPosition selectLeader selectMax selectMin selectNoPlayer selectPlayer selectRandom selectRandomWeighted selectWeapon selectWeaponTurret sendAUMessage sendSimpleCommand sendTask sendTaskResult sendUDPMessage serverCommand serverCommandAvailable serverCommandExecutable serverName serverTime set set3DENAttribute set3DENAttributes set3DENGrid set3DENIconsVisible set3DENLayer set3DENLinesVisible set3DENLogicType set3DENMissionAttribute set3DENMissionAttributes set3DENModelsVisible set3DENObjectType set3DENSelected setAccTime setActualCollectiveRTD setAirplaneThrottle setAirportSide setAmmo setAmmoCargo setAmmoOnPylon setAnimSpeedCoef setAperture setApertureNew setArmoryPoints setAttributes setAutonomous setBehaviour setBleedingRemaining setBrakesRTD setCameraInterest setCamShakeDefParams setCamShakeParams setCamUseTI setCaptive setCenterOfMass setCollisionLight setCombatMode setCompassOscillation setConvoySeparation setCuratorCameraAreaCeiling setCuratorCoef setCuratorEditingAreaType setCuratorWaypointCost setCurrentChannel setCurrentTask setCurrentWaypoint setCustomAimCoef setCustomWeightRTD setDamage setDammage setDate setDebriefingText setDefaultCamera setDestination setDetailMapBlendPars setDir setDirection setDrawIcon setDriveOnPath setDropInterval setDynamicSimulationDistance setDynamicSimulationDistanceCoef setEditorMode setEditorObjectScope setEffectCondition setEngineRPMRTD setFace setFaceAnimation setFatigue setFeatureType setFlagAnimationPhase setFlagOwner setFlagSide setFlagTexture setFog setFormation setFormationTask setFormDir setFriend setFromEditor setFSMVariable setFuel setFuelCargo setGroupIcon setGroupIconParams setGroupIconsSelectable setGroupIconsVisible setGroupId setGroupIdGlobal setGroupOwner setGusts setHideBehind setHit setHitIndex setHitPointDamage setHorizonParallaxCoef setHUDMovementLevels setIdentity setImportance setInfoPanel setLeader setLightAmbient setLightAttenuation setLightBrightness setLightColor setLightDayLight setLightFlareMaxDistance setLightFlareSize setLightIntensity setLightnings setLightUseFlare setLocalWindParams setMagazineTurretAmmo setMarkerAlpha setMarkerAlphaLocal setMarkerBrush setMarkerBrushLocal setMarkerColor setMarkerColorLocal setMarkerDir setMarkerDirLocal setMarkerPos setMarkerPosLocal setMarkerShape setMarkerShapeLocal setMarkerSize setMarkerSizeLocal setMarkerText setMarkerTextLocal setMarkerType setMarkerTypeLocal setMass setMimic setMousePosition setMusicEffect setMusicEventHandler setName setNameSound setObjectArguments setObjectMaterial setObjectMaterialGlobal setObjectProxy setObjectTexture setObjectTextureGlobal setObjectViewDistance setOvercast setOwner setOxygenRemaining setParticleCircle setParticleClass setParticleFire setParticleParams setParticleRandom setPilotCameraDirection setPilotCameraRotation setPilotCameraTarget setPilotLight setPiPEffect setPitch setPlateNumber setPlayable setPlayerRespawnTime setPos setPosASL setPosASL2 setPosASLW setPosATL setPosition setPosWorld setPylonLoadOut setPylonsPriority setRadioMsg setRain setRainbow setRandomLip setRank setRectangular setRepairCargo setRotorBrakeRTD setShadowDistance setShotParents setSide setSimpleTaskAlwaysVisible setSimpleTaskCustomData setSimpleTaskDescription setSimpleTaskDestination setSimpleTaskTarget setSimpleTaskType setSimulWeatherLayers setSize setSkill setSlingLoad setSoundEffect setSpeaker setSpeech setSpeedMode setStamina setStaminaScheme setStatValue setSuppression setSystemOfUnits setTargetAge setTaskMarkerOffset setTaskResult setTaskState setTerrainGrid setText setTimeMultiplier setTitleEffect setTrafficDensity setTrafficDistance setTrafficGap setTrafficSpeed setTriggerActivation setTriggerArea setTriggerStatements setTriggerText setTriggerTimeout setTriggerType setType setUnconscious setUnitAbility setUnitLoadout setUnitPos setUnitPosWeak setUnitRank setUnitRecoilCoefficient setUnitTrait setUnloadInCombat setUserActionText setUserMFDText setUserMFDvalue setVariable setVectorDir setVectorDirAndUp setVectorUp setVehicleAmmo setVehicleAmmoDef setVehicleArmor setVehicleCargo setVehicleId setVehicleLock setVehiclePosition setVehicleRadar setVehicleReceiveRemoteTargets setVehicleReportOwnPosition setVehicleReportRemoteTargets setVehicleTIPars setVehicleVarName setVelocity setVelocityModelSpace setVelocityTransformation setViewDistance setVisibleIfTreeCollapsed setWantedRPMRTD setWaves setWaypointBehaviour setWaypointCombatMode setWaypointCompletionRadius setWaypointDescription setWaypointForceBehaviour setWaypointFormation setWaypointHousePosition setWaypointLoiterRadius setWaypointLoiterType setWaypointName setWaypointPosition setWaypointScript setWaypointSpeed setWaypointStatements setWaypointTimeout setWaypointType setWaypointVisible setWeaponReloadingTime setWind setWindDir setWindForce setWindStr setWingForceScaleRTD setWPPos show3DIcons showChat showCinemaBorder showCommandingMenu showCompass showCuratorCompass showGPS showHUD showLegend showMap shownArtilleryComputer shownChat shownCompass shownCuratorCompass showNewEditorObject shownGPS shownHUD shownMap shownPad shownRadio shownScoretable shownUAVFeed shownWarrant shownWatch showPad showRadio showScoretable showSubtitles showUAVFeed showWarrant showWatch showWaypoint showWaypoints side sideChat sideEnemy sideFriendly sideRadio simpleTasks simulationEnabled simulCloudDensity simulCloudOcclusion simulInClouds simulWeatherSync sin size sizeOf skill skillFinal skipTime sleep sliderPosition sliderRange sliderSetPosition sliderSetRange sliderSetSpeed sliderSpeed slingLoadAssistantShown soldierMagazines someAmmo sort soundVolume spawn speaker speed speedMode splitString sqrt squadParams stance startLoadingScreen step stop stopEngineRTD stopped str sunOrMoon supportInfo suppressFor surfaceIsWater surfaceNormal surfaceType swimInDepth switchableUnits switchAction switchCamera switchGesture switchLight switchMove synchronizedObjects synchronizedTriggers synchronizedWaypoints synchronizeObjectsAdd synchronizeObjectsRemove synchronizeTrigger synchronizeWaypoint systemChat systemOfUnits tan targetKnowledge targets targetsAggregate targetsQuery taskAlwaysVisible taskChildren taskCompleted taskCustomData taskDescription taskDestination taskHint taskMarkerOffset taskParent taskResult taskState taskType teamMember teamName teams teamSwitch teamSwitchEnabled teamType terminate terrainIntersect terrainIntersectASL terrainIntersectAtASL text textLog textLogFormat tg time timeMultiplier titleCut titleFadeOut titleObj titleRsc titleText toArray toFixed toLower toString toUpper triggerActivated triggerActivation triggerArea triggerAttachedVehicle triggerAttachObject triggerAttachVehicle triggerDynamicSimulation triggerStatements triggerText triggerTimeout triggerTimeoutCurrent triggerType turretLocal turretOwner turretUnit tvAdd tvClear tvCollapse tvCollapseAll tvCount tvCurSel tvData tvDelete tvExpand tvExpandAll tvPicture tvSetColor tvSetCurSel tvSetData tvSetPicture tvSetPictureColor tvSetPictureColorDisabled tvSetPictureColorSelected tvSetPictureRight tvSetPictureRightColor tvSetPictureRightColorDisabled tvSetPictureRightColorSelected tvSetText tvSetTooltip tvSetValue tvSort tvSortByValue tvText tvTooltip tvValue type typeName typeOf UAVControl uiNamespace uiSleep unassignCurator unassignItem unassignTeam unassignVehicle underwater uniform uniformContainer uniformItems uniformMagazines unitAddons unitAimPosition unitAimPositionVisual unitBackpack unitIsUAV unitPos unitReady unitRecoilCoefficient units unitsBelowHeight unlinkItem unlockAchievement unregisterTask updateDrawIcon updateMenuItem updateObjectTree useAISteeringComponent useAudioTimeForMoves userInputDisabled vectorAdd vectorCos vectorCrossProduct vectorDiff vectorDir vectorDirVisual vectorDistance vectorDistanceSqr vectorDotProduct vectorFromTo vectorMagnitude vectorMagnitudeSqr vectorModelToWorld vectorModelToWorldVisual vectorMultiply vectorNormalized vectorUp vectorUpVisual vectorWorldToModel vectorWorldToModelVisual vehicle vehicleCargoEnabled vehicleChat vehicleRadio vehicleReceiveRemoteTargets vehicleReportOwnPosition vehicleReportRemoteTargets vehicles vehicleVarName velocity velocityModelSpace verifySignature vest vestContainer vestItems vestMagazines viewDistance visibleCompass visibleGPS visibleMap visiblePosition visiblePositionASL visibleScoretable visibleWatch waves waypointAttachedObject waypointAttachedVehicle waypointAttachObject waypointAttachVehicle waypointBehaviour waypointCombatMode waypointCompletionRadius waypointDescription waypointForceBehaviour waypointFormation waypointHousePosition waypointLoiterRadius waypointLoiterType waypointName waypointPosition waypoints waypointScript waypointsEnabledUAV waypointShow waypointSpeed waypointStatements waypointTimeout waypointTimeoutCurrent waypointType waypointVisible weaponAccessories weaponAccessoriesCargo weaponCargo weaponDirection weaponInertia weaponLowered weapons weaponsItems weaponsItemsCargo weaponState weaponsTurret weightRTD WFSideText wind ", + literal: + "blufor civilian configNull controlNull displayNull east endl false grpNull independent lineBreak locationNull nil objNull opfor pi resistance scriptNull sideAmbientLife sideEmpty sideLogic sideUnknown taskNull teamMemberNull true west", + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.NUMBER_MODE, + { className: "variable", begin: /\b_+[a-zA-Z]\w*/ }, + { className: "title", begin: /[a-zA-Z][a-zA-Z0-9]+_fnc_\w*/ }, + t, + n, + ], + illegal: /#|^\$ /, + }; +}; +var Bb = function (e) { + var t = e.COMMENT("--", "$"); + return { + name: "SQL (more)", + aliases: ["mysql", "oracle"], + disableAutodetect: !0, + case_insensitive: !0, + illegal: /[<>{}*]/, + contains: [ + { + beginKeywords: + "begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment values with", + end: /;/, + endsWithParent: !0, + keywords: { + $pattern: /[\w\.]+/, + keyword: + "as abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias all allocate allow alter always analyze ancillary and anti any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound bucket buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain explode export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force foreign form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour hours http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lateral lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minutes minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notnull notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second seconds section securefile security seed segment select self semi sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tablesample tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unnest unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace window with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek", + literal: "true false null unknown", + built_in: + "array bigint binary bit blob bool boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text time timestamp tinyint varchar varchar2 varying void", + }, + contains: [ + { + className: "string", + begin: "'", + end: "'", + contains: [{ begin: "''" }], + }, + { + className: "string", + begin: '"', + end: '"', + contains: [{ begin: '""' }], + }, + { className: "string", begin: "`", end: "`" }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + e.HASH_COMMENT_MODE, + ], + }, + e.C_BLOCK_COMMENT_MODE, + t, + e.HASH_COMMENT_MODE, + ], + }; +}; +function Gb(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function Yb() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return Gb(e); + }) + .join(""); + return a; +} +function Hb() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return Gb(e); + }) + .join("|") + + ")"; + return a; +} +var Vb = function (e) { + var t = e.COMMENT("--", "$"), + n = ["true", "false", "unknown"], + a = [ + "bigint", + "binary", + "blob", + "boolean", + "char", + "character", + "clob", + "date", + "dec", + "decfloat", + "decimal", + "float", + "int", + "integer", + "interval", + "nchar", + "nclob", + "national", + "numeric", + "real", + "row", + "smallint", + "time", + "timestamp", + "varchar", + "varying", + "varbinary", + ], + r = [ + "abs", + "acos", + "array_agg", + "asin", + "atan", + "avg", + "cast", + "ceil", + "ceiling", + "coalesce", + "corr", + "cos", + "cosh", + "count", + "covar_pop", + "covar_samp", + "cume_dist", + "dense_rank", + "deref", + "element", + "exp", + "extract", + "first_value", + "floor", + "json_array", + "json_arrayagg", + "json_exists", + "json_object", + "json_objectagg", + "json_query", + "json_table", + "json_table_primitive", + "json_value", + "lag", + "last_value", + "lead", + "listagg", + "ln", + "log", + "log10", + "lower", + "max", + "min", + "mod", + "nth_value", + "ntile", + "nullif", + "percent_rank", + "percentile_cont", + "percentile_disc", + "position", + "position_regex", + "power", + "rank", + "regr_avgx", + "regr_avgy", + "regr_count", + "regr_intercept", + "regr_r2", + "regr_slope", + "regr_sxx", + "regr_sxy", + "regr_syy", + "row_number", + "sin", + "sinh", + "sqrt", + "stddev_pop", + "stddev_samp", + "substring", + "substring_regex", + "sum", + "tan", + "tanh", + "translate", + "translate_regex", + "treat", + "trim", + "trim_array", + "unnest", + "upper", + "value_of", + "var_pop", + "var_samp", + "width_bucket", + ], + i = [ + "create table", + "insert into", + "primary key", + "foreign key", + "not null", + "alter table", + "add constraint", + "grouping sets", + "on overflow", + "character set", + "respect nulls", + "ignore nulls", + "nulls first", + "nulls last", + "depth first", + "breadth first", + ], + o = r, + s = [] + .concat( + [ + "abs", + "acos", + "all", + "allocate", + "alter", + "and", + "any", + "are", + "array", + "array_agg", + "array_max_cardinality", + "as", + "asensitive", + "asin", + "asymmetric", + "at", + "atan", + "atomic", + "authorization", + "avg", + "begin", + "begin_frame", + "begin_partition", + "between", + "bigint", + "binary", + "blob", + "boolean", + "both", + "by", + "call", + "called", + "cardinality", + "cascaded", + "case", + "cast", + "ceil", + "ceiling", + "char", + "char_length", + "character", + "character_length", + "check", + "classifier", + "clob", + "close", + "coalesce", + "collate", + "collect", + "column", + "commit", + "condition", + "connect", + "constraint", + "contains", + "convert", + "copy", + "corr", + "corresponding", + "cos", + "cosh", + "count", + "covar_pop", + "covar_samp", + "create", + "cross", + "cube", + "cume_dist", + "current", + "current_catalog", + "current_date", + "current_default_transform_group", + "current_path", + "current_role", + "current_row", + "current_schema", + "current_time", + "current_timestamp", + "current_path", + "current_role", + "current_transform_group_for_type", + "current_user", + "cursor", + "cycle", + "date", + "day", + "deallocate", + "dec", + "decimal", + "decfloat", + "declare", + "default", + "define", + "delete", + "dense_rank", + "deref", + "describe", + "deterministic", + "disconnect", + "distinct", + "double", + "drop", + "dynamic", + "each", + "element", + "else", + "empty", + "end", + "end_frame", + "end_partition", + "end-exec", + "equals", + "escape", + "every", + "except", + "exec", + "execute", + "exists", + "exp", + "external", + "extract", + "false", + "fetch", + "filter", + "first_value", + "float", + "floor", + "for", + "foreign", + "frame_row", + "free", + "from", + "full", + "function", + "fusion", + "get", + "global", + "grant", + "group", + "grouping", + "groups", + "having", + "hold", + "hour", + "identity", + "in", + "indicator", + "initial", + "inner", + "inout", + "insensitive", + "insert", + "int", + "integer", + "intersect", + "intersection", + "interval", + "into", + "is", + "join", + "json_array", + "json_arrayagg", + "json_exists", + "json_object", + "json_objectagg", + "json_query", + "json_table", + "json_table_primitive", + "json_value", + "lag", + "language", + "large", + "last_value", + "lateral", + "lead", + "leading", + "left", + "like", + "like_regex", + "listagg", + "ln", + "local", + "localtime", + "localtimestamp", + "log", + "log10", + "lower", + "match", + "match_number", + "match_recognize", + "matches", + "max", + "member", + "merge", + "method", + "min", + "minute", + "mod", + "modifies", + "module", + "month", + "multiset", + "national", + "natural", + "nchar", + "nclob", + "new", + "no", + "none", + "normalize", + "not", + "nth_value", + "ntile", + "null", + "nullif", + "numeric", + "octet_length", + "occurrences_regex", + "of", + "offset", + "old", + "omit", + "on", + "one", + "only", + "open", + "or", + "order", + "out", + "outer", + "over", + "overlaps", + "overlay", + "parameter", + "partition", + "pattern", + "per", + "percent", + "percent_rank", + "percentile_cont", + "percentile_disc", + "period", + "portion", + "position", + "position_regex", + "power", + "precedes", + "precision", + "prepare", + "primary", + "procedure", + "ptf", + "range", + "rank", + "reads", + "real", + "recursive", + "ref", + "references", + "referencing", + "regr_avgx", + "regr_avgy", + "regr_count", + "regr_intercept", + "regr_r2", + "regr_slope", + "regr_sxx", + "regr_sxy", + "regr_syy", + "release", + "result", + "return", + "returns", + "revoke", + "right", + "rollback", + "rollup", + "row", + "row_number", + "rows", + "running", + "savepoint", + "scope", + "scroll", + "search", + "second", + "seek", + "select", + "sensitive", + "session_user", + "set", + "show", + "similar", + "sin", + "sinh", + "skip", + "smallint", + "some", + "specific", + "specifictype", + "sql", + "sqlexception", + "sqlstate", + "sqlwarning", + "sqrt", + "start", + "static", + "stddev_pop", + "stddev_samp", + "submultiset", + "subset", + "substring", + "substring_regex", + "succeeds", + "sum", + "symmetric", + "system", + "system_time", + "system_user", + "table", + "tablesample", + "tan", + "tanh", + "then", + "time", + "timestamp", + "timezone_hour", + "timezone_minute", + "to", + "trailing", + "translate", + "translate_regex", + "translation", + "treat", + "trigger", + "trim", + "trim_array", + "true", + "truncate", + "uescape", + "union", + "unique", + "unknown", + "unnest", + "update ", + "upper", + "user", + "using", + "value", + "values", + "value_of", + "var_pop", + "var_samp", + "varbinary", + "varchar", + "varying", + "versioning", + "when", + "whenever", + "where", + "width_bucket", + "window", + "with", + "within", + "without", + "year", + ], + ["add", "asc", "collation", "desc", "final", "first", "last", "view"], + ) + .filter(function (e) { + return !r.includes(e); + }), + l = { + begin: Yb(/\b/, Hb.apply(void 0, o), /\s*\(/), + keywords: { built_in: o }, + }; + return { + name: "SQL", + case_insensitive: !0, + illegal: /[{}]|<\//, + keywords: { + $pattern: /\b[\w\.]+/, + keyword: (function (e) { + var t = + arguments.length > 1 && void 0 !== arguments[1] ? arguments[1] : {}, + n = t.exceptions, + a = t.when, + r = a; + return ( + (n = n || []), + e.map(function (e) { + return e.match(/\|\d+$/) || n.includes(e) + ? e + : r(e) + ? "".concat(e, "|0") + : e; + }) + ); + })(s, { + when: function (e) { + return e.length < 3; + }, + }), + literal: n, + type: a, + built_in: [ + "current_catalog", + "current_date", + "current_default_transform_group", + "current_path", + "current_role", + "current_schema", + "current_transform_group_for_type", + "current_user", + "session_user", + "system_time", + "system_user", + "current_time", + "localtime", + "current_timestamp", + "localtimestamp", + ], + }, + contains: [ + { + begin: Hb.apply(void 0, i), + keywords: { + $pattern: /[\w\.]+/, + keyword: s.concat(i), + literal: n, + type: a, + }, + }, + { + className: "type", + begin: Hb.apply(void 0, [ + "double precision", + "large object", + "with timezone", + "without timezone", + ]), + }, + l, + { className: "variable", begin: /@[a-z0-9]+/ }, + { + className: "string", + variants: [{ begin: /'/, end: /'/, contains: [{ begin: /''/ }] }], + }, + { begin: /"/, end: /"/, contains: [{ begin: /""/ }] }, + e.C_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t, + { + className: "operator", + begin: /[-+*/=%^~]|&&?|\|\|?|!=?|<(?:=>?|<|>)?|>[>=]?/, + relevance: 0, + }, + ], + }; +}; +var qb = function (e) { + return { + name: "Stan", + aliases: ["stanfuncs"], + keywords: { + $pattern: e.IDENT_RE, + title: [ + "functions", + "model", + "data", + "parameters", + "quantities", + "transformed", + "generated", + ], + keyword: [ + "for", + "in", + "if", + "else", + "while", + "break", + "continue", + "return", + ] + .concat([ + "int", + "real", + "vector", + "ordered", + "positive_ordered", + "simplex", + "unit_vector", + "row_vector", + "matrix", + "cholesky_factor_corr|10", + "cholesky_factor_cov|10", + "corr_matrix|10", + "cov_matrix|10", + "void", + ]) + .concat([ + "print", + "reject", + "increment_log_prob|10", + "integrate_ode|10", + "integrate_ode_rk45|10", + "integrate_ode_bdf|10", + "algebra_solver", + ]), + built_in: [ + "Phi", + "Phi_approx", + "abs", + "acos", + "acosh", + "algebra_solver", + "append_array", + "append_col", + "append_row", + "asin", + "asinh", + "atan", + "atan2", + "atanh", + "bernoulli_cdf", + "bernoulli_lccdf", + "bernoulli_lcdf", + "bernoulli_logit_lpmf", + "bernoulli_logit_rng", + "bernoulli_lpmf", + "bernoulli_rng", + "bessel_first_kind", + "bessel_second_kind", + "beta_binomial_cdf", + "beta_binomial_lccdf", + "beta_binomial_lcdf", + "beta_binomial_lpmf", + "beta_binomial_rng", + "beta_cdf", + "beta_lccdf", + "beta_lcdf", + "beta_lpdf", + "beta_rng", + "binary_log_loss", + "binomial_cdf", + "binomial_coefficient_log", + "binomial_lccdf", + "binomial_lcdf", + "binomial_logit_lpmf", + "binomial_lpmf", + "binomial_rng", + "block", + "categorical_logit_lpmf", + "categorical_logit_rng", + "categorical_lpmf", + "categorical_rng", + "cauchy_cdf", + "cauchy_lccdf", + "cauchy_lcdf", + "cauchy_lpdf", + "cauchy_rng", + "cbrt", + "ceil", + "chi_square_cdf", + "chi_square_lccdf", + "chi_square_lcdf", + "chi_square_lpdf", + "chi_square_rng", + "cholesky_decompose", + "choose", + "col", + "cols", + "columns_dot_product", + "columns_dot_self", + "cos", + "cosh", + "cov_exp_quad", + "crossprod", + "csr_extract_u", + "csr_extract_v", + "csr_extract_w", + "csr_matrix_times_vector", + "csr_to_dense_matrix", + "cumulative_sum", + "determinant", + "diag_matrix", + "diag_post_multiply", + "diag_pre_multiply", + "diagonal", + "digamma", + "dims", + "dirichlet_lpdf", + "dirichlet_rng", + "distance", + "dot_product", + "dot_self", + "double_exponential_cdf", + "double_exponential_lccdf", + "double_exponential_lcdf", + "double_exponential_lpdf", + "double_exponential_rng", + "e", + "eigenvalues_sym", + "eigenvectors_sym", + "erf", + "erfc", + "exp", + "exp2", + "exp_mod_normal_cdf", + "exp_mod_normal_lccdf", + "exp_mod_normal_lcdf", + "exp_mod_normal_lpdf", + "exp_mod_normal_rng", + "expm1", + "exponential_cdf", + "exponential_lccdf", + "exponential_lcdf", + "exponential_lpdf", + "exponential_rng", + "fabs", + "falling_factorial", + "fdim", + "floor", + "fma", + "fmax", + "fmin", + "fmod", + "frechet_cdf", + "frechet_lccdf", + "frechet_lcdf", + "frechet_lpdf", + "frechet_rng", + "gamma_cdf", + "gamma_lccdf", + "gamma_lcdf", + "gamma_lpdf", + "gamma_p", + "gamma_q", + "gamma_rng", + "gaussian_dlm_obs_lpdf", + "get_lp", + "gumbel_cdf", + "gumbel_lccdf", + "gumbel_lcdf", + "gumbel_lpdf", + "gumbel_rng", + "head", + "hypergeometric_lpmf", + "hypergeometric_rng", + "hypot", + "inc_beta", + "int_step", + "integrate_ode", + "integrate_ode_bdf", + "integrate_ode_rk45", + "inv", + "inv_Phi", + "inv_chi_square_cdf", + "inv_chi_square_lccdf", + "inv_chi_square_lcdf", + "inv_chi_square_lpdf", + "inv_chi_square_rng", + "inv_cloglog", + "inv_gamma_cdf", + "inv_gamma_lccdf", + "inv_gamma_lcdf", + "inv_gamma_lpdf", + "inv_gamma_rng", + "inv_logit", + "inv_sqrt", + "inv_square", + "inv_wishart_lpdf", + "inv_wishart_rng", + "inverse", + "inverse_spd", + "is_inf", + "is_nan", + "lbeta", + "lchoose", + "lgamma", + "lkj_corr_cholesky_lpdf", + "lkj_corr_cholesky_rng", + "lkj_corr_lpdf", + "lkj_corr_rng", + "lmgamma", + "lmultiply", + "log", + "log10", + "log1m", + "log1m_exp", + "log1m_inv_logit", + "log1p", + "log1p_exp", + "log2", + "log_determinant", + "log_diff_exp", + "log_falling_factorial", + "log_inv_logit", + "log_mix", + "log_rising_factorial", + "log_softmax", + "log_sum_exp", + "logistic_cdf", + "logistic_lccdf", + "logistic_lcdf", + "logistic_lpdf", + "logistic_rng", + "logit", + "lognormal_cdf", + "lognormal_lccdf", + "lognormal_lcdf", + "lognormal_lpdf", + "lognormal_rng", + "machine_precision", + "matrix_exp", + "max", + "mdivide_left_spd", + "mdivide_left_tri_low", + "mdivide_right_spd", + "mdivide_right_tri_low", + "mean", + "min", + "modified_bessel_first_kind", + "modified_bessel_second_kind", + "multi_gp_cholesky_lpdf", + "multi_gp_lpdf", + "multi_normal_cholesky_lpdf", + "multi_normal_cholesky_rng", + "multi_normal_lpdf", + "multi_normal_prec_lpdf", + "multi_normal_rng", + "multi_student_t_lpdf", + "multi_student_t_rng", + "multinomial_lpmf", + "multinomial_rng", + "multiply_log", + "multiply_lower_tri_self_transpose", + "neg_binomial_2_cdf", + "neg_binomial_2_lccdf", + "neg_binomial_2_lcdf", + "neg_binomial_2_log_lpmf", + "neg_binomial_2_log_rng", + "neg_binomial_2_lpmf", + "neg_binomial_2_rng", + "neg_binomial_cdf", + "neg_binomial_lccdf", + "neg_binomial_lcdf", + "neg_binomial_lpmf", + "neg_binomial_rng", + "negative_infinity", + "normal_cdf", + "normal_lccdf", + "normal_lcdf", + "normal_lpdf", + "normal_rng", + "not_a_number", + "num_elements", + "ordered_logistic_lpmf", + "ordered_logistic_rng", + "owens_t", + "pareto_cdf", + "pareto_lccdf", + "pareto_lcdf", + "pareto_lpdf", + "pareto_rng", + "pareto_type_2_cdf", + "pareto_type_2_lccdf", + "pareto_type_2_lcdf", + "pareto_type_2_lpdf", + "pareto_type_2_rng", + "pi", + "poisson_cdf", + "poisson_lccdf", + "poisson_lcdf", + "poisson_log_lpmf", + "poisson_log_rng", + "poisson_lpmf", + "poisson_rng", + "positive_infinity", + "pow", + "print", + "prod", + 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"tanh", + "target", + "tcrossprod", + "tgamma", + "to_array_1d", + "to_array_2d", + "to_matrix", + "to_row_vector", + "to_vector", + "trace", + "trace_gen_quad_form", + "trace_quad_form", + "trigamma", + "trunc", + "uniform_cdf", + "uniform_lccdf", + "uniform_lcdf", + "uniform_lpdf", + "uniform_rng", + "variance", + "von_mises_lpdf", + "von_mises_rng", + "weibull_cdf", + "weibull_lccdf", + "weibull_lcdf", + "weibull_lpdf", + "weibull_rng", + "wiener_lpdf", + "wishart_lpdf", + "wishart_rng", + ], + }, + contains: [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/#/, /$/, { + relevance: 0, + keywords: { "meta-keyword": "include" }, + }), + e.COMMENT(/\/\*/, /\*\//, { + relevance: 0, + contains: [{ className: "doctag", begin: /@(return|param)/ }], + }), + { begin: /<\s*lower\s*=/, keywords: "lower" }, + { begin: /[<,]\s*upper\s*=/, keywords: "upper" }, + { className: "keyword", begin: /\btarget\s*\+=/, relevance: 10 }, + { + begin: "~\\s*(" + e.IDENT_RE + ")\\s*\\(", + keywords: [ + "bernoulli", + 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ztir_5 ztjoin_5 ztnb ztnb_p ztp ztp_p zts_5 ztset_5 ztspli_5 ztsum_5 zttoct_5 ztvary_5 ztweib_5", + contains: [ + { className: "symbol", begin: /`[a-zA-Z0-9_]+'/ }, + { className: "variable", begin: /\$\{?[a-zA-Z0-9_]+\}?/ }, + { + className: "string", + variants: [{ begin: '`"[^\r\n]*?"\'' }, { begin: '"[^\r\n"]*"' }], + }, + { + className: "built_in", + variants: [ + { + begin: + 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+ }, + ], + }, + e.COMMENT("^[ \t]*\\*.*$", !1), + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + ], + }; +}; +var Wb = function (e) { + return { + name: "STEP Part 21", + aliases: ["p21", "step", "stp"], + case_insensitive: !0, + keywords: { + $pattern: "[A-Z_][A-Z0-9_.]*", + keyword: "HEADER ENDSEC DATA", + }, + contains: [ + { className: "meta", begin: "ISO-10303-21;", relevance: 10 }, + { className: "meta", begin: "END-ISO-10303-21;", relevance: 10 }, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + e.COMMENT("/\\*\\*!", "\\*/"), + e.C_NUMBER_MODE, + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + { className: "string", begin: "'", end: "'" }, + { + className: "symbol", + variants: [{ begin: "#", end: "\\d+", illegal: "\\W" }], + }, + ], + }; + }, + $b = [ + "a", + "abbr", + "address", + "article", + "aside", + "audio", + "b", + "blockquote", + "body", + "button", + "canvas", + "caption", + "cite", + "code", + "dd", + "del", + "details", + "dfn", + "div", + "dl", + "dt", + "em", + "fieldset", + "figcaption", + "figure", + "footer", + "form", + "h1", + "h2", + "h3", + "h4", + "h5", + "h6", + "header", + "hgroup", + "html", + "i", + "iframe", + "img", + "input", + "ins", + "kbd", + "label", + "legend", + "li", + "main", + "mark", + "menu", + "nav", + "object", + "ol", + "p", + "q", + "quote", + "samp", + "section", + "span", + "strong", + "summary", + "sup", + "table", + "tbody", + "td", + "textarea", + "tfoot", + "th", + "thead", + "time", + "tr", + "ul", + "var", + "video", + ], + Qb = [ + "any-hover", + "any-pointer", + "aspect-ratio", + "color", + "color-gamut", + "color-index", + "device-aspect-ratio", + "device-height", + "device-width", + "display-mode", + "forced-colors", + "grid", + "height", + "hover", + "inverted-colors", + "monochrome", + "orientation", + "overflow-block", + "overflow-inline", + "pointer", + "prefers-color-scheme", + "prefers-contrast", + "prefers-reduced-motion", + "prefers-reduced-transparency", + "resolution", + "scan", + "scripting", + "update", + "width", + "min-width", + "max-width", + "min-height", + "max-height", + ], + Kb = [ + "active", + "any-link", + "blank", + "checked", + "current", + "default", + "defined", + "dir", + "disabled", + "drop", + "empty", + "enabled", + "first", + "first-child", + "first-of-type", + "fullscreen", + "future", + "focus", + "focus-visible", + "focus-within", + "has", + "host", + "host-context", + "hover", + "indeterminate", + "in-range", + "invalid", + "is", + "lang", + "last-child", + "last-of-type", + "left", + "link", + "local-link", + "not", + "nth-child", + "nth-col", + "nth-last-child", + "nth-last-col", + "nth-last-of-type", + "nth-of-type", + "only-child", + "only-of-type", + "optional", + "out-of-range", + "past", + "placeholder-shown", + "read-only", + "read-write", + "required", + "right", + "root", + "scope", + "target", + "target-within", + "user-invalid", + "valid", + "visited", + "where", + ], + jb = [ + "after", + "backdrop", + "before", + "cue", + "cue-region", + "first-letter", + "first-line", + "grammar-error", + "marker", + "part", + "placeholder", + "selection", + "slotted", + "spelling-error", + ], + Xb = [ + "align-content", + "align-items", + "align-self", + "animation", + "animation-delay", + "animation-direction", + "animation-duration", + "animation-fill-mode", + "animation-iteration-count", + "animation-name", + "animation-play-state", + "animation-timing-function", + "auto", + "backface-visibility", + "background", + "background-attachment", + "background-clip", + "background-color", + "background-image", + "background-origin", + "background-position", + "background-repeat", + "background-size", + "border", + "border-bottom", + "border-bottom-color", + "border-bottom-left-radius", + "border-bottom-right-radius", + "border-bottom-style", + "border-bottom-width", + "border-collapse", + "border-color", + "border-image", + "border-image-outset", + "border-image-repeat", + "border-image-slice", + "border-image-source", + "border-image-width", + "border-left", + "border-left-color", + "border-left-style", + "border-left-width", + "border-radius", + "border-right", + "border-right-color", + "border-right-style", + "border-right-width", + "border-spacing", + "border-style", + "border-top", + "border-top-color", + "border-top-left-radius", + "border-top-right-radius", + "border-top-style", + "border-top-width", + "border-width", + "bottom", + "box-decoration-break", + "box-shadow", + "box-sizing", + "break-after", + "break-before", + "break-inside", + "caption-side", + "clear", + "clip", + "clip-path", + "color", + "column-count", + "column-fill", + "column-gap", + "column-rule", + "column-rule-color", + "column-rule-style", + "column-rule-width", + "column-span", + "column-width", + "columns", + "content", + "counter-increment", + "counter-reset", + "cursor", + "direction", + "display", + "empty-cells", + "filter", + "flex", + "flex-basis", + "flex-direction", + "flex-flow", + "flex-grow", + "flex-shrink", + "flex-wrap", + "float", + "font", + "font-display", + "font-family", + "font-feature-settings", + "font-kerning", + "font-language-override", + "font-size", + "font-size-adjust", + "font-smoothing", + "font-stretch", + "font-style", + "font-variant", + "font-variant-ligatures", + "font-variation-settings", + "font-weight", + "height", + "hyphens", + "icon", + "image-orientation", + "image-rendering", + "image-resolution", + "ime-mode", + "inherit", + "initial", + "justify-content", + "left", + "letter-spacing", + "line-height", + "list-style", + "list-style-image", + "list-style-position", + "list-style-type", + "margin", + "margin-bottom", + "margin-left", + "margin-right", + "margin-top", + "marks", + "mask", + "max-height", + "max-width", + "min-height", + "min-width", + "nav-down", + "nav-index", + "nav-left", + "nav-right", + "nav-up", + "none", + "normal", + "object-fit", + "object-position", + "opacity", + "order", + "orphans", + "outline", + "outline-color", + "outline-offset", + "outline-style", + "outline-width", + "overflow", + "overflow-wrap", + "overflow-x", + "overflow-y", + "padding", + "padding-bottom", + "padding-left", + "padding-right", + "padding-top", + "page-break-after", + "page-break-before", + "page-break-inside", + "perspective", + "perspective-origin", + "pointer-events", + "position", + "quotes", + "resize", + "right", + "src", + "tab-size", + "table-layout", + "text-align", + "text-align-last", + "text-decoration", + "text-decoration-color", + "text-decoration-line", + "text-decoration-style", + "text-indent", + "text-overflow", + "text-rendering", + "text-shadow", + "text-transform", + "text-underline-position", + "top", + "transform", + "transform-origin", + "transform-style", + "transition", + "transition-delay", + "transition-duration", + "transition-property", + "transition-timing-function", + "unicode-bidi", + "vertical-align", + "visibility", + "white-space", + "widows", + "width", + "word-break", + "word-spacing", + "word-wrap", + "z-index", + ].reverse(); +var Zb = function (e) { + var t = (function (e) { + return { + IMPORTANT: { className: "meta", begin: "!important" }, + HEXCOLOR: { + className: "number", + begin: "#([a-fA-F0-9]{6}|[a-fA-F0-9]{3})", + }, + ATTRIBUTE_SELECTOR_MODE: { + className: "selector-attr", + begin: /\[/, + end: /\]/, + illegal: "$", + contains: [e.APOS_STRING_MODE, e.QUOTE_STRING_MODE], + }, + }; + })(e), + n = { className: "variable", begin: "\\$" + e.IDENT_RE }, + a = "(?=[.\\s\\n[:,(])"; + return { + name: "Stylus", + aliases: ["styl"], + case_insensitive: !1, + keywords: "if else for in", + illegal: + "(" + + [ + "\\?", + "(\\bReturn\\b)", + "(\\bEnd\\b)", + "(\\bend\\b)", + "(\\bdef\\b)", + ";", + "#\\s", + "\\*\\s", + "===\\s", + "\\|", + "%", + ].join("|") + + ")", + contains: [ + e.QUOTE_STRING_MODE, + e.APOS_STRING_MODE, + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + t.HEXCOLOR, + { + begin: "\\.[a-zA-Z][a-zA-Z0-9_-]*(?=[.\\s\\n[:,(])", + className: "selector-class", + }, + { + begin: "#[a-zA-Z][a-zA-Z0-9_-]*(?=[.\\s\\n[:,(])", + className: "selector-id", + }, + { begin: "\\b(" + $b.join("|") + ")" + a, className: "selector-tag" }, + { className: "selector-pseudo", begin: "&?:(" + Kb.join("|") + ")" + a }, + { className: "selector-pseudo", begin: "&?::(" + jb.join("|") + ")" + a }, + t.ATTRIBUTE_SELECTOR_MODE, + { + className: "keyword", + begin: /@media/, + starts: { + end: /[{;}]/, + keywords: { + $pattern: /[a-z-]+/, + keyword: "and or not only", + attribute: Qb.join(" "), + }, + contains: [e.CSS_NUMBER_MODE], + }, + }, + { + className: "keyword", + begin: + "@((-(o|moz|ms|webkit)-)?(" + + [ + "charset", + "css", + "debug", + "extend", + "font-face", + "for", + "import", + "include", + "keyframes", + "media", + "mixin", + "page", + "warn", + "while", + ].join("|") + + "))\\b", + }, + n, + e.CSS_NUMBER_MODE, + { + className: "function", + begin: "^[a-zA-Z][a-zA-Z0-9_-]*\\(.*\\)", + illegal: "[\\n]", + returnBegin: !0, + contains: [ + { className: "title", begin: "\\b[a-zA-Z][a-zA-Z0-9_-]*" }, + { + className: "params", + begin: /\(/, + end: /\)/, + contains: [ + t.HEXCOLOR, + n, + e.APOS_STRING_MODE, + e.CSS_NUMBER_MODE, + e.QUOTE_STRING_MODE, + ], + }, + ], + }, + { + className: "attribute", + begin: "\\b(" + Xb.join("|") + ")\\b", + starts: { + end: /;|$/, + contains: [ + t.HEXCOLOR, + n, + e.APOS_STRING_MODE, + e.QUOTE_STRING_MODE, + e.CSS_NUMBER_MODE, + e.C_BLOCK_COMMENT_MODE, + t.IMPORTANT, + ], + illegal: /\./, + relevance: 0, + }, + }, + ], + }; +}; +var Jb = function (e) { + return { + name: "SubUnit", + case_insensitive: !0, + contains: [ + { className: "string", begin: "\\[\n(multipart)?", end: "\\]\n" }, + { + className: "string", + begin: "\\d{4}-\\d{2}-\\d{2}(\\s+)\\d{2}:\\d{2}:\\d{2}.\\d+Z", + }, + { className: "string", begin: "(\\+|-)\\d+" }, + { + className: "keyword", + relevance: 10, + variants: [ + { + begin: + "^(test|testing|success|successful|failure|error|skip|xfail|uxsuccess)(:?)\\s+(test)?", + }, + { begin: "^progress(:?)(\\s+)?(pop|push)?" }, + { begin: "^tags:" }, + { begin: "^time:" }, + ], + }, + ], + }; +}; +function eT(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function tT(e) { + return nT("(?=", e, ")"); +} +function nT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return eT(e); + }) + .join(""); + return a; +} +function aT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return eT(e); + }) + .join("|") + + ")"; + return a; +} +var rT = function (e) { + return nT(/\b/, e, /\w$/.test(e) ? /\b/ : /\B/); + }, + iT = ["Protocol", "Type"].map(rT), + oT = ["init", "self"].map(rT), + sT = ["Any", "Self"], + lT = [ + "associatedtype", + "async", + "await", + /as\?/, + /as!/, + "as", + "break", + "case", + "catch", + "class", + "continue", + "convenience", + "default", + "defer", + "deinit", + "didSet", + "do", + "dynamic", + "else", + "enum", + "extension", + "fallthrough", + /fileprivate\(set\)/, + "fileprivate", + "final", + "for", + "func", + "get", + "guard", + "if", + "import", + "indirect", + "infix", + /init\?/, + /init!/, + "inout", + /internal\(set\)/, + "internal", + "in", + "is", + "lazy", + "let", + "mutating", + "nonmutating", + /open\(set\)/, + "open", + "operator", + "optional", + "override", + "postfix", + "precedencegroup", + "prefix", + /private\(set\)/, + "private", + "protocol", + /public\(set\)/, + "public", + "repeat", + "required", + "rethrows", + "return", + "set", + "some", + "static", + "struct", + "subscript", + "super", + "switch", + "throws", + "throw", + /try\?/, + /try!/, + "try", + "typealias", + /unowned\(safe\)/, + /unowned\(unsafe\)/, + "unowned", + "var", + "weak", + "where", + "while", + "willSet", + ], + cT = ["false", "nil", "true"], + _T = [ + "assignment", + "associativity", + "higherThan", + "left", + "lowerThan", + "none", + "right", + ], + dT = [ + "#colorLiteral", + "#column", + "#dsohandle", + "#else", + "#elseif", + "#endif", + "#error", + "#file", + "#fileID", + "#fileLiteral", + "#filePath", + "#function", + "#if", + "#imageLiteral", + "#keyPath", + "#line", + "#selector", + "#sourceLocation", + "#warn_unqualified_access", + "#warning", + ], + uT = [ + "abs", + "all", + "any", + "assert", + "assertionFailure", + "debugPrint", + "dump", + "fatalError", + "getVaList", + "isKnownUniquelyReferenced", + "max", + "min", + "numericCast", + "pointwiseMax", + "pointwiseMin", + "precondition", + "preconditionFailure", + "print", + "readLine", + "repeatElement", + "sequence", + "stride", + "swap", + "swift_unboxFromSwiftValueWithType", + "transcode", + "type", + "unsafeBitCast", + "unsafeDowncast", + "withExtendedLifetime", + "withUnsafeMutablePointer", + "withUnsafePointer", + "withVaList", + "withoutActuallyEscaping", + "zip", + ], + mT = aT( + /[/=\-+!*%<>&|^~?]/, + /[\u00A1-\u00A7]/, + /[\u00A9\u00AB]/, + /[\u00AC\u00AE]/, + /[\u00B0\u00B1]/, + /[\u00B6\u00BB\u00BF\u00D7\u00F7]/, + /[\u2016-\u2017]/, + /[\u2020-\u2027]/, + /[\u2030-\u203E]/, + /[\u2041-\u2053]/, + /[\u2055-\u205E]/, + /[\u2190-\u23FF]/, + /[\u2500-\u2775]/, + /[\u2794-\u2BFF]/, + /[\u2E00-\u2E7F]/, + /[\u3001-\u3003]/, + /[\u3008-\u3020]/, + /[\u3030]/, + ), + pT = aT( + mT, + /[\u0300-\u036F]/, + /[\u1DC0-\u1DFF]/, + /[\u20D0-\u20FF]/, + /[\uFE00-\uFE0F]/, + /[\uFE20-\uFE2F]/, + ), + gT = nT(mT, pT, "*"), + ET = aT( + /[a-zA-Z_]/, + /[\u00A8\u00AA\u00AD\u00AF\u00B2-\u00B5\u00B7-\u00BA]/, + /[\u00BC-\u00BE\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u00FF]/, + /[\u0100-\u02FF\u0370-\u167F\u1681-\u180D\u180F-\u1DBF]/, + /[\u1E00-\u1FFF]/, + /[\u200B-\u200D\u202A-\u202E\u203F-\u2040\u2054\u2060-\u206F]/, + /[\u2070-\u20CF\u2100-\u218F\u2460-\u24FF\u2776-\u2793]/, + /[\u2C00-\u2DFF\u2E80-\u2FFF]/, + /[\u3004-\u3007\u3021-\u302F\u3031-\u303F\u3040-\uD7FF]/, + /[\uF900-\uFD3D\uFD40-\uFDCF\uFDF0-\uFE1F\uFE30-\uFE44]/, + /[\uFE47-\uFEFE\uFF00-\uFFFD]/, + ), + ST = aT(ET, /\d/, /[\u0300-\u036F\u1DC0-\u1DFF\u20D0-\u20FF\uFE20-\uFE2F]/), + bT = nT(ET, ST, "*"), + TT = nT(/[A-Z]/, ST, "*"), + fT = [ + "autoclosure", + nT(/convention\(/, aT("swift", "block", "c"), /\)/), + "discardableResult", + "dynamicCallable", + "dynamicMemberLookup", + "escaping", + "frozen", + "GKInspectable", + "IBAction", + "IBDesignable", + "IBInspectable", + "IBOutlet", + "IBSegueAction", + "inlinable", + "main", + "nonobjc", + "NSApplicationMain", + "NSCopying", + "NSManaged", + nT(/objc\(/, bT, /\)/), + "objc", + "objcMembers", + "propertyWrapper", + "requires_stored_property_inits", + "testable", + "UIApplicationMain", + "unknown", + "usableFromInline", + ], + CT = [ + "iOS", + "iOSApplicationExtension", + "macOS", + "macOSApplicationExtension", + "macCatalyst", + "macCatalystApplicationExtension", + "watchOS", + "watchOSApplicationExtension", + "tvOS", + "tvOSApplicationExtension", + "swift", + ]; +var NT = function (e) { + var t = { match: /\s+/, relevance: 0 }, + n = e.COMMENT("/\\*", "\\*/", { contains: ["self"] }), + a = [e.C_LINE_COMMENT_MODE, n], + r = { + className: "keyword", + begin: nT(/\./, tT(aT.apply(void 0, c(iT).concat(c(oT))))), + end: aT.apply(void 0, c(iT).concat(c(oT))), + excludeBegin: !0, + }, + i = { match: nT(/\./, aT.apply(void 0, lT)), relevance: 0 }, + o = lT + .filter(function (e) { + return "string" == typeof e; + }) + .concat(["_|0"]), + s = lT + .filter(function (e) { + return "string" != typeof e; + }) + .concat(sT) + .map(rT), + l = { + variants: [ + { className: "keyword", match: aT.apply(void 0, c(s).concat(c(oT))) }, + ], + }, + d = { $pattern: aT(/\b\w+/, /#\w+/), keyword: o.concat(dT), literal: cT }, + u = [r, i, l], + m = [ + { match: nT(/\./, aT.apply(void 0, uT)), relevance: 0 }, + { + className: "built_in", + match: nT(/\b/, aT.apply(void 0, uT), /(?=\()/), + }, + ], + p = { match: /->/, relevance: 0 }, + g = [ + p, + { + className: "operator", + relevance: 0, + variants: [{ match: gT }, { match: "\\.(\\.|".concat(pT, ")+") }], + }, + ], + E = "([0-9]_*)+", + S = "([0-9a-fA-F]_*)+", + b = { + className: "number", + relevance: 0, + variants: [ + { + match: + "\\b(".concat(E, ")(\\.(").concat(E, "))?") + + "([eE][+-]?(".concat(E, "))?\\b"), + }, + { + match: + "\\b0x(".concat(S, ")(\\.(").concat(S, "))?") + + "([pP][+-]?(".concat(E, "))?\\b"), + }, + { match: /\b0o([0-7]_*)+\b/ }, + { match: /\b0b([01]_*)+\b/ }, + ], + }, + T = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + className: "subst", + variants: [ + { match: nT(/\\/, e, /[0\\tnr"']/) }, + { match: nT(/\\/, e, /u\{[0-9a-fA-F]{1,8}\}/) }, + ], + }; + }, + f = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + className: "subst", + match: nT(/\\/, e, /[\t ]*(?:[\r\n]|\r\n)/), + }; + }, + C = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + className: "subst", + label: "interpol", + begin: nT(/\\/, e, /\(/), + end: /\)/, + }; + }, + N = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { + begin: nT(e, /"""/), + end: nT(/"""/, e), + contains: [T(e), f(e), C(e)], + }; + }, + R = function () { + var e = + arguments.length > 0 && void 0 !== arguments[0] ? arguments[0] : ""; + return { begin: nT(e, /"/), end: nT(/"/, e), contains: [T(e), C(e)] }; + }, + v = { + className: "string", + variants: [ + N(), + N("#"), + N("##"), + N("###"), + R(), + R("#"), + R("##"), + R("###"), + ], + }, + O = { match: nT(/`/, bT, /`/) }, + h = [ + O, + { className: "variable", match: /\$\d+/ }, + { className: "variable", match: "\\$".concat(ST, "+") }, + ], + y = [ + { + match: /(@|#)available/, + className: "keyword", + starts: { + contains: [ + { + begin: /\(/, + end: /\)/, + keywords: CT, + contains: [].concat(g, [b, v]), + }, + ], + }, + }, + { className: "keyword", match: nT(/@/, aT.apply(void 0, fT)) }, + { className: "meta", match: nT(/@/, bT) }, + ], + I = { + match: tT(/\b[A-Z]/), + relevance: 0, + contains: [ + { + className: "type", + match: nT( + /(AV|CA|CF|CG|CI|CL|CM|CN|CT|MK|MP|MTK|MTL|NS|SCN|SK|UI|WK|XC)/, + ST, + "+", + ), + }, + { className: "type", match: TT, relevance: 0 }, + { match: /[?!]+/, relevance: 0 }, + { match: /\.\.\./, relevance: 0 }, + { match: nT(/\s+&\s+/, tT(TT)), relevance: 0 }, + ], + }, + A = { + begin: //, + keywords: d, + contains: [].concat(a, u, y, [p, I]), + }; + I.contains.push(A); + var D, + M = { + begin: /\(/, + end: /\)/, + relevance: 0, + keywords: d, + contains: [ + "self", + { match: nT(bT, /\s*:/), keywords: "_|0", relevance: 0 }, + ].concat(a, u, m, g, [b, v], h, y, [I]), + }, + L = { + beginKeywords: "func", + contains: [ + { + className: "title", + match: aT(O.match, bT, gT), + endsParent: !0, + relevance: 0, + }, + t, + ], + }, + w = { begin: //, contains: [].concat(a, [I]) }, + x = { + begin: /\(/, + end: /\)/, + keywords: d, + contains: [ + { + begin: aT(tT(nT(bT, /\s*:/)), tT(nT(bT, /\s+/, bT, /\s*:/))), + end: /:/, + relevance: 0, + contains: [ + { className: "keyword", match: /\b_\b/ }, + { className: "params", match: bT }, + ], + }, + ].concat(a, u, g, [b, v], y, [I, M]), + endsParent: !0, + illegal: /["']/, + }, + P = { + className: "function", + match: tT(/\bfunc\b/), + contains: [L, w, x, t], + illegal: [/\[/, /%/], + }, + k = { + className: "function", + match: /\b(subscript|init[?!]?)\s*(?=[<(])/, + keywords: { keyword: "subscript init init? init!", $pattern: /\w+[?!]?/ }, + contains: [w, x, t], + illegal: /\[|%/, + }, + U = { + beginKeywords: "operator", + end: e.MATCH_NOTHING_RE, + contains: [ + { className: "title", match: gT, endsParent: !0, relevance: 0 }, + ], + }, + F = { + beginKeywords: "precedencegroup", + end: e.MATCH_NOTHING_RE, + contains: [ + { className: "title", match: TT, relevance: 0 }, + { + begin: /{/, + end: /}/, + relevance: 0, + endsParent: !0, + keywords: [].concat(_T, cT), + contains: [I], + }, + ], + }, + B = (function (e, t) { + var n = + ("undefined" != typeof Symbol && e[Symbol.iterator]) || e["@@iterator"]; + if (!n) { + if ( + Array.isArray(e) || + (n = _(e)) || + (t && e && "number" == typeof e.length) + ) { + n && (e = n); + var a = 0, + r = function () {}; + return { + s: r, + n: function () { + return a >= e.length ? { done: !0 } : { done: !1, value: e[a++] }; + }, + e: function (e) { + throw e; + }, + f: r, + }; + } + throw new TypeError( + "Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.", + ); + } + var i, + o = !0, + s = !1; + return { + s: function () { + n = n.call(e); + }, + n: function () { + var e = n.next(); + return (o = e.done), e; + }, + e: function (e) { + (s = !0), (i = e); + }, + f: function () { + try { + o || null == n.return || n.return(); + } finally { + if (s) throw i; + } + }, + }; + })(v.variants); + try { + for (B.s(); !(D = B.n()).done; ) { + var G = D.value.contains.find(function (e) { + return "interpol" === e.label; + }); + G.keywords = d; + var Y = [].concat(u, m, g, [b, v], h); + G.contains = [].concat(c(Y), [ + { begin: /\(/, end: /\)/, contains: ["self"].concat(c(Y)) }, + ]); + } + } catch (e) { + B.e(e); + } finally { + B.f(); + } + return { + name: "Swift", + keywords: d, + contains: [].concat( + a, + [ + P, + k, + { + className: "class", + beginKeywords: "struct protocol class extension enum", + end: "\\{", + excludeEnd: !0, + keywords: d, + contains: [ + e.inherit(e.TITLE_MODE, { + begin: /[A-Za-z$_][\u00C0-\u02B80-9A-Za-z$_]*/, + }), + ].concat(u), + }, + U, + F, + { + beginKeywords: "import", + end: /$/, + contains: [].concat(a), + relevance: 0, + }, + ], + u, + m, + g, + [b, v], + h, + y, + [I, M], + ), + }; +}; +var RT = function (e) { + return { + name: "Tagger Script", + contains: [ + { + className: "comment", + begin: /\$noop\(/, + end: /\)/, + contains: [ + { begin: /\(/, end: /\)/, contains: ["self", { begin: /\\./ }] }, + ], + relevance: 10, + }, + { + className: "keyword", + begin: /\$(?!noop)[a-zA-Z][_a-zA-Z0-9]*/, + end: /\(/, + excludeEnd: !0, + }, + { className: "variable", begin: /%[_a-zA-Z0-9:]*/, end: "%" }, + { className: "symbol", begin: /\\./ }, + ], + }; +}; +var vT = function (e) { + var t = "true false yes no null", + n = "[\\w#;/?:@&=+$,.~*'()[\\]]+", + a = { + className: "string", + relevance: 0, + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /\S+/ }, + ], + contains: [ + e.BACKSLASH_ESCAPE, + { + className: "template-variable", + variants: [ + { begin: /\{\{/, end: /\}\}/ }, + { begin: /%\{/, end: /\}/ }, + ], + }, + ], + }, + r = e.inherit(a, { + variants: [ + { begin: /'/, end: /'/ }, + { begin: /"/, end: /"/ }, + { begin: /[^\s,{}[\]]+/ }, + ], + }), + i = { + className: "number", + begin: + "\\b[0-9]{4}(-[0-9][0-9]){0,2}([Tt \\t][0-9][0-9]?(:[0-9][0-9]){2})?(\\.[0-9]*)?([ \\t])*(Z|[-+][0-9][0-9]?(:[0-9][0-9])?)?\\b", + }, + o = { + end: ",", + endsWithParent: !0, + excludeEnd: !0, + keywords: t, + relevance: 0, + }, + s = { begin: /\{/, end: /\}/, contains: [o], illegal: "\\n", relevance: 0 }, + l = { + begin: "\\[", + end: "\\]", + contains: [o], + illegal: "\\n", + relevance: 0, + }, + c = [ + { + className: "attr", + variants: [ + { begin: "\\w[\\w :\\/.-]*:(?=[ \t]|$)" }, + { begin: '"\\w[\\w :\\/.-]*":(?=[ \t]|$)' }, + { begin: "'\\w[\\w :\\/.-]*':(?=[ \t]|$)" }, + ], + }, + { className: "meta", begin: "^---\\s*$", relevance: 10 }, + { + className: "string", + begin: "[\\|>]([1-9]?[+-])?[ ]*\\n( +)[^ ][^\\n]*\\n(\\2[^\\n]+\\n?)*", + }, + { + begin: "<%[%=-]?", + end: "[%-]?%>", + subLanguage: "ruby", + excludeBegin: !0, + excludeEnd: !0, + relevance: 0, + }, + { className: "type", begin: "!\\w+!" + n }, + { className: "type", begin: "!<" + n + ">" }, + { className: "type", begin: "!" + n }, + { className: "type", begin: "!!" + n }, + { className: "meta", begin: "&" + e.UNDERSCORE_IDENT_RE + "$" }, + { className: "meta", begin: "\\*" + e.UNDERSCORE_IDENT_RE + "$" }, + { className: "bullet", begin: "-(?=[ ]|$)", relevance: 0 }, + e.HASH_COMMENT_MODE, + { beginKeywords: t, keywords: { literal: t } }, + i, + { className: "number", begin: e.C_NUMBER_RE + "\\b", relevance: 0 }, + s, + l, + a, + ], + _ = [].concat(c); + return ( + _.pop(), + _.push(r), + (o.contains = _), + { name: "YAML", case_insensitive: !0, aliases: ["yml"], contains: c } + ); +}; +var OT = function (e) { + return { + name: "Test Anything Protocol", + case_insensitive: !0, + contains: [ + e.HASH_COMMENT_MODE, + { + className: "meta", + variants: [ + { begin: "^TAP version (\\d+)$" }, + { begin: "^1\\.\\.(\\d+)$" }, + ], + }, + { begin: /---$/, end: "\\.\\.\\.$", subLanguage: "yaml", relevance: 0 }, + { className: "number", begin: " (\\d+) " }, + { + className: "symbol", + variants: [{ begin: "^ok" }, { begin: "^not ok" }], + }, + ], + }; +}; +function hT(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function yT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return hT(e); + }) + .join(""); + return a; +} +var IT = function (e) { + var t, + n = /[a-zA-Z_][a-zA-Z0-9_]*/, + a = { + className: "number", + variants: [e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE], + }; + return { + name: "Tcl", + aliases: ["tk"], + keywords: + "after append apply array auto_execok auto_import auto_load auto_mkindex auto_mkindex_old auto_qualify auto_reset bgerror binary break catch cd chan clock close concat continue dde dict encoding eof error eval exec exit expr fblocked fconfigure fcopy file fileevent filename flush for foreach format gets glob global history http if incr info interp join lappend|10 lassign|10 lindex|10 linsert|10 list llength|10 load lrange|10 lrepeat|10 lreplace|10 lreverse|10 lsearch|10 lset|10 lsort|10 mathfunc mathop memory msgcat namespace open package parray pid pkg::create pkg_mkIndex platform platform::shell proc puts pwd read refchan regexp registry regsub|10 rename return safe scan seek set socket source split string subst switch tcl_endOfWord tcl_findLibrary tcl_startOfNextWord tcl_startOfPreviousWord tcl_wordBreakAfter tcl_wordBreakBefore tcltest tclvars tell time tm trace unknown unload unset update uplevel upvar variable vwait while", + contains: [ + e.COMMENT(";[ \\t]*#", "$"), + e.COMMENT("^[ \\t]*#", "$"), + { + beginKeywords: "proc", + end: "[\\{]", + excludeEnd: !0, + contains: [ + { + className: "title", + begin: "[ \\t\\n\\r]+(::)?[a-zA-Z_]((::)?[a-zA-Z0-9_])*", + end: "[ \\t\\n\\r]", + endsWithParent: !0, + excludeEnd: !0, + }, + ], + }, + { + className: "variable", + variants: [ + { + begin: yT(/\$/, ((t = /::/), yT("(", t, ")?")), n, "(::", n, ")*"), + }, + { + begin: "\\$\\{(::)?[a-zA-Z_]((::)?[a-zA-Z0-9_])*", + end: "\\}", + contains: [a], + }, + ], + }, + { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [e.inherit(e.QUOTE_STRING_MODE, { illegal: null })], + }, + a, + ], + }; 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+function kT(e) { + return e ? ("string" == typeof e ? e : e.source) : null; +} +function UT(e) { + return FT("(?=", e, ")"); +} +function FT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return kT(e); + }) + .join(""); + return a; +} +var BT = function (e) { + var t = { + $pattern: LT, + keyword: wT.concat([ + "type", + "namespace", + "typedef", + "interface", + "public", + "private", + "protected", + "implements", + "declare", + "abstract", + "readonly", + ]), + literal: xT, + built_in: PT.concat([ + "any", + "void", + "number", + "boolean", + "string", + "object", + "never", + "enum", + ]), + }, + n = { className: "meta", begin: "@[A-Za-z$_][0-9A-Za-z$_]*" }, + a = function (e, t, n) { + var a = e.contains.findIndex(function (e) { + return e.label === t; + }); + if (-1 === a) throw new Error("can not find mode to replace"); + e.contains.splice(a, 1, n); + }, + r = (function (e) { + var t = LT, + n = "<>", + a = "", + r = { + begin: /<[A-Za-z0-9\\._:-]+/, + end: /\/[A-Za-z0-9\\._:-]+>|\/>/, + isTrulyOpeningTag: function (e, t) { + var n = e[0].length + e.index, + a = e.input[n]; + "<" !== a + ? 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("string" == typeof e ? e : e.source) : null; +} +function HT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = t + .map(function (e) { + return YT(e); + }) + .join(""); + return a; +} +function VT() { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var a = + "(" + + t + .map(function (e) { + return YT(e); + }) + .join("|") + + ")"; + return a; +} +var qT = function (e) { + var t = /\d{1,2}\/\d{1,2}\/\d{4}/, + n = /\d{4}-\d{1,2}-\d{1,2}/, + a = /(\d|1[012])(:\d+){0,2} *(AM|PM)/, + r = /\d{1,2}(:\d{1,2}){1,2}/, + i = { + className: "literal", + variants: [ + { begin: HT(/# */, VT(n, t), / *#/) }, + { begin: HT(/# */, r, / *#/) }, + { begin: HT(/# */, a, / *#/) }, + { begin: HT(/# */, VT(n, t), / +/, VT(a, r), / *#/) }, + ], + }, + o = e.COMMENT(/'''/, /$/, { + contains: [{ className: "doctag", begin: /<\/?/, end: />/ }], + }), + s = e.COMMENT(null, /$/, { + variants: [{ begin: /'/ }, { begin: /([\t ]|^)REM(?=\s)/ }], + }); + return { + name: "Visual Basic .NET", + aliases: ["vb"], + case_insensitive: !0, + classNameAliases: { label: "symbol" }, + keywords: { + keyword: + "addhandler alias aggregate ansi as async assembly auto binary by byref byval call case catch class compare const continue custom declare default delegate dim distinct do each equals else elseif end enum erase error event exit explicit finally for friend from function get global goto group handles if implements imports in inherits interface into iterator join key let lib loop me mid module mustinherit mustoverride mybase myclass namespace narrowing new next notinheritable notoverridable of off on operator option optional order overloads overridable overrides paramarray partial preserve private property protected public raiseevent readonly redim removehandler resume return select set shadows shared skip static step stop structure strict sub synclock take text then throw to try unicode until using when where while widening with withevents writeonly yield", + built_in: + "addressof and andalso await directcast gettype getxmlnamespace is isfalse isnot istrue like mod nameof new not or orelse trycast typeof xor cbool cbyte cchar cdate cdbl cdec cint clng cobj csbyte cshort csng cstr cuint culng cushort", + type: "boolean byte char date decimal double integer long object sbyte short single string uinteger ulong ushort", + literal: "true false nothing", + }, + illegal: "//|\\{|\\}|endif|gosub|variant|wend|^\\$ ", + contains: [ + { className: "string", begin: /"(""|[^/n])"C\b/ }, + { + className: "string", + begin: /"/, + end: /"/, + illegal: /\n/, + contains: [{ begin: /""/ }], + }, + i, + { + className: "number", + relevance: 0, + variants: [ + { + begin: + /\b\d[\d_]*((\.[\d_]+(E[+-]?[\d_]+)?)|(E[+-]?[\d_]+))[RFD@!#]?/, + }, + { begin: /\b\d[\d_]*((U?[SIL])|[%&])?/ }, + { begin: /&H[\dA-F_]+((U?[SIL])|[%&])?/ }, + { begin: /&O[0-7_]+((U?[SIL])|[%&])?/ }, + { begin: /&B[01_]+((U?[SIL])|[%&])?/ }, + ], + }, + { className: "label", begin: /^\w+:/ }, + o, + s, + { + className: "meta", + begin: + /[\t ]*#(const|disable|else|elseif|enable|end|externalsource|if|region)\b/, + end: /$/, + keywords: { + "meta-keyword": + "const disable else elseif enable end externalsource if region then", + }, + contains: [s], + }, + ], + }; +}; +function zT(e) { + return e ? 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$writeh $writeo $monitor $monitorb $monitorh $monitoro $writememb $dumpfile $dumpoff $dumpall $dumpflush $dumpportsoff $dumpportsall $dumpportsflush $fclose $fdisplay $fdisplayb $fdisplayh $fdisplayo $fstrobe $fstrobeb $fstrobeh $fstrobeo $swrite $swriteb $swriteh $swriteo $fscanf $fread $fseek $fflush $feof $fopen $fwrite $fwriteb $fwriteh $fwriteo $fmonitor $fmonitorb $fmonitorh $fmonitoro $sformat $sformatf $fgetc $ungetc $fgets $sscanf $rewind $ftell $ferror", + }, + contains: [ + e.C_BLOCK_COMMENT_MODE, + e.C_LINE_COMMENT_MODE, + e.QUOTE_STRING_MODE, + { + className: "number", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + { begin: "\\b((\\d+'(b|h|o|d|B|H|O|D))[0-9xzXZa-fA-F_]+)" }, + { begin: "\\B(('(b|h|o|d|B|H|O|D))[0-9xzXZa-fA-F_]+)" }, + { begin: "\\b([0-9_])+", relevance: 0 }, + ], + }, + { + className: "variable", + variants: [ + { begin: "#\\((?!parameter).+\\)" }, + { begin: "\\.\\w+", relevance: 0 }, + ], + }, + { + className: "meta", + begin: "`", + end: "$", + keywords: { + "meta-keyword": + "define __FILE__ __LINE__ begin_keywords celldefine default_nettype define else elsif end_keywords endcelldefine endif ifdef ifndef include line nounconnected_drive pragma resetall timescale unconnected_drive undef undefineall", + }, + relevance: 0, + }, + ], + }; +}; +var XT = function (e) { + return { + name: "VHDL", + case_insensitive: !0, + keywords: { + keyword: + "abs access after alias all and architecture array assert assume assume_guarantee attribute begin block body buffer bus case component configuration constant context cover disconnect downto default else elsif end entity exit fairness file for force function generate generic group guarded if impure in inertial inout is label library linkage literal loop map mod nand new next nor not null of on open or others out package parameter port postponed procedure process property protected pure range record register reject release rem report restrict restrict_guarantee return rol ror select sequence severity shared signal sla sll sra srl strong subtype then to transport type unaffected units until use variable view vmode vprop vunit wait when while with xnor xor", + built_in: + "boolean bit character integer time delay_length natural positive string bit_vector file_open_kind file_open_status std_logic std_logic_vector unsigned signed boolean_vector integer_vector std_ulogic std_ulogic_vector unresolved_unsigned u_unsigned unresolved_signed u_signed real_vector time_vector", + literal: "false true note warning error failure line text side width", + }, + illegal: /\{/, + contains: [ + e.C_BLOCK_COMMENT_MODE, + e.COMMENT("--", "$"), + e.QUOTE_STRING_MODE, + { + className: "number", + begin: + "\\b(\\d(_|\\d)*#\\w+(\\.\\w+)?#([eE][-+]?\\d(_|\\d)*)?|\\d(_|\\d)*(\\.\\d(_|\\d)*)?([eE][-+]?\\d(_|\\d)*)?)", + relevance: 0, + }, + { + className: "string", + begin: "'(U|X|0|1|Z|W|L|H|-)'", + contains: [e.BACKSLASH_ESCAPE], + }, + { + className: "symbol", + begin: "'[A-Za-z](_?[A-Za-z0-9])*", + contains: [e.BACKSLASH_ESCAPE], + }, + ], + }; +}; +var ZT = function (e) { + return { + name: "Vim Script", + keywords: { + $pattern: /[!#@\w]+/, + keyword: + "N|0 P|0 X|0 a|0 ab abc abo al am an|0 ar arga argd arge argdo argg argl argu as au aug aun b|0 bN ba bad bd be bel bf bl bm bn bo bp br brea breaka breakd breakl bro bufdo buffers bun bw c|0 cN cNf ca cabc caddb cad caddf cal cat cb cc ccl cd ce cex cf cfir cgetb cgete cg changes chd che checkt cl cla clo cm cmapc cme cn cnew cnf cno cnorea cnoreme co col colo com comc comp con conf cope cp cpf cq cr cs cst cu cuna cunme cw delm deb debugg delc delf dif diffg diffo diffp diffpu diffs diffthis dig di dl dell dj dli do doautoa dp dr ds dsp e|0 ea ec echoe echoh echom echon el elsei em en endfo endf endt endw ene ex exe exi exu f|0 files filet fin fina fini fir fix fo foldc foldd folddoc foldo for fu go gr grepa gu gv ha helpf helpg helpt hi hid his ia iabc if ij il im imapc ime ino inorea inoreme int is isp iu iuna iunme j|0 ju k|0 keepa kee keepj lN lNf l|0 lad laddb laddf la lan lat lb lc lch lcl lcs le lefta let lex lf lfir lgetb lgete lg lgr lgrepa lh ll lla lli lmak lm lmapc lne lnew lnf ln loadk lo loc lockv lol lope lp lpf lr ls lt lu lua luad luaf lv lvimgrepa lw m|0 ma mak map mapc marks mat me menut mes mk mks mksp mkv mkvie mod mz mzf nbc nb nbs new nm nmapc nme nn nnoreme noa no noh norea noreme norm nu nun nunme ol o|0 om omapc ome on ono onoreme opt ou ounme ow p|0 profd prof pro promptr pc ped pe perld po popu pp pre prev ps pt ptN ptf ptj ptl ptn ptp ptr pts pu pw py3 python3 py3d py3f py pyd pyf quita qa rec red redi redr redraws reg res ret retu rew ri rightb rub rubyd rubyf rund ru rv sN san sa sal sav sb sbN sba sbf sbl sbm sbn sbp sbr scrip scripte scs se setf setg setl sf sfir sh sim sig sil sl sla sm smap smapc sme sn sni sno snor snoreme sor so spelld spe spelli spellr spellu spellw sp spr sre st sta startg startr star stopi stj sts sun sunm sunme sus sv sw sy synti sync tN tabN tabc tabdo tabe tabf tabfir tabl tabm tabnew tabn tabo tabp tabr tabs tab ta tags tc tcld tclf te tf th tj tl tm tn to tp tr try ts tu u|0 undoj undol una unh unl unlo unm unme uns up ve verb vert vim vimgrepa vi viu vie vm vmapc vme vne vn vnoreme vs vu vunme windo w|0 wN wa wh wi winc winp wn wp wq wqa ws wu wv x|0 xa xmapc xm xme xn xnoreme xu xunme y|0 z|0 ~ Next Print append abbreviate abclear aboveleft all amenu anoremenu args argadd argdelete argedit argglobal arglocal argument ascii autocmd augroup aunmenu buffer bNext ball badd bdelete behave belowright bfirst blast bmodified bnext botright bprevious brewind break breakadd breakdel breaklist browse bunload bwipeout change cNext cNfile cabbrev cabclear caddbuffer caddexpr caddfile call catch cbuffer cclose center cexpr cfile cfirst cgetbuffer cgetexpr cgetfile chdir checkpath checktime clist clast close cmap cmapclear cmenu cnext cnewer cnfile cnoremap cnoreabbrev cnoremenu copy colder colorscheme command comclear compiler continue confirm copen cprevious cpfile cquit crewind cscope cstag cunmap cunabbrev cunmenu cwindow delete delmarks debug debuggreedy delcommand delfunction diffupdate diffget diffoff diffpatch diffput diffsplit digraphs display deletel djump dlist doautocmd doautoall deletep drop dsearch dsplit edit earlier echo echoerr echohl echomsg else elseif emenu endif endfor endfunction endtry endwhile enew execute exit exusage file filetype find finally finish first fixdel fold foldclose folddoopen folddoclosed foldopen function global goto grep grepadd gui gvim hardcopy help helpfind helpgrep helptags highlight hide history insert iabbrev iabclear ijump ilist imap imapclear imenu inoremap inoreabbrev inoremenu intro isearch isplit iunmap iunabbrev iunmenu join jumps keepalt keepmarks keepjumps lNext lNfile list laddexpr laddbuffer laddfile last language later lbuffer lcd lchdir lclose lcscope left leftabove lexpr lfile lfirst lgetbuffer lgetexpr lgetfile lgrep lgrepadd lhelpgrep llast llist lmake lmap lmapclear lnext lnewer lnfile lnoremap loadkeymap loadview lockmarks lockvar lolder lopen lprevious lpfile lrewind ltag lunmap luado luafile lvimgrep lvimgrepadd lwindow move mark make mapclear match menu menutranslate messages mkexrc mksession mkspell mkvimrc mkview mode mzscheme mzfile nbclose nbkey nbsart next nmap nmapclear nmenu nnoremap nnoremenu noautocmd noremap nohlsearch noreabbrev noremenu normal number nunmap nunmenu oldfiles open omap omapclear omenu only onoremap onoremenu options ounmap ounmenu ownsyntax print profdel profile promptfind promptrepl pclose pedit perl perldo pop popup ppop preserve previous psearch ptag ptNext ptfirst ptjump ptlast ptnext ptprevious ptrewind ptselect put pwd py3do py3file python pydo pyfile quit quitall qall read recover redo redir redraw redrawstatus registers resize retab return rewind right rightbelow ruby rubydo rubyfile rundo runtime rviminfo substitute sNext sandbox sargument sall saveas sbuffer sbNext sball sbfirst sblast sbmodified sbnext sbprevious sbrewind scriptnames scriptencoding scscope set setfiletype setglobal setlocal sfind sfirst shell simalt sign silent sleep slast smagic smapclear smenu snext sniff snomagic snoremap snoremenu sort source spelldump spellgood spellinfo spellrepall spellundo spellwrong split sprevious srewind stop stag startgreplace startreplace startinsert stopinsert stjump stselect sunhide sunmap sunmenu suspend sview swapname syntax syntime syncbind tNext tabNext tabclose tabedit tabfind tabfirst tablast tabmove tabnext tabonly tabprevious tabrewind tag tcl tcldo tclfile tearoff tfirst throw tjump tlast tmenu tnext topleft tprevious trewind tselect tunmenu undo undojoin undolist unabbreviate unhide unlet unlockvar unmap unmenu unsilent update vglobal version verbose vertical vimgrep vimgrepadd visual viusage view vmap vmapclear vmenu vnew vnoremap vnoremenu vsplit vunmap vunmenu write wNext wall while winsize wincmd winpos wnext wprevious wqall wsverb wundo wviminfo xit xall xmapclear xmap xmenu xnoremap xnoremenu xunmap xunmenu yank", + built_in: + "synIDtrans atan2 range matcharg did_filetype asin feedkeys xor argv complete_check add getwinposx getqflist getwinposy screencol clearmatches empty extend getcmdpos mzeval garbagecollect setreg ceil sqrt diff_hlID inputsecret get getfperm getpid filewritable shiftwidth max sinh isdirectory synID system inputrestore winline atan visualmode inputlist tabpagewinnr round getregtype mapcheck hasmapto histdel argidx findfile sha256 exists toupper getcmdline taglist string getmatches bufnr strftime winwidth bufexists strtrans tabpagebuflist setcmdpos remote_read printf setloclist getpos getline bufwinnr float2nr len getcmdtype diff_filler luaeval resolve libcallnr foldclosedend reverse filter has_key bufname str2float strlen setline getcharmod setbufvar index searchpos shellescape undofile foldclosed setqflist buflisted strchars str2nr virtcol floor remove undotree remote_expr winheight gettabwinvar reltime cursor tabpagenr finddir localtime acos getloclist search tanh matchend rename gettabvar strdisplaywidth type abs py3eval setwinvar tolower wildmenumode log10 spellsuggest bufloaded synconcealed nextnonblank server2client complete settabwinvar executable input wincol setmatches getftype hlID inputsave searchpair or screenrow line settabvar histadd deepcopy strpart remote_peek and eval getftime submatch screenchar winsaveview matchadd mkdir screenattr getfontname libcall reltimestr getfsize winnr invert pow getbufline byte2line soundfold repeat fnameescape tagfiles sin strwidth spellbadword trunc maparg log lispindent hostname setpos globpath remote_foreground getchar synIDattr fnamemodify cscope_connection stridx winbufnr indent min complete_add nr2char searchpairpos inputdialog values matchlist items hlexists strridx browsedir expand fmod pathshorten line2byte argc count getwinvar glob foldtextresult getreg foreground cosh matchdelete has char2nr simplify histget searchdecl iconv winrestcmd pumvisible writefile foldlevel haslocaldir keys cos matchstr foldtext histnr tan tempname getcwd byteidx getbufvar islocked escape eventhandler remote_send serverlist winrestview synstack pyeval prevnonblank readfile cindent filereadable changenr exp", + }, + illegal: /;/, + contains: [ + e.NUMBER_MODE, + { className: "string", begin: "'", end: "'", illegal: "\\n" }, + { className: "string", begin: /"(\\"|\n\\|[^"\n])*"/ }, + e.COMMENT('"', "$"), + { className: "variable", begin: /[bwtglsav]:[\w\d_]*/ }, + { + className: "function", + beginKeywords: "function function!", + end: "$", + relevance: 0, + contains: [ + e.TITLE_MODE, + { className: "params", begin: "\\(", end: "\\)" }, + ], + }, + { className: "symbol", begin: /<[\w-]+>/ }, + ], + }; +}; +var JT = function (e) { + return { + name: "Intel x86 Assembly", + case_insensitive: !0, + keywords: { + $pattern: "[.%]?" + e.IDENT_RE, + keyword: + "lock rep repe repz repne repnz xaquire xrelease bnd nobnd aaa aad aam aas adc add and arpl bb0_reset bb1_reset bound bsf bsr bswap bt btc btr bts call cbw cdq cdqe clc cld cli clts cmc cmp cmpsb cmpsd cmpsq cmpsw cmpxchg cmpxchg486 cmpxchg8b cmpxchg16b cpuid cpu_read cpu_write cqo cwd cwde daa das dec div dmint emms enter equ f2xm1 fabs fadd faddp fbld fbstp fchs fclex fcmovb fcmovbe fcmove fcmovnb fcmovnbe fcmovne fcmovnu fcmovu fcom fcomi fcomip fcomp fcompp fcos fdecstp fdisi fdiv fdivp fdivr fdivrp femms feni ffree ffreep fiadd ficom ficomp fidiv fidivr fild fimul fincstp finit fist fistp fisttp fisub fisubr fld fld1 fldcw fldenv fldl2e fldl2t fldlg2 fldln2 fldpi fldz fmul fmulp fnclex fndisi fneni fninit fnop fnsave fnstcw fnstenv fnstsw fpatan fprem fprem1 fptan frndint frstor fsave fscale fsetpm fsin fsincos fsqrt fst fstcw fstenv fstp fstsw fsub fsubp fsubr fsubrp ftst fucom fucomi fucomip fucomp fucompp fxam fxch fxtract fyl2x fyl2xp1 hlt ibts icebp idiv imul in inc incbin insb insd insw int int01 int1 int03 int3 into invd invpcid invlpg invlpga iret iretd iretq iretw jcxz jecxz jrcxz jmp jmpe lahf lar lds lea leave les lfence lfs lgdt lgs lidt lldt lmsw loadall loadall286 lodsb lodsd lodsq lodsw loop loope loopne loopnz loopz lsl lss ltr mfence monitor mov movd movq movsb movsd movsq movsw movsx movsxd movzx mul mwait neg nop not or out outsb outsd outsw packssdw packsswb packuswb paddb paddd paddsb paddsiw paddsw paddusb paddusw paddw pand pandn pause paveb pavgusb pcmpeqb pcmpeqd pcmpeqw pcmpgtb pcmpgtd pcmpgtw pdistib pf2id pfacc pfadd pfcmpeq pfcmpge pfcmpgt pfmax pfmin pfmul pfrcp pfrcpit1 pfrcpit2 pfrsqit1 pfrsqrt pfsub pfsubr pi2fd pmachriw pmaddwd pmagw pmulhriw pmulhrwa pmulhrwc pmulhw pmullw pmvgezb pmvlzb pmvnzb pmvzb pop popa popad popaw popf popfd popfq popfw por prefetch prefetchw pslld psllq psllw psrad psraw psrld psrlq psrlw psubb psubd psubsb psubsiw psubsw psubusb psubusw psubw punpckhbw punpckhdq punpckhwd punpcklbw punpckldq punpcklwd push pusha pushad pushaw pushf pushfd pushfq pushfw pxor rcl rcr rdshr rdmsr rdpmc rdtsc rdtscp ret retf retn rol ror rdm rsdc rsldt rsm rsts sahf sal salc sar sbb scasb scasd scasq scasw sfence sgdt shl shld shr shrd sidt sldt skinit smi smint smintold smsw stc std sti stosb stosd stosq stosw str sub svdc svldt svts swapgs syscall sysenter sysexit sysret test ud0 ud1 ud2b ud2 ud2a umov verr verw fwait wbinvd wrshr wrmsr xadd xbts xchg xlatb xlat xor cmove cmovz cmovne cmovnz cmova cmovnbe cmovae cmovnb cmovb cmovnae cmovbe cmovna cmovg cmovnle cmovge cmovnl cmovl cmovnge cmovle cmovng cmovc cmovnc cmovo cmovno cmovs cmovns cmovp cmovpe cmovnp cmovpo je jz jne jnz ja jnbe jae jnb jb jnae jbe jna jg jnle jge jnl jl jnge jle jng jc jnc jo jno js jns jpo jnp jpe jp sete setz setne setnz seta setnbe setae setnb setnc setb setnae setcset setbe setna setg setnle setge setnl setl setnge setle setng sets setns seto setno setpe setp setpo setnp addps addss andnps andps cmpeqps cmpeqss cmpleps cmpless cmpltps cmpltss cmpneqps cmpneqss cmpnleps cmpnless cmpnltps cmpnltss cmpordps cmpordss cmpunordps cmpunordss cmpps cmpss comiss cvtpi2ps cvtps2pi cvtsi2ss cvtss2si cvttps2pi cvttss2si divps divss ldmxcsr maxps maxss minps minss movaps movhps movlhps movlps movhlps movmskps movntps movss movups mulps mulss orps rcpps rcpss rsqrtps rsqrtss shufps sqrtps sqrtss stmxcsr subps subss ucomiss unpckhps unpcklps xorps fxrstor fxrstor64 fxsave fxsave64 xgetbv xsetbv xsave xsave64 xsaveopt xsaveopt64 xrstor xrstor64 prefetchnta prefetcht0 prefetcht1 prefetcht2 maskmovq movntq pavgb pavgw pextrw pinsrw pmaxsw pmaxub pminsw pminub pmovmskb pmulhuw psadbw pshufw pf2iw pfnacc pfpnacc pi2fw pswapd maskmovdqu clflush movntdq movnti movntpd movdqa movdqu movdq2q movq2dq paddq pmuludq pshufd pshufhw pshuflw pslldq psrldq psubq punpckhqdq punpcklqdq addpd addsd andnpd andpd cmpeqpd cmpeqsd cmplepd cmplesd cmpltpd cmpltsd cmpneqpd cmpneqsd cmpnlepd cmpnlesd cmpnltpd cmpnltsd cmpordpd cmpordsd cmpunordpd cmpunordsd cmppd comisd cvtdq2pd cvtdq2ps cvtpd2dq cvtpd2pi cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd haddps hsubpd hsubps lddqu movddup movshdup movsldup clgi stgi vmcall vmclear vmfunc vmlaunch vmload vmmcall vmptrld vmptrst vmread vmresume vmrun vmsave vmwrite vmxoff vmxon invept invvpid pabsb pabsw pabsd palignr phaddw phaddd phaddsw phsubw phsubd phsubsw pmaddubsw pmulhrsw pshufb psignb psignw psignd extrq insertq movntsd movntss lzcnt blendpd blendps blendvpd blendvps dppd dpps extractps insertps movntdqa mpsadbw packusdw pblendvb pblendw pcmpeqq pextrb pextrd pextrq phminposuw pinsrb pinsrd pinsrq pmaxsb pmaxsd pmaxud pmaxuw pminsb pminsd pminud pminuw pmovsxbw pmovsxbd pmovsxbq pmovsxwd pmovsxwq pmovsxdq pmovzxbw pmovzxbd pmovzxbq pmovzxwd pmovzxwq pmovzxdq pmuldq pmulld ptest roundpd roundps roundsd roundss crc32 pcmpestri pcmpestrm pcmpistri pcmpistrm pcmpgtq popcnt getsec pfrcpv pfrsqrtv movbe aesenc aesenclast aesdec aesdeclast aesimc aeskeygenassist vaesenc vaesenclast vaesdec vaesdeclast vaesimc vaeskeygenassist vaddpd vaddps vaddsd vaddss vaddsubpd vaddsubps vandpd vandps vandnpd vandnps vblendpd vblendps vblendvpd vblendvps vbroadcastss vbroadcastsd vbroadcastf128 vcmpeq_ospd vcmpeqpd vcmplt_ospd vcmpltpd vcmple_ospd vcmplepd vcmpunord_qpd vcmpunordpd vcmpneq_uqpd vcmpneqpd vcmpnlt_uspd vcmpnltpd vcmpnle_uspd vcmpnlepd vcmpord_qpd vcmpordpd vcmpeq_uqpd vcmpnge_uspd vcmpngepd vcmpngt_uspd vcmpngtpd vcmpfalse_oqpd vcmpfalsepd vcmpneq_oqpd vcmpge_ospd vcmpgepd vcmpgt_ospd vcmpgtpd vcmptrue_uqpd vcmptruepd vcmplt_oqpd vcmple_oqpd vcmpunord_spd vcmpneq_uspd vcmpnlt_uqpd vcmpnle_uqpd vcmpord_spd vcmpeq_uspd vcmpnge_uqpd vcmpngt_uqpd vcmpfalse_ospd vcmpneq_ospd vcmpge_oqpd vcmpgt_oqpd vcmptrue_uspd vcmppd vcmpeq_osps vcmpeqps vcmplt_osps vcmpltps vcmple_osps vcmpleps vcmpunord_qps vcmpunordps vcmpneq_uqps vcmpneqps vcmpnlt_usps vcmpnltps vcmpnle_usps vcmpnleps vcmpord_qps vcmpordps vcmpeq_uqps vcmpnge_usps vcmpngeps vcmpngt_usps vcmpngtps vcmpfalse_oqps vcmpfalseps vcmpneq_oqps vcmpge_osps vcmpgeps vcmpgt_osps vcmpgtps vcmptrue_uqps vcmptrueps vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oqsd vcmptrue_ussd vcmpsd vcmpeq_osss vcmpeqss vcmplt_osss vcmpltss vcmple_osss vcmpless vcmpunord_qss vcmpunordss vcmpneq_uqss vcmpneqss vcmpnlt_usss vcmpnltss vcmpnle_usss vcmpnless vcmpord_qss vcmpordss vcmpeq_uqss vcmpnge_usss vcmpngess vcmpngt_usss vcmpngtss vcmpfalse_oqss vcmpfalsess vcmpneq_oqss vcmpge_osss vcmpgess vcmpgt_osss vcmpgtss vcmptrue_uqss vcmptruess vcmplt_oqss vcmple_oqss vcmpunord_sss vcmpneq_usss vcmpnlt_uqss vcmpnle_uqss vcmpord_sss vcmpeq_usss vcmpnge_uqss vcmpngt_uqss vcmpfalse_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmovd vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmpeqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmulld vpmuludq vpmuldq vpor 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vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfnmadd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmaddsd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpmacswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq vpcmpleq vpcmpneqq vpcmpnltq vpcmpnleq vpcmpq vpcmpequd vpcmpltud vpcmpleud vpcmpnequd vpcmpnltud vpcmpnleud vpcmpud vpcmpequq vpcmpltuq vpcmpleuq vpcmpnequq vpcmpnltuq vpcmpnleuq vpcmpuq vpcompressd vpcompressq vpermi2d vpermi2pd vpermi2ps vpermi2q vpermt2d vpermt2pd vpermt2ps vpermt2q vpexpandd vpexpandq vpmaxsq vpmaxuq vpminsq vpminuq vpmovdb vpmovdw vpmovqb vpmovqd vpmovqw vpmovsdb vpmovsdw vpmovsqb vpmovsqd vpmovsqw vpmovusdb vpmovusdw vpmovusqb vpmovusqd vpmovusqw vpord vporq vprold vprolq vprolvd vprolvq vprord vprorq vprorvd vprorvq vpscatterdd vpscatterdq vpscatterqd vpscatterqq vpsraq vpsravq vpternlogd vpternlogq vptestmd vptestmq vptestnmd vptestnmq vpxord vpxorq vrcp14pd vrcp14ps vrcp14sd vrcp14ss vrndscalepd vrndscaleps vrndscalesd vrndscaless vrsqrt14pd vrsqrt14ps vrsqrt14sd vrsqrt14ss vscalefpd vscalefps vscalefsd vscalefss vscatterdpd vscatterdps vscatterqpd vscatterqps vshuff32x4 vshuff64x2 vshufi32x4 vshufi64x2 kandnw kandw kmovw knotw kortestw korw kshiftlw kshiftrw kunpckbw kxnorw kxorw vpbroadcastmb2q vpbroadcastmw2d vpconflictd vpconflictq vplzcntd vplzcntq vexp2pd vexp2ps vrcp28pd vrcp28ps vrcp28sd vrcp28ss vrsqrt28pd vrsqrt28ps vrsqrt28sd vrsqrt28ss vgatherpf0dpd vgatherpf0dps vgatherpf0qpd vgatherpf0qps vgatherpf1dpd vgatherpf1dps vgatherpf1qpd vgatherpf1qps vscatterpf0dpd vscatterpf0dps vscatterpf0qpd vscatterpf0qps vscatterpf1dpd vscatterpf1dps vscatterpf1qpd vscatterpf1qps prefetchwt1 bndmk bndcl bndcu bndcn bndmov bndldx bndstx sha1rnds4 sha1nexte sha1msg1 sha1msg2 sha256rnds2 sha256msg1 sha256msg2 hint_nop0 hint_nop1 hint_nop2 hint_nop3 hint_nop4 hint_nop5 hint_nop6 hint_nop7 hint_nop8 hint_nop9 hint_nop10 hint_nop11 hint_nop12 hint_nop13 hint_nop14 hint_nop15 hint_nop16 hint_nop17 hint_nop18 hint_nop19 hint_nop20 hint_nop21 hint_nop22 hint_nop23 hint_nop24 hint_nop25 hint_nop26 hint_nop27 hint_nop28 hint_nop29 hint_nop30 hint_nop31 hint_nop32 hint_nop33 hint_nop34 hint_nop35 hint_nop36 hint_nop37 hint_nop38 hint_nop39 hint_nop40 hint_nop41 hint_nop42 hint_nop43 hint_nop44 hint_nop45 hint_nop46 hint_nop47 hint_nop48 hint_nop49 hint_nop50 hint_nop51 hint_nop52 hint_nop53 hint_nop54 hint_nop55 hint_nop56 hint_nop57 hint_nop58 hint_nop59 hint_nop60 hint_nop61 hint_nop62 hint_nop63", + built_in: + "ip eip rip al ah bl bh cl ch dl dh sil dil bpl spl r8b r9b r10b r11b r12b r13b r14b r15b ax bx cx dx si di bp sp r8w r9w r10w r11w r12w r13w r14w r15w eax ebx ecx edx esi edi ebp esp eip r8d r9d r10d r11d r12d r13d r14d r15d rax rbx rcx rdx rsi rdi rbp rsp r8 r9 r10 r11 r12 r13 r14 r15 cs ds es fs gs ss st st0 st1 st2 st3 st4 st5 st6 st7 mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7 xmm0 xmm1 xmm2 xmm3 xmm4 xmm5 xmm6 xmm7 xmm8 xmm9 xmm10 xmm11 xmm12 xmm13 xmm14 xmm15 xmm16 xmm17 xmm18 xmm19 xmm20 xmm21 xmm22 xmm23 xmm24 xmm25 xmm26 xmm27 xmm28 xmm29 xmm30 xmm31 ymm0 ymm1 ymm2 ymm3 ymm4 ymm5 ymm6 ymm7 ymm8 ymm9 ymm10 ymm11 ymm12 ymm13 ymm14 ymm15 ymm16 ymm17 ymm18 ymm19 ymm20 ymm21 ymm22 ymm23 ymm24 ymm25 ymm26 ymm27 ymm28 ymm29 ymm30 ymm31 zmm0 zmm1 zmm2 zmm3 zmm4 zmm5 zmm6 zmm7 zmm8 zmm9 zmm10 zmm11 zmm12 zmm13 zmm14 zmm15 zmm16 zmm17 zmm18 zmm19 zmm20 zmm21 zmm22 zmm23 zmm24 zmm25 zmm26 zmm27 zmm28 zmm29 zmm30 zmm31 k0 k1 k2 k3 k4 k5 k6 k7 bnd0 bnd1 bnd2 bnd3 cr0 cr1 cr2 cr3 cr4 cr8 dr0 dr1 dr2 dr3 dr8 tr3 tr4 tr5 tr6 tr7 r0 r1 r2 r3 r4 r5 r6 r7 r0b r1b r2b r3b r4b r5b r6b r7b r0w r1w r2w r3w r4w r5w r6w r7w r0d r1d r2d r3d r4d r5d r6d r7d r0h r1h r2h r3h r0l r1l r2l r3l r4l r5l r6l r7l r8l r9l r10l r11l r12l r13l r14l r15l db dw dd dq dt ddq do dy dz resb resw resd resq rest resdq reso resy resz incbin equ times byte word dword qword nosplit rel abs seg wrt strict near far a32 ptr", + meta: "%define %xdefine %+ %undef %defstr %deftok %assign %strcat %strlen %substr %rotate %elif %else %endif %if %ifmacro %ifctx %ifidn %ifidni %ifid %ifnum %ifstr %iftoken %ifempty %ifenv %error %warning %fatal %rep %endrep %include %push %pop %repl %pathsearch %depend %use %arg %stacksize %local %line %comment %endcomment .nolist __FILE__ __LINE__ __SECT__ __BITS__ __OUTPUT_FORMAT__ __DATE__ __TIME__ __DATE_NUM__ __TIME_NUM__ __UTC_DATE__ __UTC_TIME__ __UTC_DATE_NUM__ __UTC_TIME_NUM__ __PASS__ struc endstruc istruc at iend align alignb sectalign daz nodaz up down zero default option assume public bits use16 use32 use64 default section segment absolute extern global common cpu float __utf16__ __utf16le__ __utf16be__ __utf32__ __utf32le__ __utf32be__ __float8__ __float16__ __float32__ __float64__ __float80m__ __float80e__ __float128l__ __float128h__ __Infinity__ __QNaN__ __SNaN__ Inf NaN QNaN SNaN float8 float16 float32 float64 float80m float80e float128l float128h __FLOAT_DAZ__ __FLOAT_ROUND__ __FLOAT__", + }, + contains: [ + e.COMMENT(";", "$", { relevance: 0 }), + { + className: "number", + variants: [ + { + begin: + "\\b(?:([0-9][0-9_]*)?\\.[0-9_]*(?:[eE][+-]?[0-9_]+)?|(0[Xx])?[0-9][0-9_]*(\\.[0-9_]*)?(?:[pP](?:[+-]?[0-9_]+)?)?)\\b", + relevance: 0, + }, + { begin: "\\$[0-9][0-9A-Fa-f]*", relevance: 0 }, + { + begin: + "\\b(?:[0-9A-Fa-f][0-9A-Fa-f_]*[Hh]|[0-9][0-9_]*[DdTt]?|[0-7][0-7_]*[QqOo]|[0-1][0-1_]*[BbYy])\\b", + }, + { + begin: + "\\b(?:0[Xx][0-9A-Fa-f_]+|0[DdTt][0-9_]+|0[QqOo][0-7_]+|0[BbYy][0-1_]+)\\b", + }, + ], + }, + e.QUOTE_STRING_MODE, + { + className: "string", + variants: [ + { begin: "'", end: "[^\\\\]'" }, + { begin: "`", end: "[^\\\\]`" }, + ], + relevance: 0, + }, + { + className: "symbol", + variants: [ + { begin: "^\\s*[A-Za-z._?][A-Za-z0-9_$#@~.?]*(:|\\s+label)" }, + { begin: "^\\s*%%[A-Za-z0-9_$#@~.?]*:" }, + ], + relevance: 0, + }, + { className: "subst", begin: "%[0-9]+", relevance: 0 }, + { className: "subst", begin: "%!S+", relevance: 0 }, + { className: "meta", begin: /^\s*\.[\w_-]+/ }, + ], + }; +}; +var ef = function (e) { + var t = { + $pattern: /[a-zA-Z][a-zA-Z0-9_?]*/, + keyword: + "if then else do while until for loop import with is as where when by data constant integer real text name boolean symbol infix prefix postfix block tree", + literal: "true false nil", + built_in: + "in mod rem and or xor not abs sign floor ceil sqrt sin cos tan asin acos atan exp expm1 log log2 log10 log1p pi at text_length text_range text_find text_replace contains page slide basic_slide title_slide title subtitle fade_in fade_out fade_at clear_color color line_color line_width texture_wrap texture_transform texture scale_?x scale_?y scale_?z? translate_?x translate_?y translate_?z? rotate_?x rotate_?y rotate_?z? rectangle circle ellipse sphere path line_to move_to quad_to curve_to theme background contents locally time mouse_?x mouse_?y mouse_buttons ObjectLoader Animate MovieCredits Slides Filters Shading Materials LensFlare Mapping VLCAudioVideo StereoDecoder PointCloud NetworkAccess RemoteControl RegExp ChromaKey Snowfall NodeJS Speech Charts", + }, + n = { className: "string", begin: '"', end: '"', illegal: "\\n" }, + a = { beginKeywords: "import", end: "$", keywords: t, contains: [n] }, + r = { + className: "function", + begin: /[a-z][^\n]*->/, + returnBegin: !0, + end: /->/, + contains: [ + e.inherit(e.TITLE_MODE, { + starts: { endsWithParent: !0, keywords: t }, + }), + ], + }; + return { + name: "XL", + aliases: ["tao"], + keywords: t, + contains: [ + e.C_LINE_COMMENT_MODE, + e.C_BLOCK_COMMENT_MODE, + n, + { className: "string", begin: "'", end: "'", illegal: "\\n" }, + { className: "string", begin: "<<", end: ">>" }, + r, + a, + { + className: "number", + begin: "[0-9]+#[0-9A-Z_]+(\\.[0-9-A-Z_]+)?#?([Ee][+-]?[0-9]+)?", + }, + e.NUMBER_MODE, + ], + }; +}; +var tf = function (e) { + return { + name: "XQuery", + aliases: ["xpath", "xq"], + case_insensitive: !1, + illegal: /(proc)|(abstract)|(extends)|(until)|(#)/, + keywords: { + $pattern: /[a-zA-Z$][a-zA-Z0-9_:-]*/, + keyword: + "module schema namespace boundary-space preserve no-preserve strip default collation base-uri ordering context decimal-format decimal-separator copy-namespaces empty-sequence except exponent-separator external grouping-separator inherit no-inherit lax minus-sign per-mille percent schema-attribute schema-element strict unordered zero-digit declare import option function validate variable for at in let where order group by return if then else tumbling sliding window start when only end previous next stable ascending descending allowing empty greatest least some every satisfies switch case typeswitch try catch and or to union intersect instance of treat as castable cast map array delete insert into replace value rename copy modify update", + type: "item document-node node attribute document element comment namespace namespace-node processing-instruction text construction xs:anyAtomicType xs:untypedAtomic xs:duration xs:time xs:decimal xs:float xs:double xs:gYearMonth xs:gYear xs:gMonthDay xs:gMonth xs:gDay xs:boolean xs:base64Binary xs:hexBinary xs:anyURI xs:QName xs:NOTATION xs:dateTime xs:dateTimeStamp xs:date xs:string xs:normalizedString xs:token xs:language xs:NMTOKEN xs:Name xs:NCName xs:ID xs:IDREF xs:ENTITY xs:integer xs:nonPositiveInteger xs:negativeInteger xs:long xs:int xs:short xs:byte xs:nonNegativeInteger xs:unisignedLong xs:unsignedInt xs:unsignedShort xs:unsignedByte xs:positiveInteger xs:yearMonthDuration xs:dayTimeDuration", + literal: + "eq ne lt le gt ge is self:: child:: descendant:: descendant-or-self:: attribute:: following:: following-sibling:: parent:: ancestor:: ancestor-or-self:: preceding:: preceding-sibling:: NaN", + }, + contains: [ + { className: "variable", begin: /[$][\w\-:]+/ }, + { + className: "built_in", + variants: [ + { + begin: /\barray:/, + end: /(?:append|filter|flatten|fold-(?:left|right)|for-each(?:-pair)?|get|head|insert-before|join|put|remove|reverse|size|sort|subarray|tail)\b/, + }, + { + begin: /\bmap:/, + end: /(?:contains|entry|find|for-each|get|keys|merge|put|remove|size)\b/, + }, + { + begin: /\bmath:/, + end: /(?:a(?:cos|sin|tan[2]?)|cos|exp(?:10)?|log(?:10)?|pi|pow|sin|sqrt|tan)\b/, + }, + { begin: /\bop:/, end: /\(/, excludeEnd: !0 }, + { begin: /\bfn:/, end: /\(/, excludeEnd: !0 }, + { + begin: + /[^/, + end: /(\/[\w._:-]+>)/, + subLanguage: "xml", + contains: [{ begin: /\{/, end: /\}/, subLanguage: "xquery" }, "self"], + }, + ], + }; +}; +var nf = function (e) { + var t = { + className: "string", + contains: [e.BACKSLASH_ESCAPE], + variants: [ + e.inherit(e.APOS_STRING_MODE, { illegal: null }), + e.inherit(e.QUOTE_STRING_MODE, { illegal: null }), + ], + }, + n = e.UNDERSCORE_TITLE_MODE, + a = { variants: [e.BINARY_NUMBER_MODE, e.C_NUMBER_MODE] }, + r = + "namespace class interface use extends function return abstract final public protected private static deprecated throw try catch Exception echo empty isset instanceof unset let var new const self require if else elseif switch case default do while loop for continue break likely unlikely __LINE__ __FILE__ __DIR__ __FUNCTION__ __CLASS__ __TRAIT__ __METHOD__ __NAMESPACE__ array boolean float double integer object resource string char long unsigned bool int uint ulong uchar true false null undefined"; + return { + name: "Zephir", + aliases: ["zep"], + keywords: r, + contains: [ + e.C_LINE_COMMENT_MODE, + e.COMMENT(/\/\*/, /\*\//, { + contains: [{ className: "doctag", begin: /@[A-Za-z]+/ }], + }), + { + className: "string", + begin: /<<<['"]?\w+['"]?$/, + end: /^\w+;/, + contains: [e.BACKSLASH_ESCAPE], + }, + { begin: /(::|->)+[a-zA-Z_\x7f-\xff][a-zA-Z0-9_\x7f-\xff]*/ }, + { + className: "function", + beginKeywords: "function fn", + end: /[;{]/, + excludeEnd: !0, + illegal: /\$|\[|%/, + contains: [ + n, + { + className: "params", + begin: /\(/, + end: /\)/, + keywords: r, + contains: ["self", e.C_BLOCK_COMMENT_MODE, t, a], + }, + ], + }, + { + className: "class", + beginKeywords: "class interface", + end: /\{/, + excludeEnd: !0, + illegal: /[:($"]/, + contains: [{ beginKeywords: "extends implements" }, n], + }, + { + beginKeywords: "namespace", + end: /;/, + illegal: /[.']/, + contains: [n], + }, + { beginKeywords: "use", end: /;/, contains: [n] }, + { begin: /=>/ }, + t, + a, + ], + }; + }, + af = Pm; +af.registerLanguage("1c", km), + af.registerLanguage("abnf", Bm), + af.registerLanguage("accesslog", Vm), + af.registerLanguage("actionscript", Wm), + af.registerLanguage("ada", $m), + af.registerLanguage("angelscript", Qm), + af.registerLanguage("apache", Km), + af.registerLanguage("applescript", Jm), + af.registerLanguage("arcade", ep), + af.registerLanguage("arduino", rp), + af.registerLanguage("armasm", ip), + af.registerLanguage("xml", _p), + af.registerLanguage("asciidoc", mp), + af.registerLanguage("aspectj", Ep), + af.registerLanguage("autohotkey", Sp), + af.registerLanguage("autoit", bp), + af.registerLanguage("avrasm", Tp), + af.registerLanguage("awk", fp), + af.registerLanguage("axapta", Cp), + af.registerLanguage("bash", vp), + af.registerLanguage("basic", Op), + af.registerLanguage("bnf", hp), + af.registerLanguage("brainfuck", yp), + af.registerLanguage("c-like", Mp), + af.registerLanguage("c", xp), + af.registerLanguage("cal", Pp), + af.registerLanguage("capnproto", kp), + af.registerLanguage("ceylon", Up), + af.registerLanguage("clean", Fp), + af.registerLanguage("clojure", Bp), + af.registerLanguage("clojure-repl", Gp), + af.registerLanguage("cmake", Yp), + af.registerLanguage("coffeescript", zp), + af.registerLanguage("coq", Wp), + af.registerLanguage("cos", $p), + af.registerLanguage("cpp", Xp), + af.registerLanguage("crmsh", Zp), + af.registerLanguage("crystal", Jp), + af.registerLanguage("csharp", eg), + af.registerLanguage("csp", tg), + af.registerLanguage("css", cg), + af.registerLanguage("d", _g), + af.registerLanguage("markdown", mg), + af.registerLanguage("dart", pg), + af.registerLanguage("delphi", gg), + af.registerLanguage("diff", Eg), + af.registerLanguage("django", Sg), + af.registerLanguage("dns", bg), + af.registerLanguage("dockerfile", Tg), + af.registerLanguage("dos", fg), + af.registerLanguage("dsconfig", Cg), + af.registerLanguage("dts", Ng), + af.registerLanguage("dust", Rg), + af.registerLanguage("ebnf", vg), + af.registerLanguage("elixir", Og), + af.registerLanguage("elm", hg), + af.registerLanguage("ruby", Ag), + af.registerLanguage("erb", Dg), + af.registerLanguage("erlang-repl", wg), + af.registerLanguage("erlang", xg), + af.registerLanguage("excel", Pg), + af.registerLanguage("fix", kg), + af.registerLanguage("flix", Ug), + af.registerLanguage("fortran", Gg), + af.registerLanguage("fsharp", Yg), + af.registerLanguage("gams", qg), + af.registerLanguage("gauss", zg), + af.registerLanguage("gcode", Wg), + af.registerLanguage("gherkin", $g), + af.registerLanguage("glsl", Qg), + af.registerLanguage("gml", Kg), + af.registerLanguage("go", jg), + af.registerLanguage("golo", Xg), + af.registerLanguage("gradle", Zg), + af.registerLanguage("groovy", nE), + af.registerLanguage("haml", aE), + af.registerLanguage("handlebars", oE), + af.registerLanguage("haskell", sE), + af.registerLanguage("haxe", lE), + af.registerLanguage("hsp", cE), + af.registerLanguage("htmlbars", mE), + af.registerLanguage("http", EE), + af.registerLanguage("hy", SE), + af.registerLanguage("inform7", bE), + af.registerLanguage("ini", CE), + af.registerLanguage("irpf90", vE), + af.registerLanguage("isbl", OE), + af.registerLanguage("java", AE), + af.registerLanguage("javascript", kE), + af.registerLanguage("jboss-cli", UE), + af.registerLanguage("json", FE), + af.registerLanguage("julia", BE), + af.registerLanguage("julia-repl", GE), + af.registerLanguage("kotlin", qE), + af.registerLanguage("lasso", zE), + af.registerLanguage("latex", QE), + af.registerLanguage("ldif", KE), + af.registerLanguage("leaf", jE), + af.registerLanguage("less", aS), + af.registerLanguage("lisp", rS), + af.registerLanguage("livecodeserver", iS), + af.registerLanguage("livescript", cS), + af.registerLanguage("llvm", uS), + af.registerLanguage("lsl", mS), + af.registerLanguage("lua", pS), + af.registerLanguage("makefile", gS), + af.registerLanguage("mathematica", CS), + af.registerLanguage("matlab", NS), + af.registerLanguage("maxima", RS), + af.registerLanguage("mel", vS), + af.registerLanguage("mercury", OS), + af.registerLanguage("mipsasm", hS), + af.registerLanguage("mizar", yS), + af.registerLanguage("perl", MS), + af.registerLanguage("mojolicious", LS), + af.registerLanguage("monkey", wS), + af.registerLanguage("moonscript", xS), + af.registerLanguage("n1ql", PS), + af.registerLanguage("nginx", kS), + af.registerLanguage("nim", US), + af.registerLanguage("nix", FS), + af.registerLanguage("node-repl", BS), + af.registerLanguage("nsis", GS), + af.registerLanguage("objectivec", YS), + af.registerLanguage("ocaml", HS), + af.registerLanguage("openscad", VS), + af.registerLanguage("oxygene", qS), + af.registerLanguage("parser3", zS), + af.registerLanguage("pf", WS), + af.registerLanguage("pgsql", $S), + af.registerLanguage("php", QS), + af.registerLanguage("php-template", KS), + af.registerLanguage("plaintext", jS), + af.registerLanguage("pony", XS), + af.registerLanguage("powershell", ZS), + af.registerLanguage("processing", JS), + af.registerLanguage("profile", eb), + af.registerLanguage("prolog", tb), + af.registerLanguage("properties", nb), + af.registerLanguage("protobuf", ab), + af.registerLanguage("puppet", rb), + af.registerLanguage("purebasic", ib), + af.registerLanguage("python", lb), + af.registerLanguage("python-repl", cb), + af.registerLanguage("q", _b), + af.registerLanguage("qml", mb), + af.registerLanguage("r", Eb), + af.registerLanguage("reasonml", Sb), + af.registerLanguage("rib", bb), + af.registerLanguage("roboconf", Tb), + af.registerLanguage("routeros", fb), + af.registerLanguage("rsl", Cb), + af.registerLanguage("ruleslanguage", Nb), + af.registerLanguage("rust", Rb), + af.registerLanguage("sas", vb), + af.registerLanguage("scala", Ob), + af.registerLanguage("scheme", hb), + af.registerLanguage("scilab", yb), + af.registerLanguage("scss", wb), + af.registerLanguage("shell", xb), + af.registerLanguage("smali", Pb), + af.registerLanguage("smalltalk", kb), + af.registerLanguage("sml", Ub), + af.registerLanguage("sqf", Fb), + af.registerLanguage("sql_more", Bb), + af.registerLanguage("sql", Vb), + af.registerLanguage("stan", qb), + af.registerLanguage("stata", zb), + af.registerLanguage("step21", Wb), + af.registerLanguage("stylus", Zb), + af.registerLanguage("subunit", Jb), + af.registerLanguage("swift", NT), + af.registerLanguage("taggerscript", RT), + af.registerLanguage("yaml", vT), + af.registerLanguage("tap", OT), + af.registerLanguage("tcl", IT), + af.registerLanguage("thrift", AT), + af.registerLanguage("tp", DT), + af.registerLanguage("twig", MT), + af.registerLanguage("typescript", BT), + af.registerLanguage("vala", GT), + af.registerLanguage("vbnet", qT), + af.registerLanguage("vbscript", QT), + af.registerLanguage("vbscript-html", KT), + af.registerLanguage("verilog", jT), + af.registerLanguage("vhdl", XT), + af.registerLanguage("vim", ZT), + af.registerLanguage("x86asm", JT), + af.registerLanguage("xl", ef), + af.registerLanguage("xquery", tf), + af.registerLanguage("zephir", nf); +var rf = af; +!(function (t, n) { + var a, + r = "hljs-ln", + i = "hljs-ln-code", + o = "hljs-ln-n", + s = "data-line-number", + l = /\r\n|\r|\n/g; + function c(e) { + try { + var a = n.querySelectorAll("code.hljs,code.nohighlight"); + for (var r in a) + a.hasOwnProperty(r) && + (a[r].classList.contains("nohljsln") || _(a[r], e)); + } catch (e) { + t.console.error("LineNumbers error: ", e); + } + } + function _(t, n) { + "object" == e(t) && (t.innerHTML = d(t, n)); + } + function d(e, t) { + var n, + a, + c = + ((n = e), + { + singleLine: (function (e) { + return !!e.singleLine && e.singleLine; + })((a = (a = t) || {})), + startFrom: (function (e, t) { + var n = 1; + isFinite(t.startFrom) && (n = t.startFrom); + var a = (function (e, t) { + return e.hasAttribute(t) ? e.getAttribute(t) : null; + })(e, "data-ln-start-from"); + return ( + null !== a && + (n = (function (e, t) { + if (!e) return 1; + var n = Number(e); + return isFinite(n) ? n : 1; + })(a)), + n + ); + })(n, a), + }); + return ( + (function e(t) { + var n = t.childNodes; + for (var a in n) { + var r; + n.hasOwnProperty(a) && + 0 < ((r = n[a]).textContent.trim().match(l) || []).length && + (0 < r.childNodes.length ? e(r) : u(r.parentNode)); + } + })(e), + (function (e, t) { + var n = m(e); + if ( + ("" === n[n.length - 1].trim() && n.pop(), + 1 < n.length || t.singleLine) + ) { + for (var a = "", l = 0, c = n.length; l < c; l++) + a += p( + '
    {6}', + [ + "hljs-ln-line", + "hljs-ln-numbers", + o, + s, + i, + l + t.startFrom, + 0 < n[l].length ? n[l] : " ", + ], + ); + return p('{1}
    ', [r, a]); + } + return e; + })(e.innerHTML, c) + ); + } + function u(e) { + var t = e.className; + if (/hljs-/.test(t)) { + for (var n = m(e.innerHTML), a = 0, r = ""; a < n.length; a++) + r += p('{1}\n', [ + t, + 0 < n[a].length ? n[a] : " ", + ]); + e.innerHTML = r.trim(); + } + } + function m(e) { + return 0 === e.length ? [] : e.split(l); + } + function p(e, t) { + return e.replace(/\{(\d+)\}/g, function (e, n) { + return void 0 !== t[n] ? t[n] : e; + }); + } + rf + ? ((rf.initLineNumbersOnLoad = function (e) { + "interactive" === n.readyState || "complete" === n.readyState + ? c(e) + : t.addEventListener("DOMContentLoaded", function () { + c(e); + }); + }), + (rf.lineNumbersBlock = _), + (rf.lineNumbersValue = function (e, t) { + if ("string" == typeof e) { + var n = document.createElement("code"); + return (n.innerHTML = e), d(n, t); + } + }), + ((a = n.createElement("style")).type = "text/css"), + (a.innerHTML = p( + ".{0}{border-collapse:collapse}.{0} td{padding:0}.{1}:before{content:attr({2})}", + [r, o, s], + )), + n.getElementsByTagName("head")[0].appendChild(a)) + : t.console.error("highlight.js not detected!"), + document.addEventListener("copy", function (e) { + var t, + n = window.getSelection(); + !(function (e) { + for (var t = e; t; ) { + if (t.className && -1 !== t.className.indexOf("hljs-ln-code")) + return 1; + t = t.parentNode; + } + })(n.anchorNode) || + ((t = + -1 !== window.navigator.userAgent.indexOf("Edge") + ? (function (e) { + for ( + var t = e.toString(), n = e.anchorNode; + "TD" !== n.nodeName; + + ) + n = n.parentNode; + for (var a = e.focusNode; "TD" !== a.nodeName; ) + a = a.parentNode; + var r = parseInt(n.dataset.lineNumber), + o = parseInt(a.dataset.lineNumber); + if (r == o) return t; + var l, + c = n.textContent, + _ = a.textContent; + for ( + o < r && + ((l = r), (r = o), (o = l), (l = c), (c = _), (_ = l)); + 0 !== t.indexOf(c); + + ) + c = c.slice(1); + for (; -1 === t.lastIndexOf(_); ) _ = _.slice(0, -1); + for ( + var d = c, + u = (function (e) { + for (var t = e; "TABLE" !== t.nodeName; ) + t = t.parentNode; + return t; + })(n), + m = r + 1; + m < o; + ++m + ) { + var g = p('.{0}[{1}="{2}"]', [i, s, m]); + d += "\n" + u.querySelector(g).textContent; + } + return d + "\n" + _; + })(n) + : n.toString()), + e.clipboardData.setData("text/plain", t), + e.preventDefault()); + }); +})(window, document); /*! * reveal.js plugin that adds syntax highlight support. */ -var of={id:"highlight",HIGHLIGHT_STEP_DELIMITER:"|",HIGHLIGHT_LINE_DELIMITER:",",HIGHLIGHT_LINE_RANGE_DELIMITER:"-",hljs:rf,init:function(e){var t=e.getConfig().highlight||{};t.highlightOnLoad="boolean"!=typeof t.highlightOnLoad||t.highlightOnLoad,t.escapeHTML="boolean"!=typeof t.escapeHTML||t.escapeHTML,Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach((function(e){e.parentNode.classList.add("code-wrapper");var n=e.querySelector('script[type="text/template"]');n&&(e.textContent=n.innerHTML),e.hasAttribute("data-trim")&&"function"==typeof e.innerHTML.trim&&(e.innerHTML=function(e){function t(e){return e.replace(/^[\s\uFEFF\xA0]+/g,"")}function n(e){for(var t=e.split("\n"),n=0;n=0&&""===t[n].trim();n--)t.splice(n,1);return t.join("\n")}return function(e){var a=n(e.innerHTML).split("\n"),r=a.reduce((function(e,n){return n.length>0&&t(n).length>0&&e>n.length-t(n).length?n.length-t(n).length:e}),Number.POSITIVE_INFINITY);return a.map((function(e,t){return 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HIGHLIGHT_LINE_DELIMITER: ",", + HIGHLIGHT_LINE_RANGE_DELIMITER: "-", + hljs: rf, + init: function (e) { + var t = e.getConfig().highlight || {}; + (t.highlightOnLoad = + "boolean" != typeof t.highlightOnLoad || t.highlightOnLoad), + (t.escapeHTML = "boolean" != typeof t.escapeHTML || t.escapeHTML), + Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach( + function (e) { + e.parentNode.classList.add("code-wrapper"); + var n = e.querySelector('script[type="text/template"]'); + n && (e.textContent = n.innerHTML), + e.hasAttribute("data-trim") && + "function" == typeof e.innerHTML.trim && + (e.innerHTML = (function (e) { + function t(e) { + return e.replace(/^[\s\uFEFF\xA0]+/g, ""); + } + function n(e) { + for ( + var t = e.split("\n"), n = 0; + n < t.length && "" === t[n].trim(); + n++ + ) + t.splice(n--, 1); + for (n = t.length - 1; n >= 0 && "" === t[n].trim(); n--) + t.splice(n, 1); + return t.join("\n"); + } + return (function (e) { + var a = n(e.innerHTML).split("\n"), + r = a.reduce(function (e, n) { + return n.length > 0 && + t(n).length > 0 && + e > n.length - t(n).length + ? n.length - t(n).length + : e; + }, Number.POSITIVE_INFINITY); + return a + .map(function (e, t) { + return e.slice(r); + }) + .join("\n"); + })(e); + })(e)), + t.escapeHTML && + !e.hasAttribute("data-noescape") && + (e.innerHTML = e.innerHTML + .replace(//g, ">")), + e.addEventListener( + "focusout", + function (e) { + rf.highlightElement(e.currentTarget); + }, + !1, + ); + }, + ), + "function" == typeof t.beforeHighlight && t.beforeHighlight(rf), + t.highlightOnLoad && + Array.from(e.getRevealElement().querySelectorAll("pre code")).forEach( + function (e) { + of.highlightBlock(e); + }, + ), + e.on("pdf-ready", function () { + [].slice + .call( + e + .getRevealElement() + .querySelectorAll("pre code[data-line-numbers].current-fragment"), + ) + .forEach(function (e) { + of.scrollHighlightedLineIntoView(e, {}, !0); + }); + }); + }, + highlightBlock: function (e) { + if ( + (rf.highlightElement(e), + 0 !== e.innerHTML.trim().length && e.hasAttribute("data-line-numbers")) + ) { + rf.lineNumbersBlock(e, { singleLine: !0 }); + var t = { currentBlock: e }, + n = of.deserializeHighlightSteps(e.getAttribute("data-line-numbers")); + if (n.length > 1) { + var a = parseInt(e.getAttribute("data-fragment-index"), 10); + ("number" != typeof a || isNaN(a)) && (a = null), + n.slice(1).forEach(function (n) { + var r = e.cloneNode(!0); + r.setAttribute( + "data-line-numbers", + of.serializeHighlightSteps([n]), + ), + r.classList.add("fragment"), + e.parentNode.appendChild(r), + of.highlightLines(r), + "number" == typeof a + ? (r.setAttribute("data-fragment-index", a), (a += 1)) + : r.removeAttribute("data-fragment-index"), + r.addEventListener( + "visible", + of.scrollHighlightedLineIntoView.bind(of, r, t), + ), + r.addEventListener( + "hidden", + of.scrollHighlightedLineIntoView.bind(of, r.previousSibling, t), + ); + }), + e.removeAttribute("data-fragment-index"), + e.setAttribute( + "data-line-numbers", + of.serializeHighlightSteps([n[0]]), + ); + } + var r = + "function" == typeof e.closest + ? e.closest("section:not(.stack)") + : null; + if (r) { + r.addEventListener("visible", function n() { + of.scrollHighlightedLineIntoView(e, t, !0), + r.removeEventListener("visible", n); + }); + } + of.highlightLines(e); + } + }, + scrollHighlightedLineIntoView: function (e, t, n) { + cancelAnimationFrame(t.animationFrameID), + t.currentBlock && (e.scrollTop = t.currentBlock.scrollTop), + (t.currentBlock = e); + var a = this.getHighlightedLineBounds(e), + r = e.offsetHeight, + i = getComputedStyle(e); + r -= parseInt(i.paddingTop) + parseInt(i.paddingBottom); + var o = e.scrollTop, + s = a.top + (Math.min(a.bottom - a.top, r) - r) / 2, + l = e.querySelector(".hljs-ln"); + if ( + (l && (s += l.offsetTop - parseInt(i.paddingTop)), + (s = Math.max(Math.min(s, e.scrollHeight - r), 0)), + !0 === n || o === s) + ) + e.scrollTop = s; + else { + if (e.scrollHeight <= r) return; + var c = 0; + !(function n() { + (c = Math.min(c + 0.02, 1)), + (e.scrollTop = o + (s - o) * of.easeInOutQuart(c)), + c < 1 && (t.animationFrameID = requestAnimationFrame(n)); + })(); + } + }, + easeInOutQuart: function (e) { + return e < 0.5 ? 8 * e * e * e * e : 1 - 8 * --e * e * e * e; + }, + getHighlightedLineBounds: function (e) { + var t = e.querySelectorAll(".highlight-line"); + if (0 === t.length) return { top: 0, bottom: 0 }; + var n = t[0], + a = t[t.length - 1]; + return { top: n.offsetTop, bottom: a.offsetTop + a.offsetHeight }; + }, + highlightLines: function (e, t) { + var n = of.deserializeHighlightSteps( + t || e.getAttribute("data-line-numbers"), + ); + n.length && + n[0].forEach(function (t) { + var n = []; + "number" == typeof t.end + ? (n = [].slice.call( + e.querySelectorAll( + "table tr:nth-child(n+" + + t.start + + "):nth-child(-n+" + + t.end + + ")", + ), + )) + : "number" == typeof t.start && + (n = [].slice.call( + e.querySelectorAll("table tr:nth-child(" + t.start + ")"), + )), + n.length && + (n.forEach(function (e) { + e.classList.add("highlight-line"); + }), + e.classList.add("has-highlights")); + }); + }, + deserializeHighlightSteps: function (e) { + return (e = (e = e.replace(/\s/g, "")).split( + of.HIGHLIGHT_STEP_DELIMITER, + )).map(function (e) { + return e.split(of.HIGHLIGHT_LINE_DELIMITER).map(function (e) { + if (/^[\d-]+$/.test(e)) { + e = e.split(of.HIGHLIGHT_LINE_RANGE_DELIMITER); + var t = parseInt(e[0], 10), + n = parseInt(e[1], 10); + return isNaN(n) ? { start: t } : { start: t, end: n }; + } + return {}; + }); + }); + }, + serializeHighlightSteps: function (e) { + return e + .map(function (e) { + return e + .map(function (e) { + return "number" == typeof e.end + ? e.start + 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(n = r = 0) + : 1 === f + ? ((n = 0), (r = l - c)) + : ((n = f - 2), (r = Mc(Lc(Rc(t), 0), l - c))), + l + n - r > 9007199254740991) + ) + throw TypeError("Maximum allowed length exceeded"); + for (u = zc(s, r), i = 0; i < r; i++) (o = c + i) in s && $c(u, i, s[o]); + if (((u.length = r), n < r)) { + for (i = c; i < l - r; i++) + (a = i + n), (o = i + r) in s ? (s[a] = s[o]) : delete s[a]; + for (i = l; i > l - r + n; i--) delete s[i - 1]; + } else if (n > r) + for (i = l - r; i > c; i--) + (a = i + n - 1), (o = i + r - 1) in s ? (s[a] = s[o]) : delete s[a]; + for (i = 0; i < n; i++) s[i + c] = arguments[i + 2]; + return (s.length = l - r + n), u; + }, + }, +); +var Nc = ki.map; +en( + { target: "Array", proto: !0, forced: !ws("map") }, + { + map: function (e) { + return Nc(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +); +var Uc = en, + qc = ei.start, + Zc = ri("trimStart"), + Gc = Zc + ? function () { + return qc(this); + } + : "".trimStart; +Uc( + { target: "String", proto: !0, forced: Zc }, + { trimStart: Gc, trimLeft: Gc }, +); +var Hc = + Object.is || + function (e, t) { + return e === t ? 0 !== e || 1 / e == 1 / t : e != e && t != t; + }, + Qc = ee, + Vc = C, + Yc = Hc, + Kc = fr; +Xn("search", 1, function (e, t, n) { + return [ + function (t) { + var n = Vc(this), + r = null == t ? void 0 : t[e]; + return void 0 !== r ? r.call(t, n) : new RegExp(t)[e](String(n)); + }, + function (e) { + var r = n(t, e, this); + if (r.done) return r.value; + var u = Qc(e), + i = String(this), + o = u.lastIndex; + Yc(o, 0) || (u.lastIndex = 0); + var a = Kc(u, i); + return Yc(u.lastIndex, o) || (u.lastIndex = o), null === a ? -1 : a.index; + }, + ]; +}); +var Xc = en, + Wc = ei.end, + Jc = ri("trimEnd"), + ef = Jc + ? function () { + return Wc(this); + } + : "".trimEnd; +Xc({ target: "String", proto: !0, forced: Jc }, { trimEnd: ef, trimRight: ef }); +var tf = ki.filter; +en( + { target: "Array", proto: !0, forced: !ws("filter") }, + { + filter: function (e) { + return tf(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +); +var nf = C, + rf = /"/g, + uf = D, + of = function (e, t, n, r) { + var u = String(nf(e)), + i = "<" + t; + return ( + "" !== n && (i += " " + n + '="' + String(r).replace(rf, """) + '"'), + i + ">" + u + "" + ); + }; +en( + { + target: "String", + proto: !0, + forced: (function (e) { + return uf(function () { + var t = ""[e]('"'); + return t !== t.toLowerCase() || t.split('"').length > 3; + }); + })("link"), + }, + { + link: function (e) { + return of(this, "a", "href", e); + }, + }, +); +var af = I, + sf = qs; +en( + { + target: "Object", + stat: !0, + forced: D(function () { + sf(1); + }), + }, + { + keys: function (e) { + return sf(af(e)); + }, + }, +); +var lf = kt.includes, + cf = pl; +en( + { target: "Array", proto: !0 }, + { + includes: function (e) { + return lf(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, +), + cf("includes"); +var ff = Tr, + pf = Pn("match"), + hf = function (e) { + if (ff(e)) throw TypeError("The method doesn't accept regular expressions"); + return e; + }, + Df = C; +function gf() { + return { + baseUrl: null, + breaks: !1, + extensions: null, + gfm: !0, + headerIds: !0, + headerPrefix: "", + highlight: null, + langPrefix: "language-", + mangle: !0, + pedantic: !1, + renderer: null, + sanitize: !1, + sanitizer: null, + silent: !1, + smartLists: !1, + smartypants: !1, + tokenizer: null, + walkTokens: null, + xhtml: !1, + }; +} +en( + { + target: "String", + proto: !0, + forced: !(function (e) { + var t = /./; + try { + "/./"[e](t); + } catch (n) { + try { + return (t[pf] = !1), "/./"[e](t); + } catch (e) {} + } + return !1; + })("includes"), + }, + { + includes: function (e) { + return !!~String(Df(this)).indexOf( + hf(e), + arguments.length > 1 ? arguments[1] : void 0, + ); + }, + }, +); +var df = { + baseUrl: null, + breaks: !1, + extensions: null, + gfm: !0, + headerIds: !0, + headerPrefix: "", + highlight: null, + langPrefix: "language-", + mangle: !0, + pedantic: !1, + renderer: null, + sanitize: !1, + sanitizer: null, + silent: !1, + smartLists: !1, + smartypants: !1, + tokenizer: null, + walkTokens: null, + xhtml: !1, +}; +var vf = /[&<>"']/, + yf = /[&<>"']/g, + Af = /[<>"']|&(?!#?\w+;)/, + mf = /[<>"']|&(?!#?\w+;)/g, + kf = { "&": "&", "<": "<", ">": ">", '"': """, "'": "'" }, + Ef = function (e) { + return kf[e]; + }; +function xf(e, t) { + if (t) { + if (vf.test(e)) return e.replace(yf, Ef); + } else if (Af.test(e)) return e.replace(mf, Ef); + return e; +} +var Ff = /&(#(?:\d+)|(?:#x[0-9A-Fa-f]+)|(?:\w+));?/gi; +function bf(e) { + return e.replace(Ff, function (e, t) { + return "colon" === (t = t.toLowerCase()) + ? ":" + : "#" === t.charAt(0) + ? "x" === t.charAt(1) + ? String.fromCharCode(parseInt(t.substring(2), 16)) + : String.fromCharCode(+t.substring(1)) + : ""; + }); +} +var Cf = /(^|[^\[])\^/g; +function wf(e, t) { + (e = e.source || e), (t = t || ""); + var n = { + replace: function (t, r) { + return ( + (r = (r = r.source || r).replace(Cf, "$1")), (e = e.replace(t, r)), n + ); + }, + getRegex: function () { + return new RegExp(e, t); + }, + }; + return n; +} +var Bf = /[^\w:]/g, + Sf = /^$|^[a-z][a-z0-9+.-]*:|^[?#]/i; +function _f(e, t, n) { + if (e) { + var r; + try { + r = decodeURIComponent(bf(n)).replace(Bf, "").toLowerCase(); + } catch (e) { + return null; + } + if ( + 0 === r.indexOf("javascript:") || + 0 === r.indexOf("vbscript:") || + 0 === r.indexOf("data:") + ) + return null; + } + t && + !Sf.test(n) && + (n = (function (e, t) { + Tf[" " + e] || + (Of.test(e) ? (Tf[" " + e] = e + "/") : (Tf[" " + e] = Pf(e, "/", !0))); + var n = -1 === (e = Tf[" " + e]).indexOf(":"); + return "//" === t.substring(0, 2) + ? n + ? t + : e.replace(Rf, "$1") + t + : "/" === t.charAt(0) + ? n + ? t + : e.replace(If, "$1") + t + : e + t; + })(t, n)); + try { + n = encodeURI(n).replace(/%25/g, "%"); + } catch (e) { + return null; + } + return n; +} +var Tf = {}, + Of = /^[^:]+:\/*[^/]*$/, + Rf = /^([^:]+:)[\s\S]*$/, + If = /^([^:]+:\/*[^/]*)[\s\S]*$/; +var jf = { exec: function () {} }; +function zf(e) { + for (var t, n, r = 1; r < arguments.length; r++) + for (n in (t = arguments[r])) + Object.prototype.hasOwnProperty.call(t, n) && (e[n] = t[n]); + return e; +} +function $f(e, t) { + var n = e + .replace(/\|/g, function (e, t, n) { + for (var r = !1, u = t; --u >= 0 && "\\" === n[u]; ) r = !r; + return r ? "|" : " |"; + }) + .split(/ \|/), + r = 0; + if ( + (n[0].trim() || n.shift(), + n.length > 0 && !n[n.length - 1].trim() && n.pop(), + n.length > t) + ) + n.splice(t); + else for (; n.length < t; ) n.push(""); + for (; r < n.length; r++) n[r] = n[r].trim().replace(/\\\|/g, "|"); + return n; +} +function Pf(e, t, n) { + var r = e.length; + if (0 === r) return ""; + for (var u = 0; u < r; ) { + var i = e.charAt(r - u - 1); + if (i !== t || n) { + if (i === t || !n) break; + u++; + } else u++; + } + return e.substr(0, r - u); +} +function Lf(e) { + e && + e.sanitize && + !e.silent && + console.warn( + "marked(): sanitize and sanitizer parameters are deprecated since version 0.7.0, should not be used and will be removed in the future. Read more here: https://marked.js.org/#/USING_ADVANCED.md#options", + ); +} +function Mf(e, t) { + if (t < 1) return ""; + for (var n = ""; t > 1; ) 1 & t && (n += e), (t >>= 1), (e += e); + return n + e; +} +function Nf(e, t, n, r) { + var u = t.href, + i = t.title ? xf(t.title) : null, + o = e[1].replace(/\\([\[\]])/g, "$1"); + if ("!" !== e[0].charAt(0)) { + r.state.inLink = !0; + var a = { + type: "link", + raw: n, + href: u, + title: i, + text: o, + tokens: r.inlineTokens(o, []), + }; + return (r.state.inLink = !1), a; + } + return { type: "image", raw: n, href: u, title: i, text: xf(o) }; +} +var Uf = (function () { + function e(n) { + t(this, e), (this.options = n || df); + } + return ( + r(e, [ + { + key: "space", + value: function (e) { + var t = this.rules.block.newline.exec(e); + if (t && t[0].length > 0) return { type: "space", raw: t[0] }; + }, + }, + { + key: "code", + value: function (e) { + var t = this.rules.block.code.exec(e); + if (t) { + var n = t[0].replace(/^ {1,4}/gm, ""); + return { + type: "code", + raw: t[0], + codeBlockStyle: "indented", + text: this.options.pedantic ? n : Pf(n, "\n"), + }; + } + }, + }, + { + key: "fences", + value: function (e) { + var t = this.rules.block.fences.exec(e); + if (t) { + var n = t[0], + r = (function (e, t) { + var n = e.match(/^(\s+)(?:```)/); + if (null === n) return t; + var r = n[1]; + return t + .split("\n") + .map(function (e) { + var t = e.match(/^\s+/); + return null === t + ? e + : o(t, 1)[0].length >= r.length + ? e.slice(r.length) + : e; + }) + .join("\n"); + })(n, t[3] || ""); + return { + type: "code", + raw: n, + lang: t[2] ? t[2].trim() : t[2], + text: r, + }; + } + }, + }, + { + key: "heading", + value: function (e) { + var t = this.rules.block.heading.exec(e); + if (t) { + var n = t[2].trim(); + if (/#$/.test(n)) { + var r = Pf(n, "#"); + this.options.pedantic + ? (n = r.trim()) + : (r && !/ $/.test(r)) || (n = r.trim()); + } + var u = { + type: "heading", + raw: t[0], + depth: t[1].length, + text: n, + tokens: [], + }; + return this.lexer.inline(u.text, u.tokens), u; + } + }, + }, + { + key: "hr", + value: function (e) { + var t = this.rules.block.hr.exec(e); + if (t) return { type: "hr", raw: t[0] }; + }, + }, + { + key: "blockquote", + value: function (e) { + var t = this.rules.block.blockquote.exec(e); + if (t) { + var n = t[0].replace(/^ *> ?/gm, ""); + return { + type: "blockquote", + raw: t[0], + tokens: this.lexer.blockTokens(n, []), + text: n, + }; + } + }, + }, + { + key: "list", + value: function (e) { + var t = this.rules.block.list.exec(e); + if (t) { + var n, + r, + u, + i, + o, + a, + s, + c, + f, + p, + h, + D, + g = t[1].trim(), + d = g.length > 1, + v = { + type: "list", + raw: "", + ordered: d, + start: d ? +g.slice(0, -1) : "", + loose: !1, + items: [], + }; + (g = d ? "\\d{1,9}\\".concat(g.slice(-1)) : "\\".concat(g)), + this.options.pedantic && (g = d ? g : "[*+-]"); + for ( + var y = new RegExp( + "^( {0,3}".concat(g, ")((?: [^\\n]*)?(?:\\n|$))"), + ); + e && + ((D = !1), (t = y.exec(e))) && + !this.rules.block.hr.test(e); + + ) { + if ( + ((n = t[0]), + (e = e.substring(n.length)), + (c = t[2].split("\n", 1)[0]), + (f = e.split("\n", 1)[0]), + this.options.pedantic + ? ((i = 2), (h = c.trimLeft())) + : ((i = (i = t[2].search(/[^ ]/)) > 4 ? 1 : i), + (h = c.slice(i)), + (i += t[1].length)), + (a = !1), + !c && + /^ *$/.test(f) && + ((n += f + "\n"), + (e = e.substring(f.length + 1)), + (D = !0)), + !D) + ) + for ( + var A = new RegExp( + "^ {0,".concat( + Math.min(3, i - 1), + "}(?:[*+-]|\\d{1,9}[.)])", + ), + ); + e && + ((c = p = e.split("\n", 1)[0]), + this.options.pedantic && + (c = c.replace(/^ {1,4}(?=( {4})*[^ ])/g, " ")), + !A.test(c)); + + ) { + if (c.search(/[^ ]/) >= i || !c.trim()) + h += "\n" + c.slice(i); + else { + if (a) break; + h += "\n" + c; + } + a || c.trim() || (a = !0), + (n += p + "\n"), + (e = e.substring(p.length + 1)); + } + v.loose || + (s ? (v.loose = !0) : /\n *\n *$/.test(n) && (s = !0)), + this.options.gfm && + (r = /^\[[ xX]\] /.exec(h)) && + ((u = "[ ] " !== r[0]), + (h = h.replace(/^\[[ xX]\] +/, ""))), + v.items.push({ + type: "list_item", + raw: n, + task: !!r, + checked: u, + loose: !1, + text: h, + }), + (v.raw += n); + } + (v.items[v.items.length - 1].raw = n.trimRight()), + (v.items[v.items.length - 1].text = h.trimRight()), + (v.raw = v.raw.trimRight()); + var m = v.items.length; + for (o = 0; o < m; o++) { + (this.lexer.state.top = !1), + (v.items[o].tokens = this.lexer.blockTokens( + v.items[o].text, + [], + )); + var k = v.items[o].tokens.filter(function (e) { + return "space" === e.type; + }), + E = k.every(function (e) { + var t, + n = 0, + r = l(e.raw.split("")); + try { + for (r.s(); !(t = r.n()).done; ) { + if (("\n" === t.value && (n += 1), n > 1)) return !0; + } + } catch (e) { + r.e(e); + } finally { + r.f(); + } + return !1; + }); + !v.loose && + k.length && + E && + ((v.loose = !0), (v.items[o].loose = !0)); + } + return v; + } + }, + }, + { + key: "html", + value: function (e) { + var t = this.rules.block.html.exec(e); + if (t) { + var n = { + type: "html", + raw: t[0], + pre: + !this.options.sanitizer && + ("pre" === t[1] || "script" === t[1] || "style" === t[1]), + text: t[0], + }; + return ( + this.options.sanitize && + ((n.type = "paragraph"), + (n.text = this.options.sanitizer + ? this.options.sanitizer(t[0]) + : xf(t[0])), + (n.tokens = []), + this.lexer.inline(n.text, n.tokens)), + n + ); + } + }, + }, + { + key: "def", + value: function (e) { + var t = this.rules.block.def.exec(e); + if (t) + return ( + t[3] && (t[3] = t[3].substring(1, t[3].length - 1)), + { + type: "def", + tag: t[1].toLowerCase().replace(/\s+/g, " "), + raw: t[0], + href: t[2], + title: t[3], + } + ); + }, + }, + { + key: "table", + value: function (e) { + var t = this.rules.block.table.exec(e); + if (t) { + var n = { + type: "table", + header: $f(t[1]).map(function (e) { + return { text: e }; + }), + align: t[2].replace(/^ *|\| *$/g, "").split(/ *\| */), + rows: + t[3] && t[3].trim() + ? t[3].replace(/\n[ \t]*$/, "").split("\n") + : [], + }; + if (n.header.length === n.align.length) { + n.raw = t[0]; + var r, + u, + i, + o, + a = n.align.length; + for (r = 0; r < a; r++) + /^ *-+: *$/.test(n.align[r]) + ? (n.align[r] = "right") + : /^ *:-+: *$/.test(n.align[r]) + ? (n.align[r] = "center") + : /^ *:-+ *$/.test(n.align[r]) + ? (n.align[r] = "left") + : (n.align[r] = null); + for (a = n.rows.length, r = 0; r < a; r++) + n.rows[r] = $f(n.rows[r], n.header.length).map(function (e) { + return { text: e }; + }); + for (a = n.header.length, u = 0; u < a; u++) + (n.header[u].tokens = []), + this.lexer.inlineTokens( + n.header[u].text, + n.header[u].tokens, + ); + for (a = n.rows.length, u = 0; u < a; u++) + for (o = n.rows[u], i = 0; i < o.length; i++) + (o[i].tokens = []), + this.lexer.inlineTokens(o[i].text, o[i].tokens); + return n; + } + } + }, + }, + { + key: "lheading", + value: function (e) { + var t = this.rules.block.lheading.exec(e); + if (t) { + var n = { + type: "heading", + raw: t[0], + depth: "=" === t[2].charAt(0) ? 1 : 2, + text: t[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "paragraph", + value: function (e) { + var t = this.rules.block.paragraph.exec(e); + if (t) { + var n = { + type: "paragraph", + raw: t[0], + text: + "\n" === t[1].charAt(t[1].length - 1) + ? t[1].slice(0, -1) + : t[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "text", + value: function (e) { + var t = this.rules.block.text.exec(e); + if (t) { + var n = { type: "text", raw: t[0], text: t[0], tokens: [] }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "escape", + value: function (e) { + var t = this.rules.inline.escape.exec(e); + if (t) return { type: "escape", raw: t[0], text: xf(t[1]) }; + }, + }, + { + key: "tag", + value: function (e) { + var t = this.rules.inline.tag.exec(e); + if (t) + return ( + !this.lexer.state.inLink && /^    /i.test(t[0]) && + (this.lexer.state.inLink = !1), + !this.lexer.state.inRawBlock && + /^<(pre|code|kbd|script)(\s|>)/i.test(t[0]) + ? (this.lexer.state.inRawBlock = !0) + : this.lexer.state.inRawBlock && + /^<\/(pre|code|kbd|script)(\s|>)/i.test(t[0]) && + (this.lexer.state.inRawBlock = !1), + { + type: this.options.sanitize ? "text" : "html", + raw: t[0], + inLink: this.lexer.state.inLink, + inRawBlock: this.lexer.state.inRawBlock, + text: this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(t[0]) + : xf(t[0]) + : t[0], + } + ); + }, + }, + { + key: "link", + value: function (e) { + var t = this.rules.inline.link.exec(e); + if (t) { + var n = t[2].trim(); + if (!this.options.pedantic && /^$/.test(n)) return; + var r = Pf(n.slice(0, -1), "\\"); + if ((n.length - r.length) % 2 == 0) return; + } else { + var u = (function (e, t) { + if (-1 === e.indexOf(t[1])) return -1; + for (var n = e.length, r = 0, u = 0; u < n; u++) + if ("\\" === e[u]) u++; + else if (e[u] === t[0]) r++; + else if (e[u] === t[1] && --r < 0) return u; + return -1; + })(t[2], "()"); + if (u > -1) { + var i = (0 === t[0].indexOf("!") ? 5 : 4) + t[1].length + u; + (t[2] = t[2].substring(0, u)), + (t[0] = t[0].substring(0, i).trim()), + (t[3] = ""); + } + } + var o = t[2], + a = ""; + if (this.options.pedantic) { + var s = /^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(o); + s && ((o = s[1]), (a = s[3])); + } else a = t[3] ? t[3].slice(1, -1) : ""; + return ( + (o = o.trim()), + /^$/.test(n) + ? o.slice(1) + : o.slice(1, -1)), + Nf( + t, + { + href: o ? o.replace(this.rules.inline._escapes, "$1") : o, + title: a ? a.replace(this.rules.inline._escapes, "$1") : a, + }, + t[0], + this.lexer, + ) + ); + } + }, + }, + { + key: "reflink", + value: function (e, t) { + var n; + if ( + (n = this.rules.inline.reflink.exec(e)) || + (n = this.rules.inline.nolink.exec(e)) + ) { + var r = (n[2] || n[1]).replace(/\s+/g, " "); + if (!(r = t[r.toLowerCase()]) || !r.href) { + var u = n[0].charAt(0); + return { type: "text", raw: u, text: u }; + } + return Nf(n, r, n[0], this.lexer); + } + }, + }, + { + key: "emStrong", + value: function (e, t) { + var n = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "", + r = this.rules.inline.emStrong.lDelim.exec(e); + if ( + r && + (!r[3] || + !n.match( + 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+ )) + ) { + var u = r[1] || r[2] || ""; + if ( + !u || + (u && ("" === n || this.rules.inline.punctuation.exec(n))) + ) { + var i, + o, + a = r[0].length - 1, + s = a, + l = 0, + c = + "*" === r[0][0] + ? this.rules.inline.emStrong.rDelimAst + : this.rules.inline.emStrong.rDelimUnd; + for ( + c.lastIndex = 0, t = t.slice(-1 * e.length + a); + null != (r = c.exec(t)); + + ) + if ((i = r[1] || r[2] || r[3] || r[4] || r[5] || r[6])) + if (((o = i.length), r[3] || r[4])) s += o; + else if (!((r[5] || r[6]) && a % 3) || (a + o) % 3) { + if (!((s -= o) > 0)) { + if ( + ((o = Math.min(o, o + s + l)), Math.min(a, o) % 2) + ) { + var f = e.slice(1, a + r.index + o); + return { + type: "em", + raw: e.slice(0, a + r.index + o + 1), + text: f, + tokens: this.lexer.inlineTokens(f, []), + }; + } + var p = e.slice(2, a + r.index + o - 1); + return { + type: "strong", + raw: e.slice(0, a + r.index + o + 1), + text: p, + tokens: this.lexer.inlineTokens(p, []), + }; + } + } else l += o; + } + } + }, + }, + { + key: "codespan", + value: function (e) { + var t = this.rules.inline.code.exec(e); + if (t) { + var n = t[2].replace(/\n/g, " "), + r = /[^ ]/.test(n), + u = /^ /.test(n) && / $/.test(n); + return ( + r && u && (n = n.substring(1, n.length - 1)), + (n = xf(n, !0)), + { type: "codespan", raw: t[0], text: n } + ); + } + }, + }, + { + key: "br", + value: function (e) { + var t = this.rules.inline.br.exec(e); + if (t) return { type: "br", raw: t[0] }; + }, + }, + { + key: "del", + value: function (e) { + var t = this.rules.inline.del.exec(e); + if (t) + return { + type: "del", + raw: t[0], + text: t[2], + tokens: this.lexer.inlineTokens(t[2], []), + }; + }, + }, + { + key: "autolink", + value: function (e, t) { + var n, + r, + u = this.rules.inline.autolink.exec(e); + if (u) + return ( + (r = + "@" === u[2] + ? "mailto:" + (n = xf(this.options.mangle ? t(u[1]) : u[1])) + : (n = xf(u[1]))), + { + type: "link", + raw: u[0], + text: n, + href: r, + tokens: [{ type: "text", raw: n, text: n }], + } + ); + }, + }, + { + key: "url", + value: function (e, t) { + var n; + if ((n = this.rules.inline.url.exec(e))) { + var r, u; + if ("@" === n[2]) + u = "mailto:" + (r = xf(this.options.mangle ? t(n[0]) : n[0])); + else { + var i; + do { + (i = n[0]), + (n[0] = this.rules.inline._backpedal.exec(n[0])[0]); + } while (i !== n[0]); + (r = xf(n[0])), (u = "www." === n[1] ? "http://" + r : r); + } + return { + type: "link", + raw: n[0], + text: r, + href: u, + tokens: [{ type: "text", raw: r, text: r }], + }; + } + }, + }, + { + key: "inlineText", + value: function (e, t) { + var n, + r = this.rules.inline.text.exec(e); + if (r) + return ( + (n = this.lexer.state.inRawBlock + ? this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(r[0]) + : xf(r[0]) + : r[0] + : xf(this.options.smartypants ? t(r[0]) : r[0])), + { type: "text", raw: r[0], text: n } + ); + }, + }, + ]), + e + ); + })(), + qf = { + newline: /^(?: *(?:\n|$))+/, + code: /^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/, + fences: + /^ {0,3}(`{3,}(?=[^`\n]*\n)|~{3,})([^\n]*)\n(?:|([\s\S]*?)\n)(?: {0,3}\1[~`]* *(?=\n|$)|$)/, + hr: /^ {0,3}((?:- *){3,}|(?:_ *){3,}|(?:\* *){3,})(?:\n+|$)/, + heading: /^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/, + blockquote: /^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/, + list: /^( {0,3}bull)( [^\n]+?)?(?:\n|$)/, + html: "^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|\\n*|$)|\\n*|$)|)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))", + def: /^ {0,3}\[(label)\]: *(?:\n *)?]+)>?(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/, + table: jf, + lheading: /^([^\n]+)\n {0,3}(=+|-+) *(?:\n+|$)/, + _paragraph: + /^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/, + text: /^[^\n]+/, + _label: /(?!\s*\])(?:\\.|[^\[\]\\])+/, + _title: /(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/, + }; +(qf.def = wf(qf.def) + .replace("label", qf._label) + .replace("title", qf._title) + .getRegex()), + (qf.bullet = /(?:[*+-]|\d{1,9}[.)])/), + (qf.listItemStart = wf(/^( *)(bull) */) + .replace("bull", qf.bullet) + .getRegex()), + (qf.list = wf(qf.list) + .replace(/bull/g, qf.bullet) + .replace( + "hr", + "\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))", + ) + .replace("def", "\\n+(?=" + qf.def.source + ")") + .getRegex()), + (qf._tag = + "address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul"), + (qf._comment = /|$)/), + (qf.html = wf(qf.html, "i") + .replace("comment", qf._comment) + .replace("tag", qf._tag) + .replace( + "attribute", + / +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/, + ) + .getRegex()), + (qf.paragraph = wf(qf._paragraph) + .replace("hr", qf.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("|table", "") + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", qf._tag) + .getRegex()), + (qf.blockquote = wf(qf.blockquote) + .replace("paragraph", qf.paragraph) + .getRegex()), + (qf.normal = zf({}, qf)), + (qf.gfm = zf({}, qf.normal, { + table: + "^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)", + })), + (qf.gfm.table = wf(qf.gfm.table) + .replace("hr", qf.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("blockquote", " {0,3}>") + .replace("code", " {4}[^\\n]") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", qf._tag) + .getRegex()), + (qf.gfm.paragraph = wf(qf._paragraph) + .replace("hr", qf.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("table", qf.gfm.table) + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", qf._tag) + .getRegex()), + (qf.pedantic = zf({}, qf.normal, { + html: wf( + "^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+? *(?:\\n{2,}|\\s*$)|\\s]*)*?/?> *(?:\\n{2,}|\\s*$))", + ) + .replace("comment", qf._comment) + .replace( + /tag/g, + "(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b", + ) + .getRegex(), + def: /^ *\[([^\]]+)\]: *]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/, + heading: /^(#{1,6})(.*)(?:\n+|$)/, + fences: jf, + paragraph: wf(qf.normal._paragraph) + .replace("hr", qf.hr) + .replace("heading", " *#{1,6} *[^\n]") + .replace("lheading", qf.lheading) + .replace("blockquote", " {0,3}>") + .replace("|fences", "") + .replace("|list", "") + .replace("|html", "") + .getRegex(), + })); +var Zf = { + escape: /^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/, + autolink: /^<(scheme:[^\s\x00-\x1f<>]*|email)>/, + url: jf, + tag: "^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^", + link: /^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/, + reflink: /^!?\[(label)\]\[(ref)\]/, + nolink: /^!?\[(ref)\](?:\[\])?/, + reflinkSearch: "reflink|nolink(?!\\()", + emStrong: { + lDelim: /^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/, + rDelimAst: + /^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/, + rDelimUnd: + /^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/, + }, + code: /^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/, + br: /^( {2,}|\\)\n(?!\s*$)/, + del: jf, + text: /^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\ 0.5 && (n = "x" + n.toString(16)), + (r += "&#" + n + ";"); + return r; +} +(Zf._punctuation = "!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~"), + (Zf.punctuation = wf(Zf.punctuation) + .replace(/punctuation/g, Zf._punctuation) + .getRegex()), + (Zf.blockSkip = /\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g), + (Zf.escapedEmSt = /\\\*|\\_/g), + (Zf._comment = wf(qf._comment).replace("(?:--\x3e|$)", "--\x3e").getRegex()), + (Zf.emStrong.lDelim = wf(Zf.emStrong.lDelim) + .replace(/punct/g, Zf._punctuation) + .getRegex()), + (Zf.emStrong.rDelimAst = wf(Zf.emStrong.rDelimAst, "g") + .replace(/punct/g, Zf._punctuation) + .getRegex()), + (Zf.emStrong.rDelimUnd = wf(Zf.emStrong.rDelimUnd, "g") + .replace(/punct/g, Zf._punctuation) + .getRegex()), + (Zf._escapes = /\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g), + (Zf._scheme = /[a-zA-Z][a-zA-Z0-9+.-]{1,31}/), + (Zf._email = + /[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/), + (Zf.autolink = wf(Zf.autolink) + .replace("scheme", Zf._scheme) + .replace("email", Zf._email) + .getRegex()), + (Zf._attribute = + /\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/), + (Zf.tag = wf(Zf.tag) + .replace("comment", Zf._comment) + .replace("attribute", Zf._attribute) + .getRegex()), + (Zf._label = /(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/), + (Zf._href = /<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/), + (Zf._title = /"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/), + (Zf.link = wf(Zf.link) + .replace("label", Zf._label) + .replace("href", Zf._href) + .replace("title", Zf._title) + .getRegex()), + (Zf.reflink = wf(Zf.reflink) + .replace("label", Zf._label) + .replace("ref", qf._label) + .getRegex()), + (Zf.nolink = wf(Zf.nolink).replace("ref", qf._label).getRegex()), + (Zf.reflinkSearch = wf(Zf.reflinkSearch, "g") + .replace("reflink", Zf.reflink) + .replace("nolink", Zf.nolink) + .getRegex()), + (Zf.normal = zf({}, Zf)), + (Zf.pedantic = zf({}, Zf.normal, { + strong: { + start: /^__|\*\*/, + middle: /^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/, + endAst: /\*\*(?!\*)/g, + endUnd: /__(?!_)/g, + }, + em: { + start: /^_|\*/, + middle: /^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/, + endAst: /\*(?!\*)/g, + endUnd: /_(?!_)/g, + }, + link: wf(/^!?\[(label)\]\((.*?)\)/) + .replace("label", Zf._label) + .getRegex(), + reflink: wf(/^!?\[(label)\]\s*\[([^\]]*)\]/) + .replace("label", Zf._label) + .getRegex(), + })), + (Zf.gfm = zf({}, Zf.normal, { + escape: wf(Zf.escape).replace("])", "~|])").getRegex(), + _extended_email: + /[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/, + url: /^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/, + _backpedal: + /(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/, + del: /^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/, + text: /^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\ 1 && void 0 !== arguments[1] + ? arguments[1] + : []; + for (this.options.pedantic && (e = e.replace(/^ +$/gm, "")); e; ) + if ( + !( + this.options.extensions && + this.options.extensions.block && + this.options.extensions.block.some(function (n) { + return ( + !!(t = n.call({ lexer: i }, e, o)) && + ((e = e.substring(t.raw.length)), o.push(t), !0) + ); + }) + ) + ) + if ((t = this.tokenizer.space(e))) + (e = e.substring(t.raw.length)), + 1 === t.raw.length && o.length > 0 + ? (o[o.length - 1].raw += "\n") + : o.push(t); + else if ((t = this.tokenizer.code(e))) + (e = e.substring(t.raw.length)), + !(n = o[o.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? o.push(t) + : ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.text), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((t = this.tokenizer.fences(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.heading(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.hr(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.blockquote(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.list(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.html(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.def(e))) + (e = e.substring(t.raw.length)), + !(n = o[o.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? this.tokens.links[t.tag] || + (this.tokens.links[t.tag] = { + href: t.href, + title: t.title, + }) + : ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.raw), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((t = this.tokenizer.table(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ((t = this.tokenizer.lheading(e))) + (e = e.substring(t.raw.length)), o.push(t); + else if ( + ((r = e), + this.options.extensions && + this.options.extensions.startBlock && + (function () { + var t = 1 / 0, + n = e.slice(1), + u = void 0; + i.options.extensions.startBlock.forEach(function (e) { + "number" == typeof (u = e.call({ lexer: this }, n)) && + u >= 0 && + (t = Math.min(t, u)); + }), + t < 1 / 0 && t >= 0 && (r = e.substring(0, t + 1)); + })(), + this.state.top && (t = this.tokenizer.paragraph(r))) + ) + (n = o[o.length - 1]), + u && "paragraph" === n.type + ? ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : o.push(t), + (u = r.length !== e.length), + (e = e.substring(t.raw.length)); + else if ((t = this.tokenizer.text(e))) + (e = e.substring(t.raw.length)), + (n = o[o.length - 1]) && "text" === n.type + ? ((n.raw += "\n" + t.raw), + (n.text += "\n" + t.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : o.push(t); + else if (e) { + var a = "Infinite loop on byte: " + e.charCodeAt(0); + if (this.options.silent) { + console.error(a); + break; + } + throw new Error(a); + } + return (this.state.top = !0), o; + }, + }, + { + key: "inline", + value: function (e, t) { + this.inlineQueue.push({ src: e, tokens: t }); + }, + }, + { + key: "inlineTokens", + value: function (e) { + var t, + n, + r, + u, + i, + o, + a = this, + s = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : [], + l = e; + if (this.tokens.links) { + var c = Object.keys(this.tokens.links); + if (c.length > 0) + for ( + ; + null != + (u = this.tokenizer.rules.inline.reflinkSearch.exec(l)); + + ) + c.includes(u[0].slice(u[0].lastIndexOf("[") + 1, -1)) && + (l = + l.slice(0, u.index) + + "[" + + Mf("a", u[0].length - 2) + + "]" + + l.slice( + this.tokenizer.rules.inline.reflinkSearch.lastIndex, + )); + } + for ( + ; + null != (u = this.tokenizer.rules.inline.blockSkip.exec(l)); + + ) + l = + l.slice(0, u.index) + + "[" + + Mf("a", u[0].length - 2) + + "]" + + l.slice(this.tokenizer.rules.inline.blockSkip.lastIndex); + for ( + ; + null != (u = this.tokenizer.rules.inline.escapedEmSt.exec(l)); + + ) + l = + l.slice(0, u.index) + + "++" + + l.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex); + for (; e; ) + if ( + (i || (o = ""), + (i = !1), + !( + this.options.extensions && + this.options.extensions.inline && + this.options.extensions.inline.some(function (n) { + return ( + !!(t = n.call({ lexer: a }, e, s)) && + ((e = e.substring(t.raw.length)), s.push(t), !0) + ); + }) + )) + ) + if ((t = this.tokenizer.escape(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.tag(e))) + (e = e.substring(t.raw.length)), + (n = s[s.length - 1]) && + "text" === t.type && + "text" === n.type + ? ((n.raw += t.raw), (n.text += t.text)) + : s.push(t); + else if ((t = this.tokenizer.link(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.reflink(e, this.tokens.links))) + (e = e.substring(t.raw.length)), + (n = s[s.length - 1]) && + "text" === t.type && + "text" === n.type + ? ((n.raw += t.raw), (n.text += t.text)) + : s.push(t); + else if ((t = this.tokenizer.emStrong(e, l, o))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.codespan(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.br(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.del(e))) + (e = e.substring(t.raw.length)), s.push(t); + else if ((t = this.tokenizer.autolink(e, Hf))) + (e = e.substring(t.raw.length)), s.push(t); + else if ( + this.state.inLink || + !(t = this.tokenizer.url(e, Hf)) + ) { + if ( + ((r = e), + this.options.extensions && + this.options.extensions.startInline && + (function () { + var t = 1 / 0, + n = e.slice(1), + u = void 0; + a.options.extensions.startInline.forEach( + function (e) { + "number" == + typeof (u = e.call({ lexer: this }, n)) && + u >= 0 && + (t = Math.min(t, u)); + }, + ), + t < 1 / 0 && t >= 0 && (r = e.substring(0, t + 1)); + })(), + (t = this.tokenizer.inlineText(r, Gf))) + ) + (e = e.substring(t.raw.length)), + "_" !== t.raw.slice(-1) && (o = t.raw.slice(-1)), + (i = !0), + (n = s[s.length - 1]) && "text" === n.type + ? ((n.raw += t.raw), (n.text += t.text)) + : s.push(t); + else if (e) { + var f = "Infinite loop on byte: " + e.charCodeAt(0); + if (this.options.silent) { + console.error(f); + break; + } + throw new Error(f); + } + } else (e = e.substring(t.raw.length)), s.push(t); + return s; + }, + }, + ], + [ + { + key: "rules", + get: function () { + return { block: qf, inline: Zf }; + }, + }, + { + key: "lex", + value: function (t, n) { + return new e(n).lex(t); + }, + }, + { + key: "lexInline", + value: function (t, n) { + return new e(n).inlineTokens(t); + }, + }, + ], + ), + e + ); + })(), + Vf = (function () { + function e(n) { + t(this, e), (this.options = n || df); + } + return ( + r(e, [ + { + key: "code", + value: function (e, t, n) { + var r = (t || "").match(/\S*/)[0]; + if (this.options.highlight) { + var u = this.options.highlight(e, r); + null != u && u !== e && ((n = !0), (e = u)); + } + return ( + (e = e.replace(/\n$/, "") + "\n"), + r + ? '
    ' +
+                  (n ? e : xf(e, !0)) +
+                  "
    \n" + : "
    " + (n ? e : xf(e, !0)) + "
    \n" + ); + }, + }, + { + key: "blockquote", + value: function (e) { + return "
    \n" + e + "
    \n"; + }, + }, + { + key: "html", + value: function (e) { + return e; + }, + }, + { + key: "heading", + value: function (e, t, n, r) { + return this.options.headerIds + ? "' + + e + + "\n" + : "" + e + "\n"; + }, + }, + { + key: "hr", + value: function () { + return this.options.xhtml ? "
    \n" : "
    \n"; + }, + }, + { + key: "list", + value: function (e, t, n) { + var r = t ? "ol" : "ul"; + return ( + "<" + + r + + (t && 1 !== n ? ' start="' + n + '"' : "") + + ">\n" + + e + + "\n" + ); + }, + }, + { + key: "listitem", + value: function (e) { + return "
  • " + e + "
    • \n"; + }, + }, + { + key: "checkbox", + value: function (e) { + return ( + " " + ); + }, + }, + { + key: "paragraph", + value: function (e) { + return "

    " + e + "

    \n"; + }, + }, + { + key: "table", + value: function (e, t) { + return ( + t && (t = "" + t + ""), + "\n\n" + e + "\n" + t + "
    \n" + ); + }, + }, + { + key: "tablerow", + value: function (e) { + return "\n" + e + "\n"; + }, + }, + { + key: "tablecell", + value: function (e, t) { + var n = t.header ? "th" : "td"; + return ( + (t.align + ? "<" + n + ' align="' + t.align + '">' + : "<" + n + ">") + + e + + "\n" + ); + }, + }, + { + key: "strong", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "em", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "codespan", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "br", + value: function () { + return this.options.xhtml ? "
    " : "
    "; + }, + }, + { + key: "del", + value: function (e) { + return "" + e + ""; + }, + }, + { + key: "link", + value: function (e, t, n) { + if ( + null === (e = _f(this.options.sanitize, this.options.baseUrl, e)) + ) + return n; + var r = '    "); + }, + }, + { + key: "image", + value: function (e, t, n) { + if ( + null === (e = _f(this.options.sanitize, this.options.baseUrl, e)) + ) + return n; + var r = '' + n + '" : ">") + ); + }, + }, + { + key: "text", + value: function (e) { + return e; + }, + }, + ]), + e + ); + })(), + Yf = (function () { + function e() { + t(this, e); + } + return ( + r(e, [ + { + key: "strong", + value: function (e) { + return e; + }, + }, + { + key: "em", + value: function (e) { + return e; + }, + }, + { + key: "codespan", + value: function (e) { + return e; + }, + }, + { + key: "del", + value: function (e) { + return e; + }, + }, + { + key: "html", + value: function (e) { + return e; + }, + }, + { + key: "text", + value: function (e) { + return e; + }, + }, + { + key: "link", + value: function (e, t, n) { + return "" + n; + }, + }, + { + key: "image", + value: function (e, t, n) { + return "" + n; + }, + }, + { + key: "br", + value: function () { + return ""; + }, + }, + ]), + e + ); + })(), + Kf = (function () { + function e() { + t(this, e), (this.seen = {}); + } + return ( + r(e, [ + { + key: "serialize", + value: function (e) { + return e + .toLowerCase() + .trim() + .replace(/<[!\/a-z].*?>/gi, "") + .replace( + /[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g, + "", + ) + .replace(/\s/g, "-"); + }, + }, + { + key: "getNextSafeSlug", + value: function (e, t) { + var n = e, + r = 0; + if (this.seen.hasOwnProperty(n)) { + r = this.seen[e]; + do { + n = e + "-" + ++r; + } while (this.seen.hasOwnProperty(n)); + } + return t || ((this.seen[e] = r), (this.seen[n] = 0)), n; + }, + }, + { + key: "slug", + value: function (e) { + var t = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : {}, + n = this.serialize(e); + return this.getNextSafeSlug(n, t.dryrun); + }, + }, + ]), + e + ); + })(), + Xf = (function () { + function e(n) { + t(this, e), + (this.options = n || df), + (this.options.renderer = this.options.renderer || new Vf()), + (this.renderer = this.options.renderer), + (this.renderer.options = this.options), + (this.textRenderer = new Yf()), + (this.slugger = new Kf()); + } + return ( + r( + e, + [ + { + key: "parse", + value: function (e) { + var t, + n, + r, + u, + i, + o, + a, + s, + l, + c, + f, + p, + h, + D, + g, + d, + v, + y, + A, + m = + !(arguments.length > 1 && void 0 !== arguments[1]) || + arguments[1], + k = "", + E = e.length; + for (t = 0; t < E; t++) + if ( + ((c = e[t]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[c.type] + ) || + (!1 === + (A = this.options.extensions.renderers[c.type].call( + { parser: this }, + c, + )) && + [ + "space", + "hr", + "heading", + "code", + "table", + "blockquote", + "list", + "html", + "paragraph", + "text", + ].includes(c.type))) + ) + switch (c.type) { + case "space": + continue; + case "hr": + k += this.renderer.hr(); + continue; + case "heading": + k += this.renderer.heading( + this.parseInline(c.tokens), + c.depth, + bf(this.parseInline(c.tokens, this.textRenderer)), + this.slugger, + ); + continue; + case "code": + k += this.renderer.code(c.text, c.lang, c.escaped); + continue; + case "table": + for ( + s = "", a = "", u = c.header.length, n = 0; + n < u; + n++ + ) + a += this.renderer.tablecell( + this.parseInline(c.header[n].tokens), + { header: !0, align: c.align[n] }, + ); + for ( + s += this.renderer.tablerow(a), + l = "", + u = c.rows.length, + n = 0; + n < u; + n++ + ) { + for ( + a = "", i = (o = c.rows[n]).length, r = 0; + r < i; + r++ + ) + a += this.renderer.tablecell( + this.parseInline(o[r].tokens), + { header: !1, align: c.align[r] }, + ); + l += this.renderer.tablerow(a); + } + k += this.renderer.table(s, l); + continue; + case "blockquote": + (l = this.parse(c.tokens)), + (k += this.renderer.blockquote(l)); + continue; + case "list": + for ( + f = c.ordered, + p = c.start, + h = c.loose, + u = c.items.length, + l = "", + n = 0; + n < u; + n++ + ) + (d = (g = c.items[n]).checked), + (v = g.task), + (D = ""), + g.task && + ((y = this.renderer.checkbox(d)), + h + ? g.tokens.length > 0 && + "paragraph" === g.tokens[0].type + ? ((g.tokens[0].text = + y + " " + g.tokens[0].text), + g.tokens[0].tokens && + g.tokens[0].tokens.length > 0 && + "text" === g.tokens[0].tokens[0].type && + (g.tokens[0].tokens[0].text = + y + " " + g.tokens[0].tokens[0].text)) + : g.tokens.unshift({ type: "text", text: y }) + : (D += y)), + (D += this.parse(g.tokens, h)), + (l += this.renderer.listitem(D, v, d)); + k += this.renderer.list(l, f, p); + continue; + case "html": + k += this.renderer.html(c.text); + continue; + case "paragraph": + k += this.renderer.paragraph(this.parseInline(c.tokens)); + continue; + case "text": + for ( + l = c.tokens ? this.parseInline(c.tokens) : c.text; + t + 1 < E && "text" === e[t + 1].type; + + ) + l += + "\n" + + ((c = e[++t]).tokens + ? this.parseInline(c.tokens) + : c.text); + k += m ? this.renderer.paragraph(l) : l; + continue; + default: + var x = 'Token with "' + c.type + '" type was not found.'; + if (this.options.silent) return void console.error(x); + throw new Error(x); + } + else k += A || ""; + return k; + }, + }, + { + key: "parseInline", + value: function (e, t) { + t = t || this.renderer; + var n, + r, + u, + i = "", + o = e.length; + for (n = 0; n < o; n++) + if ( + ((r = e[n]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[r.type] + ) || + (!1 === + (u = this.options.extensions.renderers[r.type].call( + { parser: this }, + r, + )) && + [ + "escape", + "html", + "link", + "image", + "strong", + "em", + "codespan", + "br", + "del", + "text", + ].includes(r.type))) + ) + switch (r.type) { + case "escape": + i += t.text(r.text); + break; + case "html": + i += t.html(r.text); + break; + case "link": + i += t.link( + r.href, + r.title, + this.parseInline(r.tokens, t), + ); + break; + case "image": + i += t.image(r.href, r.title, r.text); + break; + case "strong": + i += t.strong(this.parseInline(r.tokens, t)); + break; + case "em": + i += t.em(this.parseInline(r.tokens, t)); + break; + case "codespan": + i += t.codespan(r.text); + break; + case "br": + i += t.br(); + break; + case "del": + i += t.del(this.parseInline(r.tokens, t)); + break; + case "text": + i += t.text(r.text); + break; + default: + var a = 'Token with "' + r.type + '" type was not found.'; + if (this.options.silent) return void console.error(a); + throw new Error(a); + } + else i += u || ""; + return i; + }, + }, + ], + [ + { + key: "parse", + value: function (t, n) { + return new e(n).parse(t); + }, + }, + { + key: "parseInline", + value: function (t, n) { + return new e(n).parseInline(t); + }, + }, + ], + ), + e + ); + })(); +function Wf(e, t, n) { + if (null == e) + throw new Error("marked(): input parameter is undefined or null"); + if ("string" != typeof e) + throw new Error( + "marked(): input parameter is of type " + + Object.prototype.toString.call(e) + + ", string expected", + ); + if ( + ("function" == typeof t && ((n = t), (t = null)), + Lf((t = zf({}, Wf.defaults, t || {}))), + n) + ) { + var r, + u = t.highlight; + try { + r = Qf.lex(e, t); + } catch (e) { + return n(e); + } + var i = function (e) { + var i; + if (!e) + try { + t.walkTokens && Wf.walkTokens(r, t.walkTokens), (i = Xf.parse(r, t)); + } catch (t) { + e = t; + } + return (t.highlight = u), e ? n(e) : n(null, i); + }; + if (!u || u.length < 3) return i(); + if ((delete t.highlight, !r.length)) return i(); + var o = 0; + return ( + Wf.walkTokens(r, function (e) { + "code" === e.type && + (o++, + setTimeout(function () { + u(e.text, e.lang, function (t, n) { + if (t) return i(t); + null != n && n !== e.text && ((e.text = n), (e.escaped = !0)), + 0 === --o && i(); + }); + }, 0)); + }), + void (0 === o && i()) + ); + } + try { + var a = Qf.lex(e, t); + return t.walkTokens && Wf.walkTokens(a, t.walkTokens), Xf.parse(a, t); + } catch (e) { + if ( + ((e.message += + "\nPlease report this to https://github.com/markedjs/marked."), + t.silent) + ) + return ( + "

    An error occurred:

    " + xf(e.message + "", !0) + "
    " + ); + throw e; + } +} +(Wf.options = Wf.setOptions = + function (e) { + var t; + return zf(Wf.defaults, e), (t = Wf.defaults), (df = t), Wf; + }), + (Wf.getDefaults = gf), + (Wf.defaults = df), + (Wf.use = function () { + for (var e = arguments.length, t = new Array(e), n = 0; n < e; n++) + t[n] = arguments[n]; + var r, + u = zf.apply(void 0, [{}].concat(t)), + i = Wf.defaults.extensions || { renderers: {}, childTokens: {} }; + t.forEach(function (e) { + if ( + (e.extensions && + ((r = !0), + e.extensions.forEach(function (e) { + if (!e.name) throw new Error("extension name required"); + if (e.renderer) { + var t = i.renderers ? i.renderers[e.name] : null; + i.renderers[e.name] = t + ? function () { + for ( + var n = arguments.length, r = new Array(n), u = 0; + u < n; + u++ + ) + r[u] = arguments[u]; + var i = e.renderer.apply(this, r); + return !1 === i && (i = t.apply(this, r)), i; + } + : e.renderer; + } + if (e.tokenizer) { + if (!e.level || ("block" !== e.level && "inline" !== e.level)) + throw new Error("extension level must be 'block' or 'inline'"); + i[e.level] + ? i[e.level].unshift(e.tokenizer) + : (i[e.level] = [e.tokenizer]), + e.start && + ("block" === e.level + ? i.startBlock + ? i.startBlock.push(e.start) + : (i.startBlock = [e.start]) + : "inline" === e.level && + (i.startInline + ? i.startInline.push(e.start) + : (i.startInline = [e.start]))); + } + e.childTokens && (i.childTokens[e.name] = e.childTokens); + })), + e.renderer && + (function () { + var t = Wf.defaults.renderer || new Vf(), + n = function (n) { + var r = t[n]; + t[n] = function () { + for ( + var u = arguments.length, i = new Array(u), o = 0; + o < u; + o++ + ) + i[o] = arguments[o]; + var a = e.renderer[n].apply(t, i); + return !1 === a && (a = r.apply(t, i)), a; + }; + }; + for (var r in e.renderer) n(r); + u.renderer = t; + })(), + e.tokenizer && + (function () { + var t = Wf.defaults.tokenizer || new Uf(), + n = function (n) { + var r = t[n]; + t[n] = function () { + for ( + var u = arguments.length, i = new Array(u), o = 0; + o < u; + o++ + ) + i[o] = arguments[o]; + var a = e.tokenizer[n].apply(t, i); + return !1 === a && (a = r.apply(t, i)), a; + }; + }; + for (var r in e.tokenizer) n(r); + u.tokenizer = t; + })(), + e.walkTokens) + ) { + var t = Wf.defaults.walkTokens; + u.walkTokens = function (n) { + e.walkTokens.call(this, n), t && t.call(this, n); + }; + } + r && (u.extensions = i), Wf.setOptions(u); + }); + }), + (Wf.walkTokens = function (e, t) { + var n, + r = l(e); + try { + var u = function () { + var e = n.value; + switch ((t.call(Wf, e), e.type)) { + case "table": + var r, + u = l(e.header); + try { + for (u.s(); !(r = u.n()).done; ) { + var i = r.value; + Wf.walkTokens(i.tokens, t); + } + } catch (e) { + u.e(e); + } finally { + u.f(); + } + var o, + a = l(e.rows); + try { + for (a.s(); !(o = a.n()).done; ) { + var s, + c = l(o.value); + try { + for (c.s(); !(s = c.n()).done; ) { + var f = s.value; + Wf.walkTokens(f.tokens, t); + } + } catch (e) { + c.e(e); + } finally { + c.f(); + } + } + } catch (e) { + a.e(e); + } finally { + a.f(); + } + break; + case "list": + Wf.walkTokens(e.items, t); + break; + default: + Wf.defaults.extensions && + Wf.defaults.extensions.childTokens && + Wf.defaults.extensions.childTokens[e.type] + ? Wf.defaults.extensions.childTokens[e.type].forEach( + function (n) { + Wf.walkTokens(e[n], t); + }, + ) + : e.tokens && Wf.walkTokens(e.tokens, t); + } + }; + for (r.s(); !(n = r.n()).done; ) u(); + } catch (e) { + r.e(e); + } finally { + r.f(); + } + }), + (Wf.parseInline = function (e, t) { + if (null == e) + throw new Error( + "marked.parseInline(): input parameter is undefined or null", + ); + if ("string" != typeof e) + throw new Error( + "marked.parseInline(): input parameter is of type " + + Object.prototype.toString.call(e) + + ", string expected", + ); + Lf((t = zf({}, Wf.defaults, t || {}))); + try { + var n = Qf.lexInline(e, t); + return ( + t.walkTokens && Wf.walkTokens(n, t.walkTokens), Xf.parseInline(n, t) + ); + } catch (e) { + if ( + ((e.message += + "\nPlease report this to https://github.com/markedjs/marked."), + t.silent) + ) + return ( + "

    An error occurred:

    " + xf(e.message + "", !0) + "
    " + ); + throw e; + } + }), + (Wf.Parser = Xf), + (Wf.parser = Xf.parse), + (Wf.Renderer = Vf), + (Wf.TextRenderer = Yf), + (Wf.Lexer = Qf), + (Wf.lexer = Qf.lex), + (Wf.Tokenizer = Uf), + (Wf.Slugger = Kf), + (Wf.parse = Wf); +var Jf = /\[([\s\d,|-]*)\]/, + ep = { "&": "&", "<": "<", ">": ">", '"': """, "'": "'" }; +export default function () { + var t; + function n(e) { + var t = ( + e.querySelector("[data-template]") || + e.querySelector("script") || + e + ).textContent, + n = (t = t.replace(new RegExp("__SCRIPT_END__", "g"), "")).match( + /^\n?(\s*)/, + )[1].length, + r = t.match(/^\n?(\t*)/)[1].length; + return ( + r > 0 + ? (t = t.replace(new RegExp("\\n?\\t{" + r + "}", "g"), "\n")) + : n > 1 && (t = t.replace(new RegExp("\\n? {" + n + "}", "g"), "\n")), + t + ); + } + function r(e) { + for (var t = e.attributes, n = [], r = 0, u = t.length; r < u; r++) { + var i = t[r].name, + o = t[r].value; + /data\-(markdown|separator|vertical|notes)/gi.test(i) || + (o ? n.push(i + '="' + o + '"') : n.push(i)); + } + return n.join(" "); + } + function o(e) { + return ( + ((e = e || {}).separator = e.separator || "\r?\n---\r?\n"), + (e.notesSeparator = e.notesSeparator || "notes?:"), + (e.attributes = e.attributes || ""), + e + ); + } + function a(e, t) { + t = o(t); + var n = e.split(new RegExp(t.notesSeparator, "mgi")); + return ( + 2 === n.length && + (e = n[0] + '"), + '" + ); + } + function s(e, t) { + t = o(t); + for ( + var n, + r, + u, + i = new RegExp( + t.separator + (t.verticalSeparator ? "|" + t.verticalSeparator : ""), + "mg", + ), + s = new RegExp(t.separator), + l = 0, + c = !0, + f = []; + (n = i.exec(e)); + + ) + !(r = s.test(n[0])) && c && f.push([]), + (u = e.substring(l, n.index)), + r && c ? f.push(u) : f[f.length - 1].push(u), + (l = i.lastIndex), + (c = r); + (c ? f : f[f.length - 1]).push(e.substring(l)); + for (var p = "", h = 0, D = f.length; h < D; h++) + f[h] instanceof Array + ? ((p += "
    "), + f[h].forEach(function (e) { + p += "
    " + a(e, t) + "
    "; + }), + (p += "
    ")) + : (p += + "
    " + + a(f[h], t) + + "
    "); + return p; + } + function l(e) { + return new Promise(function (t) { + var u = []; + [].slice + .call( + e.querySelectorAll( + "section[data-markdown]:not([data-markdown-parsed])", + ), + ) + .forEach(function (e, t) { + e.getAttribute("data-markdown").length + ? u.push( + (function (e) { + return new Promise(function (t, n) { + var r = new XMLHttpRequest(), + u = e.getAttribute("data-markdown"), + i = e.getAttribute("data-charset"); + null != i && + "" != i && + r.overrideMimeType("text/html; charset=" + i), + (r.onreadystatechange = function (e, r) { + 4 === r.readyState && + ((r.status >= 200 && r.status < 300) || 0 === r.status + ? t(r, u) + : n(r, u)); + }.bind(this, e, r)), + r.open("GET", u, !0); + try { + r.send(); + } catch (e) { + console.warn( + "Failed to get the Markdown file " + + u + + ". Make sure that the presentation and the file are served by a HTTP server and the file can be found there. " + + e, + ), + t(r, u); + } + }); + })(e).then( + function (t, n) { + e.outerHTML = s(t.responseText, { + separator: e.getAttribute("data-separator"), + verticalSeparator: e.getAttribute( + "data-separator-vertical", + ), + notesSeparator: e.getAttribute("data-separator-notes"), + attributes: r(e), + }); + }, + function (t, n) { + e.outerHTML = + '
    ERROR: The attempt to fetch ' + + n + + " failed with HTTP status " + + t.status + + ".Check your browser's JavaScript console for more details.

    Remember that you need to serve the presentation HTML from a HTTP server.

    "; + }, + ), + ) + : (e.outerHTML = s(n(e), { + separator: e.getAttribute("data-separator"), + verticalSeparator: e.getAttribute("data-separator-vertical"), + notesSeparator: e.getAttribute("data-separator-notes"), + attributes: r(e), + })); + }), + Promise.all(u).then(t); + }); + } + function c(e, t, n) { + var r, + u, + i = new RegExp(n, "mg"), + o = new RegExp('([^"= ]+?)="([^"]+?)"|(data-[^"= ]+?)(?=[" ])', "mg"), + a = e.nodeValue; + if ((r = i.exec(a))) { + var s = r[1]; + for ( + a = a.substring(0, r.index) + a.substring(i.lastIndex), e.nodeValue = a; + (u = o.exec(s)); + + ) + u[2] ? t.setAttribute(u[1], u[2]) : t.setAttribute(u[3], ""); + return !0; + } + return !1; + } + function f(e, t, n, r, u) { + if (null != t && null != t.childNodes && t.childNodes.length > 0) + for (var i = t, o = 0; o < t.childNodes.length; o++) { + var a = t.childNodes[o]; + if (o > 0) + for (var s = o - 1; s >= 0; ) { + var l = t.childNodes[s]; + if ("function" == typeof l.setAttribute && "BR" != l.tagName) { + i = l; + break; + } + s -= 1; + } + var p = e; + "section" == a.nodeName && ((p = a), (i = a)), + ("function" != typeof a.setAttribute && + a.nodeType != Node.COMMENT_NODE) || + f(p, a, i, r, u); + } + t.nodeType == Node.COMMENT_NODE && 0 == c(t, n, r) && c(t, e, u); + } + function p() { + var e = t + .getRevealElement() + .querySelectorAll("[data-markdown]:not([data-markdown-parsed])"); + return ( + [].slice.call(e).forEach(function (e) { + e.setAttribute("data-markdown-parsed", !0); + var t = e.querySelector("aside.notes"), + r = n(e); + (e.innerHTML = Wf(r)), + f( + e, + e, + null, + e.getAttribute("data-element-attributes") || + e.parentNode.getAttribute("data-element-attributes") || + "\\.element\\s*?(.+?)$", + e.getAttribute("data-attributes") || + e.parentNode.getAttribute("data-attributes") || + "\\.slide:\\s*?(\\S.+?)$", + ), + t && e.appendChild(t); + }), + Promise.resolve() + ); + } + return { + id: "markdown", + init: function (n) { + var r = (t = n).getConfig().markdown || {}, + o = r.renderer, + a = r.animateLists, + s = i(r, ["renderer", "animateLists"]); + return ( + o || + ((o = new Wf.Renderer()).code = function (e, t) { + var n = ""; + return ( + Jf.test(t) && + ((n = t.match(Jf)[1].trim()), + (n = 'data-line-numbers="'.concat(n, '"')), + (t = t.replace(Jf, "").trim())), + (e = e.replace(/([&<>'"])/g, function (e) { + return ep[e]; + })), + "
    ')
+                .concat(e, "
    ") + ); + }), + !0 === a && + (o.listitem = function (e) { + return '
  • '.concat(e, "
    • "); + }), + Wf.setOptions( + (function (t) { + for (var n = 1; n < arguments.length; n++) { + var r = null != arguments[n] ? arguments[n] : {}; + n % 2 + ? e(Object(r), !0).forEach(function (e) { + u(t, e, r[e]); + }) + : Object.getOwnPropertyDescriptors + ? 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    ") + ); + }), + !0 === a && + (o.listitem = function (e) { + return '
  • '.concat(e, "
    • "); + }), + gp.setOptions( + (function (t) { + for (var n = 1; n < arguments.length; n++) { + var r = null != arguments[n] ? arguments[n] : {}; + n % 2 + ? e(Object(r), !0).forEach(function (e) { + u(t, e, r[e]); + }) + : Object.getOwnPropertyDescriptors + ? Object.defineProperties( + t, + Object.getOwnPropertyDescriptors(r), + ) + : e(Object(r)).forEach(function (e) { + Object.defineProperty( + t, + e, + Object.getOwnPropertyDescriptor(r, e), + ); + }); + } + return t; + })({ renderer: o }, s), + ), + l(t.getRevealElement()).then(p) + ); + }, + processSlides: l, + convertSlides: p, + slidify: s, + marked: gp, + }; + }; +}); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/markdown/plugin.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/markdown/plugin.js index db1cbf2..e78356c 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/markdown/plugin.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/markdown/plugin.js @@ -4,472 +4,508 @@ * of external markdown documents. */ -import { marked } from 'marked'; +import { marked } from "marked"; -const DEFAULT_SLIDE_SEPARATOR = '\r?\n---\r?\n', - DEFAULT_NOTES_SEPARATOR = 'notes?:', - DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR = '\\\.element\\\s*?(.+?)$', - DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR = '\\\.slide:\\\s*?(\\\S.+?)$'; +const DEFAULT_SLIDE_SEPARATOR = "\r?\n---\r?\n", + DEFAULT_NOTES_SEPARATOR = "notes?:", + DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR = "\\.element\\s*?(.+?)$", + DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR = "\\.slide:\\s*?(\\S.+?)$"; -const SCRIPT_END_PLACEHOLDER = '__SCRIPT_END__'; +const SCRIPT_END_PLACEHOLDER = "__SCRIPT_END__"; const CODE_LINE_NUMBER_REGEX = /\[([\s\d,|-]*)\]/; const HTML_ESCAPE_MAP = { - '&': '&', - '<': '<', - '>': '>', - '"': '"', - "'": ''' + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", }; const Plugin = () => { - - // The reveal.js instance this plugin is attached to - let deck; - - /** - * Retrieves the markdown contents of a slide section - * element. Normalizes leading tabs/whitespace. - */ - function getMarkdownFromSlide( section ) { - - // look for a ' ); - - var leadingWs = text.match( /^\n?(\s*)/ )[1].length, - leadingTabs = text.match( /^\n?(\t*)/ )[1].length; - - if( leadingTabs > 0 ) { - text = text.replace( new RegExp('\\n?\\t{' + leadingTabs + '}','g'), '\n' ); - } - else if( leadingWs > 1 ) { - text = text.replace( new RegExp('\\n? {' + leadingWs + '}', 'g'), '\n' ); - } - - return text; - - } - - /** - * Given a markdown slide section element, this will - * return all arguments that aren't related to markdown - * parsing. Used to forward any other user-defined arguments - * to the output markdown slide. - */ - function getForwardedAttributes( section ) { - - var attributes = section.attributes; - var result = []; - - for( var i = 0, len = attributes.length; i < len; i++ ) { - var name = attributes[i].name, - value = attributes[i].value; - - // disregard attributes that are used for markdown loading/parsing - if( /data\-(markdown|separator|vertical|notes)/gi.test( name ) ) continue; - - if( value ) { - result.push( name + '="' + value + '"' ); - } - else { - result.push( name ); - } - } - - return result.join( ' ' ); - - } - - /** - * Inspects the given options and fills out default - * values for what's not defined. - */ - function getSlidifyOptions( options ) { - - options = options || {}; - options.separator = options.separator || DEFAULT_SLIDE_SEPARATOR; - options.notesSeparator = options.notesSeparator || DEFAULT_NOTES_SEPARATOR; - options.attributes = options.attributes || ''; - - return options; - - } - - /** - * Helper function for constructing a markdown slide. - */ - function createMarkdownSlide( content, options ) { - - options = getSlidifyOptions( options ); - - var notesMatch = content.split( new RegExp( options.notesSeparator, 'mgi' ) ); - - if( notesMatch.length === 2 ) { - content = notesMatch[0] + ''; - } - - // prevent script end tags in the content from interfering - // with parsing - content = content.replace( /<\/script>/g, SCRIPT_END_PLACEHOLDER ); - - return ''; - - } - - /** - * Parses a data string into multiple slides based - * on the passed in separator arguments. - */ - function slidify( markdown, options ) { - - options = getSlidifyOptions( options ); - - var separatorRegex = new RegExp( options.separator + ( options.verticalSeparator ? '|' + options.verticalSeparator : '' ), 'mg' ), - horizontalSeparatorRegex = new RegExp( options.separator ); - - var matches, - lastIndex = 0, - isHorizontal, - wasHorizontal = true, - content, - sectionStack = []; - - // iterate until all blocks between separators are stacked up - while( matches = separatorRegex.exec( markdown ) ) { - var notes = null; - - // determine direction (horizontal by default) - isHorizontal = horizontalSeparatorRegex.test( matches[0] ); - - if( !isHorizontal && wasHorizontal ) { - // create vertical stack - sectionStack.push( [] ); - } - - // pluck slide content from markdown input - content = markdown.substring( lastIndex, matches.index ); - - if( isHorizontal && wasHorizontal ) { - // add to horizontal stack - sectionStack.push( content ); - } - else { - // add to vertical stack - sectionStack[sectionStack.length-1].push( content ); - } - - lastIndex = separatorRegex.lastIndex; - wasHorizontal = isHorizontal; - } - - // add the remaining slide - ( wasHorizontal ? sectionStack : sectionStack[sectionStack.length-1] ).push( markdown.substring( lastIndex ) ); - - var markdownSections = ''; - - // flatten the hierarchical stack, and insert
    tags - for( var i = 0, len = sectionStack.length; i < len; i++ ) { - // vertical - if( sectionStack[i] instanceof Array ) { - markdownSections += '
    '; - - sectionStack[i].forEach( function( child ) { - markdownSections += '
    ' + createMarkdownSlide( child, options ) + '
    '; - } ); - - markdownSections += '
    '; - } - else { - markdownSections += '
    ' + createMarkdownSlide( sectionStack[i], options ) + '
    '; - } - } - - return markdownSections; - - } - - /** - * Parses any current data-markdown slides, splits - * multi-slide markdown into separate sections and - * handles loading of external markdown. - */ - function processSlides( scope ) { - - return new Promise( function( resolve ) { - - var externalPromises = []; - - [].slice.call( scope.querySelectorAll( 'section[data-markdown]:not([data-markdown-parsed])') ).forEach( function( section, i ) { - - if( section.getAttribute( 'data-markdown' ).length ) { - - externalPromises.push( loadExternalMarkdown( section ).then( - - // Finished loading external file - function( xhr, url ) { - section.outerHTML = slidify( xhr.responseText, { - separator: section.getAttribute( 'data-separator' ), - verticalSeparator: section.getAttribute( 'data-separator-vertical' ), - notesSeparator: section.getAttribute( 'data-separator-notes' ), - attributes: getForwardedAttributes( section ) - }); - }, - - // Failed to load markdown - function( xhr, url ) { - section.outerHTML = '
    ' + - 'ERROR: The attempt to fetch ' + url + ' failed with HTTP status ' + xhr.status + '.' + - 'Check your browser\'s JavaScript console for more details.' + - '

    Remember that you need to serve the presentation HTML from a HTTP server.

    ' + - '
    '; - } - - ) ); - - } - else { - - section.outerHTML = slidify( getMarkdownFromSlide( section ), { - separator: section.getAttribute( 'data-separator' ), - verticalSeparator: section.getAttribute( 'data-separator-vertical' ), - notesSeparator: section.getAttribute( 'data-separator-notes' ), - attributes: getForwardedAttributes( section ) - }); - - } - - }); - - Promise.all( externalPromises ).then( resolve ); - - } ); - - } - - function loadExternalMarkdown( section ) { - - return new Promise( function( resolve, reject ) { - - var xhr = new XMLHttpRequest(), - url = section.getAttribute( 'data-markdown' ); - - var datacharset = section.getAttribute( 'data-charset' ); - - // see https://developer.mozilla.org/en-US/docs/Web/API/element.getAttribute#Notes - if( datacharset != null && datacharset != '' ) { - xhr.overrideMimeType( 'text/html; charset=' + datacharset ); - } - - xhr.onreadystatechange = function( section, xhr ) { - if( xhr.readyState === 4 ) { - // file protocol yields status code 0 (useful for local debug, mobile applications etc.) - if ( ( xhr.status >= 200 && xhr.status < 300 ) || xhr.status === 0 ) { - - resolve( xhr, url ); - - } - else { - - reject( xhr, url ); - - } - } - }.bind( this, section, xhr ); - - xhr.open( 'GET', url, true ); - - try { - xhr.send(); - } - catch ( e ) { - console.warn( 'Failed to get the Markdown file ' + url + '. Make sure that the presentation and the file are served by a HTTP server and the file can be found there. ' + e ); - resolve( xhr, url ); - } - - } ); - - } - - /** - * Check if a node value has the attributes pattern. - * If yes, extract it and add that value as one or several attributes - * to the target element. - * - * You need Cache Killer on Chrome to see the effect on any FOM transformation - * directly on refresh (F5) - * http://stackoverflow.com/questions/5690269/disabling-chrome-cache-for-website-development/7000899#answer-11786277 - */ - function addAttributeInElement( node, elementTarget, separator ) { - - var mardownClassesInElementsRegex = new RegExp( separator, 'mg' ); - var mardownClassRegex = new RegExp( "([^\"= ]+?)=\"([^\"]+?)\"|(data-[^\"= ]+?)(?=[\" ])", 'mg' ); - var nodeValue = node.nodeValue; - var matches, - matchesClass; - if( matches = mardownClassesInElementsRegex.exec( nodeValue ) ) { - - var classes = matches[1]; - nodeValue = nodeValue.substring( 0, matches.index ) + nodeValue.substring( mardownClassesInElementsRegex.lastIndex ); - node.nodeValue = nodeValue; - while( matchesClass = mardownClassRegex.exec( classes ) ) { - if( matchesClass[2] ) { - elementTarget.setAttribute( matchesClass[1], matchesClass[2] ); - } else { - elementTarget.setAttribute( matchesClass[3], "" ); - } - } - return true; - } - return false; - } - - /** - * Add attributes to the parent element of a text node, - * or the element of an attribute node. - */ - function addAttributes( section, element, previousElement, separatorElementAttributes, separatorSectionAttributes ) { - - if ( element != null && element.childNodes != undefined && element.childNodes.length > 0 ) { - var previousParentElement = element; - for( var i = 0; i < element.childNodes.length; i++ ) { - var childElement = element.childNodes[i]; - if ( i > 0 ) { - var j = i - 1; - while ( j >= 0 ) { - var aPreviousChildElement = element.childNodes[j]; - if ( typeof aPreviousChildElement.setAttribute == 'function' && aPreviousChildElement.tagName != "BR" ) { - previousParentElement = aPreviousChildElement; - break; - } - j = j - 1; - } - } - var parentSection = section; - if( childElement.nodeName == "section" ) { - parentSection = childElement ; - previousParentElement = childElement ; - } - if ( typeof childElement.setAttribute == 'function' || childElement.nodeType == Node.COMMENT_NODE ) { - addAttributes( parentSection, childElement, previousParentElement, separatorElementAttributes, separatorSectionAttributes ); - } - } - } - - if ( element.nodeType == Node.COMMENT_NODE ) { - if ( addAttributeInElement( element, previousElement, separatorElementAttributes ) == false ) { - addAttributeInElement( element, section, separatorSectionAttributes ); - } - } - } - - /** - * Converts any current data-markdown slides in the - * DOM to HTML. - */ - function convertSlides() { - - var sections = deck.getRevealElement().querySelectorAll( '[data-markdown]:not([data-markdown-parsed])'); - - [].slice.call( sections ).forEach( function( section ) { - - section.setAttribute( 'data-markdown-parsed', true ) - - var notes = section.querySelector( 'aside.notes' ); - var markdown = getMarkdownFromSlide( section ); - - section.innerHTML = marked( markdown ); - addAttributes( section, section, null, section.getAttribute( 'data-element-attributes' ) || - section.parentNode.getAttribute( 'data-element-attributes' ) || - DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR, - section.getAttribute( 'data-attributes' ) || - section.parentNode.getAttribute( 'data-attributes' ) || - DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR); - - // If there were notes, we need to re-add them after - // having overwritten the section's HTML - if( notes ) { - section.appendChild( notes ); - } - - } ); - - return Promise.resolve(); - - } - - function escapeForHTML( input ) { - - return input.replace( /([&<>'"])/g, char => HTML_ESCAPE_MAP[char] ); - - } - - return { - id: 'markdown', - - /** - * Starts processing and converting Markdown within the - * current reveal.js deck. - */ - init: function( reveal ) { - - deck = reveal; - - let { renderer, animateLists, ...markedOptions } = deck.getConfig().markdown || {}; - - if( !renderer ) { - renderer = new marked.Renderer(); - - renderer.code = ( code, language ) => { - - // Off by default - let lineNumbers = ''; - - // Users can opt in to show line numbers and highlight - // specific lines. - // ```javascript [] show line numbers - // ```javascript [1,4-8] highlights lines 1 and 4-8 - if( CODE_LINE_NUMBER_REGEX.test( language ) ) { - lineNumbers = language.match( CODE_LINE_NUMBER_REGEX )[1].trim(); - lineNumbers = `data-line-numbers="${lineNumbers}"`; - language = language.replace( CODE_LINE_NUMBER_REGEX, '' ).trim(); - } - - // Escape before this gets injected into the DOM to - // avoid having the HTML parser alter our code before - // highlight.js is able to read it - code = escapeForHTML( code ); - - return `
    ${code}
    `; - }; - } - - if( animateLists === true ) { - renderer.listitem = text => `
  • ${text}
    • `; - } - - marked.setOptions( { - renderer, - ...markedOptions - } ); - - return processSlides( deck.getRevealElement() ).then( convertSlides ); - - }, - - // TODO: Do these belong in the API? - processSlides: processSlides, - convertSlides: convertSlides, - slidify: slidify, - marked: marked - } - + // The reveal.js instance this plugin is attached to + let deck; + + /** + * Retrieves the markdown contents of a slide section + * element. Normalizes leading tabs/whitespace. + */ + function getMarkdownFromSlide(section) { + // look for a "); + + var leadingWs = text.match(/^\n?(\s*)/)[1].length, + leadingTabs = text.match(/^\n?(\t*)/)[1].length; + + if (leadingTabs > 0) { + text = text.replace( + new RegExp("\\n?\\t{" + leadingTabs + "}", "g"), + "\n", + ); + } else if (leadingWs > 1) { + text = text.replace(new RegExp("\\n? {" + leadingWs + "}", "g"), "\n"); + } + + return text; + } + + /** + * Given a markdown slide section element, this will + * return all arguments that aren't related to markdown + * parsing. Used to forward any other user-defined arguments + * to the output markdown slide. + */ + function getForwardedAttributes(section) { + var attributes = section.attributes; + var result = []; + + for (var i = 0, len = attributes.length; i < len; i++) { + var name = attributes[i].name, + value = attributes[i].value; + + // disregard attributes that are used for markdown loading/parsing + if (/data\-(markdown|separator|vertical|notes)/gi.test(name)) continue; + + if (value) { + result.push(name + '="' + value + '"'); + } else { + result.push(name); + } + } + + return result.join(" "); + } + + /** + * Inspects the given options and fills out default + * values for what's not defined. + */ + function getSlidifyOptions(options) { + options = options || {}; + options.separator = options.separator || DEFAULT_SLIDE_SEPARATOR; + options.notesSeparator = options.notesSeparator || DEFAULT_NOTES_SEPARATOR; + options.attributes = options.attributes || ""; + + return options; + } + + /** + * Helper function for constructing a markdown slide. + */ + function createMarkdownSlide(content, options) { + options = getSlidifyOptions(options); + + var notesMatch = content.split(new RegExp(options.notesSeparator, "mgi")); + + if (notesMatch.length === 2) { + content = + notesMatch[0] + + '"; + } + + // prevent script end tags in the content from interfering + // with parsing + content = content.replace(/<\/script>/g, SCRIPT_END_PLACEHOLDER); + + return '"; + } + + /** + * Parses a data string into multiple slides based + * on the passed in separator arguments. + */ + function slidify(markdown, options) { + options = getSlidifyOptions(options); + + var separatorRegex = new RegExp( + options.separator + + (options.verticalSeparator ? "|" + options.verticalSeparator : ""), + "mg", + ), + horizontalSeparatorRegex = new RegExp(options.separator); + + var matches, + lastIndex = 0, + isHorizontal, + wasHorizontal = true, + content, + sectionStack = []; + + // iterate until all blocks between separators are stacked up + while ((matches = separatorRegex.exec(markdown))) { + var notes = null; + + // determine direction (horizontal by default) + isHorizontal = horizontalSeparatorRegex.test(matches[0]); + + if (!isHorizontal && wasHorizontal) { + // create vertical stack + sectionStack.push([]); + } + + // pluck slide content from markdown input + content = markdown.substring(lastIndex, matches.index); + + if (isHorizontal && wasHorizontal) { + // add to horizontal stack + sectionStack.push(content); + } else { + // add to vertical stack + sectionStack[sectionStack.length - 1].push(content); + } + + lastIndex = separatorRegex.lastIndex; + wasHorizontal = isHorizontal; + } + + // add the remaining slide + (wasHorizontal ? sectionStack : sectionStack[sectionStack.length - 1]).push( + markdown.substring(lastIndex), + ); + + var markdownSections = ""; + + // flatten the hierarchical stack, and insert
    tags + for (var i = 0, len = sectionStack.length; i < len; i++) { + // vertical + if (sectionStack[i] instanceof Array) { + markdownSections += "
    "; + + sectionStack[i].forEach(function (child) { + markdownSections += + "
    " + + createMarkdownSlide(child, options) + + "
    "; + }); + + markdownSections += "
    "; + } else { + markdownSections += + "
    " + + createMarkdownSlide(sectionStack[i], options) + + "
    "; + } + } + + return markdownSections; + } + + /** + * Parses any current data-markdown slides, splits + * multi-slide markdown into separate sections and + * handles loading of external markdown. + */ + function processSlides(scope) { + return new Promise(function (resolve) { + var externalPromises = []; + + [].slice + .call( + scope.querySelectorAll( + "section[data-markdown]:not([data-markdown-parsed])", + ), + ) + .forEach(function (section, i) { + if (section.getAttribute("data-markdown").length) { + externalPromises.push( + loadExternalMarkdown(section).then( + // Finished loading external file + function (xhr, url) { + section.outerHTML = slidify(xhr.responseText, { + separator: section.getAttribute("data-separator"), + verticalSeparator: section.getAttribute( + "data-separator-vertical", + ), + notesSeparator: section.getAttribute( + "data-separator-notes", + ), + attributes: getForwardedAttributes(section), + }); + }, + + // Failed to load markdown + function (xhr, url) { + section.outerHTML = + '
    ' + + "ERROR: The attempt to fetch " + + url + + " failed with HTTP status " + + xhr.status + + "." + + "Check your browser's JavaScript console for more details." + + "

    Remember that you need to serve the presentation HTML from a HTTP server.

    " + + "
    "; + }, + ), + ); + } else { + section.outerHTML = slidify(getMarkdownFromSlide(section), { + separator: section.getAttribute("data-separator"), + verticalSeparator: section.getAttribute( + "data-separator-vertical", + ), + notesSeparator: section.getAttribute("data-separator-notes"), + attributes: getForwardedAttributes(section), + }); + } + }); + + Promise.all(externalPromises).then(resolve); + }); + } + + function loadExternalMarkdown(section) { + return new Promise(function (resolve, reject) { + var xhr = new XMLHttpRequest(), + url = section.getAttribute("data-markdown"); + + var datacharset = section.getAttribute("data-charset"); + + // see https://developer.mozilla.org/en-US/docs/Web/API/element.getAttribute#Notes + if (datacharset != null && datacharset != "") { + xhr.overrideMimeType("text/html; charset=" + datacharset); + } + + xhr.onreadystatechange = function (section, xhr) { + if (xhr.readyState === 4) { + // file protocol yields status code 0 (useful for local debug, mobile applications etc.) + if ((xhr.status >= 200 && xhr.status < 300) || xhr.status === 0) { + resolve(xhr, url); + } else { + reject(xhr, url); + } + } + }.bind(this, section, xhr); + + xhr.open("GET", url, true); + + try { + xhr.send(); + } catch (e) { + console.warn( + "Failed to get the Markdown file " + + url + + ". Make sure that the presentation and the file are served by a HTTP server and the file can be found there. " + + e, + ); + resolve(xhr, url); + } + }); + } + + /** + * Check if a node value has the attributes pattern. + * If yes, extract it and add that value as one or several attributes + * to the target element. + * + * You need Cache Killer on Chrome to see the effect on any FOM transformation + * directly on refresh (F5) + * http://stackoverflow.com/questions/5690269/disabling-chrome-cache-for-website-development/7000899#answer-11786277 + */ + function addAttributeInElement(node, elementTarget, separator) { + var mardownClassesInElementsRegex = new RegExp(separator, "mg"); + var mardownClassRegex = new RegExp( + '([^"= ]+?)="([^"]+?)"|(data-[^"= ]+?)(?=[" ])', + "mg", + ); + var nodeValue = node.nodeValue; + var matches, matchesClass; + if ((matches = mardownClassesInElementsRegex.exec(nodeValue))) { + var classes = matches[1]; + nodeValue = + nodeValue.substring(0, matches.index) + + nodeValue.substring(mardownClassesInElementsRegex.lastIndex); + node.nodeValue = nodeValue; + while ((matchesClass = mardownClassRegex.exec(classes))) { + if (matchesClass[2]) { + elementTarget.setAttribute(matchesClass[1], matchesClass[2]); + } else { + elementTarget.setAttribute(matchesClass[3], ""); + } + } + return true; + } + return false; + } + + /** + * Add attributes to the parent element of a text node, + * or the element of an attribute node. + */ + function addAttributes( + section, + element, + previousElement, + separatorElementAttributes, + separatorSectionAttributes, + ) { + if ( + element != null && + element.childNodes != undefined && + element.childNodes.length > 0 + ) { + var previousParentElement = element; + for (var i = 0; i < element.childNodes.length; i++) { + var childElement = element.childNodes[i]; + if (i > 0) { + var j = i - 1; + while (j >= 0) { + var aPreviousChildElement = element.childNodes[j]; + if ( + typeof aPreviousChildElement.setAttribute == "function" && + aPreviousChildElement.tagName != "BR" + ) { + previousParentElement = aPreviousChildElement; + break; + } + j = j - 1; + } + } + var parentSection = section; + if (childElement.nodeName == "section") { + parentSection = childElement; + previousParentElement = childElement; + } + if ( + typeof childElement.setAttribute == "function" || + childElement.nodeType == Node.COMMENT_NODE + ) { + addAttributes( + parentSection, + childElement, + previousParentElement, + separatorElementAttributes, + separatorSectionAttributes, + ); + } + } + } + + if (element.nodeType == Node.COMMENT_NODE) { + if ( + addAttributeInElement( + element, + previousElement, + separatorElementAttributes, + ) == false + ) { + addAttributeInElement(element, section, separatorSectionAttributes); + } + } + } + + /** + * Converts any current data-markdown slides in the + * DOM to HTML. + */ + function convertSlides() { + var sections = deck + .getRevealElement() + .querySelectorAll("[data-markdown]:not([data-markdown-parsed])"); + + [].slice.call(sections).forEach(function (section) { + section.setAttribute("data-markdown-parsed", true); + + var notes = section.querySelector("aside.notes"); + var markdown = getMarkdownFromSlide(section); + + section.innerHTML = marked(markdown); + addAttributes( + section, + section, + null, + section.getAttribute("data-element-attributes") || + section.parentNode.getAttribute("data-element-attributes") || + DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR, + section.getAttribute("data-attributes") || + section.parentNode.getAttribute("data-attributes") || + DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR, + ); + + // If there were notes, we need to re-add them after + // having overwritten the section's HTML + if (notes) { + section.appendChild(notes); + } + }); + + return Promise.resolve(); + } + + function escapeForHTML(input) { + return input.replace(/([&<>'"])/g, (char) => HTML_ESCAPE_MAP[char]); + } + + return { + id: "markdown", + + /** + * Starts processing and converting Markdown within the + * current reveal.js deck. + */ + init: function (reveal) { + deck = reveal; + + let { renderer, animateLists, ...markedOptions } = + deck.getConfig().markdown || {}; + + if (!renderer) { + renderer = new marked.Renderer(); + + renderer.code = (code, language) => { + // Off by default + let lineNumbers = ""; + + // Users can opt in to show line numbers and highlight + // specific lines. + // ```javascript [] show line numbers + // ```javascript [1,4-8] highlights lines 1 and 4-8 + if (CODE_LINE_NUMBER_REGEX.test(language)) { + lineNumbers = language.match(CODE_LINE_NUMBER_REGEX)[1].trim(); + lineNumbers = `data-line-numbers="${lineNumbers}"`; + language = language.replace(CODE_LINE_NUMBER_REGEX, "").trim(); + } + + // Escape before this gets injected into the DOM to + // avoid having the HTML parser alter our code before + // highlight.js is able to read it + code = escapeForHTML(code); + + return `
    ${code}
    `; + }; + } + + if (animateLists === true) { + renderer.listitem = (text) => `
  • ${text}
    • `; + } + + marked.setOptions({ + renderer, + ...markedOptions, + }); + + return processSlides(deck.getRevealElement()).then(convertSlides); + }, + + // TODO: Do these belong in the API? + processSlides: processSlides, + convertSlides: convertSlides, + slidify: slidify, + marked: marked, + }; }; export default Plugin; diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/katex.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/katex.js index a8b47c4..3b927b6 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/katex.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/katex.js @@ -6,91 +6,87 @@ * @author Gerhard Burger */ export const KaTeX = () => { - let deck; - - let defaultOptions = { - version: 'latest', - delimiters: [ - {left: '$$', right: '$$', display: true}, // Note: $$ has to come before $ - {left: '$', right: '$', display: false}, - {left: '\\(', right: '\\)', display: false}, - {left: '\\[', right: '\\]', display: true} - ], - ignoredTags: ['script', 'noscript', 'style', 'textarea', 'pre'] - } - - const loadCss = src => { - let link = document.createElement('link'); - link.rel = 'stylesheet'; - link.href = src; - document.head.appendChild(link); - }; - - /** - * Loads a JavaScript file and returns a Promise for when it is loaded - * Credits: https://aaronsmith.online/easily-load-an-external-script-using-javascript/ - */ - const loadScript = src => { - return new Promise((resolve, reject) => { - const script = document.createElement('script') - script.type = 'text/javascript' - script.onload = resolve - script.onerror = reject - script.src = src - document.head.append(script) - }) - }; - - async function loadScripts(urls) { - for(const url of urls) { - await loadScript(url); - } - } - - return { - id: 'katex', - - init: function (reveal) { - - deck = reveal; - - let revealOptions = deck.getConfig().katex || {}; - - let options = {...defaultOptions, ...revealOptions}; - const {local, version, extensions, ...katexOptions} = options; - - let baseUrl = options.local || 'https://cdn.jsdelivr.net/npm/katex'; - let versionString = options.local ? '' : '@' + options.version; - - let cssUrl = baseUrl + versionString + '/dist/katex.min.css'; - let katexUrl = baseUrl + versionString + '/dist/katex.min.js'; - let mhchemUrl = baseUrl + versionString + '/dist/contrib/mhchem.min.js' - let karUrl = baseUrl + versionString + '/dist/contrib/auto-render.min.js'; - - let katexScripts = [katexUrl]; - if(options.extensions && options.extensions.includes("mhchem")) { - katexScripts.push(mhchemUrl); - } - katexScripts.push(karUrl); - - const renderMath = () => { - renderMathInElement(reveal.getSlidesElement(), katexOptions); - deck.layout(); - } - - loadCss(cssUrl); - - // For some reason dynamically loading with defer attribute doesn't result in the expected behavior, the below code does - loadScripts(katexScripts).then(() => { - if( deck.isReady() ) { - renderMath(); - } - else { - deck.on( 'ready', renderMath.bind( this ) ); - } - }); - - } - } - + let deck; + + let defaultOptions = { + version: "latest", + delimiters: [ + { left: "$$", right: "$$", display: true }, // Note: $$ has to come before $ + { left: "$", right: "$", display: false }, + { left: "\\(", right: "\\)", display: false }, + { left: "\\[", right: "\\]", display: true }, + ], + ignoredTags: ["script", "noscript", "style", "textarea", "pre"], + }; + + const loadCss = (src) => { + let link = document.createElement("link"); + link.rel = "stylesheet"; + link.href = src; + document.head.appendChild(link); + }; + + /** + * Loads a JavaScript file and returns a Promise for when it is loaded + * Credits: https://aaronsmith.online/easily-load-an-external-script-using-javascript/ + */ + const loadScript = (src) => { + return new Promise((resolve, reject) => { + const script = document.createElement("script"); + script.type = "text/javascript"; + script.onload = resolve; + script.onerror = reject; + script.src = src; + document.head.append(script); + }); + }; + + async function loadScripts(urls) { + for (const url of urls) { + await loadScript(url); + } + } + + return { + id: "katex", + + init: function (reveal) { + deck = reveal; + + let revealOptions = deck.getConfig().katex || {}; + + let options = { ...defaultOptions, ...revealOptions }; + const { local, version, extensions, ...katexOptions } = options; + + let baseUrl = options.local || "https://cdn.jsdelivr.net/npm/katex"; + let versionString = options.local ? "" : "@" + options.version; + + let cssUrl = baseUrl + versionString + "/dist/katex.min.css"; + let katexUrl = baseUrl + versionString + "/dist/katex.min.js"; + let mhchemUrl = baseUrl + versionString + "/dist/contrib/mhchem.min.js"; + let karUrl = baseUrl + versionString + "/dist/contrib/auto-render.min.js"; + + let katexScripts = [katexUrl]; + if (options.extensions && options.extensions.includes("mhchem")) { + katexScripts.push(mhchemUrl); + } + katexScripts.push(karUrl); + + const renderMath = () => { + renderMathInElement(reveal.getSlidesElement(), katexOptions); + deck.layout(); + }; + + loadCss(cssUrl); + + // For some reason dynamically loading with defer attribute doesn't result in the expected behavior, the below code does + loadScripts(katexScripts).then(() => { + if (deck.isReady()) { + renderMath(); + } else { + deck.on("ready", renderMath.bind(this)); + } + }); + }, + }; }; diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/math.esm.js 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return { + id: "mathjax3", + init: function (e) { + var n = e.getConfig().mathjax3 || {}, + r = on(on({}, t), n); + (r.tex = on(on({}, t.tex), n.tex)), + (r.options = on(on({}, t.options), n.options)), + (r.startup = on(on({}, t.startup), n.startup)); + var o = + r.mathjax || + "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"; + (r.mathjax = null), + (window.MathJax = r), + (function (t, e) { + var n = document.createElement("script"); + (n.type = "text/javascript"), + (n.id = "MathJax-script"), + (n.src = t), + (n.async = !0), + (n.onload = function () { + "function" == typeof e && (e.call(), (e = null)); + }), + document.head.appendChild(n); + })(o, function () { + Reveal.addEventListener("slidechanged", function (t) { + MathJax.typeset(); + }); + }); + }, + }; + }, + })); +export default ha; diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/math.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/math/math.js index 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"\\d{1,9}\\".concat(h.slice(-1)) : "\\".concat(h)), + this.options.pedantic && (h = g ? h : "[*+-]"); + for ( + var m = new RegExp( + "^( {0,3}".concat(h, ")((?: [^\\n]*)?(?:\\n|$))"), + ); + t && + ((f = !1), (e = m.exec(t))) && + !this.rules.block.hr.test(t); + + ) { + if ( + ((n = e[0]), + (t = t.substring(n.length)), + (l = e[2].split("\n", 1)[0]), + (c = t.split("\n", 1)[0]), + this.options.pedantic + ? ((u = 2), (d = l.trimLeft())) + : ((u = (u = e[2].search(/[^ ]/)) > 4 ? 1 : u), + (d = l.slice(u)), + (u += e[1].length)), + (o = !1), + !l && + /^ *$/.test(c) && + ((n += c + "\n"), + (t = t.substring(c.length + 1)), + (f = !0)), + !f) + ) + for ( + var v = new RegExp( + "^ {0,".concat( + Math.min(3, u - 1), + "}(?:[*+-]|\\d{1,9}[.)])", + ), + ); + t && + ((l = p = t.split("\n", 1)[0]), + this.options.pedantic && + (l = l.replace(/^ {1,4}(?=( {4})*[^ ])/g, " ")), + !v.test(l)); + + ) { + if (l.search(/[^ ]/) >= u || !l.trim()) + d += "\n" + l.slice(u); + else { + if (o) break; + d += "\n" + l; + } + o || l.trim() || (o = !0), + (n += p + "\n"), + (t = t.substring(p.length + 1)); + } + D.loose || + (s ? (D.loose = !0) : /\n *\n *$/.test(n) && (s = !0)), + this.options.gfm && + (r = /^\[[ xX]\] /.exec(d)) && + ((i = "[ ] " !== r[0]), + (d = d.replace(/^\[[ xX]\] +/, ""))), + D.items.push({ + type: "list_item", + raw: n, + task: !!r, + checked: i, + loose: !1, + text: d, + }), + (D.raw += n); + } + (D.items[D.items.length - 1].raw = n.trimRight()), + (D.items[D.items.length - 1].text = d.trimRight()), + (D.raw = D.raw.trimRight()); + var y = D.items.length; + for (a = 0; a < y; a++) { + (this.lexer.state.top = !1), + (D.items[a].tokens = this.lexer.blockTokens( + D.items[a].text, + [], + )); + var k = D.items[a].tokens.filter(function (t) { + return "space" === t.type; + }), + E = k.every(function (t) { + var e, + n = 0, + r = pr(t.raw.split("")); + try { + for (r.s(); !(e = r.n()).done; ) { + if (("\n" === e.value && (n += 1), n > 1)) return !0; + } + } catch (t) { + r.e(t); + } finally { + r.f(); + } + return !1; + }); + !D.loose && + k.length && + E && + ((D.loose = !0), (D.items[a].loose = !0)); + } + return D; + } + }, + }, + { + key: "html", + value: function (t) { + var e = this.rules.block.html.exec(t); + if (e) { + var n = { + type: "html", + raw: e[0], + pre: + !this.options.sanitizer && + ("pre" === e[1] || "script" === e[1] || "style" === e[1]), + text: e[0], + }; + return ( + this.options.sanitize && + ((n.type = "paragraph"), + (n.text = this.options.sanitizer + ? this.options.sanitizer(e[0]) + : Fo(e[0])), + (n.tokens = []), + this.lexer.inline(n.text, n.tokens)), + n + ); + } + }, + }, + { + key: "def", + value: function (t) { + var e = this.rules.block.def.exec(t); + if (e) + return ( + e[3] && (e[3] = e[3].substring(1, e[3].length - 1)), + { + type: "def", + tag: e[1].toLowerCase().replace(/\s+/g, " "), + raw: e[0], + href: e[2], + title: e[3], + } + ); + }, + }, + { + key: "table", + value: function (t) { + var e = this.rules.block.table.exec(t); + if (e) { + var n = { + type: "table", + header: No(e[1]).map(function (t) { + return { text: t }; + }), + align: e[2].replace(/^ *|\| *$/g, "").split(/ *\| */), + rows: + e[3] && e[3].trim() + ? e[3].replace(/\n[ \t]*$/, "").split("\n") + : [], + }; + if (n.header.length === n.align.length) { + n.raw = e[0]; + var r, + i, + u, + a, + o = n.align.length; + for (r = 0; r < o; r++) + /^ *-+: *$/.test(n.align[r]) + ? (n.align[r] = "right") + : /^ *:-+: *$/.test(n.align[r]) + ? (n.align[r] = "center") + : /^ *:-+ *$/.test(n.align[r]) + ? (n.align[r] = "left") + : (n.align[r] = null); + for (o = n.rows.length, r = 0; r < o; r++) + n.rows[r] = No(n.rows[r], n.header.length).map(function (t) { + return { text: t }; + }); + for (o = n.header.length, i = 0; i < o; i++) + (n.header[i].tokens = []), + this.lexer.inlineTokens( + n.header[i].text, + n.header[i].tokens, + ); + for (o = n.rows.length, i = 0; i < o; i++) + for (a = n.rows[i], u = 0; u < a.length; u++) + (a[u].tokens = []), + this.lexer.inlineTokens(a[u].text, a[u].tokens); + return n; + } + } + }, + }, + { + key: "lheading", + value: function (t) { + var e = this.rules.block.lheading.exec(t); + if (e) { + var n = { + type: "heading", + raw: e[0], + depth: "=" === e[2].charAt(0) ? 1 : 2, + text: e[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "paragraph", + value: function (t) { + var e = this.rules.block.paragraph.exec(t); + if (e) { + var n = { + type: "paragraph", + raw: e[0], + text: + "\n" === e[1].charAt(e[1].length - 1) + ? e[1].slice(0, -1) + : e[1], + tokens: [], + }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "text", + value: function (t) { + var e = this.rules.block.text.exec(t); + if (e) { + var n = { type: "text", raw: e[0], text: e[0], tokens: [] }; + return this.lexer.inline(n.text, n.tokens), n; + } + }, + }, + { + key: "escape", + value: function (t) { + var e = this.rules.inline.escape.exec(t); + if (e) return { type: "escape", raw: e[0], text: Fo(e[1]) }; + }, + }, + { + key: "tag", + value: function (t) { + var e = this.rules.inline.tag.exec(t); + if (e) + return ( + !this.lexer.state.inLink && /^    /i.test(e[0]) && + (this.lexer.state.inLink = !1), + !this.lexer.state.inRawBlock && + /^<(pre|code|kbd|script)(\s|>)/i.test(e[0]) + ? (this.lexer.state.inRawBlock = !0) + : this.lexer.state.inRawBlock && + /^<\/(pre|code|kbd|script)(\s|>)/i.test(e[0]) && + (this.lexer.state.inRawBlock = !1), + { + type: this.options.sanitize ? "text" : "html", + raw: e[0], + inLink: this.lexer.state.inLink, + inRawBlock: this.lexer.state.inRawBlock, + text: this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(e[0]) + : Fo(e[0]) + : e[0], + } + ); + }, + }, + { + key: "link", + value: function (t) { + var e = this.rules.inline.link.exec(t); + if (e) { + var n = e[2].trim(); + if (!this.options.pedantic && /^$/.test(n)) return; + var r = Uo(n.slice(0, -1), "\\"); + if ((n.length - r.length) % 2 == 0) return; + } else { + var i = (function (t, e) { + if (-1 === t.indexOf(e[1])) return -1; + for (var n = t.length, r = 0, i = 0; i < n; i++) + if ("\\" === t[i]) i++; + else if (t[i] === e[0]) r++; + else if (t[i] === e[1] && --r < 0) return i; + return -1; + })(e[2], "()"); + if (i > -1) { + var u = (0 === e[0].indexOf("!") ? 5 : 4) + e[1].length + i; + (e[2] = e[2].substring(0, i)), + (e[0] = e[0].substring(0, u).trim()), + (e[3] = ""); + } + } + var a = e[2], + o = ""; + if (this.options.pedantic) { + var s = /^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(a); + s && ((a = s[1]), (o = s[3])); + } else o = e[3] ? e[3].slice(1, -1) : ""; + return ( + (a = a.trim()), + /^$/.test(n) + ? a.slice(1) + : a.slice(1, -1)), + Ho( + e, + { + href: a ? a.replace(this.rules.inline._escapes, "$1") : a, + title: o ? o.replace(this.rules.inline._escapes, "$1") : o, + }, + e[0], + this.lexer, + ) + ); + } + }, + }, + { + key: "reflink", + value: function (t, e) { + var n; + if ( + (n = this.rules.inline.reflink.exec(t)) || + (n = this.rules.inline.nolink.exec(t)) + ) { + var r = (n[2] || n[1]).replace(/\s+/g, " "); + if (!(r = e[r.toLowerCase()]) || !r.href) { + var i = n[0].charAt(0); + return { type: "text", raw: i, text: i }; + } + return Ho(n, r, n[0], this.lexer); + } + }, + }, + { + key: "emStrong", + value: function (t, e) { + var n = + arguments.length > 2 && void 0 !== arguments[2] + ? arguments[2] + : "", + r = this.rules.inline.emStrong.lDelim.exec(t); + if ( + r && + (!r[3] || + !n.match( + 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+ )) + ) { + var i = r[1] || r[2] || ""; + if ( + !i || + (i && ("" === n || this.rules.inline.punctuation.exec(n))) + ) { + var u, + a, + o = r[0].length - 1, + s = o, + l = 0, + c = + "*" === r[0][0] + ? this.rules.inline.emStrong.rDelimAst + : this.rules.inline.emStrong.rDelimUnd; + for ( + c.lastIndex = 0, e = e.slice(-1 * t.length + o); + null != (r = c.exec(e)); + + ) + if ((u = r[1] || r[2] || r[3] || r[4] || r[5] || r[6])) + if (((a = u.length), r[3] || r[4])) s += a; + else if (!((r[5] || r[6]) && o % 3) || (o + a) % 3) { + if (!((s -= a) > 0)) { + if ( + ((a = Math.min(a, a + s + l)), Math.min(o, a) % 2) + ) { + var p = t.slice(1, o + r.index + a); + return { + type: "em", + raw: t.slice(0, o + r.index + a + 1), + text: p, + tokens: this.lexer.inlineTokens(p, []), + }; + } + var d = t.slice(2, o + r.index + a - 1); + return { + type: "strong", + raw: t.slice(0, o + r.index + a + 1), + text: d, + tokens: this.lexer.inlineTokens(d, []), + }; + } + } else l += a; + } + } + }, + }, + { + key: "codespan", + value: function (t) { + var e = this.rules.inline.code.exec(t); + if (e) { + var n = e[2].replace(/\n/g, " "), + r = /[^ ]/.test(n), + i = /^ /.test(n) && / $/.test(n); + return ( + r && i && (n = n.substring(1, n.length - 1)), + (n = Fo(n, !0)), + { type: "codespan", raw: e[0], text: n } + ); + } + }, + }, + { + key: "br", + value: function (t) { + var e = this.rules.inline.br.exec(t); + if (e) return { type: "br", raw: e[0] }; + }, + }, + { + key: "del", + value: function (t) { + var e = this.rules.inline.del.exec(t); + if (e) + return { + type: "del", + raw: e[0], + text: e[2], + tokens: this.lexer.inlineTokens(e[2], []), + }; + }, + }, + { + key: "autolink", + value: function (t, e) { + var n, + r, + i = this.rules.inline.autolink.exec(t); + if (i) + return ( + (r = + "@" === i[2] + ? "mailto:" + (n = Fo(this.options.mangle ? e(i[1]) : i[1])) + : (n = Fo(i[1]))), + { + type: "link", + raw: i[0], + text: n, + href: r, + tokens: [{ type: "text", raw: n, text: n }], + } + ); + }, + }, + { + key: "url", + value: function (t, e) { + var n; + if ((n = this.rules.inline.url.exec(t))) { + var r, i; + if ("@" === n[2]) + i = "mailto:" + (r = Fo(this.options.mangle ? e(n[0]) : n[0])); + else { + var u; + do { + (u = n[0]), + (n[0] = this.rules.inline._backpedal.exec(n[0])[0]); + } while (u !== n[0]); + (r = Fo(n[0])), (i = "www." === n[1] ? "http://" + r : r); + } + return { + type: "link", + raw: n[0], + text: r, + href: i, + tokens: [{ type: "text", raw: r, text: r }], + }; + } + }, + }, + { + key: "inlineText", + value: function (t, e) { + var n, + r = this.rules.inline.text.exec(t); + if (r) + return ( + (n = this.lexer.state.inRawBlock + ? this.options.sanitize + ? this.options.sanitizer + ? this.options.sanitizer(r[0]) + : Fo(r[0]) + : r[0] + : Fo(this.options.smartypants ? e(r[0]) : r[0])), + { type: "text", raw: r[0], text: n } + ); + }, + }, + ]), + t + ); + })(), + Jo = { + newline: /^(?: *(?:\n|$))+/, + code: /^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/, + fences: + /^ {0,3}(`{3,}(?=[^`\n]*\n)|~{3,})([^\n]*)\n(?:|([\s\S]*?)\n)(?: {0,3}\1[~`]* *(?=\n|$)|$)/, + hr: /^ {0,3}((?:- *){3,}|(?:_ *){3,}|(?:\* *){3,})(?:\n+|$)/, + heading: /^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/, + blockquote: /^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/, + list: /^( {0,3}bull)( [^\n]+?)?(?:\n|$)/, + html: "^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|\\n*|$)|\\n*|$)|)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))", + def: /^ {0,3}\[(label)\]: *(?:\n *)?]+)>?(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/, + table: Mo, + lheading: /^([^\n]+)\n {0,3}(=+|-+) *(?:\n+|$)/, + _paragraph: + /^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/, + text: /^[^\n]+/, + _label: /(?!\s*\])(?:\\.|[^\[\]\\])+/, + _title: /(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/, + }; +(Jo.def = _o(Jo.def) + .replace("label", Jo._label) + .replace("title", Jo._title) + .getRegex()), + (Jo.bullet = /(?:[*+-]|\d{1,9}[.)])/), + (Jo.listItemStart = _o(/^( *)(bull) */) + .replace("bull", Jo.bullet) + .getRegex()), + (Jo.list = _o(Jo.list) + .replace(/bull/g, Jo.bullet) + .replace( + "hr", + "\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))", + ) + .replace("def", "\\n+(?=" + Jo.def.source + ")") + .getRegex()), + (Jo._tag = + "address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul"), + (Jo._comment = /|$)/), + (Jo.html = _o(Jo.html, "i") + .replace("comment", Jo._comment) + .replace("tag", Jo._tag) + .replace( + "attribute", + / +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/, + ) + .getRegex()), + (Jo.paragraph = _o(Jo._paragraph) + .replace("hr", Jo.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("|table", "") + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", Jo._tag) + .getRegex()), + (Jo.blockquote = _o(Jo.blockquote) + .replace("paragraph", Jo.paragraph) + .getRegex()), + (Jo.normal = jo({}, Jo)), + (Jo.gfm = jo({}, Jo.normal, { + table: + "^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)", + })), + (Jo.gfm.table = _o(Jo.gfm.table) + .replace("hr", Jo.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("blockquote", " {0,3}>") + .replace("code", " {4}[^\\n]") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", Jo._tag) + .getRegex()), + (Jo.gfm.paragraph = _o(Jo._paragraph) + .replace("hr", Jo.hr) + .replace("heading", " {0,3}#{1,6} ") + .replace("|lheading", "") + .replace("table", Jo.gfm.table) + .replace("blockquote", " {0,3}>") + .replace("fences", " {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n") + .replace("list", " {0,3}(?:[*+-]|1[.)]) ") + .replace( + "html", + ")|<(?:script|pre|style|textarea|!--)", + ) + .replace("tag", Jo._tag) + .getRegex()), + (Jo.pedantic = jo({}, Jo.normal, { + html: _o( + "^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+? *(?:\\n{2,}|\\s*$)|\\s]*)*?/?> *(?:\\n{2,}|\\s*$))", + ) + .replace("comment", Jo._comment) + .replace( + /tag/g, + "(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b", + ) + .getRegex(), + def: /^ *\[([^\]]+)\]: *]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/, + heading: /^(#{1,6})(.*)(?:\n+|$)/, + fences: Mo, + paragraph: _o(Jo.normal._paragraph) + .replace("hr", Jo.hr) + .replace("heading", " *#{1,6} *[^\n]") + .replace("lheading", Jo.lheading) + .replace("blockquote", " {0,3}>") + .replace("|fences", "") + .replace("|list", "") + .replace("|html", "") + .getRegex(), + })); +var Vo = { + escape: /^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/, + autolink: /^<(scheme:[^\s\x00-\x1f<>]*|email)>/, + url: Mo, + tag: "^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^", + link: /^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/, + reflink: /^!?\[(label)\]\[(ref)\]/, + nolink: /^!?\[(ref)\](?:\[\])?/, + reflinkSearch: "reflink|nolink(?!\\()", + emStrong: { + lDelim: /^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/, + rDelimAst: + /^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/, + rDelimUnd: + /^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/, + }, + code: /^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/, + br: /^( {2,}|\\)\n(?!\s*$)/, + del: Mo, + text: /^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\ 0.5 && (n = "x" + n.toString(16)), + (r += "&#" + n + ";"); + return r; +} +(Vo._punctuation = "!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~"), + (Vo.punctuation = _o(Vo.punctuation) + .replace(/punctuation/g, Vo._punctuation) + .getRegex()), + (Vo.blockSkip = /\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g), + (Vo.escapedEmSt = /\\\*|\\_/g), + (Vo._comment = _o(Jo._comment).replace("(?:--\x3e|$)", "--\x3e").getRegex()), + (Vo.emStrong.lDelim = _o(Vo.emStrong.lDelim) + .replace(/punct/g, Vo._punctuation) + .getRegex()), + (Vo.emStrong.rDelimAst = _o(Vo.emStrong.rDelimAst, "g") + .replace(/punct/g, Vo._punctuation) + .getRegex()), + (Vo.emStrong.rDelimUnd = _o(Vo.emStrong.rDelimUnd, "g") + .replace(/punct/g, Vo._punctuation) + .getRegex()), + (Vo._escapes = /\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g), + (Vo._scheme = /[a-zA-Z][a-zA-Z0-9+.-]{1,31}/), + (Vo._email = + /[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/), + (Vo.autolink = _o(Vo.autolink) + .replace("scheme", Vo._scheme) + .replace("email", Vo._email) + .getRegex()), + (Vo._attribute = + /\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/), + (Vo.tag = _o(Vo.tag) + .replace("comment", Vo._comment) + .replace("attribute", Vo._attribute) + .getRegex()), + (Vo._label = /(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/), + (Vo._href = /<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/), + (Vo._title = /"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/), + (Vo.link = _o(Vo.link) + .replace("label", Vo._label) + .replace("href", Vo._href) + .replace("title", Vo._title) + .getRegex()), + (Vo.reflink = _o(Vo.reflink) + .replace("label", Vo._label) + .replace("ref", Jo._label) + .getRegex()), + (Vo.nolink = _o(Vo.nolink).replace("ref", Jo._label).getRegex()), + (Vo.reflinkSearch = _o(Vo.reflinkSearch, "g") + .replace("reflink", Vo.reflink) + .replace("nolink", Vo.nolink) + .getRegex()), + (Vo.normal = jo({}, Vo)), + (Vo.pedantic = jo({}, Vo.normal, { + strong: { + start: /^__|\*\*/, + middle: /^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/, + endAst: /\*\*(?!\*)/g, + endUnd: /__(?!_)/g, + }, + em: { + start: /^_|\*/, + middle: /^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/, + endAst: /\*(?!\*)/g, + endUnd: /_(?!_)/g, + }, + link: _o(/^!?\[(label)\]\((.*?)\)/) + .replace("label", Vo._label) + .getRegex(), + reflink: _o(/^!?\[(label)\]\s*\[([^\]]*)\]/) + .replace("label", Vo._label) + .getRegex(), + })), + (Vo.gfm = jo({}, Vo.normal, { + escape: _o(Vo.escape).replace("])", "~|])").getRegex(), + _extended_email: + /[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/, + url: /^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/, + _backpedal: + /(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/, + del: /^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/, + text: /^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\ 1 && void 0 !== arguments[1] + ? arguments[1] + : []; + for (this.options.pedantic && (t = t.replace(/^ +$/gm, "")); t; ) + if ( + !( + this.options.extensions && + this.options.extensions.block && + this.options.extensions.block.some(function (n) { + return ( + !!(e = n.call({ lexer: u }, t, a)) && + ((t = t.substring(e.raw.length)), a.push(e), !0) + ); + }) + ) + ) + if ((e = this.tokenizer.space(t))) + (t = t.substring(e.raw.length)), + 1 === e.raw.length && a.length > 0 + ? (a[a.length - 1].raw += "\n") + : a.push(e); + else if ((e = this.tokenizer.code(t))) + (t = t.substring(e.raw.length)), + !(n = a[a.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? a.push(e) + : ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.text), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((e = this.tokenizer.fences(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.heading(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.hr(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.blockquote(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.list(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.html(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.def(t))) + (t = t.substring(e.raw.length)), + !(n = a[a.length - 1]) || + ("paragraph" !== n.type && "text" !== n.type) + ? this.tokens.links[e.tag] || + (this.tokens.links[e.tag] = { + href: e.href, + title: e.title, + }) + : ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.raw), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)); + else if ((e = this.tokenizer.table(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ((e = this.tokenizer.lheading(t))) + (t = t.substring(e.raw.length)), a.push(e); + else if ( + ((r = t), + this.options.extensions && + this.options.extensions.startBlock && + (function () { + var e = 1 / 0, + n = t.slice(1), + i = void 0; + u.options.extensions.startBlock.forEach(function (t) { + "number" == typeof (i = t.call({ lexer: this }, n)) && + i >= 0 && + (e = Math.min(e, i)); + }), + e < 1 / 0 && e >= 0 && (r = t.substring(0, e + 1)); + })(), + this.state.top && (e = this.tokenizer.paragraph(r))) + ) + (n = a[a.length - 1]), + i && "paragraph" === n.type + ? ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : a.push(e), + (i = r.length !== t.length), + (t = t.substring(e.raw.length)); + else if ((e = this.tokenizer.text(t))) + (t = t.substring(e.raw.length)), + (n = a[a.length - 1]) && "text" === n.type + ? ((n.raw += "\n" + e.raw), + (n.text += "\n" + e.text), + this.inlineQueue.pop(), + (this.inlineQueue[this.inlineQueue.length - 1].src = + n.text)) + : a.push(e); + else if (t) { + var o = "Infinite loop on byte: " + t.charCodeAt(0); + if (this.options.silent) { + console.error(o); + break; + } + throw new Error(o); + } + return (this.state.top = !0), a; + }, + }, + { + key: "inline", + value: function (t, e) { + this.inlineQueue.push({ src: t, tokens: e }); + }, + }, + { + key: "inlineTokens", + value: function (t) { + var e, + n, + r, + i, + u, + a, + o = this, + s = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : [], + l = t; + if (this.tokens.links) { + var c = Object.keys(this.tokens.links); + if (c.length > 0) + for ( + ; + null != + (i = this.tokenizer.rules.inline.reflinkSearch.exec(l)); + + ) + c.includes(i[0].slice(i[0].lastIndexOf("[") + 1, -1)) && + (l = + l.slice(0, i.index) + + "[" + + Zo("a", i[0].length - 2) + + "]" + + l.slice( + this.tokenizer.rules.inline.reflinkSearch.lastIndex, + )); + } + for ( + ; + null != (i = this.tokenizer.rules.inline.blockSkip.exec(l)); + + ) + l = + l.slice(0, i.index) + + "[" + + Zo("a", i[0].length - 2) + + "]" + + l.slice(this.tokenizer.rules.inline.blockSkip.lastIndex); + for ( + ; + null != (i = this.tokenizer.rules.inline.escapedEmSt.exec(l)); + + ) + l = + l.slice(0, i.index) + + "++" + + l.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex); + for (; t; ) + if ( + (u || (a = ""), + (u = !1), + !( + this.options.extensions && + this.options.extensions.inline && + this.options.extensions.inline.some(function (n) { + return ( + !!(e = n.call({ lexer: o }, t, s)) && + ((t = t.substring(e.raw.length)), s.push(e), !0) + ); + }) + )) + ) + if ((e = this.tokenizer.escape(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.tag(t))) + (t = t.substring(e.raw.length)), + (n = s[s.length - 1]) && + "text" === e.type && + "text" === n.type + ? ((n.raw += e.raw), (n.text += e.text)) + : s.push(e); + else if ((e = this.tokenizer.link(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.reflink(t, this.tokens.links))) + (t = t.substring(e.raw.length)), + (n = s[s.length - 1]) && + "text" === e.type && + "text" === n.type + ? ((n.raw += e.raw), (n.text += e.text)) + : s.push(e); + else if ((e = this.tokenizer.emStrong(t, l, a))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.codespan(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.br(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.del(t))) + (t = t.substring(e.raw.length)), s.push(e); + else if ((e = this.tokenizer.autolink(t, Qo))) + (t = t.substring(e.raw.length)), s.push(e); + else if ( + this.state.inLink || + !(e = this.tokenizer.url(t, Qo)) + ) { + if ( + ((r = t), + this.options.extensions && + this.options.extensions.startInline && + (function () { + var e = 1 / 0, + n = t.slice(1), + i = void 0; + o.options.extensions.startInline.forEach( + function (t) { + "number" == + typeof (i = t.call({ lexer: this }, n)) && + i >= 0 && + (e = Math.min(e, i)); + }, + ), + e < 1 / 0 && e >= 0 && (r = t.substring(0, e + 1)); + })(), + (e = this.tokenizer.inlineText(r, Ko))) + ) + (t = t.substring(e.raw.length)), + "_" !== e.raw.slice(-1) && (a = e.raw.slice(-1)), + (u = !0), + (n = s[s.length - 1]) && "text" === n.type + ? ((n.raw += e.raw), (n.text += e.text)) + : s.push(e); + else if (t) { + var p = "Infinite loop on byte: " + t.charCodeAt(0); + if (this.options.silent) { + console.error(p); + break; + } + throw new Error(p); + } + } else (t = t.substring(e.raw.length)), s.push(e); + return s; + }, + }, + ], + [ + { + key: "rules", + get: function () { + return { block: Jo, inline: Vo }; + }, + }, + { + key: "lex", + value: function (e, n) { + return new t(n).lex(e); + }, + }, + { + key: "lexInline", + value: function (e, n) { + return new t(n).inlineTokens(e); + }, + }, + ], + ), + t + ); + })(), + Yo = (function () { + function t(e) { + ur(this, t), (this.options = e || ko); + } + return ( + or(t, [ + { + key: "code", + value: function (t, e, n) { + var r = (e || "").match(/\S*/)[0]; + if (this.options.highlight) { + var i = this.options.highlight(t, r); + null != i && i !== t && ((n = !0), (t = i)); + } + return ( + (t = t.replace(/\n$/, "") + "\n"), + r + ? '
    ' +
+                  (n ? t : Fo(t, !0)) +
+                  "
    \n" + : "
    " + (n ? t : Fo(t, !0)) + "
    \n" + ); + }, + }, + { + key: "blockquote", + value: function (t) { + return "
    \n" + t + "
    \n"; + }, + }, + { + key: "html", + value: function (t) { + return t; + }, + }, + { + key: "heading", + value: function (t, e, n, r) { + return this.options.headerIds + ? "' + + t + + "\n" + : "" + t + "\n"; + }, + }, + { + key: "hr", + value: function () { + return this.options.xhtml ? "
    \n" : "
    \n"; + }, + }, + { + key: "list", + value: function (t, e, n) { + var r = e ? "ol" : "ul"; + return ( + "<" + + r + + (e && 1 !== n ? ' start="' + n + '"' : "") + + ">\n" + + t + + "\n" + ); + }, + }, + { + key: "listitem", + value: function (t) { + return "
  • " + t + "
    • \n"; + }, + }, + { + key: "checkbox", + value: function (t) { + return ( + " " + ); + }, + }, + { + key: "paragraph", + value: function (t) { + return "

    " + t + "

    \n"; + }, + }, + { + key: "table", + value: function (t, e) { + return ( + e && (e = "" + e + ""), + "\n\n" + t + "\n" + e + "
    \n" + ); + }, + }, + { + key: "tablerow", + value: function (t) { + return "\n" + t + "\n"; + }, + }, + { + key: "tablecell", + value: function (t, e) { + var n = e.header ? "th" : "td"; + return ( + (e.align + ? "<" + n + ' align="' + e.align + '">' + : "<" + n + ">") + + t + + "\n" + ); + }, + }, + { + key: "strong", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "em", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "codespan", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "br", + value: function () { + return this.options.xhtml ? "
    " : "
    "; + }, + }, + { + key: "del", + value: function (t) { + return "" + t + ""; + }, + }, + { + key: "link", + value: function (t, e, n) { + if ( + null === (t = Io(this.options.sanitize, this.options.baseUrl, t)) + ) + return n; + var r = '    "); + }, + }, + { + key: "image", + value: function (t, e, n) { + if ( + null === (t = Io(this.options.sanitize, this.options.baseUrl, t)) + ) + return n; + var r = '' + n + '" : ">") + ); + }, + }, + { + key: "text", + value: function (t) { + return t; + }, + }, + ]), + t + ); + })(), + Xo = (function () { + function t() { + ur(this, t); + } + return ( + or(t, [ + { + key: "strong", + value: function (t) { + return t; + }, + }, + { + key: "em", + value: function (t) { + return t; + }, + }, + { + key: "codespan", + value: function (t) { + return t; + }, + }, + { + key: "del", + value: function (t) { + return t; + }, + }, + { + key: "html", + value: function (t) { + return t; + }, + }, + { + key: "text", + value: function (t) { + return t; + }, + }, + { + key: "link", + value: function (t, e, n) { + return "" + n; + }, + }, + { + key: "image", + value: function (t, e, n) { + return "" + n; + }, + }, + { + key: "br", + value: function () { + return ""; + }, + }, + ]), + t + ); + })(), + ts = (function () { + function t() { + ur(this, t), (this.seen = {}); + } + return ( + or(t, [ + { + key: "serialize", + value: function (t) { + return t + .toLowerCase() + .trim() + .replace(/<[!\/a-z].*?>/gi, "") + .replace( + /[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g, + "", + ) + .replace(/\s/g, "-"); + }, + }, + { + key: "getNextSafeSlug", + value: function (t, e) { + var n = t, + r = 0; + if (this.seen.hasOwnProperty(n)) { + r = this.seen[t]; + do { + n = t + "-" + ++r; + } while (this.seen.hasOwnProperty(n)); + } + return e || ((this.seen[t] = r), (this.seen[n] = 0)), n; + }, + }, + { + key: "slug", + value: function (t) { + var e = + arguments.length > 1 && void 0 !== arguments[1] + ? arguments[1] + : {}, + n = this.serialize(t); + return this.getNextSafeSlug(n, e.dryrun); + }, + }, + ]), + t + ); + })(), + es = (function () { + function t(e) { + ur(this, t), + (this.options = e || ko), + (this.options.renderer = this.options.renderer || new Yo()), + (this.renderer = this.options.renderer), + (this.renderer.options = this.options), + (this.textRenderer = new Xo()), + (this.slugger = new ts()); + } + return ( + or( + t, + [ + { + key: "parse", + value: function (t) { + var e, + n, + r, + i, + u, + a, + o, + s, + l, + c, + p, + d, + f, + h, + g, + D, + m, + v, + y, + k = + !(arguments.length > 1 && void 0 !== arguments[1]) || + arguments[1], + E = "", + x = t.length; + for (e = 0; e < x; e++) + if ( + ((c = t[e]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[c.type] + ) || + (!1 === + (y = this.options.extensions.renderers[c.type].call( + { parser: this }, + c, + )) && + [ + "space", + "hr", + "heading", + "code", + "table", + "blockquote", + "list", + "html", + "paragraph", + "text", + ].includes(c.type))) + ) + switch (c.type) { + case "space": + continue; + case "hr": + E += this.renderer.hr(); + continue; + case "heading": + E += this.renderer.heading( + this.parseInline(c.tokens), + c.depth, + Bo(this.parseInline(c.tokens, this.textRenderer)), + this.slugger, + ); + continue; + case "code": + E += this.renderer.code(c.text, c.lang, c.escaped); + continue; + case "table": + for ( + s = "", o = "", i = c.header.length, n = 0; + n < i; + n++ + ) + o += this.renderer.tablecell( + this.parseInline(c.header[n].tokens), + { header: !0, align: c.align[n] }, + ); + for ( + s += this.renderer.tablerow(o), + l = "", + i = c.rows.length, + n = 0; + n < i; + n++ + ) { + for ( + o = "", u = (a = c.rows[n]).length, r = 0; + r < u; + r++ + ) + o += this.renderer.tablecell( + this.parseInline(a[r].tokens), + { header: !1, align: c.align[r] }, + ); + l += this.renderer.tablerow(o); + } + E += this.renderer.table(s, l); + continue; + case "blockquote": + (l = this.parse(c.tokens)), + (E += this.renderer.blockquote(l)); + continue; + case "list": + for ( + p = c.ordered, + d = c.start, + f = c.loose, + i = c.items.length, + l = "", + n = 0; + n < i; + n++ + ) + (D = (g = c.items[n]).checked), + (m = g.task), + (h = ""), + g.task && + ((v = this.renderer.checkbox(D)), + f + ? g.tokens.length > 0 && + "paragraph" === g.tokens[0].type + ? ((g.tokens[0].text = + v + " " + g.tokens[0].text), + g.tokens[0].tokens && + g.tokens[0].tokens.length > 0 && + "text" === g.tokens[0].tokens[0].type && + (g.tokens[0].tokens[0].text = + v + " " + g.tokens[0].tokens[0].text)) + : g.tokens.unshift({ type: "text", text: v }) + : (h += v)), + (h += this.parse(g.tokens, f)), + (l += this.renderer.listitem(h, m, D)); + E += this.renderer.list(l, p, d); + continue; + case "html": + E += this.renderer.html(c.text); + continue; + case "paragraph": + E += this.renderer.paragraph(this.parseInline(c.tokens)); + continue; + case "text": + for ( + l = c.tokens ? this.parseInline(c.tokens) : c.text; + e + 1 < x && "text" === t[e + 1].type; + + ) + l += + "\n" + + ((c = t[++e]).tokens + ? this.parseInline(c.tokens) + : c.text); + E += k ? this.renderer.paragraph(l) : l; + continue; + default: + var A = 'Token with "' + c.type + '" type was not found.'; + if (this.options.silent) return void console.error(A); + throw new Error(A); + } + else E += y || ""; + return E; + }, + }, + { + key: "parseInline", + value: function (t, e) { + e = e || this.renderer; + var n, + r, + i, + u = "", + a = t.length; + for (n = 0; n < a; n++) + if ( + ((r = t[n]), + !( + this.options.extensions && + this.options.extensions.renderers && + this.options.extensions.renderers[r.type] + ) || + (!1 === + (i = this.options.extensions.renderers[r.type].call( + { parser: this }, + r, + )) && + [ + "escape", + "html", + "link", + "image", + "strong", + "em", + "codespan", + "br", + "del", + "text", + ].includes(r.type))) + ) + switch (r.type) { + case "escape": + u += e.text(r.text); + break; + case "html": + u += e.html(r.text); + break; + case "link": + u += e.link( + r.href, + r.title, + this.parseInline(r.tokens, e), + ); + break; + case "image": + u += e.image(r.href, r.title, r.text); + break; + case "strong": + u += e.strong(this.parseInline(r.tokens, e)); + break; + case "em": + u += e.em(this.parseInline(r.tokens, e)); + break; + case "codespan": + u += e.codespan(r.text); + break; + case "br": + u += e.br(); + break; + case "del": + u += e.del(this.parseInline(r.tokens, e)); + break; + case "text": + u += e.text(r.text); + break; + default: + var o = 'Token with "' + r.type + '" type was not found.'; + if (this.options.silent) return void console.error(o); + throw new Error(o); + } + else u += i || ""; + return u; + }, + }, + ], + [ + { + key: "parse", + value: function (e, n) { + return new t(n).parse(e); + }, + }, + { + key: "parseInline", + value: function (e, n) { + return new t(n).parseInline(e); + }, + }, + ], + ), + t + ); + })(); +function ns(t, e, n) { + if (null == t) + throw new Error("marked(): input parameter is undefined or null"); + if ("string" != typeof t) + throw new Error( + "marked(): input parameter is of type " + + Object.prototype.toString.call(t) + + ", string expected", + ); + if ( + ("function" == typeof e && ((n = e), (e = null)), + qo((e = jo({}, ns.defaults, e || {}))), + n) + ) { + var r, + i = e.highlight; + try { + r = Go.lex(t, e); + } catch (t) { + return n(t); + } + var u = function (t) { + var u; + if (!t) + try { + e.walkTokens && ns.walkTokens(r, e.walkTokens), (u = es.parse(r, e)); + } catch (e) { + t = e; + } + return (e.highlight = i), t ? n(t) : n(null, u); + }; + if (!i || i.length < 3) return u(); + if ((delete e.highlight, !r.length)) return u(); + var a = 0; + return ( + ns.walkTokens(r, function (t) { + "code" === t.type && + (a++, + setTimeout(function () { + i(t.text, t.lang, function (e, n) { + if (e) return u(e); + null != n && n !== t.text && ((t.text = n), (t.escaped = !0)), + 0 === --a && u(); + }); + }, 0)); + }), + void (0 === a && u()) + ); + } + try { + var o = Go.lex(t, e); + return e.walkTokens && ns.walkTokens(o, e.walkTokens), es.parse(o, e); + } catch (t) { + if ( + ((t.message += + "\nPlease report this to https://github.com/markedjs/marked."), + e.silent) + ) + return ( + "

    An error occurred:

    " + Fo(t.message + "", !0) + "
    " + ); + throw t; + } +} +(ns.options = ns.setOptions = + function (t) { + var e; + return jo(ns.defaults, t), (e = ns.defaults), (ko = e), ns; + }), + (ns.getDefaults = yo), + (ns.defaults = ko), + (ns.use = function () { + for (var t = arguments.length, e = new Array(t), n = 0; n < t; n++) + e[n] = arguments[n]; + var r, + i = jo.apply(void 0, [{}].concat(e)), + u = ns.defaults.extensions || { renderers: {}, childTokens: {} }; + e.forEach(function (t) { + if ( + (t.extensions && + ((r = !0), + t.extensions.forEach(function (t) { + if (!t.name) throw new Error("extension name required"); + if (t.renderer) { + var e = u.renderers ? u.renderers[t.name] : null; + u.renderers[t.name] = e + ? function () { + for ( + var n = arguments.length, r = new Array(n), i = 0; + i < n; + i++ + ) + r[i] = arguments[i]; + var u = t.renderer.apply(this, r); + return !1 === u && (u = e.apply(this, r)), u; + } + : t.renderer; + } + if (t.tokenizer) { + if (!t.level || ("block" !== t.level && "inline" !== t.level)) + throw new Error("extension level must be 'block' or 'inline'"); + u[t.level] + ? u[t.level].unshift(t.tokenizer) + : (u[t.level] = [t.tokenizer]), + t.start && + ("block" === t.level + ? u.startBlock + ? u.startBlock.push(t.start) + : (u.startBlock = [t.start]) + : "inline" === t.level && + (u.startInline + ? u.startInline.push(t.start) + : (u.startInline = [t.start]))); + } + t.childTokens && (u.childTokens[t.name] = t.childTokens); + })), + t.renderer && + (function () { + var e = ns.defaults.renderer || new Yo(), + n = function (n) { + var r = e[n]; + e[n] = function () { + for ( + var i = arguments.length, u = new Array(i), a = 0; + a < i; + a++ + ) + u[a] = arguments[a]; + var o = t.renderer[n].apply(e, u); + return !1 === o && (o = r.apply(e, u)), o; + }; + }; + for (var r in t.renderer) n(r); + i.renderer = e; + })(), + t.tokenizer && + (function () { + var e = ns.defaults.tokenizer || new Wo(), + n = function (n) { + var r = e[n]; + e[n] = function () { + for ( + var i = arguments.length, u = new Array(i), a = 0; + a < i; + a++ + ) + u[a] = arguments[a]; + var o = t.tokenizer[n].apply(e, u); + return !1 === o && (o = r.apply(e, u)), o; + }; + }; + for (var r in t.tokenizer) n(r); + i.tokenizer = e; + })(), + t.walkTokens) + ) { + var e = ns.defaults.walkTokens; + i.walkTokens = function (n) { + t.walkTokens.call(this, n), e && e.call(this, n); + }; + } + r && (i.extensions = u), ns.setOptions(i); + }); + }), + (ns.walkTokens = function (t, e) { + var n, + r = pr(t); + try { + var i = function () { + var t = n.value; + switch ((e.call(ns, t), t.type)) { + case "table": + var r, + i = pr(t.header); + try { + for (i.s(); !(r = i.n()).done; ) { + var u = r.value; + ns.walkTokens(u.tokens, e); + } + } catch (t) { + i.e(t); + } finally { + i.f(); + } + var a, + o = pr(t.rows); + try { + for (o.s(); !(a = o.n()).done; ) { + var s, + l = pr(a.value); + try { + for (l.s(); !(s = l.n()).done; ) { + var c = s.value; + ns.walkTokens(c.tokens, e); + } + } catch (t) { + l.e(t); + } finally { + l.f(); + } + } + } catch (t) { + o.e(t); + } finally { + o.f(); + } + break; + case "list": + ns.walkTokens(t.items, e); + break; + default: + ns.defaults.extensions && + ns.defaults.extensions.childTokens && + ns.defaults.extensions.childTokens[t.type] + ? ns.defaults.extensions.childTokens[t.type].forEach( + function (n) { + ns.walkTokens(t[n], e); + }, + ) + : t.tokens && ns.walkTokens(t.tokens, e); + } + }; + for (r.s(); !(n = r.n()).done; ) i(); + } catch (t) { + r.e(t); + } finally { + r.f(); + } + }), + (ns.parseInline = function (t, e) { + if (null == t) + throw new Error( + "marked.parseInline(): input parameter is undefined or null", + ); + if ("string" != typeof t) + throw new Error( + "marked.parseInline(): input parameter is of type " + + Object.prototype.toString.call(t) + + ", string expected", + ); + qo((e = jo({}, ns.defaults, e || {}))); + try { + var n = Go.lexInline(t, e); + return ( + e.walkTokens && ns.walkTokens(n, e.walkTokens), es.parseInline(n, e) + ); + } catch (t) { + if ( + ((t.message += + "\nPlease report this to https://github.com/markedjs/marked."), + e.silent) + ) + return ( + "

    An error occurred:

    " + Fo(t.message + "", !0) + "
    " + ); + throw t; + } + }), + (ns.Parser = es), + (ns.parser = es.parse), + (ns.Renderer = Yo), + (ns.TextRenderer = Xo), + (ns.Lexer = Go), + (ns.lexer = Go.lex), + (ns.Tokenizer = Wo), + (ns.Slugger = ts), + (ns.parse = ns); +export default function () { + var t, + e, + n = null; + function r() { + if (n && !n.closed) n.focus(); + else { + if ( + (((n = window.open( + "about:blank", + "reveal.js - Notes", + "width=1100,height=700", + )).marked = ns), + n.document.write( + "\x3c!--\n\tNOTE: You need to build the notes plugin after making changes to this file.\n--\x3e\n\n\t\n\t\t\n\n\t\treveal.js - Speaker View\n\n\t\t\n\t\n\n\t\n\n\t\t
    Loading speaker view...
    \n\n\t\t
    \n\t\t
    Upcoming
    \n\t\t
    \n\t\t\t
    \n\t\t\t\t

    Time Click to Reset

    \n\t\t\t\t
    \n\t\t\t\t\t0:00 AM\n\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t00:00:00\n\t\t\t\t
    \n\t\t\t\t
    \n\n\t\t\t\t

    Pacing – Time to finish current slide

    \n\t\t\t\t
    \n\t\t\t\t\t00:00:00\n\t\t\t\t
    \n\t\t\t
    \n\n\t\t\t
    \n\t\t\t\t

    Notes

    \n\t\t\t\t
    \n\t\t\t
    \n\t\t
    \n\t\t
    \n\t\t\t\n\t\t\t\n\t\t
    \n\n\t\t\n\t\n", + ), + !n) + ) + return void alert( + "Speaker view popup failed to open. Please make sure popups are allowed and reopen the speaker view.", + ); + (r = e.getConfig().url), + (i = + "string" == typeof r + ? r + : window.location.protocol + + "//" + + window.location.host + + window.location.pathname + + window.location.search), + (t = setInterval(function () { + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "connect", + state: e.getState(), + url: i, + }), + "*", + ); + }, 500)), + window.addEventListener("message", u); + } + var r, i; + } + function i(t) { + var r = e.getCurrentSlide(), + i = r.querySelector("aside.notes"), + u = r.querySelector(".current-fragment"), + a = { + namespace: "reveal-notes", + type: "state", + notes: "", + markdown: !1, + whitespace: "normal", + state: e.getState(), + }; + if ( + (r.hasAttribute("data-notes") && + ((a.notes = r.getAttribute("data-notes")), (a.whitespace = "pre-wrap")), + u) + ) { + var o = u.querySelector("aside.notes"); + o + ? (i = o) + : u.hasAttribute("data-notes") && + ((a.notes = u.getAttribute("data-notes")), + (a.whitespace = "pre-wrap"), + (i = null)); + } + i && + ((a.notes = i.innerHTML), + (a.markdown = "string" == typeof i.getAttribute("data-markdown"))), + n.postMessage(JSON.stringify(a), "*"); + } + function u(r) { + var i, + u, + o, + s, + l = JSON.parse(r.data); + l && "reveal-notes" === l.namespace && "connected" === l.type + ? (clearInterval(t), a()) + : l && + "reveal-notes" === l.namespace && + "call" === l.type && + ((i = l.methodName), + (u = l.arguments), + (o = l.callId), + (s = e[i].apply(e, u)), + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "return", + result: s, + callId: o, + }), + "*", + )); + } + function a() { + e.on("slidechanged", i), + e.on("fragmentshown", i), + e.on("fragmenthidden", i), + e.on("overviewhidden", i), + e.on("overviewshown", i), + e.on("paused", i), + e.on("resumed", i), + i(); + } + return { + id: "notes", + init: function (t) { + (e = t), + /receiver/i.test(window.location.search) || + (null !== window.location.search.match(/(\?|\&)notes/gi) + ? r() + : window.addEventListener("message", function (t) { + if (!n && "string" == typeof t.data) { + var e; + try { + e = JSON.parse(t.data); + } catch (t) {} + e && + "reveal-notes" === e.namespace && + "heartbeat" === e.type && + ((r = t.source), + n && !n.closed + ? n.focus() + : ((n = r), window.addEventListener("message", u), a())); + } + var r; + }), + e.addKeyBinding( + { keyCode: 83, key: "S", description: "Speaker notes view" }, + function () { + r(); + }, + )); + }, + open: r, + }; +} diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/notes.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/notes.js index ddbc03b..5024838 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/notes.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/notes.js @@ -1 +1,4401 @@ -!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).RevealNotes=e()}(this,(function(){"use strict";var t="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof 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Please make sure popups are allowed and reopen the speaker view.", + ); + (r = e.getConfig().url), + (i = + "string" == typeof r + ? r + : window.location.protocol + + "//" + + window.location.host + + window.location.pathname + + window.location.search), + (t = setInterval(function () { + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "connect", + state: e.getState(), + url: i, + }), + "*", + ); + }, 500)), + window.addEventListener("message", u); + } + var r, i; + } + function i(t) { + var r = e.getCurrentSlide(), + i = r.querySelector("aside.notes"), + u = r.querySelector(".current-fragment"), + a = { + namespace: "reveal-notes", + type: "state", + notes: "", + markdown: !1, + whitespace: "normal", + state: e.getState(), + }; + if ( + (r.hasAttribute("data-notes") && + ((a.notes = r.getAttribute("data-notes")), + (a.whitespace = "pre-wrap")), + u) + ) { + var o = u.querySelector("aside.notes"); + o + ? (i = o) + : u.hasAttribute("data-notes") && + ((a.notes = u.getAttribute("data-notes")), + (a.whitespace = "pre-wrap"), + (i = null)); + } + i && + ((a.notes = i.innerHTML), + (a.markdown = "string" == typeof i.getAttribute("data-markdown"))), + n.postMessage(JSON.stringify(a), "*"); + } + function u(r) { + var i, + u, + o, + s, + l = JSON.parse(r.data); + l && "reveal-notes" === l.namespace && "connected" === l.type + ? (clearInterval(t), a()) + : l && + "reveal-notes" === l.namespace && + "call" === l.type && + ((i = l.methodName), + (u = l.arguments), + (o = l.callId), + (s = e[i].apply(e, u)), + n.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "return", + result: s, + callId: o, + }), + "*", + )); + } + function a() { + e.on("slidechanged", i), + e.on("fragmentshown", i), + e.on("fragmenthidden", i), + e.on("overviewhidden", i), + e.on("overviewshown", i), + e.on("paused", i), + e.on("resumed", i), + i(); + } + return { + id: "notes", + init: function (t) { + (e = t), + /receiver/i.test(window.location.search) || + (null !== window.location.search.match(/(\?|\&)notes/gi) + ? r() + : window.addEventListener("message", function (t) { + if (!n && "string" == typeof t.data) { + var e; + try { + e = JSON.parse(t.data); + } catch (t) {} + e && + "reveal-notes" === e.namespace && + "heartbeat" === e.type && + ((r = t.source), + n && !n.closed + ? n.focus() + : ((n = r), + window.addEventListener("message", u), + a())); + } + var r; + }), + e.addKeyBinding( + { keyCode: 83, key: "S", description: "Speaker notes view" }, + function () { + r(); + }, + )); + }, + open: r, + }; + }; +}); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/plugin.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/plugin.js index c80afa8..be08c6e 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/plugin.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/plugin.js @@ -1,6 +1,6 @@ -import speakerViewHTML from './speaker-view.html'; +import speakerViewHTML from "./speaker-view.html"; -import { marked } from 'marked'; +import { marked } from "marked"; /** * Handles opening of and synchronization with the reveal.js @@ -14,223 +14,230 @@ import { marked } from 'marked'; * to the notes window */ const Plugin = () => { - - let connectInterval; - let speakerWindow = null; - let deck; - - /** - * Opens a new speaker view window. - */ - function openSpeakerWindow() { - - // If a window is already open, focus it - if( speakerWindow && !speakerWindow.closed ) { - speakerWindow.focus(); - } - else { - speakerWindow = window.open( 'about:blank', 'reveal.js - Notes', 'width=1100,height=700' ); - speakerWindow.marked = marked; - speakerWindow.document.write( speakerViewHTML ); - - if( !speakerWindow ) { - alert( 'Speaker view popup failed to open. Please make sure popups are allowed and reopen the speaker view.' ); - return; - } - - connect(); - } - - } - - /** - * Reconnect with an existing speaker view window. - */ - function reconnectSpeakerWindow( reconnectWindow ) { - - if( speakerWindow && !speakerWindow.closed ) { - speakerWindow.focus(); - } - else { - speakerWindow = reconnectWindow; - window.addEventListener( 'message', onPostMessage ); - onConnected(); - } - - } - - /** - * Connect to the notes window through a postmessage handshake. - * Using postmessage enables us to work in situations where the - * origins differ, such as a presentation being opened from the - * file system. - */ - function connect() { - - const presentationURL = deck.getConfig().url; - - const url = typeof presentationURL === 'string' ? presentationURL : - window.location.protocol + '//' + window.location.host + window.location.pathname + window.location.search; - - // Keep trying to connect until we get a 'connected' message back - connectInterval = setInterval( function() { - speakerWindow.postMessage( JSON.stringify( { - namespace: 'reveal-notes', - type: 'connect', - state: deck.getState(), - url - } ), '*' ); - }, 500 ); - - window.addEventListener( 'message', onPostMessage ); - - } - - /** - * Calls the specified Reveal.js method with the provided argument - * and then pushes the result to the notes frame. - */ - function callRevealApi( methodName, methodArguments, callId ) { - - let result = deck[methodName].apply( deck, methodArguments ); - speakerWindow.postMessage( JSON.stringify( { - namespace: 'reveal-notes', - type: 'return', - result, - callId - } ), '*' ); - - } - - /** - * Posts the current slide data to the notes window. - */ - function post( event ) { - - let slideElement = deck.getCurrentSlide(), - notesElement = slideElement.querySelector( 'aside.notes' ), - fragmentElement = slideElement.querySelector( '.current-fragment' ); - - let messageData = { - namespace: 'reveal-notes', - type: 'state', - notes: '', - markdown: false, - whitespace: 'normal', - state: deck.getState() - }; - - // Look for notes defined in a slide attribute - if( slideElement.hasAttribute( 'data-notes' ) ) { - messageData.notes = slideElement.getAttribute( 'data-notes' ); - messageData.whitespace = 'pre-wrap'; - } - - // Look for notes defined in a fragment - if( fragmentElement ) { - let fragmentNotes = fragmentElement.querySelector( 'aside.notes' ); - if( fragmentNotes ) { - notesElement = fragmentNotes; - } - else if( fragmentElement.hasAttribute( 'data-notes' ) ) { - messageData.notes = fragmentElement.getAttribute( 'data-notes' ); - messageData.whitespace = 'pre-wrap'; - - // In case there are slide notes - notesElement = null; - } - } - - // Look for notes defined in an aside element - if( notesElement ) { - messageData.notes = notesElement.innerHTML; - messageData.markdown = typeof notesElement.getAttribute( 'data-markdown' ) === 'string'; - } - - speakerWindow.postMessage( JSON.stringify( messageData ), '*' ); - - } - - function onPostMessage( event ) { - - let data = JSON.parse( event.data ); - if( data && data.namespace === 'reveal-notes' && data.type === 'connected' ) { - clearInterval( connectInterval ); - onConnected(); - } - else if( data && data.namespace === 'reveal-notes' && data.type === 'call' ) { - callRevealApi( data.methodName, data.arguments, data.callId ); - } - - } - - /** - * Called once we have established a connection to the notes - * window. - */ - function onConnected() { - - // Monitor events that trigger a change in state - deck.on( 'slidechanged', post ); - deck.on( 'fragmentshown', post ); - deck.on( 'fragmenthidden', post ); - deck.on( 'overviewhidden', post ); - deck.on( 'overviewshown', post ); - deck.on( 'paused', post ); - deck.on( 'resumed', post ); - - // Post the initial state - post(); - - } - - return { - id: 'notes', - - init: function( reveal ) { - - deck = reveal; - - if( !/receiver/i.test( window.location.search ) ) { - - // If the there's a 'notes' query set, open directly - if( window.location.search.match( /(\?|\&)notes/gi ) !== null ) { - openSpeakerWindow(); - } - else { - // Keep listening for speaker view hearbeats. If we receive a - // heartbeat from an orphaned window, reconnect it. This ensures - // that we remain connected to the notes even if the presentation - // is reloaded. - window.addEventListener( 'message', event => { - - if( !speakerWindow && typeof event.data === 'string' ) { - let data; - - try { - data = JSON.parse( event.data ); - } - catch( error ) {} - - if( data && data.namespace === 'reveal-notes' && data.type === 'heartbeat' ) { - reconnectSpeakerWindow( event.source ); - } - } - }); - } - - // Open the notes when the 's' key is hit - deck.addKeyBinding({keyCode: 83, key: 'S', description: 'Speaker notes view'}, function() { - openSpeakerWindow(); - } ); - - } - - }, - - open: openSpeakerWindow - }; - + let connectInterval; + let speakerWindow = null; + let deck; + + /** + * Opens a new speaker view window. + */ + function openSpeakerWindow() { + // If a window is already open, focus it + if (speakerWindow && !speakerWindow.closed) { + speakerWindow.focus(); + } else { + speakerWindow = window.open( + "about:blank", + "reveal.js - Notes", + "width=1100,height=700", + ); + speakerWindow.marked = marked; + speakerWindow.document.write(speakerViewHTML); + + if (!speakerWindow) { + alert( + "Speaker view popup failed to open. Please make sure popups are allowed and reopen the speaker view.", + ); + return; + } + + connect(); + } + } + + /** + * Reconnect with an existing speaker view window. + */ + function reconnectSpeakerWindow(reconnectWindow) { + if (speakerWindow && !speakerWindow.closed) { + speakerWindow.focus(); + } else { + speakerWindow = reconnectWindow; + window.addEventListener("message", onPostMessage); + onConnected(); + } + } + + /** + * Connect to the notes window through a postmessage handshake. + * Using postmessage enables us to work in situations where the + * origins differ, such as a presentation being opened from the + * file system. + */ + function connect() { + const presentationURL = deck.getConfig().url; + + const url = + typeof presentationURL === "string" + ? presentationURL + : window.location.protocol + + "//" + + window.location.host + + window.location.pathname + + window.location.search; + + // Keep trying to connect until we get a 'connected' message back + connectInterval = setInterval(function () { + speakerWindow.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "connect", + state: deck.getState(), + url, + }), + "*", + ); + }, 500); + + window.addEventListener("message", onPostMessage); + } + + /** + * Calls the specified Reveal.js method with the provided argument + * and then pushes the result to the notes frame. + */ + function callRevealApi(methodName, methodArguments, callId) { + let result = deck[methodName].apply(deck, methodArguments); + speakerWindow.postMessage( + JSON.stringify({ + namespace: "reveal-notes", + type: "return", + result, + callId, + }), + "*", + ); + } + + /** + * Posts the current slide data to the notes window. + */ + function post(event) { + let slideElement = deck.getCurrentSlide(), + notesElement = slideElement.querySelector("aside.notes"), + fragmentElement = slideElement.querySelector(".current-fragment"); + + let messageData = { + namespace: "reveal-notes", + type: "state", + notes: "", + markdown: false, + whitespace: "normal", + state: deck.getState(), + }; + + // Look for notes defined in a slide attribute + if (slideElement.hasAttribute("data-notes")) { + messageData.notes = slideElement.getAttribute("data-notes"); + messageData.whitespace = "pre-wrap"; + } + + // Look for notes defined in a fragment + if (fragmentElement) { + let fragmentNotes = fragmentElement.querySelector("aside.notes"); + if (fragmentNotes) { + notesElement = fragmentNotes; + } else if (fragmentElement.hasAttribute("data-notes")) { + messageData.notes = fragmentElement.getAttribute("data-notes"); + messageData.whitespace = "pre-wrap"; + + // In case there are slide notes + notesElement = null; + } + } + + // Look for notes defined in an aside element + if (notesElement) { + messageData.notes = notesElement.innerHTML; + messageData.markdown = + typeof notesElement.getAttribute("data-markdown") === "string"; + } + + speakerWindow.postMessage(JSON.stringify(messageData), "*"); + } + + function onPostMessage(event) { + let data = JSON.parse(event.data); + if ( + data && + data.namespace === "reveal-notes" && + data.type === "connected" + ) { + clearInterval(connectInterval); + onConnected(); + } else if ( + data && + data.namespace === "reveal-notes" && + data.type === "call" + ) { + callRevealApi(data.methodName, data.arguments, data.callId); + } + } + + /** + * Called once we have established a connection to the notes + * window. + */ + function onConnected() { + // Monitor events that trigger a change in state + deck.on("slidechanged", post); + deck.on("fragmentshown", post); + deck.on("fragmenthidden", post); + deck.on("overviewhidden", post); + deck.on("overviewshown", post); + deck.on("paused", post); + deck.on("resumed", post); + + // Post the initial state + post(); + } + + return { + id: "notes", + + init: function (reveal) { + deck = reveal; + + if (!/receiver/i.test(window.location.search)) { + // If the there's a 'notes' query set, open directly + if (window.location.search.match(/(\?|\&)notes/gi) !== null) { + openSpeakerWindow(); + } else { + // Keep listening for speaker view hearbeats. If we receive a + // heartbeat from an orphaned window, reconnect it. This ensures + // that we remain connected to the notes even if the presentation + // is reloaded. + window.addEventListener("message", (event) => { + if (!speakerWindow && typeof event.data === "string") { + let data; + + try { + data = JSON.parse(event.data); + } catch (error) {} + + if ( + data && + data.namespace === "reveal-notes" && + data.type === "heartbeat" + ) { + reconnectSpeakerWindow(event.source); + } + } + }); + } + + // Open the notes when the 's' key is hit + deck.addKeyBinding( + { keyCode: 83, key: "S", description: "Speaker notes view" }, + function () { + openSpeakerWindow(); + }, + ); + } + }, + + open: openSpeakerWindow, + }; }; export default Plugin; diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/speaker-view.html b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/speaker-view.html index 93ac3c0..dcb838c 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/speaker-view.html +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/notes/speaker-view.html @@ -2,883 +2,912 @@ NOTE: You need to build the notes plugin after making changes to this file. --> - - - - reveal.js - Speaker View - - - - - - -
    Loading speaker view...
    - -
    -
    Upcoming
    -
    -
    -

    Time Click to Reset

    -
    - 0:00 AM -
    -
    - 00:00:00 -
    -
    - - - -
    - - -
    -
    - - -
    - - - - \ No newline at end of file + + + + reveal.js - Speaker View + + + + + +
    Loading speaker view...
    + +
    +
    + Upcoming +
    +
    +
    +

    + Time Click to Reset +

    +
    + 0:00 AM +
    +
    + 00:00:00 +
    +
    + + + +
    + + +
    +
    + + +
    + + + + diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/pdf-export/pdfexport.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/pdf-export/pdfexport.js index bf9104c..d052cc6 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/pdf-export/pdfexport.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/pdf-export/pdfexport.js @@ -1,111 +1,125 @@ -var PdfExport = ( function( _Reveal ){ +var PdfExport = (function (_Reveal) { + var Reveal = _Reveal; + var setStylesheet = null; + var installAltKeyBindings = null; - var Reveal = _Reveal; - var setStylesheet = null; - var installAltKeyBindings = null; + function getRevealJsPath() { + var regex = /\b[^/]+\/reveal.css$/i; + var script = Array.from(document.querySelectorAll("link")).find( + function (e) { + return e.attributes.href && e.attributes.href.value.search(regex) >= 0; + }, + ); + if (!script) { + console.error( + "reveal.css could not be found in included elements. Did you rename this file?", + ); + return ""; + } + return script.attributes.href.value.replace(regex, ""); + } - function getRevealJsPath(){ - var regex = /\b[^/]+\/reveal.css$/i; - var script = Array.from( document.querySelectorAll( 'link' ) ).find( function( e ){ - return e.attributes.href && e.attributes.href.value.search( regex ) >= 0; - }); - if( !script ){ - console.error( 'reveal.css could not be found in included elements. Did you rename this file?' ); - return ''; - } - return script.attributes.href.value.replace( regex, '' ); - } + function setStylesheet3(pdfExport) { + var link = document.querySelector("#print"); + if (!link) { + link = document.createElement("link"); + link.rel = "stylesheet"; + link.id = "print"; + document.querySelector("head").appendChild(link); + } + var style = "paper"; + if (pdfExport) { + style = "pdf"; + } + link.href = getRevealJsPath() + "css/print/" + style + ".css"; + } - function setStylesheet3( pdfExport ){ - var link = document.querySelector( '#print' ); - if( !link ){ - link = document.createElement( 'link' ); - link.rel = 'stylesheet'; - link.id = 'print'; - document.querySelector( 'head' ).appendChild( link ); - } - var style = 'paper'; - if( pdfExport ){ - style = 'pdf'; - } - link.href = getRevealJsPath() + 'css/print/' + style + '.css'; - } + function setStylesheet4(pdfExport) {} - function setStylesheet4( pdfExport ){ - } + function installAltKeyBindings3() {} - function installAltKeyBindings3(){ - } + function installAltKeyBindings4() { + if (isPrintingPDF()) { + var config = Reveal.getConfig(); + var shortcut = config.pdfExportShortcut || "E"; + window.addEventListener( + "keydown", + function (e) { + if ( + e.target.nodeName.toUpperCase() == "BODY" && + (e.key.toUpperCase() == shortcut.toUpperCase() || + e.keyCode == shortcut.toUpperCase().charCodeAt(0)) + ) { + e.preventDefault(); + togglePdfExport(); + return false; + } + }, + true, + ); + } + } - function installAltKeyBindings4(){ - if( isPrintingPDF() ){ - var config = Reveal.getConfig(); - var shortcut = config.pdfExportShortcut || 'E'; - window.addEventListener( 'keydown', function( e ){ - if( e.target.nodeName.toUpperCase() == 'BODY' - && ( e.key.toUpperCase() == shortcut.toUpperCase() || e.keyCode == shortcut.toUpperCase().charCodeAt( 0 ) ) ){ - e.preventDefault(); - togglePdfExport(); - return false; - } - }, true ); - } - } - - function isPrintingPDF(){ - return ( /print-pdf/gi ).test( window.location.search ); - } + function isPrintingPDF() { + return /print-pdf/gi.test(window.location.search); + } - function togglePdfExport(){ - var url_doc = new URL( document.URL ); - var query_doc = new URLSearchParams( url_doc.searchParams ); - if( isPrintingPDF() ){ - query_doc.delete( 'print-pdf' ); - }else{ - query_doc.set( 'print-pdf', '' ); - } - url_doc.search = ( query_doc.toString() ? '?' + query_doc.toString() : '' ); - window.location.href = url_doc.toString(); - } + function togglePdfExport() { + var url_doc = new URL(document.URL); + var query_doc = new URLSearchParams(url_doc.searchParams); + if (isPrintingPDF()) { + query_doc.delete("print-pdf"); + } else { + query_doc.set("print-pdf", ""); + } + url_doc.search = query_doc.toString() ? "?" + query_doc.toString() : ""; + window.location.href = url_doc.toString(); + } - function installKeyBindings(){ - var config = Reveal.getConfig(); - var shortcut = config.pdfExportShortcut || 'E'; - Reveal.addKeyBinding({ - keyCode: shortcut.toUpperCase().charCodeAt( 0 ), - key: shortcut.toUpperCase(), - description: 'PDF export mode' - }, togglePdfExport ); - installAltKeyBindings(); - } + function installKeyBindings() { + var config = Reveal.getConfig(); + var shortcut = config.pdfExportShortcut || "E"; + Reveal.addKeyBinding( + { + keyCode: shortcut.toUpperCase().charCodeAt(0), + key: shortcut.toUpperCase(), + description: "PDF export mode", + }, + togglePdfExport, + ); + installAltKeyBindings(); + } - function install(){ - installKeyBindings(); - setStylesheet( isPrintingPDF() ); - } + function install() { + installKeyBindings(); + setStylesheet(isPrintingPDF()); + } - var Plugin = { - } + var Plugin = {}; - if( Reveal && Reveal.VERSION && Reveal.VERSION.length && Reveal.VERSION[ 0 ] == '3' ){ - // reveal 3.x - setStylesheet = setStylesheet3; - installAltKeyBindings = installAltKeyBindings3; - install(); - }else{ - // must be reveal 4.x - setStylesheet = setStylesheet4; - installAltKeyBindings = installAltKeyBindings4; - Plugin.id = 'pdf-export'; - Plugin.init = function( _Reveal ){ - Reveal = _Reveal; - install(); - }; - Plugin.togglePdfExport = function () { + if ( + Reveal && + Reveal.VERSION && + Reveal.VERSION.length && + Reveal.VERSION[0] == "3" + ) { + // reveal 3.x + setStylesheet = setStylesheet3; + installAltKeyBindings = installAltKeyBindings3; + install(); + } else { + // must be reveal 4.x + setStylesheet = setStylesheet4; + installAltKeyBindings = installAltKeyBindings4; + Plugin.id = "pdf-export"; + Plugin.init = function (_Reveal) { + Reveal = _Reveal; + install(); + }; + Plugin.togglePdfExport = function () { togglePdfExport(); }; - } - - return Plugin; + } -})( Reveal ); + return Plugin; +})(Reveal); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js index a69ca1d..6ea7afc 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.js @@ -10,7 +10,7 @@ window.QuartoLineHighlight = function () { }; const regex = new RegExp( - "^[\\d" + Object.values(delimiters).join("") + "]+$" + "^[\\d" + Object.values(delimiters).join("") + "]+$", ); function handleLinesSelector(deck, attr) { @@ -56,7 +56,7 @@ window.QuartoLineHighlight = function () { // each clone should follow in an incremental sequence let fragmentIndex = parseInt( code.getAttribute(kFragmentIndex), - 10 + 10, ); fragmentIndex = typeof fragmentIndex !== "number" || isNaN(fragmentIndex) @@ -70,7 +70,7 @@ window.QuartoLineHighlight = function () { var fragmentBlock = code.cloneNode(true); fragmentBlock.setAttribute( "data-code-line-numbers", - joinLineNumbers([step]) + joinLineNumbers([step]), ); fragmentBlock.classList.add("fragment"); @@ -81,7 +81,7 @@ window.QuartoLineHighlight = function () { if (span.hasAttribute("id")) { span.setAttribute( "id", - span.getAttribute("id").concat("-" + stepN) + span.getAttribute("id").concat("-" + stepN), ); } }); @@ -106,23 +106,23 @@ window.QuartoLineHighlight = function () { scrollHighlightedLineIntoView.bind( this, fragmentBlock, - scrollState - ) + scrollState, + ), ); fragmentBlock.addEventListener( "hidden", scrollHighlightedLineIntoView.bind( this, fragmentBlock.previousSibling, - scrollState - ) + scrollState, + ), ); - } + }, ); code.removeAttribute(kFragmentIndex); code.setAttribute( kCodeLineNumbersAttr, - joinLineNumbers([highlightSteps[0]]) + joinLineNumbers([highlightSteps[0]]), ); } @@ -136,7 +136,7 @@ window.QuartoLineHighlight = function () { scrollHighlightedLineIntoView(code, scrollState, true); slide.removeEventListener( "visible", - scrollFirstHighlightIntoView + scrollFirstHighlightIntoView, ); }; slide.addEventListener("visible", scrollFirstHighlightIntoView); @@ -151,7 +151,7 @@ window.QuartoLineHighlight = function () { function highlightCodeBlock(codeBlock) { const highlightSteps = splitLineNumbers( - codeBlock.getAttribute(kCodeLineNumbersAttr) + codeBlock.getAttribute(kCodeLineNumbersAttr), ); if (highlightSteps.length) { @@ -169,20 +169,20 @@ window.QuartoLineHighlight = function () { highlight.first + "):nth-of-type(-n+" + highlight.last + - ")" - ) + ")", + ), ); } else if (typeof highlight.first === "number") { spanToHighlight = [].slice.call( codeBlock.querySelectorAll( - ":scope > span:nth-of-type(" + highlight.first + ")" - ) + ":scope > span:nth-of-type(" + highlight.first + ")", + ), ); } if (spanToHighlight.length) { // Add a class on and to select line to highlight spanToHighlight.forEach((span) => - span.classList.add("highlight-line") + span.classList.add("highlight-line"), ); codeBlock.classList.add("has-line-highlights"); } @@ -226,7 +226,7 @@ window.QuartoLineHighlight = function () { // Make sure the scroll target is within bounds targetTop = Math.max( Math.min(targetTop, block.scrollHeight - viewportHeight), - 0 + 0, ); if (skipAnimation === true || startTop === targetTop) { @@ -339,8 +339,8 @@ window.QuartoLineHighlight = function () { deck .getRevealElement() .querySelectorAll( - "pre code[data-code-line-numbers].current-fragment" - ) + "pre code[data-code-line-numbers].current-fragment", + ), ) .forEach(function (block) { scrollHighlightedLineIntoView(block, {}, true); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-support/support.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-support/support.js index 25a0bc0..d69acb8 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-support/support.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/quarto-support/support.js @@ -7,14 +7,14 @@ window.QuartoSupport = function () { // helper for theme toggling function toggleBackgroundTheme(el, onDarkBackground, onLightBackground) { if (onDarkBackground) { - el.classList.add('has-dark-background') + el.classList.add("has-dark-background"); } else { - el.classList.remove('has-dark-background') + el.classList.remove("has-dark-background"); } if (onLightBackground) { - el.classList.add('has-light-background') + el.classList.add("has-light-background"); } else { - el.classList.remove('has-light-background') + el.classList.remove("has-light-background"); } } @@ -96,7 +96,7 @@ window.QuartoSupport = function () { return false; } }, - false + false, ); } }); @@ -130,14 +130,20 @@ window.QuartoSupport = function () { deck.on("slidechanged", function (ev) { const revealParent = deck.getRevealElement(); const slideNumberEl = revealParent.querySelector(".slide-number"); - const onDarkBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-dark-background'); - const onLightBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-light-background'); + const onDarkBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-dark-background"); + const onLightBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-light-background"); toggleBackgroundTheme(slideNumberEl, onDarkBackground, onLightBackground); - }) + }); } - // add footer text - function addFooter(deck) { + // add footer text + function addFooter(deck) { const revealParent = deck.getRevealElement(); const defaultFooterDiv = document.querySelector(".footer-default"); if (defaultFooterDiv) { @@ -146,23 +152,37 @@ window.QuartoSupport = function () { if (!isPrintView()) { deck.on("slidechanged", function (ev) { const prevSlideFooter = document.querySelector( - ".reveal > .footer:not(.footer-default)" + ".reveal > .footer:not(.footer-default)", ); if (prevSlideFooter) { prevSlideFooter.remove(); } const currentSlideFooter = ev.currentSlide.querySelector(".footer"); - const onDarkBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-dark-background') - const onLightBackground = Reveal.getSlideBackground(ev.indexh, ev.indexv).classList.contains('has-light-background') + const onDarkBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-dark-background"); + const onLightBackground = Reveal.getSlideBackground( + ev.indexh, + ev.indexv, + ).classList.contains("has-light-background"); if (currentSlideFooter) { defaultFooterDiv.style.display = "none"; const slideFooter = currentSlideFooter.cloneNode(true); handleLinkClickEvents(deck, slideFooter); deck.getRevealElement().appendChild(slideFooter); - toggleBackgroundTheme(slideFooter, onDarkBackground, onLightBackground) + toggleBackgroundTheme( + slideFooter, + onDarkBackground, + onLightBackground, + ); } else { defaultFooterDiv.style.display = "block"; - toggleBackgroundTheme(defaultFooterDiv, onDarkBackground, onLightBackground) + toggleBackgroundTheme( + defaultFooterDiv, + onDarkBackground, + onLightBackground, + ); } }); } @@ -216,7 +236,7 @@ window.QuartoSupport = function () { const config = deck.getConfig(); let buttons = !!config.chalkboard.buttons; const slideButtons = ev.currentSlide.getAttribute( - "data-chalkboard-buttons" + "data-chalkboard-buttons", ); if (slideButtons) { if (slideButtons === "true" || slideButtons === "1") { @@ -306,7 +326,7 @@ window.QuartoSupport = function () { // remove all whitespace text nodes // whitespace nodes cause the columns to be misaligned // since they have inline-block layout - // + // // Quarto emits no whitespace nodes, but third-party tooling // has bugs that can cause whitespace nodes to be emitted. // See https://github.com/quarto-dev/quarto-cli/issues/8382 diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.css b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.css index 5a300fd..60b0f8d 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.css +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.css @@ -1,5 +1,5 @@ .slide-menu-wrapper { - font-family: 'Source Sans Pro', Helvetica, sans-serif; + font-family: "Source Sans Pro", Helvetica, sans-serif; } .slide-menu-wrapper .slide-menu { @@ -293,8 +293,8 @@ * Theme and Transitions buttons */ -.slide-menu-wrapper div[data-panel='Themes'] li, -.slide-menu-wrapper div[data-panel='Transitions'] li { +.slide-menu-wrapper div[data-panel="Themes"] li, +.slide-menu-wrapper div[data-panel="Transitions"] li { display: block; text-align: left; cursor: pointer; @@ -326,7 +326,10 @@ height: 0; background-color: #000; opacity: 0; - transition: opacity 0.3s, width 0s 0.3s, height 0s 0.3s; + transition: + opacity 0.3s, + width 0s 0.3s, + height 0s 0.3s; } .slide-menu-wrapper .slide-menu-overlay.active { diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.js index 5369df3..684d236 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/reveal-menu/menu.js @@ -1 +1,2256 @@ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e=e||self).RevealMenu=t()}(this,(function(){"use strict";var e="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof 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Array : n)(0 === t ? 0 : t) + ); + }, + We = [].push, + He = function (e) { + var t = 1 == e, + n = 2 == e, + r = 3 == e, + i = 4 == e, + a = 6 == e, + o = 5 == e || a; + return function (s, l, c, u) { + for ( + var f, + d, + h = Ae(s), + m = p(h), + v = (function (e, t, n) { + if ((Oe(e), void 0 === t)) return e; + switch (n) { + case 0: + return function () { + return e.call(t); + }; + case 1: + return function (n) { + return e.call(t, n); + }; + case 2: + return function (n, r) { + return e.call(t, n, r); + }; + case 3: + return function (n, r, i) { + return e.call(t, n, r, i); + }; + } + return function () { + return e.apply(t, arguments); + }; + })(l, c, 3), + g = oe(m.length), + y = 0, + b = u || Fe, + S = t ? b(s, g) : n ? b(s, 0) : void 0; + g > y; + y++ + ) + if ((o || y in m) && ((d = v((f = m[y]), y, h)), e)) + if (t) S[y] = d; + else if (d) + switch (e) { + case 3: + return !0; + case 5: + return f; + case 6: + return y; + case 2: + We.call(S, f); + } + else if (i) return !1; + return a ? -1 : r || i ? i : S; + }; + }, + Ue = { + forEach: He(0), + map: He(1), + filter: He(2), + some: He(3), + every: He(4), + find: He(5), + findIndex: He(6), + }, + $e = function (e, t) { + var n = [][e]; + return ( + !!n && + i(function () { + n.call( + null, + t || + function () { + throw 1; + }, + 1, + ); + }) + ); + }, + De = Object.defineProperty, + qe = {}, + Be = function (e) { + throw e; + }, + Ge = function (e, t) { + if (b(qe, e)) return qe[e]; + t || (t = {}); + var n = [][e], + r = !!b(t, "ACCESSORS") && t.ACCESSORS, + o = b(t, 0) ? t[0] : Be, + s = b(t, 1) ? t[1] : void 0; + return (qe[e] = + !!n && + !i(function () { + if (r && !a) return !0; + var e = { length: -1 }; + r ? De(e, 1, { enumerable: !0, get: Be }) : (e[1] = 1), + n.call(e, o, s); + })); + }, + Ve = Ue.every, + Ke = $e("every"), + ze = Ge("every"); + Ce( + { target: "Array", proto: !0, forced: !Ke || !ze }, + { + every: function (e) { + return Ve(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ); + var Xe, + Ye, + Je = te("navigator", "userAgent") || "", + Ze = r.process, + Qe = Ze && Ze.versions, + et = Qe && Qe.v8; + et + ? (Ye = (Xe = et.split("."))[0] + Xe[1]) + : Je && + (!(Xe = Je.match(/Edge\/(\d+)/)) || Xe[1] >= 74) && + (Xe = Je.match(/Chrome\/(\d+)/)) && + (Ye = Xe[1]); + var tt = Ye && +Ye, + nt = Ne("species"), + rt = function (e) { + return ( + tt >= 51 || + !i(function () { + var t = []; + return ( + ((t.constructor = {})[nt] = function () { + return { foo: 1 }; + }), + 1 !== t[e](Boolean).foo + ); + }) + ); + }, + it = Ue.filter, + at = rt("filter"), + ot = Ge("filter"); + Ce( + { target: "Array", proto: !0, forced: !at || !ot }, + { + filter: function (e) { + return it(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ); + var st = Ue.forEach, + lt = $e("forEach"), + ct = Ge("forEach"), + ut = + lt && ct + ? [].forEach + : function (e) { + return st(this, e, arguments.length > 1 ? arguments[1] : void 0); + }; + Ce({ target: "Array", proto: !0, forced: [].forEach != ut }, { forEach: ut }); + var ft = fe.indexOf, + dt = [].indexOf, + pt = !!dt && 1 / [1].indexOf(1, -0) < 0, + ht = $e("indexOf"), + mt = Ge("indexOf", { ACCESSORS: !0, 1: 0 }); + Ce( + { target: "Array", proto: !0, forced: pt || !ht || !mt }, + { + indexOf: function (e) { + return pt + ? dt.apply(this, arguments) || 0 + : ft(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ), + Ce({ target: "Array", stat: !0 }, { isArray: ke }); + var vt = [].join, + gt = p != Object, + yt = $e("join", ","); + Ce( + { target: "Array", proto: !0, forced: gt || !yt }, + { + join: function (e) { + return vt.call(m(this), void 0 === e ? "," : e); + }, + }, + ); + var bt = Math.min, + St = [].lastIndexOf, + Et = !!St && 1 / [1].lastIndexOf(1, -0) < 0, + xt = $e("lastIndexOf"), + wt = Ge("indexOf", { ACCESSORS: !0, 1: 0 }), + Lt = + Et || !xt || !wt + ? function (e) { + if (Et) return St.apply(this, arguments) || 0; + var t = m(this), + n = oe(t.length), + r = n - 1; + for ( + arguments.length > 1 && (r = bt(r, ie(arguments[1]))), + r < 0 && (r = n + r); + r >= 0; + r-- + ) + if (r in t && t[r] === e) return r || 0; + return -1; + } + : St; + Ce( + { target: "Array", proto: !0, forced: Lt !== [].lastIndexOf }, + { lastIndexOf: Lt }, + ); + var Tt = Ue.map, + Ct = rt("map"), + Ot = Ge("map"); + Ce( + { target: "Array", proto: !0, forced: !Ct || !Ot }, + { + map: function (e) { + return Tt(this, e, arguments.length > 1 ? arguments[1] : void 0); + }, + }, + ); + var At = function (e, t, n) { + var r = g(t); + r in e ? O.f(e, r, c(0, n)) : (e[r] = n); + }, + kt = rt("slice"), + It = Ge("slice", { ACCESSORS: !0, 0: 0, 1: 2 }), + Pt = Ne("species"), + Mt = [].slice, + Rt = Math.max; + Ce( + { target: "Array", proto: !0, forced: !kt || !It }, + { + slice: function (e, t) { + var n, + r, + i, + a = m(this), + o = oe(a.length), + s = ce(e, o), + l = ce(void 0 === t ? o : t, o); + if ( + ke(a) && + ("function" != typeof (n = a.constructor) || + (n !== Array && !ke(n.prototype)) + ? v(n) && null === (n = n[Pt]) && (n = void 0) + : (n = void 0), + n === Array || void 0 === n) + ) + return Mt.call(a, s, l); + for ( + r = new (void 0 === n ? Array : n)(Rt(l - s, 0)), i = 0; + s < l; + s++, i++ + ) + s in a && At(r, i, a[s]); + return (r.length = i), r; + }, + }, + ); + var jt = O.f, + Nt = Function.prototype, + _t = Nt.toString, + Ft = /^\s*function ([^ (]*)/; + a && + !("name" in Nt) && + jt(Nt, "name", { + configurable: !0, + get: function () { + try { + return _t.call(this).match(Ft)[1]; + } catch (e) { + return ""; + } + }, + }); + var Wt = he.f, + Ht = {}.toString, + Ut = + "object" == typeof window && window && Object.getOwnPropertyNames + ? Object.getOwnPropertyNames(window) + : [], + $t = function (e) { + return Ut && "[object Window]" == Ht.call(e) + ? (function (e) { + try { + return Wt(e); + } catch (e) { + return Ut.slice(); + } + })(e) + : Wt(m(e)); + }; + Ce( + { + target: "Object", + stat: !0, + forced: i(function () { + return !Object.getOwnPropertyNames(1); + }), + }, + { getOwnPropertyNames: $t }, + ); + var Dt = "\t\n\v\f\r                　\u2028\u2029\ufeff", + qt = "[" + Dt + "]", + Bt = RegExp("^" + qt + qt + "*"), + Gt = RegExp(qt + qt + "*$"), + Vt = function (e) { + return function (t) { + var n = String(h(t)); + return ( + 1 & e && (n = n.replace(Bt, "")), 2 & e && (n = n.replace(Gt, "")), n + ); + }; + }, + Kt = { start: Vt(1), end: Vt(2), trim: Vt(3) }, + zt = Kt.trim, + Xt = r.parseFloat, + Yt = + 1 / Xt(Dt + "-0") != -1 / 0 + ? function (e) { + var t = zt(String(e)), + n = Xt(t); + return 0 === n && "-" == t.charAt(0) ? -0 : n; + } + : Xt; + Ce({ global: !0, forced: parseFloat != Yt }, { parseFloat: Yt }); + var Jt = Kt.trim, + Zt = r.parseInt, + Qt = /^[+-]?0[Xx]/, + en = + 8 !== Zt(Dt + "08") || 22 !== Zt(Dt + "0x16") + ? function (e, t) { + var n = Jt(String(e)); + return Zt(n, t >>> 0 || (Qt.test(n) ? 16 : 10)); + } + : Zt; + Ce({ global: !0, forced: parseInt != en }, { parseInt: en }); + var tn = function () { + var e = T(this), + t = ""; + return ( + e.global && (t += "g"), + e.ignoreCase && (t += "i"), + e.multiline && (t += "m"), + e.dotAll && (t += "s"), + e.unicode && (t += "u"), + e.sticky && (t += "y"), + t + ); + }; + function nn(e, t) { + return RegExp(e, t); + } + var rn, + an, + on = { + UNSUPPORTED_Y: i(function () { + var e = nn("a", "y"); + return (e.lastIndex = 2), null != e.exec("abcd"); + }), + BROKEN_CARET: i(function () { + var e = nn("^r", "gy"); + return (e.lastIndex = 2), null != e.exec("str"); + }), + }, + sn = RegExp.prototype.exec, + ln = String.prototype.replace, + cn = sn, + un = + ((rn = /a/), + (an = /b*/g), + sn.call(rn, "a"), + sn.call(an, "a"), + 0 !== rn.lastIndex || 0 !== an.lastIndex), + fn = on.UNSUPPORTED_Y || on.BROKEN_CARET, + dn = void 0 !== /()??/.exec("")[1]; + (un || dn || fn) && + (cn = function (e) { + var t, + n, + r, + i, + a = this, + o = fn && a.sticky, + s = tn.call(a), + l = a.source, + c = 0, + u = e; + return ( + o && + (-1 === (s = s.replace("y", "")).indexOf("g") && (s += "g"), + (u = String(e).slice(a.lastIndex)), + a.lastIndex > 0 && + (!a.multiline || (a.multiline && "\n" !== e[a.lastIndex - 1])) && + ((l = "(?: " + l + ")"), (u = " " + u), c++), + (n = new RegExp("^(?:" + l + ")", s))), + dn && (n = new RegExp("^" + l + "$(?!\\s)", s)), + un && (t = a.lastIndex), + (r = sn.call(o ? n : a, u)), + o + ? r + ? ((r.input = r.input.slice(c)), + (r[0] = r[0].slice(c)), + (r.index = a.lastIndex), + (a.lastIndex += r[0].length)) + : (a.lastIndex = 0) + : un && r && (a.lastIndex = a.global ? r.index + r[0].length : t), + dn && + r && + r.length > 1 && + ln.call(r[0], n, function () { + for (i = 1; i < arguments.length - 2; i++) + void 0 === arguments[i] && (r[i] = void 0); + }), + r + ); + }); + var pn = cn; + Ce({ target: "RegExp", proto: !0, forced: /./.exec !== pn }, { exec: pn }); + var hn, + mn = Ne("match"), + vn = function (e) { + var t; + return v(e) && (void 0 !== (t = e[mn]) ? !!t : "RegExp" == f(e)); + }, + gn = function (e) { + if (vn(e)) + throw TypeError("The method doesn't accept regular expressions"); + return e; + }, + yn = Ne("match"), + bn = function (e) { + var t = /./; + try { + "/./"[e](t); + } catch (n) { + try { + return (t[yn] = !1), "/./"[e](t); + } catch (e) {} + } + return !1; + }, + Sn = L.f, + En = "".endsWith, + xn = Math.min, + wn = bn("endsWith"); + Ce( + { + target: "String", + proto: !0, + forced: + !!( + wn || ((hn = Sn(String.prototype, "endsWith")), !hn || hn.writable) + ) && !wn, + }, + { + endsWith: function (e) { + var t = String(h(this)); + gn(e); + var n = arguments.length > 1 ? arguments[1] : void 0, + r = oe(t.length), + i = void 0 === n ? r : xn(oe(n), r), + a = String(e); + return En ? En.call(t, a, i) : t.slice(i - a.length, i) === a; + }, + }, + ); + var Ln = Ne("species"), + Tn = !i(function () { + var e = /./; + return ( + (e.exec = function () { + var e = []; + return (e.groups = { a: "7" }), e; + }), + "7" !== "".replace(e, "$    ") + ); + }), + Cn = "$0" === "a".replace(/./, "$0"), + On = Ne("replace"), + An = !!/./[On] && "" === /./[On]("a", "$0"), + kn = !i(function () { + var e = /(?:)/, + t = e.exec; + e.exec = function () { + return t.apply(this, arguments); + }; + var n = "ab".split(e); + return 2 !== n.length || "a" !== n[0] || "b" !== n[1]; + }), + In = function (e, t, n, r) { + var a = Ne(e), + o = !i(function () { + var t = {}; + return ( + (t[a] = function () { + return 7; + }), + 7 != ""[e](t) + ); + }), + s = + o && + !i(function () { + var t = !1, + n = /a/; + return ( + "split" === e && + (((n = {}).constructor = {}), + (n.constructor[Ln] = function () { + return n; + }), + (n.flags = ""), + (n[a] = /./[a])), + (n.exec = function () { + return (t = !0), null; + }), + n[a](""), + !t + ); + }); + if ( + !o || + !s || + ("replace" === e && (!Tn || !Cn || An)) || + ("split" === e && !kn) + ) { + var l = /./[a], + c = n( + a, + ""[e], + function (e, t, n, r, i) { + return t.exec === pn + ? o && !i + ? { done: !0, value: l.call(t, n, r) } + : { done: !0, value: e.call(n, t, r) } + : { done: !1 }; + }, + { + REPLACE_KEEPS_$0: Cn, + REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE: An, + }, + ), + u = c[0], + f = c[1]; + Z(String.prototype, e, u), + Z( + RegExp.prototype, + a, + 2 == t + ? function (e, t) { + return f.call(e, this, t); + } + : function (e) { + return f.call(e, this); + }, + ); + } + r && A(RegExp.prototype[a], "sham", !0); + }, + Pn = function (e) { + return function (t, n) { + var r, + i, + a = String(h(t)), + o = ie(n), + s = a.length; + return o < 0 || o >= s + ? e + ? "" + : void 0 + : (r = a.charCodeAt(o)) < 55296 || + r > 56319 || + o + 1 === s || + (i = a.charCodeAt(o + 1)) < 56320 || + i > 57343 + ? e + ? a.charAt(o) + : r + : e + ? a.slice(o, o + 2) + : i - 56320 + ((r - 55296) << 10) + 65536; + }; + }, + Mn = { codeAt: Pn(!1), charAt: Pn(!0) }.charAt, + Rn = function (e, t, n) { + return t + (n ? Mn(e, t).length : 1); + }, + jn = function (e, t) { + var n = e.exec; + if ("function" == typeof n) { + var r = n.call(e, t); + if ("object" != typeof r) + throw TypeError( + "RegExp exec method returned something other than an Object or null", + ); + return r; + } + if ("RegExp" !== f(e)) + throw TypeError("RegExp#exec called on incompatible receiver"); + return pn.call(e, t); + }, + Nn = Math.max, + _n = Math.min, + Fn = Math.floor, + Wn = /\$([$&'`]|\d\d?|<[^>]*>)/g, + Hn = /\$([$&'`]|\d\d?)/g; + In("replace", 2, function (e, t, n, r) { + var i = r.REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE, + a = r.REPLACE_KEEPS_$0, + o = i ? "$" : "$0"; + return [ + function (n, r) { + var i = h(this), + a = null == n ? void 0 : n[e]; + return void 0 !== a ? a.call(n, i, r) : t.call(String(i), n, r); + }, + function (e, r) { + if ((!i && a) || ("string" == typeof r && -1 === r.indexOf(o))) { + var l = n(t, e, this, r); + if (l.done) return l.value; + } + var c = T(e), + u = String(this), + f = "function" == typeof r; + f || (r = String(r)); + var d = c.global; + if (d) { + var p = c.unicode; + c.lastIndex = 0; + } + for (var h = []; ; ) { + var m = jn(c, u); + if (null === m) break; + if ((h.push(m), !d)) break; + "" === String(m[0]) && (c.lastIndex = Rn(u, oe(c.lastIndex), p)); + } + for (var v, g = "", y = 0, b = 0; b < h.length; b++) { + m = h[b]; + for ( + var S = String(m[0]), + E = Nn(_n(ie(m.index), u.length), 0), + x = [], + w = 1; + w < m.length; + w++ + ) + x.push(void 0 === (v = m[w]) ? v : String(v)); + var L = m.groups; + if (f) { + var C = [S].concat(x, E, u); + void 0 !== L && C.push(L); + var O = String(r.apply(void 0, C)); + } else O = s(S, u, E, x, L, r); + E >= y && ((g += u.slice(y, E) + O), (y = E + S.length)); + } + return g + u.slice(y); + }, + ]; + function s(e, n, r, i, a, o) { + var s = r + e.length, + l = i.length, + c = Hn; + return ( + void 0 !== a && ((a = Ae(a)), (c = Wn)), + t.call(o, c, function (t, o) { + var c; + switch (o.charAt(0)) { + case "$": + return "$"; + case "&": + return e; + case "`": + return n.slice(0, r); + case "'": + return n.slice(s); + case "<": + c = a[o.slice(1, -1)]; + break; + default: + var u = +o; + if (0 === u) return t; + if (u > l) { + var f = Fn(u / 10); + return 0 === f + ? t + : f <= l + ? void 0 === i[f - 1] + ? o.charAt(1) + : i[f - 1] + o.charAt(1) + : t; + } + c = i[u - 1]; + } + return void 0 === c ? "" : c; + }) + ); + } + }); + var Un = + Object.is || + function (e, t) { + return e === t ? 0 !== e || 1 / e == 1 / t : e != e && t != t; + }; + In("search", 1, function (e, t, n) { + return [ + function (t) { + var n = h(this), + r = null == t ? void 0 : t[e]; + return void 0 !== r ? r.call(t, n) : new RegExp(t)[e](String(n)); + }, + function (e) { + var r = n(t, e, this); + if (r.done) return r.value; + var i = T(e), + a = String(this), + o = i.lastIndex; + Un(o, 0) || (i.lastIndex = 0); + var s = jn(i, a); + return ( + Un(i.lastIndex, o) || (i.lastIndex = o), null === s ? -1 : s.index + ); + }, + ]; + }); + var $n = Ne("species"), + Dn = [].push, + qn = Math.min, + Bn = !i(function () { + return !RegExp(4294967295, "y"); + }); + In( + "split", + 2, + function (e, t, n) { + var r; + return ( + (r = + "c" == "abbc".split(/(b)*/)[1] || + 4 != "test".split(/(?:)/, -1).length || + 2 != "ab".split(/(?:ab)*/).length || + 4 != ".".split(/(.?)(.?)/).length || + ".".split(/()()/).length > 1 || + "".split(/.?/).length + ? function (e, n) { + var r = String(h(this)), + i = void 0 === n ? 4294967295 : n >>> 0; + if (0 === i) return []; + if (void 0 === e) return [r]; + if (!vn(e)) return t.call(r, e, i); + for ( + var a, + o, + s, + l = [], + c = + (e.ignoreCase ? "i" : "") + + (e.multiline ? "m" : "") + + (e.unicode ? "u" : "") + + (e.sticky ? "y" : ""), + u = 0, + f = new RegExp(e.source, c + "g"); + (a = pn.call(f, r)) && + !( + (o = f.lastIndex) > u && + (l.push(r.slice(u, a.index)), + a.length > 1 && + a.index < r.length && + Dn.apply(l, a.slice(1)), + (s = a[0].length), + (u = o), + l.length >= i) + ); + + ) + f.lastIndex === a.index && f.lastIndex++; + return ( + u === r.length + ? (!s && f.test("")) || l.push("") + : l.push(r.slice(u)), + l.length > i ? l.slice(0, i) : l + ); + } + : "0".split(void 0, 0).length + ? function (e, n) { + return void 0 === e && 0 === n ? [] : t.call(this, e, n); + } + : t), + [ + function (t, n) { + var i = h(this), + a = null == t ? void 0 : t[e]; + return void 0 !== a ? a.call(t, i, n) : r.call(String(i), t, n); + }, + function (e, i) { + var a = n(r, e, this, i, r !== t); + if (a.done) return a.value; + var o = T(e), + s = String(this), + l = (function (e, t) { + var n, + r = T(e).constructor; + return void 0 === r || null == (n = T(r)[$n]) ? t : Oe(n); + })(o, RegExp), + c = o.unicode, + u = + (o.ignoreCase ? "i" : "") + + (o.multiline ? "m" : "") + + (o.unicode ? "u" : "") + + (Bn ? "y" : "g"), + f = new l(Bn ? o : "^(?:" + o.source + ")", u), + d = void 0 === i ? 4294967295 : i >>> 0; + if (0 === d) return []; + if (0 === s.length) return null === jn(f, s) ? [s] : []; + for (var p = 0, h = 0, m = []; h < s.length; ) { + f.lastIndex = Bn ? h : 0; + var v, + g = jn(f, Bn ? s : s.slice(h)); + if ( + null === g || + (v = qn(oe(f.lastIndex + (Bn ? 0 : h)), s.length)) === p + ) + h = Rn(s, h, c); + else { + if ((m.push(s.slice(p, h)), m.length === d)) return m; + for (var y = 1; y <= g.length - 1; y++) + if ((m.push(g[y]), m.length === d)) return m; + h = p = v; + } + } + return m.push(s.slice(p)), m; + }, + ] + ); + }, + !Bn, + ); + var Gn = L.f, + Vn = "".startsWith, + Kn = Math.min, + zn = bn("startsWith"); + Ce( + { + target: "String", + proto: !0, + forced: + !( + !zn && + !!(function () { + var e = Gn(String.prototype, "startsWith"); + return e && !e.writable; + })() + ) && !zn, + }, + { + startsWith: function (e) { + var t = String(h(this)); + gn(e); + var n = oe(Kn(arguments.length > 1 ? arguments[1] : void 0, t.length)), + r = String(e); + return Vn ? Vn.call(t, r, n) : t.slice(n, n + r.length) === r; + }, + }, + ); + var Xn, + Yn = Kt.trim; + Ce( + { + target: "String", + proto: !0, + forced: + ((Xn = "trim"), + i(function () { + return !!Dt[Xn]() || "​…᠎" != "​…᠎"[Xn]() || Dt[Xn].name !== Xn; + })), + }, + { + trim: function () { + return Yn(this); + }, + }, + ); + for (var Jn in { + CSSRuleList: 0, + CSSStyleDeclaration: 0, + CSSValueList: 0, + ClientRectList: 0, + DOMRectList: 0, + DOMStringList: 0, + DOMTokenList: 1, + DataTransferItemList: 0, + FileList: 0, + HTMLAllCollection: 0, + HTMLCollection: 0, + HTMLFormElement: 0, + HTMLSelectElement: 0, + MediaList: 0, + MimeTypeArray: 0, + NamedNodeMap: 0, + NodeList: 1, + PaintRequestList: 0, + Plugin: 0, + PluginArray: 0, + SVGLengthList: 0, + SVGNumberList: 0, + SVGPathSegList: 0, + SVGPointList: 0, + SVGStringList: 0, + SVGTransformList: 0, + SourceBufferList: 0, + StyleSheetList: 0, + TextTrackCueList: 0, + TextTrackList: 0, + TouchList: 0, + }) { + var Zn = r[Jn], + Qn = Zn && Zn.prototype; + if (Qn && Qn.forEach !== ut) + try { + A(Qn, "forEach", ut); + } catch (e) { + Qn.forEach = ut; + } + } + var er = [].slice, + tr = function (e) { + return function (t, n) { + var r = arguments.length > 2, + i = r ? er.call(arguments, 2) : void 0; + return e( + r + ? function () { + ("function" == typeof t ? t : Function(t)).apply(this, i); + } + : t, + n, + ); + }; + }; + Ce( + { global: !0, bind: !0, forced: /MSIE .\./.test(Je) }, + { setTimeout: tr(r.setTimeout), setInterval: tr(r.setInterval) }, + ); + return ( + String.prototype.startsWith || + (String.prototype.startsWith = function (e, t) { + return this.substr(t || 0, e.length) === e; + }), + String.prototype.endsWith || + (String.prototype.endsWith = function (e, t) { + return ( + (void 0 === t || t > this.length) && (t = this.length), + this.substring(t - e.length, t) === e + ); + }), + function () { + var e, + t, + n, + r, + i = + (e = /(msie) ([\w.]+)/.exec( + window.navigator.userAgent.toLowerCase(), + )) && "msie" === e[1] + ? parseFloat(e[2]) + : null, + a = !1; + function o(e) { + ((r = e.menu || {}).path = + r.path || + (function () { + var e; + if (document.querySelector('script[src$="menu.js"]')) { + var t = document.querySelector('script[src$="menu.js"]'); + t && (e = t.src.slice(0, -7)); + } else + e = ( + "undefined" == typeof document + ? new (require("url").URL)("file:" + __filename).href + : (document.currentScript && document.currentScript.src) || + new URL("menu.js", document.baseURI).href + ).slice( + 0, + ("undefined" == typeof document + ? new (require("url").URL)("file:" + __filename).href + : (document.currentScript && document.currentScript.src) || + new URL("menu.js", document.baseURI).href + ).lastIndexOf("/") + 1, + ); + return e; + })() || + "plugin/menu/"), + r.path.endsWith("/") || (r.path += "/"), + void 0 === r.side && (r.side = "left"), + void 0 === r.numbers && (r.numbers = !1), + "string" != typeof r.titleSelector && + (r.titleSelector = "h1, h2, h3, h4, h5"), + void 0 === r.hideMissingTitles && (r.hideMissingTitles = !1), + void 0 === r.useTextContentForMissingTitles && + (r.useTextContentForMissingTitles = !1), + void 0 === r.markers && (r.markers = !0), + "string" != typeof r.themesPath && (r.themesPath = "dist/theme/"), + r.themesPath.endsWith("/") || (r.themesPath += "/"), + O("link#theme") || (r.themes = !1), + !0 === r.themes + ? (r.themes = [ + { name: "Black", theme: r.themesPath + "black.css" }, + { name: "White", theme: r.themesPath + "white.css" }, + { name: "League", theme: r.themesPath + "league.css" }, + { name: "Sky", theme: r.themesPath + "sky.css" }, + { name: "Beige", theme: r.themesPath + "beige.css" }, + { name: "Simple", theme: r.themesPath + "simple.css" }, + { name: "Serif", theme: r.themesPath + "serif.css" }, + { name: "Blood", theme: r.themesPath + "blood.css" }, + { name: "Night", theme: r.themesPath + "night.css" }, + { name: "Moon", theme: r.themesPath + "moon.css" }, + { name: "Solarized", theme: r.themesPath + "solarized.css" }, + ]) + : Array.isArray(r.themes) || (r.themes = !1), + void 0 === r.transitions && (r.transitions = !1), + !0 === r.transitions + ? (r.transitions = [ + "None", + "Fade", + "Slide", + "Convex", + "Concave", + "Zoom", + ]) + : !1 === r.transitions || + (Array.isArray(r.transitions) && + r.transitions.every(function (e) { + return "string" == typeof e; + })) || + (console.error( + "reveal.js-menu error: transitions config value must be 'true' or an array of strings, eg ['None', 'Fade', 'Slide')", + ), + (r.transitions = !1)), + i && i <= 9 && (r.transitions = !1), + void 0 === r.openButton && (r.openButton = !0), + void 0 === r.openSlideNumber && (r.openSlideNumber = !1), + void 0 === r.keyboard && (r.keyboard = !0), + void 0 === r.sticky && (r.sticky = !1), + void 0 === r.autoOpen && (r.autoOpen = !0), + void 0 === r.delayInit && (r.delayInit = !1), + void 0 === r.openOnInit && (r.openOnInit = !1); + } + var s = !0; + function l() { + s = !1; + } + function c() { + O("nav.slide-menu").addEventListener("mousemove", function e(t) { + O("nav.slide-menu").removeEventListener("mousemove", e), (s = !0); + }); + } + function u(e) { + var t = + (function (e) { + for ( + var t = 0, n = 0; + e && !isNaN(e.offsetLeft) && !isNaN(e.offsetTop); + + ) + (t += e.offsetLeft - e.scrollLeft), + (n += e.offsetTop - e.scrollTop), + (e = e.offsetParent); + return { top: n, left: t }; + })(e).top - e.offsetParent.offsetTop; + if (t < 0) return -t; + var n = + e.offsetParent.offsetHeight - + (e.offsetTop - e.offsetParent.scrollTop + e.offsetHeight); + return n < 0 ? n : 0; + } + function f(e) { + var t = u(e); + t && (l(), e.scrollIntoView(t > 0), c()); + } + function d(e) { + l(), (e.offsetParent.scrollTop = e.offsetTop), c(); + } + function p(e) { + l(), + (e.offsetParent.scrollTop = + e.offsetTop - e.offsetParent.offsetHeight + e.offsetHeight), + c(); + } + function h(e) { + e.classList.add("selected"), f(e), r.sticky && r.autoOpen && E(e); + } + function m(e) { + if (b()) + switch ((e.stopImmediatePropagation(), e.keyCode)) { + case 72: + case 37: + !(function () { + var e = + parseInt( + O(".active-toolbar-button").getAttribute("data-button"), + ) - 1; + e < 0 && (e = T - 1); + S( + null, + O( + '.toolbar-panel-button[data-button="' + e + '"]', + ).getAttribute("data-panel"), + ); + })(); + break; + case 76: + case 39: + (l = + (parseInt( + O(".active-toolbar-button").getAttribute("data-button"), + ) + + 1) % + T), + S( + null, + O( + '.toolbar-panel-button[data-button="' + l + '"]', + ).getAttribute("data-panel"), + ); + break; + case 75: + case 38: + if ( + (s = + O(".active-menu-panel .slide-menu-items li.selected") || + O(".active-menu-panel .slide-menu-items li.active")) + ) + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h( + O( + '.active-menu-panel .slide-menu-items li[data-item="' + + (parseInt(s.getAttribute("data-item")) - 1) + + '"]', + ) || s, + ); + else + (o = O( + ".active-menu-panel .slide-menu-items li.slide-menu-item", + )) && h(o); + break; + case 74: + case 40: + if ( + (s = + O(".active-menu-panel .slide-menu-items li.selected") || + O(".active-menu-panel .slide-menu-items li.active")) + ) + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h( + O( + '.active-menu-panel .slide-menu-items li[data-item="' + + (parseInt(s.getAttribute("data-item")) + 1) + + '"]', + ) || s, + ); + else + (o = O( + ".active-menu-panel .slide-menu-items li.slide-menu-item", + )) && h(o); + break; + case 33: + case 85: + var t = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return u(e) > 0; + }, + ), + n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ), + r = + t.length > 0 && + Math.abs(u(t[t.length - 1])) < t[t.length - 1].clientHeight + ? t[t.length - 1] + : n[0]; + r && + (r.classList.contains("selected") && + t.length > 0 && + (p(r), + (r = + (n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ))[0] == r + ? t[t.length - 1] + : n[0])), + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h(r), + d(r)); + break; + case 34: + case 68: + n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ); + var i = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return u(e) < 0; + }, + ), + a = + i.length > 0 && Math.abs(u(i[0])) < i[0].clientHeight + ? i[0] + : n[n.length - 1]; + a && + (a.classList.contains("selected") && + i.length > 0 && + (d(a), + (a = + (n = A(".active-menu-panel .slide-menu-items li").filter( + function (e) { + return 0 == u(e); + }, + ))[n.length - 1] == a + ? i[0] + : n[n.length - 1])), + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + h(a), + p(a)); + break; + case 36: + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + (o = O( + ".active-menu-panel .slide-menu-items li:first-of-type", + )) && (o.classList.add("selected"), f(o)); + break; + case 35: + var o; + A(".active-menu-panel .slide-menu-items li").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + (o = O( + ".active-menu-panel .slide-menu-items:last-of-type li:last-of-type", + )) && (o.classList.add("selected"), f(o)); + break; + case 32: + case 13: + var s; + (s = O(".active-menu-panel .slide-menu-items li.selected")) && + E(s, !0); + break; + case 27: + g(null, !0); + } + var l; + } + function v(e) { + (e && e.preventDefault(), b()) || + (O("body").classList.add("slide-menu-active"), + O(".reveal").classList.add("has-" + r.effect + "-" + r.side), + O(".slide-menu").classList.add("active"), + O(".slide-menu-overlay").classList.add("active"), + r.themes && + (A('div[data-panel="Themes"] li').forEach(function (e) { + e.classList.remove("active"); + }), + A( + 'li[data-theme="' + O("link#theme").getAttribute("href") + '"]', + ).forEach(function (e) { + e.classList.add("active"); + })), + r.transitions && + (A('div[data-panel="Transitions"] li').forEach(function (e) { + e.classList.remove("active"); + }), + A('li[data-transition="' + n.transition + '"]').forEach( + function (e) { + e.classList.add("active"); + }, + )), + A(".slide-menu-panel li.active").forEach(function (e) { + e.classList.add("selected"), f(e); + })); + } + function g(e, t) { + e && e.preventDefault(), + (r.sticky && !t) || + (O("body").classList.remove("slide-menu-active"), + O(".reveal").classList.remove("has-" + r.effect + "-" + r.side), + O(".slide-menu").classList.remove("active"), + O(".slide-menu-overlay").classList.remove("active"), + A(".slide-menu-panel li.selected").forEach(function (e) { + e.classList.remove("selected"); + })); + } + function y(e) { + b() ? g(e, !0) : v(e); + } + function b() { + return O("body").classList.contains("slide-menu-active"); + } + function S(e, t) { + v(e); + var n = t; + "string" != typeof t && + (n = e.currentTarget.getAttribute("data-panel")), + O(".slide-menu-toolbar > li.active-toolbar-button").classList.remove( + "active-toolbar-button", + ), + O('li[data-panel="' + n + '"]').classList.add( + "active-toolbar-button", + ), + O(".slide-menu-panel.active-menu-panel").classList.remove( + "active-menu-panel", + ), + O('div[data-panel="' + n + '"]').classList.add("active-menu-panel"); + } + function E(e, n) { + var i = parseInt(e.getAttribute("data-slide-h")), + a = parseInt(e.getAttribute("data-slide-v")), + o = e.getAttribute("data-theme"), + s = e.getAttribute("data-highlight-theme"), + l = e.getAttribute("data-transition"); + isNaN(i) || isNaN(a) || t.slide(i, a), + o && I("theme", o), + s && I("highlight-theme", s), + l && t.configure({ transition: l }); + var c = O("a", e); + c && + (n || + !r.sticky || + (r.autoOpen && c.href.startsWith("#")) || + c.href.startsWith( + window.location.origin + window.location.pathname + "#", + )) && + c.click(), + g(); + } + function x(e) { + "A" !== e.target.nodeName && e.preventDefault(), E(e.currentTarget); + } + function w() { + var e = t.getState(); + A("li.slide-menu-item, li.slide-menu-item-vertical").forEach( + function (t) { + t.classList.remove("past"), + t.classList.remove("active"), + t.classList.remove("future"); + var n = parseInt(t.getAttribute("data-slide-h")), + r = parseInt(t.getAttribute("data-slide-v")); + n < e.indexh || (n === e.indexh && r < e.indexv) + ? t.classList.add("past") + : n === e.indexh && r === e.indexv + ? t.classList.add("active") + : t.classList.add("future"); + }, + ); + } + function L() { + var e = window.getComputedStyle(O(".reveal")); + O(".slide-menu").style.fontFamily = e.fontFamily; + } + var T = 0; + function C() { + if (!a) { + var e = function (e, t, n, r, i, a) { + var o = { + "data-button": "" + T++, + class: + "toolbar-panel-button" + (a ? " active-toolbar-button" : ""), + }; + t && (o["data-panel"] = t); + var s = k("li", o); + return ( + n.startsWith("fa-") + ? s.appendChild(k("i", { class: r + " " + n })) + : (s.innerHTML = n + ""), + s.appendChild(k("br"), O("i", s)), + s.appendChild( + k("span", { class: "slide-menu-toolbar-label" }, e), + O("i", s), + ), + (s.onclick = i), + d.appendChild(s), + s + ); + }, + i = function (e, i, a, o, s) { + function l(e, t) { + if ("" === e) return null; + var n = t ? O(e, i) : O(e); + return n ? n.textContent : null; + } + var c = + i.getAttribute("data-menu-title") || + l(".menu-title", i) || + l(r.titleSelector, i); + if ( + (!c && + r.useTextContentForMissingTitles && + (c = i.textContent.trim()) && + (c = + c + .split("\n") + .map(function (e) { + return e.trim(); + }) + .join(" ") + .trim() + .replace(/^(.{16}[^\s]*).*/, "$1") + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/'/g, "'") + "..."), + !c) + ) { + if (r.hideMissingTitles) return ""; + (e += " no-title"), (c = "Slide " + (a + 1)); + } + var u = k("li", { + class: e, + "data-item": a, + "data-slide-h": o, + "data-slide-v": void 0 === s ? 0 : s, + }); + if ( + (r.markers && + (u.appendChild( + k("i", { class: "fas fa-check-circle fa-fw past" }), + ), + u.appendChild( + k("i", { + class: "fas fa-arrow-alt-circle-right fa-fw active", + }), + ), + u.appendChild( + k("i", { class: "far fa-circle fa-fw future" }), + )), + r.numbers) + ) { + var f = [], + d = "h.v"; + switch ( + ("string" == typeof r.numbers + ? (d = r.numbers) + : "string" == typeof n.slideNumber && (d = n.slideNumber), + d) + ) { + case "c": + f.push(a + 1); + break; + case "c/t": + f.push(a + 1, "/", t.getTotalSlides()); + break; + case "h/v": + f.push(o + 1), + "number" != typeof s || isNaN(s) || f.push("/", s + 1); + break; + default: + f.push(o + 1), + "number" != typeof s || isNaN(s) || f.push(".", s + 1); + } + u.appendChild( + k( + "span", + { class: "slide-menu-item-number" }, + f.join("") + ". ", + ), + ); + } + return ( + u.appendChild(k("span", { class: "slide-menu-item-title" }, c)), + u + ); + }, + o = function (e) { + s && + (A(".active-menu-panel .slide-menu-items li.selected").forEach( + function (e) { + e.classList.remove("selected"); + }, + ), + e.currentTarget.classList.add("selected")); + }, + l = O(".reveal").parentElement, + c = k("div", { class: "slide-menu-wrapper" }); + l.appendChild(c); + var u = k("nav", { class: "slide-menu slide-menu--" + r.side }); + "string" == typeof r.width && + (-1 != ["normal", "wide", "third", "half", "full"].indexOf(r.width) + ? u.classList.add("slide-menu--" + r.width) + : (u.classList.add("slide-menu--custom"), + (u.style.width = r.width))), + c.appendChild(u), + L(); + var f = k("div", { class: "slide-menu-overlay" }); + c.appendChild(f), + (f.onclick = function () { + g(null, !0); + }); + var d = k("ol", { class: "slide-menu-toolbar" }); + O(".slide-menu").appendChild(d), + e("Slides", "Slides", "fa-images", "fas", S, !0), + r.custom && + r.custom.forEach(function (t, n, r) { + e(t.title, "Custom" + n, t.icon, null, S); + }), + r.themes && e("Themes", "Themes", "fa-adjust", "fas", S), + r.transitions && + e("Transitions", "Transitions", "fa-sticky-note", "fas", S); + var p = k("li", { id: "close", class: "toolbar-panel-button" }); + if ( + (p.appendChild(k("i", { class: "fas fa-times" })), + p.appendChild(k("br")), + p.appendChild( + k("span", { class: "slide-menu-toolbar-label" }, "Close"), + ), + (p.onclick = function () { + g(null, !0); + }), + d.appendChild(p), + (function e() { + if ( + document.querySelector( + "section[data-markdown]:not([data-markdown-parsed])", + ) + ) + setTimeout(e, 100); + else { + var t = k("div", { + "data-panel": "Slides", + class: "slide-menu-panel active-menu-panel", + }); + t.appendChild(k("ul", { class: "slide-menu-items" })), + u.appendChild(t); + var n = O( + '.slide-menu-panel[data-panel="Slides"] > .slide-menu-items', + ), + r = 0; + A(".slides > section").forEach(function (e, t) { + var a = A("section", e); + if (a.length > 0) + a.forEach(function (e, a) { + var o = i( + 0 === a + ? "slide-menu-item" + : "slide-menu-item-vertical", + e, + r, + t, + a, + ); + o && n.appendChild(o), r++; + }); + else { + var o = i("slide-menu-item", e, r, t); + o && n.appendChild(o), r++; + } + }), + A(".slide-menu-item, .slide-menu-item-vertical").forEach( + function (e) { + e.onclick = x; + }, + ), + w(); + } + })(), + t.addEventListener("slidechanged", w), + r.custom) + ) { + var h = function () { + this.status >= 200 && this.status < 300 + ? ((this.panel.innerHTML = this.responseText), C(this.panel)) + : I(this); + }, + E = function () { + I(this); + }, + C = function (e) { + A("ul.slide-menu-items li.slide-menu-item", e).forEach( + function (e, t) { + e.setAttribute("data-item", t + 1), + (e.onclick = x), + e.addEventListener("mouseenter", o); + }, + ); + }, + I = function (e) { + var t = + "

    ERROR: The attempt to fetch " + + e.responseURL + + " failed with HTTP status " + + e.status + + " (" + + e.statusText + + ").

    Remember that you need to serve the presentation HTML from a HTTP server.

    "; + e.panel.innerHTML = t; + }; + r.custom.forEach(function (e, t, n) { + var r = k("div", { + "data-panel": "Custom" + t, + class: "slide-menu-panel slide-menu-custom-panel", + }); + e.content + ? ((r.innerHTML = e.content), C(r)) + : e.src && + (function (e, t) { + var n = new XMLHttpRequest(); + (n.panel = e), + (n.arguments = Array.prototype.slice.call(arguments, 2)), + (n.onload = h), + (n.onerror = E), + n.open("get", t, !0), + n.send(null); + })(r, e.src), + u.appendChild(r); + }); + } + if (r.themes) { + var P = k("div", { + class: "slide-menu-panel", + "data-panel": "Themes", + }); + u.appendChild(P); + var M = k("ul", { class: "slide-menu-items" }); + P.appendChild(M), + r.themes.forEach(function (e, t) { + var n = { class: "slide-menu-item", "data-item": "" + (t + 1) }; + e.theme && (n["data-theme"] = e.theme), + e.highlightTheme && + (n["data-highlight-theme"] = e.highlightTheme); + var r = k("li", n, e.name); + M.appendChild(r), (r.onclick = x); + }); + } + if (r.transitions) { + P = k("div", { + class: "slide-menu-panel", + "data-panel": "Transitions", + }); + u.appendChild(P); + M = k("ul", { class: "slide-menu-items" }); + P.appendChild(M), + r.transitions.forEach(function (e, t) { + var n = k( + "li", + { + class: "slide-menu-item", + "data-transition": e.toLowerCase(), + "data-item": "" + (t + 1), + }, + e, + ); + M.appendChild(n), (n.onclick = x); + }); + } + if (r.openButton) { + var R = k("div", { class: "slide-menu-button" }), + j = k("a", { href: "#" }); + j.appendChild(k("i", { class: "fas fa-bars" })), + R.appendChild(j), + O(".reveal").appendChild(R), + (R.onclick = v); + } + if (r.openSlideNumber) O("div.slide-number").onclick = v; + A(".slide-menu-panel .slide-menu-items li").forEach(function (e) { + e.addEventListener("mouseenter", o); + }); + } + if (r.keyboard) { + if ( + (document.addEventListener("keydown", m, !1), + window.addEventListener("message", function (e) { + var t; + try { + t = JSON.parse(e.data); + } catch (e) {} + t && + "triggerKey" === t.method && + m({ + keyCode: t.args[0], + stopImmediatePropagation: function () {}, + }); + }), + n.keyboardCondition && "function" == typeof n.keyboardCondition) + ) { + var N = n.keyboardCondition; + n.keyboardCondition = function (e) { + return N(e) && (!b() || 77 == e.keyCode); + }; + } else + n.keyboardCondition = function (e) { + return !b() || 77 == e.keyCode; + }; + t.addKeyBinding( + { keyCode: 77, key: "M", description: "Toggle menu" }, + y, + ); + } + r.openOnInit && v(), (a = !0); + } + function O(e, t) { + return t || (t = document), t.querySelector(e); + } + function A(e, t) { + return ( + t || (t = document), Array.prototype.slice.call(t.querySelectorAll(e)) + ); + } + function k(e, t, n) { + var r = document.createElement(e); + return ( + t && + Object.getOwnPropertyNames(t).forEach(function (e) { + r.setAttribute(e, t[e]); + }), + n && (r.innerHTML = n), + r + ); + } + function I(e, t) { + var n = O("link#" + e), + r = n.parentElement, + i = n.nextElementSibling; + n.remove(); + var a = n.cloneNode(); + a.setAttribute("href", t), + (a.onload = function () { + L(); + }), + r.insertBefore(a, i); + } + function P(e, t, n) { + n.call(); + } + function M() { + var e, + a, + o, + s = !i || i >= 9; + t.isSpeakerNotes() && + window.location.search.endsWith("controls=false") && + (s = !1), + s && + (r.delayInit || C(), + (e = "menu-ready"), + (o = document.createEvent("HTMLEvents", 1, 2)).initEvent(e, !0, !0), + (function (e, t) { + for (var n in t) e[n] = t[n]; + })(o, a), + document.querySelector(".reveal").dispatchEvent(o), + n.postMessageEvents && + window.parent !== window.self && + window.parent.postMessage( + JSON.stringify({ + namespace: "reveal", + eventName: e, + state: t.getState(), + }), + "*", + )); + } + return { + id: "menu", + init: function (e) { + o((n = (t = e).getConfig())), + P(r.path + "menu.css", "stylesheet", function () { + void 0 === r.loadIcons || r.loadIcons + ? P(r.path + "font-awesome/css/all.css", "stylesheet", M) + : M(); + }); + }, + toggle: y, + openMenu: v, + closeMenu: g, + openPanel: S, + isOpen: b, + initialiseMenu: C, + isMenuInitialised: function () { + return a; + }, + }; + } + ); +}); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/plugin.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/plugin.js index 5d09ce6..e6b25f9 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/plugin.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/plugin.js @@ -6,238 +6,242 @@ */ const Plugin = () => { - - // The reveal.js instance this plugin is attached to - let deck; - - let searchElement; - let searchButton; - let searchInput; - - let matchedSlides; - let currentMatchedIndex; - let searchboxDirty; - let hilitor; - - function render() { - - searchElement = document.createElement( 'div' ); - searchElement.classList.add( 'searchbox' ); - searchElement.style.position = 'absolute'; - searchElement.style.top = '10px'; - searchElement.style.right = '10px'; - searchElement.style.zIndex = 10; - - //embedded base64 search icon Designed by Sketchdock - http://www.sketchdock.com/: - searchElement.innerHTML = ` + // The reveal.js instance this plugin is attached to + let deck; + + let searchElement; + let searchButton; + let searchInput; + + let matchedSlides; + let currentMatchedIndex; + let searchboxDirty; + let hilitor; + + function render() { + searchElement = document.createElement("div"); + searchElement.classList.add("searchbox"); + searchElement.style.position = "absolute"; + searchElement.style.top = "10px"; + searchElement.style.right = "10px"; + searchElement.style.zIndex = 10; + + //embedded base64 search icon Designed by Sketchdock - http://www.sketchdock.com/: + searchElement.innerHTML = ` `; - searchInput = searchElement.querySelector( '.searchinput' ); - searchInput.style.width = '240px'; - searchInput.style.fontSize = '14px'; - searchInput.style.padding = '4px 6px'; - searchInput.style.color = '#000'; - searchInput.style.background = '#fff'; - searchInput.style.borderRadius = '2px'; - searchInput.style.border = '0'; - searchInput.style.outline = '0'; - searchInput.style.boxShadow = '0 2px 18px rgba(0, 0, 0, 0.2)'; - searchInput.style['-webkit-appearance'] = 'none'; - - deck.getRevealElement().appendChild( searchElement ); - - // searchButton.addEventListener( 'click', function(event) { - // doSearch(); - // }, false ); - - searchInput.addEventListener( 'keyup', function( event ) { - switch (event.keyCode) { - case 13: - event.preventDefault(); - doSearch(); - searchboxDirty = false; - break; - default: - searchboxDirty = true; - } - }, false ); - - closeSearch(); - - } - - function openSearch() { - if( !searchElement ) render(); - - searchElement.style.display = 'inline'; - searchInput.focus(); - searchInput.select(); - } - - function closeSearch() { - if( !searchElement ) render(); - - searchElement.style.display = 'none'; - if(hilitor) hilitor.remove(); - } - - function toggleSearch() { - if( !searchElement ) render(); - - if (searchElement.style.display !== 'inline') { - openSearch(); - } - else { - closeSearch(); - } - } - - function doSearch() { - //if there's been a change in the search term, perform a new search: - if (searchboxDirty) { - var searchstring = searchInput.value; - - if (searchstring === '') { - if(hilitor) hilitor.remove(); - matchedSlides = null; - } - else { - //find the keyword amongst the slides - hilitor = new Hilitor("slidecontent"); - matchedSlides = hilitor.apply(searchstring); - currentMatchedIndex = 0; - } - } - - if (matchedSlides) { - //navigate to the next slide that has the keyword, wrapping to the first if necessary - if (matchedSlides.length && (matchedSlides.length <= currentMatchedIndex)) { - currentMatchedIndex = 0; - } - if (matchedSlides.length > currentMatchedIndex) { - deck.slide(matchedSlides[currentMatchedIndex].h, matchedSlides[currentMatchedIndex].v); - currentMatchedIndex++; - } - } - } - - // Original JavaScript code by Chirp Internet: www.chirp.com.au - // Please acknowledge use of this code by including this header. - // 2/2013 jon: modified regex to display any match, not restricted to word boundaries. - function Hilitor(id, tag) { - - var targetNode = document.getElementById(id) || document.body; - var hiliteTag = tag || "EM"; - var skipTags = new RegExp("^(?:" + hiliteTag + "|SCRIPT|FORM)$"); - var colors = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"]; - var wordColor = []; - var colorIdx = 0; - var matchRegex = ""; - var matchingSlides = []; - - this.setRegex = function(input) - { - input = input.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|"); - matchRegex = new RegExp("(" + input + ")","i"); - } - - this.getRegex = function() - { - return matchRegex.toString().replace(/^\/\\b\(|\)\\b\/i$/g, "").replace(/\|/g, " "); - } - - // recursively apply word highlighting - this.hiliteWords = function(node) - { - if(node == undefined || !node) return; - if(!matchRegex) return; - if(skipTags.test(node.nodeName)) return; - - if(node.hasChildNodes()) { - for(var i=0; i < node.childNodes.length; i++) - this.hiliteWords(node.childNodes[i]); - } - if(node.nodeType == 3) { // NODE_TEXT - var nv, regs; - if((nv = node.nodeValue) && (regs = matchRegex.exec(nv))) { - //find the slide's section element and save it in our list of matching slides - var secnode = node; - while (secnode != null && secnode.nodeName != 'SECTION') { - secnode = secnode.parentNode; - } - - var slideIndex = deck.getIndices(secnode); - var slidelen = matchingSlides.length; - var alreadyAdded = false; - for (var i=0; i < slidelen; i++) { - if ( (matchingSlides[i].h === slideIndex.h) && (matchingSlides[i].v === slideIndex.v) ) { - alreadyAdded = true; - } - } - if (! alreadyAdded) { - matchingSlides.push(slideIndex); - } - - if(!wordColor[regs[0].toLowerCase()]) { - wordColor[regs[0].toLowerCase()] = colors[colorIdx++ % colors.length]; - } - - var match = document.createElement(hiliteTag); - match.appendChild(document.createTextNode(regs[0])); - match.style.backgroundColor = wordColor[regs[0].toLowerCase()]; - match.style.fontStyle = "inherit"; - match.style.color = "#000"; - - var after = node.splitText(regs.index); - after.nodeValue = after.nodeValue.substring(regs[0].length); - node.parentNode.insertBefore(match, after); - } - } - }; - - // remove highlighting - this.remove = function() - { - var arr = document.getElementsByTagName(hiliteTag); - var el; - while(arr.length && (el = arr[0])) { - el.parentNode.replaceChild(el.firstChild, el); - } - }; - - // start highlighting at target node - this.apply = function(input) - { - if(input == undefined || !input) return; - this.remove(); - this.setRegex(input); - this.hiliteWords(targetNode); - return matchingSlides; - }; - - } - - return { - - id: 'search', - - init: reveal => { - - deck = reveal; - deck.registerKeyboardShortcut( 'CTRL + Shift + F', 'Search' ); - - document.addEventListener( 'keydown', function( event ) { - if( event.key == "F" && (event.ctrlKey || event.metaKey) ) { //Control+Shift+f - event.preventDefault(); - toggleSearch(); - } - }, false ); - - }, - - open: openSearch - - } + searchInput = searchElement.querySelector(".searchinput"); + searchInput.style.width = "240px"; + searchInput.style.fontSize = "14px"; + searchInput.style.padding = "4px 6px"; + searchInput.style.color = "#000"; + searchInput.style.background = "#fff"; + searchInput.style.borderRadius = "2px"; + searchInput.style.border = "0"; + searchInput.style.outline = "0"; + searchInput.style.boxShadow = "0 2px 18px rgba(0, 0, 0, 0.2)"; + searchInput.style["-webkit-appearance"] = "none"; + + deck.getRevealElement().appendChild(searchElement); + + // searchButton.addEventListener( 'click', function(event) { + // doSearch(); + // }, false ); + + searchInput.addEventListener( + "keyup", + function (event) { + switch (event.keyCode) { + case 13: + event.preventDefault(); + doSearch(); + searchboxDirty = false; + break; + default: + searchboxDirty = true; + } + }, + false, + ); + + closeSearch(); + } + + function openSearch() { + if (!searchElement) render(); + + searchElement.style.display = "inline"; + searchInput.focus(); + searchInput.select(); + } + + function closeSearch() { + if (!searchElement) render(); + + searchElement.style.display = "none"; + if (hilitor) hilitor.remove(); + } + + function toggleSearch() { + if (!searchElement) render(); + + if (searchElement.style.display !== "inline") { + openSearch(); + } else { + closeSearch(); + } + } + + function doSearch() { + //if there's been a change in the search term, perform a new search: + if (searchboxDirty) { + var searchstring = searchInput.value; + + if (searchstring === "") { + if (hilitor) hilitor.remove(); + matchedSlides = null; + } else { + //find the keyword amongst the slides + hilitor = new Hilitor("slidecontent"); + matchedSlides = hilitor.apply(searchstring); + currentMatchedIndex = 0; + } + } + + if (matchedSlides) { + //navigate to the next slide that has the keyword, wrapping to the first if necessary + if (matchedSlides.length && matchedSlides.length <= currentMatchedIndex) { + currentMatchedIndex = 0; + } + if (matchedSlides.length > currentMatchedIndex) { + deck.slide( + matchedSlides[currentMatchedIndex].h, + matchedSlides[currentMatchedIndex].v, + ); + currentMatchedIndex++; + } + } + } + + // Original JavaScript code by Chirp Internet: www.chirp.com.au + // Please acknowledge use of this code by including this header. + // 2/2013 jon: modified regex to display any match, not restricted to word boundaries. + function Hilitor(id, tag) { + var targetNode = document.getElementById(id) || document.body; + var hiliteTag = tag || "EM"; + var skipTags = new RegExp("^(?:" + hiliteTag + "|SCRIPT|FORM)$"); + var colors = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"]; + var wordColor = []; + var colorIdx = 0; + var matchRegex = ""; + var matchingSlides = []; + + this.setRegex = function (input) { + input = input.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|"); + matchRegex = new RegExp("(" + input + ")", "i"); + }; + + this.getRegex = function () { + return matchRegex + .toString() + .replace(/^\/\\b\(|\)\\b\/i$/g, "") + .replace(/\|/g, " "); + }; + + // recursively apply word highlighting + this.hiliteWords = function (node) { + if (node == undefined || !node) return; + if (!matchRegex) return; + if (skipTags.test(node.nodeName)) return; + + if (node.hasChildNodes()) { + for (var i = 0; i < node.childNodes.length; i++) + this.hiliteWords(node.childNodes[i]); + } + if (node.nodeType == 3) { + // NODE_TEXT + var nv, regs; + if ((nv = node.nodeValue) && (regs = matchRegex.exec(nv))) { + //find the slide's section element and save it in our list of matching slides + var secnode = node; + while (secnode != null && secnode.nodeName != "SECTION") { + secnode = secnode.parentNode; + } + + var slideIndex = deck.getIndices(secnode); + var slidelen = matchingSlides.length; + var alreadyAdded = false; + for (var i = 0; i < slidelen; i++) { + if ( + matchingSlides[i].h === slideIndex.h && + matchingSlides[i].v === slideIndex.v + ) { + alreadyAdded = true; + } + } + if (!alreadyAdded) { + matchingSlides.push(slideIndex); + } + + if (!wordColor[regs[0].toLowerCase()]) { + wordColor[regs[0].toLowerCase()] = + colors[colorIdx++ % colors.length]; + } + + var match = document.createElement(hiliteTag); + match.appendChild(document.createTextNode(regs[0])); + match.style.backgroundColor = wordColor[regs[0].toLowerCase()]; + match.style.fontStyle = "inherit"; + match.style.color = "#000"; + + var after = node.splitText(regs.index); + after.nodeValue = after.nodeValue.substring(regs[0].length); + node.parentNode.insertBefore(match, after); + } + } + }; + + // remove highlighting + this.remove = function () { + var arr = document.getElementsByTagName(hiliteTag); + var el; + while (arr.length && (el = arr[0])) { + el.parentNode.replaceChild(el.firstChild, el); + } + }; + + // start highlighting at target node + this.apply = function (input) { + if (input == undefined || !input) return; + this.remove(); + this.setRegex(input); + this.hiliteWords(targetNode); + return matchingSlides; + }; + } + + return { + id: "search", + + init: (reveal) => { + deck = reveal; + deck.registerKeyboardShortcut("CTRL + Shift + F", "Search"); + + document.addEventListener( + "keydown", + function (event) { + if (event.key == "F" && (event.ctrlKey || event.metaKey)) { + //Control+Shift+f + event.preventDefault(); + toggleSearch(); + } + }, + false, + ); + }, + + open: openSearch, + }; }; -export default Plugin; \ No newline at end of file +export default Plugin; diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/search.esm.js 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((r.input = r.input.slice(l)), + (r[0] = r[0].slice(l)), + (r.index = i.lastIndex), + (i.lastIndex += r[0].length)) + : (i.lastIndex = 0) + : Zn && r && (i.lastIndex = i.global ? r.index + r[0].length : t), + tr && + r && + r.length > 1 && + Jn.call(r[0], n, function () { + for (o = 1; o < arguments.length - 2; o++) + void 0 === arguments[o] && (r[o] = void 0); + }), + r + ); + }); +var nr = Qn; +(function (e, t) { + var n, + r, + o, + i, + c, + a = e.target, + u = e.global, + l = e.stat; + if ((n = u ? Fn : l ? Fn[a] || Wn(a, {}) : (Fn[a] || {}).prototype)) + for (r in t) { + if ( + ((i = t[r]), + (o = e.noTargetGet ? (c = zn(n, r)) && c.value : n[r]), + !Vn(u ? r : a + (l ? 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"" + : void 0 + : (r = i.charCodeAt(c)) < 55296 || + r > 56319 || + c + 1 === a || + (o = i.charCodeAt(c + 1)) < 56320 || + o > 57343 + ? e + ? i.charAt(c) + : r + : e + ? i.slice(c, c + 2) + : o - 56320 + ((r - 55296) << 10) + 65536; + }; + }, + Tr = { codeAt: Rr(!1), charAt: Rr(!0) }.charAt, + _r = U, + jr = Math.floor, + Pr = "".replace, + Ir = /\$([$&'`]|\d{1,2}|<[^>]*>)/g, + Cr = /\$([$&'`]|\d{1,2})/g, + Nr = B, + Ar = nr, + kr = function (e, t, n, r) { + var o = dr(e), + i = !gr(function () { + var t = {}; + return ( + (t[o] = function () { + return 7; + }), + 7 != ""[e](t) + ); + }), + c = + i && + !gr(function () { + var t = !1, + n = /a/; + return ( + "split" === e && + (((n = {}).constructor = {}), + (n.constructor[yr] = function () { + return n; + }), + (n.flags = ""), + (n[o] = /./[o])), + (n.exec = function () { + return (t = !0), null; + }), + n[o](""), + !t + ); + }); + if ( + !i || + !c || + ("replace" === e && (!xr || !br || mr)) || + ("split" === e && !Sr) + ) { + var a = /./[o], + u = n( + o, + ""[e], + function (e, t, n, r, o) { + var c = t.exec; + return c === pr || c === vr.exec + ? i && !o + ? { done: !0, value: a.call(t, n, r) } + : { done: !0, value: e.call(n, t, r) } + : { done: !1 }; + }, + { + REPLACE_KEEPS_$0: br, + REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE: mr, + }, + ), + l = u[0], + f = u[1]; + sr(String.prototype, e, l), + sr( + vr, + o, + 2 == t + ? function (e, t) { + return f.call(e, this, t); + } + : function (e) { + return f.call(e, this); + }, + ); + } + r && hr(vr[o], "sham", !0); + }, + $r = h, + Lr = ee, + Mr = J, + Ur = L, + Dr = function (e, t, n) { + return t + (n ? Tr(e, t).length : 1); + }, + Fr = function (e, t, n, r, o, i) { + var c = n + e.length, + a = r.length, + u = Cr; + return ( + void 0 !== o && ((o = _r(o)), (u = Ir)), + Pr.call(i, u, function (i, u) { + var l; + switch (u.charAt(0)) { + case "$": + return "$"; + case "&": + return e; + case "`": + return t.slice(0, n); + case "'": + return t.slice(c); + case "<": + l = o[u.slice(1, -1)]; + break; + default: + var f = +u; + if (0 === f) return i; + if (f > a) { + var s = jr(f / 10); + return 0 === s + ? i + : s <= a + ? void 0 === r[s - 1] + ? u.charAt(1) + : r[s - 1] + u.charAt(1) + : i; + } + l = r[f - 1]; + } + return void 0 === l ? 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"$" : "$0"; + return [ + function (n, r) { + var o = Ur(this), + i = null == n ? void 0 : n[e]; + return void 0 !== i ? i.call(n, o, r) : t.call(String(o), n, r); + }, + function (e, r) { + if ((!o && i) || ("string" == typeof r && -1 === r.indexOf(c))) { + var a = n(t, e, this, r); + if (a.done) return a.value; + } + var u = $r(e), + l = String(this), + f = "function" == typeof r; + f || (r = String(r)); + var s = u.global; + if (s) { + var p = u.unicode; + u.lastIndex = 0; + } + for (var g = []; ; ) { + var d = zr(u, l); + if (null === d) break; + if ((g.push(d), !s)) break; + "" === String(d[0]) && (u.lastIndex = Dr(l, Lr(u.lastIndex), p)); + } + for (var h, y = "", v = 0, x = 0; x < g.length; x++) { + d = g[x]; + for ( + var b = String(d[0]), + E = Kr(Br(Mr(d.index), l.length), 0), + m = [], + S = 1; + S < d.length; + S++ + ) + m.push(void 0 === (h = d[S]) ? h : String(h)); + var w = d.groups; + if (f) { + var O = [b].concat(m, E, l); + void 0 !== w && O.push(w); + var R = String(r.apply(void 0, O)); + } else R = Fr(b, l, E, m, w, r); + E >= v && ((y += l.slice(v, E) + R), (v = E + b.length)); + } + return y + l.slice(v); + }, + ]; +}); +var Wr = {}; +Wr[Ze("toStringTag")] = "z"; +var Gr = "[object z]" === String(Wr), + Vr = Gr, + Yr = B, + qr = Ze("toStringTag"), + Xr = + "Arguments" == + Yr( + (function () { + return arguments; + })(), + ), + Hr = Vr + ? Yr + : function (e) { + var t, n, r; + return void 0 === e + ? "Undefined" + : null === e + ? "Null" + : "string" == + typeof (n = (function (e, t) { + try { + return e[t]; + } catch (e) {} + })((t = Object(e)), qr)) + ? n + : Xr + ? Yr(t) + : "Object" == (r = Yr(t)) && "function" == typeof t.callee + ? "Arguments" + : r; + }, + Jr = Gr + ? {}.toString + : function () { + return "[object " + Hr(this) + "]"; + }, + Qr = Gr, + Zr = ut.exports, + eo = Jr; +Qr || Zr(Object.prototype, "toString", eo, { unsafe: !0 }); /*! * Handles finding a text string anywhere in the slides and showing the next occurrence to the user * by navigatating to that slide and highlighting it. * * @author Jon Snyder , February 2013 - */;export default function(){var e,t,n,r,o,i,c;function a(){(t=document.createElement("div")).classList.add("searchbox"),t.style.position="absolute",t.style.top="10px",t.style.right="10px",t.style.zIndex=10,t.innerHTML='\n\t\t',(n=t.querySelector(".searchinput")).style.width="240px",n.style.fontSize="14px",n.style.padding="4px 6px",n.style.color="#000",n.style.background="#fff",n.style.borderRadius="2px",n.style.border="0",n.style.outline="0",n.style.boxShadow="0 2px 18px rgba(0, 0, 0, 0.2)",n.style["-webkit-appearance"]="none",e.getRevealElement().appendChild(t),n.addEventListener("keyup",(function(t){switch(t.keyCode){case 13:t.preventDefault(),function(){if(i){var t=n.value;""===t?(c&&c.remove(),r=null):(c=new f("slidecontent"),r=c.apply(t),o=0)}r&&(r.length&&r.length<=o&&(o=0),r.length>o&&(e.slide(r[o].h,r[o].v),o++))}(),i=!1;break;default:i=!0}}),!1),l()}function u(){t||a(),t.style.display="inline",n.focus(),n.select()}function l(){t||a(),t.style.display="none",c&&c.remove()}function f(t,n){var r=document.getElementById(t)||document.body,o=n||"EM",i=new RegExp("^(?:"+o+"|SCRIPT|FORM)$"),c=["#ff6","#a0ffff","#9f9","#f99","#f6f"],a=[],u=0,l="",f=[];this.setRegex=function(e){e=e.replace(/^[^\w]+|[^\w]+$/g,"").replace(/[^\w'-]+/g,"|"),l=new RegExp("("+e+")","i")},this.getRegex=function(){return l.toString().replace(/^\/\\b\(|\)\\b\/i$/g,"").replace(/\|/g," ")},this.hiliteWords=function(t){if(null!=t&&t&&l&&!i.test(t.nodeName)){if(t.hasChildNodes())for(var n=0;n\n\t\t'), + ((n = t.querySelector(".searchinput")).style.width = "240px"), + (n.style.fontSize = "14px"), + (n.style.padding = "4px 6px"), + (n.style.color = "#000"), + (n.style.background = "#fff"), + (n.style.borderRadius = "2px"), + (n.style.border = "0"), + (n.style.outline = "0"), + (n.style.boxShadow = "0 2px 18px rgba(0, 0, 0, 0.2)"), + (n.style["-webkit-appearance"] = "none"), + e.getRevealElement().appendChild(t), + n.addEventListener( + "keyup", + function (t) { + switch (t.keyCode) { + case 13: + t.preventDefault(), + (function () { + if (i) { + var t = n.value; + "" === t + ? (c && c.remove(), (r = null)) + : ((c = new f("slidecontent")), + (r = c.apply(t)), + (o = 0)); + } + r && + (r.length && r.length <= o && (o = 0), + r.length > o && (e.slide(r[o].h, r[o].v), o++)); + })(), + (i = !1); + break; + default: + i = !0; + } + }, + !1, + ), + l(); + } + function u() { + t || a(), (t.style.display = "inline"), n.focus(), n.select(); + } + function l() { + t || a(), (t.style.display = "none"), c && c.remove(); + } + function f(t, n) { + var r = document.getElementById(t) || document.body, + o = n || "EM", + i = new RegExp("^(?:" + o + "|SCRIPT|FORM)$"), + c = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"], + a = [], + u = 0, + l = "", + f = []; + (this.setRegex = function (e) { + (e = e.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|")), + (l = new RegExp("(" + e + ")", "i")); + }), + (this.getRegex = function () { + return l + .toString() + .replace(/^\/\\b\(|\)\\b\/i$/g, "") + .replace(/\|/g, " "); + }), + (this.hiliteWords = function (t) { + if (null != t && t && l && !i.test(t.nodeName)) { + if (t.hasChildNodes()) + for (var n = 0; n < t.childNodes.length; n++) + this.hiliteWords(t.childNodes[n]); + var r, s; + if (3 == t.nodeType) + if ((r = t.nodeValue) && (s = l.exec(r))) { + for (var p = t; null != p && "SECTION" != p.nodeName; ) + p = p.parentNode; + var g = e.getIndices(p), + d = f.length, + h = !1; + for (n = 0; n < d; n++) + f[n].h === g.h && f[n].v === g.v && (h = !0); + h || f.push(g), + a[s[0].toLowerCase()] || + (a[s[0].toLowerCase()] = c[u++ % c.length]); + var y = document.createElement(o); + y.appendChild(document.createTextNode(s[0])), + (y.style.backgroundColor = a[s[0].toLowerCase()]), + (y.style.fontStyle = "inherit"), + (y.style.color = "#000"); + var v = t.splitText(s.index); + (v.nodeValue = v.nodeValue.substring(s[0].length)), + t.parentNode.insertBefore(y, v); + } + } + }), + (this.remove = function () { + for ( + var e, t = document.getElementsByTagName(o); + t.length && (e = t[0]); + + ) + e.parentNode.replaceChild(e.firstChild, e); + }), + (this.apply = function (e) { + if (null != e && e) + return this.remove(), this.setRegex(e), this.hiliteWords(r), f; + }); + } + return { + id: "search", + init: function (n) { + (e = n).registerKeyboardShortcut("CTRL + Shift + F", "Search"), + document.addEventListener( + "keydown", + function (e) { + "F" == e.key && + (e.ctrlKey || e.metaKey) && + (e.preventDefault(), + t || a(), + "inline" !== t.style.display ? u() : l()); + }, + !1, + ); + }, + open: u, + }; +} diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/search.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/search.js index bcabf72..1a3c93b 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/search.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/search/search.js @@ -1,7 +1,1163 @@ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof 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(e[t] = n) + : Ft(t, n); + })(Function.prototype, "toString", function () { + return ("function" == typeof this && Kt(this).source) || zt(this); + }); + var Gt = Le, + Vt = S, + Yt = n, + qt = et("species"), + Xt = n, + Ht = o, + Jt = p, + Qt = function (e, t, n) { + var r, o; + return ( + E && + "function" == typeof (r = t.constructor) && + r !== n && + m((o = r.prototype)) && + o !== n.prototype && + E(e, o), + e + ); + }, + Zt = S.f, + en = $.f, + tn = function (e) { + var t; + return tt(e) && (void 0 !== (t = e[rt]) ? !!t : "RegExp" == nt(e)); + }, + nn = it, + rn = ct, + on = lt.exports, + cn = t, + an = Lt.enforce, + un = function (e) { + var t = Gt(e), + n = Vt.f; + Yt && + t && + !t[qt] && + n(t, qt, { + configurable: !0, + get: function () { + return this; + }, + }); + }, + ln = et("match"), + fn = Ht.RegExp, + sn = fn.prototype, + pn = /a/g, + dn = /a/g, + gn = new fn(pn) !== pn, + hn = rn.UNSUPPORTED_Y; + if ( + Xt && + Jt( + "RegExp", + !gn || + hn || + cn(function () { + return ( + (dn[ln] = !1), fn(pn) != pn || fn(dn) == dn || "/a/i" != fn(pn, "i") + ); + }), + ) + ) { + for ( + var yn = function (e, t) { + var n, + r = this instanceof yn, + o = tn(e), + i = void 0 === t; + if (!r && o && e.constructor === yn && i) return e; + gn + ? o && !i && (e = e.source) + : e instanceof yn && (i && (t = nn.call(e)), (e = e.source)), + hn && (n = !!t && t.indexOf("y") > -1) && (t = t.replace(/y/g, "")); + var c = Qt(gn ? new fn(e, t) : fn(e, t), r ? this : sn, yn); + hn && n && (an(c).sticky = !0); + return c; + }, + vn = function (e) { + (e in yn) || + Zt(yn, e, { + configurable: !0, + get: function () { + return fn[e]; + }, + set: function (t) { + fn[e] = t; + }, + }); + }, + xn = en(fn), + bn = 0; + xn.length > bn; + + ) + vn(xn[bn++]); + (sn.constructor = yn), (yn.prototype = sn), on(Ht, "RegExp", yn); + } + un("RegExp"); + var mn = {}, + En = {}, + Sn = {}.propertyIsEnumerable, + wn = Object.getOwnPropertyDescriptor, + On = wn && !Sn.call({ 1: 2 }, 1); + En.f = On + ? function (e) { + var t = wn(this, e); + return !!t && t.enumerable; + } + : Sn; + var Rn = n, + Tn = En, + _n = ve, + jn = q, + Pn = P, + In = z, + Cn = _, + Nn = Object.getOwnPropertyDescriptor; + mn.f = Rn + ? Nn + : function (e, t) { + if (((e = jn(e)), (t = Pn(t, !0)), Cn)) + try { + return Nn(e, t); + } catch (e) {} + if (In(e, t)) return _n(!Tn.f.call(e, t), e[t]); + }; + var An = {}; + An.f = Object.getOwnPropertySymbols; + var kn = $, + $n = An, + Ln = h, + Mn = + Le("Reflect", "ownKeys") || + function (e) { + var t = kn.f(Ln(e)), + n = $n.f; + return n ? t.concat(n(e)) : t; + }, + Un = z, + Dn = Mn, + Fn = mn, + zn = S, + Kn = o, + Bn = mn.f, + Wn = me, + Gn = lt.exports, + Vn = we, + Yn = function (e, t) { + for (var n = Dn(t), r = zn.f, o = Fn.f, i = 0; i < n.length; i++) { + var c = n[i]; + Un(e, c) || r(e, c, o(t, c)); + } + }, + qn = p, + Xn = it, + Hn = ct, + Jn = ye.exports, + Qn = RegExp.prototype.exec, + Zn = Jn("native-string-replace", String.prototype.replace), + er = Qn, + tr = (function () { + var e = /a/, + t = /b*/g; + return ( + Qn.call(e, "a"), Qn.call(t, "a"), 0 !== e.lastIndex || 0 !== t.lastIndex + ); + })(), + nr = Hn.UNSUPPORTED_Y || Hn.BROKEN_CARET, + rr = void 0 !== /()??/.exec("")[1]; + (tr || rr || nr) && + (er = function (e) { + var t, + n, + r, + o, + i = this, + c = nr && i.sticky, + a = Xn.call(i), + u = i.source, + l = 0, + f = e; + return ( + c && + (-1 === (a = a.replace("y", "")).indexOf("g") && (a += "g"), + (f = String(e).slice(i.lastIndex)), + i.lastIndex > 0 && + (!i.multiline || (i.multiline && "\n" !== e[i.lastIndex - 1])) && + ((u = "(?: " + u + ")"), (f = " " + f), l++), + (n = new RegExp("^(?:" + u + ")", a))), + rr && (n = new RegExp("^" + u + "$(?!\\s)", a)), + tr && (t = i.lastIndex), + (r = Qn.call(c ? n : i, f)), + c + ? r + ? ((r.input = r.input.slice(l)), + (r[0] = r[0].slice(l)), + (r.index = i.lastIndex), + (i.lastIndex += r[0].length)) + : (i.lastIndex = 0) + : tr && r && (i.lastIndex = i.global ? r.index + r[0].length : t), + rr && + r && + r.length > 1 && + Zn.call(r[0], n, function () { + for (o = 1; o < arguments.length - 2; o++) + void 0 === arguments[o] && (r[o] = void 0); + }), + r + ); + }); + var or = er; + (function (e, t) { + var n, + r, + o, + i, + c, + a = e.target, + u = e.global, + l = e.stat; + if ((n = u ? Kn : l ? Kn[a] || Vn(a, {}) : (Kn[a] || {}).prototype)) + for (r in t) { + if ( + ((i = t[r]), + (o = e.noTargetGet ? (c = Bn(n, r)) && c.value : n[r]), + !qn(u ? r : a + (l ? "." : "#") + r, e.forced) && void 0 !== o) + ) { + if (typeof i == typeof o) continue; + Yn(i, o); + } + (e.sham || (o && o.sham)) && Wn(i, "sham", !0), Gn(n, r, i, e); + } + })({ target: "RegExp", proto: !0, forced: /./.exec !== or }, { exec: or }); + var ir = lt.exports, + cr = h, + ar = t, + ur = it, + lr = "toString", + fr = RegExp.prototype, + sr = fr.toString, + pr = ar(function () { + return "/a/b" != sr.call({ source: "a", flags: "b" }); + }), + dr = sr.name != lr; + (pr || dr) && + ir( + RegExp.prototype, + lr, + function () { + var e = cr(this), + t = String(e.source), + n = e.flags; + return ( + "/" + + t + + "/" + + String( + void 0 === n && e instanceof RegExp && !("flags" in fr) + ? ur.call(e) + : n, + ) + ); + }, + { unsafe: !0 }, + ); + var gr = lt.exports, + hr = or, + yr = t, + vr = et, + xr = me, + br = vr("species"), + mr = RegExp.prototype, + Er = !yr(function () { + var e = /./; + return ( + (e.exec = function () { + var e = []; + return (e.groups = { a: "7" }), e; + }), + "7" !== "".replace(e, "$") + ); + }), + Sr = "$0" === "a".replace(/./, "$0"), + wr = vr("replace"), + Or = !!/./[wr] && "" === /./[wr]("a", "$0"), + Rr = !yr(function () { + var e = /(?:)/, + t = e.exec; + e.exec = function () { + return t.apply(this, arguments); + }; + var n = "ab".split(e); + return 2 !== n.length || "a" !== n[0] || "b" !== n[1]; + }), + Tr = J, + _r = L, + jr = function (e) { + return function (t, n) { + var r, + o, + i = String(_r(t)), + c = Tr(n), + a = i.length; + return c < 0 || c >= a + ? e + ? "" + : void 0 + : (r = i.charCodeAt(c)) < 55296 || + r > 56319 || + c + 1 === a || + (o = i.charCodeAt(c + 1)) < 56320 || + o > 57343 + ? e + ? i.charAt(c) + : r + : e + ? i.slice(c, c + 2) + : o - 56320 + ((r - 55296) << 10) + 65536; + }; + }, + Pr = { codeAt: jr(!1), charAt: jr(!0) }.charAt, + Ir = U, + Cr = Math.floor, + Nr = "".replace, + Ar = /\$([$&'`]|\d{1,2}|<[^>]*>)/g, + kr = /\$([$&'`]|\d{1,2})/g, + $r = B, + Lr = or, + Mr = function (e, t, n, r) { + var o = vr(e), + i = !yr(function () { + var t = {}; + return ( + (t[o] = function () { + return 7; + }), + 7 != ""[e](t) + ); + }), + c = + i && + !yr(function () { + var t = !1, + n = /a/; + return ( + "split" === e && + (((n = {}).constructor = {}), + (n.constructor[br] = function () { + return n; + }), + (n.flags = ""), + (n[o] = /./[o])), + (n.exec = function () { + return (t = !0), null; + }), + n[o](""), + !t + ); + }); + if ( + !i || + !c || + ("replace" === e && (!Er || !Sr || Or)) || + ("split" === e && !Rr) + ) { + var a = /./[o], + u = n( + o, + ""[e], + function (e, t, n, r, o) { + var c = t.exec; + return c === hr || c === mr.exec + ? i && !o + ? { done: !0, value: a.call(t, n, r) } + : { done: !0, value: e.call(n, t, r) } + : { done: !1 }; + }, + { + REPLACE_KEEPS_$0: Sr, + REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE: Or, + }, + ), + l = u[0], + f = u[1]; + gr(String.prototype, e, l), + gr( + mr, + o, + 2 == t + ? function (e, t) { + return f.call(e, this, t); + } + : function (e) { + return f.call(e, this); + }, + ); + } + r && xr(mr[o], "sham", !0); + }, + Ur = h, + Dr = ee, + Fr = J, + zr = L, + Kr = function (e, t, n) { + return t + (n ? Pr(e, t).length : 1); + }, + Br = function (e, t, n, r, o, i) { + var c = n + e.length, + a = r.length, + u = kr; + return ( + void 0 !== o && ((o = Ir(o)), (u = Ar)), + Nr.call(i, u, function (i, u) { + var l; + switch (u.charAt(0)) { + case "$": + return "$"; + case "&": + return e; + case "`": + return t.slice(0, n); + case "'": + return t.slice(c); + case "<": + l = o[u.slice(1, -1)]; + break; + default: + var f = +u; + if (0 === f) return i; + if (f > a) { + var s = Cr(f / 10); + return 0 === s + ? i + : s <= a + ? void 0 === r[s - 1] + ? u.charAt(1) + : r[s - 1] + u.charAt(1) + : i; + } + l = r[f - 1]; + } + return void 0 === l ? "" : l; + }) + ); + }, + Wr = function (e, t) { + var n = e.exec; + if ("function" == typeof n) { + var r = n.call(e, t); + if ("object" != typeof r) + throw TypeError( + "RegExp exec method returned something other than an Object or null", + ); + return r; + } + if ("RegExp" !== $r(e)) + throw TypeError("RegExp#exec called on incompatible receiver"); + return Lr.call(e, t); + }, + Gr = Math.max, + Vr = Math.min; + Mr("replace", 2, function (e, t, n, r) { + var o = r.REGEXP_REPLACE_SUBSTITUTES_UNDEFINED_CAPTURE, + i = r.REPLACE_KEEPS_$0, + c = o ? "$" : "$0"; + return [ + function (n, r) { + var o = zr(this), + i = null == n ? void 0 : n[e]; + return void 0 !== i ? i.call(n, o, r) : t.call(String(o), n, r); + }, + function (e, r) { + if ((!o && i) || ("string" == typeof r && -1 === r.indexOf(c))) { + var a = n(t, e, this, r); + if (a.done) return a.value; + } + var u = Ur(e), + l = String(this), + f = "function" == typeof r; + f || (r = String(r)); + var s = u.global; + if (s) { + var p = u.unicode; + u.lastIndex = 0; + } + for (var d = []; ; ) { + var g = Wr(u, l); + if (null === g) break; + if ((d.push(g), !s)) break; + "" === String(g[0]) && (u.lastIndex = Kr(l, Dr(u.lastIndex), p)); + } + for (var h, y = "", v = 0, x = 0; x < d.length; x++) { + g = d[x]; + for ( + var b = String(g[0]), + m = Gr(Vr(Fr(g.index), l.length), 0), + E = [], + S = 1; + S < g.length; + S++ + ) + E.push(void 0 === (h = g[S]) ? h : String(h)); + var w = g.groups; + if (f) { + var O = [b].concat(E, m, l); + void 0 !== w && O.push(w); + var R = String(r.apply(void 0, O)); + } else R = Br(b, l, m, E, w, r); + m >= v && ((y += l.slice(v, m) + R), (v = m + b.length)); + } + return y + l.slice(v); + }, + ]; + }); + var Yr = {}; + Yr[et("toStringTag")] = "z"; + var qr = "[object z]" === String(Yr), + Xr = qr, + Hr = B, + Jr = et("toStringTag"), + Qr = + "Arguments" == + Hr( + (function () { + return arguments; + })(), + ), + Zr = Xr + ? Hr + : function (e) { + var t, n, r; + return void 0 === e + ? "Undefined" + : null === e + ? "Null" + : "string" == + typeof (n = (function (e, t) { + try { + return e[t]; + } catch (e) {} + })((t = Object(e)), Jr)) + ? n + : Qr + ? Hr(t) + : "Object" == (r = Hr(t)) && "function" == typeof t.callee + ? "Arguments" + : r; + }, + eo = qr + ? {}.toString + : function () { + return "[object " + Zr(this) + "]"; + }, + to = qr, + no = lt.exports, + ro = eo; + to || no(Object.prototype, "toString", ro, { unsafe: !0 }); + /*! + * Handles finding a text string anywhere in the slides and showing the next occurrence to the user + * by navigatating to that slide and highlighting it. + * + * @author Jon Snyder , February 2013 + */ + return function () { + var e, t, n, r, o, i, c; + function a() { + (t = document.createElement("div")).classList.add("searchbox"), + (t.style.position = "absolute"), + (t.style.top = "10px"), + (t.style.right = "10px"), + (t.style.zIndex = 10), + (t.innerHTML = + '\n\t\t'), + ((n = t.querySelector(".searchinput")).style.width = "240px"), + (n.style.fontSize = "14px"), + (n.style.padding = "4px 6px"), + (n.style.color = "#000"), + (n.style.background = "#fff"), + (n.style.borderRadius = "2px"), + (n.style.border = "0"), + (n.style.outline = "0"), + (n.style.boxShadow = "0 2px 18px rgba(0, 0, 0, 0.2)"), + (n.style["-webkit-appearance"] = "none"), + e.getRevealElement().appendChild(t), + n.addEventListener( + "keyup", + function (t) { + switch (t.keyCode) { + case 13: + t.preventDefault(), + (function () { + if (i) { + var t = n.value; + "" === t + ? (c && c.remove(), (r = null)) + : ((c = new f("slidecontent")), + (r = c.apply(t)), + (o = 0)); + } + r && + (r.length && r.length <= o && (o = 0), + r.length > o && (e.slide(r[o].h, r[o].v), o++)); + })(), + (i = !1); + break; + default: + i = !0; + } + }, + !1, + ), + l(); + } + function u() { + t || a(), (t.style.display = "inline"), n.focus(), n.select(); + } + function l() { + t || a(), (t.style.display = "none"), c && c.remove(); + } + function f(t, n) { + var r = document.getElementById(t) || document.body, + o = n || "EM", + i = new RegExp("^(?:" + o + "|SCRIPT|FORM)$"), + c = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"], + a = [], + u = 0, + l = "", + f = []; + (this.setRegex = function (e) { + (e = e.replace(/^[^\w]+|[^\w]+$/g, "").replace(/[^\w'-]+/g, "|")), + (l = new RegExp("(" + e + ")", "i")); + }), + (this.getRegex = function () { + return l + .toString() + .replace(/^\/\\b\(|\)\\b\/i$/g, "") + .replace(/\|/g, " "); + }), + (this.hiliteWords = function (t) { + if (null != t && t && l && !i.test(t.nodeName)) { + if (t.hasChildNodes()) + for (var n = 0; n < t.childNodes.length; n++) + this.hiliteWords(t.childNodes[n]); + var r, s; + if (3 == t.nodeType) + if ((r = t.nodeValue) && (s = l.exec(r))) { + for (var p = t; null != p && "SECTION" != p.nodeName; ) + p = p.parentNode; + var d = e.getIndices(p), + g = f.length, + h = !1; + for (n = 0; n < g; n++) + f[n].h === d.h && f[n].v === d.v && (h = !0); + h || f.push(d), + a[s[0].toLowerCase()] || + (a[s[0].toLowerCase()] = c[u++ % c.length]); + var y = document.createElement(o); + y.appendChild(document.createTextNode(s[0])), + (y.style.backgroundColor = a[s[0].toLowerCase()]), + (y.style.fontStyle = "inherit"), + (y.style.color = "#000"); + var v = t.splitText(s.index); + (v.nodeValue = v.nodeValue.substring(s[0].length)), + t.parentNode.insertBefore(y, v); + } + } + }), + (this.remove = function () { + for ( + var e, t = document.getElementsByTagName(o); + t.length && (e = t[0]); + + ) + e.parentNode.replaceChild(e.firstChild, e); + }), + (this.apply = function (e) { + if (null != e && e) + return this.remove(), this.setRegex(e), this.hiliteWords(r), f; + }); + } + return { + id: "search", + init: function (n) { + (e = n).registerKeyboardShortcut("CTRL + Shift + F", "Search"), + document.addEventListener( + "keydown", + function (e) { + "F" == e.key && + (e.ctrlKey || e.metaKey) && + (e.preventDefault(), + t || a(), + "inline" !== t.style.display ? u() : l()); + }, + !1, + ); + }, + open: u, + }; + }; +}); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/plugin.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/plugin.js index 960fb81..4a0ec82 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/plugin.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/plugin.js @@ -2,37 +2,38 @@ * reveal.js Zoom plugin */ const Plugin = { - - id: 'zoom', - - init: function( reveal ) { - - reveal.getRevealElement().addEventListener( 'mousedown', function( event ) { - var defaultModifier = /Linux/.test( window.navigator.platform ) ? 'ctrl' : 'alt'; - - var modifier = ( reveal.getConfig().zoomKey ? reveal.getConfig().zoomKey : defaultModifier ) + 'Key'; - var zoomLevel = ( reveal.getConfig().zoomLevel ? reveal.getConfig().zoomLevel : 2 ); - - if( event[ modifier ] && !reveal.isOverview() ) { - event.preventDefault(); - - zoom.to({ - x: event.clientX, - y: event.clientY, - scale: zoomLevel, - pan: false - }); - } - } ); - - }, - - destroy: () => { - - zoom.reset(); - - } - + id: "zoom", + + init: function (reveal) { + reveal.getRevealElement().addEventListener("mousedown", function (event) { + var defaultModifier = /Linux/.test(window.navigator.platform) + ? "ctrl" + : "alt"; + + var modifier = + (reveal.getConfig().zoomKey + ? reveal.getConfig().zoomKey + : defaultModifier) + "Key"; + var zoomLevel = reveal.getConfig().zoomLevel + ? reveal.getConfig().zoomLevel + : 2; + + if (event[modifier] && !reveal.isOverview()) { + event.preventDefault(); + + zoom.to({ + x: event.clientX, + y: event.clientY, + scale: zoomLevel, + pan: false, + }); + } + }); + }, + + destroy: () => { + zoom.reset(); + }, }; export default () => Plugin; @@ -44,221 +45,243 @@ export default () => Plugin; * * Copyright (C) 2011-2014 Hakim El Hattab, http://hakim.se */ -var zoom = (function(){ - - // The current zoom level (scale) - var level = 1; - - // The current mouse position, used for panning - var mouseX = 0, - mouseY = 0; - - // Timeout before pan is activated - var panEngageTimeout = -1, - panUpdateInterval = -1; - - // Check for transform support so that we can fallback otherwise - var supportsTransforms = 'transform' in document.body.style; - - if( supportsTransforms ) { - // The easing that will be applied when we zoom in/out - document.body.style.transition = 'transform 0.8s ease'; - } - - // Zoom out if the user hits escape - document.addEventListener( 'keyup', function( event ) { - if( level !== 1 && event.keyCode === 27 ) { - zoom.out(); - } - } ); - - // Monitor mouse movement for panning - document.addEventListener( 'mousemove', function( event ) { - if( level !== 1 ) { - mouseX = event.clientX; - mouseY = event.clientY; - } - } ); - - /** - * Applies the CSS required to zoom in, prefers the use of CSS3 - * transforms but falls back on zoom for IE. - * - * @param {Object} rect - * @param {Number} scale - */ - function magnify( rect, scale ) { - - var scrollOffset = getScrollOffset(); - - // Ensure a width/height is set - rect.width = rect.width || 1; - rect.height = rect.height || 1; - - // Center the rect within the zoomed viewport - rect.x -= ( window.innerWidth - ( rect.width * scale ) ) / 2; - rect.y -= ( window.innerHeight - ( rect.height * scale ) ) / 2; - - if( supportsTransforms ) { - // Reset - if( scale === 1 ) { - document.body.style.transform = ''; - } - // Scale - else { - var origin = scrollOffset.x +'px '+ scrollOffset.y +'px', - transform = 'translate('+ -rect.x +'px,'+ -rect.y +'px) scale('+ scale +')'; - - document.body.style.transformOrigin = origin; - document.body.style.transform = transform; - } - } - else { - // Reset - if( scale === 1 ) { - document.body.style.position = ''; - document.body.style.left = ''; - document.body.style.top = ''; - document.body.style.width = ''; - document.body.style.height = ''; - document.body.style.zoom = ''; - } - // Scale - else { - document.body.style.position = 'relative'; - document.body.style.left = ( - ( scrollOffset.x + rect.x ) / scale ) + 'px'; - document.body.style.top = ( - ( scrollOffset.y + rect.y ) / scale ) + 'px'; - document.body.style.width = ( scale * 100 ) + '%'; - document.body.style.height = ( scale * 100 ) + '%'; - document.body.style.zoom = scale; - } - } - - level = scale; - - if( document.documentElement.classList ) { - if( level !== 1 ) { - document.documentElement.classList.add( 'zoomed' ); - } - else { - document.documentElement.classList.remove( 'zoomed' ); - } - } - } - - /** - * Pan the document when the mosue cursor approaches the edges - * of the window. - */ - function pan() { - var range = 0.12, - rangeX = window.innerWidth * range, - rangeY = window.innerHeight * range, - scrollOffset = getScrollOffset(); - - // Up - if( mouseY < rangeY ) { - window.scroll( scrollOffset.x, scrollOffset.y - ( 1 - ( mouseY / rangeY ) ) * ( 14 / level ) ); - } - // Down - else if( mouseY > window.innerHeight - rangeY ) { - window.scroll( scrollOffset.x, scrollOffset.y + ( 1 - ( window.innerHeight - mouseY ) / rangeY ) * ( 14 / level ) ); - } - - // Left - if( mouseX < rangeX ) { - window.scroll( scrollOffset.x - ( 1 - ( mouseX / rangeX ) ) * ( 14 / level ), scrollOffset.y ); - } - // Right - else if( mouseX > window.innerWidth - rangeX ) { - window.scroll( scrollOffset.x + ( 1 - ( window.innerWidth - mouseX ) / rangeX ) * ( 14 / level ), scrollOffset.y ); - } - } - - function getScrollOffset() { - return { - x: window.scrollX !== undefined ? window.scrollX : window.pageXOffset, - y: window.scrollY !== undefined ? window.scrollY : window.pageYOffset - } - } - - return { - /** - * Zooms in on either a rectangle or HTML element. - * - * @param {Object} options - * - element: HTML element to zoom in on - * OR - * - x/y: coordinates in non-transformed space to zoom in on - * - width/height: the portion of the screen to zoom in on - * - scale: can be used instead of width/height to explicitly set scale - */ - to: function( options ) { - - // Due to an implementation limitation we can't zoom in - // to another element without zooming out first - if( level !== 1 ) { - zoom.out(); - } - else { - options.x = options.x || 0; - options.y = options.y || 0; - - // If an element is set, that takes precedence - if( !!options.element ) { - // Space around the zoomed in element to leave on screen - var padding = 20; - var bounds = options.element.getBoundingClientRect(); - - options.x = bounds.left - padding; - options.y = bounds.top - padding; - options.width = bounds.width + ( padding * 2 ); - options.height = bounds.height + ( padding * 2 ); - } - - // If width/height values are set, calculate scale from those values - if( options.width !== undefined && options.height !== undefined ) { - options.scale = Math.max( Math.min( window.innerWidth / options.width, window.innerHeight / options.height ), 1 ); - } - - if( options.scale > 1 ) { - options.x *= options.scale; - options.y *= options.scale; - - magnify( options, options.scale ); - - if( options.pan !== false ) { - - // Wait with engaging panning as it may conflict with the - // zoom transition - panEngageTimeout = setTimeout( function() { - panUpdateInterval = setInterval( pan, 1000 / 60 ); - }, 800 ); - - } - } - } - }, - - /** - * Resets the document zoom state to its default. - */ - out: function() { - clearTimeout( panEngageTimeout ); - clearInterval( panUpdateInterval ); - - magnify( { x: 0, y: 0 }, 1 ); - - level = 1; - }, - - // Alias - magnify: function( options ) { this.to( options ) }, - reset: function() { this.out() }, - - zoomLevel: function() { - return level; - } - } - +var zoom = (function () { + // The current zoom level (scale) + var level = 1; + + // The current mouse position, used for panning + var mouseX = 0, + mouseY = 0; + + // Timeout before pan is activated + var panEngageTimeout = -1, + panUpdateInterval = -1; + + // Check for transform support so that we can fallback otherwise + var supportsTransforms = "transform" in document.body.style; + + if (supportsTransforms) { + // The easing that will be applied when we zoom in/out + document.body.style.transition = "transform 0.8s ease"; + } + + // Zoom out if the user hits escape + document.addEventListener("keyup", function (event) { + if (level !== 1 && event.keyCode === 27) { + zoom.out(); + } + }); + + // Monitor mouse movement for panning + document.addEventListener("mousemove", function (event) { + if (level !== 1) { + mouseX = event.clientX; + mouseY = event.clientY; + } + }); + + /** + * Applies the CSS required to zoom in, prefers the use of CSS3 + * transforms but falls back on zoom for IE. + * + * @param {Object} rect + * @param {Number} scale + */ + function magnify(rect, scale) { + var scrollOffset = getScrollOffset(); + + // Ensure a width/height is set + rect.width = rect.width || 1; + rect.height = rect.height || 1; + + // Center the rect within the zoomed viewport + rect.x -= (window.innerWidth - rect.width * scale) / 2; + rect.y -= (window.innerHeight - rect.height * scale) / 2; + + if (supportsTransforms) { + // Reset + if (scale === 1) { + document.body.style.transform = ""; + } + // Scale + else { + var origin = scrollOffset.x + "px " + scrollOffset.y + "px", + transform = + "translate(" + + -rect.x + + "px," + + -rect.y + + "px) scale(" + + scale + + ")"; + + document.body.style.transformOrigin = origin; + document.body.style.transform = transform; + } + } else { + // Reset + if (scale === 1) { + document.body.style.position = ""; + document.body.style.left = ""; + document.body.style.top = ""; + document.body.style.width = ""; + document.body.style.height = ""; + document.body.style.zoom = ""; + } + // Scale + else { + document.body.style.position = "relative"; + document.body.style.left = -(scrollOffset.x + rect.x) / scale + "px"; + document.body.style.top = -(scrollOffset.y + rect.y) / scale + "px"; + document.body.style.width = scale * 100 + "%"; + document.body.style.height = scale * 100 + "%"; + document.body.style.zoom = scale; + } + } + + level = scale; + + if (document.documentElement.classList) { + if (level !== 1) { + document.documentElement.classList.add("zoomed"); + } else { + document.documentElement.classList.remove("zoomed"); + } + } + } + + /** + * Pan the document when the mosue cursor approaches the edges + * of the window. + */ + function pan() { + var range = 0.12, + rangeX = window.innerWidth * range, + rangeY = window.innerHeight * range, + scrollOffset = getScrollOffset(); + + // Up + if (mouseY < rangeY) { + window.scroll( + scrollOffset.x, + scrollOffset.y - (1 - mouseY / rangeY) * (14 / level), + ); + } + // Down + else if (mouseY > window.innerHeight - rangeY) { + window.scroll( + scrollOffset.x, + scrollOffset.y + + (1 - (window.innerHeight - mouseY) / rangeY) * (14 / level), + ); + } + + // Left + if (mouseX < rangeX) { + window.scroll( + scrollOffset.x - (1 - mouseX / rangeX) * (14 / level), + scrollOffset.y, + ); + } + // Right + else if (mouseX > window.innerWidth - rangeX) { + window.scroll( + scrollOffset.x + + (1 - (window.innerWidth - mouseX) / rangeX) * (14 / level), + scrollOffset.y, + ); + } + } + + function getScrollOffset() { + return { + x: window.scrollX !== undefined ? window.scrollX : window.pageXOffset, + y: window.scrollY !== undefined ? window.scrollY : window.pageYOffset, + }; + } + + return { + /** + * Zooms in on either a rectangle or HTML element. + * + * @param {Object} options + * - element: HTML element to zoom in on + * OR + * - x/y: coordinates in non-transformed space to zoom in on + * - width/height: the portion of the screen to zoom in on + * - scale: can be used instead of width/height to explicitly set scale + */ + to: function (options) { + // Due to an implementation limitation we can't zoom in + // to another element without zooming out first + if (level !== 1) { + zoom.out(); + } else { + options.x = options.x || 0; + options.y = options.y || 0; + + // If an element is set, that takes precedence + if (!!options.element) { + // Space around the zoomed in element to leave on screen + var padding = 20; + var bounds = options.element.getBoundingClientRect(); + + options.x = bounds.left - padding; + options.y = bounds.top - padding; + options.width = bounds.width + padding * 2; + options.height = bounds.height + padding * 2; + } + + // If width/height values are set, calculate scale from those values + if (options.width !== undefined && options.height !== undefined) { + options.scale = Math.max( + Math.min( + window.innerWidth / options.width, + window.innerHeight / options.height, + ), + 1, + ); + } + + if (options.scale > 1) { + options.x *= options.scale; + options.y *= options.scale; + + magnify(options, options.scale); + + if (options.pan !== false) { + // Wait with engaging panning as it may conflict with the + // zoom transition + panEngageTimeout = setTimeout(function () { + panUpdateInterval = setInterval(pan, 1000 / 60); + }, 800); + } + } + } + }, + + /** + * Resets the document zoom state to its default. + */ + out: function () { + clearTimeout(panEngageTimeout); + clearInterval(panUpdateInterval); + + magnify({ x: 0, y: 0 }, 1); + + level = 1; + }, + + // Alias + magnify: function (options) { + this.to(options); + }, + reset: function () { + this.out(); + }, + + zoomLevel: function () { + return level; + }, + }; })(); diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.esm.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.esm.js index c0e8d7b..fcb3807 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.esm.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.esm.js @@ -1,4 +1,144 @@ /*! * reveal.js Zoom plugin */ -var e={id:"zoom",init:function(e){e.getRevealElement().addEventListener("mousedown",(function(n){var o=/Linux/.test(window.navigator.platform)?"ctrl":"alt",i=(e.getConfig().zoomKey?e.getConfig().zoomKey:o)+"Key",d=e.getConfig().zoomLevel?e.getConfig().zoomLevel:2;n[i]&&!e.isOverview()&&(n.preventDefault(),t.to({x:n.clientX,y:n.clientY,scale:d,pan:!1}))}))},destroy:function(){t.reset()}},t=function(){var e=1,n=0,o=0,i=-1,d=-1,l="transform"in document.body.style;function s(t,n){var o=r();if(t.width=t.width||1,t.height=t.height||1,t.x-=(window.innerWidth-t.width*n)/2,t.y-=(window.innerHeight-t.height*n)/2,l)if(1===n)document.body.style.transform="";else{var i=o.x+"px "+o.y+"px",d="translate("+-t.x+"px,"+-t.y+"px) scale("+n+")";document.body.style.transformOrigin=i,document.body.style.transform=d}else 1===n?(document.body.style.position="",document.body.style.left="",document.body.style.top="",document.body.style.width="",document.body.style.height="",document.body.style.zoom=""):(document.body.style.position="relative",document.body.style.left=-(o.x+t.x)/n+"px",document.body.style.top=-(o.y+t.y)/n+"px",document.body.style.width=100*n+"%",document.body.style.height=100*n+"%",document.body.style.zoom=n);e=n,document.documentElement.classList&&(1!==e?document.documentElement.classList.add("zoomed"):document.documentElement.classList.remove("zoomed"))}function c(){var t=.12*window.innerWidth,i=.12*window.innerHeight,d=r();owindow.innerHeight-i&&window.scroll(d.x,d.y+(1-(window.innerHeight-o)/i)*(14/e)),nwindow.innerWidth-t&&window.scroll(d.x+(1-(window.innerWidth-n)/t)*(14/e),d.y)}function r(){return{x:void 0!==window.scrollX?window.scrollX:window.pageXOffset,y:void 0!==window.scrollY?window.scrollY:window.pageYOffset}}return l&&(document.body.style.transition="transform 0.8s ease"),document.addEventListener("keyup",(function(n){1!==e&&27===n.keyCode&&t.out()})),document.addEventListener("mousemove",(function(t){1!==e&&(n=t.clientX,o=t.clientY)})),{to:function(n){if(1!==e)t.out();else{if(n.x=n.x||0,n.y=n.y||0,n.element){var o=n.element.getBoundingClientRect();n.x=o.left-20,n.y=o.top-20,n.width=o.width+40,n.height=o.height+40}void 0!==n.width&&void 0!==n.height&&(n.scale=Math.max(Math.min(window.innerWidth/n.width,window.innerHeight/n.height),1)),n.scale>1&&(n.x*=n.scale,n.y*=n.scale,s(n,n.scale),!1!==n.pan&&(i=setTimeout((function(){d=setInterval(c,1e3/60)}),800)))}},out:function(){clearTimeout(i),clearInterval(d),s({x:0,y:0},1),e=1},magnify:function(e){this.to(e)},reset:function(){this.out()},zoomLevel:function(){return e}}}();export default function(){return e} +var e = { + id: "zoom", + init: function (e) { + e.getRevealElement().addEventListener("mousedown", function (n) { + var o = /Linux/.test(window.navigator.platform) ? "ctrl" : "alt", + i = (e.getConfig().zoomKey ? e.getConfig().zoomKey : o) + "Key", + d = e.getConfig().zoomLevel ? e.getConfig().zoomLevel : 2; + n[i] && + !e.isOverview() && + (n.preventDefault(), + t.to({ x: n.clientX, y: n.clientY, scale: d, pan: !1 })); + }); + }, + destroy: function () { + t.reset(); + }, + }, + t = (function () { + var e = 1, + n = 0, + o = 0, + i = -1, + d = -1, + l = "transform" in document.body.style; + function s(t, n) { + var o = r(); + if ( + ((t.width = t.width || 1), + (t.height = t.height || 1), + (t.x -= (window.innerWidth - t.width * n) / 2), + (t.y -= (window.innerHeight - t.height * n) / 2), + l) + ) + if (1 === n) document.body.style.transform = ""; + else { + var i = o.x + "px " + o.y + "px", + d = "translate(" + -t.x + "px," + -t.y + "px) scale(" + n + ")"; + (document.body.style.transformOrigin = i), + (document.body.style.transform = d); + } + else + 1 === n + ? ((document.body.style.position = ""), + (document.body.style.left = ""), + (document.body.style.top = ""), + (document.body.style.width = ""), + (document.body.style.height = ""), + (document.body.style.zoom = "")) + : ((document.body.style.position = "relative"), + (document.body.style.left = -(o.x + t.x) / n + "px"), + (document.body.style.top = -(o.y + t.y) / n + "px"), + (document.body.style.width = 100 * n + "%"), + (document.body.style.height = 100 * n + "%"), + (document.body.style.zoom = n)); + (e = n), + document.documentElement.classList && + (1 !== e + ? document.documentElement.classList.add("zoomed") + : document.documentElement.classList.remove("zoomed")); + } + function c() { + var t = 0.12 * window.innerWidth, + i = 0.12 * window.innerHeight, + d = r(); + o < i + ? window.scroll(d.x, d.y - (14 / e) * (1 - o / i)) + : o > window.innerHeight - i && + window.scroll( + d.x, + d.y + (1 - (window.innerHeight - o) / i) * (14 / e), + ), + n < t + ? window.scroll(d.x - (14 / e) * (1 - n / t), d.y) + : n > window.innerWidth - t && + window.scroll( + d.x + (1 - (window.innerWidth - n) / t) * (14 / e), + d.y, + ); + } + function r() { + return { + x: void 0 !== window.scrollX ? window.scrollX : window.pageXOffset, + y: void 0 !== window.scrollY ? window.scrollY : window.pageYOffset, + }; + } + return ( + l && (document.body.style.transition = "transform 0.8s ease"), + document.addEventListener("keyup", function (n) { + 1 !== e && 27 === n.keyCode && t.out(); + }), + document.addEventListener("mousemove", function (t) { + 1 !== e && ((n = t.clientX), (o = t.clientY)); + }), + { + to: function (n) { + if (1 !== e) t.out(); + else { + if (((n.x = n.x || 0), (n.y = n.y || 0), n.element)) { + var o = n.element.getBoundingClientRect(); + (n.x = o.left - 20), + (n.y = o.top - 20), + (n.width = o.width + 40), + (n.height = o.height + 40); + } + void 0 !== n.width && + void 0 !== n.height && + (n.scale = Math.max( + Math.min( + window.innerWidth / n.width, + window.innerHeight / n.height, + ), + 1, + )), + n.scale > 1 && + ((n.x *= n.scale), + (n.y *= n.scale), + s(n, n.scale), + !1 !== n.pan && + (i = setTimeout(function () { + d = setInterval(c, 1e3 / 60); + }, 800))); + } + }, + out: function () { + clearTimeout(i), clearInterval(d), s({ x: 0, y: 0 }, 1), (e = 1); + }, + magnify: function (e) { + this.to(e); + }, + reset: function () { + this.out(); + }, + zoomLevel: function () { + return e; + }, + } + ); + })(); +export default function () { + return e; +} diff --git a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.js b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.js index b52804d..95f31d8 100644 --- a/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.js +++ b/content/slides/strucutral_estimation_files/libs/revealjs/plugin/zoom/zoom.js @@ -1,4 +1,155 @@ -!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).RevealZoom=t()}(this,(function(){"use strict"; -/*! - * reveal.js Zoom plugin - */var e={id:"zoom",init:function(e){e.getRevealElement().addEventListener("mousedown",(function(o){var n=/Linux/.test(window.navigator.platform)?"ctrl":"alt",i=(e.getConfig().zoomKey?e.getConfig().zoomKey:n)+"Key",d=e.getConfig().zoomLevel?e.getConfig().zoomLevel:2;o[i]&&!e.isOverview()&&(o.preventDefault(),t.to({x:o.clientX,y:o.clientY,scale:d,pan:!1}))}))},destroy:function(){t.reset()}},t=function(){var e=1,o=0,n=0,i=-1,d=-1,l="transform"in document.body.style;function s(t,o){var n=r();if(t.width=t.width||1,t.height=t.height||1,t.x-=(window.innerWidth-t.width*o)/2,t.y-=(window.innerHeight-t.height*o)/2,l)if(1===o)document.body.style.transform="";else{var i=n.x+"px "+n.y+"px",d="translate("+-t.x+"px,"+-t.y+"px) scale("+o+")";document.body.style.transformOrigin=i,document.body.style.transform=d}else 1===o?(document.body.style.position="",document.body.style.left="",document.body.style.top="",document.body.style.width="",document.body.style.height="",document.body.style.zoom=""):(document.body.style.position="relative",document.body.style.left=-(n.x+t.x)/o+"px",document.body.style.top=-(n.y+t.y)/o+"px",document.body.style.width=100*o+"%",document.body.style.height=100*o+"%",document.body.style.zoom=o);e=o,document.documentElement.classList&&(1!==e?document.documentElement.classList.add("zoomed"):document.documentElement.classList.remove("zoomed"))}function c(){var t=.12*window.innerWidth,i=.12*window.innerHeight,d=r();nwindow.innerHeight-i&&window.scroll(d.x,d.y+(1-(window.innerHeight-n)/i)*(14/e)),owindow.innerWidth-t&&window.scroll(d.x+(1-(window.innerWidth-o)/t)*(14/e),d.y)}function r(){return{x:void 0!==window.scrollX?window.scrollX:window.pageXOffset,y:void 0!==window.scrollY?window.scrollY:window.pageYOffset}}return l&&(document.body.style.transition="transform 0.8s ease"),document.addEventListener("keyup",(function(o){1!==e&&27===o.keyCode&&t.out()})),document.addEventListener("mousemove",(function(t){1!==e&&(o=t.clientX,n=t.clientY)})),{to:function(o){if(1!==e)t.out();else{if(o.x=o.x||0,o.y=o.y||0,o.element){var n=o.element.getBoundingClientRect();o.x=n.left-20,o.y=n.top-20,o.width=n.width+40,o.height=n.height+40}void 0!==o.width&&void 0!==o.height&&(o.scale=Math.max(Math.min(window.innerWidth/o.width,window.innerHeight/o.height),1)),o.scale>1&&(o.x*=o.scale,o.y*=o.scale,s(o,o.scale),!1!==o.pan&&(i=setTimeout((function(){d=setInterval(c,1e3/60)}),800)))}},out:function(){clearTimeout(i),clearInterval(d),s({x:0,y:0},1),e=1},magnify:function(e){this.to(e)},reset:function(){this.out()},zoomLevel:function(){return e}}}();return function(){return e}})); +!(function (e, t) { + "object" == typeof exports && "undefined" != typeof module + ? (module.exports = t()) + : "function" == typeof define && define.amd + ? define(t) + : ((e = + "undefined" != typeof globalThis + ? globalThis + : e || self).RevealZoom = t()); +})(this, function () { + "use strict"; + /*! + * reveal.js Zoom plugin + */ var e = { + id: "zoom", + init: function (e) { + e.getRevealElement().addEventListener("mousedown", function (o) { + var n = /Linux/.test(window.navigator.platform) ? "ctrl" : "alt", + i = (e.getConfig().zoomKey ? e.getConfig().zoomKey : n) + "Key", + d = e.getConfig().zoomLevel ? e.getConfig().zoomLevel : 2; + o[i] && + !e.isOverview() && + (o.preventDefault(), + t.to({ x: o.clientX, y: o.clientY, scale: d, pan: !1 })); + }); + }, + destroy: function () { + t.reset(); + }, + }, + t = (function () { + var e = 1, + o = 0, + n = 0, + i = -1, + d = -1, + l = "transform" in document.body.style; + function s(t, o) { + var n = r(); + if ( + ((t.width = t.width || 1), + (t.height = t.height || 1), + (t.x -= (window.innerWidth - t.width * o) / 2), + (t.y -= (window.innerHeight - t.height * o) / 2), + l) + ) + if (1 === o) document.body.style.transform = ""; + else { + var i = n.x + "px " + n.y + "px", + d = "translate(" + -t.x + "px," + -t.y + "px) scale(" + o + ")"; + (document.body.style.transformOrigin = i), + (document.body.style.transform = d); + } + else + 1 === o + ? ((document.body.style.position = ""), + (document.body.style.left = ""), + (document.body.style.top = ""), + (document.body.style.width = ""), + (document.body.style.height = ""), + (document.body.style.zoom = "")) + : ((document.body.style.position = "relative"), + (document.body.style.left = -(n.x + t.x) / o + "px"), + (document.body.style.top = -(n.y + t.y) / o + "px"), + (document.body.style.width = 100 * o + "%"), + (document.body.style.height = 100 * o + "%"), + (document.body.style.zoom = o)); + (e = o), + document.documentElement.classList && + (1 !== e + ? document.documentElement.classList.add("zoomed") + : document.documentElement.classList.remove("zoomed")); + } + function c() { + var t = 0.12 * window.innerWidth, + i = 0.12 * window.innerHeight, + d = r(); + n < i + ? window.scroll(d.x, d.y - (14 / e) * (1 - n / i)) + : n > window.innerHeight - i && + window.scroll( + d.x, + d.y + (1 - (window.innerHeight - n) / i) * (14 / e), + ), + o < t + ? window.scroll(d.x - (14 / e) * (1 - o / t), d.y) + : o > window.innerWidth - t && + window.scroll( + d.x + (1 - (window.innerWidth - o) / t) * (14 / e), + d.y, + ); + } + function r() { + return { + x: void 0 !== window.scrollX ? window.scrollX : window.pageXOffset, + y: void 0 !== window.scrollY ? window.scrollY : window.pageYOffset, + }; + } + return ( + l && (document.body.style.transition = "transform 0.8s ease"), + document.addEventListener("keyup", function (o) { + 1 !== e && 27 === o.keyCode && t.out(); + }), + document.addEventListener("mousemove", function (t) { + 1 !== e && ((o = t.clientX), (n = t.clientY)); + }), + { + to: function (o) { + if (1 !== e) t.out(); + else { + if (((o.x = o.x || 0), (o.y = o.y || 0), o.element)) { + var n = o.element.getBoundingClientRect(); + (o.x = n.left - 20), + (o.y = n.top - 20), + (o.width = n.width + 40), + (o.height = n.height + 40); + } + void 0 !== o.width && + void 0 !== o.height && + (o.scale = Math.max( + Math.min( + window.innerWidth / o.width, + window.innerHeight / o.height, + ), + 1, + )), + o.scale > 1 && + ((o.x *= o.scale), + (o.y *= o.scale), + s(o, o.scale), + !1 !== o.pan && + (i = setTimeout(function () { + d = setInterval(c, 1e3 / 60); + }, 800))); + } + }, + out: function () { + clearTimeout(i), clearInterval(d), s({ x: 0, y: 0 }, 1), (e = 1); + }, + magnify: function (e) { + this.to(e); + }, + reset: function () { + this.out(); + }, + zoomLevel: function () { + return e; + }, + } + ); + })(); + return function () { + return e; + }; +}); diff --git a/content/slides/tables/fullbeq_results.html b/content/slides/tables/fullbeq_results.html index cc58f91..99d1434 100644 --- a/content/slides/tables/fullbeq_results.html +++ b/content/slides/tables/fullbeq_results.html @@ -1,6 +1,6 @@ - + @@ -58,4 +58,4 @@ -
    value
    \ No newline at end of file + diff --git a/content/slides/tables/lcim_results.html b/content/slides/tables/lcim_results.html index 9fe13f2..004801c 100644 --- a/content/slides/tables/lcim_results.html +++ b/content/slides/tables/lcim_results.html @@ -1,6 +1,6 @@ - + @@ -36,4 +36,4 @@ -
    value***
    \ No newline at end of file + diff --git a/content/slides/tables/savres.tex b/content/slides/tables/savres.tex index 3b9d92b..c5db51f 100644 --- a/content/slides/tables/savres.tex +++ b/content/slides/tables/savres.tex @@ -62,4 +62,4 @@ 91. Wise/prudent thing to do; good discipline to save; habit 92. Liquidity; to have cash available/on hand -1. Don't/can't save; "have no money" - ``` \ No newline at end of file + ``` diff --git a/content/slides/tables/table1.tex b/content/slides/tables/table1.tex index bc68dda..29fde53 100644 --- a/content/slides/tables/table1.tex +++ b/content/slides/tables/table1.tex @@ -75,4 +75,4 @@ 28% - \ No newline at end of file + diff --git a/content/slides/tables/table2.tex b/content/slides/tables/table2.tex index a83eb15..609b65a 100644 --- a/content/slides/tables/table2.tex +++ b/content/slides/tables/table2.tex @@ -75,4 +75,4 @@ 21% - \ No newline at end of file + diff --git a/content/slides/tables/trp_results.html b/content/slides/tables/trp_results.html index a71ece0..0c72ac1 100644 --- a/content/slides/tables/trp_results.html +++ b/content/slides/tables/trp_results.html @@ -1,6 +1,6 @@ - + @@ -58,4 +58,4 @@ -
    value**
    \ No newline at end of file + diff --git a/content/slides/tables/wgbeq_results.html b/content/slides/tables/wgbeq_results.html index 66fc7be..428f9f7 100644 --- a/content/slides/tables/wgbeq_results.html +++ b/content/slides/tables/wgbeq_results.html @@ -1,6 +1,6 @@ - + @@ -69,4 +69,4 @@ -
    value
    \ No newline at end of file + diff --git a/content/tables/TRP/Portfolio_estimate_results.csv b/content/tables/TRP/Portfolio_estimate_results.csv index 4372856..ed64d84 100644 --- a/content/tables/TRP/Portfolio_estimate_results.csv +++ b/content/tables/TRP/Portfolio_estimate_results.csv @@ -1,33 +1,33 @@ -CRRA,9.252286005027539 -time_to_estimate,60.753241539001465 -params,{'CRRA': 9.252286005027539} -criterion,0.6423582605057705 -start_criterion,0.6339648081630582 -start_params,{'CRRA': 9.252342476844415} -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,1 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 9.252342476844415}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 9.250458955049714}, {'CRRA': 8.789725353002193}, {'CRRA': 9.231195795349796}, {'CRRA': 9.23850082029189}, {'CRRA': 9.191470898591822}, {'CRRA': 9.310169617324693}, {'CRRA': 9.250657961285336}, {'CRRA': 9.250710524700677}, {'CRRA': 9.248963361277593}, {'CRRA': 9.253968442160518}, {'CRRA': 9.255956673124432}, {'CRRA': 9.254149574984424}, {'CRRA': 9.25143892777441}, {'CRRA': 9.251890702309414}, {'CRRA': 9.252568364111916}, {'CRRA': 9.252455420478165}, {'CRRA': 9.252286005027539}], 'criterion': [0.6423583236273489, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.642369169571296, 0.6492959495923358, 0.6423762404892832, 0.6423752797264956, 0.6425219143922716, 0.642514400144132, 0.6423685692715722, 0.6423684112220268, 0.6423805942108727, 0.6423699960365742, 0.6423863674367805, 0.6423725213049074, 0.6423641176751165, 0.6423603114677403, 0.6423590691348976, 0.642358466533662, 0.6423582605057704], 'runtime': [0.0, 3.3893006040002547, 3.7743795520000276, 3.9942729670001427, 4.308895564000068, 4.500098180999885, 4.797666575000221, 5.097781724000015, 5.30452481400016, 5.586473449000096, 5.8145816500000365, 6.019138186999953, 6.258510488999946, 21.4172058070003, 22.745588674999908, 24.07027555800005, 25.37667203000001, 26.848738280999896, 28.141902802999994, 29.463674151000305, 30.76145256100017, 32.056981691000146, 33.34122019300003, 34.749019994000264, 36.131662315000085, 37.520465677000175, 38.87502066600018, 40.19993009200016, 41.659364199000265, 43.079727706000085, 44.41924671800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" -convergence_report, +CRRA,9.252286005027539 +time_to_estimate,60.753241539001465 +params,{'CRRA': 9.252286005027539} +criterion,0.6423582605057705 +start_criterion,0.6339648081630582 +start_params,{'CRRA': 9.252342476844415} +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,1 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 9.252342476844415}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 10.177576724528857}, {'CRRA': 8.327108229159974}, {'CRRA': 9.250458955049714}, {'CRRA': 8.789725353002193}, {'CRRA': 9.231195795349796}, {'CRRA': 9.23850082029189}, {'CRRA': 9.191470898591822}, {'CRRA': 9.310169617324693}, {'CRRA': 9.250657961285336}, {'CRRA': 9.250710524700677}, {'CRRA': 9.248963361277593}, {'CRRA': 9.253968442160518}, {'CRRA': 9.255956673124432}, {'CRRA': 9.254149574984424}, {'CRRA': 9.25143892777441}, {'CRRA': 9.251890702309414}, {'CRRA': 9.252568364111916}, {'CRRA': 9.252455420478165}, {'CRRA': 9.252286005027539}], 'criterion': [0.6423583236273489, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.672466609452884, 0.6726961956566793, 0.642369169571296, 0.6492959495923358, 0.6423762404892832, 0.6423752797264956, 0.6425219143922716, 0.642514400144132, 0.6423685692715722, 0.6423684112220268, 0.6423805942108727, 0.6423699960365742, 0.6423863674367805, 0.6423725213049074, 0.6423641176751165, 0.6423603114677403, 0.6423590691348976, 0.642358466533662, 0.6423582605057704], 'runtime': [0.0, 3.3893006040002547, 3.7743795520000276, 3.9942729670001427, 4.308895564000068, 4.500098180999885, 4.797666575000221, 5.097781724000015, 5.30452481400016, 5.586473449000096, 5.8145816500000365, 6.019138186999953, 6.258510488999946, 21.4172058070003, 22.745588674999908, 24.07027555800005, 25.37667203000001, 26.848738280999896, 28.141902802999994, 29.463674151000305, 30.76145256100017, 32.056981691000146, 33.34122019300003, 34.749019994000264, 36.131662315000085, 37.520465677000175, 38.87502066600018, 40.19993009200016, 41.659364199000265, 43.079727706000085, 44.41924671800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]}" +convergence_report, multistart_info,"{'start_parameters': [{'CRRA': 9.252342476844415}], 'local_optima': [Minimize with 1 free parameters terminated. The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 9.827e-08* 9.827e-08* -relative_params_change 6.104e-06* 6.104e-06* -absolute_criterion_change 6.312e-08* 6.312e-08* -absolute_params_change 5.647e-05 5.647e-05 + one_step five_steps +relative_criterion_change 9.827e-08* 9.827e-08* +relative_params_change 6.104e-06* 6.104e-06* +absolute_criterion_change 6.312e-08* 6.312e-08* +absolute_params_change 5.647e-05 5.647e-05 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252342476844415}, {'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.64235832, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, - 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25234248]), radius=0.9252342476844415, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6423583236273489, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.9252342476844415, shift=array([9.25234248])), vector_model=VectorModel(intercepts=array([ 0.04871268, 0.12404506, 0.14884697, 0.1938148 , 0.21740598, 0.23241888, 0.23335722, 0.06701942, -0.08019005, -0.06712962, -0.40905677, -0.41755326, -0.12516467, -0.09880123, -0.089428 , @@ -1670,4 +1670,4 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([9.25234248] [ 4.64375 ], [17.6375 ], [ 3.4625 ]]), 'exploration_results': array([0.64235832, 0.6831279 , 0.69939713, 1.02360977, 1.18227674, - 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" diff --git a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv index 229e4a5..4cd4e64 100644 --- a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv +++ b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv @@ -1,32 +1,32 @@ -CRRA,9.206775856414323 -BeqShift,45.64298427855443 -BeqFac,23.05054873023735 -time_to_estimate,236.11751127243042 -params,"{'CRRA': 9.206775856414323, 'BeqShift': 45.64298427855443, 'BeqFac': 23.05054873023735}" -criterion,0.6411981344087744 -start_criterion,0.6327696850981256 -start_params,"{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,3 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 10.556800566878689, 'BeqShift': 48.18211054060359, 'BeqFac': 19.257926550618446}, {'CRRA': 6.326950770325419, 'BeqShift': 48.028109248006054, 'BeqFac': 20.231068322997192}, {'CRRA': 5.376086840779585, 'BeqShift': 47.88380177945927, 'BeqFac': 24.370194536190226}, {'CRRA': 8.836842627116226, 'BeqShift': 41.31414348521892, 'BeqFac': 22.987721550281236}, {'CRRA': 8.588658189640931, 'BeqShift': 44.17145102421629, 'BeqFac': 27.216744679593038}, {'CRRA': 5.236533581737813, 'BeqShift': 43.73675712771144, 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0.6414329200652998, 0.6416044108046374, 0.64132278013026, 0.6414343864325133, 0.6414243782009376, 0.6414087932865302, 0.6412954271044502, 0.6412865692543277, 0.6412854794294207, 0.641279581055819, 0.6413818350153835, 0.6413360725922467, 0.6412388217057458, 0.6412877040107962, 0.6412706976786109, 0.6412739272492612, 0.6412528783892343, 0.6412455571674617, 0.6412437419225396, 0.6412523925678478, 0.641237872543212, 0.6412525940364707, 0.6412266965010901, 0.6412304930550558, 0.6412272191205531, 0.641233030463995, 0.6412251230472868, 0.6412487097674622, 0.6412413355178332, 0.6412231729718957, 0.641203166149811, 0.6412728891948222, 0.6412280723530474, 0.6412001975141021, 0.6411981649680744, 0.6412079437030589, 0.6411984036181763, 0.6411986911783254, 0.6412027480647225, 0.641202525461889, 0.6411987437639856, 0.6412015723843267, 0.641200500556385, 0.6412001120408095, 0.6412009516967749, 0.6412010184458711, 0.6412040999888278, 0.6411982806489596, 0.6412043369021567, 0.641199213576318, 0.6411985861573757, 0.6411988531624396, 0.6411981410601364, 0.6411983435068044, 0.6411982218360789, 0.6411983974477781, 0.641198802565166, 0.6411983487460016, 0.6411983387176948, 0.6411981887862126, 0.6411981618258916, 0.6411982859124767, 0.6411981835432701, 0.6411986552585525, 0.6411982826614723, 0.6411982163849296, 0.6411981768576345, 0.6411981415280087, 0.641198183511717, 0.6411981767918296, 0.6411981639977796, 0.6411981458535818, 0.6411981948719611, 0.6411981750765026, 0.6411981395569295, 0.6411981554481666, 0.6411981703995496, 0.6411981486118316, 0.6411981344087745], 'runtime': [0.0, 1.6660772569994151, 1.8709459969995805, 2.0942662709994693, 2.2992738539996935, 2.573140214999512, 2.793285908999678, 3.0239409299992985, 3.21622742999989, 3.619086903999232, 3.837496295999699, 4.064787764999892, 4.257744123999146, 5.771405386999504, 7.115736492999531, 8.483594589999484, 10.179792636999991, 10.376474048999626, 10.572450558999662, 10.776888375999988, 10.970461102999252, 11.2191199709996, 11.477609637999194, 11.685499741999593, 11.92565495799954, 12.199407783999959, 12.418102449999424, 12.646197193999797, 14.152243655999882, 15.625608827999713, 16.969351719999395, 18.28108473499924, 19.585364371999276, 20.901589727999635, 22.48934374199962, 22.68442717599919, 22.880525249999664, 23.091497723999964, 23.296080404999884, 23.49989032999929, 23.722511152999687, 24.103386021000006, 24.334308810999573, 24.55911114099945, 24.749386731999948, 24.971330100999694, 26.516184221999538, 27.830082762999155, 29.15997776199947, 30.618337350999354, 32.042491603999224, 33.47518897499958, 34.98603190199992, 36.43389601199942, 37.84166763199937, 39.21184449299926, 40.55340066699955, 41.92501889799951, 43.2515812369993, 44.711765780999485, 46.01470684099968, 47.659159168999395, 47.85348247499951, 48.05084095099937, 48.25375765299941, 48.457000436999806, 48.651789432999976, 48.87244264999936, 49.13459188999968, 49.35882806399968, 49.59038818599947, 49.81993929499913, 50.037629032999575, 51.526061966999805, 52.89645295799983, 54.40159700000004, 55.99065433299984, 57.3362580489993, 58.66196896999918, 59.97153813599925, 61.57381356899987, 61.760147432999474, 62.01530456399996, 62.21522431899939, 62.42186290299924, 62.657604192999315, 62.87575448699954, 63.08432635399913, 63.47667616999934, 63.669311561999166, 63.89146974599953, 64.14564449699992, 65.63578254099957, 66.97582318299919, 68.28952709699934, 69.91957877599998, 70.13097531499989, 70.39026367599945, 70.66363012599959, 70.86506305199964, 71.1010670679998, 71.33640461300001, 71.55638564799938, 71.81790237399946, 72.03107159499996, 72.23905201799971, 72.46167476499977, 74.07334276299935, 75.45991226499973, 77.02644426299958, 78.62224931799938, 78.81875102399954, 79.01513964099922, 79.23094035199938, 79.42576199999985, 79.6535652449993, 79.8579345169992, 80.10478922699986, 80.32257100899915, 80.57707195499916, 80.81765127299968, 81.04075872399972, 82.53920170899983], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 30, 31, 32, 33, 34, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 38, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 42, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45]}" +convergence_report,"{'one_step': {'relative_criterion_change': 3.5828565118959094e-08, 'relative_params_change': 0.1730113235913555, 'absolute_criterion_change': 2.297320911281986e-08, 'absolute_params_change': 5.876738890647083}, 'five_steps': {'relative_criterion_change': 3.5828565118959094e-08, 'relative_params_change': 0.1730113235913555, 'absolute_criterion_change': 2.297320911281986e-08, 'absolute_params_change': 5.876738890647083}}" multistart_info,"{'start_parameters': [{'CRRA': 9.20677821614649, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 9.275230386313043, 'BeqShift': 45.88119053785236, 'BeqFac': 23.014811093019418}, {'CRRA': 9.128116958674036, 'BeqShift': 48.90833875417502, 'BeqFac': 23.98172788815444}], 'local_optima': [Minimize with 3 free parameters terminated. The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 1.182e-08* 2.384e-08* -relative_params_change 7.164e-07* 7.164e-07* -absolute_criterion_change 7.578e-09** 1.529e-08* -absolute_params_change 3.435e-05 3.435e-05 + one_step five_steps +relative_criterion_change 1.182e-08* 2.384e-08* +relative_params_change 7.164e-07* 7.164e-07* +absolute_criterion_change 7.578e-09** 1.529e-08* +absolute_params_change 3.435e-05 3.435e-05 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. @@ -34,11 +34,11 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 8.029e-09** 7.847e-06* -relative_params_change 2.558e-07* 0.0001336 -absolute_criterion_change 5.148e-09** 5.032e-06* -absolute_params_change 5.08e-06* 0.00307 + one_step five_steps +relative_criterion_change 8.029e-09** 7.847e-06* +relative_params_change 2.558e-07* 0.0001336 +absolute_criterion_change 5.148e-09** 5.032e-06* +absolute_params_change 5.08e-06* 0.00307 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. @@ -46,18 +46,18 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 5.493e-08* 6.761e-06* -relative_params_change 1.236e-05 5.445e-05 -absolute_criterion_change 3.522e-08* 4.335e-06* -absolute_params_change 0.0006154 0.000671 + one_step five_steps +relative_criterion_change 5.493e-08* 6.761e-06* +relative_params_change 1.236e-05 5.445e-05 +absolute_criterion_change 3.522e-08* 4.335e-06* +absolute_params_change 0.0006154 0.000671 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.206778216146489, 'BeqShift': 50.64405071849033, 'BeqFac': 26.1368726540768}, {'CRRA': 9.368749999999999, 'BeqShift': 39.375, 'BeqFac': 18.75}, {'CRRA': 8.778125, 'BeqShift': 63.4375, 'BeqFac': 28.125}, {'CRRA': 9.959375, 'BeqShift': 6.5625, 'BeqFac': 84.375}, {'CRRA': 8.1875, 'BeqShift': 26.25, 'BeqFac': 62.5}, {'CRRA': 10.549999999999999, 'BeqShift': 35.0, 'BeqFac': 50.0}, {'CRRA': 7.596874999999999, 'BeqShift': 50.3125, 'BeqFac': 71.875}, {'CRRA': 7.00625, 'BeqShift': 13.125, 'BeqFac': 31.25}, {'CRRA': 11.73125, 'BeqShift': 30.625, 'BeqFac': 6.25}, {'CRRA': 12.321874999999999, 'BeqShift': 67.8125, 'BeqFac': 96.875}, {'CRRA': 6.415625, 'BeqShift': 19.6875, 'BeqFac': 15.625}, {'CRRA': 12.9125, 'BeqShift': 8.75, 'BeqFac': 87.5}, {'CRRA': 5.824999999999999, 'BeqShift': 52.5, 'BeqFac': 75.0}, {'CRRA': 13.503124999999999, 'BeqShift': 45.9375, 'BeqFac': 3.125}, {'CRRA': 5.234375, 'BeqShift': 59.0625, 'BeqFac': 9.375}, {'CRRA': 14.093749999999998, 'BeqShift': 56.875, 'BeqFac': 43.75}, {'CRRA': 14.684375, 'BeqShift': 24.0625, 'BeqFac': 59.375}, {'CRRA': 4.64375, 'BeqShift': 21.875, 'BeqFac': 93.75}, {'CRRA': 15.274999999999999, 'BeqShift': 17.5, 'BeqFac': 25.0}, {'CRRA': 4.053125, 'BeqShift': 10.9375, 'BeqFac': 53.125}, {'CRRA': 15.865624999999998, 'BeqShift': 54.6875, 'BeqFac': 65.625}, {'CRRA': 3.4625, 'BeqShift': 43.75, 'BeqFac': 37.5}, {'CRRA': 16.45625, 'BeqShift': 48.125, 'BeqFac': 81.25}, {'CRRA': 17.046875, 'BeqShift': 15.3125, 'BeqFac': 21.875}, {'CRRA': 2.871875, 'BeqShift': 32.8125, 'BeqFac': 46.875}, {'CRRA': 2.28125, 'BeqShift': 65.625, 'BeqFac': 56.25}, {'CRRA': 17.6375, 'BeqShift': 61.25, 'BeqFac': 12.5}, {'CRRA': 18.228125, 'BeqShift': 28.4375, 'BeqFac': 78.125}, {'CRRA': 18.81875, 'BeqShift': 4.375, 'BeqFac': 68.75}, {'CRRA': 19.409375, 'BeqShift': 41.5625, 'BeqFac': 34.375}], 'exploration_results': array([0.64119816, 0.64211564, 0.64842422, 0.66164008, 0.68191739, 0.7044766 , 0.74714868, 0.84823283, 0.8626059 , 0.97951322, 0.98996599, 1.12431839, 1.18221304, 1.30406645, 1.43892243, 1.52011014, 1.78024917, 1.78131285, 2.0971452 , 2.24344059, 2.48116605, 2.89787 , 2.94379242, 3.4976294 , 3.77080322, - 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" + 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.27523039, 45.88119054, 23.01481109]), radius=4.5881190537852365, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 70., 100.]))), model_indices=[0], model=ScalarModel(intercept=0.6414954627541696, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]), scale=4.5881190537852365, shift=array([ 9.27523039, 45.88119054, 23.01481109])), vector_model=VectorModel(intercepts=array([ 0.04902623, 0.12501294, 0.1503741 , 0.19587585, 0.21991651, @@ -11059,4 +11059,4 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.27523039 0.98996599, 1.12431839, 1.18221304, 1.30406645, 1.43892243, 1.52011014, 1.78024917, 1.78131285, 2.0971452 , 2.24344059, 2.48116605, 2.89787 , 2.94379242, 3.4976294 , 3.77080322, - 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}}" + 4.13701934, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}}" diff --git a/content/tables/TRP/WealthPortfolio_estimate_results.csv b/content/tables/TRP/WealthPortfolio_estimate_results.csv index c995cfc..49b3e99 100644 --- a/content/tables/TRP/WealthPortfolio_estimate_results.csv +++ b/content/tables/TRP/WealthPortfolio_estimate_results.csv @@ -1,31 +1,31 @@ -CRRA,5.335577372664163 -WealthShare,0.1706005756625005 -time_to_estimate,202.92073488235474 -params,"{'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}" -criterion,0.2421983863534466 -start_criterion,0.23890510137815316 -start_params,"{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,2 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}, {'CRRA': 5.521836423832173, 'WealthShare': 0.01}, {'CRRA': 6.355319463479058, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 6.288942410133527, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.34434498212577297}, {'CRRA': 6.227220953739785, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 5.871508721893411, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.6700100023631337}, {'CRRA': 5.428342638379923, 'WealthShare': 0.6950727963110019}, {'CRRA': 5.320570644267783, 'WealthShare': 0.39643867888963663}, {'CRRA': 5.320570644267783, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.129876895883085, 'WealthShare': 0.14106398655854144}, {'CRRA': 5.5792578490706015, 'WealthShare': 0.1204690698151589}, {'CRRA': 5.696390372221354, 'WealthShare': 0.1330305333078522}, {'CRRA': 5.764661943417508, 'WealthShare': 0.16014227844012982}, {'CRRA': 5.618872311285, 'WealthShare': 0.1672901244784106}, {'CRRA': 5.360185106482181, 'WealthShare': 0.15621623896813125}, {'CRRA': 5.4728571815195926, 'WealthShare': 0.16421014778193593}, {'CRRA': 5.545825951300451, 'WealthShare': 0.16306458747685207}, {'CRRA': 5.582398435175709, 'WealthShare': 0.1685319632864777}, {'CRRA': 5.509355844762908, 'WealthShare': 0.16418872811097143}, {'CRRA': 5.545831963262839, 'WealthShare': 0.1638629137313917}, {'CRRA': 5.564154615154598, 'WealthShare': 0.16862293277010182}, {'CRRA': 5.527673129514142, 'WealthShare': 0.16928670293386877}, {'CRRA': 5.454701789923809, 'WealthShare': 0.16996692667547664}, {'CRRA': 5.308764908353464, 'WealthShare': 0.17181847436097414}, {'CRRA': 5.0970292962882615, 'WealthShare': 0.1601742594497508}, {'CRRA': 5.454606416226443, 'WealthShare': 0.16345692042672777}, {'CRRA': 5.381637011093981, 'WealthShare': 0.1642816207639356}, {'CRRA': 5.272807055506822, 'WealthShare': 0.16562610692660415}, {'CRRA': 5.326977544603738, 'WealthShare': 0.17075638690650644}, {'CRRA': 5.290536727275727, 'WealthShare': 0.1654939898644262}, {'CRRA': 5.34522111280142, 'WealthShare': 0.17069763426574053}, {'CRRA': 5.336100860393708, 'WealthShare': 0.17086100179753094}, {'CRRA': 5.354343627621392, 'WealthShare': 0.1706887162046996}, {'CRRA': 5.345222085332012, 'WealthShare': 0.17075761374684867}, {'CRRA': 5.331561427746527, 'WealthShare': 0.1728165644941382}, {'CRRA': 5.333816121531683, 'WealthShare': 0.17032107739522934}, {'CRRA': 5.337237317831098, 'WealthShare': 0.17035022974547837}, {'CRRA': 5.335530348688637, 'WealthShare': 0.17082137941624043}, {'CRRA': 5.336665303591373, 'WealthShare': 0.17028845981607194}, {'CRRA': 5.334960873512694, 'WealthShare': 0.17089262349762813}, {'CRRA': 5.335767080396652, 'WealthShare': 0.170983623728545}, {'CRRA': 5.3355388649474635, 'WealthShare': 0.1706791061179343}, {'CRRA': 5.335333663634032, 'WealthShare': 0.17047473426234255}, {'CRRA': 5.335554589018792, 'WealthShare': 0.17053744817902458}, {'CRRA': 5.335343081312356, 'WealthShare': 0.17034634254745087}, {'CRRA': 5.33559928332507, 'WealthShare': 0.17040210920806037}, {'CRRA': 5.335580340581636, 'WealthShare': 0.1706038967420634}, {'CRRA': 5.335454088688302, 'WealthShare': 0.17053748476047093}, {'CRRA': 5.335633144546121, 'WealthShare': 0.17065220536865475}, {'CRRA': 5.335596501563392, 'WealthShare': 0.170572140462395}, {'CRRA': 5.335595445166626, 'WealthShare': 0.1705940558344041}, {'CRRA': 5.335583910614445, 'WealthShare': 0.17061205807039284}, {'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}], 'criterion': [0.2500249942408325, 1.1791007356386276, 0.9197651867022865, 1.2595025990222253, 33.4909498055013, 1.4882139243569137, 0.9530633832431904, 1.2595025990222253, 36.978934548688066, 26.44344185974536, 41.385698106382975, 3.0981667931180743, 42.608449629101216, 0.2728700599071999, 0.3342173205988753, 0.29198968043547885, 0.2481648865140715, 0.24373445213195505, 0.25114503099349234, 0.24406213167656582, 0.24489531582637777, 0.24315780951732555, 0.24426227832547462, 0.24453862473009094, 0.24306497226632034, 0.24285876672075696, 0.24259437399648875, 0.24243354455545987, 0.25079678704331854, 0.24441635199641626, 0.24414569958048554, 0.2439788766246561, 0.24225547458407035, 0.24394629290622924, 0.24227514910123488, 0.24223921155653808, 0.24227947343936865, 0.24227295597179582, 0.24250574148919085, 0.24223949661361607, 0.24225288167948408, 0.2422306308030337, 0.2422548267052162, 0.24224813492764274, 0.24225797040581512, 0.242202396887387, 0.24220875273143883, 0.2422004189428712, 0.24224389187159454, 0.24223135088965633, 0.2421984742592525, 0.24219915623837365, 0.24220019073252083, 0.24219911551794257, 0.2421985513731777, 0.24219868532057515, 0.24219838635344662], 'runtime': [0.0, 1.296167903999958, 1.3449491520000265, 1.3858779940001114, 1.4279043609999462, 1.4686698719997366, 1.514770218999729, 1.5592568910001319, 1.6084964980000223, 1.6590743809997548, 1.7047839209999438, 1.761857082000006, 1.8163866790000611, 3.181013745999735, 4.325823927999863, 5.600623542999983, 6.755316591999872, 7.917994205000014, 9.077396046000104, 10.294875745999889, 11.503319416000068, 12.720127907000006, 14.032752316999904, 15.207649181999841, 16.429454532999898, 17.670099559999926, 18.8472972149998, 20.135531741999785, 21.277609068999936, 22.423084480999933, 23.553762927999742, 24.684132019000117, 25.817971458000102, 26.998730913000145, 28.1869263640001, 29.34812033800017, 30.50678410599994, 31.65761656199993, 32.799299537000024, 33.94402660300011, 35.26958909099994, 36.47545633799973, 37.66156821799996, 38.83101894899983, 39.99145682000017, 41.17240058000016, 42.34739114900003, 43.51756579399989, 44.678886351000074, 45.83479911199993, 46.989458055999876, 48.12758910999992, 49.42098368999996, 50.618441345000065, 51.84377754599973, 52.98982088799994, 54.21043696800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" -convergence_report,"{'one_step': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}, 'five_steps': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}}" +CRRA,5.335577372664163 +WealthShare,0.1706005756625005 +time_to_estimate,202.92073488235474 +params,"{'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}" +criterion,0.2421983863534466 +start_criterion,0.23890510137815316 +start_params,"{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}" +algorithm,multistart_tranquilo_ls +direction,minimize +n_free,2 +message,Absolute criterion change smaller than tolerance. +success, +n_criterion_evaluations, +n_derivative_evaluations, +n_iterations, +history,"{'params': [{'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}, {'CRRA': 5.521836423832173, 'WealthShare': 0.01}, {'CRRA': 6.355319463479058, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 6.288942410133527, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.34434498212577297}, {'CRRA': 6.227220953739785, 'WealthShare': 0.01}, {'CRRA': 5.320570644267783, 'WealthShare': 0.01}, {'CRRA': 5.871508721893411, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.355319463479058, 'WealthShare': 0.6700100023631337}, {'CRRA': 5.428342638379923, 'WealthShare': 0.6950727963110019}, {'CRRA': 5.320570644267783, 'WealthShare': 0.39643867888963663}, {'CRRA': 5.320570644267783, 'WealthShare': 0.6950727963110019}, {'CRRA': 6.129876895883085, 'WealthShare': 0.14106398655854144}, {'CRRA': 5.5792578490706015, 'WealthShare': 0.1204690698151589}, {'CRRA': 5.696390372221354, 'WealthShare': 0.1330305333078522}, {'CRRA': 5.764661943417508, 'WealthShare': 0.16014227844012982}, {'CRRA': 5.618872311285, 'WealthShare': 0.1672901244784106}, {'CRRA': 5.360185106482181, 'WealthShare': 0.15621623896813125}, {'CRRA': 5.4728571815195926, 'WealthShare': 0.16421014778193593}, {'CRRA': 5.545825951300451, 'WealthShare': 0.16306458747685207}, {'CRRA': 5.582398435175709, 'WealthShare': 0.1685319632864777}, {'CRRA': 5.509355844762908, 'WealthShare': 0.16418872811097143}, {'CRRA': 5.545831963262839, 'WealthShare': 0.1638629137313917}, {'CRRA': 5.564154615154598, 'WealthShare': 0.16862293277010182}, {'CRRA': 5.527673129514142, 'WealthShare': 0.16928670293386877}, {'CRRA': 5.454701789923809, 'WealthShare': 0.16996692667547664}, {'CRRA': 5.308764908353464, 'WealthShare': 0.17181847436097414}, {'CRRA': 5.0970292962882615, 'WealthShare': 0.1601742594497508}, {'CRRA': 5.454606416226443, 'WealthShare': 0.16345692042672777}, {'CRRA': 5.381637011093981, 'WealthShare': 0.1642816207639356}, {'CRRA': 5.272807055506822, 'WealthShare': 0.16562610692660415}, {'CRRA': 5.326977544603738, 'WealthShare': 0.17075638690650644}, {'CRRA': 5.290536727275727, 'WealthShare': 0.1654939898644262}, {'CRRA': 5.34522111280142, 'WealthShare': 0.17069763426574053}, {'CRRA': 5.336100860393708, 'WealthShare': 0.17086100179753094}, {'CRRA': 5.354343627621392, 'WealthShare': 0.1706887162046996}, {'CRRA': 5.345222085332012, 'WealthShare': 0.17075761374684867}, {'CRRA': 5.331561427746527, 'WealthShare': 0.1728165644941382}, {'CRRA': 5.333816121531683, 'WealthShare': 0.17032107739522934}, {'CRRA': 5.337237317831098, 'WealthShare': 0.17035022974547837}, {'CRRA': 5.335530348688637, 'WealthShare': 0.17082137941624043}, {'CRRA': 5.336665303591373, 'WealthShare': 0.17028845981607194}, {'CRRA': 5.334960873512694, 'WealthShare': 0.17089262349762813}, {'CRRA': 5.335767080396652, 'WealthShare': 0.170983623728545}, {'CRRA': 5.3355388649474635, 'WealthShare': 0.1706791061179343}, {'CRRA': 5.335333663634032, 'WealthShare': 0.17047473426234255}, {'CRRA': 5.335554589018792, 'WealthShare': 0.17053744817902458}, {'CRRA': 5.335343081312356, 'WealthShare': 0.17034634254745087}, {'CRRA': 5.33559928332507, 'WealthShare': 0.17040210920806037}, {'CRRA': 5.335580340581636, 'WealthShare': 0.1706038967420634}, {'CRRA': 5.335454088688302, 'WealthShare': 0.17053748476047093}, {'CRRA': 5.335633144546121, 'WealthShare': 0.17065220536865475}, {'CRRA': 5.335596501563392, 'WealthShare': 0.170572140462395}, {'CRRA': 5.335595445166626, 'WealthShare': 0.1705940558344041}, {'CRRA': 5.335583910614445, 'WealthShare': 0.17061205807039284}, {'CRRA': 5.335577372664163, 'WealthShare': 0.1706005756625005}], 'criterion': [0.2500249942408325, 1.1791007356386276, 0.9197651867022865, 1.2595025990222253, 33.4909498055013, 1.4882139243569137, 0.9530633832431904, 1.2595025990222253, 36.978934548688066, 26.44344185974536, 41.385698106382975, 3.0981667931180743, 42.608449629101216, 0.2728700599071999, 0.3342173205988753, 0.29198968043547885, 0.2481648865140715, 0.24373445213195505, 0.25114503099349234, 0.24406213167656582, 0.24489531582637777, 0.24315780951732555, 0.24426227832547462, 0.24453862473009094, 0.24306497226632034, 0.24285876672075696, 0.24259437399648875, 0.24243354455545987, 0.25079678704331854, 0.24441635199641626, 0.24414569958048554, 0.2439788766246561, 0.24225547458407035, 0.24394629290622924, 0.24227514910123488, 0.24223921155653808, 0.24227947343936865, 0.24227295597179582, 0.24250574148919085, 0.24223949661361607, 0.24225288167948408, 0.2422306308030337, 0.2422548267052162, 0.24224813492764274, 0.24225797040581512, 0.242202396887387, 0.24220875273143883, 0.2422004189428712, 0.24224389187159454, 0.24223135088965633, 0.2421984742592525, 0.24219915623837365, 0.24220019073252083, 0.24219911551794257, 0.2421985513731777, 0.24219868532057515, 0.24219838635344662], 'runtime': [0.0, 1.296167903999958, 1.3449491520000265, 1.3858779940001114, 1.4279043609999462, 1.4686698719997366, 1.514770218999729, 1.5592568910001319, 1.6084964980000223, 1.6590743809997548, 1.7047839209999438, 1.761857082000006, 1.8163866790000611, 3.181013745999735, 4.325823927999863, 5.600623542999983, 6.755316591999872, 7.917994205000014, 9.077396046000104, 10.294875745999889, 11.503319416000068, 12.720127907000006, 14.032752316999904, 15.207649181999841, 16.429454532999898, 17.670099559999926, 18.8472972149998, 20.135531741999785, 21.277609068999936, 22.423084480999933, 23.553762927999742, 24.684132019000117, 25.817971458000102, 26.998730913000145, 28.1869263640001, 29.34812033800017, 30.50678410599994, 31.65761656199993, 32.799299537000024, 33.94402660300011, 35.26958909099994, 36.47545633799973, 37.66156821799996, 38.83101894899983, 39.99145682000017, 41.17240058000016, 42.34739114900003, 43.51756579399989, 44.678886351000074, 45.83479911199993, 46.989458055999876, 48.12758910999992, 49.42098368999996, 50.618441345000065, 51.84377754599973, 52.98982088799994, 54.21043696800007], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" +convergence_report,"{'one_step': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}, 'five_steps': {'relative_criterion_change': 0.00012034146417080751, 'relative_params_change': 0.004344123651656158, 'absolute_criterion_change': 2.9146508433580687e-05, 'absolute_params_change': 0.01844539352010625}}" multistart_info,"{'start_parameters': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 5.837945053873421, 'WealthShare': 0.17769838670536425}], 'local_optima': [Minimize with 2 free parameters terminated. The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 5.606e-08* 5.606e-08* -relative_params_change 0.0001132 0.0001132 -absolute_criterion_change 1.358e-08* 1.358e-08* -absolute_params_change 3.272e-05 3.272e-05 + one_step five_steps +relative_criterion_change 5.606e-08* 5.606e-08* +relative_params_change 0.0001132 0.0001132 +absolute_criterion_change 1.358e-08* 1.358e-08* +absolute_params_change 3.272e-05 3.272e-05 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -33,17 +33,17 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 3.629e-07* 0.0001686 -relative_params_change 1.947e-05 0.00153 -absolute_criterion_change 8.791e-08* 4.083e-05 -absolute_params_change 4.454e-06* 0.0005847 + one_step five_steps +relative_criterion_change 3.629e-07* 0.0001686 +relative_params_change 1.947e-05 0.00153 +absolute_criterion_change 8.791e-08* 4.083e-05 +absolute_params_change 4.454e-06* 0.0005847 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.35399091577092, 'WealthShare': 0.1710302407154898}, {'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([2.42227546e-01, 3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, - 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83794505, 0.17769839]), radius=0.5837945053873421, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.2500249942408325, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], [0., 0.]]), scale=0.5837945053873421, shift=array([5.83794505, 0.17769839])), vector_model=VectorModel(intercepts=array([ 0.04097158, 0.08958026, 0.08816431, 0.11232637, 0.12576489, 0.13980983, 0.15782978, 0.17549271, 0.09352102, 0.15057374, @@ -7383,4 +7383,4 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([5.83794505, 1.79325436e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, - 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}}" + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}}" diff --git a/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv b/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv index bb2a13f..4178f42 100644 --- a/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv @@ -21,11 +21,11 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 6.101e-09** 6.101e-09** -relative_params_change 1.69e-07* 1.69e-07* -absolute_criterion_change 2.005e-06* 2.005e-06* -absolute_params_change 1.205e-06* 1.205e-06* + one_step five_steps +relative_criterion_change 6.101e-09** 6.101e-09** +relative_params_change 1.69e-07* 1.69e-07* +absolute_criterion_change 2.005e-06* 2.005e-06* +absolute_params_change 1.205e-06* 1.205e-06* (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -34,10 +34,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 1.804e-06* 0.01593 -relative_params_change 0.0001065 0.1347 -absolute_criterion_change 0.000593 5.236 -absolute_params_change 0.0007487 0.9471 +relative_criterion_change 1.804e-06* 0.01593 +relative_params_change 0.0001065 0.1347 +absolute_criterion_change 0.000593 5.236 +absolute_params_change 0.0007487 0.9471 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.130705399496962, 'DiscFac': 1.1}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 328.68572765, 420.32021458, 528.04271383, 663.30843919, 755.01356649, 860.62318106, 905.15922637, 916.6930808 , diff --git a/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv index 5b50fff..062a68d 100644 --- a/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv @@ -21,11 +21,11 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 9.23e-09** 9.23e-09** -relative_params_change 6.761e-07* 6.761e-07* -absolute_criterion_change 3.027e-06* 3.027e-06* -absolute_params_change 4.083e-06* 4.083e-06* + one_step five_steps +relative_criterion_change 9.23e-09** 9.23e-09** +relative_params_change 6.761e-07* 6.761e-07* +absolute_criterion_change 3.027e-06* 3.027e-06* +absolute_params_change 4.083e-06* 4.083e-06* (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -33,11 +33,11 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 8.88e-07* 0.0001055 -relative_params_change 5.646e-05 0.006636 -absolute_criterion_change 0.0002913 0.03461 -absolute_params_change 0.000341 0.04007 + one_step five_steps +relative_criterion_change 8.88e-07* 0.0001055 +relative_params_change 5.646e-05 0.006636 +absolute_criterion_change 0.0002913 0.03461 +absolute_params_change 0.000341 0.04007 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.039365487749873, 'DiscFac': 1.1}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}], 'exploration_results': array([ 327.98396556, 444.08066157, 540.65943804, 544.96572798, 689.47008041, 769.45866328, 909.59665878, 921.36892514, diff --git a/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv b/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv index dab8b0c..6e78c41 100644 --- a/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv @@ -21,11 +21,11 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 6.781e-06* 6.781e-06* -relative_params_change 0.0001716 0.0001716 -absolute_criterion_change 0.003615 0.003615 -absolute_params_change 0.0001744 0.0001744 + one_step five_steps +relative_criterion_change 6.781e-06* 6.781e-06* +relative_params_change 0.0001716 0.0001716 +absolute_criterion_change 0.003615 0.003615 +absolute_params_change 0.0001744 0.0001744 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -33,11 +33,11 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 7.135e-06* 0.002522 -relative_params_change 0.0007284 0.01496 -absolute_criterion_change 0.003805 1.345 -absolute_params_change 0.0007105 0.09367 + one_step five_steps +relative_criterion_change 7.135e-06* 0.002522 +relative_params_change 0.0007284 0.01496 +absolute_criterion_change 0.003805 1.345 +absolute_params_change 0.0007105 0.09367 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.14177124762114, 'DiscFac': 0.9700614043534316}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 533.15737537, 554.91392947, 580.45087475, 623.67135041, 630.89444653, 637.20876422, 645.36720654, 650.8682765 , diff --git a/content/tables/min/IndShock_estimate_results.csv b/content/tables/min/IndShock_estimate_results.csv index 7bee283..126a30b 100644 --- a/content/tables/min/IndShock_estimate_results.csv +++ b/content/tables/min/IndShock_estimate_results.csv @@ -21,21 +21,21 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 1.078e-06* 1.078e-06* -relative_params_change 5.967e-06* 5.967e-06* -absolute_criterion_change 0.00054 0.00054 -absolute_params_change 1.992e-05 1.992e-05 + one_step five_steps +relative_criterion_change 1.078e-06* 1.078e-06* +relative_params_change 5.967e-06* 5.967e-06* +absolute_criterion_change 0.00054 0.00054 +absolute_params_change 1.992e-05 1.992e-05 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 3.829e-05 0.00369 -relative_params_change 0.003188 0.01772 -absolute_criterion_change 0.01919 1.85 -absolute_params_change 0.02136 0.107 +relative_criterion_change 3.829e-05 0.00369 +relative_params_change 0.003188 0.01772 +absolute_criterion_change 0.01919 1.85 +absolute_params_change 0.02136 0.107 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.518758193778226, 'DiscFac': 0.9740911850209896}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 501.12751638, 519.11435204, 538.30979579, 597.0207673 , 611.52511798, 615.89547778, 622.99770198, 631.34502173, diff --git a/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv index 12d3111..686cf98 100644 --- a/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv @@ -22,20 +22,20 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.02632 0.2707 -relative_params_change 0.0005553 0.0918 -absolute_criterion_change 0.03186 0.3276 -absolute_params_change 0.0006017 1.071 +relative_criterion_change 0.02632 0.2707 +relative_params_change 0.0005553 0.0918 +absolute_criterion_change 0.03186 0.3276 +absolute_params_change 0.0006017 1.071 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.449 1.086 -relative_params_change 0.004166 0.07772 -absolute_criterion_change 0.3226 0.7803 -absolute_params_change 0.005346 0.9465 +relative_criterion_change 0.449 1.086 +relative_params_change 0.004166 0.07772 +absolute_criterion_change 0.3226 0.7803 +absolute_params_change 0.005346 0.9465 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 12.279830990813757, 'DiscFac': 1.0776706629061286}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.25226984, 1.54798499, 1.77139048, 1.79567843, 2.19243348, 2.99563577, 3.4003551 , 3.46407238, 3.67340234, 3.72448066, diff --git a/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv index fa2a732..9dbfeaa 100644 --- a/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv @@ -21,11 +21,11 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 8.19e-07* 8.19e-07* -relative_params_change 9.248e-07* 9.248e-07* -absolute_criterion_change 1.3e-06* 1.3e-06* -absolute_params_change 3.236e-06* 3.236e-06* + one_step five_steps +relative_criterion_change 8.19e-07* 8.19e-07* +relative_params_change 9.248e-07* 9.248e-07* +absolute_criterion_change 1.3e-06* 1.3e-06* +absolute_params_change 3.236e-06* 3.236e-06* (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -34,10 +34,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.0005557 0.01853 -relative_params_change 0.006672 0.05371 -absolute_criterion_change 0.0008913 0.02972 -absolute_params_change 0.02942 0.2354 +relative_criterion_change 0.0005557 0.01853 +relative_params_change 0.006672 0.05371 +absolute_criterion_change 0.0008913 0.02972 +absolute_params_change 0.02942 0.2354 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.242030481097337, 'DiscFac': 0.9833811989466528}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.58762926, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, diff --git a/content/tables/min/Portfolio_estimate_results.csv b/content/tables/min/Portfolio_estimate_results.csv index ac57ad0..33b0825 100644 --- a/content/tables/min/Portfolio_estimate_results.csv +++ b/content/tables/min/Portfolio_estimate_results.csv @@ -21,11 +21,11 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 0.02496 0.02496 -relative_params_change 0.0002006 0.0002006 -absolute_criterion_change 0.03277 0.03277 -absolute_params_change 0.001071 0.001071 + one_step five_steps +relative_criterion_change 0.02496 0.02496 +relative_params_change 0.0002006 0.0002006 +absolute_criterion_change 0.03277 0.03277 +absolute_params_change 0.001071 0.001071 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 10.865980160003074, 'DiscFac': 0.8065707082845225}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.66924823, 1.74531296, 2.10648864, 2.17581064, 2.66803924, 2.70500778, 2.87740953, 3.0406658 , diff --git a/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv index d1e8335..edb999f 100644 --- a/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv @@ -22,20 +22,20 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.02632 0.2707 -relative_params_change 0.0005553 0.0918 -absolute_criterion_change 0.03186 0.3276 -absolute_params_change 0.0006017 1.071 +relative_criterion_change 0.02632 0.2707 +relative_params_change 0.0005553 0.0918 +absolute_criterion_change 0.03186 0.3276 +absolute_params_change 0.0006017 1.071 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.6327 0.9982 -relative_params_change 0.0007548 0.07704 -absolute_criterion_change 0.4355 0.687 -absolute_params_change 0.002656 1.007 +relative_criterion_change 0.6327 0.9982 +relative_params_change 0.0007548 0.07704 +absolute_criterion_change 0.4355 0.687 +absolute_params_change 0.002656 1.007 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.25226984, 1.77139048, 1.79567843, 2.19243348, 2.99563577, 3.4003551 , 3.46407238, 3.67340234, 3.72448066, 4.28414873, diff --git a/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv index aaf7f87..b87147c 100644 --- a/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv @@ -22,10 +22,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 5.482e-05 0.01069 -relative_params_change 0.008558 0.06089 -absolute_criterion_change 8.708e-05 0.01698 -absolute_params_change 0.0362 0.2564 +relative_criterion_change 5.482e-05 0.01069 +relative_params_change 0.008558 0.06089 +absolute_criterion_change 8.708e-05 0.01698 +absolute_params_change 0.0362 0.2564 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -34,10 +34,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.0003018 0.01028 -relative_params_change 0.002003 0.05369 -absolute_criterion_change 0.0004793 0.01633 -absolute_params_change 0.007394 0.228 +relative_criterion_change 0.0003018 0.01028 +relative_params_change 0.002003 0.05369 +absolute_criterion_change 0.0004793 0.01633 +absolute_params_change 0.007394 0.228 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.74175066, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, diff --git a/content/tables/min/WarmGlowPortfolio_estimate_results.csv b/content/tables/min/WarmGlowPortfolio_estimate_results.csv index acb3ef0..b21ab12 100644 --- a/content/tables/min/WarmGlowPortfolio_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolio_estimate_results.csv @@ -20,31 +20,31 @@ multistart_info,"{'start_parameters': [{'CRRA': 15.274999999999999, 'BeqFac': 32 Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 0.4439 1.468 -relative_params_change 0.0003056 0.0003056 -absolute_criterion_change 0.9678 3.2 -absolute_params_change 0.005363 0.005363 + one_step five_steps +relative_criterion_change 0.4439 1.468 +relative_params_change 0.0003056 0.0003056 +absolute_criterion_change 0.9678 3.2 +absolute_params_change 0.005363 0.005363 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - one_step five_steps -relative_criterion_change 1.405 17.75 -relative_params_change 0.003117 0.003984 -absolute_criterion_change 4.239 53.57 -absolute_params_change 0.05189 0.06976 + one_step five_steps +relative_criterion_change 1.405 17.75 +relative_params_change 0.003117 0.003984 +absolute_criterion_change 4.239 53.57 +absolute_params_change 0.05189 0.06976 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.02464 2.477 -relative_params_change 3.439e-07* 2.186 -absolute_criterion_change 0.2897 29.12 -absolute_params_change 1.353e-06* 8.505 +relative_criterion_change 0.02464 2.477 +relative_params_change 3.439e-07* 2.186 +absolute_criterion_change 0.2897 29.12 +absolute_params_change 1.353e-06* 8.505 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 15.274999999999999, 'BeqFac': 32.5, 'BeqShift': 17.5}, {'CRRA': 3.4625, 'BeqFac': 51.25, 'BeqShift': 26.25}, {'CRRA': 18.228125, 'BeqFac': 40.3125, 'BeqShift': 54.6875}, {'CRRA': 4.64375, 'BeqFac': 35.625, 'BeqShift': 65.625}, {'CRRA': 2.871875, 'BeqFac': 43.4375, 'BeqShift': 32.8125}, {'CRRA': 17.6375, 'BeqFac': 63.75, 'BeqShift': 8.75}, {'CRRA': 15.865624999999998, 'BeqFac': 59.0625, 'BeqShift': 45.9375}, {'CRRA': 12.321874999999999, 'BeqFac': 68.4375, 'BeqShift': 67.8125}, {'CRRA': 2.28125, 'BeqFac': 66.875, 'BeqShift': 39.375}, {'CRRA': 9.959375, 'BeqFac': 24.6875, 'BeqShift': 59.0625}, {'CRRA': 8.778125, 'BeqFac': 65.3125, 'BeqShift': 19.6875}, {'CRRA': 9.368749999999999, 'BeqFac': 48.125, 'BeqShift': 13.125}, {'CRRA': 9.370461268457287, 'BeqFac': 67.92926162554892, 'BeqShift': 52.186320909731975}, {'CRRA': 8.1875, 'BeqFac': 38.75, 'BeqShift': 43.75}, {'CRRA': 18.81875, 'BeqFac': 23.125, 'BeqShift': 48.125}, {'CRRA': 13.503124999999999, 'BeqFac': 52.8125, 'BeqShift': 2.1875}, {'CRRA': 14.093749999999998, 'BeqFac': 60.625, 'BeqShift': 30.625}, {'CRRA': 4.053125, 'BeqFac': 27.8125, 'BeqShift': 37.1875}, {'CRRA': 6.415625, 'BeqFac': 34.0625, 'BeqShift': 10.9375}, {'CRRA': 16.45625, 'BeqFac': 54.375, 'BeqShift': 56.875}, {'CRRA': 11.73125, 'BeqFac': 41.875, 'BeqShift': 4.375}, {'CRRA': 17.046875, 'BeqFac': 30.9375, 'BeqShift': 15.3125}, {'CRRA': 19.409375, 'BeqFac': 49.6875, 'BeqShift': 24.0625}, {'CRRA': 10.549999999999999, 'BeqFac': 45.0, 'BeqShift': 35.0}, {'CRRA': 5.824999999999999, 'BeqFac': 57.5, 'BeqShift': 52.5}, {'CRRA': 5.234375, 'BeqFac': 62.1875, 'BeqShift': 6.5625}, {'CRRA': 12.9125, 'BeqFac': 26.25, 'BeqShift': 61.25}, {'CRRA': 7.596874999999999, 'BeqFac': 55.9375, 'BeqShift': 50.3125}, {'CRRA': 14.684375, 'BeqFac': 37.1875, 'BeqShift': 41.5625}, {'CRRA': 7.00625, 'BeqFac': 29.375, 'BeqShift': 21.875}], 'exploration_results': array([2.67005783e+00, 8.75572079e+00, 9.08018789e+00, 1.68017528e+01, 8.75438745e+01, 9.36743973e+01, 1.00926173e+02, 1.11632625e+02, diff --git a/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv index a9ce77c..561ccb4 100644 --- a/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv @@ -22,10 +22,10 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.02247 0.09591 -relative_params_change 0.1151 0.2751 -absolute_criterion_change 7.462 31.86 -absolute_params_change 0.4044 0.9669 +relative_criterion_change 0.02247 0.09591 +relative_params_change 0.1151 0.2751 +absolute_criterion_change 7.462 31.86 +absolute_params_change 0.4044 0.9669 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. diff --git a/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv index 2917098..91c3f77 100644 --- a/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv @@ -22,10 +22,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.02962 0.1773 -relative_params_change 0.1778 0.4224 -absolute_criterion_change 9.634 57.67 -absolute_params_change 0.8087 1.921 +relative_criterion_change 0.02962 0.1773 +relative_params_change 0.1778 0.4224 +absolute_criterion_change 9.634 57.67 +absolute_params_change 0.8087 1.921 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. diff --git a/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv index 0284e79..4267bd8 100644 --- a/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv @@ -20,10 +20,10 @@ multistart_info,"{'start_parameters': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRR Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.9326 0.9326 -relative_params_change 0.1316 0.1316 -absolute_criterion_change 527.7 527.7 -absolute_params_change 0.323 0.323 +relative_criterion_change 0.9326 0.9326 +relative_params_change 0.1316 0.1316 +absolute_criterion_change 527.7 527.7 +absolute_params_change 0.323 0.323 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. diff --git a/content/tables/min/WarmGlow_estimate_results.csv b/content/tables/min/WarmGlow_estimate_results.csv index 7b65c20..aaa9d20 100644 --- a/content/tables/min/WarmGlow_estimate_results.csv +++ b/content/tables/min/WarmGlow_estimate_results.csv @@ -24,10 +24,10 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.4266 0.611 -relative_params_change 0.2042 0.2042 -absolute_criterion_change 254.4 364.4 -absolute_params_change 0.674 0.674 +relative_criterion_change 0.4266 0.611 +relative_params_change 0.2042 0.2042 +absolute_criterion_change 254.4 364.4 +absolute_params_change 0.674 0.674 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([1069.96402275, 1180.14464257, 1201.13045726, 2198.69465259])}" algorithm_output,"{'states': [State(trustregion=Region(center=array([3.78766627, 0.8916693 ]), radius=0.3787666272489316, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=960.8060529870695, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], diff --git a/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv index 7def6fe..9444b84 100644 --- a/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv @@ -22,20 +22,20 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.02632 0.2707 -relative_params_change 0.0005553 0.0918 -absolute_criterion_change 0.03186 0.3276 -absolute_params_change 0.0006017 1.071 +relative_criterion_change 0.02632 0.2707 +relative_params_change 0.0005553 0.0918 +absolute_criterion_change 0.03186 0.3276 +absolute_params_change 0.0006017 1.071 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.6327 0.9982 -relative_params_change 0.0007548 0.07704 -absolute_criterion_change 0.4355 0.687 -absolute_params_change 0.002656 1.007 +relative_criterion_change 0.6327 0.9982 +relative_params_change 0.0007548 0.07704 +absolute_criterion_change 0.4355 0.687 +absolute_params_change 0.002656 1.007 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.25226984, 1.77139048, 1.79567843, 2.19243348, 2.99563577, 3.4003551 , 3.46407238, 3.67340234, 3.72448066, 4.28414873, diff --git a/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv index 5de5794..6624a23 100644 --- a/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv @@ -22,10 +22,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 5.482e-05 0.01069 -relative_params_change 0.008558 0.06089 -absolute_criterion_change 8.708e-05 0.01698 -absolute_params_change 0.0362 0.2564 +relative_criterion_change 5.482e-05 0.01069 +relative_params_change 0.008558 0.06089 +absolute_criterion_change 8.708e-05 0.01698 +absolute_params_change 0.0362 0.2564 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. @@ -34,10 +34,10 @@ The tranquilo_ls algorithm reported: Absolute criterion change smaller than tole Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.0003018 0.01028 -relative_params_change 0.002003 0.05369 -absolute_criterion_change 0.0004793 0.01633 -absolute_params_change 0.007394 0.228 +relative_criterion_change 0.0003018 0.01028 +relative_params_change 0.002003 0.05369 +absolute_criterion_change 0.0004793 0.01633 +absolute_params_change 0.007394 0.228 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.74175066, 2.04247528, 2.9717037 , 3.76994253, 3.80031518, 4.27735638, 4.32695188, 4.78264529, 5.25346612, 5.79528191, diff --git a/content/tables/min/WealthPortfolio_estimate_results.csv b/content/tables/min/WealthPortfolio_estimate_results.csv index 74b3cea..84e4e27 100644 --- a/content/tables/min/WealthPortfolio_estimate_results.csv +++ b/content/tables/min/WealthPortfolio_estimate_results.csv @@ -22,10 +22,10 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: one_step five_steps -relative_criterion_change 0.4931 0.7235 -relative_params_change 0.2452 0.2452 -absolute_criterion_change 0.5564 0.8164 -absolute_params_change 1.918 1.918 +relative_criterion_change 0.4931 0.7235 +relative_params_change 0.2452 0.2452 +absolute_criterion_change 0.5564 0.8164 +absolute_params_change 1.918 1.918 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 5.0, 'DiscFac': 0.95}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([ 1.74531296, 2.10648864, 2.17581064, 2.66803924, 2.70500778, 2.87740953, 3.0406658 , 3.37839016, diff --git a/myst.yml b/myst.yml index bcaf280..50ec6dc 100644 --- a/myst.yml +++ b/myst.yml @@ -11,7 +11,9 @@ site: url: https://mystmd.org/guide domains: [] project: - title: Structural Estimation of Life Cycle Models with Wealth in the Utility Function + title: + Structural Estimation of Life Cycle Models with Wealth in the Utility + Function # description: keywords: structural estimation, life cycle, wealth in the utility authors: diff --git a/src/README.md b/src/README.md index 2fca85e..04a6889 100644 --- a/src/README.md +++ b/src/README.md @@ -1,7 +1,9 @@ # Description -1. The "Stata" directory is a clone of the corresponding directory in the original online version - of the `SolvingMicoDSOPs` project, available at http://econ.jhu.edu/people/ccarroll/Topics/EstimatingMicroDSOPs.zip - * That directory is being added here to clarify the origin of the SCFdata.txt file -1. Original Matlab and Mathematica code to solve the model here is also available in that zip archive - +1. The "Stata" directory is a clone of the corresponding directory in the + original online version of the `SolvingMicoDSOPs` project, available at + http://econ.jhu.edu/people/ccarroll/Topics/EstimatingMicroDSOPs.zip + - That directory is being added here to clarify the origin of the SCFdata.txt + file +1. Original Matlab and Mathematica code to solve the model here is also + available in that zip archive diff --git a/src/do_all.py b/src/do_all.py index d359127..659f3fc 100644 --- a/src/do_all.py +++ b/src/do_all.py @@ -45,6 +45,8 @@ still run. """ +from __future__ import annotations + from estimark.estimation import estimate from estimark.options import ( all_replications, @@ -61,7 +63,7 @@ def run_replication(): [1] IndShockConsumerType - 2 PortfolioConsumerType + 2 PortfolioConsumerType 3 BequestWarmGlowConsumerType diff --git a/src/estimark/agents.py b/src/estimark/agents.py index e874bd8..77d71c2 100644 --- a/src/estimark/agents.py +++ b/src/estimark/agents.py @@ -8,6 +8,9 @@ income as defined in ConsIndShockModel. """ +from __future__ import annotations + +import numpy as np from HARK.ConsumptionSaving.ConsBequestModel import ( BequestWarmGlowConsumerType, BequestWarmGlowPortfolioType, @@ -16,7 +19,6 @@ from HARK.ConsumptionSaving.ConsPortfolioModel import PortfolioConsumerType from HARK.ConsumptionSaving.ConsWealthPortfolioModel import WealthPortfolioConsumerType from HARK.core import AgentType -import numpy as np # ===================================================== # Define objects and functions used for the estimation @@ -50,7 +52,7 @@ def sim_birth(self, which_agents): self.t_age[which_agents] = 0 # Which period of the cycle each agents is currently in self.t_cycle[which_agents] = 0 - + def sim_death(self): return np.zeros(self.AgentCount, dtype=bool) diff --git a/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv b/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv index 8c6bf4f..9ead805 100644 --- a/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv +++ b/src/estimark/content/tables/min/PortfolioDiscFac_estimate_results.csv @@ -1,15400 +1,15418 @@ -CRRA,6.614528749695077 -DiscFac,1.0357967813213964 -time_to_estimate,207.89406180381775 -params,"{'CRRA': 6.614528749695077, 'DiscFac': 1.0357967813213964}" -criterion,0.5414098309112865 -start_criterion,0.6339659358362199 -start_params,"{'CRRA': 9.252106996349742, 'DiscFac': 1.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,2 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 9.474902352534894, 'DiscFac': 0.9999364724331312}, {'CRRA': 8.635210994449684, 'DiscFac': 0.5395034896195957}, {'CRRA': 10.314593710620104, 'DiscFac': 0.839446855669377}, {'CRRA': 8.635210994449684, 'DiscFac': 0.8283678716666038}, {'CRRA': 10.314593710620104, 'DiscFac': 1.0999999747595868}, {'CRRA': 10.314593710620104, 'DiscFac': 0.5389047702194539}, {'CRRA': 10.099775031645393, 'DiscFac': 0.5}, {'CRRA': 8.635210994449684, 'DiscFac': 1.0754042612834813}, {'CRRA': 10.314593710620104, 'DiscFac': 0.8992682338268907}, {'CRRA': 10.02804372855336, 'DiscFac': 1.1}, {'CRRA': 8.635210994449684, 'DiscFac': 1.0126290881448918}, {'CRRA': 9.383098701781327, 'DiscFac': 0.5}, {'CRRA': 9.565093432029592, 'DiscFac': 1.1}, {'CRRA': 8.784457936968376, 'DiscFac': 0.9310439834648917}, {'CRRA': 9.8947480315775, 'DiscFac': 0.8184105648091622}, {'CRRA': 9.684825192056197, 'DiscFac': 0.885136695992917}, {'CRRA': 9.579863772295544, 'DiscFac': 0.9102910870194282}, {'CRRA': 9.534018703276786, 'DiscFac': 0.9930620756112296}, {'CRRA': 9.506611879960504, 'DiscFac': 1.0031675966210665}, {'CRRA': 9.491850351794207, 'DiscFac': 1.0059290988491987}, {'CRRA': 9.521258624865157, 'DiscFac': 1.0009457308801484}, {'CRRA': 9.477048409069202, 'DiscFac': 1.0062117721069057}, {'CRRA': 9.50732214323413, 'DiscFac': 1.004629074015044}, {'CRRA': 9.462245565165952, 'DiscFac': 1.0064356785650799}, {'CRRA': 9.491997724043722, 'DiscFac': 1.0094319823652904}, {'CRRA': 9.44744469326394, 'DiscFac': 1.0067935306460525}, {'CRRA': 9.417841489167358, 'DiscFac': 1.0074286188478525}, {'CRRA': 9.35862836816774, 'DiscFac': 1.0081996514934115}, {'CRRA': 9.253666948407089, 'DiscFac': 1.0095565528258796}, {'CRRA': 9.043744108885786, 'DiscFac': 1.0122071451332528}, {'CRRA': 8.62389842984318, 'DiscFac': 0.9893524404100075}, {'CRRA': 8.833821269364483, 'DiscFac': 1.0053062205953809}, {'CRRA': 8.938782689125135, 'DiscFac': 1.0068771581248388}, {'CRRA': 9.102802873067144, 'DiscFac': 1.0041054814625276}, {'CRRA': 9.014132800170033, 'DiscFac': 1.01223101590572}, {'CRRA': 8.95483028166163, 'DiscFac': 1.0072403330168547}, {'CRRA': 8.98452584702955, 'DiscFac': 1.0125866853161116}, {'CRRA': 8.925216978198963, 'DiscFac': 1.0077189333941095}, {'CRRA': 8.954918939383921, 'DiscFac': 1.0129457463899745}, {'CRRA': 8.895705048120474, 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1.0358776886253191}, {'CRRA': 6.629456449634945, 'DiscFac': 1.0358183726658614}, {'CRRA': 6.607097916397772, 'DiscFac': 1.0357328241754022}, {'CRRA': 6.6107995764389855, 'DiscFac': 1.0359145478364045}, {'CRRA': 6.616352565428071, 'DiscFac': 1.0360591907672831}, {'CRRA': 6.613866667200956, 'DiscFac': 1.0350546159580063}, {'CRRA': 6.614037476326552, 'DiscFac': 1.035954042885339}, {'CRRA': 6.6147307972095755, 'DiscFac': 1.0357723735949134}, {'CRRA': 6.614487361640625, 'DiscFac': 1.035683617182909}, {'CRRA': 6.614501671721159, 'DiscFac': 1.0358563997053487}, {'CRRA': 6.614528749695077, 'DiscFac': 1.0357967813213964}], 'criterion': [0.6448272261874601, 3.2321994025011787, 1.9091666596987835, 2.3916847958894416, 3.478393276809091, 3.001530893143964, 3.102577078428783, 1.7262922340264317, 1.3964408207071262, 3.413103793830339, 0.6007967457515286, 3.1979455519934517, 3.319523330141687, 1.4100381767073222, 2.151878868775669, 1.681054561744293, 1.451186887621807, 0.6676758699239331, 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0.5414533040238656, 0.5414101559834423, 0.5414570248057565, 0.541461501749771, 0.5414940722460778, 0.5415308885058294, 0.5415401970146225, 0.5414670862447538, 0.541630603918333, 0.5417428852802306, 0.5414925969685491, 0.5414213817815339, 0.5415235212662285, 0.5414473204202261, 0.5414098309112865], 'runtime': [0.0, 1.6021721996366978, 1.763861000072211, 1.9364263000898063, 2.105490399990231, 2.2869786000810564, 2.4659180999733508, 2.645659300033003, 2.8395737996324897, 3.035836899653077, 3.223421999718994, 3.405466000083834, 3.596803499851376, 4.85236749984324, 5.994861399754882, 7.14662199979648, 8.282688799779862, 9.429990599863231, 10.606538099702448, 11.902987699955702, 13.051619499921799, 14.198331299703568, 15.403642300050706, 16.55112239997834, 17.69306949991733, 18.856871999800205, 20.002126100007445, 21.29282119963318, 22.501489299815148, 23.68054739991203, 24.829328400082886, 25.971021299716085, 27.116726099979132, 28.259377599693835, 29.404241499956697, 30.751218599732965, 31.8990707998164, 33.04312939988449, 34.19053489994258, 35.35309229977429, 36.50230869976804, 37.67961739981547, 38.97866219980642, 40.138869300018996, 41.282837899867445, 42.43159359972924, 43.61818069964647, 44.7674082997255, 45.915902299806476, 47.063161599915475, 48.3554062996991, 49.515675500035286, 50.720047399867326, 51.87775920005515, 53.02564329979941, 54.18041200004518, 55.32891159970313, 56.47521140007302, 57.76730199996382, 58.93786389986053, 60.09956929972395, 61.246673699934036, 62.39065769966692, 63.537203199695796, 64.6880405000411, 65.84563069976866, 67.12054200004786, 68.2633369998075, 69.40824959985912, 70.57097999984398, 71.71744199981913, 72.86136729968712, 74.00794119993225, 75.30863319989294, 76.46647779969499, 77.64264229964465, 78.78975840006024, 79.93701329967007, 81.08435950009152, 82.23052149964496, 83.39726469991729, 84.69916970003396, 85.85893659992144], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]}" -convergence_report,"{'one_step': {'relative_criterion_change': 1.525257377346415e-05, 'relative_params_change': 0.004758913549058271, 'absolute_criterion_change': 8.257893387653148e-06, 'absolute_params_change': 0.031397306790084616}, 'five_steps': {'relative_criterion_change': 1.525257377346415e-05, 'relative_params_change': 0.004758913549058271, 'absolute_criterion_change': 8.257893387653148e-06, 'absolute_params_change': 0.031397306790084616}}" -multistart_info,"{'start_parameters': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 9.474902352534894, 'DiscFac': 0.9999364724331312}], 'local_optima': [Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.183e-07* 3.672e-05 -relative_params_change 8.471e-07* 0.001055 -absolute_criterion_change 6.406e-08* 1.988e-05 -absolute_params_change 1e-06* 0.006933 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 6.004e-07* 0.03035 -relative_params_change 4.702e-06* 0.1395 -absolute_criterion_change 3.251e-07* 0.01643 -absolute_params_change 2.892e-05 0.9212 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([0.64235819, 0.99955613, 1.1121001 , 1.75705837, 1.77123339, - 1.82132756, 1.94396028, 2.07018913, 2.11467658, 2.15218408, - 2.24402179, 2.54668134, 2.78807842, 2.90740886, 3.01539999, - 3.32546076, 3.5830362 , 4.07905962, 4.08578359, 6.94508729])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=0.6448272261874601, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=0, candidate_x=array([9.47490235, 0.99993647]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.7324177488592093, linear_terms=array([-0.1604269 , -1.70678719]), square_terms=array([[0.05311507, 0.46725062], - [0.46725062, 4.78691623]]), scale=array([0.83969136, 0.3 ]), shift=array([9.47490235, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=13, candidate_x=array([8.78445794, 0.93104398]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-3.2925872216365843, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.4596011524812236, linear_terms=array([-0.15193583, -0.53365863]), square_terms=array([[0.20583856, 0.85488875], - [0.85488875, 3.85975947]]), scale=array([0.41984568, 0.2599546 ]), shift=array([9.47490235, 0.8400454 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=14, candidate_x=array([9.89474803, 0.81841056]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-3.246228222983773, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), old_indices_discarded=array([1, 2, 3, 5, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 6, 7, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.47748362589833987, linear_terms=array([0.03029103, 0.35911003]), square_terms=array([[0.02625602, 0.18108694], - [0.18108694, 1.39847469]]), scale=array([0.20992284, 0.15499318]), shift=array([9.47490235, 0.94500682])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=15, candidate_x=array([9.68482519, 0.8851367 ]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-3.7543621103084996, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 6, 7, 9, 10, 11, 12, 13, 14]), old_indices_discarded=array([1, 2, 3, 4, 5, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 11, 12, 14, 15]), model=ScalarModel(intercept=0.781511080377176, linear_terms=array([-0.06570334, 0.6958516 ]), square_terms=array([[ 0.00455801, -0.05593978], - [-0.05593978, 0.75231219]]), scale=array([0.10496142, 0.10251247]), shift=array([9.47490235, 0.99748753])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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model=ScalarModel(intercept=0.6108530298545867, linear_terms=array([-0.10139668, -0.14696738]), square_terms=array([[0.07792252, 0.32866092], - [0.32866092, 1.49842941]]), scale=0.05921813970334309, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=17, candidate_x=array([9.5340187 , 0.99306208]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-0.3116075291616809, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=0.6954288371659393, linear_terms=array([-0.10174434, -0.15897912]), square_terms=array([[0.03333137, 0.10554599], - [0.10554599, 0.37011472]]), scale=0.029609069851671544, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=18, candidate_x=array([9.50661188, 1.0031676 ]), index=18, x=array([9.50661188, 1.0031676 ]), fval=0.6396317123330025, rho=0.05607187891543219, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.03187372417953975, relative_step_length=1.0764851560421564, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.50661188, 1.0031676 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.6396317123330028, linear_terms=array([ 0.00028177, -0.01430195]), square_terms=array([[2.43882216e-05, 1.35799469e-03], - [1.35799469e-03, 8.34202654e-02]]), scale=0.014804534925835772, shift=array([9.50661188, 1.0031676 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=19, candidate_x=array([9.49185035, 1.0059291 ]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=1.4693827516516844, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.015017609941676606, relative_step_length=1.0143925504521585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6412257536348447, linear_terms=array([-0.06905576, -0.03758833]), square_terms=array([[0.03588925, 0.1094543 ], - [0.1094543 , 0.37040144]]), scale=0.029609069851671544, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=20, candidate_x=array([9.52125862, 1.00094573]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=-0.11096998055275568, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6375966987395302, linear_terms=array([ 0.00059101, -0.00033053]), square_terms=array([[2.18308578e-05, 1.28615347e-03], - [1.28615347e-03, 8.40653356e-02]]), scale=0.014804534925835772, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=21, candidate_x=array([9.47704841, 1.00621177]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=1.2946325204884988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.01480464158988585, relative_step_length=1.0000072048227528, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6740948994884155, linear_terms=array([-0.0775299 , -0.08137508]), square_terms=array([[0.03091198, 0.10157856], - [0.10157856, 0.37039842]]), scale=0.029609069851671544, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=22, candidate_x=array([9.50732214, 1.00462907]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=-0.029474268266789803, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6367076097989959, linear_terms=array([ 6.38911855e-04, -5.64861763e-05]), square_terms=array([[1.99591697e-05, 1.23030124e-03], - [1.23030124e-03, 8.44345370e-02]]), scale=0.014804534925835772, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=23, candidate_x=array([9.46224557, 1.00643568]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=1.1173242341721383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014804537200668226, relative_step_length=1.0000001536578127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6376967017750665, linear_terms=array([-0.00887642, -0.07715602]), square_terms=array([[0.00091426, 0.02162778], - [0.02162778, 0.54194842]]), scale=0.029609069851671544, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=24, candidate_x=array([9.49199772, 1.00943198]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=-0.23439049876600507, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6357439312845821, linear_terms=array([ 0.00057539, -0.00082847]), square_terms=array([[2.06319310e-05, 1.26644672e-03], - [1.26644672e-03, 8.60694145e-02]]), scale=0.014804534925835772, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=25, candidate_x=array([9.44744469, 1.00679353]), index=25, x=array([9.44744469, 1.00679353]), fval=0.6347642471305107, rho=1.4044689675006616, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014805197302692493, relative_step_length=1.000044741483609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.44744469, 1.00679353]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6347153364849043, linear_terms=array([ 0.00136442, -0.00288838]), square_terms=array([[7.21853077e-05, 4.81168593e-03], - [4.81168593e-03, 3.57552054e-01]]), scale=0.029609069851671544, shift=array([9.44744469, 1.00679353])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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old_indices_discarded=array([17]), step_length=0.02961001570090617, relative_step_length=1.0000319445777717, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.41784149, 1.00742862]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6332385782456339, linear_terms=array([0.0028514 , 0.00022792]), square_terms=array([[2.79552380e-04, 1.89198753e-02], - [1.89198753e-02, 1.43266898e+00]]), scale=0.05921813970334309, shift=array([9.41784149, 1.00742862])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=27, candidate_x=array([9.35862837, 1.00819965]), index=27, x=array([9.35862837, 1.00819965]), fval=0.6303511715372276, rho=1.0071263421242136, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 17, 20]), step_length=0.0592181407159667, relative_step_length=1.0000000170998888, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.35862837, 1.00819965]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.639178807035496, linear_terms=array([ 0.00132575, -0.26376211]), square_terms=array([[8.65219778e-04, 5.51863712e-02], - [5.51863712e-02, 3.95321986e+00]]), scale=array([0.10496142, 0.09838088]), shift=array([9.35862837, 1.00161912])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=28, candidate_x=array([9.25366695, 1.00955655]), index=28, x=array([9.25366695, 1.00955655]), fval=0.6252147257628975, rho=1.035476052619481, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 20, 22]), step_length=0.10497019014652463, relative_step_length=0.8863009769673553, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25366695, 1.00955655]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3525263472005808, linear_terms=array([-0.05604803, -3.65995549]), square_terms=array([[3.37929225e-03, 1.66025609e-01], - [1.66025609e-01, 9.20973251e+00]]), scale=array([0.20992284, 0.15018314]), shift=array([9.25366695, 0.94981686])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=29, candidate_x=array([9.04374411, 1.01220715]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=1.0345307568988933, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 22]), step_length=0.20993957271621416, relative_step_length=0.8862975676368734, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), model=ScalarModel(intercept=2.887557528467335, linear_terms=array([ 0.32145309, -7.82327945]), square_terms=array([[ 0.01909841, -0.39706853], - [-0.39706853, 13.16541827]]), scale=array([0.41984568, 0.25381927]), shift=array([9.04374411, 0.84618073])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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12, 14, 15, 16, 17, 18, 19, 20, - 21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.1785163391833393, linear_terms=array([ 0.04245163, -3.2666068 ]), square_terms=array([[ 1.11423119e-03, -2.87009785e-02], - [-2.87009785e-02, 8.89866124e+00]]), scale=array([0.20992284, 0.14885785]), shift=array([9.04374411, 0.95114215])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=31, candidate_x=array([8.83382127, 1.00530622]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.21904622845287416, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 6, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, - 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=0.588971346798013, linear_terms=array([ 0.01505103, -0.13000888]), square_terms=array([[ 2.31746838e-04, -3.20175653e-03], - [-3.20175653e-03, 3.75543886e+00]]), scale=array([0.10496142, 0.09637714]), shift=array([9.04374411, 1.00362286])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=32, candidate_x=array([8.93878269, 1.00687716]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.19293832377303985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), old_indices_discarded=array([ 0, 3, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32]), model=ScalarModel(intercept=0.6214336044267341, linear_terms=array([0.00147887, 0.10259686]), square_terms=array([[4.10977860e-04, 1.78962346e-02], - [1.78962346e-02, 8.79816604e-01]]), scale=0.05921813970334309, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=33, candidate_x=array([9.10280287, 1.00410548]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-2.08297420159985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33]), model=ScalarModel(intercept=0.6151402665456009, linear_terms=array([0.00149709, 0.00482735]), square_terms=array([[7.65543339e-05, 5.14393571e-03], - [5.14393571e-03, 3.91745261e-01]]), scale=0.029609069851671544, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=34, candidate_x=array([9.0141328 , 1.01223102]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=0.8565054081815938, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.02961131833730782, relative_step_length=1.0000759390837854, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.620881773286023, linear_terms=array([0.00197631, 0.09173778]), square_terms=array([[3.44046695e-04, 1.64408522e-02], - [1.64408522e-02, 8.92929811e-01]]), scale=0.05921813970334309, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=35, candidate_x=array([8.95483028, 1.00724033]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=-0.906107936879546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6137867229839677, linear_terms=array([0.00142032, 0.00043433]), square_terms=array([[7.69832064e-05, 5.16913372e-03], - [5.16913372e-03, 3.92730596e-01]]), scale=0.029609069851671544, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=36, candidate_x=array([8.98452585, 1.01258669]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=0.9657336720601446, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.029609089398228004, relative_step_length=1.0000006601543567, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.619467823965291, linear_terms=array([0.00209097, 0.09020298]), square_terms=array([[3.29303090e-04, 1.61351934e-02], - [1.61351934e-02, 9.00329160e-01]]), scale=0.05921813970334309, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=37, candidate_x=array([8.92521698, 1.00771893]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=-0.8416732621911007, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.6124263604451471, linear_terms=array([0.00140759, 0.00037964]), square_terms=array([[7.68743253e-05, 5.17639009e-03], - [5.17639009e-03, 3.94127433e-01]]), scale=0.029609069851671544, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=38, candidate_x=array([8.95491894, 1.01294575]), index=38, x=array([8.95491894, 1.01294575]), fval=0.6110646963985666, rho=1.04699122192598, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.02960908484218274, relative_step_length=1.0000005062810575, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.95491894, 1.01294575]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6110062274424244, linear_terms=array([0.0028986 , 0.00157643]), square_terms=array([[3.04730334e-04, 2.06301143e-02], - [2.06301143e-02, 1.58200616e+00]]), scale=0.05921813970334309, shift=array([8.95491894, 1.01294575])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=39, candidate_x=array([8.89570505, 1.01365764]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=0.9122655509396086, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([13]), step_length=0.05921817039076989, relative_step_length=1.0000005182099092, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.614727205905659, linear_terms=array([-0.00711409, -0.08425954]), square_terms=array([[1.97921783e-03, 6.38199381e-02], - [6.38199381e-02, 2.30738160e+00]]), scale=array([0.10496142, 0.09565189]), shift=array([8.89570505, 1.00434811])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=40, candidate_x=array([9.00066647, 1.00519543]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=-1.8130574184159354, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([ 0, 1, 3, 7, 10, 21, 23, 25, 26, 27, 28, 29, 30, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.6082515870760768, linear_terms=array([0.00289756, 0.00212777]), square_terms=array([[2.93803015e-04, 2.03395974e-02], - [2.03395974e-02, 1.60156668e+00]]), scale=0.05921813970334309, shift=array([8.89570505, 1.01365764])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=41, candidate_x=array([8.83649067, 1.01432978]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=0.9335608646751216, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([ 7, 10, 13, 29, 30, 33]), step_length=0.05921819354659514, relative_step_length=1.0000009092357902, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), model=ScalarModel(intercept=0.610651531374723, linear_terms=array([ 0.00125715, -0.10509161]), square_terms=array([[7.56634919e-04, 3.91907529e-02], - [3.91907529e-02, 2.39236786e+00]]), scale=array([0.10496142, 0.09531582]), shift=array([8.83649067, 1.00468418])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=42, candidate_x=array([8.73152925, 1.01043262]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.2539961161624663, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), old_indices_discarded=array([ 1, 3, 7, 10, 26, 27, 28, 29, 30, 33, 34, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6128960194558581, linear_terms=array([0.00323829, 0.08375858]), square_terms=array([[2.19797272e-04, 1.29854569e-02], - [1.29854569e-02, 9.21144727e-01]]), scale=0.05921813970334309, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=43, candidate_x=array([8.77720289, 1.00979089]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.636748464236313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), old_indices_discarded=array([ 3, 7, 10, 29, 30, 33, 34, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), model=ScalarModel(intercept=0.6129958444671166, linear_terms=array([0.00125322, 0.0420077 ]), square_terms=array([[7.02244693e-05, 3.72273429e-03], - [3.72273429e-03, 2.29436197e-01]]), scale=0.029609069851671544, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=44, candidate_x=array([8.80679818, 1.00940247]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-1.1780504359812205, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), old_indices_discarded=array([35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 39, 41, 43, 44]), model=ScalarModel(intercept=0.6059146038962105, linear_terms=array([0.00063063, 0.00133693]), square_terms=array([[1.93356047e-05, 1.31514692e-03], - [1.31514692e-03, 1.01583452e-01]]), scale=0.014804534925835772, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=45, candidate_x=array([8.82168485, 1.01432664]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=1.020928649746183, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 39, 41, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.014805816548052337, relative_step_length=1.0000865695695937, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6115555266177718, linear_terms=array([0.00124126, 0.04101205]), square_terms=array([[7.04241015e-05, 3.73186729e-03], - [3.73186729e-03, 2.30210452e-01]]), scale=0.029609069851671544, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=46, candidate_x=array([8.79199479, 1.00954484]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=-1.230884779808124, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), old_indices_discarded=array([32, 35, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6052045803357664, linear_terms=array([5.75893275e-04, 2.08251016e-05]), square_terms=array([[2.14888603e-05, 1.39552818e-03], - [1.39552818e-03, 1.02157333e-01]]), scale=0.014804534925835772, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=47, candidate_x=array([8.80688164, 1.01452473]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=1.1411150863578703, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.014804535502910653, relative_step_length=1.0000000389796022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.6105358682194937, linear_terms=array([0.00053509, 0.04163391]), square_terms=array([[1.09771507e-04, 4.72352439e-03], - [4.72352439e-03, 2.30297392e-01]]), scale=0.029609069851671544, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=48, candidate_x=array([8.8363726 , 1.00857494]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=-2.17105462493869, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([32, 37, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6045817009488239, linear_terms=array([ 0.00060537, -0.00012916]), square_terms=array([[2.04142540e-05, 1.36233150e-03], - [1.36233150e-03, 1.02744088e-01]]), scale=0.014804534925835772, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - 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44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.01480454508906136, relative_step_length=1.0000006864940802, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.79207864, 1.01473837]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.6039320017441308, linear_terms=array([1.22941931e-03, 2.77206681e-05]), square_terms=array([[7.95912011e-05, 5.38269834e-03], - [5.38269834e-03, 4.12173721e-01]]), scale=0.029609069851671544, shift=array([8.79207864, 1.01473837])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=50, candidate_x=array([8.76247205, 1.01512188]), index=50, x=array([8.76247205, 1.01512188]), fval=0.6024855719631016, rho=1.1053283609927005, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), old_indices_discarded=array([13, 32, 37, 39, 42]), step_length=0.02960907001757359, relative_step_length=1.000000005603082, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.76247205, 1.01512188]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.6026578745853444, linear_terms=array([0.0024301, 0.0002779]), square_terms=array([[3.16909370e-04, 2.15042159e-02], - [2.15042159e-02, 1.65484964e+00]]), scale=0.05921813970334309, shift=array([8.76247205, 1.01512188])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=51, candidate_x=array([8.70325877, 1.0158803 ]), index=51, x=array([8.70325877, 1.0158803 ]), fval=0.5997907962729061, rho=1.1192868878296924, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), old_indices_discarded=array([ 3, 7, 10, 13, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 48]), step_length=0.05921814073463714, relative_step_length=1.0000000174151713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), model=ScalarModel(intercept=0.5745601532643059, linear_terms=array([ 0.01933147, -0.20828263]), square_terms=array([[ 3.96552248e-04, -7.70697193e-03], - [-7.70697193e-03, 3.60952666e+00]]), scale=array([0.10496142, 0.09454056]), shift=array([8.70325877, 1.00545944])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=52, candidate_x=array([8.59829735, 1.01071291]), index=51, x=array([8.70325877, 1.0158803 ]), fval=0.5997907962729061, rho=-0.2234956303402016, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), old_indices_discarded=array([ 1, 3, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, - 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), model=ScalarModel(intercept=0.5848648139367352, linear_terms=array([ 0.01259792, -0.01987223]), square_terms=array([[ 2.31944576e-04, -1.32546665e-02], - [-1.32546665e-02, 2.24249333e+00]]), scale=0.05921813970334309, shift=array([8.70325877, 1.0158803 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=53, candidate_x=array([8.64403857, 1.01605409]), index=53, x=array([8.64403857, 1.01605409]), fval=0.5974935659720187, rho=0.1838940908968012, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), old_indices_discarded=array([ 3, 13, 31, 32, 35, 36, 37, 38, 39, 41, 44, 45, 47, 48, 52]), step_length=0.05922045545199665, relative_step_length=1.0000391053934683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.64403857, 1.01605409]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6233574060676437, linear_terms=array([ 0.01619734, -0.70631774]), square_terms=array([[2.97413525e-04, 7.75950577e-04], - [7.75950577e-04, 5.79975163e+00]]), scale=array([0.10496142, 0.09445367]), shift=array([8.64403857, 1.00554633])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=54, candidate_x=array([8.53907715, 1.01706193]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=0.26745528391814205, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, - 46, 47, 48, 49]), step_length=0.10496625831674383, relative_step_length=0.8862677791178409, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.7023662250726452, linear_terms=array([ 0.06492856, -1.1147206 ]), square_terms=array([[ 3.47923203e-03, -8.06332216e-02], - [-8.06332216e-02, 3.61802648e+00]]), scale=array([0.20992284, 0.14643046]), shift=array([8.53907715, 0.95356954])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=55, candidate_x=array([8.32915431, 0.99542161]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=-1.7530958161469754, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, - 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, - 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.615086708073995, linear_terms=array([ 0.00978289, -0.72244786]), square_terms=array([[4.17284358e-04, 3.04748646e-02], - [3.04748646e-02, 5.74255506e+00]]), scale=array([0.10496142, 0.09394975]), shift=array([8.53907715, 1.00605025])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=56, candidate_x=array([8.43411573, 1.01836827]), index=56, x=array([8.43411573, 1.01836827]), fval=0.5885914443329772, rho=0.3283224223143453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, - 45, 46, 47, 48, 49, 50]), step_length=0.10496954879325875, relative_step_length=0.8862955617916246, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43411573, 1.01836827]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=1.9169446392811247, linear_terms=array([-0.05516787, -6.07713486]), square_terms=array([[2.74937852e-03, 1.66261876e-01], - [1.66261876e-01, 1.36583309e+01]]), scale=array([0.20992284, 0.14577728]), shift=array([8.43411573, 0.95422272])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=57, candidate_x=array([8.22419289, 1.02085937]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=0.46355807804061855, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, - 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, - 45, 46, 47, 48, 49, 50]), step_length=0.2099376195491761, relative_step_length=0.8862893219918404, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=3.341466430284669, linear_terms=array([-1.05103033, -7.77084409]), square_terms=array([[ 0.18279549, 1.34509322], - [ 1.34509322, 11.10009818]]), scale=array([0.41984568, 0.24949316]), shift=array([8.22419289, 0.85050684])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=58, candidate_x=array([8.64403857, 0.99493627]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=-1.0936320536105784, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, - 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.9832433701127465, linear_terms=array([-0.11608732, -6.1156747 ]), square_terms=array([[5.73554046e-03, 2.62479555e-01], - [2.62479555e-01, 1.32048414e+01]]), scale=array([0.20992284, 0.14453174]), shift=array([8.22419289, 0.95546826])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=59, candidate_x=array([8.01427005, 1.02527945]), index=59, x=array([8.01427005, 1.02527945]), fval=0.5734330166298551, rho=1.093147952169101, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 1, 3, 13, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, - 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 58]), step_length=0.20996936856056853, relative_step_length=0.8864233561389423, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.01427005, 1.02527945]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=9.981376675556422, linear_terms=array([ -0.67948996, -26.92467299]), square_terms=array([[2.66781078e-02, 9.72846991e-01], - [9.72846991e-01, 3.85178573e+01]]), scale=array([0.41984568, 0.24728311]), shift=array([8.01427005, 0.85271689])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=60, candidate_x=array([7.90516021, 1.02719534]), index=60, x=array([7.90516021, 1.02719534]), fval=0.570654333265082, rho=28.312992905781908, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, - 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.1091266633945737, relative_step_length=0.23034889296854485, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.90516021, 1.02719534]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=6.968668322923406, linear_terms=array([ -0.3193095 , -18.62779191]), square_terms=array([[1.10008541e-02, 4.89131866e-01], - [4.89131866e-01, 2.70983093e+01]]), scale=array([0.41984568, 0.24632517]), shift=array([7.90516021, 0.85367483])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=61, candidate_x=array([7.53569564, 1.02680097]), index=61, x=array([7.53569564, 1.02680097]), fval=0.557843378100565, rho=0.7126019978012866, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, - 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.36946477399899075, relative_step_length=0.7798809111740238, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53569564, 1.02680097]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), model=ScalarModel(intercept=12.13842084066151, linear_terms=array([ -0.90389113, -30.58695776]), square_terms=array([[ 0.04679947, 1.22766505], - [ 1.22766505, 40.38597232]]), scale=array([0.83969136, 0.3 ]), shift=array([7.53569564, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=62, candidate_x=array([6.69600428, 1.03632926]), index=62, x=array([6.69600428, 1.03632926]), fval=0.5424834330682993, rho=0.7246036090517496, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, - 53, 58]), step_length=0.8397454168164292, relative_step_length=0.88628398011064, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69600428, 1.03632926]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), model=ScalarModel(intercept=13.436478042611053, linear_terms=array([ -1.77594432, -32.96460288]), square_terms=array([[ 0.1709616 , 2.30049165], - [ 2.30049165, 42.12835475]]), scale=array([1.67938272, 0.3 ]), shift=array([6.69600428, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=63, candidate_x=array([6.58843012, 1.03594893]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=0.501065213354614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, - 51, 53, 58]), step_length=0.10757483992757301, relative_step_length=0.05676831059836341, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=13.527389310158597, linear_terms=array([ -1.76412438, -33.07272766]), square_terms=array([[ 0.17044821, 2.26794554], - [ 2.26794554, 42.11033571]]), scale=array([1.67938272, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=64, candidate_x=array([5.99457255, 1.04132824]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-2.858177349740313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, - 51, 52, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=13.033433276409284, linear_terms=array([ -0.7928235 , -31.91157346]), square_terms=array([[3.91876133e-02, 1.01921230e+00], - [1.01921230e+00, 4.07663306e+01]]), scale=array([0.83969136, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=65, candidate_x=array([6.28160798, 1.03757835]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.670491030486846, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, - 51, 52, 53, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=7.57645714444444, linear_terms=array([ -0.29574748, -19.22814953]), square_terms=array([[9.87830891e-03, 4.07101559e-01], - [4.07101559e-01, 2.62853990e+01]]), scale=array([0.41984568, 0.24194837]), shift=array([6.58843012, 0.85805163])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=66, candidate_x=array([6.34718361, 1.03719355]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.608088619606695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 30, 31, 32, 35, 37, 38, 39, 41, 42, 43, 44, 45, - 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=2.51780763816455, linear_terms=array([-0.09621641, -7.35783746]), square_terms=array([[3.42399524e-03, 1.80214137e-01], - [1.80214137e-01, 1.37071817e+01]]), scale=array([0.20992284, 0.13698695]), shift=array([6.58843012, 0.96301305])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=67, candidate_x=array([6.48488598, 1.03743422]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.0724059676808537, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 62, 63, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67]), model=ScalarModel(intercept=0.7479032481802277, linear_terms=array([-0.01574272, -1.69550858]), square_terms=array([[8.99601089e-04, 6.45218243e-02], - [6.45218243e-02, 6.96729079e+00]]), scale=array([0.10496142, 0.08450624]), shift=array([6.58843012, 1.01549376])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=68, candidate_x=array([6.60273304, 1.03595193]), index=68, x=array([6.60273304, 1.03595193]), fval=0.5414533040238657, rho=1.1451990693564231, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.014302919721513424, relative_step_length=0.12076468285870495, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.60273304, 1.03595193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.7438421645946036, linear_terms=array([-0.01554274, -1.67681543]), square_terms=array([[8.98462125e-04, 6.42713904e-02], - [6.42713904e-02, 6.94910166e+00]]), scale=array([0.10496142, 0.08450474]), shift=array([6.60273304, 1.01549526])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=69, candidate_x=array([6.61449989, 1.0357986 ]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=10.755716308584471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.011767854290167887, relative_step_length=0.09936021588249544, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.7410229267529151, linear_terms=array([-0.01539784, -1.66026198]), square_terms=array([[8.96829948e-04, 6.39617448e-02], - [6.39617448e-02, 6.90724557e+00]]), scale=array([0.10496142, 0.08458141]), shift=array([6.61449989, 1.01541859])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=70, candidate_x=array([6.62265463, 1.03568817]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-22.25987391656959, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.5414839920684693, linear_terms=array([4.15483856e-05, 3.93468494e-03]), square_terms=array([[2.84874482e-04, 2.52791748e-02], - [2.52791748e-02, 3.36942768e+00]]), scale=0.05921813970334309, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=71, candidate_x=array([6.60701913, 1.03578557]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-16.79541545743258, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.5414615742402571, linear_terms=array([8.4752230e-05, 4.0438811e-03]), square_terms=array([[6.98826564e-05, 6.29275999e-03], - [6.29275999e-03, 8.41875305e-01]]), scale=0.029609069851671544, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 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candidate_index=72, candidate_x=array([6.58489072, 1.03587769]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-1.588880786010452, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.5414393947813702, linear_terms=array([-2.11246402e-05, -1.93873062e-03]), square_terms=array([[1.87338726e-05, 1.64576236e-03], - [1.64576236e-03, 2.06670312e-01]]), scale=0.014804534925835772, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=73, candidate_x=array([6.62945645, 1.03581837]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-10.089924952581605, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.007402267462917886, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.5414619458203902, linear_terms=array([6.9562924e-06, 8.5447900e-04]), square_terms=array([[4.61608585e-06, 4.03102152e-04], - [4.03102152e-04, 5.06614459e-02]]), scale=0.007402267462917886, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.003701133731458943, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 69, 70, 71, 73, 74]), model=ScalarModel(intercept=0.5414709833059852, linear_terms=array([ 2.31365098e-06, -2.97464372e-04]), square_terms=array([[1.14990591e-06, 1.00511445e-04], - [1.00511445e-04, 1.26982949e-02]]), scale=0.003701133731458943, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 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69, 70, 71, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0018505668657294715, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 70, 71, 74, 75]), model=ScalarModel(intercept=0.5414505447289019, linear_terms=array([-8.39195189e-06, -4.82590678e-04]), square_terms=array([[2.93663647e-07, 2.57520778e-05], - [2.57520778e-05, 3.23949356e-03]]), scale=0.0018505668657294715, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0009252834328647358, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 75, 76]), model=ScalarModel(intercept=0.5414101559834426, linear_terms=array([6.06884618e-06, 6.19553068e-04]), square_terms=array([[7.09654175e-08, 5.99044034e-06], - [5.99044034e-06, 7.63676033e-04]]), scale=0.0009252834328647358, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0004626417164323679, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 76, 77]), model=ScalarModel(intercept=0.5414101559834427, linear_terms=array([ 5.92027706e-05, -8.95483752e-05]), square_terms=array([[1.36984529e-08, 3.03717688e-07], - [3.03717688e-07, 2.08099140e-04]]), scale=0.0004626417164323679, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 77, 78]), model=ScalarModel(intercept=0.5414101559834427, linear_terms=array([-3.31705191e-05, 8.65285911e-06]), square_terms=array([[2.22032765e-08, 8.67788726e-07], - [8.67788726e-07, 5.06632311e-05]]), scale=0.00023132085821618394, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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square_terms=array([[5.66196704e-10, 3.16088422e-08], - [3.16088422e-08, 3.25790992e-06]]), scale=5.7830214554045985e-05, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 6, 7, 9, 10, 11, 12, 13, 14]), old_indices_discarded=array([1, 2, 3, 4, 5, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.252107, 1. ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=0.4913066605981889, linear_terms=array([0.01109994, 0.20097942]), square_terms=array([[0.01277879, 0.08046678], - [0.08046678, 0.57798568]]), scale=array([0.10249333, 0.10124666]), shift=array([9.252107 , 0.99875334])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.252107, 1. ]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=0.6662963313940111, linear_terms=array([-0.15809224, -0.38864171]), square_terms=array([[0.08582577, 0.3626811 ], - [0.3626811 , 1.64746182]]), scale=0.05782566872718589, shift=array([9.252107, 1. ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=17, candidate_x=array([9.31141951, 1.00056177]), index=17, x=array([9.31141951, 1.00056177]), fval=0.6408823601480558, rho=0.012603910502834279, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.059315175075835086, relative_step_length=1.0257585667651592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.31141951, 1.00056177]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=0.5717127918231406, linear_terms=array([-0.03750891, -0.05450237]), square_terms=array([[0.01170099, 0.06888337], - [0.06888337, 0.44451557]]), scale=0.028912834363592946, shift=array([9.31141951, 1.00056177])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=18, candidate_x=array([9.3405367 , 0.99965144]), index=17, x=array([9.31141951, 1.00056177]), fval=0.6408823601480558, rho=-0.09273270389117051, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.31141951, 1.00056177]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18]), model=ScalarModel(intercept=0.641670882731942, linear_terms=array([ 0.0001693, -0.0200567]), square_terms=array([[1.68433034e-05, 9.19250943e-04], - [9.19250943e-04, 5.69319975e-02]]), scale=0.014456417181796473, shift=array([9.31141951, 1.00056177])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6012358381863223, linear_terms=array([-0.03042216, -0.04242261]), square_terms=array([[0.0084916 , 0.0594053 ], - [0.0594053 , 0.45360295]]), scale=0.028912834363592946, shift=array([9.2970465 , 1.00584119])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=20, candidate_x=array([9.32606997, 1.00479922]), index=19, x=array([9.2970465 , 1.00584119]), fval=0.6292604132865477, rho=-0.08086081452522284, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2970465 , 1.00584119]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6292100231114055, linear_terms=array([ 0.00052506, -0.01570005]), square_terms=array([[1.75334423e-05, 1.15319018e-03], - [1.15319018e-03, 8.52466648e-02]]), scale=0.014456417181796473, shift=array([9.2970465 , 1.00584119])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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[0.05731219, 0.45543714]]), scale=0.028912834363592946, shift=array([9.28262709, 1.00867427])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - 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shift=array([9.28262709, 1.00867427])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=23, candidate_x=array([9.2681721 , 1.00887763]), index=23, x=array([9.2681721 , 1.00887763]), fval=0.626016100574723, rho=0.9614924551145125, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014456422294036085, relative_step_length=1.0000003536311624, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2681721 , 1.00887763]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6302545671695479, linear_terms=array([0.00190945, 0.02595137]), square_terms=array([[4.59886870e-05, 3.02614441e-03], - [3.02614441e-03, 2.38414991e-01]]), scale=0.028912834363592946, shift=array([9.2681721 , 1.00887763])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=24, candidate_x=array([9.2392219 , 1.00611615]), index=23, x=array([9.2681721 , 1.00887763]), fval=0.626016100574723, rho=-0.27020250132363843, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2681721 , 1.00887763]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6260945388463117, linear_terms=array([ 7.51624753e-04, -5.54521229e-05]), square_terms=array([[1.66698146e-05, 1.13418053e-03], - [1.13418053e-03, 8.70860695e-02]]), scale=0.014456417181796473, shift=array([9.2681721 , 1.00887763])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=25, candidate_x=array([9.25371701, 1.00907341]), index=25, x=array([9.25371701, 1.00907341]), fval=0.6253205234400713, rho=0.9257747492589474, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014456419134255051, relative_step_length=1.000000135058262, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25371701, 1.00907341]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6253360883435206, linear_terms=array([1.50448575e-03, 8.41583250e-05]), square_terms=array([[6.56507364e-05, 4.50340306e-03], - [4.50340306e-03, 3.49079339e-01]]), scale=0.028912834363592946, shift=array([9.25371701, 1.00907341])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=26, candidate_x=array([9.22480647, 1.00943785]), index=26, x=array([9.22480647, 1.00943785]), fval=0.6238879213243532, rho=0.955377431374471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([12, 16, 18]), step_length=0.028912835624545444, relative_step_length=1.0000000436122063, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.22480647, 1.00943785]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6238467663285244, linear_terms=array([0.00300031, 0.00025396]), square_terms=array([[2.60662493e-04, 1.79499400e-02], - [1.79499400e-02, 1.39799232e+00]]), scale=0.05782566872718589, shift=array([9.22480647, 1.00943785])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=27, candidate_x=array([9.16698541, 1.0101682 ]), index=27, x=array([9.16698541, 1.0101682 ]), fval=0.6211424674970292, rho=0.9207546180217053, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 18, 20]), step_length=0.05782566998640155, relative_step_length=1.0000000217760674, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.16698541, 1.0101682 ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6293604631753875, linear_terms=array([ 0.00168088, -0.25402434]), square_terms=array([[7.94437328e-04, 5.20961396e-02], - [5.20961396e-02, 3.88207149e+00]]), scale=array([0.10249333, 0.09616256]), shift=array([9.16698541, 1.00383744])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=28, candidate_x=array([9.06449208, 1.01142033]), index=28, x=array([9.06449208, 1.01142033]), fval=0.6161874288905664, rho=0.9826654333073533, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 18, 20]), step_length=0.10250097734076673, relative_step_length=0.8862930563272275, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.06449208, 1.01142033]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3269180344206313, linear_terms=array([-0.0530022 , -3.58500408]), square_terms=array([[3.16437378e-03, 1.58383379e-01], - [1.58383379e-01, 9.04125674e+00]]), scale=array([0.20498666, 0.14678316]), shift=array([9.06449208, 0.95321684])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=29, candidate_x=array([8.85950542, 1.01399005]), index=29, x=array([8.85950542, 1.01399005]), fval=0.6068057893338722, rho=0.9768016993555535, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 22]), step_length=0.20500276489564104, relative_step_length=0.8862965591579142, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.85950542, 1.01399005]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 21, 22, 24, 27, 28, 29]), model=ScalarModel(intercept=5.056588947128265, linear_terms=array([ 0.12014523, -14.39462599]), square_terms=array([[ 4.11394441e-03, -9.76345780e-02], - [-9.76345780e-02, 2.30839568e+01]]), scale=array([0.40997332, 0.24799163]), shift=array([8.85950542, 0.85200837])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=30, candidate_x=array([8.5580007 , 1.00630498]), index=29, x=array([8.85950542, 1.01399005]), fval=0.6068057893338722, rho=-0.3014966051278916, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 21, 22, 24, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, - 20, 23, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.85950542, 1.01399005]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 13, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.9805939633807261, linear_terms=array([-0.03836743, -1.98882772]), square_terms=array([[3.07342158e-03, 1.18191343e-01], - [1.18191343e-01, 5.34723779e+00]]), scale=array([0.20498666, 0.1454983 ]), shift=array([8.85950542, 0.9545017 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=31, candidate_x=array([8.65451876, 1.01183367]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=0.4338323431380853, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 13, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 1, 3, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, - 23]), step_length=0.20499800027263237, relative_step_length=0.8862759600748705, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=2.6746347212609862, linear_terms=array([ 0.41935614, -7.16647929]), square_terms=array([[ 0.03391117, -0.55595491], - [-0.55595491, 12.02901321]]), scale=array([0.40997332, 0.24906982]), shift=array([8.65451876, 0.85093018])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=32, candidate_x=array([8.24454545, 0.98780609]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.992201463503947, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 27, 28, 29, 30, 31]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, - 21, 22, 23, 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.8053556101878039, linear_terms=array([ 0.0500788 , -1.40901645]), square_terms=array([[ 1.81988958e-03, -5.72982752e-02], - [-5.72982752e-02, 4.04450417e+00]]), scale=array([0.20498666, 0.14657649]), shift=array([8.65451876, 0.95342351])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=33, candidate_x=array([8.44953211, 1.002411 ]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.2944780565240657, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, - 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 28, 29, 30, 31, 33]), model=ScalarModel(intercept=0.5580455956991042, linear_terms=array([0.02153789, 0.02119119]), square_terms=array([[ 0.0023124 , -0.05543391], - [-0.05543391, 1.707328 ]]), scale=array([0.10249333, 0.09532983]), shift=array([8.65451876, 1.00467017])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=34, candidate_x=array([8.55202543, 1.00039176]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.872524866103317, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 28, 29, 30, 31, 33]), old_indices_discarded=array([ 1, 26, 27, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 29, 30, 31, 33, 34]), model=ScalarModel(intercept=0.5796812293248299, linear_terms=array([0.01586132, 0.02969879]), square_terms=array([[ 0.00226369, -0.03434719], - [-0.03434719, 0.60957011]]), scale=0.05782566872718589, shift=array([8.65451876, 1.01183367])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=35, candidate_x=array([8.59693373, 1.00593781]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.1577436588415695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 29, 30, 31, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 31, 34, 35]), model=ScalarModel(intercept=0.6034966185125235, linear_terms=array([-0.00034619, -0.03914939]), square_terms=array([[9.39236593e-05, 5.48779016e-03], - [5.48779016e-03, 3.65303262e-01]]), scale=0.028912834363592946, shift=array([8.65451876, 1.01183367])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=36, candidate_x=array([8.62565678, 1.0153636 ]), index=36, x=array([8.62565678, 1.0153636 ]), fval=0.5971978537018936, rho=2.442794438040451, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.02907704145844469, relative_step_length=1.0056793842065692, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.62565678, 1.0153636 ]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 31, 33, 34, 35, 36]), model=ScalarModel(intercept=0.5603293659645237, linear_terms=array([-0.00236478, 0.12100806]), square_terms=array([[0.00302626, 0.06759657], - [0.06759657, 1.59874941]]), scale=0.05782566872718589, shift=array([8.62565678, 1.0153636 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=37, candidate_x=array([8.68324715, 1.00858284]), index=36, x=array([8.62565678, 1.0153636 ]), fval=0.5971978537018936, rho=-1.2581050712790383, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 30, 31, 33, 34, 35, 36]), old_indices_discarded=array([ 3, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.62565678, 1.0153636 ]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 31, 34, 35, 36, 37]), model=ScalarModel(intercept=0.5981101022806663, linear_terms=array([ 0.00137081, -0.01014437]), square_terms=array([[6.44240543e-05, 4.61221760e-03], - [4.61221760e-03, 3.91431444e-01]]), scale=0.028912834363592946, shift=array([8.62565678, 1.0153636 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=38, candidate_x=array([8.59675474, 1.01644935]), index=38, x=array([8.59675474, 1.01644935]), fval=0.5956021578624653, rho=0.987318065496306, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.02892243332840774, relative_step_length=1.0003319966729682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.59675474, 1.01644935]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 31, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.5967490097134395, linear_terms=array([0.00266649, 0.00222244]), square_terms=array([[2.84220825e-04, 1.95806140e-02], - [1.95806140e-02, 1.56758521e+00]]), scale=0.05782566872718589, shift=array([8.59675474, 1.01644935])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=39, candidate_x=array([8.53893254, 1.01708857]), index=39, x=array([8.53893254, 1.01708857]), fval=0.5930797801438898, rho=0.9626177327032385, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 31, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 3, 7, 13, 29]), step_length=0.05782572833136036, relative_step_length=1.0000010307563367, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53893254, 1.01708857]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 31, 33, 34, 35, 36, 38, 39]), model=ScalarModel(intercept=0.6164032682482006, linear_terms=array([-0.0014308, -0.4276386]), square_terms=array([[9.03112375e-04, 5.61729802e-02], - [5.61729802e-02, 4.04976927e+00]]), scale=array([0.10249333, 0.09270238]), shift=array([8.53893254, 1.00729762])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=40, candidate_x=array([8.43643921, 1.01837244]), index=40, x=array([8.43643921, 1.01837244]), fval=0.5886586453047548, rho=0.9960093815093017, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 31, 33, 34, 35, 36, 38, 39]), old_indices_discarded=array([ 1, 3, 7, 13, 28, 29, 32, 37]), step_length=0.10250137012000532, relative_step_length=0.8862964525632524, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43643921, 1.01837244]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 31, 32, 35, 36, 39, 40]), model=ScalarModel(intercept=0.724464510197967, linear_terms=array([ 0.0588604 , -1.15277065]), square_terms=array([[ 2.63888902e-03, -4.80501605e-02], - [-4.80501605e-02, 3.54022042e+00]]), scale=array([0.20498666, 0.14330711]), shift=array([8.43643921, 0.95669289])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=41, candidate_x=array([8.23145255, 1.00141166]), index=40, x=array([8.43643921, 1.01837244]), fval=0.5886586453047548, rho=-1.288535811487196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 31, 32, 35, 36, 39, 40]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, - 26, 27, 28, 29, 33, 34, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43643921, 1.01837244]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 33, 34, 35, 38, 39, 40]), model=ScalarModel(intercept=0.6190068223081573, linear_terms=array([-0.00232714, -0.74138835]), square_terms=array([[9.27865058e-04, 6.83995451e-02], - [6.83995451e-02, 5.89689117e+00]]), scale=array([0.10249333, 0.09206044]), shift=array([8.43643921, 1.00793956])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=42, candidate_x=array([8.33394588, 1.02058172]), index=42, x=array([8.33394588, 1.02058172]), fval=0.5842263244969528, rho=0.6656700898189453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 33, 34, 35, 38, 39, 40]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 36, 37, 41]), step_length=0.10251713719284976, relative_step_length=0.8864327853821501, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.33394588, 1.02058172]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 32, 33, 39, 40, 41, 42]), model=ScalarModel(intercept=0.8040646506922852, linear_terms=array([-0.16272345, -1.28751653]), square_terms=array([[0.03910645, 0.3480799 ], - [0.3480799 , 3.42094897]]), scale=array([0.20498666, 0.14220247]), shift=array([8.33394588, 0.95779753])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=43, candidate_x=array([8.53893254, 0.99684816]), index=42, x=array([8.33394588, 1.02058172]), fval=0.5842263244969528, rho=-2.655032004787166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 32, 33, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, - 26, 27, 28, 29, 31, 34, 35, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.33394588, 1.02058172]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 33, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.5905582188830226, linear_terms=array([ 0.00921118, -0.6113136 ]), square_terms=array([[4.08156812e-04, 2.99902373e-02], - [2.99902373e-02, 5.44560063e+00]]), scale=array([0.10249333, 0.09095581]), shift=array([8.33394588, 1.00904419])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=44, candidate_x=array([8.23145255, 1.01975565]), index=44, x=array([8.23145255, 1.01975565]), fval=0.5809782664932625, rho=0.24916298067878628, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 33, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 1, 3, 13, 29, 30, 31, 34, 35, 36, 37, 38]), step_length=0.10249665809363835, relative_step_length=0.8862557091834465, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.23145255, 1.01975565]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 32, 33, 40, 41, 42, 44]), model=ScalarModel(intercept=0.9956483394129002, linear_terms=array([-0.42250664, -1.67357518]), square_terms=array([[0.14817312, 0.69125356], - [0.69125356, 3.60029371]]), scale=array([0.20498666, 0.1426155 ]), shift=array([8.23145255, 0.9573845 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=45, candidate_x=array([8.43643921, 0.9962964 ]), index=44, x=array([8.23145255, 1.01975565]), fval=0.5809782664932625, rho=-1.1980641129471428, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 32, 33, 40, 41, 42, 44]), old_indices_discarded=array([ 0, 1, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, - 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.23145255, 1.01975565]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 33, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.600332066842147, linear_terms=array([-0.00086165, -0.70849018]), square_terms=array([[8.18384405e-04, 5.99221947e-02], - [5.99221947e-02, 5.42443420e+00]]), scale=array([0.10249333, 0.09136884]), shift=array([8.23145255, 1.00863116])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=46, candidate_x=array([8.12895922, 1.02157425]), index=46, x=array([8.12895922, 1.02157425]), fval=0.5767016574644672, rho=0.6023906618426587, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 33, 40, 41, 42, 44, 45]), old_indices_discarded=array([ 1, 3, 30, 31, 34, 35, 36, 37, 38, 39, 43]), step_length=0.10250946224206313, relative_step_length=0.8863664225457526, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.12895922, 1.02157425]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 40, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=1.8860503514071725, linear_terms=array([-0.11681592, -5.841289 ]), square_terms=array([[5.93972205e-03, 2.64135430e-01], - [2.64135430e-01, 1.28731339e+01]]), scale=array([0.20498666, 0.1417062 ]), shift=array([8.12895922, 0.9582938 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=47, candidate_x=array([7.92397257, 1.02550171]), index=47, x=array([7.92397257, 1.02550171]), fval=0.5700309839906248, rho=2.144194285592646, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 40, 41, 42, 44, 45, 46]), old_indices_discarded=array([ 0, 1, 3, 13, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, - 36, 37, 38, 39, 43]), step_length=0.2050242792857033, relative_step_length=0.8863895731711363, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.92397257, 1.02550171]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 40, 41, 42, 44, 46, 47]), model=ScalarModel(intercept=9.701952266205492, linear_terms=array([ -0.61691483, -26.37740675]), square_terms=array([[2.29677817e-02, 8.91869339e-01], - [8.91869339e-01, 3.80689975e+01]]), scale=array([0.40997332, 0.2422358 ]), shift=array([7.92397257, 0.8577642 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=48, candidate_x=array([7.81188801, 1.02714111]), index=48, x=array([7.81188801, 1.02714111]), fval=0.5665120380760114, rho=16.57798751629955, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 40, 41, 42, 44, 46, 47]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, - 37, 38, 39, 43, 45]), step_length=0.1120965490592223, relative_step_length=0.24231572138854002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.81188801, 1.02714111]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 41, 42, 44, 46, 47, 48]), model=ScalarModel(intercept=9.766219925631294, linear_terms=array([ -0.6871664 , -26.30219402]), square_terms=array([[2.73943250e-02, 9.72602328e-01], - [9.72602328e-01, 3.76072310e+01]]), scale=array([0.40997332, 0.2414161 ]), shift=array([7.81188801, 0.8585839 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=49, candidate_x=array([8.22186132, 1.02118484]), index=48, x=array([7.81188801, 1.02714111]), fval=0.5665120380760114, rho=-2.3061269690172246, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 41, 42, 44, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, - 37, 38, 39, 40, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.81188801, 1.02714111]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 32, 41, 42, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=1.4654889570621736, linear_terms=array([-0.05047381, -4.00812911]), square_terms=array([[2.45887025e-03, 1.30332665e-01], - [1.30332665e-01, 8.90254327e+00]]), scale=array([0.20498666, 0.13892277]), shift=array([7.81188801, 0.96107723])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=50, candidate_x=array([7.60690135, 1.02565727]), index=50, x=array([7.60690135, 1.02565727]), fval=0.5602123257733056, rho=0.5841994228455609, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 32, 41, 42, 44, 46, 47, 48, 49]), old_indices_discarded=array([ 1, 3, 7, 13, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45]), step_length=0.20499202897310145, relative_step_length=0.886250144119507, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.60690135, 1.02565727]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([32, 41, 42, 44, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=7.123730519114742, linear_terms=array([ -0.32734999, -18.89524377]), square_terms=array([[1.11231711e-02, 4.88743382e-01], - [4.88743382e-01, 2.71922839e+01]]), scale=array([0.40997332, 0.24215803]), shift=array([7.60690135, 0.85784197])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=51, candidate_x=array([7.19692803, 1.03046403]), index=51, x=array([7.19692803, 1.03046403]), fval=0.549027411692029, rho=1.0036558314595654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([32, 41, 42, 44, 46, 47, 48, 49, 50]), old_indices_discarded=array([ 0, 1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, - 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, - 40, 43, 45]), step_length=0.4100014945883672, relative_step_length=0.8862878363817586, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.19692803, 1.03046403]), radius=0.9252106996349743, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([32, 41, 44, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=12.73480276563746, linear_terms=array([ -0.87087078, -31.77015852]), square_terms=array([[ 0.042882 , 1.15994563], - [ 1.15994563, 41.41239327]]), scale=array([0.81994663, 0.3 ]), shift=array([7.19692803, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=52, candidate_x=array([6.3769814 , 1.03855253]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=0.4587040426302159, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([32, 41, 44, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, - 35, 36, 37, 38, 39, 40, 42, 43, 45]), step_length=0.8199865279119269, relative_step_length=0.8862700444725059, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=1.8504213992699485, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([41, 44, 46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=14.222023285880177, linear_terms=array([ -1.72856364, -34.55682834]), square_terms=array([[ 0.15979702, 2.19225084], - [ 2.19225084, 43.64084662]]), scale=array([1.63989327, 0.3 ]), shift=array([6.3769814, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=53, candidate_x=array([6.13394911, 1.03978719]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-4.2277721907011685, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 44, 46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.9252106996349743, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([44, 46, 47, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=15.125592672308542, linear_terms=array([ -0.92742527, -36.66916769]), square_terms=array([[4.28554863e-02, 1.16804426e+00], - [1.16804426e+00, 4.61029190e+01]]), scale=array([0.81994663, 0.3 ]), shift=array([6.3769814, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=54, candidate_x=array([6.27747973, 1.03953523]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-13.31771442582917, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 47, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=10.065029563372587, linear_terms=array([ -0.39719221, -25.86425371]), square_terms=array([[1.16915302e-02, 5.40173955e-01], - [5.40173955e-01, 3.51273033e+01]]), scale=array([0.40997332, 0.23571039]), shift=array([6.3769814 , 0.86428961])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=55, candidate_x=array([6.31179845, 1.0384196 ]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-0.784363594729585, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 47, 48, 49, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 1, 3, 7, 10, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, - 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 52, 53, 54, 55]), model=ScalarModel(intercept=2.3920584590081724, linear_terms=array([-0.08950127, -6.81205893]), square_terms=array([[3.19260557e-03, 1.63216758e-01], - [1.63216758e-01, 1.25472782e+01]]), scale=array([0.20498666, 0.13321706]), shift=array([6.3769814 , 0.96678294])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=56, candidate_x=array([6.54738856, 1.0376674 ]), index=56, x=array([6.54738856, 1.0376674 ]), fval=0.5422070151116223, rho=0.9994517465051278, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([51, 52, 53, 54, 55]), old_indices_discarded=array([], dtype=int32), step_length=0.17040945911786537, relative_step_length=0.7367379524905946, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.54738856, 1.0376674 ]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 48, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=12.845919206247771, linear_terms=array([ -0.48580072, -33.75567653]), square_terms=array([[1.32030757e-02, 6.68795751e-01], - [6.68795751e-01, 4.63083580e+01]]), scale=array([0.40997332, 0.23615296]), shift=array([6.54738856, 0.86384704])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=57, candidate_x=array([6.35000299, 1.03762871]), index=56, x=array([6.54738856, 1.0376674 ]), fval=0.5422070151116223, rho=-0.3154098619261682, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([47, 48, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, - 41, 42, 43, 44, 45, 46, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.54738856, 1.0376674 ]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=2.0362311686925794, linear_terms=array([-0.07626161, -5.56401149]), square_terms=array([[2.97339012e-03, 1.42670586e-01], - [1.42670586e-01, 1.03610380e+01]]), scale=array([0.20498666, 0.13365963]), shift=array([6.54738856, 0.96634037])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=58, candidate_x=array([6.47538896, 1.03876377]), index=56, x=array([6.54738856, 1.0376674 ]), fval=0.5422070151116223, rho=-10.672128030780884, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([50, 51, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.54738856, 1.0376674 ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.7236091885770913, linear_terms=array([-0.01427721, -1.59977577]), square_terms=array([[8.28292861e-04, 6.05950908e-02], - [6.05950908e-02, 7.01428649e+00]]), scale=array([0.10249333, 0.08241296]), shift=array([6.54738856, 1.01758704])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=59, candidate_x=array([6.64988189, 1.03567133]), index=59, x=array([6.64988189, 1.03567133]), fval=0.541686191657866, rho=0.45049343577625856, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.10251276421985119, relative_step_length=0.8863949736880112, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.64988189, 1.03567133]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 52, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=2.5086291679932122, linear_terms=array([-0.09264234, -7.47903766]), square_terms=array([[3.27350442e-03, 1.77310087e-01], - [1.77310087e-01, 1.42260200e+01]]), scale=array([0.20498666, 0.13465766]), shift=array([6.64988189, 0.96534234])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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square_terms=array([[3.29232175e-03, 1.89561505e-01], - [1.89561505e-01, 1.70827528e+01]]), scale=array([0.20498666, 0.13397188]), shift=array([6.53909631, 0.96602812])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=61, candidate_x=array([6.66768984, 1.03562877]), index=60, x=array([6.53909631, 1.0370429 ]), fval=0.5416503769197223, rho=-0.4655053649797833, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([52, 53, 54, 55, 56, 57, 58, 59, 60]), old_indices_discarded=array([50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.53909631, 1.0370429 ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.7186345379760894, linear_terms=array([-0.01421716, -1.5209564 ]), square_terms=array([[8.40989846e-04, 6.01480200e-02], - [6.01480200e-02, 6.53773842e+00]]), scale=array([0.10249333, 0.08272521]), shift=array([6.53909631, 1.01727479])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=62, candidate_x=array([6.61897887, 1.03592701]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=0.6103070390488394, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([53]), step_length=0.07989035553686327, relative_step_length=0.6907862658171386, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 56, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=4.084195599052262, linear_terms=array([ -0.15194779, -13.11124864]), square_terms=array([[4.33717818e-03, 2.79546070e-01], - [2.79546070e-01, 2.42461864e+01]]), scale=array([0.20498666, 0.13452982]), shift=array([6.61897887, 0.96547018])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=64, candidate_x=array([6.76282269, 1.03712945]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=-0.822486077235511, accepted=False, new_indices=array([63]), old_indices_used=array([52, 56, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([50, 51, 53, 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 56, 58, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.8537489797787152, linear_terms=array([-0.02180749, -2.41772558]), square_terms=array([[1.03255446e-03, 8.41806686e-02], - [8.41806686e-02, 9.32527369e+00]]), scale=array([0.10249333, 0.08328316]), shift=array([6.61897887, 1.01671684])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=65, candidate_x=array([6.61232843, 1.03835811]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=-0.7324310982234294, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([52, 56, 58, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([51, 53, 54, 55, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.5447400521949106, linear_terms=array([-0.00137124, -0.18835934]), square_terms=array([[3.31269825e-04, 3.32990005e-02], - [3.32990005e-02, 4.49770488e+00]]), scale=0.05782566872718589, shift=array([6.61897887, 1.03592701])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=66, candidate_x=array([6.56122348, 1.03877707]), index=62, x=array([6.61897887, 1.03592701]), fval=0.5415174113025932, rho=-0.7212604565703155, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([52, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61897887, 1.03592701]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 64, 65, 66]), model=ScalarModel(intercept=0.5416031178062954, linear_terms=array([4.51099211e-05, 1.50726294e-03]), square_terms=array([[6.53193566e-05, 5.88023268e-03], - [5.88023268e-03, 7.83342000e-01]]), scale=0.028912834363592946, shift=array([6.61897887, 1.03592701])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=67, candidate_x=array([6.59006647, 1.03608841]), index=67, x=array([6.59006647, 1.03608841]), fval=0.5414379715211688, rho=3.2219246695022767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.028912851176776357, relative_step_length=1.000000581512805, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.59006647, 1.03608841]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 65, 66, 67]), model=ScalarModel(intercept=0.5415547803952224, linear_terms=array([ 1.01377641e-05, -3.95364543e-05]), square_terms=array([[2.63397868e-04, 2.35619632e-02], - [2.35619632e-02, 3.13951084e+00]]), scale=0.05782566872718589, shift=array([6.59006647, 1.03608841])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=68, candidate_x=array([6.58309627, 1.03614145]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=13.747183659712503, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 65, 66, 67]), old_indices_discarded=array([52, 55, 57, 63, 64]), step_length=0.0069704007281480235, relative_step_length=0.1205416362936239, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 59, 60, 61, 62, 65, 66, 67, 68]), model=ScalarModel(intercept=0.5414796419298218, linear_terms=array([0.00011312, 0.00042998]), square_terms=array([[2.70149950e-04, 2.41187611e-02], - [2.41187611e-02, 3.15020230e+00]]), scale=0.05782566872718589, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=69, candidate_x=array([6.52527224, 1.03657627]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-2.4940420439925477, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 59, 60, 61, 62, 65, 66, 67, 68]), old_indices_discarded=array([52, 55, 57, 58, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 59, 60, 62, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.5415011318321497, linear_terms=array([ 1.74351890e-05, -2.75129179e-04]), square_terms=array([[6.75592967e-05, 6.01725598e-03], - [6.01725598e-03, 7.88582554e-01]]), scale=0.028912834363592946, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=70, candidate_x=array([6.55418436, 1.03637223]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-11.73867706052995, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 59, 60, 62, 65, 66, 67, 68, 69]), old_indices_discarded=array([58, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 60, 62, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.5414799019531137, linear_terms=array([-3.44991235e-06, -9.05048244e-05]), square_terms=array([[1.67238718e-05, 1.49276761e-03], - [1.49276761e-03, 1.97781855e-01]]), scale=0.014456417181796473, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=71, candidate_x=array([6.59042582, 1.03609275]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-21.95038108304595, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 60, 62, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0072282085908982364, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 62, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.5414632259336039, linear_terms=array([2.83264621e-06, 2.05882321e-05]), square_terms=array([[4.16386161e-06, 3.73300224e-04], - [3.73300224e-04, 4.94351827e-02]]), scale=0.0072282085908982364, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=72, candidate_x=array([6.57586825, 1.03619302]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-7.761982760893527, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 62, 65, 66, 67, 68, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0036141042954491182, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([67, 68, 71, 72]), model=ScalarModel(intercept=0.5414388981465452, linear_terms=array([2.71386294e-05, 3.84330426e-03]), square_terms=array([[1.04795962e-06, 9.32698230e-05], - [9.32698230e-05, 1.23060327e-02]]), scale=0.0036141042954491182, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=73, candidate_x=array([6.58668802, 1.03498566]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.948451935681875, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 68, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0018070521477245591, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([67, 68, 71, 72, 73]), model=ScalarModel(intercept=0.5414389397529535, linear_terms=array([3.45115217e-07, 6.30331745e-05]), square_terms=array([[2.70925737e-07, 2.39729562e-05], - [2.39729562e-05, 3.08425190e-03]]), scale=0.0018070521477245591, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 73, 74]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([1.41688629e-05, 6.76977808e-05]), square_terms=array([[6.26837304e-08, 5.47876928e-06], - [5.47876928e-06, 7.68259730e-04]]), scale=0.0009035260738622796, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=75, candidate_x=array([6.58219221, 1.03606956]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-3.312310435431627, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0004517630369311398, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 74, 75]), model=ScalarModel(intercept=0.5414293228153648, linear_terms=array([-2.29729551e-06, -2.98650390e-04]), square_terms=array([[1.73350246e-08, 1.53237458e-06], - [1.53237458e-06, 2.01506003e-04]]), scale=0.0004517630369311398, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=76, candidate_x=array([6.58295466, 1.03657045]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.935555347524213, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 74, 75]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0002258815184655699, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 75, 76]), model=ScalarModel(intercept=0.5414293228153644, linear_terms=array([-1.64383026e-05, 4.29476624e-05]), square_terms=array([[5.97250724e-09, 4.20181109e-07], - [4.20181109e-07, 4.91845731e-05]]), scale=0.0002258815184655699, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=77, candidate_x=array([6.58327632, 1.03600203]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.9615211218296009, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 75, 76]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.00011294075923278494, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 76, 77]), model=ScalarModel(intercept=0.5414293228153655, linear_terms=array([4.21812548e-05, 3.81108957e-05]), square_terms=array([[1.41803201e-08, 1.76223696e-07], - [1.76223696e-07, 1.23417525e-05]]), scale=0.00011294075923278494, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=78, candidate_x=array([6.58300196, 1.03607308]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.9288183820802519, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 76, 77]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78]), model=ScalarModel(intercept=0.5414293228153648, linear_terms=array([-1.22674426e-05, -2.40256385e-05]), square_terms=array([[1.26112163e-09, 5.06903136e-08], - [5.06903136e-08, 3.10638384e-06]]), scale=5.647037961639247e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=79, candidate_x=array([6.58311861, 1.03619332]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.12812387344724027, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 77, 78]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([-2.27116547e-05, 1.08555655e-05]), square_terms=array([[4.35976955e-09, 1.69031789e-08], - [1.69031789e-08, 7.57228023e-07]]), scale=2.8235189808196236e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=80, candidate_x=array([6.58312214, 1.03612944]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.35421190384338624, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 78, 79]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=1.4117594904098118e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 79, 80]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([ 4.26256780e-06, -1.29794683e-06]), square_terms=array([[1.87697604e-10, 7.48543093e-10], - [7.48543093e-10, 1.92469128e-07]]), scale=1.4117594904098118e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=81, candidate_x=array([6.58308246, 1.03614548]), index=81, x=array([6.58308246, 1.03614548]), fval=0.5414254298290705, rho=0.85915882442786, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([68, 79, 80]), old_indices_discarded=array([], dtype=int32), step_length=1.4381793867319074e-05, relative_step_length=1.018714162363751, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58308246, 1.03614548]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79, 80, 81]), model=ScalarModel(intercept=0.5414393336189507, linear_terms=array([-6.83138000e-06, -5.43556504e-06]), square_terms=array([[8.38980666e-10, 1.12053013e-08], - [1.12053013e-08, 7.72680051e-07]]), scale=2.8235189808196236e-05, shift=array([6.58308246, 1.03614548])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=82, candidate_x=array([6.58310557, 1.03616229]), index=82, x=array([6.58310557, 1.03616229]), fval=0.5414243786888143, rho=0.12104247845813768, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([68, 78, 79, 80, 81]), old_indices_discarded=array([], dtype=int32), step_length=2.8574644124841038e-05, relative_step_length=1.0120223847953826, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58310557, 1.03616229]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.5414289185236869, linear_terms=array([-9.17816477e-06, -1.57462672e-05]), square_terms=array([[1.32415510e-09, 4.69483871e-08], - [4.69483871e-08, 3.08710853e-06]]), scale=5.647037961639247e-05, shift=array([6.58310557, 1.03616229])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58310557, 1.03616229]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.541436888279065, linear_terms=array([-1.16278729e-05, 4.19591953e-06]), square_terms=array([[9.75111772e-10, 1.22776981e-08], - [1.22776981e-08, 7.67636851e-07]]), scale=2.8235189808196236e-05, shift=array([6.58310557, 1.03616229])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, - 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, - -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, - -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=84, candidate_x=array([6.58313299, 1.03615297]), index=84, x=array([6.58313299, 1.03615297]), fval=0.5414181528663952, rho=0.4925977840854048, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([68, 78, 79, 80, 81, 82, 83]), old_indices_discarded=array([], dtype=int32), step_length=2.8960480777937282e-05, relative_step_length=1.0256874834087535, 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scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=91, candidate_x=array([6.58313347, 1.03615385]), index=91, x=array([6.58313347, 1.03615385]), fval=0.5414180888046741, rho=0.09302207306449327, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([84, 89, 90]), old_indices_discarded=array([], dtype=int32), step_length=9.99999999895458e-07, relative_step_length=0.9999999998954582, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 92 entries., 'history': {'params': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 8.432160362616244, 'DiscFac': 0.5396951369531111}, {'CRRA': 10.07205363008324, 'DiscFac': 0.839644435905758}, {'CRRA': 8.432160362616244, 'DiscFac': 0.8285194430703369}, {'CRRA': 10.07205363008324, 'DiscFac': 1.0999990254130083}, {'CRRA': 10.07205363008324, 'DiscFac': 0.5384287157628791}, {'CRRA': 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[0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=16, candidate_x=array([9.57986377, 0.91029109]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-2.2880982913831005, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 11, 12, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=0.6108530298545867, linear_terms=array([-0.10139668, -0.14696738]), square_terms=array([[0.07792252, 0.32866092], - [0.32866092, 1.49842941]]), scale=0.05921813970334309, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=17, candidate_x=array([9.5340187 , 0.99306208]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-0.3116075291616809, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=0.6954288371659393, linear_terms=array([-0.10174434, -0.15897912]), square_terms=array([[0.03333137, 0.10554599], - [0.10554599, 0.37011472]]), scale=0.029609069851671544, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=18, candidate_x=array([9.50661188, 1.0031676 ]), index=18, x=array([9.50661188, 1.0031676 ]), fval=0.6396317123330025, rho=0.05607187891543219, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.03187372417953975, relative_step_length=1.0764851560421564, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.50661188, 1.0031676 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.6396317123330028, linear_terms=array([ 0.00028177, -0.01430195]), square_terms=array([[2.43882216e-05, 1.35799469e-03], - [1.35799469e-03, 8.34202654e-02]]), scale=0.014804534925835772, shift=array([9.50661188, 1.0031676 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=19, candidate_x=array([9.49185035, 1.0059291 ]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=1.4693827516516844, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.015017609941676606, relative_step_length=1.0143925504521585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6412257536348447, linear_terms=array([-0.06905576, -0.03758833]), square_terms=array([[0.03588925, 0.1094543 ], - [0.1094543 , 0.37040144]]), scale=0.029609069851671544, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=20, candidate_x=array([9.52125862, 1.00094573]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=-0.11096998055275568, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6375966987395302, linear_terms=array([ 0.00059101, -0.00033053]), square_terms=array([[2.18308578e-05, 1.28615347e-03], - [1.28615347e-03, 8.40653356e-02]]), scale=0.014804534925835772, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=21, candidate_x=array([9.47704841, 1.00621177]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=1.2946325204884988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.01480464158988585, relative_step_length=1.0000072048227528, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6740948994884155, linear_terms=array([-0.0775299 , -0.08137508]), square_terms=array([[0.03091198, 0.10157856], - [0.10157856, 0.37039842]]), scale=0.029609069851671544, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=22, candidate_x=array([9.50732214, 1.00462907]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=-0.029474268266789803, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6367076097989959, linear_terms=array([ 6.38911855e-04, -5.64861763e-05]), square_terms=array([[1.99591697e-05, 1.23030124e-03], - [1.23030124e-03, 8.44345370e-02]]), scale=0.014804534925835772, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=23, candidate_x=array([9.46224557, 1.00643568]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=1.1173242341721383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014804537200668226, relative_step_length=1.0000001536578127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6376967017750665, linear_terms=array([-0.00887642, -0.07715602]), square_terms=array([[0.00091426, 0.02162778], - [0.02162778, 0.54194842]]), scale=0.029609069851671544, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=24, candidate_x=array([9.49199772, 1.00943198]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=-0.23439049876600507, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6357439312845821, linear_terms=array([ 0.00057539, -0.00082847]), square_terms=array([[2.06319310e-05, 1.26644672e-03], - [1.26644672e-03, 8.60694145e-02]]), scale=0.014804534925835772, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=25, candidate_x=array([9.44744469, 1.00679353]), index=25, x=array([9.44744469, 1.00679353]), fval=0.6347642471305107, rho=1.4044689675006616, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014805197302692493, relative_step_length=1.000044741483609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.44744469, 1.00679353]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6347153364849043, linear_terms=array([ 0.00136442, -0.00288838]), square_terms=array([[7.21853077e-05, 4.81168593e-03], - [4.81168593e-03, 3.57552054e-01]]), scale=0.029609069851671544, shift=array([9.44744469, 1.00679353])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=26, candidate_x=array([9.41784149, 1.00742862]), index=26, x=array([9.41784149, 1.00742862]), fval=0.6332046797989737, rho=1.1053204142647344, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([17]), step_length=0.02961001570090617, relative_step_length=1.0000319445777717, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.41784149, 1.00742862]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6332385782456339, linear_terms=array([0.0028514 , 0.00022792]), square_terms=array([[2.79552380e-04, 1.89198753e-02], - [1.89198753e-02, 1.43266898e+00]]), scale=0.05921813970334309, shift=array([9.41784149, 1.00742862])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=27, candidate_x=array([9.35862837, 1.00819965]), index=27, x=array([9.35862837, 1.00819965]), fval=0.6303511715372276, rho=1.0071263421242136, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 17, 20]), step_length=0.0592181407159667, relative_step_length=1.0000000170998888, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.35862837, 1.00819965]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.639178807035496, linear_terms=array([ 0.00132575, -0.26376211]), square_terms=array([[8.65219778e-04, 5.51863712e-02], - [5.51863712e-02, 3.95321986e+00]]), scale=array([0.10496142, 0.09838088]), shift=array([9.35862837, 1.00161912])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=28, candidate_x=array([9.25366695, 1.00955655]), index=28, x=array([9.25366695, 1.00955655]), fval=0.6252147257628975, rho=1.035476052619481, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 20, 22]), step_length=0.10497019014652463, relative_step_length=0.8863009769673553, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25366695, 1.00955655]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3525263472005808, linear_terms=array([-0.05604803, -3.65995549]), square_terms=array([[3.37929225e-03, 1.66025609e-01], - [1.66025609e-01, 9.20973251e+00]]), scale=array([0.20992284, 0.15018314]), shift=array([9.25366695, 0.94981686])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=29, candidate_x=array([9.04374411, 1.01220715]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=1.0345307568988933, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 22]), step_length=0.20993957271621416, relative_step_length=0.8862975676368734, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), model=ScalarModel(intercept=2.887557528467335, linear_terms=array([ 0.32145309, -7.82327945]), square_terms=array([[ 0.01909841, -0.39706853], - [-0.39706853, 13.16541827]]), scale=array([0.41984568, 0.25381927]), shift=array([9.04374411, 0.84618073])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=30, candidate_x=array([8.62389843, 0.98935244]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-1.1829304881995348, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, - 21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.1785163391833393, linear_terms=array([ 0.04245163, -3.2666068 ]), square_terms=array([[ 1.11423119e-03, -2.87009785e-02], - [-2.87009785e-02, 8.89866124e+00]]), scale=array([0.20992284, 0.14885785]), shift=array([9.04374411, 0.95114215])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=31, candidate_x=array([8.83382127, 1.00530622]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.21904622845287416, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 6, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, - 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=0.588971346798013, linear_terms=array([ 0.01505103, -0.13000888]), square_terms=array([[ 2.31746838e-04, -3.20175653e-03], - [-3.20175653e-03, 3.75543886e+00]]), scale=array([0.10496142, 0.09637714]), shift=array([9.04374411, 1.00362286])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=32, candidate_x=array([8.93878269, 1.00687716]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.19293832377303985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), old_indices_discarded=array([ 0, 3, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32]), model=ScalarModel(intercept=0.6214336044267341, linear_terms=array([0.00147887, 0.10259686]), square_terms=array([[4.10977860e-04, 1.78962346e-02], - [1.78962346e-02, 8.79816604e-01]]), scale=0.05921813970334309, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=33, candidate_x=array([9.10280287, 1.00410548]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-2.08297420159985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33]), model=ScalarModel(intercept=0.6151402665456009, linear_terms=array([0.00149709, 0.00482735]), square_terms=array([[7.65543339e-05, 5.14393571e-03], - [5.14393571e-03, 3.91745261e-01]]), scale=0.029609069851671544, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=34, candidate_x=array([9.0141328 , 1.01223102]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=0.8565054081815938, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.02961131833730782, relative_step_length=1.0000759390837854, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.620881773286023, linear_terms=array([0.00197631, 0.09173778]), square_terms=array([[3.44046695e-04, 1.64408522e-02], - [1.64408522e-02, 8.92929811e-01]]), scale=0.05921813970334309, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=35, candidate_x=array([8.95483028, 1.00724033]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=-0.906107936879546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6137867229839677, linear_terms=array([0.00142032, 0.00043433]), square_terms=array([[7.69832064e-05, 5.16913372e-03], - [5.16913372e-03, 3.92730596e-01]]), scale=0.029609069851671544, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=36, candidate_x=array([8.98452585, 1.01258669]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=0.9657336720601446, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.029609089398228004, relative_step_length=1.0000006601543567, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.619467823965291, linear_terms=array([0.00209097, 0.09020298]), square_terms=array([[3.29303090e-04, 1.61351934e-02], - [1.61351934e-02, 9.00329160e-01]]), scale=0.05921813970334309, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=37, candidate_x=array([8.92521698, 1.00771893]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=-0.8416732621911007, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.6124263604451471, linear_terms=array([0.00140759, 0.00037964]), square_terms=array([[7.68743253e-05, 5.17639009e-03], - [5.17639009e-03, 3.94127433e-01]]), scale=0.029609069851671544, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=38, candidate_x=array([8.95491894, 1.01294575]), index=38, x=array([8.95491894, 1.01294575]), fval=0.6110646963985666, rho=1.04699122192598, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.02960908484218274, relative_step_length=1.0000005062810575, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.95491894, 1.01294575]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.6110062274424244, linear_terms=array([0.0028986 , 0.00157643]), square_terms=array([[3.04730334e-04, 2.06301143e-02], - [2.06301143e-02, 1.58200616e+00]]), scale=0.05921813970334309, shift=array([8.95491894, 1.01294575])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=39, candidate_x=array([8.89570505, 1.01365764]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=0.9122655509396086, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([13]), step_length=0.05921817039076989, relative_step_length=1.0000005182099092, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.614727205905659, linear_terms=array([-0.00711409, -0.08425954]), square_terms=array([[1.97921783e-03, 6.38199381e-02], - [6.38199381e-02, 2.30738160e+00]]), scale=array([0.10496142, 0.09565189]), shift=array([8.89570505, 1.00434811])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=40, candidate_x=array([9.00066647, 1.00519543]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=-1.8130574184159354, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([ 0, 1, 3, 7, 10, 21, 23, 25, 26, 27, 28, 29, 30, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.6082515870760768, linear_terms=array([0.00289756, 0.00212777]), square_terms=array([[2.93803015e-04, 2.03395974e-02], - [2.03395974e-02, 1.60156668e+00]]), scale=0.05921813970334309, shift=array([8.89570505, 1.01365764])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=41, candidate_x=array([8.83649067, 1.01432978]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=0.9335608646751216, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([ 7, 10, 13, 29, 30, 33]), step_length=0.05921819354659514, relative_step_length=1.0000009092357902, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), model=ScalarModel(intercept=0.610651531374723, linear_terms=array([ 0.00125715, -0.10509161]), square_terms=array([[7.56634919e-04, 3.91907529e-02], - [3.91907529e-02, 2.39236786e+00]]), scale=array([0.10496142, 0.09531582]), shift=array([8.83649067, 1.00468418])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=42, candidate_x=array([8.73152925, 1.01043262]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.2539961161624663, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), old_indices_discarded=array([ 1, 3, 7, 10, 26, 27, 28, 29, 30, 33, 34, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6128960194558581, linear_terms=array([0.00323829, 0.08375858]), square_terms=array([[2.19797272e-04, 1.29854569e-02], - [1.29854569e-02, 9.21144727e-01]]), scale=0.05921813970334309, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=43, candidate_x=array([8.77720289, 1.00979089]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.636748464236313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), old_indices_discarded=array([ 3, 7, 10, 29, 30, 33, 34, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), model=ScalarModel(intercept=0.6129958444671166, linear_terms=array([0.00125322, 0.0420077 ]), square_terms=array([[7.02244693e-05, 3.72273429e-03], - [3.72273429e-03, 2.29436197e-01]]), scale=0.029609069851671544, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=44, candidate_x=array([8.80679818, 1.00940247]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-1.1780504359812205, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), old_indices_discarded=array([35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 39, 41, 43, 44]), model=ScalarModel(intercept=0.6059146038962105, linear_terms=array([0.00063063, 0.00133693]), square_terms=array([[1.93356047e-05, 1.31514692e-03], - [1.31514692e-03, 1.01583452e-01]]), scale=0.014804534925835772, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=45, candidate_x=array([8.82168485, 1.01432664]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=1.020928649746183, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 39, 41, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.014805816548052337, relative_step_length=1.0000865695695937, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6115555266177718, linear_terms=array([0.00124126, 0.04101205]), square_terms=array([[7.04241015e-05, 3.73186729e-03], - [3.73186729e-03, 2.30210452e-01]]), scale=0.029609069851671544, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=46, candidate_x=array([8.79199479, 1.00954484]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=-1.230884779808124, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), old_indices_discarded=array([32, 35, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6052045803357664, linear_terms=array([5.75893275e-04, 2.08251016e-05]), square_terms=array([[2.14888603e-05, 1.39552818e-03], - [1.39552818e-03, 1.02157333e-01]]), scale=0.014804534925835772, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=47, candidate_x=array([8.80688164, 1.01452473]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=1.1411150863578703, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.014804535502910653, relative_step_length=1.0000000389796022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.6105358682194937, linear_terms=array([0.00053509, 0.04163391]), square_terms=array([[1.09771507e-04, 4.72352439e-03], - [4.72352439e-03, 2.30297392e-01]]), scale=0.029609069851671544, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=48, candidate_x=array([8.8363726 , 1.00857494]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=-2.17105462493869, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([32, 37, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6045817009488239, linear_terms=array([ 0.00060537, -0.00012916]), square_terms=array([[2.04142540e-05, 1.36233150e-03], - [1.36233150e-03, 1.02744088e-01]]), scale=0.014804534925835772, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=49, candidate_x=array([8.79207864, 1.01473837]), index=49, x=array([8.79207864, 1.01473837]), fval=0.6038388333603965, rho=1.0929767802825647, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.01480454508906136, relative_step_length=1.0000006864940802, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.79207864, 1.01473837]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.6039320017441308, linear_terms=array([1.22941931e-03, 2.77206681e-05]), square_terms=array([[7.95912011e-05, 5.38269834e-03], - [5.38269834e-03, 4.12173721e-01]]), scale=0.029609069851671544, shift=array([8.79207864, 1.01473837])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=50, candidate_x=array([8.76247205, 1.01512188]), index=50, x=array([8.76247205, 1.01512188]), fval=0.6024855719631016, rho=1.1053283609927005, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), old_indices_discarded=array([13, 32, 37, 39, 42]), step_length=0.02960907001757359, relative_step_length=1.000000005603082, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.76247205, 1.01512188]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.6026578745853444, linear_terms=array([0.0024301, 0.0002779]), square_terms=array([[3.16909370e-04, 2.15042159e-02], - [2.15042159e-02, 1.65484964e+00]]), scale=0.05921813970334309, shift=array([8.76247205, 1.01512188])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=51, candidate_x=array([8.70325877, 1.0158803 ]), index=51, x=array([8.70325877, 1.0158803 ]), fval=0.5997907962729061, rho=1.1192868878296924, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), old_indices_discarded=array([ 3, 7, 10, 13, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 48]), step_length=0.05921814073463714, relative_step_length=1.0000000174151713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), model=ScalarModel(intercept=0.5745601532643059, linear_terms=array([ 0.01933147, -0.20828263]), square_terms=array([[ 3.96552248e-04, -7.70697193e-03], - [-7.70697193e-03, 3.60952666e+00]]), scale=array([0.10496142, 0.09454056]), shift=array([8.70325877, 1.00545944])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=52, candidate_x=array([8.59829735, 1.01071291]), index=51, x=array([8.70325877, 1.0158803 ]), fval=0.5997907962729061, rho=-0.2234956303402016, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), old_indices_discarded=array([ 1, 3, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, - 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), model=ScalarModel(intercept=0.5848648139367352, linear_terms=array([ 0.01259792, -0.01987223]), square_terms=array([[ 2.31944576e-04, -1.32546665e-02], - [-1.32546665e-02, 2.24249333e+00]]), scale=0.05921813970334309, shift=array([8.70325877, 1.0158803 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=53, candidate_x=array([8.64403857, 1.01605409]), index=53, x=array([8.64403857, 1.01605409]), fval=0.5974935659720187, rho=0.1838940908968012, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), old_indices_discarded=array([ 3, 13, 31, 32, 35, 36, 37, 38, 39, 41, 44, 45, 47, 48, 52]), step_length=0.05922045545199665, relative_step_length=1.0000391053934683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.64403857, 1.01605409]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6233574060676437, linear_terms=array([ 0.01619734, -0.70631774]), square_terms=array([[2.97413525e-04, 7.75950577e-04], - [7.75950577e-04, 5.79975163e+00]]), scale=array([0.10496142, 0.09445367]), shift=array([8.64403857, 1.00554633])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=54, candidate_x=array([8.53907715, 1.01706193]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=0.26745528391814205, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, - 46, 47, 48, 49]), step_length=0.10496625831674383, relative_step_length=0.8862677791178409, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.7023662250726452, linear_terms=array([ 0.06492856, -1.1147206 ]), square_terms=array([[ 3.47923203e-03, -8.06332216e-02], - [-8.06332216e-02, 3.61802648e+00]]), scale=array([0.20992284, 0.14643046]), shift=array([8.53907715, 0.95356954])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=55, candidate_x=array([8.32915431, 0.99542161]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=-1.7530958161469754, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, - 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, - 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.615086708073995, linear_terms=array([ 0.00978289, -0.72244786]), square_terms=array([[4.17284358e-04, 3.04748646e-02], - [3.04748646e-02, 5.74255506e+00]]), scale=array([0.10496142, 0.09394975]), shift=array([8.53907715, 1.00605025])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=56, candidate_x=array([8.43411573, 1.01836827]), index=56, x=array([8.43411573, 1.01836827]), fval=0.5885914443329772, rho=0.3283224223143453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, - 45, 46, 47, 48, 49, 50]), step_length=0.10496954879325875, relative_step_length=0.8862955617916246, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43411573, 1.01836827]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=1.9169446392811247, linear_terms=array([-0.05516787, -6.07713486]), square_terms=array([[2.74937852e-03, 1.66261876e-01], - [1.66261876e-01, 1.36583309e+01]]), scale=array([0.20992284, 0.14577728]), shift=array([8.43411573, 0.95422272])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=57, candidate_x=array([8.22419289, 1.02085937]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=0.46355807804061855, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, - 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, - 45, 46, 47, 48, 49, 50]), step_length=0.2099376195491761, relative_step_length=0.8862893219918404, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=3.341466430284669, linear_terms=array([-1.05103033, -7.77084409]), square_terms=array([[ 0.18279549, 1.34509322], - [ 1.34509322, 11.10009818]]), scale=array([0.41984568, 0.24949316]), shift=array([8.22419289, 0.85050684])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=58, candidate_x=array([8.64403857, 0.99493627]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=-1.0936320536105784, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, - 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.9832433701127465, linear_terms=array([-0.11608732, -6.1156747 ]), square_terms=array([[5.73554046e-03, 2.62479555e-01], - [2.62479555e-01, 1.32048414e+01]]), scale=array([0.20992284, 0.14453174]), shift=array([8.22419289, 0.95546826])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=59, candidate_x=array([8.01427005, 1.02527945]), index=59, x=array([8.01427005, 1.02527945]), fval=0.5734330166298551, rho=1.093147952169101, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 1, 3, 13, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, - 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 58]), step_length=0.20996936856056853, relative_step_length=0.8864233561389423, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.01427005, 1.02527945]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=9.981376675556422, linear_terms=array([ -0.67948996, -26.92467299]), square_terms=array([[2.66781078e-02, 9.72846991e-01], - [9.72846991e-01, 3.85178573e+01]]), scale=array([0.41984568, 0.24728311]), shift=array([8.01427005, 0.85271689])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=60, candidate_x=array([7.90516021, 1.02719534]), index=60, x=array([7.90516021, 1.02719534]), fval=0.570654333265082, rho=28.312992905781908, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, - 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.1091266633945737, relative_step_length=0.23034889296854485, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.90516021, 1.02719534]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=6.968668322923406, linear_terms=array([ -0.3193095 , -18.62779191]), square_terms=array([[1.10008541e-02, 4.89131866e-01], - [4.89131866e-01, 2.70983093e+01]]), scale=array([0.41984568, 0.24632517]), shift=array([7.90516021, 0.85367483])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=61, candidate_x=array([7.53569564, 1.02680097]), index=61, x=array([7.53569564, 1.02680097]), fval=0.557843378100565, rho=0.7126019978012866, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, - 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.36946477399899075, relative_step_length=0.7798809111740238, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53569564, 1.02680097]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), model=ScalarModel(intercept=12.13842084066151, linear_terms=array([ -0.90389113, -30.58695776]), square_terms=array([[ 0.04679947, 1.22766505], - [ 1.22766505, 40.38597232]]), scale=array([0.83969136, 0.3 ]), shift=array([7.53569564, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=62, candidate_x=array([6.69600428, 1.03632926]), index=62, x=array([6.69600428, 1.03632926]), fval=0.5424834330682993, rho=0.7246036090517496, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, - 53, 58]), step_length=0.8397454168164292, relative_step_length=0.88628398011064, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69600428, 1.03632926]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), model=ScalarModel(intercept=13.436478042611053, linear_terms=array([ -1.77594432, -32.96460288]), square_terms=array([[ 0.1709616 , 2.30049165], - [ 2.30049165, 42.12835475]]), scale=array([1.67938272, 0.3 ]), shift=array([6.69600428, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=63, candidate_x=array([6.58843012, 1.03594893]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=0.501065213354614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, - 51, 53, 58]), step_length=0.10757483992757301, relative_step_length=0.05676831059836341, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=13.527389310158597, linear_terms=array([ -1.76412438, -33.07272766]), square_terms=array([[ 0.17044821, 2.26794554], - [ 2.26794554, 42.11033571]]), scale=array([1.67938272, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=64, candidate_x=array([5.99457255, 1.04132824]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-2.858177349740313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, - 51, 52, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=13.033433276409284, linear_terms=array([ -0.7928235 , -31.91157346]), square_terms=array([[3.91876133e-02, 1.01921230e+00], - [1.01921230e+00, 4.07663306e+01]]), scale=array([0.83969136, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=65, candidate_x=array([6.28160798, 1.03757835]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.670491030486846, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, - 51, 52, 53, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=7.57645714444444, linear_terms=array([ -0.29574748, -19.22814953]), square_terms=array([[9.87830891e-03, 4.07101559e-01], - [4.07101559e-01, 2.62853990e+01]]), scale=array([0.41984568, 0.24194837]), shift=array([6.58843012, 0.85805163])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=66, candidate_x=array([6.34718361, 1.03719355]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.608088619606695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 30, 31, 32, 35, 37, 38, 39, 41, 42, 43, 44, 45, - 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=2.51780763816455, linear_terms=array([-0.09621641, -7.35783746]), square_terms=array([[3.42399524e-03, 1.80214137e-01], - [1.80214137e-01, 1.37071817e+01]]), scale=array([0.20992284, 0.13698695]), shift=array([6.58843012, 0.96301305])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=67, candidate_x=array([6.48488598, 1.03743422]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.0724059676808537, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 62, 63, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67]), model=ScalarModel(intercept=0.7479032481802277, linear_terms=array([-0.01574272, -1.69550858]), square_terms=array([[8.99601089e-04, 6.45218243e-02], - [6.45218243e-02, 6.96729079e+00]]), scale=array([0.10496142, 0.08450624]), shift=array([6.58843012, 1.01549376])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=68, candidate_x=array([6.60273304, 1.03595193]), index=68, x=array([6.60273304, 1.03595193]), fval=0.5414533040238657, rho=1.1451990693564231, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.014302919721513424, relative_step_length=0.12076468285870495, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.60273304, 1.03595193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.7438421645946036, linear_terms=array([-0.01554274, -1.67681543]), square_terms=array([[8.98462125e-04, 6.42713904e-02], - [6.42713904e-02, 6.94910166e+00]]), scale=array([0.10496142, 0.08450474]), shift=array([6.60273304, 1.01549526])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - 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0.08458141]), shift=array([6.61449989, 1.01541859])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, - -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), 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x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-10.089924952581605, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.007402267462917886, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.5414619458203902, linear_terms=array([6.9562924e-06, 8.5447900e-04]), square_terms=array([[4.61608585e-06, 4.03102152e-04], - [4.03102152e-04, 5.06614459e-02]]), scale=0.007402267462917886, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, - 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, - -0.39325651, -0.40243304, -0.13275181, 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45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 1.525257377346415e-05, 'relative_params_change': 0.004758913549058271, 'absolute_criterion_change': 8.257893387653148e-06, 'absolute_params_change': 0.031397306790084616}, 'five_steps': {'relative_criterion_change': 1.525257377346415e-05, 'relative_params_change': 0.004758913549058271, 'absolute_criterion_change': 8.257893387653148e-06, 'absolute_params_change': 0.031397306790084616}}" + +multistart_info,"{'start_parameters': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 9.474902352534894, 'DiscFac': 0.9999364724331312}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.183e-07* 3.672e-05 +relative_params_change 8.471e-07* 0.001055 +absolute_criterion_change 6.406e-08* 1.988e-05 +absolute_params_change 1e-06* 0.006933 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 6.004e-07* 0.03035 +relative_params_change 4.702e-06* 0.1395 +absolute_criterion_change 3.251e-07* 0.01643 +absolute_params_change 2.892e-05 0.9212 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 16.45625, 'DiscFac': 0.9125000000000001}, {'CRRA': 14.093749999999998, 'DiscFac': 0.9875}, {'CRRA': 17.046875, 'DiscFac': 0.6312500000000001}, {'CRRA': 7.596874999999999, 'DiscFac': 0.9312500000000001}, {'CRRA': 18.81875, 'DiscFac': 0.5375}, {'CRRA': 15.274999999999999, 'DiscFac': 0.65}, {'CRRA': 11.73125, 'DiscFac': 0.7625000000000001}, {'CRRA': 10.549999999999999, 'DiscFac': 0.8}, {'CRRA': 9.368749999999999, 'DiscFac': 0.8375}, {'CRRA': 5.824999999999999, 'DiscFac': 0.9500000000000001}, {'CRRA': 12.9125, 'DiscFac': 0.575}, {'CRRA': 17.6375, 'DiscFac': 1.0250000000000001}, {'CRRA': 8.1875, 'DiscFac': 0.7250000000000001}, {'CRRA': 12.321874999999999, 'DiscFac': 1.08125}, {'CRRA': 7.00625, 'DiscFac': 0.6125}, {'CRRA': 4.64375, 'DiscFac': 0.6875}, {'CRRA': 3.4625, 'DiscFac': 0.875}, {'CRRA': 2.871875, 'DiscFac': 0.78125}, {'CRRA': 2.28125, 'DiscFac': 1.0625}], 'exploration_results': array([0.64235819, 0.99955613, 1.1121001 , 1.75705837, 1.77123339, + 1.82132756, 1.94396028, 2.07018913, 2.11467658, 2.15218408, + 2.24402179, 2.54668134, 2.78807842, 2.90740886, 3.01539999, + 3.32546076, 3.5830362 , 4.07905962, 4.08578359, 6.94508729])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=[0], model=ScalarModel(intercept=0.6448272261874601, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.7324177488592093, linear_terms=array([-0.1604269 , -1.70678719]), square_terms=array([[0.05311507, 0.46725062], + [0.46725062, 4.78691623]]), scale=array([0.83969136, 0.3 ]), shift=array([9.47490235, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.4596011524812236, linear_terms=array([-0.15193583, -0.53365863]), square_terms=array([[0.20583856, 0.85488875], + [0.85488875, 3.85975947]]), scale=array([0.41984568, 0.2599546 ]), shift=array([9.47490235, 0.8400454 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=14, candidate_x=array([9.89474803, 0.81841056]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-3.246228222983773, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 4, 6, 7, 9, 10, 11, 12, 13]), old_indices_discarded=array([1, 2, 3, 5, 8]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 6, 7, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.47748362589833987, linear_terms=array([0.03029103, 0.35911003]), square_terms=array([[0.02625602, 0.18108694], + [0.18108694, 1.39847469]]), scale=array([0.20992284, 0.15499318]), shift=array([9.47490235, 0.94500682])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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upper=array([20. , 1.1]))), model_indices=array([ 0, 9, 11, 12, 14, 15]), model=ScalarModel(intercept=0.781511080377176, linear_terms=array([-0.06570334, 0.6958516 ]), square_terms=array([[ 0.00455801, -0.05593978], + [-0.05593978, 0.75231219]]), scale=array([0.10496142, 0.10251247]), shift=array([9.47490235, 0.99748753])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=16, candidate_x=array([9.57986377, 0.91029109]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-2.2880982913831005, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 11, 12, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=0.6108530298545867, linear_terms=array([-0.10139668, -0.14696738]), square_terms=array([[0.07792252, 0.32866092], + [0.32866092, 1.49842941]]), scale=0.05921813970334309, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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square_terms=array([[0.03333137, 0.10554599], + [0.10554599, 0.37011472]]), scale=0.029609069851671544, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=18, candidate_x=array([9.50661188, 1.0031676 ]), index=18, x=array([9.50661188, 1.0031676 ]), fval=0.6396317123330025, rho=0.05607187891543219, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.03187372417953975, relative_step_length=1.0764851560421564, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.50661188, 1.0031676 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.6396317123330028, linear_terms=array([ 0.00028177, -0.01430195]), square_terms=array([[2.43882216e-05, 1.35799469e-03], + [1.35799469e-03, 8.34202654e-02]]), scale=0.014804534925835772, shift=array([9.50661188, 1.0031676 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=19, candidate_x=array([9.49185035, 1.0059291 ]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=1.4693827516516844, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.015017609941676606, relative_step_length=1.0143925504521585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6412257536348447, linear_terms=array([-0.06905576, -0.03758833]), square_terms=array([[0.03588925, 0.1094543 ], + [0.1094543 , 0.37040144]]), scale=0.029609069851671544, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=20, candidate_x=array([9.52125862, 1.00094573]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=-0.11096998055275568, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6375966987395302, linear_terms=array([ 0.00059101, -0.00033053]), square_terms=array([[2.18308578e-05, 1.28615347e-03], + [1.28615347e-03, 8.40653356e-02]]), scale=0.014804534925835772, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=21, candidate_x=array([9.47704841, 1.00621177]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=1.2946325204884988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.01480464158988585, relative_step_length=1.0000072048227528, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6740948994884155, linear_terms=array([-0.0775299 , -0.08137508]), square_terms=array([[0.03091198, 0.10157856], + [0.10157856, 0.37039842]]), scale=0.029609069851671544, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=22, candidate_x=array([9.50732214, 1.00462907]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=-0.029474268266789803, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6367076097989959, linear_terms=array([ 6.38911855e-04, -5.64861763e-05]), square_terms=array([[1.99591697e-05, 1.23030124e-03], + [1.23030124e-03, 8.44345370e-02]]), scale=0.014804534925835772, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=23, candidate_x=array([9.46224557, 1.00643568]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=1.1173242341721383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014804537200668226, relative_step_length=1.0000001536578127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6376967017750665, linear_terms=array([-0.00887642, -0.07715602]), square_terms=array([[0.00091426, 0.02162778], + [0.02162778, 0.54194842]]), scale=0.029609069851671544, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=24, candidate_x=array([9.49199772, 1.00943198]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=-0.23439049876600507, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6357439312845821, linear_terms=array([ 0.00057539, -0.00082847]), square_terms=array([[2.06319310e-05, 1.26644672e-03], + [1.26644672e-03, 8.60694145e-02]]), scale=0.014804534925835772, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 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18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014805197302692493, relative_step_length=1.000044741483609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.44744469, 1.00679353]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6347153364849043, linear_terms=array([ 0.00136442, -0.00288838]), square_terms=array([[7.21853077e-05, 4.81168593e-03], + [4.81168593e-03, 3.57552054e-01]]), scale=0.029609069851671544, shift=array([9.44744469, 1.00679353])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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old_indices_discarded=array([17]), step_length=0.02961001570090617, relative_step_length=1.0000319445777717, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.41784149, 1.00742862]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6332385782456339, linear_terms=array([0.0028514 , 0.00022792]), square_terms=array([[2.79552380e-04, 1.89198753e-02], + [1.89198753e-02, 1.43266898e+00]]), scale=0.05921813970334309, shift=array([9.41784149, 1.00742862])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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step_length=0.0592181407159667, relative_step_length=1.0000000170998888, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.35862837, 1.00819965]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.639178807035496, linear_terms=array([ 0.00132575, -0.26376211]), square_terms=array([[8.65219778e-04, 5.51863712e-02], + [5.51863712e-02, 3.95321986e+00]]), scale=array([0.10496142, 0.09838088]), shift=array([9.35862837, 1.00161912])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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step_length=0.10497019014652463, relative_step_length=0.8863009769673553, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25366695, 1.00955655]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3525263472005808, linear_terms=array([-0.05604803, -3.65995549]), square_terms=array([[3.37929225e-03, 1.66025609e-01], + [1.66025609e-01, 9.20973251e+00]]), scale=array([0.20992284, 0.15018314]), shift=array([9.25366695, 0.94981686])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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14, 15, 16, 17, + 18, 20, 22]), step_length=0.20993957271621416, relative_step_length=0.8862975676368734, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), model=ScalarModel(intercept=2.887557528467335, linear_terms=array([ 0.32145309, -7.82327945]), square_terms=array([[ 0.01909841, -0.39706853], + [-0.39706853, 13.16541827]]), scale=array([0.41984568, 0.25381927]), shift=array([9.04374411, 0.84618073])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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12, 14, 15, 16, 17, 18, 19, 20, + 21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.1785163391833393, linear_terms=array([ 0.04245163, -3.2666068 ]), square_terms=array([[ 1.11423119e-03, -2.87009785e-02], + [-2.87009785e-02, 8.89866124e+00]]), scale=array([0.20992284, 0.14885785]), shift=array([9.04374411, 0.95114215])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, + 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=0.588971346798013, linear_terms=array([ 0.01505103, -0.13000888]), square_terms=array([[ 2.31746838e-04, -3.20175653e-03], + [-3.20175653e-03, 3.75543886e+00]]), scale=array([0.10496142, 0.09637714]), shift=array([9.04374411, 1.00362286])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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17, 18, 19, 20, 21, 22, 23, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32]), model=ScalarModel(intercept=0.6214336044267341, linear_terms=array([0.00147887, 0.10259686]), square_terms=array([[4.10977860e-04, 1.78962346e-02], + [1.78962346e-02, 8.79816604e-01]]), scale=0.05921813970334309, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33]), model=ScalarModel(intercept=0.6151402665456009, linear_terms=array([0.00149709, 0.00482735]), square_terms=array([[7.65543339e-05, 5.14393571e-03], + [5.14393571e-03, 3.91745261e-01]]), scale=0.029609069851671544, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=34, candidate_x=array([9.0141328 , 1.01223102]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=0.8565054081815938, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.02961131833730782, relative_step_length=1.0000759390837854, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.620881773286023, linear_terms=array([0.00197631, 0.09173778]), square_terms=array([[3.44046695e-04, 1.64408522e-02], + [1.64408522e-02, 8.92929811e-01]]), scale=0.05921813970334309, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=35, candidate_x=array([8.95483028, 1.00724033]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=-0.906107936879546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6137867229839677, linear_terms=array([0.00142032, 0.00043433]), square_terms=array([[7.69832064e-05, 5.16913372e-03], + [5.16913372e-03, 3.92730596e-01]]), scale=0.029609069851671544, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=36, candidate_x=array([8.98452585, 1.01258669]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=0.9657336720601446, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.029609089398228004, relative_step_length=1.0000006601543567, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.619467823965291, linear_terms=array([0.00209097, 0.09020298]), square_terms=array([[3.29303090e-04, 1.61351934e-02], + [1.61351934e-02, 9.00329160e-01]]), scale=0.05921813970334309, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([0.00140759, 0.00037964]), square_terms=array([[7.68743253e-05, 5.17639009e-03], + [5.17639009e-03, 3.94127433e-01]]), scale=0.029609069851671544, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[3.04730334e-04, 2.06301143e-02], + [2.06301143e-02, 1.58200616e+00]]), scale=0.05921813970334309, shift=array([8.95491894, 1.01294575])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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+ [6.38199381e-02, 2.30738160e+00]]), scale=array([0.10496142, 0.09565189]), shift=array([8.89570505, 1.00434811])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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1.60156668e+00]]), scale=0.05921813970334309, shift=array([8.89570505, 1.01365764])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=41, candidate_x=array([8.83649067, 1.01432978]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=0.9335608646751216, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([ 7, 10, 13, 29, 30, 33]), step_length=0.05921819354659514, relative_step_length=1.0000009092357902, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), model=ScalarModel(intercept=0.610651531374723, linear_terms=array([ 0.00125715, -0.10509161]), square_terms=array([[7.56634919e-04, 3.91907529e-02], + [3.91907529e-02, 2.39236786e+00]]), scale=array([0.10496142, 0.09531582]), shift=array([8.83649067, 1.00468418])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=42, candidate_x=array([8.73152925, 1.01043262]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.2539961161624663, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), old_indices_discarded=array([ 1, 3, 7, 10, 26, 27, 28, 29, 30, 33, 34, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6128960194558581, linear_terms=array([0.00323829, 0.08375858]), square_terms=array([[2.19797272e-04, 1.29854569e-02], + [1.29854569e-02, 9.21144727e-01]]), scale=0.05921813970334309, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=43, candidate_x=array([8.77720289, 1.00979089]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.636748464236313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), old_indices_discarded=array([ 3, 7, 10, 29, 30, 33, 34, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), model=ScalarModel(intercept=0.6129958444671166, linear_terms=array([0.00125322, 0.0420077 ]), square_terms=array([[7.02244693e-05, 3.72273429e-03], + [3.72273429e-03, 2.29436197e-01]]), scale=0.029609069851671544, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=44, candidate_x=array([8.80679818, 1.00940247]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-1.1780504359812205, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), old_indices_discarded=array([35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 39, 41, 43, 44]), model=ScalarModel(intercept=0.6059146038962105, linear_terms=array([0.00063063, 0.00133693]), square_terms=array([[1.93356047e-05, 1.31514692e-03], + [1.31514692e-03, 1.01583452e-01]]), scale=0.014804534925835772, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=45, candidate_x=array([8.82168485, 1.01432664]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=1.020928649746183, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 39, 41, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.014805816548052337, relative_step_length=1.0000865695695937, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6115555266177718, linear_terms=array([0.00124126, 0.04101205]), square_terms=array([[7.04241015e-05, 3.73186729e-03], + [3.73186729e-03, 2.30210452e-01]]), scale=0.029609069851671544, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=46, candidate_x=array([8.79199479, 1.00954484]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=-1.230884779808124, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), old_indices_discarded=array([32, 35, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6052045803357664, linear_terms=array([5.75893275e-04, 2.08251016e-05]), square_terms=array([[2.14888603e-05, 1.39552818e-03], + [1.39552818e-03, 1.02157333e-01]]), scale=0.014804534925835772, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=47, candidate_x=array([8.80688164, 1.01452473]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=1.1411150863578703, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.014804535502910653, relative_step_length=1.0000000389796022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.6105358682194937, linear_terms=array([0.00053509, 0.04163391]), square_terms=array([[1.09771507e-04, 4.72352439e-03], + [4.72352439e-03, 2.30297392e-01]]), scale=0.029609069851671544, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=48, candidate_x=array([8.8363726 , 1.00857494]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=-2.17105462493869, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([32, 37, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6045817009488239, linear_terms=array([ 0.00060537, -0.00012916]), square_terms=array([[2.04142540e-05, 1.36233150e-03], + [1.36233150e-03, 1.02744088e-01]]), scale=0.014804534925835772, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + 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44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.01480454508906136, relative_step_length=1.0000006864940802, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.79207864, 1.01473837]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.6039320017441308, linear_terms=array([1.22941931e-03, 2.77206681e-05]), square_terms=array([[7.95912011e-05, 5.38269834e-03], + [5.38269834e-03, 4.12173721e-01]]), scale=0.029609069851671544, shift=array([8.79207864, 1.01473837])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([13, 32, 37, 39, 42]), step_length=0.02960907001757359, relative_step_length=1.000000005603082, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.76247205, 1.01512188]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.6026578745853444, linear_terms=array([0.0024301, 0.0002779]), square_terms=array([[3.16909370e-04, 2.15042159e-02], + [2.15042159e-02, 1.65484964e+00]]), scale=0.05921813970334309, shift=array([8.76247205, 1.01512188])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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13, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 48]), step_length=0.05921814073463714, relative_step_length=1.0000000174151713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), model=ScalarModel(intercept=0.5745601532643059, linear_terms=array([ 0.01933147, -0.20828263]), square_terms=array([[ 3.96552248e-04, -7.70697193e-03], + [-7.70697193e-03, 3.60952666e+00]]), scale=array([0.10496142, 0.09454056]), shift=array([8.70325877, 1.00545944])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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old_indices_discarded=array([ 1, 3, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, + 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), model=ScalarModel(intercept=0.5848648139367352, linear_terms=array([ 0.01259792, -0.01987223]), square_terms=array([[ 2.31944576e-04, -1.32546665e-02], + [-1.32546665e-02, 2.24249333e+00]]), scale=0.05921813970334309, shift=array([8.70325877, 1.0158803 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([ 3, 13, 31, 32, 35, 36, 37, 38, 39, 41, 44, 45, 47, 48, 52]), step_length=0.05922045545199665, relative_step_length=1.0000391053934683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.64403857, 1.01605409]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6233574060676437, linear_terms=array([ 0.01619734, -0.70631774]), square_terms=array([[2.97413525e-04, 7.75950577e-04], + [7.75950577e-04, 5.79975163e+00]]), scale=array([0.10496142, 0.09445367]), shift=array([8.64403857, 1.00554633])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 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43, 50, 51, 52, 53]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, + 46, 47, 48, 49]), step_length=0.10496625831674383, relative_step_length=0.8862677791178409, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.7023662250726452, linear_terms=array([ 0.06492856, -1.1147206 ]), square_terms=array([[ 3.47923203e-03, -8.06332216e-02], + [-8.06332216e-02, 3.61802648e+00]]), scale=array([0.20992284, 0.14643046]), shift=array([8.53907715, 0.95356954])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, 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dtype=int32), old_indices_used=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.615086708073995, linear_terms=array([ 0.00978289, -0.72244786]), square_terms=array([[4.17284358e-04, 3.04748646e-02], + [3.04748646e-02, 5.74255506e+00]]), scale=array([0.10496142, 0.09394975]), shift=array([8.53907715, 1.00605025])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=56, candidate_x=array([8.43411573, 1.01836827]), index=56, x=array([8.43411573, 1.01836827]), fval=0.5885914443329772, rho=0.3283224223143453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.10496954879325875, relative_step_length=0.8862955617916246, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43411573, 1.01836827]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=1.9169446392811247, linear_terms=array([-0.05516787, -6.07713486]), square_terms=array([[2.74937852e-03, 1.66261876e-01], + [1.66261876e-01, 1.36583309e+01]]), scale=array([0.20992284, 0.14577728]), shift=array([8.43411573, 0.95422272])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=57, candidate_x=array([8.22419289, 1.02085937]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=0.46355807804061855, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, + 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.2099376195491761, relative_step_length=0.8862893219918404, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=3.341466430284669, linear_terms=array([-1.05103033, -7.77084409]), square_terms=array([[ 0.18279549, 1.34509322], + [ 1.34509322, 11.10009818]]), scale=array([0.41984568, 0.24949316]), shift=array([8.22419289, 0.85050684])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=58, candidate_x=array([8.64403857, 0.99493627]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=-1.0936320536105784, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.9832433701127465, linear_terms=array([-0.11608732, -6.1156747 ]), square_terms=array([[5.73554046e-03, 2.62479555e-01], + [2.62479555e-01, 1.32048414e+01]]), scale=array([0.20992284, 0.14453174]), shift=array([8.22419289, 0.95546826])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=59, candidate_x=array([8.01427005, 1.02527945]), index=59, x=array([8.01427005, 1.02527945]), fval=0.5734330166298551, rho=1.093147952169101, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 1, 3, 13, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, + 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 58]), step_length=0.20996936856056853, relative_step_length=0.8864233561389423, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.01427005, 1.02527945]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=9.981376675556422, linear_terms=array([ -0.67948996, -26.92467299]), square_terms=array([[2.66781078e-02, 9.72846991e-01], + [9.72846991e-01, 3.85178573e+01]]), scale=array([0.41984568, 0.24728311]), shift=array([8.01427005, 0.85271689])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=60, candidate_x=array([7.90516021, 1.02719534]), index=60, x=array([7.90516021, 1.02719534]), fval=0.570654333265082, rho=28.312992905781908, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.1091266633945737, relative_step_length=0.23034889296854485, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.90516021, 1.02719534]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=6.968668322923406, linear_terms=array([ -0.3193095 , -18.62779191]), square_terms=array([[1.10008541e-02, 4.89131866e-01], + [4.89131866e-01, 2.70983093e+01]]), scale=array([0.41984568, 0.24632517]), shift=array([7.90516021, 0.85367483])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=61, candidate_x=array([7.53569564, 1.02680097]), index=61, x=array([7.53569564, 1.02680097]), fval=0.557843378100565, rho=0.7126019978012866, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.36946477399899075, relative_step_length=0.7798809111740238, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53569564, 1.02680097]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), model=ScalarModel(intercept=12.13842084066151, linear_terms=array([ -0.90389113, -30.58695776]), square_terms=array([[ 0.04679947, 1.22766505], + [ 1.22766505, 40.38597232]]), scale=array([0.83969136, 0.3 ]), shift=array([7.53569564, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=62, candidate_x=array([6.69600428, 1.03632926]), index=62, x=array([6.69600428, 1.03632926]), fval=0.5424834330682993, rho=0.7246036090517496, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, + 53, 58]), step_length=0.8397454168164292, relative_step_length=0.88628398011064, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69600428, 1.03632926]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), model=ScalarModel(intercept=13.436478042611053, linear_terms=array([ -1.77594432, -32.96460288]), square_terms=array([[ 0.1709616 , 2.30049165], + [ 2.30049165, 42.12835475]]), scale=array([1.67938272, 0.3 ]), shift=array([6.69600428, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=63, candidate_x=array([6.58843012, 1.03594893]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=0.501065213354614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 53, 58]), step_length=0.10757483992757301, relative_step_length=0.05676831059836341, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=13.527389310158597, linear_terms=array([ -1.76412438, -33.07272766]), square_terms=array([[ 0.17044821, 2.26794554], + [ 2.26794554, 42.11033571]]), scale=array([1.67938272, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=64, candidate_x=array([5.99457255, 1.04132824]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-2.858177349740313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=13.033433276409284, linear_terms=array([ -0.7928235 , -31.91157346]), square_terms=array([[3.91876133e-02, 1.01921230e+00], + [1.01921230e+00, 4.07663306e+01]]), scale=array([0.83969136, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=65, candidate_x=array([6.28160798, 1.03757835]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.670491030486846, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=7.57645714444444, linear_terms=array([ -0.29574748, -19.22814953]), square_terms=array([[9.87830891e-03, 4.07101559e-01], + [4.07101559e-01, 2.62853990e+01]]), scale=array([0.41984568, 0.24194837]), shift=array([6.58843012, 0.85805163])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=66, candidate_x=array([6.34718361, 1.03719355]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.608088619606695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 30, 31, 32, 35, 37, 38, 39, 41, 42, 43, 44, 45, + 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=2.51780763816455, linear_terms=array([-0.09621641, -7.35783746]), square_terms=array([[3.42399524e-03, 1.80214137e-01], + [1.80214137e-01, 1.37071817e+01]]), scale=array([0.20992284, 0.13698695]), shift=array([6.58843012, 0.96301305])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=67, candidate_x=array([6.48488598, 1.03743422]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.0724059676808537, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 62, 63, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67]), model=ScalarModel(intercept=0.7479032481802277, linear_terms=array([-0.01574272, -1.69550858]), square_terms=array([[8.99601089e-04, 6.45218243e-02], + [6.45218243e-02, 6.96729079e+00]]), scale=array([0.10496142, 0.08450624]), shift=array([6.58843012, 1.01549376])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=68, candidate_x=array([6.60273304, 1.03595193]), index=68, x=array([6.60273304, 1.03595193]), fval=0.5414533040238657, rho=1.1451990693564231, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.014302919721513424, relative_step_length=0.12076468285870495, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.60273304, 1.03595193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.7438421645946036, linear_terms=array([-0.01554274, -1.67681543]), square_terms=array([[8.98462125e-04, 6.42713904e-02], + [6.42713904e-02, 6.94910166e+00]]), scale=array([0.10496142, 0.08450474]), shift=array([6.60273304, 1.01549526])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=69, candidate_x=array([6.61449989, 1.0357986 ]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=10.755716308584471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.011767854290167887, relative_step_length=0.09936021588249544, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.7410229267529151, linear_terms=array([-0.01539784, -1.66026198]), square_terms=array([[8.96829948e-04, 6.39617448e-02], + [6.39617448e-02, 6.90724557e+00]]), scale=array([0.10496142, 0.08458141]), shift=array([6.61449989, 1.01541859])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=70, candidate_x=array([6.62265463, 1.03568817]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-22.25987391656959, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.5414839920684693, linear_terms=array([4.15483856e-05, 3.93468494e-03]), square_terms=array([[2.84874482e-04, 2.52791748e-02], + [2.52791748e-02, 3.36942768e+00]]), scale=0.05921813970334309, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=71, candidate_x=array([6.60701913, 1.03578557]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-16.79541545743258, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.5414615742402571, linear_terms=array([8.4752230e-05, 4.0438811e-03]), square_terms=array([[6.98826564e-05, 6.29275999e-03], + [6.29275999e-03, 8.41875305e-01]]), scale=0.029609069851671544, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 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candidate_index=72, candidate_x=array([6.58489072, 1.03587769]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-1.588880786010452, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.5414393947813702, linear_terms=array([-2.11246402e-05, -1.93873062e-03]), square_terms=array([[1.87338726e-05, 1.64576236e-03], + [1.64576236e-03, 2.06670312e-01]]), scale=0.014804534925835772, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=73, candidate_x=array([6.62945645, 1.03581837]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-10.089924952581605, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.007402267462917886, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.5414619458203902, linear_terms=array([6.9562924e-06, 8.5447900e-04]), square_terms=array([[4.61608585e-06, 4.03102152e-04], + [4.03102152e-04, 5.06614459e-02]]), scale=0.007402267462917886, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, 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69, 70, 71, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0018505668657294715, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 70, 71, 74, 75]), model=ScalarModel(intercept=0.5414505447289019, linear_terms=array([-8.39195189e-06, -4.82590678e-04]), square_terms=array([[2.93663647e-07, 2.57520778e-05], + [2.57520778e-05, 3.23949356e-03]]), scale=0.0018505668657294715, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0009252834328647358, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 75, 76]), model=ScalarModel(intercept=0.5414101559834426, linear_terms=array([6.06884618e-06, 6.19553068e-04]), square_terms=array([[7.09654175e-08, 5.99044034e-06], + [5.99044034e-06, 7.63676033e-04]]), scale=0.0009252834328647358, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.0004626417164323679, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 76, 77]), model=ScalarModel(intercept=0.5414101559834427, linear_terms=array([ 5.92027706e-05, -8.95483752e-05]), square_terms=array([[1.36984529e-08, 3.03717688e-07], + [3.03717688e-07, 2.08099140e-04]]), scale=0.0004626417164323679, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([69, 77, 78]), model=ScalarModel(intercept=0.5414101559834427, linear_terms=array([-3.31705191e-05, 8.65285911e-06]), square_terms=array([[2.22032765e-08, 8.67788726e-07], + [8.67788726e-07, 5.06632311e-05]]), scale=0.00023132085821618394, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[5.66196704e-10, 3.16088422e-08], + [3.16088422e-08, 3.25790992e-06]]), scale=5.7830214554045985e-05, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=2.8915107277022992e-05, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.252107, 1. ]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=0.6662963313940111, linear_terms=array([-0.15809224, -0.38864171]), square_terms=array([[0.08582577, 0.3626811 ], + [0.3626811 , 1.64746182]]), scale=0.05782566872718589, shift=array([9.252107, 1. ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18]), model=ScalarModel(intercept=0.641670882731942, linear_terms=array([ 0.0001693, -0.0200567]), square_terms=array([[1.68433034e-05, 9.19250943e-04], + [9.19250943e-04, 5.69319975e-02]]), scale=0.014456417181796473, shift=array([9.31141951, 1.00056177])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=22, candidate_x=array([9.31163449, 1.00761214]), index=21, x=array([9.28262709, 1.00867427]), fval=0.6267379609535012, rho=-0.06817765537610261, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.28262709, 1.00867427]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6268638818552913, linear_terms=array([ 7.50654479e-04, -8.32943002e-05]), square_terms=array([[1.70290383e-05, 1.14364707e-03], + [1.14364707e-03, 8.64634329e-02]]), scale=0.014456417181796473, shift=array([9.28262709, 1.00867427])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=23, candidate_x=array([9.2681721 , 1.00887763]), index=23, x=array([9.2681721 , 1.00887763]), fval=0.626016100574723, rho=0.9614924551145125, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014456422294036085, relative_step_length=1.0000003536311624, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2681721 , 1.00887763]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6302545671695479, linear_terms=array([0.00190945, 0.02595137]), square_terms=array([[4.59886870e-05, 3.02614441e-03], + [3.02614441e-03, 2.38414991e-01]]), scale=0.028912834363592946, shift=array([9.2681721 , 1.00887763])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=24, candidate_x=array([9.2392219 , 1.00611615]), index=23, x=array([9.2681721 , 1.00887763]), fval=0.626016100574723, rho=-0.27020250132363843, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.2681721 , 1.00887763]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6260945388463117, linear_terms=array([ 7.51624753e-04, -5.54521229e-05]), square_terms=array([[1.66698146e-05, 1.13418053e-03], + [1.13418053e-03, 8.70860695e-02]]), scale=0.014456417181796473, shift=array([9.2681721 , 1.00887763])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=25, candidate_x=array([9.25371701, 1.00907341]), index=25, x=array([9.25371701, 1.00907341]), fval=0.6253205234400713, rho=0.9257747492589474, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014456419134255051, relative_step_length=1.000000135058262, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25371701, 1.00907341]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6253360883435206, linear_terms=array([1.50448575e-03, 8.41583250e-05]), square_terms=array([[6.56507364e-05, 4.50340306e-03], + [4.50340306e-03, 3.49079339e-01]]), scale=0.028912834363592946, shift=array([9.25371701, 1.00907341])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=26, candidate_x=array([9.22480647, 1.00943785]), index=26, x=array([9.22480647, 1.00943785]), fval=0.6238879213243532, rho=0.955377431374471, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([12, 16, 18]), step_length=0.028912835624545444, relative_step_length=1.0000000436122063, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.22480647, 1.00943785]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6238467663285244, linear_terms=array([0.00300031, 0.00025396]), square_terms=array([[2.60662493e-04, 1.79499400e-02], + [1.79499400e-02, 1.39799232e+00]]), scale=0.05782566872718589, shift=array([9.22480647, 1.00943785])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=27, candidate_x=array([9.16698541, 1.0101682 ]), index=27, x=array([9.16698541, 1.0101682 ]), fval=0.6211424674970292, rho=0.9207546180217053, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 18, 20]), step_length=0.05782566998640155, relative_step_length=1.0000000217760674, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.16698541, 1.0101682 ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.6293604631753875, linear_terms=array([ 0.00168088, -0.25402434]), square_terms=array([[7.94437328e-04, 5.20961396e-02], + [5.20961396e-02, 3.88207149e+00]]), scale=array([0.10249333, 0.09616256]), shift=array([9.16698541, 1.00383744])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=28, candidate_x=array([9.06449208, 1.01142033]), index=28, x=array([9.06449208, 1.01142033]), fval=0.6161874288905664, rho=0.9826654333073533, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 18, 20]), step_length=0.10250097734076673, relative_step_length=0.8862930563272275, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.06449208, 1.01142033]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3269180344206313, linear_terms=array([-0.0530022 , -3.58500408]), square_terms=array([[3.16437378e-03, 1.58383379e-01], + [1.58383379e-01, 9.04125674e+00]]), scale=array([0.20498666, 0.14678316]), shift=array([9.06449208, 0.95321684])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=29, candidate_x=array([8.85950542, 1.01399005]), index=29, x=array([8.85950542, 1.01399005]), fval=0.6068057893338722, rho=0.9768016993555535, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 22]), step_length=0.20500276489564104, relative_step_length=0.8862965591579142, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.85950542, 1.01399005]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 21, 22, 24, 27, 28, 29]), model=ScalarModel(intercept=5.056588947128265, linear_terms=array([ 0.12014523, -14.39462599]), square_terms=array([[ 4.11394441e-03, -9.76345780e-02], + [-9.76345780e-02, 2.30839568e+01]]), scale=array([0.40997332, 0.24799163]), shift=array([8.85950542, 0.85200837])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=30, candidate_x=array([8.5580007 , 1.00630498]), index=29, x=array([8.85950542, 1.01399005]), fval=0.6068057893338722, rho=-0.3014966051278916, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 21, 22, 24, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, + 20, 23, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.85950542, 1.01399005]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 13, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.9805939633807261, linear_terms=array([-0.03836743, -1.98882772]), square_terms=array([[3.07342158e-03, 1.18191343e-01], + [1.18191343e-01, 5.34723779e+00]]), scale=array([0.20498666, 0.1454983 ]), shift=array([8.85950542, 0.9545017 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=31, candidate_x=array([8.65451876, 1.01183367]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=0.4338323431380853, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 13, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 1, 3, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, + 23]), step_length=0.20499800027263237, relative_step_length=0.8862759600748705, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=2.6746347212609862, linear_terms=array([ 0.41935614, -7.16647929]), square_terms=array([[ 0.03391117, -0.55595491], + [-0.55595491, 12.02901321]]), scale=array([0.40997332, 0.24906982]), shift=array([8.65451876, 0.85093018])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=32, candidate_x=array([8.24454545, 0.98780609]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.992201463503947, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 27, 28, 29, 30, 31]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, + 21, 22, 23, 24, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.8053556101878039, linear_terms=array([ 0.0500788 , -1.40901645]), square_terms=array([[ 1.81988958e-03, -5.72982752e-02], + [-5.72982752e-02, 4.04450417e+00]]), scale=array([0.20498666, 0.14657649]), shift=array([8.65451876, 0.95342351])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=33, candidate_x=array([8.44953211, 1.002411 ]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.2944780565240657, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 0, 1, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 28, 29, 30, 31, 33]), model=ScalarModel(intercept=0.5580455956991042, linear_terms=array([0.02153789, 0.02119119]), square_terms=array([[ 0.0023124 , -0.05543391], + [-0.05543391, 1.707328 ]]), scale=array([0.10249333, 0.09532983]), shift=array([8.65451876, 1.00467017])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=34, candidate_x=array([8.55202543, 1.00039176]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.872524866103317, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 28, 29, 30, 31, 33]), old_indices_discarded=array([ 1, 26, 27, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 29, 30, 31, 33, 34]), model=ScalarModel(intercept=0.5796812293248299, linear_terms=array([0.01586132, 0.02969879]), square_terms=array([[ 0.00226369, -0.03434719], + [-0.03434719, 0.60957011]]), scale=0.05782566872718589, shift=array([8.65451876, 1.01183367])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=35, candidate_x=array([8.59693373, 1.00593781]), index=31, x=array([8.65451876, 1.01183367]), fval=0.6028983591291701, rho=-1.1577436588415695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 13, 29, 30, 31, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.65451876, 1.01183367]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 31, 34, 35]), model=ScalarModel(intercept=0.6034966185125235, linear_terms=array([-0.00034619, -0.03914939]), square_terms=array([[9.39236593e-05, 5.48779016e-03], + [5.48779016e-03, 3.65303262e-01]]), scale=0.028912834363592946, shift=array([8.65451876, 1.01183367])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=36, candidate_x=array([8.62565678, 1.0153636 ]), index=36, x=array([8.62565678, 1.0153636 ]), fval=0.5971978537018936, rho=2.442794438040451, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.02907704145844469, relative_step_length=1.0056793842065692, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.62565678, 1.0153636 ]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 31, 33, 34, 35, 36]), model=ScalarModel(intercept=0.5603293659645237, linear_terms=array([-0.00236478, 0.12100806]), square_terms=array([[0.00302626, 0.06759657], + [0.06759657, 1.59874941]]), scale=0.05782566872718589, shift=array([8.62565678, 1.0153636 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=37, candidate_x=array([8.68324715, 1.00858284]), index=36, x=array([8.62565678, 1.0153636 ]), fval=0.5971978537018936, rho=-1.2581050712790383, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 30, 31, 33, 34, 35, 36]), old_indices_discarded=array([ 3, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.62565678, 1.0153636 ]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 31, 34, 35, 36, 37]), model=ScalarModel(intercept=0.5981101022806663, linear_terms=array([ 0.00137081, -0.01014437]), square_terms=array([[6.44240543e-05, 4.61221760e-03], + [4.61221760e-03, 3.91431444e-01]]), scale=0.028912834363592946, shift=array([8.62565678, 1.0153636 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=38, candidate_x=array([8.59675474, 1.01644935]), index=38, x=array([8.59675474, 1.01644935]), fval=0.5956021578624653, rho=0.987318065496306, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.02892243332840774, relative_step_length=1.0003319966729682, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.59675474, 1.01644935]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 31, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.5967490097134395, linear_terms=array([0.00266649, 0.00222244]), square_terms=array([[2.84220825e-04, 1.95806140e-02], + [1.95806140e-02, 1.56758521e+00]]), scale=0.05782566872718589, shift=array([8.59675474, 1.01644935])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=39, candidate_x=array([8.53893254, 1.01708857]), index=39, x=array([8.53893254, 1.01708857]), fval=0.5930797801438898, rho=0.9626177327032385, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 31, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 3, 7, 13, 29]), step_length=0.05782572833136036, relative_step_length=1.0000010307563367, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53893254, 1.01708857]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 31, 33, 34, 35, 36, 38, 39]), model=ScalarModel(intercept=0.6164032682482006, linear_terms=array([-0.0014308, -0.4276386]), square_terms=array([[9.03112375e-04, 5.61729802e-02], + [5.61729802e-02, 4.04976927e+00]]), scale=array([0.10249333, 0.09270238]), shift=array([8.53893254, 1.00729762])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=40, candidate_x=array([8.43643921, 1.01837244]), index=40, x=array([8.43643921, 1.01837244]), fval=0.5886586453047548, rho=0.9960093815093017, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 31, 33, 34, 35, 36, 38, 39]), old_indices_discarded=array([ 1, 3, 7, 13, 28, 29, 32, 37]), step_length=0.10250137012000532, relative_step_length=0.8862964525632524, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43643921, 1.01837244]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 31, 32, 35, 36, 39, 40]), model=ScalarModel(intercept=0.724464510197967, linear_terms=array([ 0.0588604 , -1.15277065]), square_terms=array([[ 2.63888902e-03, -4.80501605e-02], + [-4.80501605e-02, 3.54022042e+00]]), scale=array([0.20498666, 0.14330711]), shift=array([8.43643921, 0.95669289])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=41, candidate_x=array([8.23145255, 1.00141166]), index=40, x=array([8.43643921, 1.01837244]), fval=0.5886586453047548, rho=-1.288535811487196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 31, 32, 35, 36, 39, 40]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 33, 34, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43643921, 1.01837244]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 33, 34, 35, 38, 39, 40]), model=ScalarModel(intercept=0.6190068223081573, linear_terms=array([-0.00232714, -0.74138835]), square_terms=array([[9.27865058e-04, 6.83995451e-02], + [6.83995451e-02, 5.89689117e+00]]), scale=array([0.10249333, 0.09206044]), shift=array([8.43643921, 1.00793956])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=42, candidate_x=array([8.33394588, 1.02058172]), index=42, x=array([8.33394588, 1.02058172]), fval=0.5842263244969528, rho=0.6656700898189453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 33, 34, 35, 38, 39, 40]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 36, 37, 41]), step_length=0.10251713719284976, relative_step_length=0.8864327853821501, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.33394588, 1.02058172]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 32, 33, 39, 40, 41, 42]), model=ScalarModel(intercept=0.8040646506922852, linear_terms=array([-0.16272345, -1.28751653]), square_terms=array([[0.03910645, 0.3480799 ], + [0.3480799 , 3.42094897]]), scale=array([0.20498666, 0.14220247]), shift=array([8.33394588, 0.95779753])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=43, candidate_x=array([8.53893254, 0.99684816]), index=42, x=array([8.33394588, 1.02058172]), fval=0.5842263244969528, rho=-2.655032004787166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 32, 33, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 31, 34, 35, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.33394588, 1.02058172]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 33, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.5905582188830226, linear_terms=array([ 0.00921118, -0.6113136 ]), square_terms=array([[4.08156812e-04, 2.99902373e-02], + [2.99902373e-02, 5.44560063e+00]]), scale=array([0.10249333, 0.09095581]), shift=array([8.33394588, 1.00904419])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=44, candidate_x=array([8.23145255, 1.01975565]), index=44, x=array([8.23145255, 1.01975565]), fval=0.5809782664932625, rho=0.24916298067878628, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 33, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 1, 3, 13, 29, 30, 31, 34, 35, 36, 37, 38]), step_length=0.10249665809363835, relative_step_length=0.8862557091834465, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.23145255, 1.01975565]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 32, 33, 40, 41, 42, 44]), model=ScalarModel(intercept=0.9956483394129002, linear_terms=array([-0.42250664, -1.67357518]), square_terms=array([[0.14817312, 0.69125356], + [0.69125356, 3.60029371]]), scale=array([0.20498666, 0.1426155 ]), shift=array([8.23145255, 0.9573845 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=45, candidate_x=array([8.43643921, 0.9962964 ]), index=44, x=array([8.23145255, 1.01975565]), fval=0.5809782664932625, rho=-1.1980641129471428, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 10, 32, 33, 40, 41, 42, 44]), old_indices_discarded=array([ 0, 1, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, + 28, 29, 30, 31, 34, 35, 36, 37, 38, 39, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.23145255, 1.01975565]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 33, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.600332066842147, linear_terms=array([-0.00086165, -0.70849018]), square_terms=array([[8.18384405e-04, 5.99221947e-02], + [5.99221947e-02, 5.42443420e+00]]), scale=array([0.10249333, 0.09136884]), shift=array([8.23145255, 1.00863116])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=46, candidate_x=array([8.12895922, 1.02157425]), index=46, x=array([8.12895922, 1.02157425]), fval=0.5767016574644672, rho=0.6023906618426587, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 33, 40, 41, 42, 44, 45]), old_indices_discarded=array([ 1, 3, 30, 31, 34, 35, 36, 37, 38, 39, 43]), step_length=0.10250946224206313, relative_step_length=0.8863664225457526, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.12895922, 1.02157425]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 40, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=1.8860503514071725, linear_terms=array([-0.11681592, -5.841289 ]), square_terms=array([[5.93972205e-03, 2.64135430e-01], + [2.64135430e-01, 1.28731339e+01]]), scale=array([0.20498666, 0.1417062 ]), shift=array([8.12895922, 0.9582938 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=47, candidate_x=array([7.92397257, 1.02550171]), index=47, x=array([7.92397257, 1.02550171]), fval=0.5700309839906248, rho=2.144194285592646, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 40, 41, 42, 44, 45, 46]), old_indices_discarded=array([ 0, 1, 3, 13, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, + 36, 37, 38, 39, 43]), step_length=0.2050242792857033, relative_step_length=0.8863895731711363, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.92397257, 1.02550171]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 40, 41, 42, 44, 46, 47]), model=ScalarModel(intercept=9.701952266205492, linear_terms=array([ -0.61691483, -26.37740675]), square_terms=array([[2.29677817e-02, 8.91869339e-01], + [8.91869339e-01, 3.80689975e+01]]), scale=array([0.40997332, 0.2422358 ]), shift=array([7.92397257, 0.8577642 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=48, candidate_x=array([7.81188801, 1.02714111]), index=48, x=array([7.81188801, 1.02714111]), fval=0.5665120380760114, rho=16.57798751629955, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 40, 41, 42, 44, 46, 47]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, + 37, 38, 39, 43, 45]), step_length=0.1120965490592223, relative_step_length=0.24231572138854002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.81188801, 1.02714111]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 32, 41, 42, 44, 46, 47, 48]), model=ScalarModel(intercept=9.766219925631294, linear_terms=array([ -0.6871664 , -26.30219402]), square_terms=array([[2.73943250e-02, 9.72602328e-01], + [9.72602328e-01, 3.76072310e+01]]), scale=array([0.40997332, 0.2414161 ]), shift=array([7.81188801, 0.8585839 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=49, candidate_x=array([8.22186132, 1.02118484]), index=48, x=array([7.81188801, 1.02714111]), fval=0.5665120380760114, rho=-2.3061269690172246, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 32, 41, 42, 44, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, + 37, 38, 39, 40, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.81188801, 1.02714111]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 32, 41, 42, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=1.4654889570621736, linear_terms=array([-0.05047381, -4.00812911]), square_terms=array([[2.45887025e-03, 1.30332665e-01], + [1.30332665e-01, 8.90254327e+00]]), scale=array([0.20498666, 0.13892277]), shift=array([7.81188801, 0.96107723])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=50, candidate_x=array([7.60690135, 1.02565727]), index=50, x=array([7.60690135, 1.02565727]), fval=0.5602123257733056, rho=0.5841994228455609, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 32, 41, 42, 44, 46, 47, 48, 49]), old_indices_discarded=array([ 1, 3, 7, 13, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45]), step_length=0.20499202897310145, relative_step_length=0.886250144119507, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.60690135, 1.02565727]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([32, 41, 42, 44, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=7.123730519114742, linear_terms=array([ -0.32734999, -18.89524377]), square_terms=array([[1.11231711e-02, 4.88743382e-01], + [4.88743382e-01, 2.71922839e+01]]), scale=array([0.40997332, 0.24215803]), shift=array([7.60690135, 0.85784197])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=51, candidate_x=array([7.19692803, 1.03046403]), index=51, x=array([7.19692803, 1.03046403]), fval=0.549027411692029, rho=1.0036558314595654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([32, 41, 42, 44, 46, 47, 48, 49, 50]), old_indices_discarded=array([ 0, 1, 3, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, + 40, 43, 45]), step_length=0.4100014945883672, relative_step_length=0.8862878363817586, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.19692803, 1.03046403]), radius=0.9252106996349743, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([32, 41, 44, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=12.73480276563746, linear_terms=array([ -0.87087078, -31.77015852]), square_terms=array([[ 0.042882 , 1.15994563], + [ 1.15994563, 41.41239327]]), scale=array([0.81994663, 0.3 ]), shift=array([7.19692803, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=52, candidate_x=array([6.3769814 , 1.03855253]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=0.4587040426302159, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([32, 41, 44, 46, 47, 48, 49, 50, 51]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, + 35, 36, 37, 38, 39, 40, 42, 43, 45]), step_length=0.8199865279119269, relative_step_length=0.8862700444725059, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=1.8504213992699485, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([41, 44, 46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=14.222023285880177, linear_terms=array([ -1.72856364, -34.55682834]), square_terms=array([[ 0.15979702, 2.19225084], + [ 2.19225084, 43.64084662]]), scale=array([1.63989327, 0.3 ]), shift=array([6.3769814, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=53, candidate_x=array([6.13394911, 1.03978719]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-4.2277721907011685, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 44, 46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.9252106996349743, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([44, 46, 47, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=15.125592672308542, linear_terms=array([ -0.92742527, -36.66916769]), square_terms=array([[4.28554863e-02, 1.16804426e+00], + [1.16804426e+00, 4.61029190e+01]]), scale=array([0.81994663, 0.3 ]), shift=array([6.3769814, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=54, candidate_x=array([6.27747973, 1.03953523]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-13.31771442582917, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 47, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([46, 47, 48, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=10.065029563372587, linear_terms=array([ -0.39719221, -25.86425371]), square_terms=array([[1.16915302e-02, 5.40173955e-01], + [5.40173955e-01, 3.51273033e+01]]), scale=array([0.40997332, 0.23571039]), shift=array([6.3769814 , 0.86428961])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=55, candidate_x=array([6.31179845, 1.0384196 ]), index=52, x=array([6.3769814 , 1.03855253]), fval=0.5426853573405204, rho=-0.784363594729585, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 47, 48, 49, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 1, 3, 7, 10, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, + 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.3769814 , 1.03855253]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 52, 53, 54, 55]), model=ScalarModel(intercept=2.3920584590081724, linear_terms=array([-0.08950127, -6.81205893]), square_terms=array([[3.19260557e-03, 1.63216758e-01], + [1.63216758e-01, 1.25472782e+01]]), scale=array([0.20498666, 0.13321706]), shift=array([6.3769814 , 0.96678294])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=56, candidate_x=array([6.54738856, 1.0376674 ]), index=56, x=array([6.54738856, 1.0376674 ]), fval=0.5422070151116223, rho=0.9994517465051278, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([51, 52, 53, 54, 55]), old_indices_discarded=array([], dtype=int32), step_length=0.17040945911786537, relative_step_length=0.7367379524905946, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.54738856, 1.0376674 ]), radius=0.46260534981748713, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([47, 48, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=12.845919206247771, linear_terms=array([ -0.48580072, -33.75567653]), square_terms=array([[1.32030757e-02, 6.68795751e-01], + [6.68795751e-01, 4.63083580e+01]]), scale=array([0.40997332, 0.23615296]), shift=array([6.54738856, 0.86384704])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=57, candidate_x=array([6.35000299, 1.03762871]), index=56, x=array([6.54738856, 1.0376674 ]), fval=0.5422070151116223, rho=-0.3154098619261682, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([47, 48, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, + 41, 42, 43, 44, 45, 46, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.54738856, 1.0376674 ]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([50, 51, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=2.0362311686925794, linear_terms=array([-0.07626161, -5.56401149]), square_terms=array([[2.97339012e-03, 1.42670586e-01], + [1.42670586e-01, 1.03610380e+01]]), scale=array([0.20498666, 0.13365963]), shift=array([6.54738856, 0.96634037])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=58, candidate_x=array([6.47538896, 1.03876377]), index=56, x=array([6.54738856, 1.0376674 ]), fval=0.5422070151116223, rho=-10.672128030780884, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([50, 51, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.54738856, 1.0376674 ]), radius=0.11565133745437178, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.7236091885770913, linear_terms=array([-0.01427721, -1.59977577]), square_terms=array([[8.28292861e-04, 6.05950908e-02], + [6.05950908e-02, 7.01428649e+00]]), scale=array([0.10249333, 0.08241296]), shift=array([6.54738856, 1.01758704])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=59, candidate_x=array([6.64988189, 1.03567133]), index=59, x=array([6.64988189, 1.03567133]), fval=0.541686191657866, rho=0.45049343577625856, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.10251276421985119, relative_step_length=0.8863949736880112, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.64988189, 1.03567133]), radius=0.23130267490874357, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([51, 52, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=2.5086291679932122, linear_terms=array([-0.09264234, 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square_terms=array([[3.29232175e-03, 1.89561505e-01], + [1.89561505e-01, 1.70827528e+01]]), scale=array([0.20498666, 0.13397188]), shift=array([6.53909631, 0.96602812])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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6.53773842e+00]]), scale=array([0.10249333, 0.08272521]), shift=array([6.53909631, 1.01727479])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.13452982]), shift=array([6.61897887, 0.96547018])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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x=array([6.59006647, 1.03608841]), fval=0.5414379715211688, rho=3.2219246695022767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 64, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.028912851176776357, relative_step_length=1.000000581512805, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.59006647, 1.03608841]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 58, 59, 60, 61, 62, 65, 66, 67]), model=ScalarModel(intercept=0.5415547803952224, linear_terms=array([ 1.01377641e-05, -3.95364543e-05]), square_terms=array([[2.63397868e-04, 2.35619632e-02], + [2.35619632e-02, 3.13951084e+00]]), scale=0.05782566872718589, shift=array([6.59006647, 1.03608841])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, 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fval=0.5414293228153648, rho=13.747183659712503, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([56, 58, 59, 60, 61, 62, 65, 66, 67]), old_indices_discarded=array([52, 55, 57, 63, 64]), step_length=0.0069704007281480235, relative_step_length=0.1205416362936239, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.05782566872718589, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 59, 60, 61, 62, 65, 66, 67, 68]), model=ScalarModel(intercept=0.5414796419298218, linear_terms=array([0.00011312, 0.00042998]), square_terms=array([[2.70149950e-04, 2.41187611e-02], + [2.41187611e-02, 3.15020230e+00]]), scale=0.05782566872718589, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=69, candidate_x=array([6.52527224, 1.03657627]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-2.4940420439925477, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 59, 60, 61, 62, 65, 66, 67, 68]), old_indices_discarded=array([52, 55, 57, 58, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.028912834363592946, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 59, 60, 62, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.5415011318321497, linear_terms=array([ 1.74351890e-05, -2.75129179e-04]), square_terms=array([[6.75592967e-05, 6.01725598e-03], + [6.01725598e-03, 7.88582554e-01]]), scale=0.028912834363592946, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=70, candidate_x=array([6.55418436, 1.03637223]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-11.73867706052995, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 59, 60, 62, 65, 66, 67, 68, 69]), old_indices_discarded=array([58, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.014456417181796473, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 60, 62, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.5414799019531137, linear_terms=array([-3.44991235e-06, -9.05048244e-05]), square_terms=array([[1.67238718e-05, 1.49276761e-03], + [1.49276761e-03, 1.97781855e-01]]), scale=0.014456417181796473, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=71, candidate_x=array([6.59042582, 1.03609275]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-21.95038108304595, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 60, 62, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0072282085908982364, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([56, 62, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.5414632259336039, linear_terms=array([2.83264621e-06, 2.05882321e-05]), square_terms=array([[4.16386161e-06, 3.73300224e-04], + [3.73300224e-04, 4.94351827e-02]]), scale=0.0072282085908982364, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=72, candidate_x=array([6.57586825, 1.03619302]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-7.761982760893527, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([56, 62, 65, 66, 67, 68, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0036141042954491182, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([67, 68, 71, 72]), model=ScalarModel(intercept=0.5414388981465452, linear_terms=array([2.71386294e-05, 3.84330426e-03]), square_terms=array([[1.04795962e-06, 9.32698230e-05], + [9.32698230e-05, 1.23060327e-02]]), scale=0.0036141042954491182, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=73, candidate_x=array([6.58668802, 1.03498566]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.948451935681875, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 68, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0018070521477245591, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([67, 68, 71, 72, 73]), model=ScalarModel(intercept=0.5414389397529535, linear_terms=array([3.45115217e-07, 6.30331745e-05]), square_terms=array([[2.70925737e-07, 2.39729562e-05], + [2.39729562e-05, 3.08425190e-03]]), scale=0.0018070521477245591, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=74, candidate_x=array([6.58490275, 1.03609048]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-33.80511316040285, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 68, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0009035260738622796, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 73, 74]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([1.41688629e-05, 6.76977808e-05]), square_terms=array([[6.26837304e-08, 5.47876928e-06], + [5.47876928e-06, 7.68259730e-04]]), scale=0.0009035260738622796, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=75, candidate_x=array([6.58219221, 1.03606956]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-3.312310435431627, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=0.0004517630369311398, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 74, 75]), model=ScalarModel(intercept=0.5414293228153648, linear_terms=array([-2.29729551e-06, -2.98650390e-04]), square_terms=array([[1.73350246e-08, 1.53237458e-06], + [1.53237458e-06, 2.01506003e-04]]), scale=0.0004517630369311398, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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square_terms=array([[5.97250724e-09, 4.20181109e-07], + [4.20181109e-07, 4.91845731e-05]]), scale=0.0002258815184655699, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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scale=0.00011294075923278494, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=78, candidate_x=array([6.58300196, 1.03607308]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.9288183820802519, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 76, 77]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78]), model=ScalarModel(intercept=0.5414293228153648, linear_terms=array([-1.22674426e-05, -2.40256385e-05]), square_terms=array([[1.26112163e-09, 5.06903136e-08], + [5.06903136e-08, 3.10638384e-06]]), scale=5.647037961639247e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=79, candidate_x=array([6.58311861, 1.03619332]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.12812387344724027, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 77, 78]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([-2.27116547e-05, 1.08555655e-05]), square_terms=array([[4.35976955e-09, 1.69031789e-08], + [1.69031789e-08, 7.57228023e-07]]), scale=2.8235189808196236e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=80, candidate_x=array([6.58312214, 1.03612944]), index=68, x=array([6.58309627, 1.03614145]), fval=0.5414293228153648, rho=-0.35421190384338624, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 78, 79]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58309627, 1.03614145]), radius=1.4117594904098118e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 79, 80]), model=ScalarModel(intercept=0.541429322815365, linear_terms=array([ 4.26256780e-06, -1.29794683e-06]), square_terms=array([[1.87697604e-10, 7.48543093e-10], + [7.48543093e-10, 1.92469128e-07]]), scale=1.4117594904098118e-05, shift=array([6.58309627, 1.03614145])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, 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old_indices_used=array([68, 79, 80]), old_indices_discarded=array([], dtype=int32), step_length=1.4381793867319074e-05, relative_step_length=1.018714162363751, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58308246, 1.03614548]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79, 80, 81]), model=ScalarModel(intercept=0.5414393336189507, linear_terms=array([-6.83138000e-06, -5.43556504e-06]), square_terms=array([[8.38980666e-10, 1.12053013e-08], + [1.12053013e-08, 7.72680051e-07]]), scale=2.8235189808196236e-05, shift=array([6.58308246, 1.03614548])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=2.8574644124841038e-05, relative_step_length=1.0120223847953826, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58310557, 1.03616229]), radius=5.647037961639247e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.5414289185236869, linear_terms=array([-9.17816477e-06, -1.57462672e-05]), square_terms=array([[1.32415510e-09, 4.69483871e-08], + [4.69483871e-08, 3.08710853e-06]]), scale=5.647037961639247e-05, shift=array([6.58310557, 1.03616229])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58310557, 1.03616229]), radius=2.8235189808196236e-05, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 78, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.541436888279065, linear_terms=array([-1.16278729e-05, 4.19591953e-06]), square_terms=array([[9.75111772e-10, 1.22776981e-08], + [1.22776981e-08, 7.67636851e-07]]), scale=2.8235189808196236e-05, shift=array([6.58310557, 1.03616229])), vector_model=VectorModel(intercepts=array([ 0.04871065, 0.12404049, 0.14884012, 0.19380771, 0.21739522, + 0.23240935, 0.23334521, 0.06700373, -0.08020904, -0.06714673, + -0.40907425, -0.41756644, -0.12515654, -0.09879383, -0.08942031, + -0.09320124, -0.09958839]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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scale=0.9252106996349743, shift=array([9.252107, 1. ])), candidate_index=91, candidate_x=array([6.58313347, 1.03615385]), index=91, x=array([6.58313347, 1.03615385]), fval=0.5414180888046741, rho=0.09302207306449327, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([84, 89, 90]), old_indices_discarded=array([], dtype=int32), step_length=9.99999999895458e-07, relative_step_length=0.9999999998954582, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 92 entries., 'history': {'params': [{'CRRA': 9.252106996349742, 'DiscFac': 1.0}, {'CRRA': 8.432160362616244, 'DiscFac': 0.5396951369531111}, {'CRRA': 10.07205363008324, 'DiscFac': 0.839644435905758}, {'CRRA': 8.432160362616244, 'DiscFac': 0.8285194430703369}, {'CRRA': 10.07205363008324, 'DiscFac': 1.0999990254130083}, {'CRRA': 10.07205363008324, 'DiscFac': 0.5384287157628791}, {'CRRA': 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model=ScalarModel(intercept=0.781511080377176, linear_terms=array([-0.06570334, 0.6958516 ]), square_terms=array([[ 0.00455801, -0.05593978], + [-0.05593978, 0.75231219]]), scale=array([0.10496142, 0.10251247]), shift=array([9.47490235, 0.99748753])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=16, candidate_x=array([9.57986377, 0.91029109]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-2.2880982913831005, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 11, 12, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 15, 16]), model=ScalarModel(intercept=0.6108530298545867, linear_terms=array([-0.10139668, -0.14696738]), square_terms=array([[0.07792252, 0.32866092], + [0.32866092, 1.49842941]]), scale=0.05921813970334309, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=17, candidate_x=array([9.5340187 , 0.99306208]), index=0, x=array([9.47490235, 0.99993647]), fval=0.6448272261874602, rho=-0.3116075291616809, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47490235, 0.99993647]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17]), model=ScalarModel(intercept=0.6954288371659393, linear_terms=array([-0.10174434, -0.15897912]), square_terms=array([[0.03333137, 0.10554599], + [0.10554599, 0.37011472]]), scale=0.029609069851671544, shift=array([9.47490235, 0.99993647])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=18, candidate_x=array([9.50661188, 1.0031676 ]), index=18, x=array([9.50661188, 1.0031676 ]), fval=0.6396317123330025, rho=0.05607187891543219, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.03187372417953975, relative_step_length=1.0764851560421564, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.50661188, 1.0031676 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.6396317123330028, linear_terms=array([ 0.00028177, -0.01430195]), square_terms=array([[2.43882216e-05, 1.35799469e-03], + [1.35799469e-03, 8.34202654e-02]]), scale=0.014804534925835772, shift=array([9.50661188, 1.0031676 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=19, candidate_x=array([9.49185035, 1.0059291 ]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=1.4693827516516844, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.015017609941676606, relative_step_length=1.0143925504521585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19]), model=ScalarModel(intercept=0.6412257536348447, linear_terms=array([-0.06905576, -0.03758833]), square_terms=array([[0.03588925, 0.1094543 ], + [0.1094543 , 0.37040144]]), scale=0.029609069851671544, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=20, candidate_x=array([9.52125862, 1.00094573]), index=19, x=array([9.49185035, 1.0059291 ]), fval=0.6370780738825652, rho=-0.11096998055275568, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.49185035, 1.0059291 ]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=0.6375966987395302, linear_terms=array([ 0.00059101, -0.00033053]), square_terms=array([[2.18308578e-05, 1.28615347e-03], + [1.28615347e-03, 8.40653356e-02]]), scale=0.014804534925835772, shift=array([9.49185035, 1.0059291 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=21, candidate_x=array([9.47704841, 1.00621177]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=1.2946325204884988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.01480464158988585, relative_step_length=1.0000072048227528, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.6740948994884155, linear_terms=array([-0.0775299 , -0.08137508]), square_terms=array([[0.03091198, 0.10157856], + [0.10157856, 0.37039842]]), scale=0.029609069851671544, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=22, candidate_x=array([9.50732214, 1.00462907]), index=21, x=array([9.47704841, 1.00621177]), fval=0.6363070785814076, rho=-0.029474268266789803, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 16, 17, 18, 19, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.47704841, 1.00621177]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.6367076097989959, linear_terms=array([ 6.38911855e-04, -5.64861763e-05]), square_terms=array([[1.99591697e-05, 1.23030124e-03], + [1.23030124e-03, 8.44345370e-02]]), scale=0.014804534925835772, shift=array([9.47704841, 1.00621177])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=23, candidate_x=array([9.46224557, 1.00643568]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=1.1173242341721383, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.014804537200668226, relative_step_length=1.0000001536578127, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.6376967017750665, linear_terms=array([-0.00887642, -0.07715602]), square_terms=array([[0.00091426, 0.02162778], + [0.02162778, 0.54194842]]), scale=0.029609069851671544, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=24, candidate_x=array([9.49199772, 1.00943198]), index=23, x=array([9.46224557, 1.00643568]), fval=0.6355934835508369, rho=-0.23439049876600507, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 12, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.46224557, 1.00643568]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.6357439312845821, linear_terms=array([ 0.00057539, -0.00082847]), square_terms=array([[2.06319310e-05, 1.26644672e-03], + [1.26644672e-03, 8.60694145e-02]]), scale=0.014804534925835772, shift=array([9.46224557, 1.00643568])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=25, candidate_x=array([9.44744469, 1.00679353]), index=25, x=array([9.44744469, 1.00679353]), fval=0.6347642471305107, rho=1.4044689675006616, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.014805197302692493, relative_step_length=1.000044741483609, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.44744469, 1.00679353]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.6347153364849043, linear_terms=array([ 0.00136442, -0.00288838]), square_terms=array([[7.21853077e-05, 4.81168593e-03], + [4.81168593e-03, 3.57552054e-01]]), scale=0.029609069851671544, shift=array([9.44744469, 1.00679353])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=26, candidate_x=array([9.41784149, 1.00742862]), index=26, x=array([9.41784149, 1.00742862]), fval=0.6332046797989737, rho=1.1053204142647344, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([17]), step_length=0.02961001570090617, relative_step_length=1.0000319445777717, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.41784149, 1.00742862]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6332385782456339, linear_terms=array([0.0028514 , 0.00022792]), square_terms=array([[2.79552380e-04, 1.89198753e-02], + [1.89198753e-02, 1.43266898e+00]]), scale=0.05921813970334309, shift=array([9.41784149, 1.00742862])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=27, candidate_x=array([9.35862837, 1.00819965]), index=27, x=array([9.35862837, 1.00819965]), fval=0.6303511715372276, rho=1.0071263421242136, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([12, 15, 16, 17, 20]), step_length=0.0592181407159667, relative_step_length=1.0000000170998888, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.35862837, 1.00819965]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.639178807035496, linear_terms=array([ 0.00132575, -0.26376211]), square_terms=array([[8.65219778e-04, 5.51863712e-02], + [5.51863712e-02, 3.95321986e+00]]), scale=array([0.10496142, 0.09838088]), shift=array([9.35862837, 1.00161912])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=28, candidate_x=array([9.25366695, 1.00955655]), index=28, x=array([9.25366695, 1.00955655]), fval=0.6252147257628975, rho=1.035476052619481, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 21, 23, 24, 25, 26, 27]), old_indices_discarded=array([11, 12, 13, 14, 15, 16, 17, 20, 22]), step_length=0.10497019014652463, relative_step_length=0.8863009769673553, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25366695, 1.00955655]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=1.3525263472005808, linear_terms=array([-0.05604803, -3.65995549]), square_terms=array([[3.37929225e-03, 1.66025609e-01], + [1.66025609e-01, 9.20973251e+00]]), scale=array([0.20992284, 0.15018314]), shift=array([9.25366695, 0.94981686])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=29, candidate_x=array([9.04374411, 1.01220715]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=1.0345307568988933, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 21, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 22]), step_length=0.20993957271621416, relative_step_length=0.8862975676368734, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 10, 13, 22, 25, 27, 28, 29]), model=ScalarModel(intercept=2.887557528467335, linear_terms=array([ 0.32145309, -7.82327945]), square_terms=array([[ 0.01909841, -0.39706853], + [-0.39706853, 13.16541827]]), scale=array([0.41984568, 0.25381927]), shift=array([9.04374411, 0.84618073])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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20, + 21, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=1.1785163391833393, linear_terms=array([ 0.04245163, -3.2666068 ]), square_terms=array([[ 1.11423119e-03, -2.87009785e-02], + [-2.87009785e-02, 8.89866124e+00]]), scale=array([0.20992284, 0.14885785]), shift=array([9.04374411, 0.95114215])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=31, candidate_x=array([8.83382127, 1.00530622]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.21904622845287416, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 23, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 6, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, + 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=0.588971346798013, linear_terms=array([ 0.01505103, -0.13000888]), square_terms=array([[ 2.31746838e-04, -3.20175653e-03], + [-3.20175653e-03, 3.75543886e+00]]), scale=array([0.10496142, 0.09637714]), shift=array([9.04374411, 1.00362286])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=32, candidate_x=array([8.93878269, 1.00687716]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-0.19293832377303985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 25, 26, 27, 28, 29, 31]), old_indices_discarded=array([ 0, 3, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32]), model=ScalarModel(intercept=0.6214336044267341, linear_terms=array([0.00147887, 0.10259686]), square_terms=array([[4.10977860e-04, 1.78962346e-02], + [1.78962346e-02, 8.79816604e-01]]), scale=0.05921813970334309, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=33, candidate_x=array([9.10280287, 1.00410548]), index=29, x=array([9.04374411, 1.01220715]), fval=0.6151402665456015, rho=-2.08297420159985, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.04374411, 1.01220715]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33]), model=ScalarModel(intercept=0.6151402665456009, linear_terms=array([0.00149709, 0.00482735]), square_terms=array([[7.65543339e-05, 5.14393571e-03], + [5.14393571e-03, 3.91745261e-01]]), scale=0.029609069851671544, shift=array([9.04374411, 1.01220715])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + 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radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.620881773286023, linear_terms=array([0.00197631, 0.09173778]), square_terms=array([[3.44046695e-04, 1.64408522e-02], + [1.64408522e-02, 8.92929811e-01]]), scale=0.05921813970334309, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=35, candidate_x=array([8.95483028, 1.00724033]), index=34, x=array([9.0141328 , 1.01223102]), fval=0.6138905842835336, rho=-0.906107936879546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.0141328 , 1.01223102]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([29, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6137867229839677, linear_terms=array([0.00142032, 0.00043433]), square_terms=array([[7.69832064e-05, 5.16913372e-03], + [5.16913372e-03, 3.92730596e-01]]), scale=0.029609069851671544, shift=array([9.0141328 , 1.01223102])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=36, candidate_x=array([8.98452585, 1.01258669]), index=36, x=array([8.98452585, 1.01258669]), fval=0.6125286403569352, rho=0.9657336720601446, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.029609089398228004, relative_step_length=1.0000006601543567, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.98452585, 1.01258669]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 28, 29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.619467823965291, linear_terms=array([0.00209097, 0.09020298]), square_terms=array([[3.29303090e-04, 1.61351934e-02], + [1.61351934e-02, 9.00329160e-01]]), scale=0.05921813970334309, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([0.00140759, 0.00037964]), square_terms=array([[7.68743253e-05, 5.17639009e-03], + [5.17639009e-03, 3.94127433e-01]]), scale=0.029609069851671544, shift=array([8.98452585, 1.01258669])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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square_terms=array([[3.04730334e-04, 2.06301143e-02], + [2.06301143e-02, 1.58200616e+00]]), scale=0.05921813970334309, shift=array([8.95491894, 1.01294575])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=39, candidate_x=array([8.89570505, 1.01365764]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=0.9122655509396086, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([13]), step_length=0.05921817039076989, relative_step_length=1.0000005182099092, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.614727205905659, linear_terms=array([-0.00711409, -0.08425954]), square_terms=array([[1.97921783e-03, 6.38199381e-02], + [6.38199381e-02, 2.30738160e+00]]), scale=array([0.10496142, 0.09565189]), shift=array([8.89570505, 1.00434811])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=40, candidate_x=array([9.00066647, 1.00519543]), index=39, x=array([8.89570505, 1.01365764]), fval=0.608454913769554, rho=-1.8130574184159354, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([ 0, 1, 3, 7, 10, 21, 23, 25, 26, 27, 28, 29, 30, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.89570505, 1.01365764]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.6082515870760768, linear_terms=array([0.00289756, 0.00212777]), square_terms=array([[2.93803015e-04, 2.03395974e-02], + [2.03395974e-02, 1.60156668e+00]]), scale=0.05921813970334309, shift=array([8.89570505, 1.01365764])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=41, candidate_x=array([8.83649067, 1.01432978]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=0.9335608646751216, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 32, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([ 7, 10, 13, 29, 30, 33]), step_length=0.05921819354659514, relative_step_length=1.0000009092357902, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), model=ScalarModel(intercept=0.610651531374723, linear_terms=array([ 0.00125715, -0.10509161]), square_terms=array([[7.56634919e-04, 3.91907529e-02], + [3.91907529e-02, 2.39236786e+00]]), scale=array([0.10496142, 0.09531582]), shift=array([8.83649067, 1.00468418])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=42, candidate_x=array([8.73152925, 1.01043262]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.2539961161624663, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 36, 37, 38, 39, 41]), old_indices_discarded=array([ 1, 3, 7, 10, 26, 27, 28, 29, 30, 33, 34, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), model=ScalarModel(intercept=0.6128960194558581, linear_terms=array([0.00323829, 0.08375858]), square_terms=array([[2.19797272e-04, 1.29854569e-02], + [1.29854569e-02, 9.21144727e-01]]), scale=0.05921813970334309, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=43, candidate_x=array([8.77720289, 1.00979089]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-0.636748464236313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 35, 37, 38, 39, 41, 42]), old_indices_discarded=array([ 3, 7, 10, 29, 30, 33, 34, 36, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), model=ScalarModel(intercept=0.6129958444671166, linear_terms=array([0.00125322, 0.0420077 ]), square_terms=array([[7.02244693e-05, 3.72273429e-03], + [3.72273429e-03, 2.29436197e-01]]), scale=0.029609069851671544, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=44, candidate_x=array([8.80679818, 1.00940247]), index=41, x=array([8.83649067, 1.01432978]), fval=0.6057905110013826, rho=-1.1780504359812205, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 32, 37, 38, 39, 41, 42, 43]), old_indices_discarded=array([35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.83649067, 1.01432978]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 39, 41, 43, 44]), model=ScalarModel(intercept=0.6059146038962105, linear_terms=array([0.00063063, 0.00133693]), square_terms=array([[1.93356047e-05, 1.31514692e-03], + [1.31514692e-03, 1.01583452e-01]]), scale=0.014804534925835772, shift=array([8.83649067, 1.01432978])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=45, candidate_x=array([8.82168485, 1.01432664]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=1.020928649746183, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 39, 41, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.014805816548052337, relative_step_length=1.0000865695695937, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.6115555266177718, linear_terms=array([0.00124126, 0.04101205]), square_terms=array([[7.04241015e-05, 3.73186729e-03], + [3.73186729e-03, 2.30210452e-01]]), scale=0.029609069851671544, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=46, candidate_x=array([8.79199479, 1.00954484]), index=45, x=array([8.82168485, 1.01432664]), fval=0.6051564916805549, rho=-1.230884779808124, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 37, 39, 41, 42, 43, 44, 45]), old_indices_discarded=array([32, 35, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.82168485, 1.01432664]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6052045803357664, linear_terms=array([5.75893275e-04, 2.08251016e-05]), square_terms=array([[2.14888603e-05, 1.39552818e-03], + [1.39552818e-03, 1.02157333e-01]]), scale=0.014804534925835772, shift=array([8.82168485, 1.01432664])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=47, candidate_x=array([8.80688164, 1.01452473]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=1.1411150863578703, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.014804535502910653, relative_step_length=1.0000000389796022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.6105358682194937, linear_terms=array([0.00053509, 0.04163391]), square_terms=array([[1.09771507e-04, 4.72352439e-03], + [4.72352439e-03, 2.30297392e-01]]), scale=0.029609069851671544, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=48, candidate_x=array([8.8363726 , 1.00857494]), index=47, x=array([8.80688164, 1.01452473]), fval=0.6045010959198394, rho=-2.17105462493869, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([13, 31, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([32, 37, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.80688164, 1.01452473]), radius=0.014804534925835772, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.6045817009488239, linear_terms=array([ 0.00060537, -0.00012916]), square_terms=array([[2.04142540e-05, 1.36233150e-03], + [1.36233150e-03, 1.02744088e-01]]), scale=0.014804534925835772, shift=array([8.80688164, 1.01452473])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=49, candidate_x=array([8.79207864, 1.01473837]), index=49, x=array([8.79207864, 1.01473837]), fval=0.6038388333603965, rho=1.0929767802825647, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.01480454508906136, relative_step_length=1.0000006864940802, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.79207864, 1.01473837]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.6039320017441308, linear_terms=array([1.22941931e-03, 2.77206681e-05]), square_terms=array([[7.95912011e-05, 5.38269834e-03], + [5.38269834e-03, 4.12173721e-01]]), scale=0.029609069851671544, shift=array([8.79207864, 1.01473837])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=50, candidate_x=array([8.76247205, 1.01512188]), index=50, x=array([8.76247205, 1.01512188]), fval=0.6024855719631016, rho=1.1053283609927005, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 41, 43, 44, 45, 46, 47, 48, 49]), old_indices_discarded=array([13, 32, 37, 39, 42]), step_length=0.02960907001757359, relative_step_length=1.000000005603082, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.76247205, 1.01512188]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.6026578745853444, linear_terms=array([0.0024301, 0.0002779]), square_terms=array([[3.16909370e-04, 2.15042159e-02], + [2.15042159e-02, 1.65484964e+00]]), scale=0.05921813970334309, shift=array([8.76247205, 1.01512188])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=51, candidate_x=array([8.70325877, 1.0158803 ]), index=51, x=array([8.70325877, 1.0158803 ]), fval=0.5997907962729061, rho=1.1192868878296924, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 42, 43, 44, 45, 46, 47, 49, 50]), old_indices_discarded=array([ 3, 7, 10, 13, 29, 30, 32, 34, 35, 36, 37, 38, 39, 40, 41, 48]), step_length=0.05921814073463714, relative_step_length=1.0000000174151713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), model=ScalarModel(intercept=0.5745601532643059, linear_terms=array([ 0.01933147, -0.20828263]), square_terms=array([[ 3.96552248e-04, -7.70697193e-03], + [-7.70697193e-03, 3.60952666e+00]]), scale=array([0.10496142, 0.09454056]), shift=array([8.70325877, 1.00545944])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=52, candidate_x=array([8.59829735, 1.01071291]), index=51, x=array([8.70325877, 1.0158803 ]), fval=0.5997907962729061, rho=-0.2234956303402016, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 13, 30, 42, 44, 45, 50, 51]), old_indices_discarded=array([ 1, 3, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, + 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.70325877, 1.0158803 ]), radius=0.05921813970334309, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 46, 49, 50, 51]), model=ScalarModel(intercept=0.5848648139367352, linear_terms=array([ 0.01259792, -0.01987223]), square_terms=array([[ 2.31944576e-04, -1.32546665e-02], + [-1.32546665e-02, 2.24249333e+00]]), scale=0.05921813970334309, shift=array([8.70325877, 1.0158803 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([ 3, 13, 31, 32, 35, 36, 37, 38, 39, 41, 44, 45, 47, 48, 52]), step_length=0.05922045545199665, relative_step_length=1.0000391053934683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.64403857, 1.01605409]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 43, 50, 51, 52, 53]), model=ScalarModel(intercept=0.6233574060676437, linear_terms=array([ 0.01619734, -0.70631774]), square_terms=array([[2.97413525e-04, 7.75950577e-04], + [7.75950577e-04, 5.79975163e+00]]), scale=array([0.10496142, 0.09445367]), shift=array([8.64403857, 1.00554633])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 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43, 50, 51, 52, 53]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, + 46, 47, 48, 49]), step_length=0.10496625831674383, relative_step_length=0.8862677791178409, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.7023662250726452, linear_terms=array([ 0.06492856, -1.1147206 ]), square_terms=array([[ 3.47923203e-03, -8.06332216e-02], + [-8.06332216e-02, 3.61802648e+00]]), scale=array([0.20992284, 0.14643046]), shift=array([8.53907715, 0.95356954])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=55, candidate_x=array([8.32915431, 0.99542161]), index=54, x=array([8.53907715, 1.01706193]), fval=0.5930898839617496, rho=-1.7530958161469754, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 42, 50, 51, 52, 53, 54]), old_indices_discarded=array([ 0, 1, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.53907715, 1.01706193]), radius=0.11843627940668618, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.615086708073995, linear_terms=array([ 0.00978289, -0.72244786]), square_terms=array([[4.17284358e-04, 3.04748646e-02], + [3.04748646e-02, 5.74255506e+00]]), scale=array([0.10496142, 0.09394975]), shift=array([8.53907715, 1.00605025])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=56, candidate_x=array([8.43411573, 1.01836827]), index=56, x=array([8.43411573, 1.01836827]), fval=0.5885914443329772, rho=0.3283224223143453, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 42, 51, 52, 53, 54, 55]), old_indices_discarded=array([ 1, 3, 13, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.10496954879325875, relative_step_length=0.8862955617916246, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.43411573, 1.01836827]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=1.9169446392811247, linear_terms=array([-0.05516787, -6.07713486]), square_terms=array([[2.74937852e-03, 1.66261876e-01], + [1.66261876e-01, 1.36583309e+01]]), scale=array([0.20992284, 0.14577728]), shift=array([8.43411573, 0.95422272])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=57, candidate_x=array([8.22419289, 1.02085937]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=0.46355807804061855, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([ 0, 1, 3, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, + 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50]), step_length=0.2099376195491761, relative_step_length=0.8862893219918404, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=3.341466430284669, linear_terms=array([-1.05103033, -7.77084409]), square_terms=array([[ 0.18279549, 1.34509322], + [ 1.34509322, 11.10009818]]), scale=array([0.41984568, 0.24949316]), shift=array([8.22419289, 0.85050684])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=58, candidate_x=array([8.64403857, 0.99493627]), index=57, x=array([8.22419289, 1.02085937]), fval=0.5799642053547169, rho=-1.0936320536105784, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 3, 7, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 0, 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.22419289, 1.02085937]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=1.9832433701127465, linear_terms=array([-0.11608732, -6.1156747 ]), square_terms=array([[5.73554046e-03, 2.62479555e-01], + [2.62479555e-01, 1.32048414e+01]]), scale=array([0.20992284, 0.14453174]), shift=array([8.22419289, 0.95546826])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=59, candidate_x=array([8.01427005, 1.02527945]), index=59, x=array([8.01427005, 1.02527945]), fval=0.5734330166298551, rho=1.093147952169101, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([ 1, 3, 13, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, + 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 58]), step_length=0.20996936856056853, relative_step_length=0.8864233561389423, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([8.01427005, 1.02527945]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), model=ScalarModel(intercept=9.981376675556422, linear_terms=array([ -0.67948996, -26.92467299]), square_terms=array([[2.66781078e-02, 9.72846991e-01], + [9.72846991e-01, 3.85178573e+01]]), scale=array([0.41984568, 0.24728311]), shift=array([8.01427005, 0.85271689])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=60, candidate_x=array([7.90516021, 1.02719534]), index=60, x=array([7.90516021, 1.02719534]), fval=0.570654333265082, rho=28.312992905781908, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 7, 10, 30, 52, 54, 55, 56, 57, 59]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.1091266633945737, relative_step_length=0.23034889296854485, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.90516021, 1.02719534]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), model=ScalarModel(intercept=6.968668322923406, linear_terms=array([ -0.3193095 , -18.62779191]), square_terms=array([[1.10008541e-02, 4.89131866e-01], + [4.89131866e-01, 2.70983093e+01]]), scale=array([0.41984568, 0.24632517]), shift=array([7.90516021, 0.85367483])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=61, candidate_x=array([7.53569564, 1.02680097]), index=61, x=array([7.53569564, 1.02680097]), fval=0.557843378100565, rho=0.7126019978012866, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([10, 30, 52, 54, 55, 56, 57, 59, 60]), old_indices_discarded=array([ 0, 1, 3, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, + 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 58]), step_length=0.36946477399899075, relative_step_length=0.7798809111740238, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([7.53569564, 1.02680097]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), model=ScalarModel(intercept=12.13842084066151, linear_terms=array([ -0.90389113, -30.58695776]), square_terms=array([[ 0.04679947, 1.22766505], + [ 1.22766505, 40.38597232]]), scale=array([0.83969136, 0.3 ]), shift=array([7.53569564, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=62, candidate_x=array([6.69600428, 1.03632926]), index=62, x=array([6.69600428, 1.03632926]), fval=0.5424834330682993, rho=0.7246036090517496, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 52, 54, 55, 56, 57, 59, 60, 61]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, + 53, 58]), step_length=0.8397454168164292, relative_step_length=0.88628398011064, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69600428, 1.03632926]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), model=ScalarModel(intercept=13.436478042611053, linear_terms=array([ -1.77594432, -32.96460288]), square_terms=array([[ 0.1709616 , 2.30049165], + [ 2.30049165, 42.12835475]]), scale=array([1.67938272, 0.3 ]), shift=array([6.69600428, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=63, candidate_x=array([6.58843012, 1.03594893]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=0.501065213354614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([52, 54, 55, 56, 57, 59, 60, 61, 62]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 53, 58]), step_length=0.10757483992757301, relative_step_length=0.05676831059836341, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=1.8949804705069788, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=13.527389310158597, linear_terms=array([ -1.76412438, -33.07272766]), square_terms=array([[ 0.17044821, 2.26794554], + [ 2.26794554, 42.11033571]]), scale=array([1.67938272, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=64, candidate_x=array([5.99457255, 1.04132824]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-2.858177349740313, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([54, 55, 56, 57, 59, 60, 61, 62, 63]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.9474902352534894, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=13.033433276409284, linear_terms=array([ -0.7928235 , -31.91157346]), square_terms=array([[3.91876133e-02, 1.01921230e+00], + [1.01921230e+00, 4.07663306e+01]]), scale=array([0.83969136, 0.3 ]), shift=array([6.58843012, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=65, candidate_x=array([6.28160798, 1.03757835]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.670491030486846, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 56, 57, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 54, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.4737451176267447, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=7.57645714444444, linear_terms=array([ -0.29574748, -19.22814953]), square_terms=array([[9.87830891e-03, 4.07101559e-01], + [4.07101559e-01, 2.62853990e+01]]), scale=array([0.41984568, 0.24194837]), shift=array([6.58843012, 0.85805163])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=66, candidate_x=array([6.34718361, 1.03719355]), index=63, x=array([6.58843012, 1.03594893]), fval=0.5414632317465802, rho=-1.608088619606695, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 59, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([ 1, 3, 7, 10, 13, 30, 31, 32, 35, 37, 38, 39, 41, 42, 43, 44, 45, + 46, 47, 48, 49, 50, 51, 52, 53, 54, 56, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.58843012, 1.03594893]), radius=0.23687255881337235, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=2.51780763816455, linear_terms=array([-0.09621641, -7.35783746]), square_terms=array([[3.42399524e-03, 1.80214137e-01], + [1.80214137e-01, 1.37071817e+01]]), scale=array([0.20992284, 0.13698695]), shift=array([6.58843012, 0.96301305])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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6.45218243e-02], + [6.45218243e-02, 6.96729079e+00]]), scale=array([0.10496142, 0.08450624]), shift=array([6.58843012, 1.01549376])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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6.94910166e+00]]), scale=array([0.10496142, 0.08450474]), shift=array([6.60273304, 1.01549526])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.08458141]), shift=array([6.61449989, 1.01541859])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.9474902352534894, shift=array([9.47490235, 0.99993647])), candidate_index=71, candidate_x=array([6.60701913, 1.03578557]), index=69, x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-16.79541545743258, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.029609069851671544, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([62, 63, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.5414615742402571, linear_terms=array([8.4752230e-05, 4.0438811e-03]), square_terms=array([[6.98826564e-05, 6.29275999e-03], + [6.29275999e-03, 8.41875305e-01]]), scale=0.029609069851671544, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 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x=array([6.61449989, 1.0357986 ]), fval=0.5414101559834424, rho=-10.089924952581605, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.007402267462917886, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([63, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.5414619458203902, linear_terms=array([6.9562924e-06, 8.5447900e-04]), square_terms=array([[4.61608585e-06, 4.03102152e-04], + [4.03102152e-04, 5.06614459e-02]]), scale=0.007402267462917886, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.61449989, 1.0357986 ]), radius=0.003701133731458943, bounds=Bounds(lower=array([1.1, 0.5]), upper=array([20. , 1.1]))), model_indices=array([68, 69, 70, 71, 73, 74]), model=ScalarModel(intercept=0.5414709833059852, linear_terms=array([ 2.31365098e-06, -2.97464372e-04]), square_terms=array([[1.14990591e-06, 1.00511445e-04], + [1.00511445e-04, 1.26982949e-02]]), scale=0.003701133731458943, shift=array([6.61449989, 1.0357986 ])), vector_model=VectorModel(intercepts=array([ 0.0503911 , 0.12838033, 0.15541499, 0.20185793, 0.22718579, + 0.24311703, 0.24583074, 0.08262932, -0.06406015, -0.05102962, + -0.39325651, -0.40243304, -0.13275181, -0.10596104, -0.09671903, + -0.10022582, -0.10660703]), linear_terms=array([[0., 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8.434115729173493, 'DiscFac': 1.0183682724359877}, {'CRRA': 8.22419288965219, 'DiscFac': 1.0208593664301888}, {'CRRA': 8.644038568694796, 'DiscFac': 0.9949362749628636}, {'CRRA': 8.014270050130888, 'DiscFac': 1.0252794547819288}, {'CRRA': 7.9051602061742, 'DiscFac': 1.0271953397940936}, {'CRRA': 7.535695642649999, 'DiscFac': 1.026800971949664}, {'CRRA': 6.696004284564788, 'DiscFac': 1.0363292572735352}, {'CRRA': 6.588430116955843, 'DiscFac': 1.0359489301964533}, {'CRRA': 5.994572552780393, 'DiscFac': 1.0413282408043845}, {'CRRA': 6.281607982220334, 'DiscFac': 1.0375783534622196}, {'CRRA': 6.347183611240269, 'DiscFac': 1.0371935498897256}, {'CRRA': 6.48488598079174, 'DiscFac': 1.0374342201775746}, {'CRRA': 6.602733036362541, 'DiscFac': 1.0359519311221903}, {'CRRA': 6.614499891643753, 'DiscFac': 1.035798596893654}, {'CRRA': 6.622654631399038, 'DiscFac': 1.0356881713897441}, {'CRRA': 6.607019125939936, 'DiscFac': 1.0357855688239106}, {'CRRA': 6.584890715486435, 'DiscFac': 1.0358776886253191}, {'CRRA': 6.629456449634945, 'DiscFac': 1.0358183726658614}, {'CRRA': 6.607097916397772, 'DiscFac': 1.0357328241754022}, {'CRRA': 6.6107995764389855, 'DiscFac': 1.0359145478364045}, {'CRRA': 6.616352565428071, 'DiscFac': 1.0360591907672831}, {'CRRA': 6.613866667200956, 'DiscFac': 1.0350546159580063}, {'CRRA': 6.614037476326552, 'DiscFac': 1.035954042885339}, {'CRRA': 6.6147307972095755, 'DiscFac': 1.0357723735949134}, {'CRRA': 6.614487361640625, 'DiscFac': 1.035683617182909}, {'CRRA': 6.614501671721159, 'DiscFac': 1.0358563997053487}, {'CRRA': 6.614528749695077, 'DiscFac': 1.0357967813213964}], 'criterion': [0.6448272261874601, 3.2321994025011787, 1.9091666596987835, 2.3916847958894416, 3.478393276809091, 3.001530893143964, 3.102577078428783, 1.7262922340264317, 1.3964408207071262, 3.413103793830339, 0.6007967457515286, 3.1979455519934517, 3.319523330141687, 1.4100381767073222, 2.151878868775669, 1.681054561744293, 1.451186887621807, 0.6676758699239331, 0.6396317123330024, 0.6370780738825651, 0.6434710277309676, 0.6363070785814076, 0.6381871027164296, 0.635593483550837, 0.6382400964218143, 0.6347642471305108, 0.6332046797989737, 0.6303511715372276, 0.6252147257628976, 0.6151402665456016, 0.739995597244996, 0.6238326932454787, 0.6190748428461661, 0.6288150875658687, 0.6138905842835336, 0.6184024304966298, 0.6125286403569352, 0.6167148454543119, 0.6110646963985666, 0.608454913769554, 0.6246685342740161, 0.6057905110013826, 0.6075289839093978, 0.609515213008583, 0.6109902971047441, 0.6051564916805547, 0.610358395328094, 0.6045010959198392, 0.6133471242213953, 0.6038388333603965, 0.6024855719631016, 0.5997907962729061, 0.6050821429018344, 0.5974935659720187, 0.5930898839617496, 0.7118379655434667, 0.5885914443329772, 0.5799642053547169, 0.6905609554101932, 0.5734330166298551, 0.570654333265082, 0.557843378100565, 0.5424834330682992, 0.5414632317465802, 0.5501696210698668, 0.5434588654753454, 0.5427098651478546, 0.5417403799848393, 0.5414533040238656, 0.5414101559834423, 0.5414570248057565, 0.541461501749771, 0.5414940722460778, 0.5415308885058294, 0.5415401970146225, 0.5414670862447538, 0.541630603918333, 0.5417428852802306, 0.5414925969685491, 0.5414213817815339, 0.5415235212662285, 0.5414473204202261, 0.5414098309112865], 'runtime': [0.0, 1.6021721996366978, 1.763861000072211, 1.9364263000898063, 2.105490399990231, 2.2869786000810564, 2.4659180999733508, 2.645659300033003, 2.8395737996324897, 3.035836899653077, 3.223421999718994, 3.405466000083834, 3.596803499851376, 4.85236749984324, 5.994861399754882, 7.14662199979648, 8.282688799779862, 9.429990599863231, 10.606538099702448, 11.902987699955702, 13.051619499921799, 14.198331299703568, 15.403642300050706, 16.55112239997834, 17.69306949991733, 18.856871999800205, 20.002126100007445, 21.29282119963318, 22.501489299815148, 23.68054739991203, 24.829328400082886, 25.971021299716085, 27.116726099979132, 28.259377599693835, 29.404241499956697, 30.751218599732965, 31.8990707998164, 33.04312939988449, 34.19053489994258, 35.35309229977429, 36.50230869976804, 37.67961739981547, 38.97866219980642, 40.138869300018996, 41.282837899867445, 42.43159359972924, 43.61818069964647, 44.7674082997255, 45.915902299806476, 47.063161599915475, 48.3554062996991, 49.515675500035286, 50.720047399867326, 51.87775920005515, 53.02564329979941, 54.18041200004518, 55.32891159970313, 56.47521140007302, 57.76730199996382, 58.93786389986053, 60.09956929972395, 61.246673699934036, 62.39065769966692, 63.537203199695796, 64.6880405000411, 65.84563069976866, 67.12054200004786, 68.2633369998075, 69.40824959985912, 70.57097999984398, 71.71744199981913, 72.86136729968712, 74.00794119993225, 75.30863319989294, 76.46647779969499, 77.64264229964465, 78.78975840006024, 79.93701329967007, 81.08435950009152, 82.23052149964496, 83.39726469991729, 84.69916970003396, 85.85893659992144], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 9.252107, 1. ], + [16.45625 , 0.9125 ], + [14.09375 , 0.9875 ], + [17.046875, 0.63125 ], + [ 7.596875, 0.93125 ], + [18.81875 , 0.5375 ], + [15.275 , 0.65 ], + [11.73125 , 0.7625 ], + [10.55 , 0.8 ], + [ 9.36875 , 0.8375 ], + [ 5.825 , 0.95 ], + [12.9125 , 0.575 ], + [17.6375 , 1.025 ], + [ 8.1875 , 0.725 ], + [12.321875, 1.08125 ], + [ 7.00625 , 0.6125 ], + [ 4.64375 , 0.6875 ], + [ 3.4625 , 0.875 ], + [ 2.871875, 0.78125 ], + [ 2.28125 , 1.0625 ]]), 'exploration_results': array([0.64235819, 0.99955613, 1.1121001 , 1.75705837, 1.77123339, + 1.82132756, 1.94396028, 2.07018913, 2.11467658, 2.15218408, + 2.24402179, 2.54668134, 2.78807842, 2.90740886, 3.01539999, + 3.32546076, 3.5830362 , 4.07905962, 4.08578359, 6.94508729])}}" diff --git a/src/estimark/content/tables/min/Portfolio_estimate_results.csv b/src/estimark/content/tables/min/Portfolio_estimate_results.csv index 08b399b..934462b 100644 --- a/src/estimark/content/tables/min/Portfolio_estimate_results.csv +++ b/src/estimark/content/tables/min/Portfolio_estimate_results.csv @@ -1,1067 +1,1084 @@ -CRRA,9.25239894900598 -time_to_estimate,45.062182664871216 -params,{'CRRA': 9.25239894900598} -criterion,0.6423583869781233 -start_criterion,0.9309824869808929 -start_params,{'CRRA': 6.614528749695077} -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,1 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 8.1875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 9.00625}, {'CRRA': 7.3687499999999995}, {'CRRA': 9.026168169426626}, {'CRRA': 7.388668169426626}, {'CRRA': 9.038959312915338}, {'CRRA': 10.676459312915338}, {'CRRA': 9.290153724085462}, {'CRRA': 7.652653724085462}, {'CRRA': 9.235117007166322}, {'CRRA': 8.416367007166322}, {'CRRA': 9.1520466847697}, {'CRRA': 9.644492007166322}, {'CRRA': 9.248676532750787}, {'CRRA': 9.658051532750788}, {'CRRA': 9.252525969850202}, {'CRRA': 9.661900969850203}, {'CRRA': 9.25239894900598}], 'criterion': [0.6831279025699917, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.6443932011688029, 0.7818234415442095, 0.6440402472439135, 0.7787614029619838, 0.6439435808488991, 0.7100198965757869, 0.642415138936857, 0.740451029529312, 0.6424025578938513, 0.6665435566959452, 0.6426661753949429, 0.6484718333650715, 0.6423803321514743, 0.6489393340909813, 0.642358861106374, 0.6490529814080408, 0.6423583869781233], 'runtime': [0.0, 3.074203699827194, 3.283573899883777, 3.498749100137502, 3.7044561998918653, 3.926607399713248, 4.110963100101799, 4.243899200111628, 4.541710100136697, 4.7094960999675095, 4.89852079981938, 5.075110800098628, 5.21526509989053, 12.235306499991566, 15.733508999925107, 16.80475839972496, 17.874723799992353, 18.94831439992413, 20.016572599764913, 21.089213099796325, 22.262267199810594, 23.334850599989295, 24.407207199838012, 25.481976099777967, 26.552139800041914, 27.62647379981354, 28.800836499780416, 29.875069699715823, 30.947488199919462, 32.024298400152475], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}" -convergence_report, -multistart_info,"{'start_parameters': [{'CRRA': 8.1875}], 'local_optima': [Minimize with 1 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 7.381e-07* 0.002468 -relative_params_change 1.373e-05 0.02307 -absolute_criterion_change 4.741e-07* 0.001585 -absolute_params_change 0.000127 0.2134 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 6.614528749695077}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.6831279 , 0.69939713, 0.93725113, 1.02360977, 1.18227674, - 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6831279025699917, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=0, candidate_x=array([8.1875]), index=0, x=array([8.1875]), fval=0.6831279025699917, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.6798922337586231, linear_terms=array([-0.06848896]), square_terms=array([[0.06703103]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=13, candidate_x=array([9.00625]), index=13, x=array([9.00625]), fval=0.6443932011688028, rho=1.1075462640286373, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.8187499999999996, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.00625]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 12, 13, 14]), model=ScalarModel(intercept=0.6451809482519073, linear_terms=array([-0.00325077]), square_terms=array([[0.26725027]]), scale=1.6375000000000002, shift=array([9.00625])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=15, candidate_x=array([9.02616817]), index=15, x=array([9.02616817]), fval=0.6440402472439134, rho=17.852277387983314, accepted=True, new_indices=array([14]), old_indices_used=array([ 0, 1, 2, 12, 13]), old_indices_discarded=array([ 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.019918169426626164, relative_step_length=0.012163767588779335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.02616817]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 12, 13, 15, 16]), model=ScalarModel(intercept=0.6447879200688609, linear_terms=array([-0.00206742]), square_terms=array([[0.26466733]]), scale=1.6375000000000002, shift=array([9.02616817])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=17, candidate_x=array([9.03895931]), index=17, x=array([9.03895931]), fval=0.6439435808488991, rho=11.971513486470478, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 2, 12, 13, 15]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]), step_length=0.012791143488712464, relative_step_length=0.007811385336618298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.03895931]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 13, 15, 17, 18]), model=ScalarModel(intercept=0.640887156447643, linear_terms=array([-0.03350583]), square_terms=array([[0.21841968]]), scale=1.6375000000000002, shift=array([9.03895931])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=19, candidate_x=array([9.29015372]), index=19, x=array([9.29015372]), fval=0.6424151389368569, rho=0.5947436919672805, accepted=True, new_indices=array([18]), old_indices_used=array([ 0, 2, 13, 15, 17]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16]), step_length=0.25119441117012364, relative_step_length=0.15340116712679305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.29015372]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 15, 18, 19, 20]), model=ScalarModel(intercept=0.6360123591878122, linear_terms=array([0.0077497]), square_terms=array([[0.23057571]]), scale=1.6375000000000002, shift=array([9.29015372])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=21, candidate_x=array([9.23511701]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=0.09660301085246886, accepted=True, new_indices=array([20]), old_indices_used=array([ 0, 2, 15, 18, 19]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17]), step_length=0.05503671691914036, relative_step_length=0.033610208805581895, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 17, 19, 21, 22]), model=ScalarModel(intercept=0.6429673760619858, linear_terms=array([0.0061267]), square_terms=array([[0.06038539]]), scale=0.8187500000000001, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=23, candidate_x=array([9.15204668]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=-0.8481705078488714, accepted=False, new_indices=array([22]), old_indices_used=array([ 2, 15, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 19, 21, 23, 24]), model=ScalarModel(intercept=0.6421324295697537, linear_terms=array([-0.00046788]), square_terms=array([[0.01412584]]), scale=0.40937500000000004, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=25, candidate_x=array([9.24867653]), index=25, x=array([9.24867653]), fval=0.6423803321514744, rho=2.8683063839230534, accepted=True, new_indices=array([24]), old_indices_used=array([ 2, 15, 19, 21, 23]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, - 20, 22]), step_length=0.013559525584465604, relative_step_length=0.03312250524449613, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.24867653]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6421484427735298, linear_terms=array([-0.00013227]), square_terms=array([[0.01406608]]), scale=0.40937500000000004, shift=array([9.24867653])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=27, candidate_x=array([9.25252597]), index=27, x=array([9.25252597]), fval=0.642358861106374, rho=34.526953193374496, accepted=True, new_indices=array([26]), old_indices_used=array([ 2, 19, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 21, 22]), step_length=0.0038494370994150984, relative_step_length=0.009403205128342224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25252597]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 26, 27, 28]), model=ScalarModel(intercept=0.64214537534659, linear_terms=array([4.36033832e-06]), square_terms=array([[0.01405292]]), scale=0.40937500000000004, shift=array([9.25252597])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=29, candidate_x=array([9.25239895]), index=29, x=array([9.25239895]), fval=0.6423583869781233, rho=700.8934507833044, accepted=True, new_indices=array([28]), old_indices_used=array([ 2, 19, 23, 26, 27]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 21, 22, 24, 25]), step_length=0.00012702084422144821, relative_step_length=0.0003102799248157513, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 30 entries., 'multistart_info': {'start_parameters': [array([8.1875])], 'local_optima': [{'solution_x': array([9.25239895]), 'solution_criterion': 0.6423583869781233, 'states': [State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6831279025699917, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=0, candidate_x=array([8.1875]), index=0, x=array([8.1875]), fval=0.6831279025699917, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.6798922337586231, linear_terms=array([-0.06848896]), square_terms=array([[0.06703103]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=13, candidate_x=array([9.00625]), index=13, x=array([9.00625]), fval=0.6443932011688028, rho=1.1075462640286373, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.8187499999999996, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.00625]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 12, 13, 14]), model=ScalarModel(intercept=0.6451809482519073, linear_terms=array([-0.00325077]), square_terms=array([[0.26725027]]), scale=1.6375000000000002, shift=array([9.00625])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=15, candidate_x=array([9.02616817]), index=15, x=array([9.02616817]), fval=0.6440402472439134, rho=17.852277387983314, accepted=True, new_indices=array([14]), old_indices_used=array([ 0, 1, 2, 12, 13]), old_indices_discarded=array([ 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.019918169426626164, relative_step_length=0.012163767588779335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.02616817]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 12, 13, 15, 16]), model=ScalarModel(intercept=0.6447879200688609, linear_terms=array([-0.00206742]), square_terms=array([[0.26466733]]), scale=1.6375000000000002, shift=array([9.02616817])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=17, candidate_x=array([9.03895931]), index=17, x=array([9.03895931]), fval=0.6439435808488991, rho=11.971513486470478, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 2, 12, 13, 15]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]), step_length=0.012791143488712464, relative_step_length=0.007811385336618298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.03895931]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 13, 15, 17, 18]), model=ScalarModel(intercept=0.640887156447643, linear_terms=array([-0.03350583]), square_terms=array([[0.21841968]]), scale=1.6375000000000002, shift=array([9.03895931])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=19, candidate_x=array([9.29015372]), index=19, x=array([9.29015372]), fval=0.6424151389368569, rho=0.5947436919672805, accepted=True, new_indices=array([18]), old_indices_used=array([ 0, 2, 13, 15, 17]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16]), step_length=0.25119441117012364, relative_step_length=0.15340116712679305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.29015372]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 15, 18, 19, 20]), model=ScalarModel(intercept=0.6360123591878122, linear_terms=array([0.0077497]), square_terms=array([[0.23057571]]), scale=1.6375000000000002, shift=array([9.29015372])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=21, candidate_x=array([9.23511701]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=0.09660301085246886, accepted=True, new_indices=array([20]), old_indices_used=array([ 0, 2, 15, 18, 19]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17]), step_length=0.05503671691914036, relative_step_length=0.033610208805581895, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 17, 19, 21, 22]), model=ScalarModel(intercept=0.6429673760619858, linear_terms=array([0.0061267]), square_terms=array([[0.06038539]]), scale=0.8187500000000001, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=23, candidate_x=array([9.15204668]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=-0.8481705078488714, accepted=False, new_indices=array([22]), old_indices_used=array([ 2, 15, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 19, 21, 23, 24]), model=ScalarModel(intercept=0.6421324295697537, linear_terms=array([-0.00046788]), square_terms=array([[0.01412584]]), scale=0.40937500000000004, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=25, candidate_x=array([9.24867653]), index=25, x=array([9.24867653]), fval=0.6423803321514744, rho=2.8683063839230534, accepted=True, new_indices=array([24]), old_indices_used=array([ 2, 15, 19, 21, 23]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, - 20, 22]), step_length=0.013559525584465604, relative_step_length=0.03312250524449613, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.24867653]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6421484427735298, linear_terms=array([-0.00013227]), square_terms=array([[0.01406608]]), scale=0.40937500000000004, shift=array([9.24867653])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=27, candidate_x=array([9.25252597]), index=27, x=array([9.25252597]), fval=0.642358861106374, rho=34.526953193374496, accepted=True, new_indices=array([26]), old_indices_used=array([ 2, 19, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 21, 22]), step_length=0.0038494370994150984, relative_step_length=0.009403205128342224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25252597]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 26, 27, 28]), model=ScalarModel(intercept=0.64214537534659, linear_terms=array([4.36033832e-06]), square_terms=array([[0.01405292]]), scale=0.40937500000000004, shift=array([9.25252597])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, - 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, - -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, - -0.0535541 , -0.06168149]), linear_terms=array([[0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.], - [0.]]), square_terms=array([[[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]], - - [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=29, candidate_x=array([9.25239895]), index=29, x=array([9.25239895]), fval=0.6423583869781233, rho=700.8934507833044, accepted=True, new_indices=array([28]), old_indices_used=array([ 2, 19, 23, 26, 27]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, - 18, 20, 21, 22, 24, 25]), step_length=0.00012702084422144821, relative_step_length=0.0003102799248157513, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 30 entries., 'history': {'params': [{'CRRA': 8.1875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 9.00625}, {'CRRA': 7.3687499999999995}, {'CRRA': 9.026168169426626}, {'CRRA': 7.388668169426626}, {'CRRA': 9.038959312915338}, {'CRRA': 10.676459312915338}, {'CRRA': 9.290153724085462}, {'CRRA': 7.652653724085462}, {'CRRA': 9.235117007166322}, {'CRRA': 8.416367007166322}, {'CRRA': 9.1520466847697}, {'CRRA': 9.644492007166322}, {'CRRA': 9.248676532750787}, {'CRRA': 9.658051532750788}, {'CRRA': 9.252525969850202}, {'CRRA': 9.661900969850203}, {'CRRA': 9.25239894900598}], 'criterion': [0.6831279025699917, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.6443932011688029, 0.7818234415442095, 0.6440402472439135, 0.7787614029619838, 0.6439435808488991, 0.7100198965757869, 0.642415138936857, 0.740451029529312, 0.6424025578938513, 0.6665435566959452, 0.6426661753949429, 0.6484718333650715, 0.6423803321514743, 0.6489393340909813, 0.642358861106374, 0.6490529814080408, 0.6423583869781233], 'runtime': [0.0, 3.074203699827194, 3.283573899883777, 3.498749100137502, 3.7044561998918653, 3.926607399713248, 4.110963100101799, 4.243899200111628, 4.541710100136697, 4.7094960999675095, 4.89852079981938, 5.075110800098628, 5.21526509989053, 12.235306499991566, 15.733508999925107, 16.80475839972496, 17.874723799992353, 18.94831439992413, 20.016572599764913, 21.089213099796325, 22.262267199810594, 23.334850599989295, 24.407207199838012, 25.481976099777967, 26.552139800041914, 27.62647379981354, 28.800836499780416, 29.875069699715823, 30.947488199919462, 32.024298400152475], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 8.1875 ], - [10.55 ], - [ 6.61452875], - [12.9125 ], - [ 5.825 ], - [14.09375 ], - [15.275 ], - [ 4.64375 ], - [17.6375 ], - [ 3.4625 ]]), 'exploration_results': array([0.6831279 , 0.69939713, 0.93725113, 1.02360977, 1.18227674, - 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" +CRRA,9.25239894900598 + +time_to_estimate,45.062182664871216 + +params,{'CRRA': 9.25239894900598} + +criterion,0.6423583869781233 + +start_criterion,0.9309824869808929 + +start_params,{'CRRA': 6.614528749695077} + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,1 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 8.1875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 9.00625}, {'CRRA': 7.3687499999999995}, {'CRRA': 9.026168169426626}, {'CRRA': 7.388668169426626}, {'CRRA': 9.038959312915338}, {'CRRA': 10.676459312915338}, {'CRRA': 9.290153724085462}, {'CRRA': 7.652653724085462}, {'CRRA': 9.235117007166322}, {'CRRA': 8.416367007166322}, {'CRRA': 9.1520466847697}, {'CRRA': 9.644492007166322}, {'CRRA': 9.248676532750787}, {'CRRA': 9.658051532750788}, {'CRRA': 9.252525969850202}, {'CRRA': 9.661900969850203}, {'CRRA': 9.25239894900598}], 'criterion': [0.6831279025699917, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.6443932011688029, 0.7818234415442095, 0.6440402472439135, 0.7787614029619838, 0.6439435808488991, 0.7100198965757869, 0.642415138936857, 0.740451029529312, 0.6424025578938513, 0.6665435566959452, 0.6426661753949429, 0.6484718333650715, 0.6423803321514743, 0.6489393340909813, 0.642358861106374, 0.6490529814080408, 0.6423583869781233], 'runtime': [0.0, 3.074203699827194, 3.283573899883777, 3.498749100137502, 3.7044561998918653, 3.926607399713248, 4.110963100101799, 4.243899200111628, 4.541710100136697, 4.7094960999675095, 4.89852079981938, 5.075110800098628, 5.21526509989053, 12.235306499991566, 15.733508999925107, 16.80475839972496, 17.874723799992353, 18.94831439992413, 20.016572599764913, 21.089213099796325, 22.262267199810594, 23.334850599989295, 24.407207199838012, 25.481976099777967, 26.552139800041914, 27.62647379981354, 28.800836499780416, 29.875069699715823, 30.947488199919462, 32.024298400152475], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 8.1875}], 'local_optima': [Minimize with 1 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 7.381e-07* 0.002468 +relative_params_change 1.373e-05 0.02307 +absolute_criterion_change 4.741e-07* 0.001585 +absolute_params_change 0.000127 0.2134 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 8.1875}, {'CRRA': 10.549999999999999}, {'CRRA': 6.614528749695077}, {'CRRA': 12.9125}, {'CRRA': 5.824999999999999}, {'CRRA': 14.093749999999998}, {'CRRA': 15.274999999999999}, {'CRRA': 4.64375}, {'CRRA': 17.6375}, {'CRRA': 3.4625}], 'exploration_results': array([0.6831279 , 0.69939713, 0.93725113, 1.02360977, 1.18227674, + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6831279025699917, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=0, candidate_x=array([8.1875]), index=0, x=array([8.1875]), fval=0.6831279025699917, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.6798922337586231, linear_terms=array([-0.06848896]), square_terms=array([[0.06703103]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=13, candidate_x=array([9.00625]), index=13, x=array([9.00625]), fval=0.6443932011688028, rho=1.1075462640286373, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.8187499999999996, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.00625]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 12, 13, 14]), model=ScalarModel(intercept=0.6451809482519073, linear_terms=array([-0.00325077]), square_terms=array([[0.26725027]]), scale=1.6375000000000002, shift=array([9.00625])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=15, candidate_x=array([9.02616817]), index=15, x=array([9.02616817]), fval=0.6440402472439134, rho=17.852277387983314, accepted=True, new_indices=array([14]), old_indices_used=array([ 0, 1, 2, 12, 13]), old_indices_discarded=array([ 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.019918169426626164, relative_step_length=0.012163767588779335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.02616817]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 12, 13, 15, 16]), model=ScalarModel(intercept=0.6447879200688609, linear_terms=array([-0.00206742]), square_terms=array([[0.26466733]]), scale=1.6375000000000002, shift=array([9.02616817])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=17, candidate_x=array([9.03895931]), index=17, x=array([9.03895931]), fval=0.6439435808488991, rho=11.971513486470478, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 2, 12, 13, 15]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]), step_length=0.012791143488712464, relative_step_length=0.007811385336618298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.03895931]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 13, 15, 17, 18]), model=ScalarModel(intercept=0.640887156447643, linear_terms=array([-0.03350583]), square_terms=array([[0.21841968]]), scale=1.6375000000000002, shift=array([9.03895931])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=19, candidate_x=array([9.29015372]), index=19, x=array([9.29015372]), fval=0.6424151389368569, rho=0.5947436919672805, accepted=True, new_indices=array([18]), old_indices_used=array([ 0, 2, 13, 15, 17]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16]), step_length=0.25119441117012364, relative_step_length=0.15340116712679305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.29015372]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 15, 18, 19, 20]), model=ScalarModel(intercept=0.6360123591878122, linear_terms=array([0.0077497]), square_terms=array([[0.23057571]]), scale=1.6375000000000002, shift=array([9.29015372])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=21, candidate_x=array([9.23511701]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=0.09660301085246886, accepted=True, new_indices=array([20]), old_indices_used=array([ 0, 2, 15, 18, 19]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17]), step_length=0.05503671691914036, relative_step_length=0.033610208805581895, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 17, 19, 21, 22]), model=ScalarModel(intercept=0.6429673760619858, linear_terms=array([0.0061267]), square_terms=array([[0.06038539]]), scale=0.8187500000000001, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=23, candidate_x=array([9.15204668]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=-0.8481705078488714, accepted=False, new_indices=array([22]), old_indices_used=array([ 2, 15, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 19, 21, 23, 24]), model=ScalarModel(intercept=0.6421324295697537, linear_terms=array([-0.00046788]), square_terms=array([[0.01412584]]), scale=0.40937500000000004, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=25, candidate_x=array([9.24867653]), index=25, x=array([9.24867653]), fval=0.6423803321514744, rho=2.8683063839230534, accepted=True, new_indices=array([24]), old_indices_used=array([ 2, 15, 19, 21, 23]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, + 20, 22]), step_length=0.013559525584465604, relative_step_length=0.03312250524449613, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.24867653]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6421484427735298, linear_terms=array([-0.00013227]), square_terms=array([[0.01406608]]), scale=0.40937500000000004, shift=array([9.24867653])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=27, candidate_x=array([9.25252597]), index=27, x=array([9.25252597]), fval=0.642358861106374, rho=34.526953193374496, accepted=True, new_indices=array([26]), old_indices_used=array([ 2, 19, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22]), step_length=0.0038494370994150984, relative_step_length=0.009403205128342224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25252597]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 26, 27, 28]), model=ScalarModel(intercept=0.64214537534659, linear_terms=array([4.36033832e-06]), square_terms=array([[0.01405292]]), scale=0.40937500000000004, shift=array([9.25252597])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=29, candidate_x=array([9.25239895]), index=29, x=array([9.25239895]), fval=0.6423583869781233, rho=700.8934507833044, accepted=True, new_indices=array([28]), old_indices_used=array([ 2, 19, 23, 26, 27]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22, 24, 25]), step_length=0.00012702084422144821, relative_step_length=0.0003102799248157513, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 30 entries., 'multistart_info': {'start_parameters': [array([8.1875])], 'local_optima': [{'solution_x': array([9.25239895]), 'solution_criterion': 0.6423583869781233, 'states': [State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=0.6831279025699917, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=0, candidate_x=array([8.1875]), index=0, x=array([8.1875]), fval=0.6831279025699917, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([8.1875]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=0.6798922337586231, linear_terms=array([-0.06848896]), square_terms=array([[0.06703103]]), scale=0.8187500000000001, shift=array([8.1875])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=13, candidate_x=array([9.00625]), index=13, x=array([9.00625]), fval=0.6443932011688028, rho=1.1075462640286373, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.8187499999999996, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.00625]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 1, 2, 12, 13, 14]), model=ScalarModel(intercept=0.6451809482519073, linear_terms=array([-0.00325077]), square_terms=array([[0.26725027]]), scale=1.6375000000000002, shift=array([9.00625])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=15, candidate_x=array([9.02616817]), index=15, x=array([9.02616817]), fval=0.6440402472439134, rho=17.852277387983314, accepted=True, new_indices=array([14]), old_indices_used=array([ 0, 1, 2, 12, 13]), old_indices_discarded=array([ 3, 4, 5, 6, 7, 8, 9, 10, 11]), step_length=0.019918169426626164, relative_step_length=0.012163767588779335, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.02616817]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 12, 13, 15, 16]), model=ScalarModel(intercept=0.6447879200688609, linear_terms=array([-0.00206742]), square_terms=array([[0.26466733]]), scale=1.6375000000000002, shift=array([9.02616817])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=17, candidate_x=array([9.03895931]), index=17, x=array([9.03895931]), fval=0.6439435808488991, rho=11.971513486470478, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 2, 12, 13, 15]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14]), step_length=0.012791143488712464, relative_step_length=0.007811385336618298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.03895931]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 13, 15, 17, 18]), model=ScalarModel(intercept=0.640887156447643, linear_terms=array([-0.03350583]), square_terms=array([[0.21841968]]), scale=1.6375000000000002, shift=array([9.03895931])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=19, candidate_x=array([9.29015372]), index=19, x=array([9.29015372]), fval=0.6424151389368569, rho=0.5947436919672805, accepted=True, new_indices=array([18]), old_indices_used=array([ 0, 2, 13, 15, 17]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16]), step_length=0.25119441117012364, relative_step_length=0.15340116712679305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.29015372]), radius=1.6375000000000002, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 0, 2, 15, 18, 19, 20]), model=ScalarModel(intercept=0.6360123591878122, linear_terms=array([0.0077497]), square_terms=array([[0.23057571]]), scale=1.6375000000000002, shift=array([9.29015372])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=21, candidate_x=array([9.23511701]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=0.09660301085246886, accepted=True, new_indices=array([20]), old_indices_used=array([ 0, 2, 15, 18, 19]), old_indices_discarded=array([ 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17]), step_length=0.05503671691914036, relative_step_length=0.033610208805581895, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.8187500000000001, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 17, 19, 21, 22]), model=ScalarModel(intercept=0.6429673760619858, linear_terms=array([0.0061267]), square_terms=array([[0.06038539]]), scale=0.8187500000000001, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=23, candidate_x=array([9.15204668]), index=21, x=array([9.23511701]), fval=0.6424025578938514, rho=-0.8481705078488714, accepted=False, new_indices=array([22]), old_indices_used=array([ 2, 15, 17, 19, 21]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.23511701]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 15, 19, 21, 23, 24]), model=ScalarModel(intercept=0.6421324295697537, linear_terms=array([-0.00046788]), square_terms=array([[0.01412584]]), scale=0.40937500000000004, shift=array([9.23511701])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=25, candidate_x=array([9.24867653]), index=25, x=array([9.24867653]), fval=0.6423803321514744, rho=2.8683063839230534, accepted=True, new_indices=array([24]), old_indices_used=array([ 2, 15, 19, 21, 23]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, + 20, 22]), step_length=0.013559525584465604, relative_step_length=0.03312250524449613, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.24867653]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 24, 25, 26]), model=ScalarModel(intercept=0.6421484427735298, linear_terms=array([-0.00013227]), square_terms=array([[0.01406608]]), scale=0.40937500000000004, shift=array([9.24867653])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=27, candidate_x=array([9.25252597]), index=27, x=array([9.25252597]), fval=0.642358861106374, rho=34.526953193374496, accepted=True, new_indices=array([26]), old_indices_used=array([ 2, 19, 23, 24, 25]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22]), step_length=0.0038494370994150984, relative_step_length=0.009403205128342224, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([9.25252597]), radius=0.40937500000000004, bounds=Bounds(lower=array([1.1]), upper=array([20.]))), model_indices=array([ 2, 19, 23, 26, 27, 28]), model=ScalarModel(intercept=0.64214537534659, linear_terms=array([4.36033832e-06]), square_terms=array([[0.01405292]]), scale=0.40937500000000004, shift=array([9.25252597])), vector_model=VectorModel(intercepts=array([ 0.04052564, 0.1019965 , 0.11618873, 0.15222976, 0.16876834, + 0.17571046, 0.17052397, -0.01190259, -0.1602348 , -0.14801994, + -0.48805873, -0.49085003, -0.08278535, -0.05708457, -0.04823565, + -0.0535541 , -0.06168149]), linear_terms=array([[0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.], + [0.]]), square_terms=array([[[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]], + + [[0.]]]), scale=0.8187500000000001, shift=array([8.1875])), candidate_index=29, candidate_x=array([9.25239895]), index=29, x=array([9.25239895]), fval=0.6423583869781233, rho=700.8934507833044, accepted=True, new_indices=array([28]), old_indices_used=array([ 2, 19, 23, 26, 27]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 20, 21, 22, 24, 25]), step_length=0.00012702084422144821, relative_step_length=0.0003102799248157513, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 30 entries., 'history': {'params': [{'CRRA': 8.1875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 7.36875}, {'CRRA': 9.00625}, {'CRRA': 9.00625}, {'CRRA': 7.3687499999999995}, {'CRRA': 9.026168169426626}, {'CRRA': 7.388668169426626}, {'CRRA': 9.038959312915338}, {'CRRA': 10.676459312915338}, {'CRRA': 9.290153724085462}, {'CRRA': 7.652653724085462}, {'CRRA': 9.235117007166322}, {'CRRA': 8.416367007166322}, {'CRRA': 9.1520466847697}, {'CRRA': 9.644492007166322}, {'CRRA': 9.248676532750787}, {'CRRA': 9.658051532750788}, {'CRRA': 9.252525969850202}, {'CRRA': 9.661900969850203}, {'CRRA': 9.25239894900598}], 'criterion': [0.6831279025699917, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.7818234415442017, 0.6443932011688029, 0.6443932011688029, 0.7818234415442095, 0.6440402472439135, 0.7787614029619838, 0.6439435808488991, 0.7100198965757869, 0.642415138936857, 0.740451029529312, 0.6424025578938513, 0.6665435566959452, 0.6426661753949429, 0.6484718333650715, 0.6423803321514743, 0.6489393340909813, 0.642358861106374, 0.6490529814080408, 0.6423583869781233], 'runtime': [0.0, 3.074203699827194, 3.283573899883777, 3.498749100137502, 3.7044561998918653, 3.926607399713248, 4.110963100101799, 4.243899200111628, 4.541710100136697, 4.7094960999675095, 4.89852079981938, 5.075110800098628, 5.21526509989053, 12.235306499991566, 15.733508999925107, 16.80475839972496, 17.874723799992353, 18.94831439992413, 20.016572599764913, 21.089213099796325, 22.262267199810594, 23.334850599989295, 24.407207199838012, 25.481976099777967, 26.552139800041914, 27.62647379981354, 28.800836499780416, 29.875069699715823, 30.947488199919462, 32.024298400152475], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 8.1875 ], + [10.55 ], + [ 6.61452875], + [12.9125 ], + [ 5.825 ], + [14.09375 ], + [15.275 ], + [ 4.64375 ], + [17.6375 ], + [ 3.4625 ]]), 'exploration_results': array([0.6831279 , 0.69939713, 0.93725113, 1.02360977, 1.18227674, + 1.26376333, 1.54278366, 1.78141341, 2.1808234 , 2.89794431])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv index 23e13f9..619dfd7 100644 --- a/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv +++ b/src/estimark/content/tables/min/WarmGlowPortfolioBeta_estimate_results.csv @@ -1,13094 +1,13113 @@ -CRRA,4.161270252410767 -BeqFac,3932.5419104702305 -DiscFac,0.9235618590930775 -time_to_estimate,235.31548237800598 -params,"{'CRRA': 4.161270252410767, 'BeqFac': 3932.5419104702305, 'DiscFac': 0.9235618590930775}" -criterion,0.07906739186138749 -start_criterion,0.14974471998399175 -start_params,"{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,3 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 1.1865624830963672, 'BeqFac': 3618.0088638043917, 'DiscFac': 1.1}, {'CRRA': 18.864750592221043, 'BeqFac': 3615.3369060843656, 'DiscFac': 1.0141895974609063}, {'CRRA': 20.0, 'BeqFac': 4139.2725657346655, 'DiscFac': 0.967186242030893}, {'CRRA': 11.813333909541619, 'BeqFac': 4191.377523809567, 'DiscFac': 0.5}, {'CRRA': 10.612091741842278, 'BeqFac': 4249.216768339459, 'DiscFac': 1.0533987310204382}, {'CRRA': 19.734405812485583, 'BeqFac': 4232.630594232504, 'DiscFac': 1.1}, {'CRRA': 1.193828208860164, 'BeqFac': 4249.216768339459, 'DiscFac': 0.8178772474441116}, {'CRRA': 1.1, 'BeqFac': 3989.879463996817, 'DiscFac': 1.0875233363571497}, {'CRRA': 19.6123309024715, 'BeqFac': 4002.81545313188, 'DiscFac': 0.5}, {'CRRA': 10.53992461925404, 'BeqFac': 3621.516955952882, 'DiscFac': 0.5}, {'CRRA': 1.1, 'BeqFac': 4191.668488920294, 'DiscFac': 0.5012402298207279}, {'CRRA': 17.486517743640487, 'BeqFac': 3633.4669300241812, 'DiscFac': 1.1}, {'CRRA': 12.695956525806718, 'BeqFac': 4249.216768339459, 'DiscFac': 0.5313513765650976}, {'CRRA': 12.977587527627069, 'BeqFac': 4090.746802775686, 'DiscFac': 0.5354925265745473}, {'CRRA': 13.161807680676663, 'BeqFac': 4011.5118199937992, 'DiscFac': 0.5553138137913001}, {'CRRA': 12.198029037880566, 'BeqFac': 3892.659345820969, 'DiscFac': 0.7092685961295264}, {'CRRA': 1.1, 'BeqFac': 3912.468091516441, 'DiscFac': 0.9140785484132952}, {'CRRA': 1.2765627298890987, 'BeqFac': 3922.3724643641767, 'DiscFac': 0.8775194603873021}, {'CRRA': 8.908960924035695, 'BeqFac': 3927.3485521141843, 'DiscFac': 0.5}, {'CRRA': 3.2360120246467066, 'BeqFac': 3937.2290236357803, 'DiscFac': 0.5}, {'CRRA': 6.049051483869841, 'BeqFac': 3937.1908812950296, 'DiscFac': 1.1}, {'CRRA': 9.006661997897792, 'BeqFac': 3928.857598545869, 'DiscFac': 1.1}, {'CRRA': 1.1, 'BeqFac': 3930.2258766560176, 'DiscFac': 0.5077364069919987}, {'CRRA': 9.011624997626022, 'BeqFac': 3927.324650788045, 'DiscFac': 0.8635917407285485}, {'CRRA': 9.011624997626022, 'BeqFac': 3937.0670585546413, 'DiscFac': 0.516988061087395}, {'CRRA': 1.1, 'BeqFac': 3937.217873428345, 'DiscFac': 1.0968673280148413}, {'CRRA': 1.2031177910382793, 'BeqFac': 3931.539996753584, 'DiscFac': 1.1}, {'CRRA': 9.011624997626022, 'BeqFac': 3936.979648919782, 'DiscFac': 0.9986737318648143}, {'CRRA': 3.8170467632114593, 'BeqFac': 3927.324650788045, 'DiscFac': 0.5}, {'CRRA': 1.642885870496578, 'BeqFac': 3927.324650788045, 'DiscFac': 1.064672740081639}, {'CRRA': 6.440258048769584, 'BeqFac': 3937.2290236357803, 'DiscFac': 0.6657716089765859}, {'CRRA': 5.666827584614865, 'BeqFac': 3929.8007439999787, 'DiscFac': 0.6004149760692965}, {'CRRA': 5.297485179725084, 'BeqFac': 3931.0387906059455, 'DiscFac': 0.65342447875882}, {'CRRA': 3.771413945779464, 'BeqFac': 3932.895860514896, 'DiscFac': 0.8968206813310258}, {'CRRA': 3.7499269222663596, 'BeqFac': 3932.4768095759414, 'DiscFac': 0.8696247373078938}, {'CRRA': 4.368950225249849, 'BeqFac': 3931.969120257049, 'DiscFac': 1.0562446232715845}, {'CRRA': 3.7553812541831513, 'BeqFac': 3931.969911104181, 'DiscFac': 0.5625768260860098}, {'CRRA': 4.3566307389438546, 'BeqFac': 3932.5730164259417, 'DiscFac': 0.5625768260860098}, {'CRRA': 3.7499269222663596, 'BeqFac': 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{'CRRA': 4.161275859925907, 'BeqFac': 3932.541912522764, 'DiscFac': 0.9235686423698032}, {'CRRA': 4.1612703469432315, 'BeqFac': 3932.541916014846, 'DiscFac': 0.9235649027864001}, {'CRRA': 4.161274439557252, 'BeqFac': 3932.5419112955856, 'DiscFac': 0.923563562230745}, {'CRRA': 4.161268588061044, 'BeqFac': 3932.5419112935697, 'DiscFac': 0.9235621223236096}, {'CRRA': 4.161264961042685, 'BeqFac': 3932.541912891103, 'DiscFac': 0.923566458371742}, {'CRRA': 4.1612706845073335, 'BeqFac': 3932.54191204673, 'DiscFac': 0.923573432878518}, {'CRRA': 4.161273169417956, 'BeqFac': 3932.5419061378893, 'DiscFac': 0.9235657502956361}, {'CRRA': 4.161267609182726, 'BeqFac': 3932.5419067553134, 'DiscFac': 0.9235645298735619}, {'CRRA': 4.161268346679939, 'BeqFac': 3932.5419060338195, 'DiscFac': 0.9235703644561561}, {'CRRA': 4.161270252410767, 'BeqFac': 3932.5419104702305, 'DiscFac': 0.9235618590930775}], 'criterion': [0.12472562593739224, 1775.1585229804136, 7.528672527611353, 5.000685908221211, 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0.07912228116224071, 0.07916469519109601, 0.07906796375411648, 0.07908384862812542, 0.0790697742885639, 0.07906858340060062, 0.0790853268788701, 0.07907490953481508, 0.07906947714323612, 0.07906933199350785, 0.07907149249698373, 0.07906810020358924, 0.07907079960068682, 0.07906938048446471, 0.07907659351953897, 0.07906938522892665, 0.07906783238396362, 0.07907470588544713, 0.07907174857897412, 0.07906914654076969, 0.07906953420777169, 0.07906927690588456, 0.07906806453744428, 0.0790680898307926, 0.07906806799767427, 0.0790681009808335, 0.07906801097061436, 0.07906778412165577, 0.07906760384602074, 0.07906743430757834, 0.07906789890259533, 0.07906809936091284, 0.0790678501168395, 0.0790677497350572, 0.07906808874933141, 0.0790673918613875], 'runtime': [0.0, 1.4576744004152715, 1.6382930004037917, 1.8128164000809193, 1.9870007000863552, 2.17121409997344, 2.3534826999530196, 2.5418712999671698, 2.7315246001817286, 2.927709100302309, 3.1174843003973365, 3.308924900367856, 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67.91691210027784, 68.10691900039092, 68.29261590028182, 68.4841554001905, 68.68089540023357, 68.87579370010644, 69.22302340017632, 69.41409680014476, 70.62386910011992, 71.77614930039272, 72.92142220027745, 74.430033700075, 74.61684990022331, 74.81267850007862, 75.00723180035129, 75.2044039000757, 75.3887424999848, 75.57385200029239, 75.76805710000917, 75.96694570034742, 76.16238610027358, 76.3546518003568, 76.55501760030165, 77.8122900002636], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 39, 40, 41, 42, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 46, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 49]}" -convergence_report,"{'one_step': {'relative_criterion_change': 0.15773345928132843, 'relative_params_change': 0.4431666467150333, 'absolute_criterion_change': 0.015773345928132843, 'absolute_params_change': 1696.956220326838}, 'five_steps': {'relative_criterion_change': 0.15773345928132843, 'relative_params_change': 0.4431666467150333, 'absolute_criterion_change': 0.015773345928132843, 'absolute_params_change': 1696.956220326838}}" -multistart_info,"{'start_parameters': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}, {'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 4.466570349505643, 'BeqFac': 4587.182667295986, 'DiscFac': 0.9284133483434123}], 'local_optima': [Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 0*** 0.1692 -relative_params_change 0*** 0.05382 -absolute_criterion_change 0*** 0.01692 -absolute_params_change 0*** 0.2525 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 4.245e-07* 5.719e-06* -relative_params_change 4.911e-07* 1.068e-05 -absolute_criterion_change 4.245e-08* 5.719e-07* -absolute_params_change 1.875e-06* 4.663e-05 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 9.277e-06* 0.001793 -relative_params_change 4.611e-05 0.002492 -absolute_criterion_change 9.277e-07* 0.0001793 -absolute_params_change 0.0002187 0.01527 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'DiscFac': 0.7250000000000001}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'DiscFac': 0.8187500000000001}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'DiscFac': 1.00625}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'DiscFac': 0.9312500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'DiscFac': 0.89375}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'DiscFac': 0.8562500000000001}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'DiscFac': 0.55625}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'DiscFac': 0.70625}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'DiscFac': 0.875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'DiscFac': 0.7625000000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'DiscFac': 1.0250000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'DiscFac': 0.6312500000000001}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'DiscFac': 0.8}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'DiscFac': 0.65}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'DiscFac': 0.59375}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'DiscFac': 0.78125}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'DiscFac': 0.66875}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'DiscFac': 0.6125}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'DiscFac': 0.9875}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'DiscFac': 0.51875}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'DiscFac': 0.5375}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'DiscFac': 0.96875}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'DiscFac': 1.0625}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'DiscFac': 1.08125}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'DiscFac': 0.8375}], 'exploration_results': array([ 0.15183336, 0.16963129, 0.3197984 , 0.64023668, 0.65671129, - 0.81183081, 0.97682658, 1.08889746, 1.16160077, 1.27832959, - 1.4056054 , 1.63187357, 1.68853551, 1.72432777, 1.75966476, - 1.80211806, 1.86015904, 1.93985391, 1.96313642, 2.01014295, - 2.05048939, 2.06445231, 2.29147195, 2.43324345, 2.49173392, - 2.54550964, 3.05245049, 4.00840374, 4.33350039, 58.25645251])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=393.2276837211913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=0.12472562593739224, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=101.88864067521602, linear_terms=array([-301.48531903, -14.07496772, 134.91730104]), square_terms=array([[ 450.60368506, 21.0269611 , -198.92102364], - [ 21.0269611 , 0.9962047 , -9.22847607], - [-198.92102364, -9.22847607, 89.91310769]]), scale=array([9.45000000e+00, 3.16939931e+02, 3.00000000e-01]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=13, candidate_x=array([1.26959565e+01, 4.24921677e+03, 5.31351377e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.004991694207075647, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=196.61384186059564, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=102.38671386491994, linear_terms=array([-301.74886293, -7.4096688 , 135.40964517]), square_terms=array([[ 4.49001106e+02, 1.10387141e+01, -1.98898743e+02], - [ 1.10387141e+01, 2.79198932e-01, 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old_indices_used=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 2, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=98.30692093029782, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=116.12151198406633, linear_terms=array([-321.94879911, 8.08795269, 184.9444489 ]), square_terms=array([[ 4.50244477e+02, -1.12411579e+01, -2.55940799e+02], - [-1.12411579e+01, 2.84655841e-01, 6.47245223e+00], - [-2.55940799e+02, 6.47245223e+00, 1.47960373e+02]]), scale=array([ 9.45 , 79.23498278, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 3, 8, 9, 14, 15]), model=ScalarModel(intercept=36.477921419964005, linear_terms=array([-138.85628947, 17.34063992, 102.44589447]), square_terms=array([[ 268.01780029, -33.176478 , -194.8794462 ], - [ -33.176478 , 4.1379421 , 24.44585578], - [-194.8794462 , 24.44585578, 145.53102569]]), scale=array([ 9.45 , 39.61749139, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=16, candidate_x=array([1.21980290e+01, 3.89265935e+03, 7.09268596e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0077322181559090096, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 8, 9, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=24.576730232574455, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 15, 16]), model=ScalarModel(intercept=137.95900758299246, linear_terms=array([ 609.72371975, 81.97626858, 1094.06151597]), square_terms=array([[1348.85665695, 181.37053476, 2420.79025551], - [ 181.37053476, 24.39149366, 325.54412825], - [2420.79025551, 325.54412825, 4345.09263337]]), scale=array([ 9.45 , 19.8087457, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=12.288365116287228, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 16, 17]), model=ScalarModel(intercept=675.1794019801806, linear_terms=array([ -2064.6019009 , 718.07582713, -10217.06091518]), square_terms=array([[ 3158.11858269, -1098.22603339, 15626.2724666 ], - [-1098.22603339, 381.93267203, -5434.33216723], - [15626.2724666 , -5434.33216723, 77322.68695368]]), scale=array([6.43190571, 9.90437285, 0.3 ]), shift=array([7.53190571e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, 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20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=108.75147073194, linear_terms=array([-309.53365492, -36.53619492, 161.80376403]), square_terms=array([[ 441.33077962, 52.10175602, -230.15109169], - [ 52.10175602, 6.15213254, -27.15013825], - [-230.15109169, -27.15013825, 120.92652934]]), scale=array([3.9558125 , 4.95218642, 0.3 ]), shift=array([5.05581250e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=31, candidate_x=array([6.44025805e+00, 3.93722902e+03, 6.65771609e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.00640748988321916, accepted=False, new_indices=array([19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=3.072091279071807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=170.59582545012594, linear_terms=array([-277.08221691, 5.5992395 , 233.76105829]), square_terms=array([[ 2.25373873e+02, -4.61116778e+00, -1.89643367e+02], - [-4.61116778e+00, 1.11464760e-01, 3.74415217e+00], - [-1.89643367e+02, 3.74415217e+00, 1.60694491e+02]]), scale=array([2.47609321, 2.47609321, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=32, candidate_x=array([5.66682758e+00, 3.92980074e+03, 6.00414976e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.006388659400412666, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([18, 19, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=1.5360456395359035, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=138.3462289851189, linear_terms=array([-147.45463528, -5.65111698, 268.26306503]), square_terms=array([[ 7.86989538e+01, 3.00126776e+00, -1.42903701e+02], - [ 3.00126776e+00, 1.18067220e-01, -5.51395279e+00], - [-1.42903701e+02, -5.51395279e+00, 2.60665493e+02]]), scale=array([1.23804661, 1.23804661, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 23, 27, 32, 33]), model=ScalarModel(intercept=1.5830056559661339, linear_terms=array([ 9.99281175, 14.43614264, -39.57256129]), square_terms=array([[ 33.80983754, 48.83787417, -133.54382225], - [ 48.83787417, 70.60333873, -193.11510202], - [-133.54382225, -193.11510202, 528.45490937]]), scale=array([0.6190233, 0.6190233, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=34, candidate_x=array([3.77141395e+00, 3.93289586e+03, 8.96820681e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0007867459648932072, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 27, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.5847700831496822, linear_terms=array([-0.41675462, -0.44190085, 2.89851388]), square_terms=array([[ 0.18454675, 0.18631726, -1.20807895], - [ 0.18631726, 0.19477978, 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43, 44, 45, 46]), old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2415835967758433, linear_terms=array([-0.15283733, -0.58871896, 3.13992098]), square_terms=array([[ 0.02040704, 0.03863901, -0.21039381], - [ 0.03863901, 0.14956944, -0.79740606], - [-0.21039381, -0.79740606, 4.2608185 ]]), scale=0.19200570494198793, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0337220474595914, linear_terms=array([-0.20696303, -0.31059194, 1.61730455]), square_terms=array([[ 0.02606724, 0.03394087, -0.17786586], - [ 0.03394087, 0.05073282, -0.26413928], - [-0.17786586, -0.26413928, 1.3775637 ]]), scale=0.09600285247099397, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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8.72088478e-01])), candidate_index=49, candidate_x=array([4.03665655e+00, 3.93225811e+03, 7.80726618e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.2193495890214448, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.11633665599043491, linear_terms=array([ 0.01692106, -0.00599296, -0.07183474]), square_terms=array([[ 0.00430738, -0.00138782, -0.01722234], - [-0.00138782, 0.00047598, 0.00583328], - 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old_indices_used=array([ 0, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.05128066423434844, relative_step_length=1.0683154284367173, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.08089110047653379, linear_terms=array([-0.00528525, -0.0001229 , 0.02824264]), square_terms=array([[ 1.16009632e-02, 2.30117604e-04, -6.86298394e-02], - [ 2.30117604e-04, 5.27144510e-06, -1.47978107e-03], - [-6.86298394e-02, -1.47978107e-03, 4.37447949e-01]]), scale=0.09600285247099397, shift=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, 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State(trustregion=Region(center=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.08002590130028917, linear_terms=array([-0.00526566, 0.000661 , 0.04112777]), square_terms=array([[ 2.05912030e-02, -2.69140553e-03, -1.45369821e-01], - [-2.69140553e-03, 4.27142888e-04, 2.22767331e-02], - [-1.45369821e-01, 2.22767331e-02, 1.17421707e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - 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model_indices=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), model=ScalarModel(intercept=0.15326138046317392, linear_terms=array([0.55415723, 0.00612001, 0.65724766]), square_terms=array([[2.16178448e+00, 2.69601220e-02, 2.58028293e+00], - [2.69601220e-02, 3.98421161e-04, 3.15716605e-02], - [2.58028293e+00, 3.15716605e-02, 3.11749315e+00]]), scale=array([0.30951165, 0.30951165, 0.24443416]), shift=array([4.16380226e+00, 3.93248621e+03, 8.55565845e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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3.93227684e+03, 8.72088478e-01])), candidate_index=65, candidate_x=array([4.03801811e+00, 3.93261118e+03, 8.85249640e-01]), index=64, x=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), fval=0.0797111995279562, rho=-0.04919187191422923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), old_indices_discarded=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45, 46, 50, 51, 52, 53, 56, - 57, 58, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), model=ScalarModel(intercept=0.07981427686533094, linear_terms=array([ 8.23222295e-04, -3.01457336e-06, -1.94680885e-04]), square_terms=array([[ 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fval=0.0797111995279562, rho=-0.4172818202820011, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 54, 55, 56, - 57, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.0797875761950249, linear_terms=array([ 5.31755989e-04, -2.45518194e-07, -8.66641581e-04]), square_terms=array([[ 1.65529454e-03, -4.13667194e-05, -8.39813544e-03], - [-4.13667194e-05, 8.98752859e-06, 1.51601069e-03], - [-8.39813544e-03, 1.51601069e-03, 2.58158286e-01]]), scale=0.09600285247099397, shift=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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45, 47, 49, 54, 55, 56, 57, 59, 60, 61]), step_length=0.10215613340917244, relative_step_length=1.0640947719760474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 63, 64, 67]), model=ScalarModel(intercept=0.0794221211152619, linear_terms=array([-8.78763547e-04, -7.32595189e-05, 1.57498998e-03]), square_terms=array([[ 1.88292059e-02, -8.43669110e-04, -1.35637703e-01], - [-8.43669110e-04, 5.18412063e-05, 7.55602994e-03], - [-1.35637703e-01, 7.55602994e-03, 1.13549976e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, 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State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 55, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.11467755048322094, linear_terms=array([ 0.4301884 , -0.10852269, 0.38403869]), square_terms=array([[ 2.8059872 , -0.69595835, 2.53665519], - [-0.69595835, 0.17347376, -0.6322991 ], - [ 2.53665519, -0.6322991 , 2.32273428]]), scale=array([0.30951165, 0.30951165, 0.24294931]), shift=array([4.16663860e+00, 3.93254477e+03, 8.57050687e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=69, candidate_x=array([4.12022455e+00, 3.93285428e+03, 9.22805613e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-0.0051254842750059655, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 42, 44, 47, 48, 49, 55, 63, 65, 66, 67, 68]), old_indices_discarded=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45, 46, 50, 51, 52, 53, 54, - 56, 57, 58, 59, 60, 61, 62, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 53, 63, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.07972737754656194, linear_terms=array([0.00089106, 0.00026728, 0.00741102]), square_terms=array([[3.83952175e-03, 2.37615327e-04, 1.80862528e-04], - [2.37615327e-04, 1.04844950e-03, 2.29140497e-02], - [1.80862528e-04, 2.29140497e-02, 5.09344565e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=70, candidate_x=array([4.12157965e+00, 3.93269953e+03, 9.14415260e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-4.697657825504714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 53, 63, 64, 65, 66, 67, 68]), old_indices_discarded=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 49, 52, 54, 55, - 56, 57, 58, 59, 60, 61, 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), model=ScalarModel(intercept=0.07970568860996624, linear_terms=array([5.17688765e-04, 7.08684557e-05, 2.23636029e-03]), square_terms=array([[1.54434855e-03, 1.10165119e-04, 2.02375366e-03], - [1.10165119e-04, 1.32782811e-04, 4.89488107e-03], - [2.02375366e-03, 4.89488107e-03, 1.84174468e-01]]), scale=0.09600285247099397, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, - 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.07966045626129445, linear_terms=array([3.20987313e-04, 4.92207090e-05, 2.88283811e-03]), square_terms=array([[ 3.79212673e-04, -2.34327242e-06, -5.41146655e-06], - [-2.34327242e-06, 8.52228643e-06, -6.54738778e-04], - [-5.41146655e-06, -6.54738778e-04, 5.14897659e-02]]), scale=0.04800142623549698, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=72, candidate_x=array([4.13514215e+00, 3.93250781e+03, 9.20459151e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-0.9016950026859181, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.02400071311774849, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 66, 68, 71, 72]), model=ScalarModel(intercept=0.07909174304369689, linear_terms=array([ 9.75729683e-05, -8.37732492e-07, -2.20345183e-03]), square_terms=array([[ 8.71775509e-04, -1.04124643e-06, -6.73499598e-03], - [-1.04124643e-06, 1.31217919e-08, 1.11732679e-05], - [-6.73499598e-03, 1.11732679e-05, 5.71455095e-02]]), scale=0.02400071311774849, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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9.23613025e-01]), fval=0.07908472064494197, rho=-0.6896313338426389, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 68, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.006000178279437123, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), model=ScalarModel(intercept=0.07925187705246434, linear_terms=array([ 4.83829824e-05, 8.99512457e-06, -1.87907241e-04]), square_terms=array([[ 5.19363336e-05, -4.83339149e-07, -4.29582543e-04], - [-4.83339149e-07, 1.47336322e-08, 3.01353003e-06], - [-4.29582543e-04, 3.01353003e-06, 3.84555377e-03]]), scale=0.006000178279437123, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - 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model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914612489984735, linear_terms=array([ 9.04725864e-05, 3.30565774e-05, -2.19601997e-05]), square_terms=array([[ 4.27792898e-05, -4.53845336e-06, -3.94771372e-04], - [-4.53845336e-06, 5.36493868e-07, 4.14551856e-05], - [-3.94771372e-04, 4.14551856e-05, 3.90706076e-03]]), scale=0.006000178279437123, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-1.0803986646623365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), old_indices_discarded=array([68, 77, 83, 89, 90, 91, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), model=ScalarModel(intercept=0.07909160054167635, linear_terms=array([-1.29680002e-05, 2.27817382e-06, 1.03758748e-06]), square_terms=array([[ 9.19566779e-07, -3.49328357e-08, -6.93344117e-06], - [-3.49328357e-08, 6.61278422e-09, 2.68258739e-07], - [-6.93344117e-06, 2.68258739e-07, 5.96608551e-05]]), 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old_indices_discarded=array([ 83, 90, 91, 100, 103, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0003750111424648202, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 94, 95, 96, 97, 98, 99, 101, 102, 104, 106]), model=ScalarModel(intercept=0.07908661788272847, linear_terms=array([-8.35298009e-06, 7.05475745e-06, 1.43187070e-06]), square_terms=array([[ 2.33438460e-07, -1.55439804e-08, -1.73669039e-06], - [-1.55439804e-08, 5.10319166e-09, 8.60392613e-08], - [-1.73669039e-06, 8.60392613e-08, 1.49265352e-05]]), scale=0.0003750111424648202, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 94, 95, 98, 102, 104, 106, 107]), model=ScalarModel(intercept=0.0790879150221595, linear_terms=array([-6.18729811e-06, 2.85069426e-06, 5.24029282e-06]), square_terms=array([[ 6.39029879e-08, -5.52613342e-09, -4.58386220e-07], - [-5.52613342e-09, 1.01866206e-09, 2.93309494e-08], - [-4.58386220e-07, 2.93309494e-08, 3.89516694e-06]]), scale=0.0001875055712324101, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=108, candidate_x=array([4.16142287e+00, 3.93254184e+03, 9.23486329e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-2.2093933698724797, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 94, 95, 98, 102, 104, 106, 107]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=9.375278561620504e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 95, 104, 107, 108]), model=ScalarModel(intercept=0.07906796375411641, linear_terms=array([-2.77746966e-05, -3.22673327e-05, -4.25319102e-05]), square_terms=array([[ 5.39542744e-08, 4.53199229e-08, -4.64131654e-08], - [ 4.53199229e-08, 4.98555993e-08, 7.22290924e-08], - [-4.64131654e-08, 7.22290924e-08, 1.14321313e-06]]), scale=9.375278561620504e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([ 95, 104, 107, 108]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=4.687639280810252e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, - 120, 121]), model=ScalarModel(intercept=0.07907082869590354, linear_terms=array([ 2.88136945e-06, -3.08394182e-07, -1.07721092e-06]), square_terms=array([[ 4.46329859e-09, -6.05308320e-10, -2.92877945e-08], - [-6.05308320e-10, 1.34067088e-10, 3.43225360e-09], - [-2.92877945e-08, 3.43225360e-09, 2.44027182e-07]]), scale=4.687639280810252e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=2.343819640405126e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=0.07907053144557313, linear_terms=array([ 1.94854337e-07, -3.55132795e-07, -5.64262326e-07]), square_terms=array([[ 1.08597546e-09, 1.16175887e-10, -7.41141155e-09], - [ 1.16175887e-10, 3.16258142e-11, -6.25413742e-10], - [-7.41141155e-09, -6.25413742e-10, 5.96099329e-08]]), scale=2.343819640405126e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 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120, 123]), model=ScalarModel(intercept=0.07907067153514401, linear_terms=array([ 2.04407661e-07, -2.51465066e-07, -2.26280892e-07]), square_terms=array([[ 2.06977844e-10, 4.21146920e-11, -1.67593686e-09], - [ 4.21146920e-11, 2.48475075e-11, -3.05589732e-10], - [-1.67593686e-09, -3.05589732e-10, 1.50170769e-08]]), scale=1.171909820202563e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 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candidate_x=array([4.16126418e+00, 3.93254192e+03, 9.23574260e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.2558930827717976, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([104, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123]), old_indices_discarded=array([111, 121, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=5.859549101012815e-06, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, - 135, 136]), model=ScalarModel(intercept=0.07906789905966807, linear_terms=array([2.53618684e-09, 2.53785426e-08, 3.95194505e-07]), square_terms=array([[ 5.35172911e-11, -7.04580653e-12, -3.54919326e-10], - [-7.04580653e-12, 3.06788632e-12, 2.82219396e-11], - [-3.54919326e-10, 2.82219396e-11, 2.96974675e-09]]), scale=5.859549101012815e-06, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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133, 134, 135, 136]), old_indices_used=array([104, 123, 124]), old_indices_discarded=array([], dtype=int32), step_length=5.859549088890873e-06, relative_step_length=0.99999999793125, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 138 entries., 'multistart_info': {'start_parameters': [array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01])], 'local_optima': [{'solution_x': array([4.49642794e+00, 2.23558572e+03, 9.79764716e-01]), 'solution_criterion': 0.09484073778952033, 'states': [State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - 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n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=31.09598891367815, linear_terms=array([-91.62457748, 35.18702724, 46.35326863]), square_terms=array([[139.00631 , -52.82239092, -67.71080652], - [-52.82239092, 20.15282885, 26.09871415], - [-67.71080652, 26.09871415, 35.14646972]]), scale=array([ 9.45 , 180.19399796, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - 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[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=223.5668701887974, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=14, candidate_x=array([1.16284187e+01, 2.14557170e+03, 5.98769297e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.0133860000302767, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([3, 6]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=28.06849241063706, linear_terms=array([-86.35465017, 10.29248796, 43.1073773 ]), square_terms=array([[136.46063816, -16.08430417, -65.95932181], - [-16.08430417, 1.90552511, 7.90048589], - [-65.95932181, 7.90048589, 33.73248494]]), scale=array([ 9.45 , 45.04849949, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 11, 14, 15]), model=ScalarModel(intercept=70.80141507437921, linear_terms=array([-19.77025943, 55.99584036, 116.00216794]), square_terms=array([[ 3.37228092, -7.98985022, -16.17070888], - [ -7.98985022, 22.21791715, 45.96590548], - [-16.17070888, 45.96590548, 95.44827251]]), scale=array([ 9.45 , 22.52424975, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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square_terms=array([[0.29433574, 0.05189857, 0.69211431], - [0.05189857, 0.01569971, 0.200252 ], - [0.69211431, 0.200252 , 2.57727413]]), scale=array([ 7.56714517, 11.26212487, 0.3 ]), shift=array([8.66714517e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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rho=-22860.524324555063, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 16, 17]), model=ScalarModel(intercept=467.19314322074354, linear_terms=array([-1065.72378965, 378.28985285, -1698.61903515]), square_terms=array([[1215.92010216, -431.55891012, 1937.88572934], - [-431.55891012, 153.18465907, -687.77512457], - [1937.88572934, -687.77512457, 3088.84974448]]), scale=array([4.75161395, 5.63106244, 0.3 ]), shift=array([5.85161395e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 17, 18]), model=ScalarModel(intercept=631.0068374845937, linear_terms=array([ -713.42087297, 227.74637403, -1894.75839998]), square_terms=array([[ 403.38912904, -128.76975477, 1071.25267549], - [-128.76975477, 41.14741941, -341.84595008], - [1071.25267549, -341.84595008, 2845.4276814 ]]), scale=array([2.81553122, 2.81553122, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=19, candidate_x=array([5.22498136e+00, 2.23285317e+03, 9.53585466e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-3.163059122264521, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.14790847307992083, linear_terms=array([-0.33207427, 0.0835986 , 0.80229467]), square_terms=array([[ 1.61949403, -0.29564339, -2.97830876], - [-0.29564339, 0.059162 , 0.58807027], - [-2.97830876, 0.58807027, 5.87252616]]), scale=array([1.40776561, 1.40776561, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], 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old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.11589494066709435, linear_terms=array([ 0.16454974, 0.04806004, -0.44293767]), square_terms=array([[ 0.76924547, 0.27501651, -2.87758723], - [ 0.27501651, 0.09970594, -1.04942116], - [-2.87758723, -1.04942116, 11.20509916]]), scale=array([0.7038828, 0.7038828, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.4031517845766621, linear_terms=array([-0.23678438, -0.13921737, 1.90006104]), square_terms=array([[ 0.14109853, 0.0676179 , -0.91471804], - [ 0.0676179 , 0.03464796, -0.47018588], - [-0.91471804, -0.47018588, 6.40611468]]), scale=array([0.3519414, 0.3519414, 0.2259707]), shift=array([4.97216547e+00, 2.23566870e+03, 8.74029299e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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2.23566870e+03, 1.00000000e+00])), candidate_index=34, candidate_x=array([4.62022407e+00, 2.23602064e+03, 8.03652173e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.31192568841383445, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([20, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.3311781411225237, linear_terms=array([-0.15829022, -0.12201639, 1.37588212]), square_terms=array([[ 0.07100082, 0.05048831, -0.56524959], - [ 0.05048831, 0.03638561, -0.40733887], - [-0.56524959, -0.40733887, 4.57262245]]), scale=array([0.1759707 , 0.1759707 , 0.13798535]), shift=array([4.97216547e+00, 2.23566870e+03, 9.62014649e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=0.7819308994641678, linear_terms=array([ 0.0074579 , -0.0259874 , 1.71946661]), square_terms=array([[ 5.56013887e-04, 1.44714906e-04, -1.22278649e-03], - [ 1.44714906e-04, 6.29275388e-04, -3.58652012e-02], - [-1.22278649e-03, -3.58652012e-02, 2.24563302e+00]]), scale=array([0.08798535, 0.08798535, 0.08798535]), shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.684129096465268, linear_terms=array([-0.0273062 , -0.01343205, 1.05830747]), square_terms=array([[ 1.32604981e-03, 5.31204096e-04, -3.19319385e-02], - [ 5.31204096e-04, 2.27658842e-04, -1.40325629e-02], - [-3.19319385e-02, -1.40325629e-02, 1.00415127e+00]]), scale=0.054581755417186864, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=223.5668701887974, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=49, candidate_x=array([4.95637410e+00, 2.23565341e+03, 9.50041339e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.30404049502893676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.1606884730144318, linear_terms=array([-0.00451786, -0.00405289, 0.12722605]), square_terms=array([[ 0.00114205, 0.00061019, -0.01818567], - [ 0.00061019, 0.00034213, -0.01019128], - [-0.01818567, -0.01019128, 0.30642055]]), scale=0.027290877708593432, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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55, 56, 57, 58, 59, 60, 61]), old_indices_used=array([ 0, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.029339079440313165, relative_step_length=1.075050782667748, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.94579579e+00, 2.23566755e+03, 9.87190354e-01]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 49, 50, 52, 54, 55, 56, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.13142051977624206, linear_terms=array([ 0.00576256, 0.00026056, -0.00133033]), square_terms=array([[ 2.27015032e-03, 1.09767205e-03, -4.97844684e-02], - [ 1.09767205e-03, 5.83638100e-04, -2.63715392e-02], - [-4.97844684e-02, -2.63715392e-02, 1.20784318e+00]]), scale=0.054581755417186864, shift=array([4.94579579e+00, 2.23566755e+03, 9.87190354e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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State(trustregion=Region(center=array([4.89130138e+00, 2.23566540e+03, 9.84967623e-01]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 48, 49, 50, 51, 52, 55, 56, 58, 59, 61, 63]), model=ScalarModel(intercept=0.1262684477348336, linear_terms=array([ 0.01011799, -0.00025361, 0.00667358]), square_terms=array([[ 0.00584699, 0.02035848, -0.15274389], - [ 0.02035848, 0.08132529, -0.60850692], - [-0.15274389, -0.60850692, 4.58703432]]), scale=0.10916351083437373, shift=array([4.89130138e+00, 2.23566540e+03, 9.84967623e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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64]), model=ScalarModel(intercept=0.14568276745179376, linear_terms=array([-0.22688217, -0.09194348, 0.79460775]), square_terms=array([[ 0.79705518, 0.32534599, -2.52015705], - [ 0.32534599, 0.13291246, -1.02588088], - [-2.52015705, -1.02588088, 8.06717203]]), scale=array([0.1759707 , 0.1759707 , 0.14779808]), shift=array([4.77904943e+00, 2.23566008e+03, 9.52201925e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - 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8.72677269e-01]), index=64, x=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), fval=0.11957247313888911, rho=-1.358818194996602, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([35, 39, 45, 46, 47, 48, 49, 52, 54, 58, 63, 64]), old_indices_discarded=array([ 0, 33, 34, 36, 37, 38, 40, 41, 42, 43, 44, 50, 51, 53, 55, 56, 57, - 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([35, 39, 46, 48, 49, 57, 58, 59, 61, 62, 63, 64]), model=ScalarModel(intercept=0.5004957915541093, linear_terms=array([-0.57918704, -0.48384567, 1.61725008]), square_terms=array([[ 0.44880458, 0.36991932, -1.20878754], - [ 0.36991932, 0.30522562, -0.99842249], - [-1.20878754, -0.99842249, 3.29756496]]), scale=0.10916351083437373, shift=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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old_indices_discarded=array([ 0, 34, 36, 37, 38, 40, 41, 42, 43, 44, 45, 47, 50, 51, 52, 53, 54, - 55, 56, 60, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([39, 46, 48, 49, 57, 58, 59, 61, 62, 63, 64, 66]), model=ScalarModel(intercept=0.27063319142748693, linear_terms=array([-0.1146274 , -0.19808018, 0.51298799]), square_terms=array([[ 0.04579955, 0.07871828, -0.19178955], - [ 0.07871828, 0.13539394, -0.32921422], - [-0.19178955, -0.32921422, 0.81551638]]), scale=0.054581755417186864, shift=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - 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radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([63, 64, 66, 67]), model=ScalarModel(intercept=0.11957247313888936, linear_terms=array([ 0.00391841, -0.01304193, -0.02667194]), square_terms=array([[ 0.00027908, -0.00230693, -0.00403301], - [-0.00230693, 0.02558587, 0.04322636], - [-0.00403301, 0.04322636, 0.07397422]]), scale=0.027290877708593432, shift=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=68, candidate_x=array([4.75839345e+00, 2.23564895e+03, 9.94942715e-01]), index=64, x=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), fval=0.11957247313888911, rho=-1.8438302886729045, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 64, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01]), radius=0.013645438854296716, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.1186316056712768, linear_terms=array([ 0.00347483, 0.00169535, -0.01719098]), square_terms=array([[ 0.00068357, 0.00053277, -0.00534077], - [ 0.00053277, 0.00042502, -0.00424712], - [-0.00534077, -0.00424712, 0.04289922]]), scale=0.013645438854296716, shift=array([4.77904943e+00, 2.23566008e+03, 9.80374550e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], 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67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.014257763675346667, relative_step_length=1.0448739558755298, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.76525937e+00, 2.23565983e+03, 9.83987893e-01]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 81]), model=ScalarModel(intercept=0.1138846587026767, linear_terms=array([ 0.00286786, 0.0001056 , -0.00362309]), square_terms=array([[ 7.58823322e-04, 1.78136736e-04, -1.27262497e-02], - [ 1.78136736e-04, 4.56758695e-05, -3.21711156e-03], - [-1.27262497e-02, -3.21711156e-03, 2.34475273e-01]]), scale=0.027290877708593432, shift=array([4.76525937e+00, 2.23565983e+03, 9.83987893e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 69, 70, 72, 74, 75, 76, 78, 79, 80, 82]), model=ScalarModel(intercept=0.1114368893687521, linear_terms=array([ 0.00547932, 0.00034892, -0.01217237]), square_terms=array([[ 1.63383022e-03, 6.41824887e-04, -3.65795371e-02], - [ 6.41824887e-04, 2.94150536e-04, -1.66544475e-02], - [-3.65795371e-02, -1.66544475e-02, 9.68829317e-01]]), scale=0.054581755417186864, shift=array([4.73797930e+00, 2.23565930e+03, 9.82933296e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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[0., 0., 0.], - [0., 0., 0.]]]), scale=223.5668701887974, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=83, candidate_x=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), index=83, x=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), fval=0.10637576541348984, rho=1.0823206627240762, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([64, 68, 69, 70, 72, 74, 75, 76, 78, 79, 80, 82]), old_indices_discarded=array([ 0, 35, 37, 39, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, - 58, 59, 60, 61, 62, 63, 65, 66, 67, 71, 73, 77, 81]), step_length=0.0546071236581967, relative_step_length=1.0004647751032545, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 66, 67, 68, 70, 74, 75, 76, 79, 80, 82, 83]), model=ScalarModel(intercept=0.107827872396005, linear_terms=array([0.00271801, 0.00465108, 0.0595416 ]), square_terms=array([[ 0.01913536, -0.00597598, -0.17725347], - [-0.00597598, 0.00225162, 0.06079365], - [-0.17725347, 0.06079365, 1.72780408]]), scale=0.10916351083437373, shift=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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x=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), fval=0.10637576541348984, rho=-0.4435220960006037, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 67, 68, 70, 74, 75, 76, 79, 80, 82, 83]), old_indices_discarded=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 69, 71, - 72, 73, 77, 78, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 66, 67, 68, 71, 72, 75, 77, 80, 81, 82, 83]), model=ScalarModel(intercept=0.10753665968218919, linear_terms=array([0.00205224, 0.00035594, 0.02486512]), square_terms=array([[ 4.13042323e-03, 1.27964751e-03, -4.10755431e-02], - [ 1.27964751e-03, 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model=ScalarModel(intercept=102.38671386491994, linear_terms=array([-301.74886293, -7.4096688 , 135.40964517]), square_terms=array([[ 4.49001106e+02, 1.10387141e+01, -1.98898743e+02], - [ 1.10387141e+01, 2.79198932e-01, -4.83711708e+00], - [-1.98898743e+02, -4.83711708e+00, 9.01425346e+01]]), scale=array([ 9.45 , 158.46996556, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - 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candidate_x=array([1.29775875e+01, 4.09074680e+03, 5.35492527e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.00494303523271701, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 2, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=98.30692093029782, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=116.12151198406633, linear_terms=array([-321.94879911, 8.08795269, 184.9444489 ]), square_terms=array([[ 4.50244477e+02, -1.12411579e+01, -2.55940799e+02], - [-1.12411579e+01, 2.84655841e-01, 6.47245223e+00], - [-2.55940799e+02, 6.47245223e+00, 1.47960373e+02]]), scale=array([ 9.45 , 79.23498278, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - 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old_indices_discarded=array([ 2, 5, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=49.15346046514891, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 3, 8, 9, 14, 15]), model=ScalarModel(intercept=36.477921419964005, linear_terms=array([-138.85628947, 17.34063992, 102.44589447]), square_terms=array([[ 268.01780029, -33.176478 , -194.8794462 ], - [ -33.176478 , 4.1379421 , 24.44585578], - [-194.8794462 , 24.44585578, 145.53102569]]), scale=array([ 9.45 , 39.61749139, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - 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16]), model=ScalarModel(intercept=137.95900758299246, linear_terms=array([ 609.72371975, 81.97626858, 1094.06151597]), square_terms=array([[1348.85665695, 181.37053476, 2420.79025551], - [ 181.37053476, 24.39149366, 325.54412825], - [2420.79025551, 325.54412825, 4345.09263337]]), scale=array([ 9.45 , 19.8087457, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - 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3.91246809e+03, 9.14078548e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-51.69148171099177, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 8, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=12.288365116287228, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 16, 17]), model=ScalarModel(intercept=675.1794019801806, linear_terms=array([ -2064.6019009 , 718.07582713, -10217.06091518]), square_terms=array([[ 3158.11858269, -1098.22603339, 15626.2724666 ], - [-1098.22603339, 381.93267203, -5434.33216723], - [15626.2724666 , -5434.33216723, 77322.68695368]]), scale=array([6.43190571, 9.90437285, 0.3 ]), shift=array([7.53190571e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=6.144182558143614, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=108.75147073194, linear_terms=array([-309.53365492, -36.53619492, 161.80376403]), square_terms=array([[ 441.33077962, 52.10175602, -230.15109169], - [ 52.10175602, 6.15213254, -27.15013825], - [-230.15109169, -27.15013825, 120.92652934]]), scale=array([3.9558125 , 4.95218642, 0.3 ]), shift=array([5.05581250e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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model=ScalarModel(intercept=170.59582545012594, linear_terms=array([-277.08221691, 5.5992395 , 233.76105829]), square_terms=array([[ 2.25373873e+02, -4.61116778e+00, -1.89643367e+02], - [-4.61116778e+00, 1.11464760e-01, 3.74415217e+00], - [-1.89643367e+02, 3.74415217e+00, 1.60694491e+02]]), scale=array([2.47609321, 2.47609321, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - 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candidate_x=array([5.66682758e+00, 3.92980074e+03, 6.00414976e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.006388659400412666, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([18, 19, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=1.5360456395359035, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=138.3462289851189, linear_terms=array([-147.45463528, -5.65111698, 268.26306503]), square_terms=array([[ 7.86989538e+01, 3.00126776e+00, -1.42903701e+02], - [ 3.00126776e+00, 1.18067220e-01, -5.51395279e+00], - [-1.42903701e+02, -5.51395279e+00, 2.60665493e+02]]), scale=array([1.23804661, 1.23804661, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=33, candidate_x=array([5.29748518e+00, 3.93103879e+03, 6.53424479e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.005882033142130255, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([19, 24, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.7680228197679517, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 23, 27, 32, 33]), model=ScalarModel(intercept=1.5830056559661339, linear_terms=array([ 9.99281175, 14.43614264, -39.57256129]), square_terms=array([[ 33.80983754, 48.83787417, -133.54382225], - [ 48.83787417, 70.60333873, -193.11510202], - [-133.54382225, -193.11510202, 528.45490937]]), scale=array([0.6190233, 0.6190233, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=34, candidate_x=array([3.77141395e+00, 3.93289586e+03, 8.96820681e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0007867459648932072, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 27, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.5847700831496822, linear_terms=array([-0.41675462, -0.44190085, 2.89851388]), square_terms=array([[ 0.18454675, 0.18631726, -1.20807895], - [ 0.18631726, 0.19477978, -1.27421791], - [-1.20807895, -1.27421791, 8.3918659 ]]), scale=array([0.30951165, 0.30951165, 0.26871159]), shift=array([4.05943857e+00, 3.93227684e+03, 8.31288413e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=47, candidate_x=array([3.96066338e+00, 3.93258635e+03, 7.66932590e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.1708886923662552, accepted=False, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2415835967758433, linear_terms=array([-0.15283733, -0.58871896, 3.13992098]), square_terms=array([[ 0.02040704, 0.03863901, -0.21039381], - [ 0.03863901, 0.14956944, -0.79740606], - [-0.21039381, -0.79740606, 4.2608185 ]]), scale=0.19200570494198793, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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old_indices_discarded=array([34, 37, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0337220474595914, linear_terms=array([-0.20696303, -0.31059194, 1.61730455]), square_terms=array([[ 0.02606724, 0.03394087, -0.17786586], - [ 0.03394087, 0.05073282, -0.26413928], - [-0.17786586, -0.26413928, 1.3775637 ]]), scale=0.09600285247099397, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - 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model_indices=array([ 0, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.11633665599043491, linear_terms=array([ 0.01692106, -0.00599296, -0.07183474]), square_terms=array([[ 0.00430738, -0.00138782, -0.01722234], - [-0.00138782, 0.00047598, 0.00583328], - [-0.01722234, 0.00583328, 0.07191651]]), scale=0.04800142623549698, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=62, candidate_x=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), index=62, x=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), fval=0.08328203055052266, rho=1.1521105575858042, accepted=True, new_indices=array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_used=array([ 0, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.05128066423434844, relative_step_length=1.0683154284367173, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.08089110047653379, linear_terms=array([-0.00528525, -0.0001229 , 0.02824264]), square_terms=array([[ 1.16009632e-02, 2.30117604e-04, -6.86298394e-02], - [ 2.30117604e-04, 5.27144510e-06, 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50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 52, 53]), step_length=0.09849549625031004, relative_step_length=1.025964267885365, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.08002590130028917, linear_terms=array([-0.00526566, 0.000661 , 0.04112777]), square_terms=array([[ 2.05912030e-02, -2.69140553e-03, -1.45369821e-01], - [-2.69140553e-03, 4.27142888e-04, 2.22767331e-02], - [-1.45369821e-01, 2.22767331e-02, 1.17421707e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), model=ScalarModel(intercept=0.15326138046317392, linear_terms=array([0.55415723, 0.00612001, 0.65724766]), square_terms=array([[2.16178448e+00, 2.69601220e-02, 2.58028293e+00], - [2.69601220e-02, 3.98421161e-04, 3.15716605e-02], - [2.58028293e+00, 3.15716605e-02, 3.11749315e+00]]), scale=array([0.30951165, 0.30951165, 0.24443416]), shift=array([4.16380226e+00, 3.93248621e+03, 8.55565845e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), model=ScalarModel(intercept=0.07981427686533094, linear_terms=array([ 8.23222295e-04, -3.01457336e-06, -1.94680885e-04]), square_terms=array([[ 4.38349908e-03, -2.78417852e-04, -2.40946662e-02], - [-2.78417852e-04, 1.45680783e-04, 9.88295037e-03], - [-2.40946662e-02, 9.88295037e-03, 6.79192813e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=66, candidate_x=array([4.12483624e+00, 3.93264097e+03, 9.17053507e-01]), index=64, x=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), fval=0.0797111995279562, rho=-0.4172818202820011, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 54, 55, 56, - 57, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.0797875761950249, linear_terms=array([ 5.31755989e-04, 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9.20412203e-01]), fval=0.07939979491586543, rho=3.2737438064381488, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 47, 49, 54, 55, 56, 57, 59, 60, 61]), step_length=0.10215613340917244, relative_step_length=1.0640947719760474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 63, 64, 67]), model=ScalarModel(intercept=0.0794221211152619, linear_terms=array([-8.78763547e-04, -7.32595189e-05, 1.57498998e-03]), square_terms=array([[ 1.88292059e-02, -8.43669110e-04, -1.35637703e-01], - [-8.43669110e-04, 5.18412063e-05, 7.55602994e-03], - [-1.35637703e-01, 7.55602994e-03, 1.13549976e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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67]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 52, - 53, 58, 61, 62, 65, 66]), step_length=0.1591995195038744, relative_step_length=0.8291395276612978, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 55, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.11467755048322094, linear_terms=array([ 0.4301884 , -0.10852269, 0.38403869]), square_terms=array([[ 2.8059872 , -0.69595835, 2.53665519], - [-0.69595835, 0.17347376, -0.6322991 ], - [ 2.53665519, -0.6322991 , 2.32273428]]), scale=array([0.30951165, 0.30951165, 0.24294931]), shift=array([4.16663860e+00, 3.93254477e+03, 8.57050687e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 53, 63, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.07972737754656194, linear_terms=array([0.00089106, 0.00026728, 0.00741102]), square_terms=array([[3.83952175e-03, 2.37615327e-04, 1.80862528e-04], - [2.37615327e-04, 1.04844950e-03, 2.29140497e-02], - [1.80862528e-04, 2.29140497e-02, 5.09344565e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - 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1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), model=ScalarModel(intercept=0.07970568860996624, linear_terms=array([5.17688765e-04, 7.08684557e-05, 2.23636029e-03]), square_terms=array([[1.54434855e-03, 1.10165119e-04, 2.02375366e-03], - [1.10165119e-04, 1.32782811e-04, 4.89488107e-03], - [2.02375366e-03, 4.89488107e-03, 1.84174468e-01]]), scale=0.09600285247099397, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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3.93227684e+03, 8.72088478e-01])), candidate_index=71, candidate_x=array([4.13217290e+00, 3.93263940e+03, 9.20311146e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-2.866324235420139, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, - 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.07966045626129445, linear_terms=array([3.20987313e-04, 4.92207090e-05, 2.88283811e-03]), square_terms=array([[ 3.79212673e-04, 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radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 72, 73]), model=ScalarModel(intercept=0.07908472064494196, linear_terms=array([-3.38525141e-05, -9.76572645e-05, 8.48540656e-04]), square_terms=array([[ 2.37169869e-04, 1.05566792e-05, -1.97718322e-03], - [ 1.05566792e-05, 7.38132067e-07, -8.17544926e-05], - [-1.97718322e-03, -8.17544926e-05, 1.75933908e-02]]), scale=0.012000356558874246, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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accepted=True, new_indices=array([75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), old_indices_used=array([68, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.006052299097263197, relative_step_length=1.0086865448656241, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914371578873403, linear_terms=array([1.82176925e-04, 7.65262019e-05, 2.45374037e-06]), square_terms=array([[ 1.92862702e-04, -9.93628574e-06, -1.67037516e-03], - [-9.93628574e-06, 7.42657125e-07, 7.94573605e-05], - [-1.67037516e-03, 7.94573605e-05, 1.54724452e-02]]), scale=0.012000356558874246, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.006000178279437123, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914612489984735, linear_terms=array([ 9.04725864e-05, 3.30565774e-05, -2.19601997e-05]), square_terms=array([[ 4.27792898e-05, -4.53845336e-06, -3.94771372e-04], - [-4.53845336e-06, 5.36493868e-07, 4.14551856e-05], - [-3.94771372e-04, 4.14551856e-05, 3.90706076e-03]]), scale=0.006000178279437123, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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model=ScalarModel(intercept=0.07916583626576876, linear_terms=array([ 3.46835434e-05, 1.37176019e-05, -3.87853430e-06]), square_terms=array([[ 1.16702087e-05, -1.13065279e-06, -1.02787515e-04], - [-1.13065279e-06, 1.19282548e-07, 9.91369374e-06], - [-1.02787515e-04, 9.91369374e-06, 9.72885293e-04]]), scale=0.0030000891397185614, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 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3.93254141e+03, 9.23023628e-01]), index=87, x=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), fval=0.07907999189313741, rho=-0.5105398504494335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 87, 89]), old_indices_discarded=array([74, 82, 85, 86, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0015000445698592807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 77, 83, 87, 89, 90]), model=ScalarModel(intercept=0.07909333025755838, linear_terms=array([-1.15137705e-05, -7.91213109e-07, 9.73368802e-05]), square_terms=array([[ 3.75332861e-06, -1.15364186e-07, -2.97559553e-05], - [-1.15364186e-07, 1.95191461e-08, 8.40989334e-07], - [-2.97559553e-05, 8.40989334e-07, 2.56198599e-04]]), scale=0.0015000445698592807, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103]), model=ScalarModel(intercept=0.07910448579668597, linear_terms=array([-9.82149673e-08, 5.86243338e-06, -2.29104355e-05]), square_terms=array([[ 8.28466084e-07, -2.44013709e-08, -6.66789179e-06], - [-2.44013709e-08, 3.18448291e-09, 1.62847966e-07], - [-6.66789179e-06, 1.62847966e-07, 5.95987805e-05]]), scale=0.0007500222849296404, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, 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0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.07909403564536577, linear_terms=array([-1.22102547e-05, 7.60923217e-06, -5.17261450e-06]), square_terms=array([[ 3.36799435e-06, -1.27039621e-07, -2.66224628e-05], - [-1.27039621e-07, 2.41088585e-08, 9.99863513e-07], - [-2.66224628e-05, 9.99863513e-07, 2.37983666e-04]]), scale=0.0015000445698592807, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=105, candidate_x=array([4.16253783e+00, 3.93254113e+03, 9.23735181e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-1.0803986646623365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), old_indices_discarded=array([68, 77, 83, 89, 90, 91, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), model=ScalarModel(intercept=0.07909160054167635, linear_terms=array([-1.29680002e-05, 2.27817382e-06, 1.03758748e-06]), square_terms=array([[ 9.19566779e-07, -3.49328357e-08, -6.93344117e-06], - [-3.49328357e-08, 6.61278422e-09, 2.68258739e-07], - [-6.93344117e-06, 2.68258739e-07, 5.96608551e-05]]), scale=0.0007500222849296404, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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fval=0.07906796375411648, rho=-0.13973162244364834, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), old_indices_discarded=array([ 83, 90, 91, 100, 103, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0003750111424648202, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 94, 95, 96, 97, 98, 99, 101, 102, 104, 106]), model=ScalarModel(intercept=0.07908661788272847, linear_terms=array([-8.35298009e-06, 7.05475745e-06, 1.43187070e-06]), square_terms=array([[ 2.33438460e-07, -1.55439804e-08, -1.73669039e-06], - [-1.55439804e-08, 5.10319166e-09, 8.60392613e-08], - [-1.73669039e-06, 8.60392613e-08, 1.49265352e-05]]), scale=0.0003750111424648202, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0001875055712324101, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 94, 95, 98, 102, 104, 106, 107]), model=ScalarModel(intercept=0.0790879150221595, linear_terms=array([-6.18729811e-06, 2.85069426e-06, 5.24029282e-06]), square_terms=array([[ 6.39029879e-08, -5.52613342e-09, -4.58386220e-07], - [-5.52613342e-09, 1.01866206e-09, 2.93309494e-08], - [-4.58386220e-07, 2.93309494e-08, 3.89516694e-06]]), scale=0.0001875055712324101, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model=ScalarModel(intercept=0.07906796375411641, linear_terms=array([-2.77746966e-05, -3.22673327e-05, -4.25319102e-05]), square_terms=array([[ 5.39542744e-08, 4.53199229e-08, -4.64131654e-08], - [ 4.53199229e-08, 4.98555993e-08, 7.22290924e-08], - [-4.64131654e-08, 7.22290924e-08, 1.14321313e-06]]), scale=9.375278561620504e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 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3.93254196e+03, 9.23633511e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.11603470278551233, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 95, 104, 107, 108]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=4.687639280810252e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, - 120, 121]), model=ScalarModel(intercept=0.07907082869590354, linear_terms=array([ 2.88136945e-06, -3.08394182e-07, -1.07721092e-06]), square_terms=array([[ 4.46329859e-09, -6.05308320e-10, -2.92877945e-08], - [-6.05308320e-10, 1.34067088e-10, 3.43225360e-09], - [-2.92877945e-08, 3.43225360e-09, 2.44027182e-07]]), scale=4.687639280810252e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=2.343819640405126e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=0.07907053144557313, linear_terms=array([ 1.94854337e-07, -3.55132795e-07, -5.64262326e-07]), square_terms=array([[ 1.08597546e-09, 1.16175887e-10, -7.41141155e-09], - [ 1.16175887e-10, 3.16258142e-11, -6.25413742e-10], - [-7.41141155e-09, -6.25413742e-10, 5.96099329e-08]]), scale=2.343819640405126e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123]), model=ScalarModel(intercept=0.07907067153514401, linear_terms=array([ 2.04407661e-07, -2.51465066e-07, -2.26280892e-07]), square_terms=array([[ 2.06977844e-10, 4.21146920e-11, -1.67593686e-09], - [ 4.21146920e-11, 2.48475075e-11, -3.05589732e-10], - [-1.67593686e-09, -3.05589732e-10, 1.50170769e-08]]), scale=1.171909820202563e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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[0., 0., 0.], - [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=124, candidate_x=array([4.16126418e+00, 3.93254192e+03, 9.23574260e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.2558930827717976, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([104, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123]), old_indices_discarded=array([111, 121, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=5.859549101012815e-06, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, - 135, 136]), model=ScalarModel(intercept=0.07906789905966807, linear_terms=array([2.53618684e-09, 2.53785426e-08, 3.95194505e-07]), square_terms=array([[ 5.35172911e-11, -7.04580653e-12, -3.54919326e-10], - [-7.04580653e-12, 3.06788632e-12, 2.82219396e-11], - [-3.54919326e-10, 2.82219396e-11, 2.96974675e-09]]), scale=5.859549101012815e-06, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, - -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, - -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, - -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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3.93254191e+03, 9.23561859e-01]), fval=0.07906739186138749, rho=1.4495418816296448, accepted=True, new_indices=array([125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136]), old_indices_used=array([104, 123, 124]), old_indices_discarded=array([], dtype=int32), step_length=5.859549088890873e-06, relative_step_length=0.99999999793125, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 138 entries., 'history': {'params': [{'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 1.1865624830963672, 'BeqFac': 3618.0088638043917, 'DiscFac': 1.1}, {'CRRA': 18.864750592221043, 'BeqFac': 3615.3369060843656, 'DiscFac': 1.0141895974609063}, {'CRRA': 20.0, 'BeqFac': 4139.2725657346655, 'DiscFac': 0.967186242030893}, {'CRRA': 11.813333909541619, 'BeqFac': 4191.377523809567, 'DiscFac': 0.5}, {'CRRA': 10.612091741842278, 'BeqFac': 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State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=28.669891670599913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 15, 16]), model=ScalarModel(intercept=4.862823488147589, linear_terms=array([ -73.44883613, -23.31734993, -131.12114654]), square_terms=array([[ 578.17685765, 182.96932654, 1029.31041988], - [ 182.96932654, 57.95652351, 325.84926908], - [1029.31041988, 325.84926908, 1832.92574964]]), scale=array([ 9.45 , 23.10781735, 0.3 ]), shift=array([1.05500000e+01, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=458.7182667295986, shift=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01])), candidate_index=17, candidate_x=array([1.10000000e+00, 4.57939284e+03, 1.00790989e+00]), index=0, x=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-37280.752006414696, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 8, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=14.334945835299957, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 16, 17]), model=ScalarModel(intercept=2230191.0097891814, linear_terms=array([ -9347809.41527515, -2363766.25708062, -22402873.36601085]), square_terms=array([[1.95905989e+07, 4.95384419e+06, 4.69506453e+07], - [4.95384419e+06, 1.25267102e+06, 1.18723370e+07], - [4.69506453e+07, 1.18723370e+07, 1.12521478e+08]]), scale=array([ 7.46023951, 11.55390867, 0.3 ]), shift=array([8.56023951e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=458.7182667295986, shift=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01])), candidate_index=18, candidate_x=array([2.60573611e+00, 4.58519579e+03, 9.65085501e-01]), index=0, x=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-1041.451062119752, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=7.167472917649978, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=80.22435907513035, linear_terms=array([-244.91564084, 13.26585911, 83.93950199]), square_terms=array([[ 375.02548269, -20.22669945, -127.79535667], - [ -20.22669945, 1.10349026, 6.99773206], - [-127.79535667, 6.99773206, 44.64312817]]), scale=array([4.57176234, 5.77695434, 0.3 ]), shift=array([5.67176234e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=458.7182667295986, shift=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01])), candidate_index=31, candidate_x=array([7.87623530e+00, 4.58140571e+03, 6.97052166e-01]), index=0, x=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-0.008943120003896764, accepted=False, new_indices=array([19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=3.583736458824989, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=93.97983279704786, linear_terms=array([-158.16807239, 51.83688273, 84.93922156]), square_terms=array([[133.49622213, -43.70152356, -71.15581564], - [-43.70152356, 14.31811665, 23.35552818], - [-71.15581564, 23.35552818, 39.27068276]]), scale=array([2.88847717, 2.88847717, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=458.7182667295986, shift=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01])), candidate_index=32, candidate_x=array([6.28886944e+00, 4.58429419e+03, 6.72481184e-01]), index=0, x=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-0.011700252101044908, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31]), old_indices_discarded=array([17, 19, 24, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=1.7918682294124946, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 20, 21, 22, 23, 26, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=81.4490928379784, linear_terms=array([-89.01247099, 16.9455529 , 111.39761411]), square_terms=array([[ 48.77806772, -9.2752547 , -60.74060208], - [ -9.2752547 , 1.76503453, 11.57507336], - [-60.74060208, 11.57507336, 77.05159691]]), scale=array([1.44423858, 1.44423858, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-0.010827579816131265, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 20, 21, 22, 23, 26, 27, 29, 30, 31, 32]), old_indices_discarded=array([17, 19, 24, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=0.8959341147062473, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 23, 32, 33]), model=ScalarModel(intercept=5.686343548503185, linear_terms=array([-1.41335436e+01, 6.60802805e-03, -3.48121455e+01]), square_terms=array([[ 1.80359552e+01, 4.54763842e-02, 4.39045113e+01], - [ 4.54763842e-02, 1.82631253e-02, -3.51143342e-02], - [ 4.39045113e+01, -3.51143342e-02, 1.08172460e+02]]), scale=array([0.72211929, 0.72211929, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 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8.21257590e-01]), index=34, x=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), fval=0.08450890381661277, rho=-0.05757584370248203, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.11199176433828091, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=2.3684676382089758, linear_terms=array([-0.10988363, -0.66436773, 2.75173228]), square_terms=array([[ 0.00591366, 0.01676982, -0.07000994], - [ 0.01676982, 0.09679265, -0.40121714], - [-0.07000994, -0.40121714, 1.66585423]]), scale=0.11199176433828091, shift=array([4.28097285e+00, 4.58790479e+03, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.055995882169140455, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.09028822664173408, linear_terms=array([-0.01061362, 0.00088466, 0.05364829]), square_terms=array([[ 1.15220286e-02, -8.95044594e-04, -5.57609462e-02], - [-8.95044594e-04, 7.85103124e-05, 4.56043843e-03], - [-5.57609462e-02, 4.56043843e-03, 2.76280980e-01]]), scale=0.055995882169140455, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.08780533673879214, linear_terms=array([-0.00204222, -0.000514 , 0.02518641]), square_terms=array([[ 9.44732408e-04, 1.61167329e-04, -9.59779358e-03], - [ 1.61167329e-04, 3.11556686e-05, -1.82922922e-03], - [-9.59779358e-03, -1.82922922e-03, 1.08278084e-01]]), scale=0.027997941084570228, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01]), radius=0.00010936695736160245, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 85, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 102]), model=ScalarModel(intercept=0.08421061771968695, linear_terms=array([1.36244245e-06, 3.65998473e-06, 1.83718615e-06]), square_terms=array([[ 1.90988019e-08, -1.56348642e-09, -1.51696258e-07], - [-1.56348642e-09, 1.79733775e-09, 1.38949776e-08], - [-1.51696258e-07, 1.38949776e-08, 1.31328339e-06]]), scale=0.00010936695736160245, shift=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], 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103]), model=ScalarModel(intercept=0.0842100948856364, linear_terms=array([6.98669087e-07, 2.12772858e-06, 6.89214124e-07]), square_terms=array([[ 4.70869613e-09, -4.52099441e-10, -3.78128159e-08], - [-4.52099441e-10, 5.76259210e-10, 3.90981776e-09], - [-3.78128159e-08, 3.90981776e-09, 3.29980284e-07]]), scale=5.4683478680801226e-05, shift=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 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candidate_x=array([4.26882502e+00, 4.58791635e+03, 9.24496345e-01]), index=85, x=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01]), fval=0.08419662491977306, rho=-0.28114607847284195, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 85, 90, 91, 92, 95, 97, 98, 99, 100, 101, 102, 103]), old_indices_discarded=array([93, 94, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01]), radius=2.7341739340400613e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 85, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, - 115, 116]), model=ScalarModel(intercept=0.08419771674559179, linear_terms=array([-1.79796518e-07, -4.84727665e-07, 6.59542682e-08]), square_terms=array([[ 9.12410658e-10, -1.60070815e-10, -9.04169307e-09], - [-1.60070815e-10, 9.81919219e-11, 1.16277977e-09], - [-9.04169307e-09, 1.16277977e-09, 9.55059994e-08]]), scale=2.7341739340400613e-05, shift=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , - -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, - -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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113, 114, 115, 116]), old_indices_used=array([ 85, 103, 104]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01]), radius=1.3670869670200306e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 85, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116]), model=ScalarModel(intercept=0.08419777229304778, linear_terms=array([-1.62551585e-08, 2.07554555e-07, 1.82199559e-07]), square_terms=array([[ 1.93862655e-10, 3.48608690e-11, -2.10716777e-09], - [ 3.48608690e-11, 8.91496168e-12, -3.59091832e-10], - [-2.10716777e-09, -3.59091832e-10, 2.37580489e-08]]), scale=1.3670869670200306e-05, shift=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , - 0.02465656, 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tolerance.', 'tranquilo_history': History for least_squares function with 119 entries., 'history': {'params': [{'CRRA': 4.466570349505643, 'BeqFac': 4587.182667295986, 'DiscFac': 0.9284133483434123}, {'CRRA': 1.1773716440521769, 'BeqFac': 4217.457589731739, 'DiscFac': 1.0189962612053658}, {'CRRA': 20.0, 'BeqFac': 4225.7618739064255, 'DiscFac': 1.0171895930759263}, {'CRRA': 20.0, 'BeqFac': 4817.154239034149, 'DiscFac': 0.9094208421939314}, {'CRRA': 12.072368029141941, 'BeqFac': 4954.33727993967, 'DiscFac': 0.5}, {'CRRA': 9.926248932086857, 'BeqFac': 4956.907744860233, 'DiscFac': 1.0940032098135037}, {'CRRA': 18.49315852332056, 'BeqFac': 4954.132040009003, 'DiscFac': 1.1}, {'CRRA': 1.2242804208400564, 'BeqFac': 4956.907744860233, 'DiscFac': 0.7608064947134148}, {'CRRA': 1.1911500855961954, 'BeqFac': 4720.043806708304, 'DiscFac': 1.1}, {'CRRA': 20.0, 'BeqFac': 4648.332190820999, 'DiscFac': 0.5002043199703005}, {'CRRA': 10.256549394930358, 'BeqFac': 4218.012526269039, 'DiscFac': 0.5}, 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75.2044039000757, 75.3887424999848, 75.57385200029239, 75.76805710000917, 75.96694570034742, 76.16238610027358, 76.3546518003568, 76.55501760030165, 77.8122900002636], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 39, 40, 41, 42, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 46, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 49]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.15773345928132843, 'relative_params_change': 0.4431666467150333, 'absolute_criterion_change': 0.015773345928132843, 'absolute_params_change': 1696.956220326838}, 'five_steps': {'relative_criterion_change': 0.15773345928132843, 'relative_params_change': 0.4431666467150333, 'absolute_criterion_change': 0.015773345928132843, 'absolute_params_change': 1696.956220326838}}" + +multistart_info,"{'start_parameters': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}, {'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 4.466570349505643, 'BeqFac': 4587.182667295986, 'DiscFac': 0.9284133483434123}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0*** 0.1692 +relative_params_change 0*** 0.05382 +absolute_criterion_change 0*** 0.01692 +absolute_params_change 0*** 0.2525 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.245e-07* 5.719e-06* +relative_params_change 4.911e-07* 1.068e-05 +absolute_criterion_change 4.245e-08* 5.719e-07* +absolute_params_change 1.875e-06* 4.663e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 9.277e-06* 0.001793 +relative_params_change 4.611e-05 0.002492 +absolute_criterion_change 9.277e-07* 0.0001793 +absolute_params_change 0.0002187 0.01527 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974, 'DiscFac': 1.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'DiscFac': 0.7250000000000001}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'DiscFac': 0.9500000000000001}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'DiscFac': 0.8187500000000001}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'DiscFac': 1.00625}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'DiscFac': 0.9312500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'DiscFac': 0.89375}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'DiscFac': 0.8562500000000001}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'DiscFac': 0.55625}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'DiscFac': 0.70625}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'DiscFac': 0.875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'DiscFac': 0.7625000000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'DiscFac': 1.0250000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'DiscFac': 0.6312500000000001}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'DiscFac': 0.8}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'DiscFac': 0.575}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'DiscFac': 0.65}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'DiscFac': 0.59375}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'DiscFac': 0.78125}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'DiscFac': 0.66875}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'DiscFac': 0.6875}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'DiscFac': 0.6125}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'DiscFac': 0.9875}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'DiscFac': 0.51875}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'DiscFac': 0.5375}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'DiscFac': 0.96875}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'DiscFac': 1.0625}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'DiscFac': 1.08125}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'DiscFac': 0.8375}], 'exploration_results': array([ 0.15183336, 0.16963129, 0.3197984 , 0.64023668, 0.65671129, + 0.81183081, 0.97682658, 1.08889746, 1.16160077, 1.27832959, + 1.4056054 , 1.63187357, 1.68853551, 1.72432777, 1.75966476, + 1.80211806, 1.86015904, 1.93985391, 1.96313642, 2.01014295, + 2.05048939, 2.06445231, 2.29147195, 2.43324345, 2.49173392, + 2.54550964, 3.05245049, 4.00840374, 4.33350039, 58.25645251])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=393.2276837211913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=0.12472562593739224, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=0, candidate_x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=393.2276837211913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=101.88864067521602, linear_terms=array([-301.48531903, -14.07496772, 134.91730104]), square_terms=array([[ 450.60368506, 21.0269611 , -198.92102364], + [ 21.0269611 , 0.9962047 , -9.22847607], + [-198.92102364, -9.22847607, 89.91310769]]), scale=array([9.45000000e+00, 3.16939931e+02, 3.00000000e-01]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=13, candidate_x=array([1.26959565e+01, 4.24921677e+03, 5.31351377e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.004991694207075647, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=196.61384186059564, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=102.38671386491994, linear_terms=array([-301.74886293, -7.4096688 , 135.40964517]), square_terms=array([[ 4.49001106e+02, 1.10387141e+01, -1.98898743e+02], + [ 1.10387141e+01, 2.79198932e-01, -4.83711708e+00], + [-1.98898743e+02, -4.83711708e+00, 9.01425346e+01]]), scale=array([ 9.45 , 158.46996556, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], 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old_indices_used=array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_discarded=array([ 2, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=98.30692093029782, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=116.12151198406633, linear_terms=array([-321.94879911, 8.08795269, 184.9444489 ]), square_terms=array([[ 4.50244477e+02, -1.12411579e+01, -2.55940799e+02], + [-1.12411579e+01, 2.84655841e-01, 6.47245223e+00], + [-2.55940799e+02, 6.47245223e+00, 1.47960373e+02]]), scale=array([ 9.45 , 79.23498278, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 3, 8, 9, 14, 15]), model=ScalarModel(intercept=36.477921419964005, linear_terms=array([-138.85628947, 17.34063992, 102.44589447]), square_terms=array([[ 268.01780029, -33.176478 , -194.8794462 ], + [ -33.176478 , 4.1379421 , 24.44585578], + [-194.8794462 , 24.44585578, 145.53102569]]), scale=array([ 9.45 , 39.61749139, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=16, candidate_x=array([1.21980290e+01, 3.89265935e+03, 7.09268596e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0077322181559090096, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 8, 9, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=24.576730232574455, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 15, 16]), model=ScalarModel(intercept=137.95900758299246, linear_terms=array([ 609.72371975, 81.97626858, 1094.06151597]), square_terms=array([[1348.85665695, 181.37053476, 2420.79025551], + [ 181.37053476, 24.39149366, 325.54412825], + [2420.79025551, 325.54412825, 4345.09263337]]), scale=array([ 9.45 , 19.8087457, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=12.288365116287228, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 16, 17]), model=ScalarModel(intercept=675.1794019801806, linear_terms=array([ -2064.6019009 , 718.07582713, -10217.06091518]), square_terms=array([[ 3158.11858269, -1098.22603339, 15626.2724666 ], + [-1098.22603339, 381.93267203, -5434.33216723], + [15626.2724666 , -5434.33216723, 77322.68695368]]), scale=array([6.43190571, 9.90437285, 0.3 ]), shift=array([7.53190571e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, 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20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=108.75147073194, linear_terms=array([-309.53365492, -36.53619492, 161.80376403]), square_terms=array([[ 441.33077962, 52.10175602, -230.15109169], + [ 52.10175602, 6.15213254, -27.15013825], + [-230.15109169, -27.15013825, 120.92652934]]), scale=array([3.9558125 , 4.95218642, 0.3 ]), shift=array([5.05581250e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_index=31, candidate_x=array([6.44025805e+00, 3.93722902e+03, 6.65771609e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.00640748988321916, accepted=False, new_indices=array([19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=3.072091279071807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=170.59582545012594, linear_terms=array([-277.08221691, 5.5992395 , 233.76105829]), square_terms=array([[ 2.25373873e+02, -4.61116778e+00, -1.89643367e+02], + [-4.61116778e+00, 1.11464760e-01, 3.74415217e+00], + [-1.89643367e+02, 3.74415217e+00, 1.60694491e+02]]), scale=array([2.47609321, 2.47609321, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([18, 19, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=1.5360456395359035, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=138.3462289851189, linear_terms=array([-147.45463528, -5.65111698, 268.26306503]), square_terms=array([[ 7.86989538e+01, 3.00126776e+00, -1.42903701e+02], + [ 3.00126776e+00, 1.18067220e-01, -5.51395279e+00], + [-1.42903701e+02, -5.51395279e+00, 2.60665493e+02]]), scale=array([1.23804661, 1.23804661, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 23, 27, 32, 33]), model=ScalarModel(intercept=1.5830056559661339, linear_terms=array([ 9.99281175, 14.43614264, -39.57256129]), square_terms=array([[ 33.80983754, 48.83787417, -133.54382225], + [ 48.83787417, 70.60333873, -193.11510202], + [-133.54382225, -193.11510202, 528.45490937]]), scale=array([0.6190233, 0.6190233, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=34, candidate_x=array([3.77141395e+00, 3.93289586e+03, 8.96820681e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.0007867459648932072, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 27, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.5847700831496822, linear_terms=array([-0.41675462, -0.44190085, 2.89851388]), square_terms=array([[ 0.18454675, 0.18631726, -1.20807895], + [ 0.18631726, 0.19477978, 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43, 44, 45, 46]), old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2415835967758433, linear_terms=array([-0.15283733, -0.58871896, 3.13992098]), square_terms=array([[ 0.02040704, 0.03863901, -0.21039381], + [ 0.03863901, 0.14956944, -0.79740606], + [-0.21039381, -0.79740606, 4.2608185 ]]), scale=0.19200570494198793, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0337220474595914, linear_terms=array([-0.20696303, -0.31059194, 1.61730455]), square_terms=array([[ 0.02606724, 0.03394087, -0.17786586], + [ 0.03394087, 0.05073282, -0.26413928], + [-0.17786586, -0.26413928, 1.3775637 ]]), scale=0.09600285247099397, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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8.72088478e-01])), candidate_index=49, candidate_x=array([4.03665655e+00, 3.93225811e+03, 7.80726618e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.2193495890214448, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.11633665599043491, linear_terms=array([ 0.01692106, -0.00599296, -0.07183474]), square_terms=array([[ 0.00430738, -0.00138782, -0.01722234], + [-0.00138782, 0.00047598, 0.00583328], + 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State(trustregion=Region(center=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.08002590130028917, linear_terms=array([-0.00526566, 0.000661 , 0.04112777]), square_terms=array([[ 2.05912030e-02, -2.69140553e-03, -1.45369821e-01], + [-2.69140553e-03, 4.27142888e-04, 2.22767331e-02], + [-1.45369821e-01, 2.22767331e-02, 1.17421707e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.15738965e+00, 3.93233146e+03, 9.28205841e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + 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model_indices=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), model=ScalarModel(intercept=0.15326138046317392, linear_terms=array([0.55415723, 0.00612001, 0.65724766]), square_terms=array([[2.16178448e+00, 2.69601220e-02, 2.58028293e+00], + [2.69601220e-02, 3.98421161e-04, 3.15716605e-02], + [2.58028293e+00, 3.15716605e-02, 3.11749315e+00]]), scale=array([0.30951165, 0.30951165, 0.24443416]), shift=array([4.16380226e+00, 3.93248621e+03, 8.55565845e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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3.93227684e+03, 8.72088478e-01])), candidate_index=65, candidate_x=array([4.03801811e+00, 3.93261118e+03, 8.85249640e-01]), index=64, x=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), fval=0.0797111995279562, rho=-0.04919187191422923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), old_indices_discarded=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 43, 45, 46, 50, 51, 52, 53, 56, + 57, 58, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), model=ScalarModel(intercept=0.07981427686533094, linear_terms=array([ 8.23222295e-04, -3.01457336e-06, -1.94680885e-04]), square_terms=array([[ 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fval=0.0797111995279562, rho=-0.4172818202820011, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 54, 55, 56, + 57, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.0797875761950249, linear_terms=array([ 5.31755989e-04, -2.45518194e-07, -8.66641581e-04]), square_terms=array([[ 1.65529454e-03, -4.13667194e-05, -8.39813544e-03], + [-4.13667194e-05, 8.98752859e-06, 1.51601069e-03], + [-8.39813544e-03, 1.51601069e-03, 2.58158286e-01]]), scale=0.09600285247099397, shift=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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45, 47, 49, 54, 55, 56, 57, 59, 60, 61]), step_length=0.10215613340917244, relative_step_length=1.0640947719760474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 63, 64, 67]), model=ScalarModel(intercept=0.0794221211152619, linear_terms=array([-8.78763547e-04, -7.32595189e-05, 1.57498998e-03]), square_terms=array([[ 1.88292059e-02, -8.43669110e-04, -1.35637703e-01], + [-8.43669110e-04, 5.18412063e-05, 7.55602994e-03], + [-1.35637703e-01, 7.55602994e-03, 1.13549976e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, 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51, 53, 63, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.07972737754656194, linear_terms=array([0.00089106, 0.00026728, 0.00741102]), square_terms=array([[3.83952175e-03, 2.37615327e-04, 1.80862528e-04], + [2.37615327e-04, 1.04844950e-03, 2.29140497e-02], + [1.80862528e-04, 2.29140497e-02, 5.09344565e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=70, candidate_x=array([4.12157965e+00, 3.93269953e+03, 9.14415260e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-4.697657825504714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 53, 63, 64, 65, 66, 67, 68]), old_indices_discarded=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 49, 52, 54, 55, + 56, 57, 58, 59, 60, 61, 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), model=ScalarModel(intercept=0.07970568860996624, linear_terms=array([5.17688765e-04, 7.08684557e-05, 2.23636029e-03]), square_terms=array([[1.54434855e-03, 1.10165119e-04, 2.02375366e-03], + [1.10165119e-04, 1.32782811e-04, 4.89488107e-03], + [2.02375366e-03, 4.89488107e-03, 1.84174468e-01]]), scale=0.09600285247099397, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, + 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.07966045626129445, linear_terms=array([3.20987313e-04, 4.92207090e-05, 2.88283811e-03]), square_terms=array([[ 3.79212673e-04, -2.34327242e-06, -5.41146655e-06], + [-2.34327242e-06, 8.52228643e-06, -6.54738778e-04], + [-5.41146655e-06, -6.54738778e-04, 5.14897659e-02]]), scale=0.04800142623549698, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.02400071311774849, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 66, 68, 71, 72]), model=ScalarModel(intercept=0.07909174304369689, linear_terms=array([ 9.75729683e-05, -8.37732492e-07, -2.20345183e-03]), square_terms=array([[ 8.71775509e-04, -1.04124643e-06, -6.73499598e-03], + [-1.04124643e-06, 1.31217919e-08, 1.11732679e-05], + [-6.73499598e-03, 1.11732679e-05, 5.71455095e-02]]), scale=0.02400071311774849, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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9.23613025e-01]), fval=0.07908472064494197, rho=-0.6896313338426389, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 68, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.006000178279437123, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), model=ScalarModel(intercept=0.07925187705246434, linear_terms=array([ 4.83829824e-05, 8.99512457e-06, -1.87907241e-04]), square_terms=array([[ 5.19363336e-05, -4.83339149e-07, -4.29582543e-04], + [-4.83339149e-07, 1.47336322e-08, 3.01353003e-06], + [-4.29582543e-04, 3.01353003e-06, 3.84555377e-03]]), scale=0.006000178279437123, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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relative_step_length=1.0086865448656241, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914371578873403, linear_terms=array([1.82176925e-04, 7.65262019e-05, 2.45374037e-06]), square_terms=array([[ 1.92862702e-04, -9.93628574e-06, -1.67037516e-03], + [-9.93628574e-06, 7.42657125e-07, 7.94573605e-05], + [-1.67037516e-03, 7.94573605e-05, 1.54724452e-02]]), scale=0.012000356558874246, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + 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model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914612489984735, linear_terms=array([ 9.04725864e-05, 3.30565774e-05, -2.19601997e-05]), square_terms=array([[ 4.27792898e-05, -4.53845336e-06, -3.94771372e-04], + [-4.53845336e-06, 5.36493868e-07, 4.14551856e-05], + [-3.94771372e-04, 4.14551856e-05, 3.90706076e-03]]), scale=0.006000178279437123, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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8.72088478e-01])), candidate_index=89, candidate_x=array([4.15545357e+00, 3.93254035e+03, 9.22788650e-01]), index=87, x=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), fval=0.07907999189313741, rho=-0.3798594883018238, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), old_indices_discarded=array([74, 82, 85, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0030000891397185614, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 87, 89]), model=ScalarModel(intercept=0.07916583626576876, linear_terms=array([ 3.46835434e-05, 1.37176019e-05, -3.87853430e-06]), square_terms=array([[ 1.16702087e-05, -1.13065279e-06, -1.02787515e-04], + [-1.13065279e-06, 1.19282548e-07, 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old_indices_used=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 87, 89]), old_indices_discarded=array([74, 82, 85, 86, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0015000445698592807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 77, 83, 87, 89, 90]), model=ScalarModel(intercept=0.07909333025755838, linear_terms=array([-1.15137705e-05, -7.91213109e-07, 9.73368802e-05]), square_terms=array([[ 3.75332861e-06, -1.15364186e-07, -2.97559553e-05], + [-1.15364186e-07, 1.95191461e-08, 8.40989334e-07], + [-2.97559553e-05, 8.40989334e-07, 2.56198599e-04]]), scale=0.0015000445698592807, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), model=ScalarModel(intercept=0.07910448579668597, linear_terms=array([-9.82149673e-08, 5.86243338e-06, -2.29104355e-05]), square_terms=array([[ 8.28466084e-07, -2.44013709e-08, -6.66789179e-06], + [-2.44013709e-08, 3.18448291e-09, 1.62847966e-07], + [-6.66789179e-06, 1.62847966e-07, 5.95987805e-05]]), scale=0.0007500222849296404, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([-1.22102547e-05, 7.60923217e-06, -5.17261450e-06]), square_terms=array([[ 3.36799435e-06, -1.27039621e-07, -2.66224628e-05], + [-1.27039621e-07, 2.41088585e-08, 9.99863513e-07], + [-2.66224628e-05, 9.99863513e-07, 2.37983666e-04]]), scale=0.0015000445698592807, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-1.0803986646623365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), old_indices_discarded=array([68, 77, 83, 89, 90, 91, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), model=ScalarModel(intercept=0.07909160054167635, linear_terms=array([-1.29680002e-05, 2.27817382e-06, 1.03758748e-06]), square_terms=array([[ 9.19566779e-07, -3.49328357e-08, -6.93344117e-06], + [-3.49328357e-08, 6.61278422e-09, 2.68258739e-07], + [-6.93344117e-06, 2.68258739e-07, 5.96608551e-05]]), 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candidate_x=array([4.16126418e+00, 3.93254192e+03, 9.23574260e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.2558930827717976, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([104, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123]), old_indices_discarded=array([111, 121, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=5.859549101012815e-06, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, + 135, 136]), model=ScalarModel(intercept=0.07906789905966807, linear_terms=array([2.53618684e-09, 2.53785426e-08, 3.95194505e-07]), square_terms=array([[ 5.35172911e-11, -7.04580653e-12, -3.54919326e-10], + [-7.04580653e-12, 3.06788632e-12, 2.82219396e-11], + [-3.54919326e-10, 2.82219396e-11, 2.96974675e-09]]), scale=5.859549101012815e-06, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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133, 134, 135, 136]), old_indices_used=array([104, 123, 124]), old_indices_discarded=array([], dtype=int32), step_length=5.859549088890873e-06, relative_step_length=0.99999999793125, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 138 entries., 'multistart_info': {'start_parameters': [array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01])], 'local_optima': [{'solution_x': array([4.49642794e+00, 2.23558572e+03, 9.79764716e-01]), 'solution_criterion': 0.09484073778952033, 'states': [State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + 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n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=31.09598891367815, linear_terms=array([-91.62457748, 35.18702724, 46.35326863]), square_terms=array([[139.00631 , -52.82239092, -67.71080652], + [-52.82239092, 20.15282885, 26.09871415], + [-67.71080652, 26.09871415, 35.14646972]]), scale=array([ 9.45 , 180.19399796, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + 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linear_terms=array([-88.87197165, 20.39453014, 49.34054691]), square_terms=array([[126.89412106, -28.78632886, -67.98866778], + [-28.78632886, 6.56253496, 15.66524874], + [-67.98866778, 15.66524874, 38.63705869]]), scale=array([ 9.45 , 90.09699898, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.0133860000302767, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([3, 6]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=28.06849241063706, linear_terms=array([-86.35465017, 10.29248796, 43.1073773 ]), square_terms=array([[136.46063816, -16.08430417, -65.95932181], + [-16.08430417, 1.90552511, 7.90048589], + [-65.95932181, 7.90048589, 33.73248494]]), scale=array([ 9.45 , 45.04849949, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 11, 14, 15]), model=ScalarModel(intercept=70.80141507437921, linear_terms=array([-19.77025943, 55.99584036, 116.00216794]), square_terms=array([[ 3.37228092, -7.98985022, -16.17070888], + [ -7.98985022, 22.21791715, 45.96590548], + [-16.17070888, 45.96590548, 95.44827251]]), scale=array([ 9.45 , 22.52424975, 0.3 ]), shift=array([1.0550000e+01, 2.2356687e+03, 8.0000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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square_terms=array([[0.29433574, 0.05189857, 0.69211431], + [0.05189857, 0.01569971, 0.200252 ], + [0.69211431, 0.200252 , 2.57727413]]), scale=array([ 7.56714517, 11.26212487, 0.3 ]), shift=array([8.66714517e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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rho=-22860.524324555063, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 16, 17]), model=ScalarModel(intercept=467.19314322074354, linear_terms=array([-1065.72378965, 378.28985285, -1698.61903515]), square_terms=array([[1215.92010216, -431.55891012, 1937.88572934], + [-431.55891012, 153.18465907, -687.77512457], + [1937.88572934, -687.77512457, 3088.84974448]]), scale=array([4.75161395, 5.63106244, 0.3 ]), shift=array([5.85161395e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 9, 17, 18]), model=ScalarModel(intercept=631.0068374845937, linear_terms=array([ -713.42087297, 227.74637403, -1894.75839998]), square_terms=array([[ 403.38912904, -128.76975477, 1071.25267549], + [-128.76975477, 41.14741941, -341.84595008], + [1071.25267549, -341.84595008, 2845.4276814 ]]), scale=array([2.81553122, 2.81553122, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), candidate_index=19, candidate_x=array([5.22498136e+00, 2.23285317e+03, 9.53585466e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-3.163059122264521, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.14790847307992083, linear_terms=array([-0.33207427, 0.0835986 , 0.80229467]), square_terms=array([[ 1.61949403, -0.29564339, -2.97830876], + [-0.29564339, 0.059162 , 0.58807027], + [-2.97830876, 0.58807027, 5.87252616]]), scale=array([1.40776561, 1.40776561, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], 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old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.11589494066709435, linear_terms=array([ 0.16454974, 0.04806004, -0.44293767]), square_terms=array([[ 0.76924547, 0.27501651, -2.87758723], + [ 0.27501651, 0.09970594, -1.04942116], + [-2.87758723, -1.04942116, 11.20509916]]), scale=array([0.7038828, 0.7038828, 0.3 ]), shift=array([4.97216547e+00, 2.23566870e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.4031517845766621, linear_terms=array([-0.23678438, -0.13921737, 1.90006104]), square_terms=array([[ 0.14109853, 0.0676179 , -0.91471804], + [ 0.0676179 , 0.03464796, -0.47018588], + [-0.91471804, -0.47018588, 6.40611468]]), scale=array([0.3519414, 0.3519414, 0.2259707]), shift=array([4.97216547e+00, 2.23566870e+03, 8.74029299e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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2.23566870e+03, 1.00000000e+00])), candidate_index=34, candidate_x=array([4.62022407e+00, 2.23602064e+03, 8.03652173e-01]), index=0, x=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), fval=0.15183336484019694, rho=-0.31192568841383445, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([20, 25, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.3311781411225237, linear_terms=array([-0.15829022, -0.12201639, 1.37588212]), square_terms=array([[ 0.07100082, 0.05048831, -0.56524959], + [ 0.05048831, 0.03638561, -0.40733887], + [-0.56524959, -0.40733887, 4.57262245]]), scale=array([0.1759707 , 0.1759707 , 0.13798535]), shift=array([4.97216547e+00, 2.23566870e+03, 9.62014649e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=0.7819308994641678, linear_terms=array([ 0.0074579 , -0.0259874 , 1.71946661]), square_terms=array([[ 5.56013887e-04, 1.44714906e-04, -1.22278649e-03], + [ 1.44714906e-04, 6.29275388e-04, -3.58652012e-02], + [-1.22278649e-03, -3.58652012e-02, 2.24563302e+00]]), scale=array([0.08798535, 0.08798535, 0.08798535]), shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.684129096465268, linear_terms=array([-0.0273062 , -0.01343205, 1.05830747]), square_terms=array([[ 1.32604981e-03, 5.31204096e-04, -3.19319385e-02], + [ 5.31204096e-04, 2.27658842e-04, -1.40325629e-02], + [-3.19319385e-02, -1.40325629e-02, 1.00415127e+00]]), scale=0.054581755417186864, shift=array([4.97216547e+00, 2.23566870e+03, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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State(trustregion=Region(center=array([4.89130138e+00, 2.23566540e+03, 9.84967623e-01]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 48, 49, 50, 51, 52, 55, 56, 58, 59, 61, 63]), model=ScalarModel(intercept=0.1262684477348336, linear_terms=array([ 0.01011799, -0.00025361, 0.00667358]), square_terms=array([[ 0.00584699, 0.02035848, -0.15274389], + [ 0.02035848, 0.08132529, -0.60850692], + [-0.15274389, -0.60850692, 4.58703432]]), scale=0.10916351083437373, shift=array([4.89130138e+00, 2.23566540e+03, 9.84967623e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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64]), model=ScalarModel(intercept=0.14568276745179376, linear_terms=array([-0.22688217, -0.09194348, 0.79460775]), square_terms=array([[ 0.79705518, 0.32534599, -2.52015705], + [ 0.32534599, 0.13291246, -1.02588088], + [-2.52015705, -1.02588088, 8.06717203]]), scale=array([0.1759707 , 0.1759707 , 0.14779808]), shift=array([4.77904943e+00, 2.23566008e+03, 9.52201925e-01])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + 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x=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), fval=0.10637576541348984, rho=-0.4435220960006037, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 67, 68, 70, 74, 75, 76, 79, 80, 82, 83]), old_indices_discarded=array([ 0, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 69, 71, + 72, 73, 77, 78, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.68340853e+00, 2.23565787e+03, 9.81541008e-01]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 66, 67, 68, 71, 72, 75, 77, 80, 81, 82, 83]), model=ScalarModel(intercept=0.10753665968218919, linear_terms=array([0.00205224, 0.00035594, 0.02486512]), square_terms=array([[ 4.13042323e-03, 1.27964751e-03, -4.10755431e-02], + [ 1.27964751e-03, 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old_indices_discarded=array([ 2, 5, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=49.15346046514891, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 3, 8, 9, 14, 15]), model=ScalarModel(intercept=36.477921419964005, linear_terms=array([-138.85628947, 17.34063992, 102.44589447]), square_terms=array([[ 268.01780029, -33.176478 , -194.8794462 ], + [ -33.176478 , 4.1379421 , 24.44585578], + [-194.8794462 , 24.44585578, 145.53102569]]), scale=array([ 9.45 , 39.61749139, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + 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16]), model=ScalarModel(intercept=137.95900758299246, linear_terms=array([ 609.72371975, 81.97626858, 1094.06151597]), square_terms=array([[1348.85665695, 181.37053476, 2420.79025551], + [ 181.37053476, 24.39149366, 325.54412825], + [2420.79025551, 325.54412825, 4345.09263337]]), scale=array([ 9.45 , 19.8087457, 0.3 ]), shift=array([1.05500000e+01, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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3.91246809e+03, 9.14078548e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-51.69148171099177, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 8, 9, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=12.288365116287228, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 16, 17]), model=ScalarModel(intercept=675.1794019801806, linear_terms=array([ -2064.6019009 , 718.07582713, -10217.06091518]), square_terms=array([[ 3158.11858269, -1098.22603339, 15626.2724666 ], + [-1098.22603339, 381.93267203, -5434.33216723], + [15626.2724666 , -5434.33216723, 77322.68695368]]), scale=array([6.43190571, 9.90437285, 0.3 ]), shift=array([7.53190571e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=6.144182558143614, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=108.75147073194, linear_terms=array([-309.53365492, -36.53619492, 161.80376403]), square_terms=array([[ 441.33077962, 52.10175602, -230.15109169], + [ 52.10175602, 6.15213254, -27.15013825], + [-230.15109169, -27.15013825, 120.92652934]]), scale=array([3.9558125 , 4.95218642, 0.3 ]), shift=array([5.05581250e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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model=ScalarModel(intercept=170.59582545012594, linear_terms=array([-277.08221691, 5.5992395 , 233.76105829]), square_terms=array([[ 2.25373873e+02, -4.61116778e+00, -1.89643367e+02], + [-4.61116778e+00, 1.11464760e-01, 3.74415217e+00], + [-1.89643367e+02, 3.74415217e+00, 1.60694491e+02]]), scale=array([2.47609321, 2.47609321, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + 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candidate_x=array([5.66682758e+00, 3.92980074e+03, 6.00414976e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.006388659400412666, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([18, 19, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=1.5360456395359035, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=138.3462289851189, linear_terms=array([-147.45463528, -5.65111698, 268.26306503]), square_terms=array([[ 7.86989538e+01, 3.00126776e+00, -1.42903701e+02], + [ 3.00126776e+00, 1.18067220e-01, -5.51395279e+00], + [-1.42903701e+02, -5.51395279e+00, 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30, 31, 32]), old_indices_discarded=array([19, 24, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.7680228197679517, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 23, 27, 32, 33]), model=ScalarModel(intercept=1.5830056559661339, linear_terms=array([ 9.99281175, 14.43614264, -39.57256129]), square_terms=array([[ 33.80983754, 48.83787417, -133.54382225], + [ 48.83787417, 70.60333873, -193.11510202], + [-133.54382225, -193.11510202, 528.45490937]]), scale=array([0.6190233, 0.6190233, 0.3 ]), shift=array([4.05943857e+00, 3.93227684e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, 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candidate_index=47, candidate_x=array([3.96066338e+00, 3.93258635e+03, 7.66932590e-01]), index=0, x=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), fval=0.12472562593739225, rho=-0.1708886923662552, accepted=False, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([ 0, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=1.2415835967758433, linear_terms=array([-0.15283733, -0.58871896, 3.13992098]), square_terms=array([[ 0.02040704, 0.03863901, -0.21039381], + [ 0.03863901, 0.14956944, -0.79740606], + [-0.21039381, -0.79740606, 4.2608185 ]]), 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old_indices_discarded=array([34, 37, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0337220474595914, linear_terms=array([-0.20696303, -0.31059194, 1.61730455]), square_terms=array([[ 0.02606724, 0.03394087, -0.17786586], + [ 0.03394087, 0.05073282, -0.26413928], + [-0.17786586, -0.26413928, 1.3775637 ]]), scale=0.09600285247099397, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + 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model_indices=array([ 0, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.11633665599043491, linear_terms=array([ 0.01692106, -0.00599296, -0.07183474]), square_terms=array([[ 0.00430738, -0.00138782, -0.01722234], + [-0.00138782, 0.00047598, 0.00583328], + [-0.01722234, 0.00583328, 0.07191651]]), scale=0.04800142623549698, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_index=62, candidate_x=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), index=62, x=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), fval=0.08328203055052266, rho=1.1521105575858042, accepted=True, new_indices=array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_used=array([ 0, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.05128066423434844, relative_step_length=1.0683154284367173, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.06698105e+00, 3.93229322e+03, 9.20092104e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.08089110047653379, linear_terms=array([-0.00528525, -0.0001229 , 0.02824264]), square_terms=array([[ 1.16009632e-02, 2.30117604e-04, -6.86298394e-02], + [ 2.30117604e-04, 5.27144510e-06, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 54, 55, 59, 61, 63, 64]), model=ScalarModel(intercept=0.15326138046317392, linear_terms=array([0.55415723, 0.00612001, 0.65724766]), square_terms=array([[2.16178448e+00, 2.69601220e-02, 2.58028293e+00], + [2.69601220e-02, 3.98421161e-04, 3.15716605e-02], + [2.58028293e+00, 3.15716605e-02, 3.11749315e+00]]), scale=array([0.30951165, 0.30951165, 0.24443416]), shift=array([4.16380226e+00, 3.93248621e+03, 8.55565845e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), model=ScalarModel(intercept=0.07981427686533094, linear_terms=array([ 8.23222295e-04, -3.01457336e-06, -1.94680885e-04]), square_terms=array([[ 4.38349908e-03, -2.78417852e-04, -2.40946662e-02], + [-2.78417852e-04, 1.45680783e-04, 9.88295037e-03], + [-2.40946662e-02, 9.88295037e-03, 6.79192813e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=66, candidate_x=array([4.12483624e+00, 3.93264097e+03, 9.17053507e-01]), index=64, x=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), fval=0.0797111995279562, rho=-0.4172818202820011, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 54, 55, 56, + 57, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16380226e+00, 3.93248621e+03, 9.20643341e-01]), radius=0.09600285247099397, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.0797875761950249, linear_terms=array([ 5.31755989e-04, 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9.20412203e-01]), fval=0.07939979491586543, rho=3.2737438064381488, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([42, 48, 50, 51, 52, 53, 58, 62, 63, 64, 65, 66]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 47, 49, 54, 55, 56, 57, 59, 60, 61]), step_length=0.10215613340917244, relative_step_length=1.0640947719760474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 50, 51, 54, 55, 56, 57, 59, 60, 63, 64, 67]), model=ScalarModel(intercept=0.0794221211152619, linear_terms=array([-8.78763547e-04, -7.32595189e-05, 1.57498998e-03]), square_terms=array([[ 1.88292059e-02, -8.43669110e-04, -1.35637703e-01], + [-8.43669110e-04, 5.18412063e-05, 7.55602994e-03], + [-1.35637703e-01, 7.55602994e-03, 1.13549976e+00]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.12942468e+00, 3.93239001e+03, 9.20412203e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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67]), old_indices_discarded=array([34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 52, + 53, 58, 61, 62, 65, 66]), step_length=0.1591995195038744, relative_step_length=0.8291395276612978, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.38401140988397586, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([41, 42, 44, 47, 48, 49, 55, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.11467755048322094, linear_terms=array([ 0.4301884 , -0.10852269, 0.38403869]), square_terms=array([[ 2.8059872 , -0.69595835, 2.53665519], + [-0.69595835, 0.17347376, -0.6322991 ], + [ 2.53665519, -0.6322991 , 2.32273428]]), scale=array([0.30951165, 0.30951165, 0.24294931]), shift=array([4.16663860e+00, 3.93254477e+03, 8.57050687e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.19200570494198793, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 53, 63, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.07972737754656194, linear_terms=array([0.00089106, 0.00026728, 0.00741102]), square_terms=array([[3.83952175e-03, 2.37615327e-04, 1.80862528e-04], + [2.37615327e-04, 1.04844950e-03, 2.29140497e-02], + [1.80862528e-04, 2.29140497e-02, 5.09344565e-01]]), scale=array([0.15475583, 0.15475583, 0.15475583]), shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + 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1.1e+00]))), model_indices=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), model=ScalarModel(intercept=0.07970568860996624, linear_terms=array([5.17688765e-04, 7.08684557e-05, 2.23636029e-03]), square_terms=array([[1.54434855e-03, 1.10165119e-04, 2.02375366e-03], + [1.10165119e-04, 1.32782811e-04, 4.89488107e-03], + [2.02375366e-03, 4.89488107e-03, 1.84174468e-01]]), scale=0.09600285247099397, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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3.93227684e+03, 8.72088478e-01])), candidate_index=71, candidate_x=array([4.13217290e+00, 3.93263940e+03, 9.20311146e-01]), index=68, x=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), fval=0.07908472064494197, rho=-2.866324235420139, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([42, 47, 48, 50, 51, 63, 64, 65, 66, 67, 68, 70]), old_indices_discarded=array([ 0, 35, 38, 41, 44, 45, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, + 62, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01]), radius=0.04800142623549698, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([42, 48, 63, 64, 65, 66, 67, 68, 70, 71]), model=ScalarModel(intercept=0.07966045626129445, linear_terms=array([3.20987313e-04, 4.92207090e-05, 2.88283811e-03]), square_terms=array([[ 3.79212673e-04, 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radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([64, 68, 72, 73]), model=ScalarModel(intercept=0.07908472064494196, linear_terms=array([-3.38525141e-05, -9.76572645e-05, 8.48540656e-04]), square_terms=array([[ 2.37169869e-04, 1.05566792e-05, -1.97718322e-03], + [ 1.05566792e-05, 7.38132067e-07, -8.17544926e-05], + [-1.97718322e-03, -8.17544926e-05, 1.75933908e-02]]), scale=0.012000356558874246, shift=array([4.16663860e+00, 3.93254477e+03, 9.23613025e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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accepted=True, new_indices=array([75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), old_indices_used=array([68, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.006052299097263197, relative_step_length=1.0086865448656241, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.012000356558874246, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 81, 82, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914371578873403, linear_terms=array([1.82176925e-04, 7.65262019e-05, 2.45374037e-06]), square_terms=array([[ 1.92862702e-04, -9.93628574e-06, -1.67037516e-03], + [-9.93628574e-06, 7.42657125e-07, 7.94573605e-05], + [-1.67037516e-03, 7.94573605e-05, 1.54724452e-02]]), scale=0.012000356558874246, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 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State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.006000178279437123, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87]), model=ScalarModel(intercept=0.07914612489984735, linear_terms=array([ 9.04725864e-05, 3.30565774e-05, -2.19601997e-05]), square_terms=array([[ 4.27792898e-05, -4.53845336e-06, -3.94771372e-04], + [-4.53845336e-06, 5.36493868e-07, 4.14551856e-05], + [-3.94771372e-04, 4.14551856e-05, 3.90706076e-03]]), scale=0.006000178279437123, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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model=ScalarModel(intercept=0.07916583626576876, linear_terms=array([ 3.46835434e-05, 1.37176019e-05, -3.87853430e-06]), square_terms=array([[ 1.16702087e-05, -1.13065279e-06, -1.02787515e-04], + [-1.13065279e-06, 1.19282548e-07, 9.91369374e-06], + [-1.02787515e-04, 9.91369374e-06, 9.72885293e-04]]), scale=0.0030000891397185614, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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3.93254141e+03, 9.23023628e-01]), index=87, x=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), fval=0.07907999189313741, rho=-0.5105398504494335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([68, 75, 76, 77, 78, 79, 80, 81, 83, 84, 87, 89]), old_indices_discarded=array([74, 82, 85, 86, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0015000445698592807, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([68, 77, 83, 87, 89, 90]), model=ScalarModel(intercept=0.07909333025755838, linear_terms=array([-1.15137705e-05, -7.91213109e-07, 9.73368802e-05]), square_terms=array([[ 3.75332861e-06, -1.15364186e-07, -2.97559553e-05], + [-1.15364186e-07, 1.95191461e-08, 8.40989334e-07], + [-2.97559553e-05, 8.40989334e-07, 2.56198599e-04]]), scale=0.0015000445698592807, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), model=ScalarModel(intercept=0.07910448579668597, linear_terms=array([-9.82149673e-08, 5.86243338e-06, -2.29104355e-05]), square_terms=array([[ 8.28466084e-07, -2.44013709e-08, -6.66789179e-06], + [-2.44013709e-08, 3.18448291e-09, 1.62847966e-07], + [-6.66789179e-06, 1.62847966e-07, 5.95987805e-05]]), scale=0.0007500222849296404, shift=array([4.16101457e+00, 3.93254256e+03, 9.23281861e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.07909403564536577, linear_terms=array([-1.22102547e-05, 7.60923217e-06, -5.17261450e-06]), square_terms=array([[ 3.36799435e-06, -1.27039621e-07, -2.66224628e-05], + [-1.27039621e-07, 2.41088585e-08, 9.99863513e-07], + [-2.66224628e-05, 9.99863513e-07, 2.37983666e-04]]), scale=0.0015000445698592807, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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scale=393.2276837211913, shift=array([4.05943857e+00, 3.93227684e+03, 8.72088478e-01])), candidate_index=105, candidate_x=array([4.16253783e+00, 3.93254113e+03, 9.23735181e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-1.0803986646623365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104]), old_indices_discarded=array([68, 77, 83, 89, 90, 91, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0007500222849296404, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), model=ScalarModel(intercept=0.07909160054167635, linear_terms=array([-1.29680002e-05, 2.27817382e-06, 1.03758748e-06]), square_terms=array([[ 9.19566779e-07, -3.49328357e-08, -6.93344117e-06], + [-3.49328357e-08, 6.61278422e-09, 2.68258739e-07], + [-6.93344117e-06, 2.68258739e-07, 5.96608551e-05]]), scale=0.0007500222849296404, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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fval=0.07906796375411648, rho=-0.13973162244364834, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 104]), old_indices_discarded=array([ 83, 90, 91, 100, 103, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0003750111424648202, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 92, 94, 95, 96, 97, 98, 99, 101, 102, 104, 106]), model=ScalarModel(intercept=0.07908661788272847, linear_terms=array([-8.35298009e-06, 7.05475745e-06, 1.43187070e-06]), square_terms=array([[ 2.33438460e-07, -1.55439804e-08, -1.73669039e-06], + [-1.55439804e-08, 5.10319166e-09, 8.60392613e-08], + [-1.73669039e-06, 8.60392613e-08, 1.49265352e-05]]), scale=0.0003750111424648202, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=0.0001875055712324101, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 87, 94, 95, 98, 102, 104, 106, 107]), model=ScalarModel(intercept=0.0790879150221595, linear_terms=array([-6.18729811e-06, 2.85069426e-06, 5.24029282e-06]), square_terms=array([[ 6.39029879e-08, -5.52613342e-09, -4.58386220e-07], + [-5.52613342e-09, 1.01866206e-09, 2.93309494e-08], + [-4.58386220e-07, 2.93309494e-08, 3.89516694e-06]]), scale=0.0001875055712324101, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.07906796375411641, linear_terms=array([-2.77746966e-05, -3.22673327e-05, -4.25319102e-05]), square_terms=array([[ 5.39542744e-08, 4.53199229e-08, -4.64131654e-08], + [ 4.53199229e-08, 4.98555993e-08, 7.22290924e-08], + [-4.64131654e-08, 7.22290924e-08, 1.14321313e-06]]), scale=9.375278561620504e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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3.93254196e+03, 9.23633511e-01]), index=104, x=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), fval=0.07906796375411648, rho=-0.11603470278551233, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 95, 104, 107, 108]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=4.687639280810252e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121]), model=ScalarModel(intercept=0.07907082869590354, linear_terms=array([ 2.88136945e-06, -3.08394182e-07, -1.07721092e-06]), square_terms=array([[ 4.46329859e-09, -6.05308320e-10, -2.92877945e-08], + [-6.05308320e-10, 1.34067088e-10, 3.43225360e-09], + [-2.92877945e-08, 3.43225360e-09, 2.44027182e-07]]), scale=4.687639280810252e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + -0.18310131, -0.00520398, -0.04192809, -0.02779983, -0.03678838, + -0.05747802, -0.07226194]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01]), radius=2.343819640405126e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([104, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=0.07907053144557313, linear_terms=array([ 1.94854337e-07, -3.55132795e-07, -5.64262326e-07]), square_terms=array([[ 1.08597546e-09, 1.16175887e-10, -7.41141155e-09], + [ 1.16175887e-10, 3.16258142e-11, -6.25413742e-10], + [-7.41141155e-09, -6.25413742e-10, 5.96099329e-08]]), scale=2.343819640405126e-05, shift=array([4.16127032e+00, 3.93254191e+03, 9.23567705e-01])), vector_model=VectorModel(intercepts=array([ 0.07188797, 0.11842249, 0.08425784, 0.06302381, 0.02761407, + -0.01169908, -0.04725341, -0.13055541, -0.1675786 , -0.01549265, + 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3.93254191e+03, 9.23561859e-01]), fval=0.07906739186138749, rho=1.4495418816296448, accepted=True, new_indices=array([125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136]), old_indices_used=array([104, 123, 124]), old_indices_discarded=array([], dtype=int32), step_length=5.859549088890873e-06, relative_step_length=0.99999999793125, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 138 entries., 'history': {'params': [{'CRRA': 4.059438573758104, 'BeqFac': 3932.2768372119126, 'DiscFac': 0.8720884775777547}, {'CRRA': 1.1865624830963672, 'BeqFac': 3618.0088638043917, 'DiscFac': 1.1}, {'CRRA': 18.864750592221043, 'BeqFac': 3615.3369060843656, 'DiscFac': 1.0141895974609063}, {'CRRA': 20.0, 'BeqFac': 4139.2725657346655, 'DiscFac': 0.967186242030893}, {'CRRA': 11.813333909541619, 'BeqFac': 4191.377523809567, 'DiscFac': 0.5}, {'CRRA': 10.612091741842278, 'BeqFac': 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vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=28.669891670599913, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 8, 9, 15, 16]), model=ScalarModel(intercept=4.862823488147589, linear_terms=array([ -73.44883613, -23.31734993, -131.12114654]), square_terms=array([[ 578.17685765, 182.96932654, 1029.31041988], + [ 182.96932654, 57.95652351, 325.84926908], + [1029.31041988, 325.84926908, 1832.92574964]]), scale=array([ 9.45 , 23.10781735, 0.3 ]), shift=array([1.05500000e+01, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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square_terms=array([[1.95905989e+07, 4.95384419e+06, 4.69506453e+07], + [4.95384419e+06, 1.25267102e+06, 1.18723370e+07], + [4.69506453e+07, 1.18723370e+07, 1.12521478e+08]]), scale=array([ 7.46023951, 11.55390867, 0.3 ]), shift=array([8.56023951e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], 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fval=0.10144929167716776, rho=-1041.451062119752, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 9, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=7.167472917649978, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=80.22435907513035, linear_terms=array([-244.91564084, 13.26585911, 83.93950199]), square_terms=array([[ 375.02548269, -20.22669945, -127.79535667], + [ -20.22669945, 1.10349026, 6.99773206], + [-127.79535667, 6.99773206, 44.64312817]]), scale=array([4.57176234, 5.77695434, 0.3 ]), shift=array([5.67176234e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 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State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=3.583736458824989, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=93.97983279704786, linear_terms=array([-158.16807239, 51.83688273, 84.93922156]), square_terms=array([[133.49622213, -43.70152356, -71.15581564], + [-43.70152356, 14.31811665, 23.35552818], + [-71.15581564, 23.35552818, 39.27068276]]), scale=array([2.88847717, 2.88847717, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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linear_terms=array([-89.01247099, 16.9455529 , 111.39761411]), square_terms=array([[ 48.77806772, -9.2752547 , -60.74060208], + [ -9.2752547 , 1.76503453, 11.57507336], + [-60.74060208, 11.57507336, 77.05159691]]), scale=array([1.44423858, 1.44423858, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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4.58718267e+03, 9.28413348e-01]), fval=0.10144929167716776, rho=-0.010827579816131265, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 20, 21, 22, 23, 26, 27, 29, 30, 31, 32]), old_indices_discarded=array([17, 19, 24, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.46657035e+00, 4.58718267e+03, 9.28413348e-01]), radius=0.8959341147062473, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 0, 18, 23, 32, 33]), model=ScalarModel(intercept=5.686343548503185, linear_terms=array([-1.41335436e+01, 6.60802805e-03, -3.48121455e+01]), square_terms=array([[ 1.80359552e+01, 4.54763842e-02, 4.39045113e+01], + [ 4.54763842e-02, 1.82631253e-02, -3.51143342e-02], + [ 4.39045113e+01, -3.51143342e-02, 1.08172460e+02]]), scale=array([0.72211929, 0.72211929, 0.3 ]), shift=array([4.46657035e+00, 4.58718267e+03, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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model=ScalarModel(intercept=2.387795744181831, linear_terms=array([-0.21651854, -1.00620205, 4.39340151]), square_terms=array([[ 0.01868472, 0.0488839 , -0.21713868], + [ 0.0488839 , 0.22001091, -0.96179267], + [-0.21713868, -0.96179267, 4.21118356]]), scale=array([0.18052982, 0.18052982, 0.17629553]), shift=array([4.28097285e+00, 4.58790479e+03, 9.23704466e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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8.21257590e-01]), index=34, x=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), fval=0.08450890381661277, rho=-0.05757584370248203, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 35, 36, 37, 39, 40, 41, 42, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.11199176433828091, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=2.3684676382089758, linear_terms=array([-0.10988363, -0.66436773, 2.75173228]), square_terms=array([[ 0.00591366, 0.01676982, -0.07000994], + [ 0.01676982, 0.09679265, -0.40121714], + [-0.07000994, -0.40121714, 1.66585423]]), scale=0.11199176433828091, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.055995882169140455, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.09028822664173408, linear_terms=array([-0.01061362, 0.00088466, 0.05364829]), square_terms=array([[ 1.15220286e-02, -8.95044594e-04, -5.57609462e-02], + [-8.95044594e-04, 7.85103124e-05, 4.56043843e-03], + [-5.57609462e-02, 4.56043843e-03, 2.76280980e-01]]), scale=0.055995882169140455, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.08780533673879214, linear_terms=array([-0.00204222, -0.000514 , 0.02518641]), square_terms=array([[ 9.44732408e-04, 1.61167329e-04, -9.59779358e-03], + [ 1.61167329e-04, 3.11556686e-05, -1.82922922e-03], + [-9.59779358e-03, -1.82922922e-03, 1.08278084e-01]]), scale=0.027997941084570228, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], 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candidate_x=array([4.25924822e+00, 4.58792176e+03, 9.19798199e-01]), index=34, x=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), fval=0.08450890381661277, rho=-0.12907089026587365, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([49, 53, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.013998970542285114, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 50, 51, 52, 54, 55, 56, 57, 58, 59, 61, 63]), model=ScalarModel(intercept=0.08746023163088124, linear_terms=array([-0.00092591, -0.00022983, 0.01191045]), square_terms=array([[ 2.22445526e-04, 3.75900215e-05, -2.31525701e-03], + [ 3.75900215e-05, 7.20689471e-06, -4.38750216e-04], + [-2.31525701e-03, -4.38750216e-04, 2.69473158e-02]]), scale=0.013998970542285114, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, -0.02088758, -0.04018239, -0.0329881 , -0.04664206, + -0.06960775, -0.0857819 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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old_indices_discarded=array([53, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01]), radius=0.006999485271142557, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([34, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.08448168161525779, linear_terms=array([-1.86013743e-04, 7.22005167e-06, 1.72000252e-03]), square_terms=array([[ 8.13031157e-05, -1.38516031e-06, -6.46926038e-04], + [-1.38516031e-06, 7.37746194e-08, 1.41206941e-05], + [-6.46926038e-04, 1.41206941e-05, 5.59742984e-03]]), scale=0.006999485271142557, shift=array([4.28097285e+00, 4.58790479e+03, 9.27938755e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, -0.00063106, -0.06576639, -0.11100278, 0.0223435 , + -0.16780024, 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113, 114, 115, 116]), old_indices_used=array([ 85, 103, 104]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01]), radius=1.3670869670200306e-05, bounds=Bounds(lower=array([1.1, 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 1.1e+00]))), model_indices=array([ 85, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116]), model=ScalarModel(intercept=0.08419777229304778, linear_terms=array([-1.62551585e-08, 2.07554555e-07, 1.82199559e-07]), square_terms=array([[ 1.93862655e-10, 3.48608690e-11, -2.10716777e-09], + [ 3.48608690e-11, 8.91496168e-12, -3.59091832e-10], + [-2.10716777e-09, -3.59091832e-10, 2.37580489e-08]]), scale=1.3670869670200306e-05, shift=array([4.26884274e+00, 4.58791640e+03, 9.24511515e-01])), vector_model=VectorModel(intercepts=array([ 0.07159608, 0.12121607, 0.09389742, 0.08054577, 0.0538135 , + 0.02465656, 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tolerance.', 'tranquilo_history': History for least_squares function with 119 entries., 'history': {'params': [{'CRRA': 4.466570349505643, 'BeqFac': 4587.182667295986, 'DiscFac': 0.9284133483434123}, {'CRRA': 1.1773716440521769, 'BeqFac': 4217.457589731739, 'DiscFac': 1.0189962612053658}, {'CRRA': 20.0, 'BeqFac': 4225.7618739064255, 'DiscFac': 1.0171895930759263}, {'CRRA': 20.0, 'BeqFac': 4817.154239034149, 'DiscFac': 0.9094208421939314}, {'CRRA': 12.072368029141941, 'BeqFac': 4954.33727993967, 'DiscFac': 0.5}, {'CRRA': 9.926248932086857, 'BeqFac': 4956.907744860233, 'DiscFac': 1.0940032098135037}, {'CRRA': 18.49315852332056, 'BeqFac': 4954.132040009003, 'DiscFac': 1.1}, {'CRRA': 1.2242804208400564, 'BeqFac': 4956.907744860233, 'DiscFac': 0.7608064947134148}, {'CRRA': 1.1911500855961954, 'BeqFac': 4720.043806708304, 'DiscFac': 1.1}, {'CRRA': 20.0, 'BeqFac': 4648.332190820999, 'DiscFac': 0.5002043199703005}, {'CRRA': 10.256549394930358, 'BeqFac': 4218.012526269039, 'DiscFac': 0.5}, 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a/src/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv +++ b/src/estimark/content/tables/min/WarmGlowPortfolioFourParams_estimate_results.csv @@ -1,41357 +1,41377 @@ -CRRA,4.130262462019113 -BeqFac,4099.4172974459125 -BeqShift,1.5605762058493682 -DiscFac,0.976477264112623 -time_to_estimate,564.7696936130524 -params,"{'CRRA': 4.130262462019113, 'BeqFac': 4099.4172974459125, 'BeqShift': 1.5605762058493682, 'DiscFac': 0.976477264112623}" -criterion,0.03160677727045485 -start_criterion,0.04761358470737734 -start_params,"{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,4 -message,Maximum number of criterion evaluations reached. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, 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109.18145349994302, 110.35773959988728, 111.52355109993368, 112.69145950023085, 113.88890310004354, 115.12849680008367, 116.31543600000441, 117.6343872002326, 118.81335589988157, 119.98585169995204, 121.1860096999444, 122.37323410017416, 123.57454850012437, 124.76921389997005, 126.4212179002352, 126.60760340001434, 126.79196709999815, 126.97290260018781, 127.15962800011039, 127.34788580005988, 127.53500150004402, 127.73213839996606, 127.93292480008677, 128.12795529980212, 128.32260590000078, 128.51027300022542, 129.75297969998792, 130.94742600014433, 132.1255693999119], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 87, 88, 89]}" -convergence_report,"{'one_step': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}, 'five_steps': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}}" -multistart_info,"{'start_parameters': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.357791964783, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 6.019017903047592, 'BeqFac': 4094.4713174272265, 'BeqShift': 8.678234369345601, 'DiscFac': 0.9603698649490326}], 'local_optima': [Minimize with 4 free parameters terminated. - -The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 3.438e-06* 0.001161 -relative_params_change 5.434e-05 0.00258 -absolute_criterion_change 3.438e-07* 0.0001161 -absolute_params_change 0.00019 0.004757 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 3.703e-08* 1.469e-05 -relative_params_change 3.819e-07* 0.001782 -absolute_criterion_change 2.006e-08* 7.959e-06* -absolute_params_change 3.648e-06* 0.03719 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. - -The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 0.0002068 0.02744 -relative_params_change 0.01485 0.1715 -absolute_criterion_change 2.068e-05 0.002744 -absolute_params_change 0.05016 0.2731 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. - -The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 0.00129 0.00488 -relative_params_change 0.001706 0.006966 -absolute_criterion_change 0.001137 0.004302 -absolute_params_change 0.02421 0.1485 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375, 'DiscFac': 0.9875}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875, 'DiscFac': 0.8562500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25, 'DiscFac': 0.875}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75, 'DiscFac': 0.7250000000000001}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125, 'DiscFac': 0.6125}, {'CRRA': 16.1609375, 'BeqFac': 156.25, 'BeqShift': 66.71875, 'DiscFac': 0.9781250000000001}, {'CRRA': 15.274999999999999, 'BeqFac': 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candidate_x=array([1.31507572e+01, 3.94692938e+03, 7.00000000e+01, 6.81223719e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-0.003076041580485302, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=102.49222305696522, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=62.025760240591936, linear_terms=array([-174.60320912, 8.27773014, 1.75673439, 164.70357842]), square_terms=array([[ 2.48797993e+02, -1.17200748e+01, -2.72607667e+00, - 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9.80265374e-01])), candidate_index=16, candidate_x=array([5.77529085e+00, 4.06149904e+03, 4.05719276e+01, 1.10000000e+00]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-2.8961115549437415, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 7, 11, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=25.623055764241304, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=26.49407321206659, linear_terms=array([-49.06494769, 9.79102818, 1.51086076, 57.27587634]), square_terms=array([[ 4.76594328e+01, -9.44277762e+00, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=12.811527882120652, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=36.484478434195694, linear_terms=array([-52.90415793, -4.33559743, 17.10456355, 79.00342384]), square_terms=array([[ 39.44693796, 3.09170401, -12.53562482, -56.43816351], - [ 3.09170401, 0.26120936, -1.01043316, -4.74528348], - [-12.53562482, -1.01043316, 4.02751365, 18.43138834], - [-56.43816351, -4.74528348, 18.43138834, 86.79993595]]), scale=array([7.40091372, 9.54747144, 5.96475664, 0.3 ]), shift=array([8.50091372e+00, 4.09968892e+03, 5.96475664e+00, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=30, candidate_x=array([1.11319430e+01, 4.10923639e+03, 0.00000000e+00, 6.76395240e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-0.01107643105638871, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=6.405763941060326, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30]), model=ScalarModel(intercept=41.718081219167644, linear_terms=array([-33.92226807, -4.49642876, 13.65470054, 92.4099355 ]), square_terms=array([[ 14.11170337, 1.8117299 , -5.57976643, -37.0017643 ], - [ 1.8117299 , 0.24365654, -0.73486485, -5.01860311], - [ -5.57976643, -0.73486485, 2.23829073, 15.09309944], - [-37.0017643 , -5.01860311, 15.09309944, 104.10895944]]), scale=array([4.77373572, 4.77373572, 3.57788878, 0.3 ]), shift=array([6.35435600e+00, 4.09968892e+03, 3.57788878e+00, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=3.202881970530163, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.1467382459502044, linear_terms=array([-0.12710713, 0.04703389, -0.03182743, 0.81280376]), square_terms=array([[ 1.08576076, -0.25975691, 0.14647556, -2.93069644], - [-0.25975691, 0.06732177, -0.04637652, 0.75351968], - [ 0.14647556, -0.04637652, 0.05546358, -0.45533981], - [-2.93069644, 0.75351968, -0.45533981, 9.03637389]]), scale=array([2.38686786, 2.38686786, 2.38445485, 0.3 ]), shift=array([6.35435600e+00, 4.09968892e+03, 2.38445485e+00, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=44, candidate_x=array([4.21584426e+00, 4.10207579e+03, 2.25255815e+00, 6.59990354e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-0.5930268001920995, accepted=False, new_indices=array([32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_used=array([ 0, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=1.6014409852650815, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.11802915488484887, linear_terms=array([ 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0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45]), model=ScalarModel(intercept=0.0949099671969752, linear_terms=array([ 0.03172615, -0.00206489, -0.00279315, 0.08759565]), square_terms=array([[ 7.86440212e-02, 1.88730963e-02, 2.33766093e-02, - -7.22673855e-01], - [ 1.88730963e-02, 6.86508066e-03, 5.16475212e-03, - -1.74059625e-01], - [ 2.33766093e-02, 5.16475212e-03, 1.07562988e-02, - -2.90285127e-01], - [-7.22673855e-01, -1.74059625e-01, -2.90285127e-01, - 8.62433420e+00]]), scale=array([0.59671696, 0.59671696, 0.59671696, 0.3 ]), shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 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model_indices=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.27541175174579846, linear_terms=array([-0.06817834, -0.05276822, 0.126152 , -0.82843349]), square_terms=array([[ 0.05944259, 0.02280401, -0.0560511 , 0.53875948], - [ 0.02280401, 0.01164477, -0.02766597, 0.24483793], - [-0.0560511 , -0.02766597, 0.06822755, -0.60762475], - [ 0.53875948, 0.24483793, -0.60762475, 5.767287 ]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.20904655]), shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 8.90953446e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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accepted=False, new_indices=array([47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]), old_indices_used=array([ 0, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.2988708045687646, linear_terms=array([ 0.05873433, -0.00758318, -0.003689 , 0.60294498]), square_terms=array([[ 2.09679873e-02, 1.70244323e-04, -6.43571996e-03, - 2.00584793e-01], - [ 1.70244323e-04, 5.37915084e-04, -1.12211972e-03, - 1.14935938e-02], - [-6.43571996e-03, -1.12211972e-03, 4.22275088e-03, - -8.44849663e-02], - [ 2.00584793e-01, 1.14935938e-02, -8.44849663e-02, - 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 47, 49, 51, 53, 55, 58, 60]), model=ScalarModel(intercept=0.2725232800460816, linear_terms=array([0.02014066, 0.01692965, 0.00516888, 0.19458607]), square_terms=array([[ 0.00974604, 0.02695117, -0.00610046, 0.10035422], - [ 0.02695117, 0.08157823, -0.01996677, 0.29440327], - [-0.00610046, -0.01996677, 0.00520399, -0.07015178], - [ 0.10035422, 0.29440327, -0.07015178, 1.10663584]]), scale=0.1000900615790676, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.44984291327620873, linear_terms=array([ 0.00794743, -0.01077897, 0.00817368, -0.21319472]), square_terms=array([[ 3.97811820e-04, 4.37458980e-04, -3.66650849e-04, - 2.01797654e-02], - [ 4.37458980e-04, 1.33275431e-03, -8.59796475e-04, - 5.23861571e-02], - [-3.66650849e-04, -8.59796475e-04, 7.72136489e-04, - -3.31731740e-02], - [ 2.01797654e-02, 5.23861571e-02, -3.31731740e-02, - 2.13462780e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07453425]), shift=array([6.36660607e+00, 4.09967655e+03, 2.39442508e+00, 1.02546575e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 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4.09974713e+03, 2.33277881e+00, 1.03111738e+00]), index=75, x=array([6.29201645e+00, 4.09974713e+03, 2.33277881e+00, 1.03111738e+00]), fval=0.3880150372231467, rho=0.612492089426925, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), old_indices_discarded=array([47, 52, 53, 55, 58, 60]), step_length=0.11989908686500553, relative_step_length=1.1979120101778487, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29201645e+00, 4.09974713e+03, 2.33277881e+00, 1.03111738e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 60, 61, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.6784397175461118, linear_terms=array([ 0.05917309, -0.09411167, 0.08046158, -1.61073661]), square_terms=array([[ 4.09804153e-03, -4.03800700e-03, 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51, 52, 53, 54, 55, 56, 57, 58, 59, 68, 69]), step_length=0.25874156763788225, relative_step_length=1.2925437528754322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 55, 59, 60, 61, 62, 64, 65, 67, 68, 70, 71, 73, 75, 76]), model=ScalarModel(intercept=0.32097874490276107, linear_terms=array([ 0.05330163, -0.04745503, -0.07450486, 0.04707809]), square_terms=array([[ 0.04450147, -0.0463324 , -0.06634309, -0.27074592], - [-0.0463324 , 0.0485459 , 0.06911336, 0.29211727], - [-0.06634309, 0.06911336, 0.09925331, 0.40416762], - [-0.27074592, 0.29211727, 0.40416762, 2.04202383]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - 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model=ScalarModel(intercept=0.3874021895959514, linear_terms=array([-1.11289514, -1.07248142, -0.06157134, -0.30876554]), square_terms=array([[6.85846414, 6.56737707, 0.304784 , 3.06229562], - [6.56737707, 6.28875786, 0.29204616, 2.9296343 ], - [0.304784 , 0.29204616, 0.01447866, 0.12773002], - [3.06229562, 2.9296343 , 0.12773002, 1.462907 ]]), scale=0.0500450307895338, shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]), model=ScalarModel(intercept=0.3752098691147475, linear_terms=array([-0.00233909, -0.00963207, 0.00186957, -0.14369637]), square_terms=array([[ 2.27269118e-04, 3.25982103e-04, -1.41010980e-05, - 9.69456874e-03], - [ 3.25982103e-04, 5.31366978e-04, -3.12154495e-05, - 1.42127747e-02], - [-1.41010980e-05, -3.12154495e-05, 1.76523696e-05, - -9.64757994e-04], - [ 9.69456874e-03, 1.42127747e-02, -9.64757994e-04, - 4.23678119e-01]]), scale=0.0250225153947669, shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), vector_model=VectorModel(intercepts=array([ 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=114, candidate_x=array([5.89553599e+00, 4.09993215e+03, 1.98226579e+00, 1.01756627e+00]), index=111, x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), fval=0.30088852241782776, rho=-0.47013410940622297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, - 111, 113]), old_indices_discarded=array([ 59, 96, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 95, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 125, 126]), model=ScalarModel(intercept=0.2942454780500071, linear_terms=array([-0.00053836, 0.01012758, 0.00658806, -0.04235502]), square_terms=array([[ 2.85341780e-04, -7.78431906e-04, -4.05296375e-04, - 1.17160568e-02], - [-7.78431906e-04, 2.28701486e-03, 1.21244865e-03, - -3.24691514e-02], - [-4.05296375e-04, 1.21244865e-03, 6.63688875e-04, - -1.73852413e-02], - [ 1.17160568e-02, -3.24691514e-02, -1.73852413e-02, - 4.95574908e-01]]), scale=0.0250225153947669, shift=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_used=array([ 94, 95, 111, 114]), old_indices_discarded=array([], dtype=int32), step_length=0.02511249539583245, relative_step_length=1.0035959614629457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92278744e+00, 4.09994717e+03, 1.94692224e+00, 1.02144230e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, - 126, 127]), model=ScalarModel(intercept=0.2943051049928589, linear_terms=array([ 0.00564431, -0.00188183, 0.00320629, -0.09902661]), square_terms=array([[ 9.27890201e-05, 7.56993238e-06, 6.61699037e-06, - 5.86812035e-03], - [ 7.56993238e-06, 3.99076703e-05, -3.70323399e-05, - 8.89741428e-03], - [ 6.61699037e-06, -3.70323399e-05, 8.59295855e-05, - -8.43245723e-03], - [ 5.86812035e-03, 8.89741428e-03, 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State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, - 126, 128]), model=ScalarModel(intercept=0.2809513915508127, linear_terms=array([0.00283903, 0.01786394, 0.01405315, 0.08551073]), square_terms=array([[ 2.87201002e-03, -8.38100805e-03, -4.93196038e-03, - 1.13296034e-01], - [-8.38100805e-03, 2.61008372e-02, 1.54731491e-02, - -3.32316666e-01], - [-4.93196038e-03, 1.54731491e-02, 9.29788666e-03, - -1.97733985e-01], - [ 1.13296034e-01, -3.32316666e-01, -1.97733985e-01, - 4.54041405e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=129, candidate_x=array([5.80416113e+00, 4.09988374e+03, 1.85123275e+00, 1.01537731e+00]), index=128, x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), fval=0.2860214168257209, rho=-0.07512022159199228, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, - 126, 128]), old_indices_discarded=array([ 46, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, - 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, - 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, - 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, - 127, 128]), model=ScalarModel(intercept=0.28601605918758766, linear_terms=array([ 0.00549346, -0.00187638, 0.00228974, -0.00413326]), square_terms=array([[ 9.45819260e-05, 2.05993647e-05, 2.22032641e-06, - 7.75797675e-03], - [ 2.05993647e-05, 6.87384635e-05, -2.72238261e-05, - 1.17088910e-02], - [ 2.22032641e-06, -2.72238261e-05, 6.49402825e-05, - -5.14755584e-03], - [ 7.75797675e-03, 1.17088910e-02, -5.14755584e-03, - 2.25853786e+00]]), scale=0.0500450307895338, shift=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 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97, 98, 99, 100, 101, 102, 103, - 104, 105, 106, 107, 108, 109, 110, 112, 113, 116, 118, 120, 121, - 122]), step_length=0.25843212314368597, relative_step_length=1.2909979226036032, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, - 131, 132]), model=ScalarModel(intercept=4.348347562426054, linear_terms=array([ 0.31927054, 0.46205338, -0.36145321, -14.31327039]), square_terms=array([[ 1.65442176e-02, 1.84070457e-02, -1.41804582e-02, - -4.63767949e-01], - [ 1.84070457e-02, 2.54749193e-02, -2.05872063e-02, - -7.55309262e-01], - [-1.41804582e-02, -2.05872063e-02, 1.89816565e-02, - 6.10967512e-01], - [-4.63767949e-01, -7.55309262e-01, 6.10967512e-01, - 2.48273398e+01]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.19027745]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.09722546e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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106, 107, 108, 109, - 110, 112, 116, 117, 118, 120, 121, 122, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, - 131, 132]), model=ScalarModel(intercept=0.6450590722968451, linear_terms=array([ 0.0687357 , 0.08298437, -0.06097559, -2.78513076]), square_terms=array([[ 4.13605440e-03, 4.60176141e-03, -3.54511454e-03, - -1.40984410e-01], - [ 4.60176141e-03, 6.36872983e-03, -5.14680157e-03, - -2.29612312e-01], - [-3.54511454e-03, -5.14680157e-03, 4.74541412e-03, - 1.85732745e-01], - [-1.40984410e-01, -2.29612312e-01, 1.85732745e-01, - 9.17763751e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11568783]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.84312166e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([129, 131, 132, 133, 134]), model=ScalarModel(intercept=0.21950785606017942, linear_terms=array([ 0.03127405, -0.02069695, 0.00761499, -0.24328735]), square_terms=array([[ 4.10019284e-03, -3.03558341e-03, 1.01299676e-03, - -8.66155089e-02], - [-3.03558341e-03, 2.31725603e-03, -7.31760870e-04, - 7.02087855e-02], - [ 1.01299676e-03, -7.31760870e-04, 3.83990035e-04, - -3.05692894e-02], - [-8.66155089e-02, 7.02087855e-02, -3.05692894e-02, - 3.73315598e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=135, candidate_x=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), index=135, x=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), fval=0.19084543631566314, rho=0.5530791700510971, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([129, 131, 132, 133, 134]), old_indices_discarded=array([], dtype=int32), step_length=0.12919783936681034, relative_step_length=1.2908158645176637, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, - 134, 135]), model=ScalarModel(intercept=0.6377265413639779, linear_terms=array([ 0.01280445, 0.03233605, 0.04550891, -2.87385806]), square_terms=array([[ 1.03672207e-03, 1.33278357e-04, 2.91938128e-04, - 1.62342101e-02], - [ 1.33278357e-04, 9.48774396e-04, 1.23085546e-03, - -8.83801170e-02], - [ 2.91938128e-04, 1.23085546e-03, 1.93567123e-03, - -1.18090773e-01], - [ 1.62342101e-02, -8.83801170e-02, -1.18090773e-01, - 9.26865853e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11748143]), shift=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 9.82518572e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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old_indices_used=array([ 94, 114, 115, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, - 134, 135]), old_indices_discarded=array([ 46, 49, 54, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, - 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, - 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, - 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 124]), step_length=0.25839641740029984, relative_step_length=1.2908195545277783, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.23784856e+00, 4.09952572e+03, 1.56470502e+00, 1.01653381e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, - 135, 136]), model=ScalarModel(intercept=3.3407898582211835, linear_terms=array([ 1.38155211e-01, -7.92390870e-03, 3.08910897e-01, -1.15990734e+01]), square_terms=array([[ 7.85599256e-03, 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old_indices_discarded=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, - 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, - 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, - 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, - 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, - 110, 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 123, 124]), step_length=0.5168470619375918, relative_step_length=1.29095500038558, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.93949008e+00, 4.09922736e+03, 1.26634654e+00, 1.00772852e+00]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 112, 114, 125, 127, 128, 129, 130, 131, 132, 133, 134, 135, - 136, 137]), model=ScalarModel(intercept=11.614247085917237, linear_terms=array([ 0.81145968, -0.51265125, 1.29401925, -33.62677892]), square_terms=array([[ 5.63481046e-02, 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old_indices_discarded=array([ 0, 32, 33, 34, 35, 36, 37, 38, 42, 44, 45, 46, 47, - 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, - 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, - 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, - 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, - 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 115, - 116, 117, 118, 119, 120, 121, 122, 123, 124, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.93949008e+00, 4.09922736e+03, 1.26634654e+00, 1.00772852e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 112, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, - 137, 138]), model=ScalarModel(intercept=2.3800270993481427, linear_terms=array([ 0.34086296, -0.36587453, 0.39270343, -9.0556698 ]), square_terms=array([[ 0.02975173, 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61, - 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, - 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 115, 116, - 117, 118, 119, 120, 121, 122, 123, 124, 126, 127]), step_length=0.5174774189042238, relative_step_length=1.2925294748056355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, - 138, 139]), model=ScalarModel(intercept=8.622726430625926, linear_terms=array([ 1.2277478 , -1.26958822, 1.35833354, -27.99550346]), square_terms=array([[ 0.11015107, -0.09196504, 0.11241212, -1.95469033], - [-0.09196504, 0.09842539, -0.10250002, 2.07845956], - [ 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76, - 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, - 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, - 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, - 118, 119, 120, 121, 122, 123, 124, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, - 139, 140]), model=ScalarModel(intercept=2.058129746040703, linear_terms=array([ 0.27064711, -0.08764093, 0.37387978, -9.44399412]), square_terms=array([[ 2.24820723e-02, -4.44313988e-03, 2.83141588e-02, - -6.06514526e-01], - [-4.44313988e-03, 3.42102034e-03, -7.54821341e-03, - 2.20553230e-01], - [ 2.83141588e-02, -7.54821341e-03, 3.74357636e-02, - 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109, 110, - 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 126, 127]), step_length=0.49338773191709673, relative_step_length=1.2323594474146116, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 42, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 141, 142]), model=ScalarModel(intercept=0.1535157442818915, linear_terms=array([ 0.16249061, -0.05439485, 0.02121035, -0.5810401 ]), square_terms=array([[ 0.13859777, -0.03700346, 0.01742136, -0.34784768], - [-0.03700346, 0.01208239, -0.00552864, 0.12680428], - [ 0.01742136, -0.00552864, 0.02104412, -0.09554744], - [-0.34784768, 0.12680428, -0.09554744, 1.56437999]]), scale=array([0.59671696, 0.59671696, 0.59671696, 0.3 ]), shift=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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122, 123, 124, 125, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, - 143]), model=ScalarModel(intercept=0.043843502447149255, linear_terms=array([ 0.01252671, 0.00052798, -0.00302499, -0.00229125]), square_terms=array([[ 0.02888995, -0.0291837 , -0.02552962, 0.08945984], - [-0.0291837 , 0.05384447, 0.05044383, -0.17830875], - [-0.02552962, 0.05044383, 0.05029034, -0.16876423], - [ 0.08945984, -0.17830875, -0.16876423, 0.59615769]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.21453342]), shift=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 8.85466580e-01])), vector_model=VectorModel(intercepts=array([ 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linear_terms=array([ 0.0729615 , -0.01994005, -0.02510471, -0.67291357]), square_terms=array([[ 9.11672153e-02, -2.53027498e-02, -2.81155848e-02, - -8.77690304e-01], - [-2.53027498e-02, 7.33288652e-03, 8.17518993e-03, - 2.54924757e-01], - [-2.81155848e-02, 8.17518993e-03, 9.50928398e-03, - 2.82797281e-01], - [-8.77690304e-01, 2.54924757e-01, 2.82797281e-01, - 8.87336174e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.1399438 ]), shift=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.60056201e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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9.26235080e-01, 9.54248156e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=-5.901698191343305, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([141, 145, 146, 147, 148, 149, 150, 151]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([146, 148, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, - 162, 163, 164]), model=ScalarModel(intercept=0.040408606819058814, linear_terms=array([ 1.79403686e-03, -1.26156607e-04, -3.91788853e-05, -1.94426398e-02]), square_terms=array([[ 1.03044977e-03, 2.02913221e-05, 3.65652688e-04, - -1.22326273e-02], - [ 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relative_step_length=1.0183954473778696, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.22484797e+00, 4.09923302e+03, 9.03726070e-01, 9.63886635e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([148, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, - 164, 165]), model=ScalarModel(intercept=0.038586828229091596, linear_terms=array([ 0.0008624 , -0.00029256, -0.00092029, -0.00362331]), square_terms=array([[ 3.80039810e-03, 1.48122981e-04, 1.95630740e-03, - -4.67627417e-02], - [ 1.48122981e-04, 1.24195108e-05, 8.98861869e-05, - -2.15859541e-03], - [ 1.95630740e-03, 8.98861869e-05, 1.24145976e-03, - -2.74189420e-02], - [-4.67627417e-02, -2.15859541e-03, -2.74189420e-02, - 6.52838862e-01]]), scale=0.0500450307895338, shift=array([4.22484797e+00, 4.09923302e+03, 9.03726070e-01, 9.63886635e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - 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rho=0.0016410550627144686, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([147, 150, 151, 152, 155, 156, 158, 160, 164, 165, 166, 167, 168, - 169, 170]), old_indices_discarded=array([137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, 153, - 154, 157, 159, 161, 162, 163]), step_length=0.21107983641988134, relative_step_length=1.0544495282038355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17571078e+00, 4.09945171e+03, 1.29350788e+00, 9.75853572e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 155, 158, 165, 166, 167, 168, 169, - 170, 171]), model=ScalarModel(intercept=0.03278405592667447, linear_terms=array([-0.00058539, -0.00037664, -0.0011525 , 0.01693014]), square_terms=array([[ 4.39506574e-03, 1.51749098e-03, 5.21452806e-04, - -4.51551090e-02], - [ 1.51749098e-03, 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relative_step_length=1.002913343419474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.73777078e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 145, 147, 150, 151, 152, 158, 165, 166, 167, 168, 169, 170, - 171, 172]), model=ScalarModel(intercept=0.035448800508248314, linear_terms=array([ 0.01859568, 0.0072831 , -0.00094604, -0.08987951]), square_terms=array([[ 4.78915876e-02, 1.75180826e-02, -6.37182435e-05, - -2.22312021e-01], - [ 1.75180826e-02, 6.76259777e-03, -5.22983618e-05, - -8.70414549e-02], - [-6.37182435e-05, -5.22983618e-05, 7.13103746e-04, - -5.40324974e-04], - [-2.22312021e-01, -8.70414549e-02, -5.40324974e-04, - 1.12940090e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.13770108]), shift=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.62298918e-01])), 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9.73777078e-01]), fval=0.0321599633543692, rho=-1.1235938647571044, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([143, 147, 150, 151, 166, 167, 168, 169, 170, 171, 172, 173]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.73777078e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([169, 171, 172, 173, 174]), model=ScalarModel(intercept=0.032159963354369214, linear_terms=array([ 5.59369311e-04, -9.52308478e-05, 1.97754626e-04, -1.30010441e-02]), square_terms=array([[ 1.15998368e-03, -1.12910017e-04, 5.17769563e-04, - -1.73140062e-02], - [-1.12910017e-04, 5.45970788e-05, -1.69795387e-04, - 5.57995869e-03], - [ 5.17769563e-04, -1.69795387e-04, 5.82895920e-04, - -1.89613008e-02], - 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1.53745960e+00, 9.76255422e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), model=ScalarModel(intercept=0.032130564354299555, linear_terms=array([-0.00156055, -0.00139965, 0.00036611, -0.025743 ]), square_terms=array([[ 4.05055456e-03, 1.48919579e-03, -6.07642697e-04, - 3.44846053e-02], - [ 1.48919579e-03, 1.66608590e-03, -7.81966055e-04, - 3.87222711e-02], - [-6.07642697e-04, -7.81966055e-04, 5.86968019e-04, - -1.75898376e-02], - [ 3.44846053e-02, 3.87222711e-02, -1.75898376e-02, - 9.06040243e-01]]), scale=0.1000900615790676, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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candidate_x=array([4.15236900e+00, 4.09955913e+03, 1.56526359e+00, 9.74785320e-01]), index=179, x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), fval=0.03162745417502315, rho=-3.5033153040189933, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178, 179, 180]), model=ScalarModel(intercept=0.031652111625055504, linear_terms=array([ 4.80604637e-05, 1.39247104e-05, -2.39867534e-05, -1.89127269e-04]), square_terms=array([[ 3.90665224e-03, 1.27857855e-04, 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relative_step_length=1.0023815838258203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, - 181]), model=ScalarModel(intercept=0.032799850622037344, linear_terms=array([-0.00120015, -0.00149617, 0.00075662, -0.03985981]), square_terms=array([[ 2.96888836e-03, 4.77998749e-04, -2.25595211e-04, - 1.47872526e-02], - [ 4.77998749e-04, 8.86980525e-04, -5.90740802e-04, - 2.76631448e-02], - [-2.25595211e-04, -5.90740802e-04, 6.06547002e-04, - -1.77861139e-02], - [ 1.47872526e-02, 2.76631448e-02, -1.77861139e-02, - 8.69630174e-01]]), scale=0.1000900615790676, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 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model=ScalarModel(intercept=0.03161885589197164, linear_terms=array([ 1.24235116e-05, 1.65270337e-05, -5.96983212e-07, -3.23007724e-04]), square_terms=array([[ 3.85807274e-03, 1.15384522e-04, 1.32757111e-03, - -5.48814365e-02], - [ 1.15384522e-04, 4.41711422e-06, 4.73125548e-05, - -2.03433014e-03], - [ 1.32757111e-03, 4.73125548e-05, 5.79485173e-04, - -2.24432493e-02], - [-5.48814365e-02, -2.03433014e-03, -2.24432493e-02, - 9.54134964e-01]]), scale=0.0500450307895338, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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new_indices=array([], dtype=int32), old_indices_used=array([172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([175, 177, 179, 181, 182, 183]), model=ScalarModel(intercept=0.03159896733455111, linear_terms=array([ 5.72777496e-04, -4.79596153e-06, 1.29876317e-04, -7.46995187e-03]), square_terms=array([[ 1.16620241e-03, 2.24403583e-06, 3.92674408e-04, - -1.60307218e-02], - [ 2.24403583e-06, 7.30065521e-09, 8.86098589e-07, - -3.50743943e-05], - [ 3.92674408e-04, 8.86098589e-07, 1.60868867e-04, - -6.16883815e-03], - [-1.60307218e-02, -3.50743943e-05, -6.16883815e-03, - 2.56901239e-01]]), scale=0.0250225153947669, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - 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model_indices=array([179, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, - 194, 195, 196]), model=ScalarModel(intercept=0.03162939399086311, linear_terms=array([-2.05014239e-05, -4.40701544e-07, -7.03347564e-06, 4.85199295e-04]), square_terms=array([[ 3.49864962e-04, -2.65476555e-06, 1.06943866e-04, - -4.62038714e-03], - [-2.65476555e-06, 3.14059178e-08, -9.72077663e-07, - 4.40297209e-05], - [ 1.06943866e-04, -9.72077663e-07, 3.86168856e-05, - -1.57987714e-03], - [-4.62038714e-03, 4.40297209e-05, -1.57987714e-03, - 7.04100722e-02]]), scale=0.01251125769738345, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], 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1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-8.714421091041428, accepted=False, new_indices=array([185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196]), old_indices_used=array([179, 181, 183, 184]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.006255628848691725, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([181, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, - 196, 197]), model=ScalarModel(intercept=0.03163372180134707, linear_terms=array([-1.40416035e-05, -1.06245855e-05, -6.25328365e-06, 2.93425222e-04]), square_terms=array([[ 8.39288020e-05, 3.25915482e-06, 2.80895929e-05, - -1.12787517e-03], - [ 3.25915482e-06, 1.50804018e-07, 1.22778171e-06, - -5.07296461e-05], - [ 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State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0031278144243458623, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, - 197, 198]), model=ScalarModel(intercept=0.03163462823062035, linear_terms=array([-9.39533430e-06, -5.39540217e-06, 2.16599626e-07, 1.49312835e-04]), square_terms=array([[ 2.34139611e-05, 1.09806616e-06, 5.53797697e-06, - -3.00949207e-04], - [ 1.09806616e-06, 5.92778360e-08, 2.79762167e-07, - -1.59957868e-05], - [ 5.53797697e-06, 2.79762167e-07, 1.56644529e-06, - -7.74361413e-05], - [-3.00949207e-04, -1.59957868e-05, -7.74361413e-05, - 4.40255574e-03]]), scale=0.0031278144243458623, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=199, candidate_x=array([4.13001034e+00, 4.09942001e+03, 1.55905409e+00, 9.76337228e-01]), index=181, x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-2.567463867080543, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, - 197, 198]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 200 entries., 'multistart_info': {'start_parameters': [array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00]), array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])], 'local_optima': [{'solution_x': array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), 'solution_criterion': 0.031736549972374545, 'states': [State(trustregion=Region(center=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00]), radius=398.5357791964783, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=[0], model=ScalarModel(intercept=0.048526887296099594, linear_terms=array([0., 0., 0., 0.]), square_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), 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rho=-0.009621076761907122, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00]), radius=24.908486199779894, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=49.21116252780055, linear_terms=array([-114.4827005 , 12.14526074, 16.2009041 , 83.22887709]), square_terms=array([[136.22560491, -14.260671 , -19.11287546, -95.67759179], - [-14.260671 , 1.50614396, 2.01037693, 10.2458899 ], - [-19.11287546, 2.01037693, 2.69121122, 13.59133153], - [-95.67759179, 10.2458899 , 13.59133153, 71.34602111]]), scale=array([ 9.45 , 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1.00128795e+00]), index=90, x=array([4.20521015e+00, 3.98536472e+03, 2.20720870e+00, 9.92417556e-01]), fval=0.04041587683749358, rho=-9.015153548302399, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 64, 69, 73, 74, 75, 76, 79, 80, 81, 82, 85, 87, 89, 90]), old_indices_discarded=array([ 0, 59, 60, 62, 63, 65, 66, 67, 68, 70, 71, 72, 77, 78, 83, 84, 86, - 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.20521015e+00, 3.98536472e+03, 2.20720870e+00, 9.92417556e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 64, 69, 73, 74, 75, 76, 77, 78, 79, 84, 87, 88, 89, 90]), model=ScalarModel(intercept=0.040131573919175975, linear_terms=array([ 0.00323797, -0.0008344 , 0.0008575 , -0.01436487]), square_terms=array([[ 1.08500426e-03, -4.90024550e-03, 3.21182359e-04, - 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.20521015e+00, 3.98536472e+03, 2.20720870e+00, 9.92417556e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([64, 69, 73, 74, 75, 76, 77, 78, 79, 84, 87, 88, 89, 90, 92]), model=ScalarModel(intercept=0.04384005770132782, linear_terms=array([-0.0030208 , 0.00836173, 0.00224383, 0.04029893]), square_terms=array([[ 0.00302054, -0.00508567, -0.00108727, -0.02795386], - [-0.00508567, 0.00916282, 0.00203708, 0.05048058], - [-0.00108727, 0.00203708, 0.000473 , 0.01113598], - [-0.02795386, 0.05048058, 0.01113598, 0.28040787]]), scale=0.024324693554472553, shift=array([4.20521015e+00, 3.98536472e+03, 2.20720870e+00, 9.92417556e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=93, candidate_x=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), index=93, x=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), fval=0.040204861958739575, rho=0.04694682324737547, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([64, 69, 73, 74, 75, 76, 77, 78, 79, 84, 87, 88, 89, 90, 92]), old_indices_discarded=array([ 0, 61, 67, 72, 80, 81, 82, 83, 85, 86, 91]), step_length=0.024864348162927093, relative_step_length=1.0221854638064007, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([73, 74, 90, 92, 93]), model=ScalarModel(intercept=0.04020486195873957, linear_terms=array([-0.00706289, -0.00175749, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, - 104, 105, 106]), model=ScalarModel(intercept=0.04013248724041306, linear_terms=array([ 7.02734078e-04, 4.25545025e-05, 1.68036148e-04, -8.04428384e-03]), square_terms=array([[ 1.09690480e-04, 1.40037264e-05, 6.92626716e-06, - -1.62751319e-03], - [ 1.40037264e-05, 2.06061675e-06, 7.87462498e-07, - -2.33069567e-04], - [ 6.92626716e-06, 7.87462498e-07, 1.26111162e-06, - -9.04427721e-05], - [-1.62751319e-03, -2.33069567e-04, -9.04427721e-05, - 2.79100169e-02]]), scale=0.006081173388618138, shift=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 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scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=107, candidate_x=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01]), index=107, x=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01]), fval=0.03860803268572889, rho=1.1129757353667895, accepted=True, new_indices=array([ 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106]), old_indices_used=array([90, 92, 93, 94]), old_indices_discarded=array([], dtype=int32), step_length=0.006407991386109553, relative_step_length=1.0537425882483644, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, - 106, 107]), model=ScalarModel(intercept=0.03867072004563482, linear_terms=array([ 5.06639540e-04, -2.54682104e-05, 2.87144909e-04, -5.38984530e-04]), square_terms=array([[ 3.43854367e-04, -6.18791687e-06, 6.33233046e-05, - -5.63275002e-03], - [-6.18791687e-06, 3.56620027e-07, -1.07897494e-06, - 8.77935577e-05], - [ 6.33233046e-05, -1.07897494e-06, 1.44913336e-05, - -1.12583308e-03], - [-5.63275002e-03, 8.77935577e-05, -1.12583308e-03, - 1.08889227e-01]]), scale=0.012162346777236276, shift=array([4.18556472e+00, 3.98534756e+03, 2.19414465e+00, 9.92502435e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, - 106, 107]), old_indices_discarded=array([73, 90, 92]), step_length=0.012406463432134685, relative_step_length=1.0200715091724988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.17514035e+00, 3.98534813e+03, 2.18746409e+00, 9.91956376e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 92, 93, 94, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, - 106, 108]), model=ScalarModel(intercept=0.038080717924361705, linear_terms=array([ 0.00084081, -0.00022709, 0.00065496, 0.00050709]), square_terms=array([[ 1.66043955e-03, 4.81512877e-04, 1.42586039e-04, - -2.52393274e-02], - [ 4.81512877e-04, 1.54741908e-04, 4.03920393e-05, - -8.17303899e-03], - [ 1.42586039e-04, 4.03920393e-05, 2.37821246e-05, - -2.23426548e-03], - [-2.52393274e-02, 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State(trustregion=Region(center=array([4.15700474e+00, 3.98535408e+03, 2.17086332e+00, 9.90922363e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, - 108, 109]), model=ScalarModel(intercept=0.03707324900316479, linear_terms=array([ 0.00134055, -0.00050342, 0.00101212, 0.00208648]), square_terms=array([[ 5.91854237e-03, 1.94274484e-03, 1.34141717e-03, - -9.61364422e-02], - [ 1.94274484e-03, 7.09489463e-04, 4.55910967e-04, - -3.55979605e-02], - [ 1.34141717e-03, 4.55910967e-04, 3.52856825e-04, - -2.30368734e-02], - [-9.61364422e-02, -3.55979605e-02, -2.30368734e-02, - 1.81990976e+00]]), scale=0.048649387108945105, shift=array([4.15700474e+00, 3.98535408e+03, 2.17086332e+00, 9.90922363e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=110, candidate_x=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01]), index=110, x=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01]), fval=0.035977133549354015, rho=0.6750767721715603, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, - 108, 109]), old_indices_discarded=array([ 0, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, - 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, - 85, 86, 87, 88, 89, 90, 91, 95, 96, 107]), step_length=0.05126390814342671, relative_step_length=1.0537421165990575, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 104, 105, 106, 108, - 109, 110]), model=ScalarModel(intercept=0.0358675631574466, linear_terms=array([ 5.58078601e-05, -1.14653349e-03, 8.86199518e-04, 2.89684507e-02]), square_terms=array([[ 2.35562544e-02, 6.54185099e-03, 7.43725653e-03, - -3.85244288e-01], - [ 6.54185099e-03, 2.02302050e-03, 2.18435526e-03, - -1.20831398e-01], - [ 7.43725653e-03, 2.18435526e-03, 2.58880660e-03, - -1.30934460e-01], - [-3.85244288e-01, -1.20831398e-01, -1.30934460e-01, - 7.34539044e+00]]), scale=0.09729877421789021, shift=array([4.12507068e+00, 3.98537340e+03, 2.13576450e+00, 9.89114421e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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fval=0.034972454852913455, rho=0.5924956878197696, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 104, 105, 106, 108, - 109, 110]), old_indices_discarded=array([ 0, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, - 84, 85, 86, 87, 88, 89, 90, 91, 95, 96, 103, 107]), step_length=0.09887174690248846, relative_step_length=1.0161664183052885, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09950363e+00, 3.98542742e+03, 2.05703239e+00, 9.86875382e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 60, 69, 73, 90, 92, 94, 95, 98, 103, 104, 106, 108, 109, - 110, 111]), model=ScalarModel(intercept=0.0505341458401392, linear_terms=array([ 0.04469009, -0.16416707, -0.08838394, -0.37577055]), square_terms=array([[ 0.06180679, 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9.86875382e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 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x=array([4.09950363e+00, 3.98542742e+03, 2.05703239e+00, 9.86875382e-01]), fval=0.034972454852913455, rho=-36.28140783180931, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 60, 69, 94, 96, 97, 99, 100, 104, 105, 107, 108, 109, 110, - 111, 113]), old_indices_discarded=array([ 61, 64, 73, 74, 89, 90, 91, 92, 93, 95, 98, 101, 102, - 103, 106, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09950363e+00, 3.98542742e+03, 2.05703239e+00, 9.86875382e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 111, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 125, 126]), model=ScalarModel(intercept=0.03687274564490144, linear_terms=array([-0.00518634, -0.00261507, -0.00207261, 0.04559937]), square_terms=array([[ 0.0060242 , 0.00276072, 0.00265885, -0.05342576], 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relative_step_length=1.0181716852450686, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09515246e+00, 3.98544448e+03, 1.97122336e+00, 9.85089031e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 115, 116, 117, 118, 119, 120, 122, 123, 125, 126, 127, 128, - 129, 130]), model=ScalarModel(intercept=0.03380035774519198, linear_terms=array([-0.00184705, -0.00045507, 0.00099379, 0.03419088]), square_terms=array([[ 2.39152620e-02, 5.11361155e-03, 1.03154248e-03, - -3.61542564e-01], - [ 5.11361155e-03, 1.25349747e-03, 1.65017220e-04, - -8.94376678e-02], - [ 1.03154248e-03, 1.65017220e-04, 2.11101288e-04, - -1.27104666e-02], - [-3.61542564e-01, -8.94376678e-02, -1.27104666e-02, - 6.42934936e+00]]), scale=0.09729877421789021, shift=array([4.09515246e+00, 3.98544448e+03, 1.97122336e+00, 9.85089031e-01])), vector_model=VectorModel(intercepts=array([ 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9.85089031e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, - 130, 131]), model=ScalarModel(intercept=0.03424609268671577, linear_terms=array([-2.25755276e-03, -7.65995429e-04, 6.14041170e-05, 3.76453713e-02]), square_terms=array([[ 6.83395663e-03, 1.94799763e-03, 1.12493586e-03, - -9.72841521e-02], - [ 1.94799763e-03, 6.27388933e-04, 3.36225682e-04, - -3.15954777e-02], - [ 1.12493586e-03, 3.36225682e-04, 2.26528041e-04, - -1.71812036e-02], - [-9.72841521e-02, -3.15954777e-02, -1.71812036e-02, - 1.60139445e+00]]), scale=0.048649387108945105, shift=array([4.09515246e+00, 3.98544448e+03, 1.97122336e+00, 9.85089031e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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candidate_x=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), index=132, x=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), fval=0.03344278008256837, rho=0.7088480558208017, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 115, 116, 117, 118, 119, 122, 123, 125, 126, 127, 128, 129, - 130, 131]), old_indices_discarded=array([ 60, 69, 109, 110, 113, 114, 120, 121, 124]), step_length=0.04884695631269484, relative_step_length=1.0040610830986894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 113, 115, 116, 117, 119, 122, 125, 126, 127, 128, 129, 130, - 131, 132]), model=ScalarModel(intercept=0.03329194108487574, linear_terms=array([ 0.00037861, -0.00120152, 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([119, 126, 128, 129, 130, 131, 132, 133, 134]), model=ScalarModel(intercept=0.033507957269472324, linear_terms=array([-9.27280516e-04, -2.83032287e-05, 1.15540832e-04, 9.32074196e-03]), square_terms=array([[ 3.68252181e-03, 1.16879464e-04, 3.76783999e-04, - -3.69008883e-02], - [ 1.16879464e-04, 4.27297274e-06, 1.31805941e-05, - -1.26793535e-03], - [ 3.76783999e-04, 1.31805941e-05, 4.85482222e-05, - -3.85976808e-03], - [-3.69008883e-02, -1.26793535e-03, -3.85976808e-03, - 3.94313772e-01]]), scale=0.024324693554472553, shift=array([4.09700718e+00, 3.98544671e+03, 1.92248591e+00, 9.83579539e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=135, candidate_x=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), index=135, x=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), fval=0.03322208936021106, rho=0.6923368728093419, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([119, 126, 128, 129, 130, 131, 132, 133, 134]), old_indices_discarded=array([], dtype=int32), step_length=0.024497657933993316, relative_step_length=1.007110649888905, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 116, 119, 122, 125, 126, 127, 128, 129, 130, 131, 132, 133, - 134, 135]), model=ScalarModel(intercept=0.033351943337015584, 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116, 119, 122, 125, 126, 127, 128, 129, 130, 131, 132, 133, - 134, 135]), old_indices_discarded=array([ 60, 111, 114, 115, 117, 118, 120, 121, 123, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 130, 131, 132, 133, 134, 135, 136]), model=ScalarModel(intercept=0.03319114993036455, linear_terms=array([-5.23016113e-05, -9.59227897e-06, 1.63111360e-04, 1.22272274e-03]), square_terms=array([[ 1.65538480e-03, -1.07905373e-05, 4.49382646e-04, - -2.37818011e-02], - [-1.07905373e-05, 9.51781004e-08, -2.54406317e-06, - 1.39408687e-04], - [ 4.49382646e-04, -2.54406317e-06, 1.38176690e-04, - -7.08221157e-03], - [-2.37818011e-02, 1.39408687e-04, -7.08221157e-03, - 3.91873909e-01]]), scale=0.024324693554472553, shift=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - 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upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 116, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, - 136, 137]), model=ScalarModel(intercept=0.033235684467905355, linear_terms=array([-2.53103045e-03, -1.23798078e-03, -6.59073825e-05, 2.22614798e-02]), square_terms=array([[ 2.43041668e-02, 8.79030260e-03, 4.02932575e-03, - -2.25980212e-01], - [ 8.79030260e-03, 3.26024408e-03, 1.47271211e-03, - -8.42717587e-02], - [ 4.02932575e-03, 1.47271211e-03, 7.11150827e-04, - -3.82927477e-02], - [-2.25980212e-01, -8.42717587e-02, -3.82927477e-02, - 2.18945052e+00]]), scale=0.048649387108945105, shift=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 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x=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01]), fval=0.033122729602777526, rho=-0.46260457325856585, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([113, 116, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, - 136, 137]), old_indices_discarded=array([111, 114, 115, 117, 118, 120, 121, 122, 123, 124, 125]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138]), model=ScalarModel(intercept=0.033088400599093445, linear_terms=array([-2.07963025e-04, 4.86452866e-05, 1.41008433e-04, 9.08365106e-05]), square_terms=array([[ 1.90554771e-03, -5.25048184e-05, 4.99344670e-04, - -2.51666427e-02], - 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 119, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, - 138, 139]), model=ScalarModel(intercept=0.0331182013788081, linear_terms=array([-1.97974768e-03, -1.06547349e-03, -5.51519758e-05, 1.81116415e-02]), square_terms=array([[ 2.38133820e-02, 8.51946412e-03, 4.28414957e-03, - -2.23713059e-01], - [ 8.51946412e-03, 3.13204553e-03, 1.55382928e-03, - -8.27366123e-02], - [ 4.28414957e-03, 1.55382928e-03, 8.16135432e-04, - -4.12818595e-02], - [-2.23713059e-01, -8.27366123e-02, -4.12818595e-02, - 2.19746582e+00]]), scale=0.048649387108945105, shift=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 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scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=140, candidate_x=array([4.11871670e+00, 3.98548023e+03, 1.82440548e+00, 9.84089102e-01]), index=139, x=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), fval=0.033044655661740516, rho=-0.568130481594652, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([113, 119, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, - 138, 139]), old_indices_discarded=array([111, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140]), model=ScalarModel(intercept=0.032951013003172214, linear_terms=array([-1.03938213e-04, -3.52725894e-06, 1.07593804e-04, 1.86042680e-03]), square_terms=array([[ 1.75065726e-03, -3.94983661e-06, 4.88863565e-04, - -2.44687091e-02], - [-3.94983661e-06, 1.79124813e-08, -1.19360841e-06, - 6.43531025e-05], - [ 4.88863565e-04, -1.19360841e-06, 1.52931166e-04, - -7.43453721e-03], - [-2.44687091e-02, 6.43531025e-05, -7.43453721e-03, - 3.86356869e-01]]), scale=0.024324693554472553, shift=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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new_indices=array([], dtype=int32), old_indices_used=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140]), old_indices_discarded=array([], dtype=int32), step_length=0.024637350930881603, relative_step_length=1.0128534970320957, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11559640e+00, 3.98544181e+03, 1.82857121e+00, 9.82636343e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, - 140, 141]), model=ScalarModel(intercept=0.03290815908255805, linear_terms=array([-4.11171815e-04, -5.00685998e-04, -1.68988412e-05, 1.54742229e-02]), square_terms=array([[ 6.62320972e-03, 2.37316712e-03, 2.54362470e-03, - -1.24735278e-01], - [ 2.37316712e-03, 9.45809690e-04, 9.92127010e-04, - -5.01657639e-02], - [ 2.54362470e-03, 9.92127010e-04, 1.08295792e-03, - -5.27667377e-02], - [-1.24735278e-01, -5.01657639e-02, -5.27667377e-02, - 2.67077637e+00]]), scale=0.048649387108945105, shift=array([4.11559640e+00, 3.98544181e+03, 1.82857121e+00, 9.82636343e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 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State(trustregion=Region(center=array([4.10380907e+00, 3.98547361e+03, 1.79311439e+00, 9.81700943e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 141, 142]), model=ScalarModel(intercept=0.0326807222536389, linear_terms=array([-0.00209921, -0.00013072, -0.00011186, 0.03140504]), square_terms=array([[ 2.65673117e-02, 1.24220947e-04, 7.48136948e-03, - -3.80223461e-01], - [ 1.24220947e-04, 1.73529823e-06, 4.33828043e-05, - -2.32018221e-03], - [ 7.48136948e-03, 4.33828043e-05, 2.37936036e-03, - -1.17532688e-01], - [-3.80223461e-01, -2.32018221e-03, -1.17532688e-01, - 6.23372854e+00]]), scale=0.09729877421789021, shift=array([4.10380907e+00, 3.98547361e+03, 1.79311439e+00, 9.81700943e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=143, candidate_x=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), index=143, x=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), fval=0.03220931715356145, rho=1.0262736108824155, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 141, 142]), old_indices_discarded=array([ 0, 59, 60, 61, 63, 64, 66, 67, 69, 70, 73, 74, 75, - 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, - 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, - 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, - 122, 123, 124, 125, 126, 127]), step_length=0.1013394261738542, relative_step_length=1.0415282925036176, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.80211212e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, - 142, 143]), model=ScalarModel(intercept=0.08296945152155982, linear_terms=array([ 7.19376948e-02, -2.23100515e-04, 2.36320257e-02, -1.08022108e+00]), square_terms=array([[ 5.84409748e-02, -8.08424984e-06, 1.70531284e-02, - -7.64859744e-01], - [-8.08424984e-06, 3.61028975e-06, -1.27774505e-05, - 2.03813182e-04], - [ 1.70531284e-02, -1.27774505e-05, 5.62834359e-03, - -2.45854953e-01], - [-7.64859744e-01, 2.03813182e-04, -2.45854953e-01, - 1.14832543e+01]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13240388]), shift=array([4.11669376e+00, 3.98550545e+03, 1.69778444e+00, 9.67596123e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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candidate_x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=0.642468592930664, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, - 142, 143]), old_indices_discarded=array([ 0, 48, 49, 52, 53, 54, 56, 59, 60, 61, 62, 63, 64, - 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, - 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, - 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, - 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, - 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 129]), step_length=0.20552612749559135, relative_step_length=1.0561599010247422, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.38919509687156084, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, - 143, 144]), model=ScalarModel(intercept=0.5158401817968741, linear_terms=array([ 0.48374106, -0.21959826, -0.03034574, -2.45076283]), square_terms=array([[ 2.73512553e-01, -1.10681409e-01, -1.36794804e-02, - -1.22742522e+00], - [-1.10681409e-01, 5.00854227e-02, 7.37119045e-03, - 5.56398366e-01], - [-1.36794804e-02, 7.37119045e-03, 2.82983352e-03, - 7.80570977e-02], - [-1.22742522e+00, 5.56398366e-01, 7.80570977e-02, - 6.20455154e+00]]), scale=array([0.29003793, 0.29003793, 0.29003793, 0.20600944]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 8.93990557e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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candidate_x=array([4.14231731e+00, 3.98555744e+03, 1.48330502e+00, 9.83650146e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-19.37116300456786, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, - 143, 144]), old_indices_discarded=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, - 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, - 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, - 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, - 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, - 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 125, 126, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, - 144, 145]), model=ScalarModel(intercept=0.034194805708801616, linear_terms=array([ 0.00897016, -0.00531212, 0.00118509, -0.10933048]), square_terms=array([[ 2.60943087e-02, -1.08818961e-02, 2.66055361e-03, - -2.23169348e-01], - [-1.08818961e-02, 6.33814787e-03, -1.14716135e-03, - 1.30275256e-01], - [ 2.66055361e-03, -1.14716135e-03, 6.17083790e-04, - -2.45327892e-02], - [-2.23169348e-01, 1.30275256e-01, -2.45327892e-02, - 2.68630749e+00]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13349996]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.66500041e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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candidate_x=array([4.14173469e+00, 3.98579549e+03, 1.44950026e+00, 9.65493838e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-6.7613204113764, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, - 144, 145]), old_indices_discarded=array([ 0, 48, 52, 53, 54, 56, 60, 61, 62, 63, 64, 65, 66, - 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, - 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, - 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, - 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, - 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.09729877421789021, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, - 145, 146]), model=ScalarModel(intercept=0.03223491974347327, linear_terms=array([-0.00435523, 0.00045162, -0.00113672, 0.06395075]), square_terms=array([[ 2.76484614e-02, -2.97346672e-03, 6.76823088e-03, - -3.49132230e-01], - [-2.97346672e-03, 3.93096161e-04, -8.27242646e-04, - 4.39088173e-02], - [ 6.76823088e-03, -8.27242646e-04, 1.87325427e-03, - -9.27089668e-02], - [-3.49132230e-01, 4.39088173e-02, -9.27089668e-02, - 5.01368528e+00]]), scale=0.09729877421789021, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), 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9.75472888e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.9167851356132374, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, - 145, 146]), old_indices_discarded=array([ 59, 130, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 144, 145, 146, 147]), model=ScalarModel(intercept=0.031852337676144094, linear_terms=array([-0.01334969, -0.000875 , -0.00212241, 0.00556698]), square_terms=array([[ 0.14858061, 0.00959735, 0.02815489, -0.28697043], - [ 0.00959735, 0.00062439, 0.00182044, -0.01825667], - [ 0.02815489, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, - 158, 159, 160]), model=ScalarModel(intercept=0.032556756784188255, linear_terms=array([-1.38069018e-03, -8.05850091e-05, -2.11446865e-04, 2.23013286e-02]), square_terms=array([[ 1.35249748e-03, 6.51569707e-05, 1.87461961e-04, - -1.84531760e-02], - [ 6.51569707e-05, 3.62304471e-06, 9.61360198e-06, - -1.02222512e-03], - [ 1.87461961e-04, 9.61360198e-06, 3.78176312e-05, - -2.78482419e-03], - [-1.84531760e-02, -1.02222512e-03, -2.78482419e-03, - 2.89607050e-01]]), scale=0.024324693554472553, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 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3.98567482e+03, 1.55173969e+00, 9.75875695e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.3377986236674669, accepted=False, new_indices=array([149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), old_indices_used=array([144, 145, 147, 148]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, - 160, 161]), model=ScalarModel(intercept=0.032618873867381576, linear_terms=array([-0.00076276, -0.00023605, -0.00050889, 0.01153604]), square_terms=array([[ 3.73432785e-04, 9.98864964e-05, 2.43531496e-04, - 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, - 161, 162]), model=ScalarModel(intercept=0.03252324635513784, linear_terms=array([-4.14065539e-04, -9.94093190e-05, -9.38607562e-05, 5.40904684e-03]), square_terms=array([[ 1.20853884e-04, 2.50219420e-05, 2.98370200e-05, - -1.40554138e-03], - [ 2.50219420e-05, 5.65327048e-06, 6.58419488e-06, - -3.17355619e-04], - [ 2.98370200e-05, 6.58419488e-06, 8.42877891e-06, - -3.71203286e-04], - [-1.40554138e-03, -3.17355619e-04, -3.71203286e-04, - 1.78905730e-02]]), scale=0.006081173388618138, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 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169, 170, 171, 172, 173, - 174, 175]), model=ScalarModel(intercept=0.03190626622045728, linear_terms=array([-5.00916898e-05, -1.60685558e-06, -3.41876508e-05, 8.86221374e-04]), square_terms=array([[ 2.04825629e-05, 2.61345798e-07, 7.17448985e-06, - -2.80536546e-04], - [ 2.61345798e-07, 2.02583758e-08, 6.64912984e-08, - -1.61121192e-06], - [ 7.17448985e-06, 6.64912984e-08, 3.00963453e-06, - -1.09754930e-04], - [-2.80536546e-04, -1.61121192e-06, -1.09754930e-04, - 4.34084080e-03]]), scale=0.003040586694309069, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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rho=0.7825513778471532, accepted=True, new_indices=array([164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), old_indices_used=array([144, 162, 163]), old_indices_discarded=array([], dtype=int32), step_length=0.00327686813422291, relative_step_length=1.077709160655106, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, - 175, 176]), model=ScalarModel(intercept=0.031782193359979234, linear_terms=array([ 1.65934087e-05, -1.10658052e-05, -5.86797451e-05, 1.70650043e-05]), square_terms=array([[ 8.16385660e-05, 4.98651617e-07, 2.63205082e-05, - -1.11853097e-03], - [ 4.98651617e-07, 7.66151913e-08, 9.65330348e-08, - 7.67793318e-07], - [ 2.63205082e-05, 9.65330348e-08, 1.07174130e-05, - -4.09379667e-04], - [-1.11853097e-03, 7.67793318e-07, -4.09379667e-04, - 1.73744472e-02]]), scale=0.006081173388618138, shift=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], 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State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, - 176, 177]), model=ScalarModel(intercept=0.03179776772837414, linear_terms=array([ 4.32340121e-06, -2.77572135e-06, -1.55094590e-05, 6.03814167e-06]), square_terms=array([[ 2.04941628e-05, 1.12208524e-07, 6.82404742e-06, - -2.79715394e-04], - [ 1.12208524e-07, 1.42062247e-08, 1.38323893e-08, - 1.90493930e-07], - [ 6.82404742e-06, 1.38323893e-08, 2.73772264e-06, - -1.05054792e-04], - [-2.79715394e-04, 1.90493930e-07, -1.05054792e-04, - 4.34256904e-03]]), scale=0.003040586694309069, shift=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 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shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=178, candidate_x=array([4.12767420e+00, 3.98565133e+03, 1.55853065e+00, 9.77392943e-01]), index=176, x=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), fval=0.03177037559889115, rho=-0.606887945704526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, - 176, 177]), old_indices_discarded=array([162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.0015202933471545345, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, - 177, 178]), model=ScalarModel(intercept=0.03179737436356608, 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164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, - 177, 178]), old_indices_discarded=array([167]), step_length=0.001533153137050571, relative_step_length=1.0084587556211475, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12778716e+00, 3.98565116e+03, 1.55693254e+00, 9.77361589e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 170, 171, 172, 174, 175, 176, 177, - 178, 179]), model=ScalarModel(intercept=0.031784323141796134, linear_terms=array([ 1.04203216e-05, -5.02575266e-06, -1.81791012e-05, 4.40512121e-07]), square_terms=array([[ 2.04146038e-05, 1.44018756e-07, 6.73868302e-06, - -2.78928952e-04], - [ 1.44018756e-07, 1.91750967e-08, 1.69350210e-08, - -1.86249381e-07], - [ 6.73868302e-06, 1.69350210e-08, 2.75520070e-06, - -1.04927558e-04], - [-2.78928952e-04, -1.86249381e-07, -1.04927558e-04, - 4.34219268e-03]]), scale=0.003040586694309069, shift=array([4.12778716e+00, 3.98565116e+03, 1.55693254e+00, 9.77361589e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 171, 172, 174, 175, 176, 177, 178, - 179, 180]), model=ScalarModel(intercept=0.03178670805953798, linear_terms=array([ 1.69560990e-06, -7.06567307e-06, -8.06971789e-06, -4.21305136e-06]), square_terms=array([[ 4.99101596e-06, -4.64918409e-08, 1.68067477e-06, - -6.90327526e-05], - [-4.64918409e-08, 9.78184881e-09, -2.52477058e-08, - 1.23452714e-06], - [ 1.68067477e-06, -2.52477058e-08, 6.96833255e-07, - -2.64272819e-05], - [-6.90327526e-05, 1.23452714e-06, -2.64272819e-05, - 1.08782041e-03]]), scale=0.0015202933471545345, shift=array([4.12778716e+00, 3.98565116e+03, 1.55693254e+00, 9.77361589e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, - -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, - 0.00122987, -0.01762768]), 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9.77381516e-01]), index=179, x=array([4.12778716e+00, 3.98565116e+03, 1.55693254e+00, 9.77361589e-01]), fval=0.03176726953457438, rho=-0.9198084241549247, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([144, 164, 165, 166, 168, 169, 171, 172, 174, 175, 176, 177, 178, - 179, 180]), old_indices_discarded=array([170, 173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12778716e+00, 3.98565116e+03, 1.55693254e+00, 9.77361589e-01]), radius=0.0007601466735772673, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([165, 166, 176, 178, 179, 180, 181]), model=ScalarModel(intercept=0.031770693465571515, linear_terms=array([ 8.84453359e-06, -2.90874581e-06, 6.04323903e-06, 1.82129910e-05]), square_terms=array([[ 1.19101573e-06, -1.54274890e-08, 3.92376854e-07, - -1.75097943e-05], - [-1.54274890e-08, 5.01297807e-09, 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State(trustregion=Region(center=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), radius=0.0015202933471545345, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 172, 175, 176, 177, 178, 179, 180, - 181, 182]), model=ScalarModel(intercept=0.03178608463955804, linear_terms=array([ 3.42972101e-07, -9.23719283e-06, -9.47276440e-06, -3.66984129e-05]), square_terms=array([[ 4.88007131e-06, 6.05773731e-08, 1.64321365e-06, - -6.82359635e-05], - [ 6.05773731e-08, 1.03064274e-08, 3.96209788e-08, - -1.24824750e-06], - [ 1.64321365e-06, 3.96209788e-08, 6.89993730e-07, - -2.63109295e-05], - [-6.82359635e-05, -1.24824750e-06, -2.63109295e-05, - 1.09136604e-03]]), scale=0.0015202933471545345, shift=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , - 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shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=183, candidate_x=array([4.12737266e+00, 3.98565235e+03, 1.55764976e+00, 9.77359212e-01]), index=182, x=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), fval=0.031736549972374545, rho=-2.2831242091794652, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([144, 164, 165, 166, 168, 169, 172, 175, 176, 177, 178, 179, 180, - 181, 182]), old_indices_discarded=array([170, 171, 173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12717358e+00, 3.98565134e+03, 1.55653153e+00, 9.77268694e-01]), radius=0.0007601466735772673, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([165, 166, 176, 178, 179, 180, 181, 182, 183]), model=ScalarModel(intercept=0.031743022378361296, linear_terms=array([ 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1.00113435e+00])), candidate_index=16, candidate_x=array([5.23938509e+00, 3.90385568e+03, 5.86832727e+01, 1.10000000e+00]), index=0, x=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), fval=1.043279826158364, rho=-3.9804649365152422, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 7, 11, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=24.173910437546425, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=70.61343619628207, linear_terms=array([-149.60881684, -15.20520525, -38.22698962, 69.94687726]), square_terms=array([[161.20374292, 16.2688619 , 40.62015243, 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State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=12.086955218773213, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=140.77239003523985, linear_terms=array([-172.90891621, -7.83111137, -2.15766299, 116.60174267]), square_terms=array([[ 1.07105586e+02, 4.80058830e+00, 1.23891759e+00, - -7.05566845e+01], - [ 4.80058830e+00, 2.17979398e-01, 6.07723262e-02, - -3.25273056e+00], - [ 1.23891759e+00, 6.07723262e-02, 2.72332254e-02, - -1.03206277e+00], - [-7.05566845e+01, -3.25273056e+00, -1.03206277e+00, - 5.01595469e+01]]), scale=array([7.03241927, 9.00750174, 9.00750174, 0.3 ]), shift=array([8.13241927e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=30, candidate_x=array([1.51648385e+01, 3.87683317e+03, 1.36457640e+01, 5.78808741e-01]), index=0, x=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), fval=1.043279826158364, rho=-0.0038980287602399517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=6.043477609386606, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=187.5605981153043, linear_terms=array([-130.64991462, -7.14678784, 1.4954385 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28, 29, 30]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=3.021738804693303, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.31987187423087415, linear_terms=array([ 0.08669126, -0.1414405 , -0.04511992, -0.93701812]), square_terms=array([[ 9.04354688e-02, -3.61449104e-02, -5.43056331e-04, - -3.12175071e-01], - [-3.61449104e-02, 1.45220794e-01, 6.28594817e-02, - 9.92180671e-01], - [-5.43056331e-04, 6.28594817e-02, 3.01737068e-02, - 4.17797683e-01], - [-3.12175071e-01, 9.92180671e-01, 4.17797683e-01, - 6.88478390e+00]]), scale=array([2.25187544, 2.25187544, 2.25187544, 0.3 ]), shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - 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9.21862396e-01]), index=46, x=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), fval=0.610030672784944, rho=-4.754375337092057, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46]), old_indices_discarded=array([31, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), radius=0.7554347011733258, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 32, 33, 37, 40, 41, 42, 45, 46, 47]), model=ScalarModel(intercept=1.5788163465465184, linear_terms=array([-0.49971524, 0.17338097, 0.11244705, -2.68460178]), square_terms=array([[ 0.11824485, -0.03484353, -0.0214855 , 0.52809251], - [-0.03484353, 0.01107004, 0.0068334 , -0.17494621], - [-0.0214855 , 0.0068334 , 0.00446103, 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), radius=0.09442933764666572, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 49, 51, 55, 57, 59, 60, 62]), model=ScalarModel(intercept=0.5342017436395708, linear_terms=array([-0.03567872, 0.03507135, -0.07223938, 0.60615755]), square_terms=array([[ 0.00489242, -0.0071859 , 0.01235855, -0.07573115], - [-0.0071859 , 0.01116723, -0.01887802, 0.11069203], - [ 0.01235855, -0.01887802, 0.03215213, -0.19085058], - [-0.07573115, 0.11069203, -0.19085058, 1.18779916]]), scale=array([0.07037111, 0.07037111, 0.07037111, 0.07037111]), shift=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, 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candidate_index=63, candidate_x=array([6.64993454e+00, 3.86733307e+03, 2.20199258e+01, 9.63254363e-01]), index=46, x=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), fval=0.610030672784944, rho=-5.047496342917129, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 51, 55, 57, 59, 60, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), radius=0.04721466882333286, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.5645793736320908, linear_terms=array([ 0.02415958, -0.02485336, 0.02479561, -0.34196441]), square_terms=array([[ 1.39237450e-03, -1.62359879e-03, 1.62536290e-03, - 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00]), radius=0.09442933764666572, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.51062010022679, linear_terms=array([ 0.01710986, -0.01628909, 0.0164215 , -0.04435784]), square_terms=array([[ 1.44592132e-03, -1.90647266e-03, 1.93858106e-03, - -5.80270672e-02], - [-1.90647266e-03, 2.93939927e-03, -3.00816915e-03, - 9.37944524e-02], - [ 1.93858106e-03, -3.00816915e-03, 3.08082456e-03, - -9.62367159e-02], - [-5.80270672e-02, 9.37944524e-02, -9.62367159e-02, - 3.30228437e+00]]), scale=array([0.07037111, 0.07037111, 0.07037111, 0.07037111]), shift=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 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model=ScalarModel(intercept=0.5106201002267896, linear_terms=array([ 0.01147966, -0.01092897, 0.01101781, -0.02976137]), square_terms=array([[ 6.50893276e-04, -8.58214219e-04, 8.72668080e-04, - -2.61213577e-02], - [-8.58214219e-04, 1.32319456e-03, -1.35415188e-03, - 4.22223379e-02], - [ 8.72668080e-04, -1.35415188e-03, 1.38685831e-03, - -4.33217428e-02], - [-2.61213577e-02, 4.22223379e-02, -4.33217428e-02, - 1.48655025e+00]]), scale=0.04721466882333286, shift=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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dtype=int32), old_indices_used=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), old_indices_discarded=array([77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00]), radius=0.02360733441166643, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78]), model=ScalarModel(intercept=0.5418281054144427, linear_terms=array([-0.00296132, 0.00311448, -0.00293785, -0.07389012]), square_terms=array([[ 1.47172302e-04, -6.61683841e-05, 5.73019881e-05, - 7.17228987e-03], - [-6.61683841e-05, 3.89561988e-05, -3.50365443e-05, - -3.54762758e-03], - [ 5.73019881e-05, -3.50365443e-05, 3.18052019e-05, - 3.12121469e-03], - [ 7.17228987e-03, -3.54762758e-03, 3.12121469e-03, - 4.03006632e-01]]), scale=0.02360733441166643, 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2.20813063e+01, 1.03510227e+00]), index=116, x=array([6.69804980e+00, 3.86727830e+03, 2.20808774e+01, 1.03496159e+00]), fval=0.5418064990534898, rho=-0.5974244675011045, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 83, 87, 94, 99, 101, 104, 106, 107, 108, 110, 111, 113, 114, - 115, 116]), old_indices_discarded=array([ 86, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 102, - 103, 105, 109, 112, 117]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69804980e+00, 3.86727830e+03, 2.20808774e+01, 1.03496159e+00]), radius=0.000368864600182288, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 83, 87, 94, 99, 104, 106, 107, 108, 110, 111, 113, 114, 115, - 116, 118]), model=ScalarModel(intercept=0.5418256537643822, linear_terms=array([ 2.30452043e-06, 8.47089151e-06, -1.51944076e-05, -5.28816905e-05]), 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old_indices_discarded=array([ 91, 93, 97, 98, 101, 102, 103, 105, 109, 112, 117]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69804980e+00, 3.86727830e+03, 2.20808774e+01, 1.03496159e+00]), radius=0.000184432300091144, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([115, 116, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, - 129, 130, 131]), model=ScalarModel(intercept=0.5418152455547458, linear_terms=array([ 5.71321381e-06, 1.51273896e-06, -8.08847284e-07, 2.26853950e-05]), square_terms=array([[ 4.01050671e-09, 1.02708114e-09, 1.01513934e-10, - 2.83508346e-07], - [ 1.02708114e-09, 8.34708031e-10, -1.13246263e-10, - 6.87407880e-08], - [ 1.01513934e-10, -1.13246263e-10, 1.34647438e-10, - 9.46090528e-09], - [ 2.83508346e-07, 6.87407880e-08, 9.46090528e-09, - 2.99118597e-05]]), scale=0.000184432300091144, 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upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, - 131, 132]), model=ScalarModel(intercept=0.5418008791380492, linear_terms=array([1.16956016e-05, 8.54910723e-06, 8.82112496e-07, 1.06001388e-05]), square_terms=array([[ 1.91075982e-08, 1.31161840e-09, -2.17307972e-09, - 1.19621246e-06], - [ 1.31161840e-09, 1.11782917e-09, 7.74250825e-11, - 9.76156809e-08], - [-2.17307972e-09, 7.74250825e-11, 2.23004529e-09, - -7.10092216e-08], - [ 1.19621246e-06, 9.76156809e-08, -7.10092216e-08, - 1.19654732e-04]]), scale=0.000368864600182288, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], 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x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-0.7561149550917772, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, - 131, 132]), old_indices_discarded=array([ 83, 87, 91, 93, 94, 97, 98, 99, 101, 102, 103, 104, 105, - 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=0.000184432300091144, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, - 132, 133]), model=ScalarModel(intercept=0.5418053134335727, linear_terms=array([ 1.59107911e-06, 1.91604113e-06, -8.39570529e-07, 4.03636914e-06]), 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old_indices_discarded=array([115, 118, 119]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=9.2216150045572e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, - 132, 134]), model=ScalarModel(intercept=0.5418036600266991, linear_terms=array([1.90610897e-06, 1.50974387e-06, 9.76797073e-08, 1.95253835e-06]), square_terms=array([[1.20515783e-09, 3.37366272e-11, 4.94787699e-11, 7.52437640e-08], - [3.37366272e-11, 1.02605967e-10, 3.57534723e-11, 1.62531668e-09], - [4.94787699e-11, 3.57534723e-11, 5.57431091e-11, 5.98222773e-09], - [7.52437640e-08, 1.62531668e-09, 5.98222773e-09, 7.49043871e-06]]), scale=9.2216150045572e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - 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0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=386.7825670007428, shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=135, candidate_x=array([6.69783593e+00, 3.86727820e+03, 2.20808947e+01, 1.03483235e+00]), index=132, x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-2.447529842628543, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, - 132, 134]), old_indices_discarded=array([119, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=4.6108075022786e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 125, 129, 130, 131, 132, 134, 135]), model=ScalarModel(intercept=0.541801225592115, linear_terms=array([-3.85833151e-06, 1.66200121e-06, -7.39584646e-07, 5.43636323e-06]), square_terms=array([[ 5.05560336e-10, 1.28395863e-10, 5.46841377e-11, - 2.23605728e-08], - [ 1.28395863e-10, 9.20178070e-11, -7.88195222e-13, - 2.29617380e-09], - [ 5.46841377e-11, -7.88195222e-13, 3.43892202e-11, - 5.45528958e-09], - [ 2.23605728e-08, 2.29617380e-09, 5.45528958e-09, - 1.89032774e-06]]), scale=4.6108075022786e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=386.7825670007428, shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=136, candidate_x=array([6.69793775e+00, 3.86727825e+03, 2.20809041e+01, 1.03481767e+00]), index=132, x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-2.670472262368463, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 120, 125, 129, 130, 131, 132, 134, 135]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=2.3054037511393e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 132, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, - 146, 147, 148]), model=ScalarModel(intercept=0.5417952329144413, linear_terms=array([ 9.66120026e-07, -1.00698628e-06, -2.74476935e-07, -9.79648793e-06]), square_terms=array([[3.32456620e-11, 1.25910740e-12, 3.31229126e-13, 2.67230800e-09], - [1.25910740e-12, 1.10109004e-11, 5.04392935e-12, 1.07277597e-09], - [3.31229126e-13, 5.04392935e-12, 2.52144478e-12, 3.73671865e-10], - [2.67230800e-09, 1.07277597e-09, 3.73671865e-10, 4.79025310e-07]]), scale=2.3054037511393e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - 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0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=386.7825670007428, shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=149, candidate_x=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), index=149, x=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), fval=0.541790689077309, rho=0.5646157203342447, accepted=True, new_indices=array([137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148]), old_indices_used=array([125, 132, 135, 136]), old_indices_discarded=array([], dtype=int32), step_length=2.3054037496676635e-05, relative_step_length=0.9999999993616578, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), radius=4.6108075022786e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 132, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, - 148, 149]), model=ScalarModel(intercept=0.5417877838400293, linear_terms=array([ 3.32286169e-07, -1.12576228e-06, -2.66782175e-07, -1.45832432e-05]), square_terms=array([[1.73587440e-10, 4.18888788e-12, 6.01676781e-12, 1.57992979e-08], - [4.18888788e-12, 6.23925265e-12, 3.51151103e-12, 3.47636777e-10], - [6.01676781e-12, 3.51151103e-12, 4.91855682e-12, 4.69589490e-10], - [1.57992979e-08, 3.47636777e-10, 4.69589490e-10, 1.89335649e-06]]), scale=4.6108075022786e-05, shift=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], 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x=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), fval=0.541790689077309, rho=-1.3326690558921785, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([125, 132, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, - 148, 149]), old_indices_discarded=array([116, 120, 128, 129, 130, 131, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), radius=2.3054037511393e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([132, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, - 149, 150]), model=ScalarModel(intercept=0.5417958106954883, linear_terms=array([-1.39628013e-06, 8.43762776e-07, -1.23740385e-07, 5.37052686e-07]), square_terms=array([[ 5.46236795e-11, -9.27306793e-12, -1.78917296e-12, - 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145, 146, 147, 148, - 149, 151]), model=ScalarModel(intercept=0.5417894820702527, linear_terms=array([-1.39931698e-07, -3.88749878e-07, -7.18191431e-08, -7.69588434e-06]), square_terms=array([[4.44331104e-11, 1.16552764e-12, 1.68416837e-12, 3.95325562e-09], - [1.16552764e-12, 1.02151397e-12, 5.46017193e-13, 7.52291998e-11], - [1.68416837e-12, 5.46017193e-13, 1.11171406e-12, 1.16911161e-10], - [3.95325562e-09, 7.52291998e-11, 1.16911161e-10, 4.73349915e-07]]), scale=2.3054037511393e-05, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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2.20809063e+01, 1.03486530e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.22806525934078076, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([132, 137, 143, 146, 149, 151, 153, 154]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=2.881754688924125e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([146, 151, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, - 165, 166, 167]), model=ScalarModel(intercept=0.5417883802201522, linear_terms=array([-1.57307408e-07, 3.76367420e-07, -8.62401148e-08, 2.16807204e-07]), square_terms=array([[ 7.45581521e-13, -3.85892702e-13, 6.62025307e-14, - 4.90598744e-11], - 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1.4408773444620624e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, - 167, 168]), model=ScalarModel(intercept=0.5417884361277132, linear_terms=array([ 6.07312569e-08, -6.26140517e-08, -2.39455329e-08, -7.21746347e-08]), square_terms=array([[1.43221103e-13, 6.68409194e-15, 5.30999617e-15, 1.31473717e-11], - [6.68409194e-15, 1.21687971e-14, 5.19067626e-15, 1.39183805e-12], - [5.30999617e-15, 5.19067626e-15, 2.72373187e-15, 5.63318896e-13], - [1.31473717e-11, 1.39183805e-12, 5.63318896e-13, 1.81511860e-09]]), scale=1.4408773444620624e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=169, candidate_x=array([6.69792294e+00, 3.86727825e+03, 2.20809015e+01, 1.03486787e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-2.724407214736079, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, - 167, 168]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, - 168, 169]), model=ScalarModel(intercept=0.5417883515717175, linear_terms=array([ 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163, 164, 165, 166, 167, - 168, 169]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, - 169, 170]), model=ScalarModel(intercept=0.5417883477472589, linear_terms=array([ 8.16206313e-08, -8.16738289e-08, -1.06357809e-07, -2.84847880e-08]), square_terms=array([[ 6.55495523e-14, -1.15249903e-15, -5.65020948e-15, - 5.70865470e-12], - [-1.15249903e-15, 3.76514587e-14, 3.27318641e-14, - 1.33386404e-12], - [-5.65020948e-15, 3.27318641e-14, 3.63103453e-14, - 1.64767596e-12], - [ 5.70865470e-12, 1.33386404e-12, 1.64767596e-12, - 8.73687479e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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rho=-0.7375647236811499, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, - 170, 171]), old_indices_discarded=array([167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 169, 170, - 171, 172]), model=ScalarModel(intercept=0.5417883710603923, linear_terms=array([ 1.43992102e-07, -3.92495599e-08, -9.39836000e-08, -9.01031376e-08]), square_terms=array([[ 8.53994530e-14, -1.24453204e-14, -2.50307988e-14, - 4.70275038e-12], - [-1.24453204e-14, 1.24566358e-14, 1.43974985e-14, - 6.71297877e-13], - [-2.50307988e-14, 1.43974985e-14, 2.77619876e-14, - 1.47049227e-12], 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, - 172, 173]), model=ScalarModel(intercept=0.5417883675324741, linear_terms=array([ 1.43818395e-07, -3.70058572e-08, -9.73353633e-08, -8.86837355e-08]), square_terms=array([[ 8.52602282e-14, -1.20995611e-14, -2.52176727e-14, - 4.70638556e-12], - [-1.20995611e-14, 1.43089742e-14, 1.24025960e-14, - 6.47585103e-13], - [-2.52176727e-14, 1.24025960e-14, 2.84449624e-14, - 1.50365410e-12], - [ 4.70638556e-12, 6.47585103e-13, 1.50365410e-12, - 8.75615148e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), 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1.03486742e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.7821082957639802, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, - 172, 173]), old_indices_discarded=array([158, 161, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 169, 170, 171, 172, - 173, 174]), model=ScalarModel(intercept=0.5417883646362189, linear_terms=array([ 1.39054002e-07, -3.67486274e-08, -9.76276358e-08, -9.03367442e-08]), square_terms=array([[ 8.31510649e-14, -1.19535743e-14, -2.39921551e-14, - 4.77544224e-12], - 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, - 174, 175]), model=ScalarModel(intercept=0.5417883618029782, linear_terms=array([ 1.06044142e-07, -4.72439863e-08, -1.20660778e-07, -9.21589515e-08]), square_terms=array([[ 6.99537303e-14, -8.73865731e-15, -1.76658488e-14, - 5.29092065e-12], - [-8.73865731e-15, 1.75516544e-14, 1.97422769e-14, - 8.07740656e-13], - [-1.76658488e-14, 1.97422769e-14, 4.40075585e-14, - 1.87057890e-12], - [ 5.29092065e-12, 8.07740656e-13, 1.87057890e-12, - 8.75720374e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=176, candidate_x=array([6.69792315e+00, 3.86727825e+03, 2.20809018e+01, 1.03486746e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.8834804979032714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, - 174, 175]), old_indices_discarded=array([158, 160, 161, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, 174, - 175, 176]), model=ScalarModel(intercept=0.5417882175351199, linear_terms=array([ 5.05553133e-08, 5.12703679e-09, -1.05733343e-07, 8.57493416e-08]), square_terms=array([[ 7.12419101e-14, -1.32051771e-16, 2.55087783e-15, - 6.19448121e-12], - [-1.32051771e-16, 7.92900914e-17, -1.62080729e-15, - -7.67851095e-14], - [ 2.55087783e-15, -1.62080729e-15, 3.32401340e-14, - 1.56473995e-12], - [ 6.19448121e-12, -7.67851095e-14, 1.56473995e-12, - 8.69914754e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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163, 164, 165, 169, 170, 171, 172, 173, 174, - 175, 176]), old_indices_discarded=array([156, 158, 160, 161, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 157, 162, 163, 164, 165, 169, 170, 171, 172, 173, 174, 175, - 176, 177]), model=ScalarModel(intercept=0.5417882385796988, linear_terms=array([ 5.40238124e-08, 2.06579943e-09, -9.65419281e-08, 5.84750328e-08]), square_terms=array([[ 7.10974253e-14, -2.37666218e-17, 1.33331214e-15, - 6.14278820e-12], - [-2.37666218e-17, 1.25861682e-17, -5.86064280e-16, - -3.02225379e-14], - [ 1.33331214e-15, -5.86064280e-16, 2.77313078e-14, - 1.43737778e-12], - [ 6.14278820e-12, -3.02225379e-14, 1.43737778e-12, - 8.70722081e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 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1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 157, 163, 164, 165, 169, 170, 171, 172, 173, 174, 175, 176, - 177, 178]), model=ScalarModel(intercept=0.5417882076829452, linear_terms=array([-5.63158410e-09, -1.00512338e-08, -8.87137958e-08, 1.24139553e-08]), square_terms=array([[8.32424888e-14, 1.91902710e-15, 1.69446003e-14, 7.03679290e-12], - [1.91902710e-15, 3.00682277e-16, 2.65393612e-15, 1.50980635e-13], - [1.69446003e-14, 2.65393612e-15, 2.34246502e-14, 1.33333428e-12], - [7.03679290e-12, 1.50980635e-13, 1.33333428e-12, 8.72095518e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , - 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, - -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, - 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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1.03486684e+00]), fval=0.5417879962626828, rho=0.38593680496603683, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([151, 157, 163, 164, 165, 169, 170, 171, 172, 173, 174, 175, 176, - 177, 178]), old_indices_discarded=array([156, 158, 159, 160, 161, 162, 166, 167, 168]), step_length=1.0046483357496498e-06, relative_step_length=1.0046483357496498, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 180 entries., 'history': {'params': [{'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 1.9420465373776397, 'BeqFac': 3579.5856142194925, 'BeqShift': 63.884357717706486, 'DiscFac': 0.6683149691599166}, {'CRRA': 1.1, 'BeqFac': 4156.0657257953635, 'BeqShift': 70.0, 'DiscFac': 0.9091286558648171}, {'CRRA': 19.746753699543984, 'BeqFac': 3660.8959750636873, 'BeqShift': 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rho=-0.01107643105638871, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=6.405763941060326, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30]), model=ScalarModel(intercept=41.718081219167644, linear_terms=array([-33.92226807, -4.49642876, 13.65470054, 92.4099355 ]), square_terms=array([[ 14.11170337, 1.8117299 , -5.57976643, -37.0017643 ], - [ 1.8117299 , 0.24365654, -0.73486485, -5.01860311], - [ -5.57976643, -0.73486485, 2.23829073, 15.09309944], - [-37.0017643 , -5.01860311, 15.09309944, 104.10895944]]), scale=array([4.77373572, 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candidate_x=array([5.75763903e+00, 4.10028564e+03, 2.13437731e+00, 7.73678270e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-1.1311868352707046, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.27541175174579846, linear_terms=array([-0.06817834, -0.05276822, 0.126152 , -0.82843349]), square_terms=array([[ 0.05944259, 0.02280401, -0.0560511 , 0.53875948], - [ 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State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.2988708045687646, linear_terms=array([ 0.05873433, -0.00758318, -0.003689 , 0.60294498]), square_terms=array([[ 2.09679873e-02, 1.70244323e-04, -6.43571996e-03, - 2.00584793e-01], - [ 1.70244323e-04, 5.37915084e-04, -1.12211972e-03, - 1.14935938e-02], - [-6.43571996e-03, -1.12211972e-03, 4.22275088e-03, - -8.44849663e-02], - [ 2.00584793e-01, 1.14935938e-02, -8.44849663e-02, - 2.28513600e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.13445693]), shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.65543066e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=60, candidate_x=array([6.20517676e+00, 4.09983810e+03, 2.23286259e+00, 9.36220917e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-4.1944986936569, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 47, 49, 51, 53, 55, 58, 60]), model=ScalarModel(intercept=0.2725232800460816, linear_terms=array([0.02014066, 0.01692965, 0.00516888, 0.19458607]), 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State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.9761443032698459, linear_terms=array([ 0.0094557 , -0.02923194, 0.02583164, -0.9717343 ]), square_terms=array([[ 1.51163653e-04, -4.84525090e-05, 2.33633959e-06, - -2.54393388e-04], - [-4.84525090e-05, 6.22775993e-04, -5.30527383e-04, - 2.31310434e-02], - [ 2.33633959e-06, -5.30527383e-04, 5.40224484e-04, - -1.94176818e-02], - [-2.54393388e-04, 2.31310434e-02, -1.94176818e-02, - 8.94373433e-01]]), scale=0.0500450307895338, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, 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4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), fval=0.3874021895959502, rho=-4.0555723632570135, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 55, 59, 60, 61, 62, 64, 65, 67, 68, 70, 71, 73, 75, 76]), old_indices_discarded=array([49, 51, 63, 66, 69, 72, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([55, 59, 60, 76, 77]), model=ScalarModel(intercept=0.3874021895959514, linear_terms=array([-1.11289514, -1.07248142, -0.06157134, -0.30876554]), square_terms=array([[6.85846414, 6.56737707, 0.304784 , 3.06229562], - [6.56737707, 6.28875786, 0.29204616, 2.9296343 ], - [0.304784 , 0.29204616, 0.01447866, 0.12773002], - [3.06229562, 2.9296343 , 0.12773002, 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candidate_x=array([6.05445907e+00, 4.10004129e+03, 2.08608577e+00, 1.02650908e+00]), index=93, x=array([6.05445907e+00, 4.10004129e+03, 2.08608577e+00, 1.02650908e+00]), fval=0.32822464823857844, rho=2.3466388930753928, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92]), old_indices_discarded=array([ 0, 49, 55, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, - 73, 74, 75, 77, 91]), step_length=0.12921041782764373, relative_step_length=1.2909415359443264, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.05445907e+00, 4.10004129e+03, 2.08608577e+00, 1.02650908e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([59, 76, 77, 78, 80, 81, 82, 84, 85, 87, 89, 90, 91, 92, 93]), model=ScalarModel(intercept=0.45930494894166957, linear_terms=array([ 0.16087669, 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old_indices_discarded=array([ 0, 46, 47, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, - 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 79, 83, 86, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.05445907e+00, 4.10004129e+03, 2.08608577e+00, 1.02650908e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([59, 76, 77, 78, 80, 81, 82, 84, 85, 87, 89, 90, 91, 92, 93]), model=ScalarModel(intercept=0.3284990171799196, linear_terms=array([ 0.00150753, -0.0103621 , -0.01222962, 0.27636184]), square_terms=array([[ 0.01256509, 0.01216675, 0.02455752, -0.15669898], - [ 0.01216675, 0.01209161, 0.02422519, -0.15867808], - [ 0.02455752, 0.02422519, 0.04869629, -0.31627154], - [-0.15669898, -0.15867808, -0.31627154, 2.12839583]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07404027]), 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, - 108, 109]), model=ScalarModel(intercept=0.32056677142311285, linear_terms=array([ 0.00446261, -0.00014495, 0.00423272, -0.00809436]), square_terms=array([[ 9.12861112e-05, 1.40030653e-05, -4.69278487e-05, - 1.10061593e-02], - [ 1.40030653e-05, 5.90060514e-06, -2.58411283e-05, - 3.19050202e-03], - [-4.69278487e-05, -2.58411283e-05, 1.82744813e-04, - -1.75467315e-02], - [ 1.10061593e-02, 3.19050202e-03, -1.75467315e-02, - 2.41272819e+00]]), scale=0.0500450307895338, shift=array([6.03544529e+00, 4.10004123e+03, 2.06983129e+00, 1.02525473e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), 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2.03586019e+00, 1.02534149e+00]), index=110, x=array([5.99870914e+00, 4.10004232e+03, 2.03586019e+00, 1.02534149e+00]), fval=0.31506887393988287, rho=1.4531169230617522, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, - 108, 109]), old_indices_discarded=array([59, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, - 92, 94, 95]), step_length=0.05004774724453496, relative_step_length=1.000054280214405, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.99870914e+00, 4.10004232e+03, 2.03586019e+00, 1.02534149e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, - 108, 110]), model=ScalarModel(intercept=0.3091099422488851, linear_terms=array([0.00887823, 0.01155048, 0.01058815, 0.06346417]), 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 94, 95, 96, 100, 101, 102, 103, 105, 106, 107, 108, 109, - 110, 111]), model=ScalarModel(intercept=0.2829838581648968, linear_terms=array([-0.00276684, 0.03094096, 0.0087251 , -0.03929999]), square_terms=array([[ 3.07948101e-03, -8.53430126e-03, -1.46741811e-03, - 1.20443717e-01], - [-8.53430126e-03, 2.48589549e-02, 4.41427265e-03, - -3.30744391e-01], - [-1.46741811e-03, 4.41427265e-03, 8.88370182e-04, - -5.83631468e-02], - [ 1.20443717e-01, -3.30744391e-01, -5.83631468e-02, - 4.81457600e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00])), 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rho=-0.47013410940622297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, - 111, 113]), old_indices_discarded=array([ 59, 96, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 95, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 125, 126]), model=ScalarModel(intercept=0.2942454780500071, linear_terms=array([-0.00053836, 0.01012758, 0.00658806, -0.04235502]), square_terms=array([[ 2.85341780e-04, -7.78431906e-04, -4.05296375e-04, - 1.17160568e-02], - [-7.78431906e-04, 2.28701486e-03, 1.21244865e-03, - -3.24691514e-02], - [-4.05296375e-04, 1.21244865e-03, 6.63688875e-04, - 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1.94692224e+00, 1.02144230e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, - 126, 127]), model=ScalarModel(intercept=0.2943051049928589, linear_terms=array([ 0.00564431, -0.00188183, 0.00320629, -0.09902661]), square_terms=array([[ 9.27890201e-05, 7.56993238e-06, 6.61699037e-06, - 5.86812035e-03], - [ 7.56993238e-06, 3.99076703e-05, -3.70323399e-05, - 8.89741428e-03], - [ 6.61699037e-06, -3.70323399e-05, 8.59295855e-05, - -8.43245723e-03], - [ 5.86812035e-03, 8.89741428e-03, -8.43245723e-03, - 2.25161796e+00]]), scale=0.0500450307895338, shift=array([5.92278744e+00, 4.09994717e+03, 1.94692224e+00, 1.02144230e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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candidate_x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), index=128, x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), fval=0.2860214168257209, rho=1.395194798708464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, - 126, 127]), old_indices_discarded=array([ 59, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, - 105, 106, 107, 108, 109, 110, 112, 113]), step_length=0.05013889760653118, relative_step_length=1.0018756471025494, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, - 126, 128]), model=ScalarModel(intercept=0.2809513915508127, linear_terms=array([0.00283903, 0.01786394, 0.01405315, 0.08551073]), square_terms=array([[ 2.87201002e-03, -8.38100805e-03, -4.93196038e-03, - 1.13296034e-01], - [-8.38100805e-03, 2.61008372e-02, 1.54731491e-02, - -3.32316666e-01], - [-4.93196038e-03, 1.54731491e-02, 9.29788666e-03, - -1.97733985e-01], - [ 1.13296034e-01, -3.32316666e-01, -1.97733985e-01, - 4.54041405e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, - 126, 128]), old_indices_discarded=array([ 46, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, - 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, - 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, - 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, - 127, 128]), model=ScalarModel(intercept=0.28601605918758766, linear_terms=array([ 0.00549346, -0.00187638, 0.00228974, -0.00413326]), square_terms=array([[ 9.45819260e-05, 2.05993647e-05, 2.22032641e-06, - 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118, 129]), step_length=0.0500760576419539, relative_step_length=1.0006199786858077, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 122, 123, 124, 125, 126, 127, 128, - 129, 130]), model=ScalarModel(intercept=0.27698506927654987, linear_terms=array([0.00886586, 0.00136488, 0.00678477, 0.02502439]), square_terms=array([[ 1.93358601e-04, 9.16030067e-06, 2.22153096e-05, - 9.68917980e-03], - [ 9.16030067e-06, 2.87649923e-05, 1.19928618e-04, - -1.05626890e-02], - [ 2.22153096e-05, 1.19928618e-04, 5.97417848e-04, - -4.71540074e-02], - [ 9.68917980e-03, -1.05626890e-02, -4.71540074e-02, - 5.10191499e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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State(trustregion=Region(center=array([5.75997591e+00, 4.09989866e+03, 1.83299422e+00, 1.02268458e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 117, 119, 123, 124, 125, 126, 127, 128, 129, - 130, 131]), model=ScalarModel(intercept=0.6958602300848257, linear_terms=array([ 0.00317513, 0.13661317, 0.00432578, -2.82344851]), square_terms=array([[ 8.92272971e-04, -9.23980154e-04, 1.92374401e-04, - 4.40514039e-02], - [-9.23980154e-04, 1.66500637e-02, -3.49295764e-04, - -3.72598035e-01], - [ 1.92374401e-04, -3.49295764e-04, 5.50846736e-04, - 6.72728739e-03], - [ 4.40514039e-02, -3.72598035e-01, 6.72728739e-03, - 9.12381886e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11324733]), shift=array([5.75997591e+00, 4.09989866e+03, 1.83299422e+00, 9.86752670e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=132, candidate_x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), index=132, x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), fval=0.22297505850584612, rho=0.8392169936307204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 117, 119, 123, 124, 125, 126, 127, 128, 129, - 130, 131]), old_indices_discarded=array([ 0, 46, 47, 49, 51, 53, 54, 55, 59, 60, 61, 62, 63, - 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, - 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, - 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, - 104, 105, 106, 107, 108, 109, 110, 112, 113, 116, 118, 120, 121, - 122]), step_length=0.25843212314368597, relative_step_length=1.2909979226036032, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, - 131, 132]), model=ScalarModel(intercept=4.348347562426054, linear_terms=array([ 0.31927054, 0.46205338, -0.36145321, -14.31327039]), square_terms=array([[ 1.65442176e-02, 1.84070457e-02, -1.41804582e-02, - -4.63767949e-01], - [ 1.84070457e-02, 2.54749193e-02, -2.05872063e-02, - -7.55309262e-01], - [-1.41804582e-02, -2.05872063e-02, 1.89816565e-02, - 6.10967512e-01], - [-4.63767949e-01, -7.55309262e-01, 6.10967512e-01, - 2.48273398e+01]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.19027745]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.09722546e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, 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2.38204183e+00, 9.80265374e-01])), candidate_index=133, candidate_x=array([5.31243818e+00, 4.09945113e+03, 1.98217346e+00, 1.00539435e+00]), index=132, x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), fval=0.22297505850584612, rho=-0.629024754891979, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, - 131, 132]), old_indices_discarded=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, - 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, - 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, - 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, - 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, - 110, 112, 116, 117, 118, 120, 121, 122, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, - 131, 132]), model=ScalarModel(intercept=0.6450590722968451, linear_terms=array([ 0.0687357 , 0.08298437, -0.06097559, -2.78513076]), square_terms=array([[ 4.13605440e-03, 4.60176141e-03, -3.54511454e-03, - -1.40984410e-01], - [ 4.60176141e-03, 6.36872983e-03, -5.14680157e-03, - -2.29612312e-01], - [-3.54511454e-03, -5.14680157e-03, 4.74541412e-03, - 1.85732745e-01], - [-1.40984410e-01, -2.29612312e-01, 1.85732745e-01, - 9.17763751e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11568783]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.84312166e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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candidate_x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), index=134, x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), fval=0.21950785606017936, rho=0.08682007098967585, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, - 131, 132]), old_indices_discarded=array([ 46, 49, 54, 55, 59, 60, 61, 62, 65, 70, 75, 76, 77, - 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, - 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, - 105, 106, 107, 108, 109, 110, 112, 116, 117, 118, 120, 121, 122, - 124, 133]), step_length=0.23555149344412082, relative_step_length=1.176697714677913, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 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1.01421638e+00]), fval=0.19084543631566314, rho=0.5530791700510971, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([129, 131, 132, 133, 134]), old_indices_discarded=array([], dtype=int32), step_length=0.12919783936681034, relative_step_length=1.2908158645176637, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, - 134, 135]), model=ScalarModel(intercept=0.6377265413639779, linear_terms=array([ 0.01280445, 0.03233605, 0.04550891, -2.87385806]), square_terms=array([[ 1.03672207e-03, 1.33278357e-04, 2.91938128e-04, - 1.62342101e-02], - [ 1.33278357e-04, 9.48774396e-04, 1.23085546e-03, - -8.83801170e-02], - [ 2.91938128e-04, 1.23085546e-03, 1.93567123e-03, 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109, 110, - 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 124]), step_length=0.25839641740029984, relative_step_length=1.2908195545277783, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.23784856e+00, 4.09952572e+03, 1.56470502e+00, 1.01653381e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, - 135, 136]), model=ScalarModel(intercept=3.3407898582211835, linear_terms=array([ 1.38155211e-01, -7.92390870e-03, 3.08910897e-01, -1.15990734e+01]), square_terms=array([[ 7.85599256e-03, -7.04702077e-05, 7.79819386e-03, - -1.81297324e-01], - [-7.04702077e-05, 2.17229466e-05, -2.97130879e-04, - 1.48620091e-02], - [ 7.79819386e-03, -2.97130879e-04, 1.53521157e-02, - -5.23356569e-01], - [-1.81297324e-01, 1.48620091e-02, -5.23356569e-01, - 2.11141364e+01]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.19091233]), shift=array([5.23784856e+00, 4.09952572e+03, 1.56470502e+00, 9.09087666e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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110, 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 123, 124]), step_length=0.5168470619375918, relative_step_length=1.29095500038558, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.93949008e+00, 4.09922736e+03, 1.26634654e+00, 1.00772852e+00]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 112, 114, 125, 127, 128, 129, 130, 131, 132, 133, 134, 135, - 136, 137]), model=ScalarModel(intercept=11.614247085917237, linear_terms=array([ 0.81145968, -0.51265125, 1.29401925, -33.62677892]), square_terms=array([[ 5.63481046e-02, -2.12532254e-02, 5.59550944e-02, - -1.09605887e+00], - [-2.12532254e-02, 1.29277848e-02, -2.78216059e-02, - 7.40676688e-01], - [ 5.59550944e-02, -2.78216059e-02, 8.09892795e-02, - -1.83895106e+00], - [-1.09605887e+00, 7.40676688e-01, -1.83895106e+00, - 4.90916110e+01]]), scale=array([0.59671696, 0.59671696, 0.59671696, 0.3 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109, 110, 111, 113, 115, - 116, 117, 118, 119, 120, 121, 122, 123, 124, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.93949008e+00, 4.09922736e+03, 1.26634654e+00, 1.00772852e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 112, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, - 137, 138]), model=ScalarModel(intercept=2.3800270993481427, linear_terms=array([ 0.34086296, -0.36587453, 0.39270343, -9.0556698 ]), square_terms=array([[ 0.02975173, -0.02652481, 0.03002866, -0.61189504], - [-0.02652481, 0.02974837, -0.03082655, 0.71348685], - [ 0.03002866, -0.03082655, 0.03469524, -0.74207167], - [-0.61189504, 0.71348685, -0.74207167, 17.95128393]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.19531498]), shift=array([4.93949008e+00, 4.09922736e+03, 1.26634654e+00, 9.04685018e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - 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relative_step_length=1.2925294748056355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, - 138, 139]), model=ScalarModel(intercept=8.622726430625926, linear_terms=array([ 1.2277478 , -1.26958822, 1.35833354, -27.99550346]), square_terms=array([[ 0.11015107, -0.09196504, 0.11241212, -1.95469033], - [-0.09196504, 0.09842539, -0.10250002, 2.07845956], - [ 0.11241212, -0.10250002, 0.11931216, -2.18708694], - [-1.95469033, 2.07845956, -2.18708694, 45.75441133]]), scale=array([0.59671696, 0.59671696, 0.59671696, 0.3 ]), shift=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=140, candidate_x=array([4.04441463e+00, 4.09892900e+03, 7.09170793e-01, 9.87246327e-01]), index=139, x=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), fval=0.07568421749234616, rho=-97.55333914328999, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, - 138, 139]), old_indices_discarded=array([ 0, 35, 36, 37, 40, 42, 44, 45, 46, 47, 48, 49, 50, - 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, - 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, - 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, - 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, - 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, - 118, 119, 120, 121, 122, 123, 124, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, - 139, 140]), model=ScalarModel(intercept=2.058129746040703, linear_terms=array([ 0.27064711, -0.08764093, 0.37387978, -9.44399412]), square_terms=array([[ 2.24820723e-02, -4.44313988e-03, 2.83141588e-02, - -6.06514526e-01], - [-4.44313988e-03, 3.42102034e-03, -7.54821341e-03, - 2.20553230e-01], - [ 2.83141588e-02, -7.54821341e-03, 3.74357636e-02, - -8.58305825e-01], - [-6.06514526e-01, 2.20553230e-01, -8.58305825e-01, - 2.22506226e+01]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.20881986]), shift=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 8.91180140e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=141, candidate_x=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), index=141, x=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), fval=0.04733711349557875, rho=1.7912506175389145, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, - 139, 140]), old_indices_discarded=array([ 45, 46, 49, 54, 59, 76, 77, 78, 79, 80, 81, 82, 83, - 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, - 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, - 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 126, 127]), step_length=0.49338773191709673, relative_step_length=1.2323594474146116, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.8007204926325407, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 42, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 141, 142]), model=ScalarModel(intercept=0.1535157442818915, linear_terms=array([ 0.16249061, -0.05439485, 0.02121035, -0.5810401 ]), square_terms=array([[ 0.13859777, -0.03700346, 0.01742136, -0.34784768], - [-0.03700346, 0.01208239, -0.00552864, 0.12680428], - [ 0.01742136, -0.00552864, 0.02104412, -0.09554744], - [-0.34784768, 0.12680428, -0.09554744, 1.56437999]]), scale=array([0.59671696, 0.59671696, 0.59671696, 0.3 ]), shift=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 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1.13237819e+00, 8.76265269e-01]), index=141, x=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), fval=0.04733711349557875, rho=-9.235171467382486, accepted=False, new_indices=array([142]), old_indices_used=array([ 42, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 141]), old_indices_discarded=array([ 0, 35, 36, 37, 44, 45, 46, 47, 48, 49, 50, 51, 52, - 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, - 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, - 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, - 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, - 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, - 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 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7.12517302e-01, 9.69291642e-01]), fval=0.04733711349557875, rho=-0.37241214656232297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, - 143]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144]), model=ScalarModel(intercept=0.07189691058029209, linear_terms=array([ 0.0729615 , -0.01994005, -0.02510471, -0.67291357]), square_terms=array([[ 9.11672153e-02, -2.53027498e-02, -2.81155848e-02, - -8.77690304e-01], - [-2.53027498e-02, 7.33288652e-03, 8.17518993e-03, - 2.54924757e-01], - [-2.81155848e-02, 8.17518993e-03, 9.50928398e-03, - 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7.93779346e-01, 9.63841347e-01]), index=146, x=array([4.28402294e+00, 4.09922505e+03, 7.93779346e-01, 9.63841347e-01]), fval=0.043027026165379276, rho=0.8364890346486437, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([138, 139, 140, 141, 145]), old_indices_discarded=array([], dtype=int32), step_length=0.10044974878343069, relative_step_length=1.0035936355587007, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.28402294e+00, 4.09922505e+03, 7.93779346e-01, 9.63841347e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144, 145, 146]), model=ScalarModel(intercept=0.051759709428797325, linear_terms=array([ 0.04242777, -0.01063285, -0.01550282, -0.40220491]), square_terms=array([[ 8.10319276e-02, -2.05701314e-02, -2.47633390e-02, - -8.14965374e-01], - [-2.05701314e-02, 5.48111505e-03, 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State(trustregion=Region(center=array([4.28402294e+00, 4.09922505e+03, 7.93779346e-01, 9.63841347e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([138, 139, 140, 141, 144, 145, 146, 147]), model=ScalarModel(intercept=0.04248835948593743, linear_terms=array([ 3.39080098e-03, -5.13012932e-05, -3.00338161e-03, -1.26979805e-02]), square_terms=array([[ 2.38039386e-02, -1.93813621e-04, 1.19579656e-03, - -3.06082467e-01], - [-1.93813621e-04, 3.41214370e-06, -1.94151353e-05, - 3.12711224e-03], - [ 1.19579656e-03, -1.94151353e-05, 4.55942268e-04, - -2.01835891e-02], - [-3.06082467e-01, 3.12711224e-03, -2.01835891e-02, - 4.22663303e+00]]), scale=0.1000900615790676, shift=array([4.28402294e+00, 4.09922505e+03, 7.93779346e-01, 9.63841347e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, 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2.38204183e+00, 9.80265374e-01])), candidate_index=148, candidate_x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=0.46328473374724694, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([138, 139, 140, 141, 144, 145, 146, 147]), old_indices_discarded=array([], dtype=int32), step_length=0.10031413807620175, relative_step_length=1.002238748718894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144, 145, 146, 147, 148, 149]), model=ScalarModel(intercept=0.03951342557884055, linear_terms=array([ 0.00394695, 0.0005794 , -0.00109741, -0.02210072]), 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([138, 140, 141, 144, 145, 146, 147, 148, 149, 150]), model=ScalarModel(intercept=0.04209180606175586, linear_terms=array([ 0.01646359, -0.00123942, 0.01043076, -0.06471733]), square_terms=array([[ 4.13794569e-02, -3.18884230e-03, 3.39064662e-02, - -1.78542915e-01], - [-3.18884230e-03, 2.65952996e-04, -2.79071151e-03, - 1.45840273e-02], - [ 3.39064662e-02, -2.79071151e-03, 2.96307444e-02, - -1.53322472e-01], - [-1.78542915e-01, 1.45840273e-02, -1.53322472e-01, - 8.08628536e-01]]), scale=0.1000900615790676, shift=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 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model_indices=array([148, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, - 164, 165]), model=ScalarModel(intercept=0.038586828229091596, linear_terms=array([ 0.0008624 , -0.00029256, -0.00092029, -0.00362331]), square_terms=array([[ 3.80039810e-03, 1.48122981e-04, 1.95630740e-03, - -4.67627417e-02], - [ 1.48122981e-04, 1.24195108e-05, 8.98861869e-05, - -2.15859541e-03], - [ 1.95630740e-03, 8.98861869e-05, 1.24145976e-03, - -2.74189420e-02], - [-4.67627417e-02, -2.15859541e-03, -2.74189420e-02, - 6.52838862e-01]]), scale=0.0500450307895338, shift=array([4.22484797e+00, 4.09923302e+03, 9.03726070e-01, 9.63886635e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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9.64726982e-01]), fval=0.037686693479215085, rho=1.0582189093134295, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([148, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, - 164, 165]), old_indices_discarded=array([141, 145, 146, 147, 149, 150, 151]), step_length=0.05116085842795206, relative_step_length=1.0222964722134136, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.20552134e+00, 4.09924683e+03, 9.49028314e-01, 9.64726982e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, - 164, 166]), model=ScalarModel(intercept=0.03746923349701317, linear_terms=array([ 0.00022178, -0.00052347, -0.00217042, 0.00839235]), square_terms=array([[ 1.70627807e-02, 9.01786077e-04, 8.09609271e-03, - -1.99839825e-01], - [ 9.01786077e-04, 7.00833597e-05, 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x=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.73777078e-01]), fval=0.0321599633543692, rho=-57.07432976877675, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([143, 145, 147, 150, 151, 152, 158, 165, 166, 167, 168, 169, 170, - 171, 172]), old_indices_discarded=array([137, 138, 139, 140, 141, 144, 146, 148, 149, 153, 154, 155, 156, - 157, 159, 160, 161, 162, 163, 164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.16250866e+00, 4.09943820e+03, 1.39207438e+00, 9.73777078e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([143, 147, 150, 151, 166, 167, 168, 169, 170, 171, 172, 173]), model=ScalarModel(intercept=0.03448842486567276, linear_terms=array([-0.00482463, -0.00068032, 0.00257991, -0.06727312]), square_terms=array([[ 5.41197136e-03, 5.75246629e-04, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=175, candidate_x=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), index=175, x=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), fval=0.03196020635668493, rho=0.6511332348539848, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([169, 171, 172, 173, 174]), old_indices_discarded=array([], dtype=int32), step_length=0.05245185261086877, relative_step_length=1.0480931230007022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175]), model=ScalarModel(intercept=0.03198863275603155, linear_terms=array([ 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176]), model=ScalarModel(intercept=0.03194809962562358, linear_terms=array([-8.08524576e-04, -1.08493288e-05, -5.49422714e-04, 1.75818827e-02]), square_terms=array([[ 3.50326784e-03, 3.48610273e-05, 1.24742847e-03, - -5.08202229e-02], - [ 3.48610273e-05, 4.48474855e-07, 1.38925490e-05, - -6.08176933e-04], - [ 1.24742847e-03, 1.38925490e-05, 5.71696697e-04, - -2.18658563e-02], - [-5.08202229e-02, -6.08176933e-04, -2.18658563e-02, - 9.21930745e-01]]), scale=0.0500450307895338, shift=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=177, candidate_x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), index=177, x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), fval=0.03175931998394472, rho=0.6690416360198942, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176]), old_indices_discarded=array([], dtype=int32), step_length=0.050499371349656076, relative_step_length=1.0090786348405505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177]), model=ScalarModel(intercept=0.03248246414257855, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178]), model=ScalarModel(intercept=0.03172573143066533, linear_terms=array([-1.14059094e-04, -1.10807797e-05, -1.03748194e-04, 1.17775825e-03]), square_terms=array([[ 3.64736289e-03, 6.02428702e-05, 1.27706960e-03, - -5.24032340e-02], - [ 6.02428702e-05, 1.22643828e-06, 2.44981157e-05, - -1.05985180e-03], - [ 1.27706960e-03, 2.44981157e-05, 5.72112824e-04, - -2.20276660e-02], - [-5.24032340e-02, -1.05985180e-03, -2.20276660e-02, - 9.34368129e-01]]), scale=0.0500450307895338, shift=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - 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0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=179, candidate_x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), index=179, x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), fval=0.03162745417502315, rho=2.510704045137157, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176, 177, 178]), old_indices_discarded=array([], dtype=int32), step_length=0.050325579243757135, relative_step_length=1.0056059203041197, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), model=ScalarModel(intercept=0.032130564354299555, linear_terms=array([-0.00156055, -0.00139965, 0.00036611, -0.025743 ]), square_terms=array([[ 4.05055456e-03, 1.48919579e-03, -6.07642697e-04, - 3.44846053e-02], - [ 1.48919579e-03, 1.66608590e-03, -7.81966055e-04, - 3.87222711e-02], - [-6.07642697e-04, -7.81966055e-04, 5.86968019e-04, - -1.75898376e-02], - [ 3.44846053e-02, 3.87222711e-02, -1.75898376e-02, - 9.06040243e-01]]), scale=0.1000900615790676, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178, 179, 180]), model=ScalarModel(intercept=0.031652111625055504, linear_terms=array([ 4.80604637e-05, 1.39247104e-05, -2.39867534e-05, -1.89127269e-04]), square_terms=array([[ 3.90665224e-03, 1.27857855e-04, 1.36034221e-03, - -5.54542919e-02], - [ 1.27857855e-04, 5.35312567e-06, 5.28469976e-05, - -2.25099856e-03], - [ 1.36034221e-03, 5.28469976e-05, 5.95652314e-04, - -2.28246303e-02], - [-5.54542919e-02, -2.25099856e-03, -2.28246303e-02, - 9.58898100e-01]]), scale=0.0500450307895338, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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- [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=181, candidate_x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), index=181, x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=0.957435706283736, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176, 177, 178, 179, 180]), old_indices_discarded=array([], dtype=int32), step_length=0.05016421722542483, relative_step_length=1.0023815838258203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, - 181]), model=ScalarModel(intercept=0.032799850622037344, linear_terms=array([-0.00120015, -0.00149617, 0.00075662, -0.03985981]), square_terms=array([[ 2.96888836e-03, 4.77998749e-04, -2.25595211e-04, - 1.47872526e-02], - [ 4.77998749e-04, 8.86980525e-04, -5.90740802e-04, - 2.76631448e-02], - [-2.25595211e-04, -5.90740802e-04, 6.06547002e-04, - -1.77861139e-02], - [ 1.47872526e-02, 2.76631448e-02, -1.77861139e-02, - 8.69630174e-01]]), scale=0.1000900615790676, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - 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x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-0.04809068155083086, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, - 181]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), model=ScalarModel(intercept=0.03161885589197164, linear_terms=array([ 1.24235116e-05, 1.65270337e-05, -5.96983212e-07, -3.23007724e-04]), square_terms=array([[ 3.85807274e-03, 1.15384522e-04, 1.32757111e-03, - -5.48814365e-02], - [ 1.15384522e-04, 4.41711422e-06, 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State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([175, 177, 179, 181, 182, 183]), model=ScalarModel(intercept=0.03159896733455111, linear_terms=array([ 5.72777496e-04, -4.79596153e-06, 1.29876317e-04, -7.46995187e-03]), square_terms=array([[ 1.16620241e-03, 2.24403583e-06, 3.92674408e-04, - -1.60307218e-02], - [ 2.24403583e-06, 7.30065521e-09, 8.86098589e-07, - -3.50743943e-05], - [ 3.92674408e-04, 8.86098589e-07, 1.60868867e-04, - -6.16883815e-03], - [-1.60307218e-02, -3.50743943e-05, -6.16883815e-03, - 2.56901239e-01]]), scale=0.0250225153947669, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , 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9.80265374e-01])), candidate_index=184, candidate_x=array([4.11715428e+00, 4.09942018e+03, 1.58180483e+00, 9.76896956e-01]), index=181, x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-0.7033836540431104, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([175, 177, 179, 181, 182, 183]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.01251125769738345, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([179, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, - 194, 195, 196]), model=ScalarModel(intercept=0.03162939399086311, linear_terms=array([-2.05014239e-05, -4.40701544e-07, -7.03347564e-06, 4.85199295e-04]), 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.006255628848691725, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([181, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, - 196, 197]), model=ScalarModel(intercept=0.03163372180134707, linear_terms=array([-1.40416035e-05, -1.06245855e-05, -6.25328365e-06, 2.93425222e-04]), square_terms=array([[ 8.39288020e-05, 3.25915482e-06, 2.80895929e-05, - -1.12787517e-03], - [ 3.25915482e-06, 1.50804018e-07, 1.22778171e-06, - -5.07296461e-05], - [ 2.80895929e-05, 1.22778171e-06, 1.11271310e-05, - -4.26777604e-04], - [-1.12787517e-03, -5.07296461e-05, -4.26777604e-04, - 1.76074601e-02]]), scale=0.006255628848691725, shift=array([4.13026246e+00, 4.09941730e+03, 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model_indices=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, - 197, 198]), model=ScalarModel(intercept=0.03163462823062035, linear_terms=array([-9.39533430e-06, -5.39540217e-06, 2.16599626e-07, 1.49312835e-04]), square_terms=array([[ 2.34139611e-05, 1.09806616e-06, 5.53797697e-06, - -3.00949207e-04], - [ 1.09806616e-06, 5.92778360e-08, 2.79762167e-07, - -1.59957868e-05], - [ 5.53797697e-06, 2.79762167e-07, 1.56644529e-06, - -7.74361413e-05], - [-3.00949207e-04, -1.59957868e-05, -7.74361413e-05, - 4.40255574e-03]]), scale=0.0031278144243458623, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, - 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, - -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, - 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=199, candidate_x=array([4.13001034e+00, 4.09942001e+03, 1.55905409e+00, 9.76337228e-01]), index=181, x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-2.567463867080543, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, - 197, 198]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Maximum number of criterion evaluations reached.', 'tranquilo_history': History for least_squares function with 200 entries., 'history': {'params': [{'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 1.5997660080756595, 'BeqFac': 3798.32770700145, 'BeqShift': 70.0, 'DiscFac': 0.7024407930795087}, {'CRRA': 1.1, 'BeqFac': 4399.8970420338355, 'BeqShift': 69.76434738104037, 'DiscFac': 0.9316481833606611}, {'CRRA': 17.36460836924718, 'BeqFac': 3912.562988977825, 'BeqShift': 70.0, 'DiscFac': 0.5294729131673447}, {'CRRA': 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9.60369865e-01])), candidate_index=46, candidate_x=array([1.71148115e+01, 4.11532983e+03, 2.95367480e+01, 5.98738718e-01]), index=33, x=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), fval=1.588023991113788, rho=-3.5378747881454817, accepted=False, new_indices=array([34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_used=array([20, 29, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6348854061273638, linear_terms=array([0.02274535, 0.06472916, 0.06290409, 1.72847375]), square_terms=array([[6.13705874e-04, 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 47]), model=ScalarModel(intercept=0.5832383280438845, linear_terms=array([-0.04366208, -0.07974917, 0.0121024 , -0.66338862]), square_terms=array([[ 1.24405099e-02, 1.84037162e-02, -3.21723600e-03, - 1.98213656e-01], - [ 1.84037162e-02, 2.76097603e-02, -4.77741170e-03, - 2.90439616e-01], - [-3.21723600e-03, -4.77741170e-03, 8.37149132e-04, - -5.12297952e-02], - [ 1.98213656e-01, 2.90439616e-01, -5.12297952e-02, - 3.19738903e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01])), 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46, 47, 48, 49]), model=ScalarModel(intercept=0.5792867438043003, linear_terms=array([ 0.0620906 , 0.05580846, -0.01892313, 1.03442734]), square_terms=array([[ 4.84671874e-02, 5.47513283e-02, -1.60884447e-02, - 6.51384141e-01], - [ 5.47513283e-02, 6.23780615e-02, -1.82999844e-02, - 7.33136697e-01], - [-1.60884447e-02, -1.82999844e-02, 5.38147700e-03, - -2.16952904e-01], - [ 6.51384141e-01, 7.33136697e-01, -2.16952904e-01, - 9.00073831e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.25071503]), shift=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.50715032e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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old_indices_discarded=array([34, 39, 41]), step_length=0.21977140924363636, relative_step_length=1.0992673076381052, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.63018525e+01, 4.11481291e+03, 2.86088479e+01, 7.58484128e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 35, 37, 38, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.5653243309971905, linear_terms=array([0.01127097, 0.00472838, 0.03641742, 0.37744159]), square_terms=array([[ 3.97059495e-03, -6.12547738e-03, 3.07970694e-02, - -2.05018401e-01], - [-6.12547738e-03, 1.02048123e-02, -5.09218091e-02, - 3.54928883e-01], - [ 3.07970694e-02, -5.09218091e-02, 2.68417429e-01, - -1.78106835e+00], - [-2.05018401e-01, 3.54928883e-01, -1.78106835e+00, - 1.26906452e+01]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.27823145]), shift=array([1.63018525e+01, 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fval=1.2823500253026578, rho=0.4037813363749846, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([33, 37, 38, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([34, 35, 39, 42]), step_length=0.2121197364289317, relative_step_length=1.0609946596949713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.61338301e+01, 4.11492371e+03, 2.85465763e+01, 7.83158466e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 37, 38, 44, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.616287359006102, linear_terms=array([ 0.1579405 , -0.06085977, -0.02629154, -0.26113511]), square_terms=array([[ 0.31070863, -0.13275671, -0.09956251, -1.66566784], - [-0.13275671, 0.05677437, 0.04270285, 0.71640616], - [-0.09956251, 0.04270285, 0.03313502, 0.55556134], - [-1.66566784, 0.71640616, 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State(trustregion=Region(center=array([1.60226351e+01, 4.11498307e+03, 2.84951098e+01, 8.86790832e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([80, 81, 82, 83, 84, 85, 86, 87]), model=ScalarModel(intercept=0.9737344396431695, linear_terms=array([ 5.08140248e-04, -9.48814342e-05, 1.72040066e-04, 1.14174914e-03]), square_terms=array([[ 9.70083861e-06, -1.52448869e-07, 4.64925012e-07, - 4.51072215e-04], - [-1.52448869e-07, 3.69663578e-08, -5.75519406e-08, - -8.12055582e-06], - [ 4.64925012e-07, -5.75519406e-08, 1.06279789e-07, - 2.27288177e-05], - [ 4.51072215e-04, -8.12055582e-06, 2.27288177e-05, - 2.15645942e-02]]), scale=0.012495334831015706, shift=array([1.60226351e+01, 4.11498307e+03, 2.84951098e+01, 8.86790832e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, 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8.67823437e+00, 9.60369865e-01])), candidate_index=88, candidate_x=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01]), index=88, x=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01]), fval=0.9728561143973231, rho=1.6707484283795988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([80, 81, 82, 83, 84, 85, 86, 87]), old_indices_discarded=array([], dtype=int32), step_length=0.012512309847297915, relative_step_length=1.0013585083162457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01]), radius=0.024990669662031412, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([65, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), model=ScalarModel(intercept=0.9744530479353398, linear_terms=array([ 0.00105277, -0.00099607, 0.00047104, 0.01403558]), 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old_indices_discarded=array([66, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([81, 82, 83, 84, 85, 86, 87, 88, 89]), model=ScalarModel(intercept=0.9730606049983257, linear_terms=array([ 4.91514813e-04, -2.73745659e-05, 8.04246166e-05, -1.02088743e-03]), square_terms=array([[ 9.70139608e-06, 2.32782735e-07, -1.25262833e-07, - 4.46639539e-04], - [ 2.32782735e-07, 9.98791914e-09, -4.25450829e-09, - 1.13008219e-05], - [-1.25262833e-07, -4.25450829e-09, 9.84307051e-09, - -7.04515004e-06], - [ 4.46639539e-04, 1.13008219e-05, -7.04515004e-06, - 2.11324714e-02]]), scale=0.012495334831015706, shift=array([1.60110358e+01, 4.11498533e+03, 2.84910190e+01, 8.86386824e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], 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candidate_index=105, candidate_x=array([1.59604407e+01, 4.11500468e+03, 2.84819210e+01, 8.88581625e-01]), index=105, x=array([1.59604407e+01, 4.11500468e+03, 2.84819210e+01, 8.88581625e-01]), fval=0.9709122582594116, rho=1.2027594033933873, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 104]), old_indices_discarded=array([ 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 103]), step_length=0.024990695516511667, relative_step_length=1.0000010345653239, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.59604407e+01, 4.11500468e+03, 2.84819210e+01, 8.88581625e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, - 104, 105]), model=ScalarModel(intercept=0.9709593031063946, linear_terms=array([ 1.38386852e-03, 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102, - 104, 105]), old_indices_discarded=array([ 38, 55, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, - 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, - 83, 84, 85, 86, 87, 88, 99, 103]), step_length=0.04998143202530104, relative_step_length=1.0000018547169698, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.59191640e+01, 4.11502516e+03, 2.84625747e+01, 8.89512165e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 89, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 104, - 105, 106]), model=ScalarModel(intercept=0.9691950560652018, linear_terms=array([ 0.00283956, -0.00127135, 0.00147188, 0.00055916]), square_terms=array([[ 8.52870166e-04, 1.58642942e-04, -1.97086495e-05, - 3.42270741e-02], - [ 1.58642942e-04, 4.16119024e-05, 3.64811022e-07, - 6.31819464e-03], - [-1.97086495e-05, 3.64811022e-07, 6.90244812e-06, - -8.46572541e-04], - [ 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relative_step_length=1.000001776993164, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.58370170e+01, 4.11506246e+03, 2.84195645e+01, 8.91270121e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 89, 91, 92, 93, 94, 96, 97, 98, 100, 101, 102, 104, 105, - 106, 107]), model=ScalarModel(intercept=0.9654979476241048, linear_terms=array([ 0.00556818, -0.00203971, 0.00462618, 0.0030042 ]), square_terms=array([[ 3.45159144e-03, 5.28318901e-04, -4.47679061e-04, - 1.37575719e-01], - [ 5.28318901e-04, 1.16985703e-04, -6.96102150e-05, - 2.10677872e-02], - [-4.47679061e-04, -6.96102150e-05, 9.58857200e-05, - -1.73752674e-02], - [ 1.37575719e-01, 2.10677872e-02, -1.73752674e-02, - 5.62615535e+00]]), scale=0.1999253572962513, shift=array([1.58370170e+01, 4.11506246e+03, 2.84195645e+01, 8.91270121e-01])), vector_model=VectorModel(intercepts=array([ 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radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 92, 93, 95, 96, 97, 100, 101, 102, 104, 105, 106, - 107, 108]), model=ScalarModel(intercept=0.9596233219847873, linear_terms=array([ 0.00425257, 0.03710232, 0.02901883, -0.34562124]), square_terms=array([[ 2.34151193e-03, -2.50628943e-03, -1.88187968e-03, - 8.76154650e-02], - [-2.50628943e-03, 4.11292206e-03, 2.90830319e-03, - -1.11807152e-01], - [-1.88187968e-03, 2.90830319e-03, 2.17215726e-03, - -8.04188023e-02], - [ 8.76154650e-02, -1.11807152e-01, -8.04188023e-02, - 3.63569521e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.25183221]), shift=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.48167794e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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candidate_x=array([1.53845479e+01, 4.11482259e+03, 2.79903380e+01, 8.64861786e-01]), index=108, x=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), fval=0.9585677871382879, rho=-0.7052809316351738, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 92, 93, 95, 96, 97, 100, 101, 102, 104, 105, 106, - 107, 108]), old_indices_discarded=array([ 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, - 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, - 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, - 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, - 87, 88, 90, 91, 94, 98, 99, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, - 107, 108]), model=ScalarModel(intercept=0.9577031847372262, linear_terms=array([ 0.03178872, 0.0310778 , -0.00399409, 0.28392775]), square_terms=array([[ 1.95437081e-03, 4.77684675e-03, -7.59671438e-04, - -4.27399704e-02], - [ 4.77684675e-03, 1.38624719e-02, -2.20752863e-03, - -1.50579650e-01], - [-7.59671438e-04, -2.20752863e-03, 3.56515716e-04, - 2.41971764e-02], - [-4.27399704e-02, -1.50579650e-01, 2.41971764e-02, - 1.98161272e+00]]), scale=0.1999253572962513, shift=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 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fval=0.9585677871382879, rho=-1.049281869869592, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, - 107, 108]), old_indices_discarded=array([ 37, 38, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, - 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, - 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 91, - 92, 94, 95, 97, 98, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 101, 102, 103, 104, 105, 106, 107, - 108, 110]), model=ScalarModel(intercept=0.9578113150011967, linear_terms=array([-0.00143314, -0.01180295, 0.00580005, 0.11249413]), square_terms=array([[ 2.42515718e-03, 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77, 78, 79, 80, 81, 82, 83, 84, - 85, 86, 87, 88, 91, 92, 94, 95, 97, 98, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([107, 108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, - 121, 122, 123]), model=ScalarModel(intercept=0.9504713730391238, linear_terms=array([ 0.00576359, -0.00071375, -0.00207317, 0.0123183 ]), square_terms=array([[2.92179329e-05, 3.65826669e-06, 1.25596683e-05, 1.93572926e-03], - [3.65826669e-06, 4.33541544e-06, 1.33464902e-05, 1.13225496e-03], - [1.25596683e-05, 1.33464902e-05, 4.21013282e-05, 3.63824896e-03], - [1.93572926e-03, 1.13225496e-03, 3.63824896e-03, 3.48459659e-01]]), scale=0.049981339324062825, shift=array([1.56825267e+01, 4.11512057e+03, 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model_indices=array([108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, - 123, 124]), model=ScalarModel(intercept=0.9487927595392658, linear_terms=array([-0.00147051, -0.00421324, -0.01362327, -0.01333188]), square_terms=array([[1.63750249e-03, 4.30941647e-04, 1.51329636e-03, 4.72095779e-02], - [4.30941647e-04, 1.22618834e-04, 4.25727547e-04, 1.22384388e-02], - [1.51329636e-03, 4.25727547e-04, 1.48789357e-03, 4.31972207e-02], - [4.72095779e-02, 1.22384388e-02, 4.31972207e-02, 1.38862042e+00]]), scale=0.09996267864812565, shift=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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fval=0.9571523832919526, rho=-0.08704504009952377, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([108, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, - 123, 124]), old_indices_discarded=array([ 37, 38, 55, 57, 60, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([108, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 125]), model=ScalarModel(intercept=0.9509907876084691, linear_terms=array([-0.00126299, -0.00562742, 0.00301372, -0.00840277]), square_terms=array([[ 4.53210625e-04, 3.06801681e-04, -1.65225453e-04, - 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01]), radius=0.024990669662031412, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([108, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 124, 126]), model=ScalarModel(intercept=0.9500782107060736, linear_terms=array([-0.00112443, -0.00068154, -0.00348962, -0.00389738]), square_terms=array([[1.43093646e-04, 2.08826756e-05, 1.14016730e-04, 3.46777637e-03], - [2.08826756e-05, 3.26531434e-06, 1.73382031e-05, 4.98398367e-04], - [1.14016730e-04, 1.73382031e-05, 9.50119687e-05, 2.71002409e-03], - [3.46777637e-03, 4.98398367e-04, 2.71002409e-03, 8.62229027e-02]]), scale=0.024990669662031412, shift=array([1.56362376e+01, 4.11512669e+03, 2.83061835e+01, 8.92627861e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, 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2.83075065e+01, 8.96050335e-01]), fval=0.956359502934222, rho=0.27822067119484756, accepted=True, new_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141]), old_indices_used=array([121, 124, 128, 129]), old_indices_discarded=array([], dtype=int32), step_length=0.0032348789465337065, relative_step_length=1.035547743307893, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56297684e+01, 4.11512757e+03, 2.83075065e+01, 8.96050335e-01]), radius=0.006247667415507853, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([124, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 141, 142]), model=ScalarModel(intercept=0.9563365939003713, linear_terms=array([0.00029643, 0.00017783, 0.00012363, 0.0004537 ]), square_terms=array([[ 2.43210903e-06, 6.37570749e-09, -3.06184031e-07, - 1.14408592e-04], - [ 6.37570749e-09, 5.95696302e-08, 2.69092843e-08, - 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scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=144, candidate_x=array([1.56169076e+01, 4.11511345e+03, 2.83032061e+01, 8.95771668e-01]), index=144, x=array([1.56169076e+01, 4.11511345e+03, 2.83032061e+01, 8.95771668e-01]), fval=0.9558021854453339, rho=0.5332151729603328, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([124, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 142, 143]), old_indices_discarded=array([108, 113, 114, 116, 118, 121, 122, 126, 127, 128, 141]), step_length=0.013729387030054918, relative_step_length=1.098761034876478, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56169076e+01, 4.11511345e+03, 2.83032061e+01, 8.95771668e-01]), radius=0.024990669662031412, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([124, 129, 130, 131, 132, 133, 134, 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rho=0.7751126903315828, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([124, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, - 143, 144]), old_indices_discarded=array([108, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, - 125, 126, 127, 128, 141, 142]), step_length=0.02569690949121279, relative_step_length=1.0282601402336318, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56001548e+01, 4.11509401e+03, 2.83019534e+01, 8.96120132e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([124, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 143, - 144, 145]), model=ScalarModel(intercept=0.9551163163125324, linear_terms=array([ 0.00108171, 0.00083652, 0.00023503, -0.00021719]), square_terms=array([[ 1.77062859e-04, -4.25828710e-06, -1.35200511e-05, - 7.80964803e-03], - [-4.25828710e-06, 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128, 129, 141, 142]), step_length=0.049981355166639124, relative_step_length=1.0000003169698235, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.55612143e+01, 4.11506385e+03, 2.82934868e+01, 8.96967997e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 122, 124, 128, 132, 133, 134, 135, 136, 137, 138, 143, 144, - 145, 146]), model=ScalarModel(intercept=0.9536335522841922, linear_terms=array([-0.01097284, 0.01056148, 0.02155033, -0.02766167]), square_terms=array([[ 4.18179471e-03, -1.79545888e-03, -3.84111252e-03, - 7.97548159e-02], - [-1.79545888e-03, 7.90774456e-04, 1.68675784e-03, - -3.34926642e-02], - [-3.84111252e-03, 1.68675784e-03, 3.64438204e-03, - -7.14999274e-02], - [ 7.97548159e-02, -3.34926642e-02, -7.14999274e-02, - 1.58324759e+00]]), scale=0.09996267864812565, shift=array([1.55612143e+01, 4.11506385e+03, 2.82934868e+01, 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4.11506385e+03, 2.82934868e+01, 8.96967997e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([129, 130, 131, 132, 135, 136, 138, 139, 140, 141, 142, 143, 144, - 145, 146]), model=ScalarModel(intercept=0.9534135923040981, linear_terms=array([0.00157329, 0.00045408, 0.00051324, 0.00013002]), square_terms=array([[ 1.70921425e-04, 5.11088898e-07, -2.51118969e-05, - 7.66257632e-03], - [ 5.11088898e-07, 1.00617208e-06, 1.91435521e-06, - 1.08035827e-04], - [-2.51118969e-05, 1.91435521e-06, 1.45702183e-05, - -9.15721799e-04], - [ 7.66257632e-03, 1.08035827e-04, -9.15721799e-04, - 3.61387982e-01]]), scale=0.049981339324062825, shift=array([1.55612143e+01, 4.11506385e+03, 2.82934868e+01, 8.96967997e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - 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new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 130, 131, 135, 136, 138, 139, 141, 143, 144, 145, 146, - 147, 148]), old_indices_discarded=array([105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, - 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 132, 133, 134, - 137, 140, 142]), step_length=0.105280468337507, relative_step_length=1.0531977510136585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54791737e+01, 4.11498376e+03, 2.83511980e+01, 8.94338923e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 137, 140, 141, 143, 144, 145, 146, 147, - 148, 149]), model=ScalarModel(intercept=0.9504811473000055, linear_terms=array([ 0.00337976, -0.00166767, 0.00065203, 0.00032394]), square_terms=array([[ 6.88080741e-05, 3.26320216e-05, -1.14612730e-05, - 4.04897732e-03], - [ 3.26320216e-05, 2.38843862e-05, -8.53007545e-06, - 2.37987944e-03], - [-1.14612730e-05, -8.53007545e-06, 3.08030290e-06, - -8.37502206e-04], - [ 4.04897732e-03, 2.37987944e-03, -8.37502206e-04, - 2.69451472e-01]]), scale=0.049981339324062825, shift=array([1.54791737e+01, 4.11498376e+03, 2.83511980e+01, 8.94338923e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 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- [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=150, candidate_x=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), index=150, x=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), fval=0.9490921495752334, rho=0.48378050592219396, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 131, 135, 136, 137, 140, 141, 143, 144, 145, 146, 147, - 148, 149]), old_indices_discarded=array([113, 114, 116, 118, 119, 121, 122, 124, 125, 127, 128, 130, 132, - 133, 134, 138, 139, 142]), step_length=0.049982181792418395, relative_step_length=1.0000168556578708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, - 149, 150]), model=ScalarModel(intercept=0.9481669388008567, linear_terms=array([ 0.00491681, -0.00191175, 0.00090832, -0.00794232]), square_terms=array([[ 4.53197713e-04, 9.69734758e-05, -4.03223571e-05, - 2.17424813e-02], - [ 9.69734758e-05, 2.70238997e-05, -1.14028932e-05, - 5.09292923e-03], - [-4.03223571e-05, -1.14028932e-05, 4.97292295e-06, - -2.10313696e-03], - [ 2.17424813e-02, 5.09292923e-03, -2.10313696e-03, - 1.09787792e+00]]), scale=0.09996267864812565, shift=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], 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0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=151, candidate_x=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), index=151, x=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), fval=0.9449793990676408, rho=0.7464682068241998, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, - 149, 150]), old_indices_discarded=array([106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, - 120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 132, 133, 134, - 137, 138, 139, 142]), step_length=0.10016713827274805, relative_step_length=1.0020453596020782, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, - 150, 151]), model=ScalarModel(intercept=0.9447208950935511, linear_terms=array([ 0.00747774, -0.00115453, 0.00019389, -0.01790295]), square_terms=array([[ 2.32523138e-03, 1.54081854e-04, -2.97872562e-06, - 1.00174053e-01], - [ 1.54081854e-04, 1.28584982e-05, -4.90411666e-07, - 6.98051990e-03], - [-2.97872562e-06, -4.90411666e-07, 4.85734809e-07, - -6.45942014e-05], - [ 1.00174053e-01, 6.98051990e-03, -6.45942014e-05, - 4.46348961e+00]]), scale=0.1999253572962513, shift=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=152, candidate_x=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), index=152, x=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), fval=0.9352639178281498, rho=1.2203446548292984, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, - 150, 151]), old_indices_discarded=array([ 37, 38, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, - 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, - 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, - 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, - 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, - 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 132, 133, 134, - 137, 138, 139, 140, 142]), step_length=0.20002826060126488, relative_step_length=1.0005147086212836, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, - 151, 152]), model=ScalarModel(intercept=1.0726433496908907, linear_terms=array([-0.02343661, -0.00889048, -0.00275083, -1.36662877]), square_terms=array([[4.86288425e-03, 6.82588240e-04, 2.46055974e-04, 1.78667944e-01], - [6.82588240e-04, 1.16954213e-04, 3.75054128e-05, 2.65804533e-02], - [2.46055974e-04, 3.75054128e-05, 1.68608179e-05, 9.53055835e-03], - [1.78667944e-01, 2.65804533e-02, 9.53055835e-03, 6.81382039e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.24785877]), shift=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 8.52141232e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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candidate_x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), index=153, x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), fval=0.9223250761250302, rho=0.7752349469770148, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, - 151, 152]), old_indices_discarded=array([ 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 111, 112, 113, 114, 115, 116, - 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, - 130, 131, 132, 133, 134, 137, 138, 139, 140, 142]), step_length=0.5161364791949027, relative_step_length=1.2908229505627111, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, - 152, 153]), model=ScalarModel(intercept=1.8249779819419052, linear_terms=array([-1.76627203e-01, -2.47141400e-03, -2.38775460e-03, -4.85764149e+00]), square_terms=array([[ 2.34532115e-02, 1.92781711e-04, 1.57766385e-04, - 5.44396171e-01], - [ 1.92781711e-04, 6.31379450e-06, -3.02452352e-06, - 5.07453896e-03], - [ 1.57766385e-04, -3.02452352e-06, 9.75935474e-06, - 4.07694325e-03], - [ 5.44396171e-01, 5.07453896e-03, 4.07694325e-03, - 1.30477707e+01]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 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scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=154, candidate_x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=1.1251274278087409, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, - 152, 153]), old_indices_discarded=array([ 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, - 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, - 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, - 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, - 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, - 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, - 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, - 139, 140, 147]), step_length=1.0323679882696024, relative_step_length=1.2909417822620541, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=1.5994028583700104, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, - 153, 154]), model=ScalarModel(intercept=1.1326265739323547, linear_terms=array([-0.09637569, -0.14379396, 0.09712327, -1.55727297]), square_terms=array([[ 0.04121845, 0.02313126, -0.01642407, 0.42608071], - [ 0.02313126, 0.01788942, -0.01256294, 0.26548283], - [-0.01642407, -0.01256294, 0.00885428, -0.18607463], - [ 0.42608071, 0.26548283, -0.18607463, 4.71648178]]), scale=array([1.19191507, 1.19191507, 1.19191507, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], 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0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=155, candidate_x=array([1.30586935e+01, 4.11715430e+03, 2.80233711e+01, 8.97432554e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=-0.7704978841646354, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, - 153, 154]), old_indices_discarded=array([ 20, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, - 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, - 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, - 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, - 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, - 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, - 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, - 121, 122, 123, 124, 126, 128, 129, 130, 131, 132, 133, 134, 135, - 137, 138, 139, 140, 141, 142, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, - 153, 154]), model=ScalarModel(intercept=1.1326265739323542, linear_terms=array([-0.04818785, -0.07189698, 0.04856163, -1.55727297]), square_terms=array([[ 1.03046127e-02, 5.78281380e-03, -4.10601701e-03, - 2.13040355e-01], - [ 5.78281380e-03, 4.47235416e-03, -3.14073444e-03, - 1.32741416e-01], - [-4.10601701e-03, -3.14073444e-03, 2.21357021e-03, - -9.30373154e-02], - [ 2.13040355e-01, 1.32741416e-01, -9.30373154e-02, - 4.71648178e+00]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - 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0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=156, candidate_x=array([1.36546511e+01, 4.11655835e+03, 2.86193287e+01, 8.98242801e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=-0.8333994975616199, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, - 153, 154]), old_indices_discarded=array([ 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, - 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, - 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, - 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, - 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, - 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, - 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, - 126, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, - 141, 142, 147, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 145, 146, 148, 149, 150, 151, 152, 153, - 154, 156]), model=ScalarModel(intercept=0.9323612178144098, linear_terms=array([-0.01259834, 0.01279683, -0.02998382, -0.59640575]), square_terms=array([[ 4.02828891e-03, -1.26463784e-03, 2.65461049e-03, - 1.14730233e-01], - [-1.26463784e-03, 4.66105839e-04, -9.83258053e-04, - -3.67550589e-02], - [ 2.65461049e-03, -9.83258053e-04, 2.08826577e-03, - 7.82898537e-02], - [ 1.14730233e-01, -3.67550589e-02, 7.82898537e-02, - 3.40732089e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.2369916 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.63008405e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, - 169]), model=ScalarModel(intercept=0.8621110105805507, linear_terms=array([ 0.05259407, 0.01444784, -0.04650949, 1.3449937 ]), square_terms=array([[ 1.06382832e-02, 1.77761267e-03, -1.34152433e-02, - 2.24240960e-01], - [ 1.77761267e-03, 3.91467956e-04, -2.20266751e-03, - 3.98071647e-02], - [-1.34152433e-02, -2.20266751e-03, 1.72375098e-02, - -2.79030554e-01], - [ 2.24240960e-01, 3.98071647e-02, -2.79030554e-01, - 4.86559567e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], 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168, 169, - 170]), model=ScalarModel(intercept=0.8521861809194315, linear_terms=array([ 0.01363795, 0.00599446, -0.00829727, 0.87225482]), square_terms=array([[ 1.25459512e-04, 6.75203715e-05, -2.81566396e-05, - 4.82302182e-03], - [ 6.75203715e-05, 1.45256704e-04, 1.00392769e-04, - -6.60298780e-03], - [-2.81566396e-05, 1.00392769e-04, 3.70387367e-04, - -2.84734924e-02], - [ 4.82302182e-03, -6.60298780e-03, -2.84734924e-02, - 2.26757906e+00]]), scale=0.09996267864812565, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - 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[0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=171, candidate_x=array([1.41707268e+01, 4.11590495e+03, 2.91978227e+01, 8.85573958e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=-0.5334571326253329, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([154, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, - 170]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 158, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, - 181, 182, 183]), model=ScalarModel(intercept=0.8751612738683985, linear_terms=array([ 0.01069739, -0.0351117 , 0.00524044, 0.03150041]), square_terms=array([[ 2.80038988e-04, -1.92877967e-03, 4.46333870e-04, - -8.04311081e-03], - [-1.92877967e-03, 1.45965092e-02, -3.44462599e-03, - 6.72455897e-02], - [ 4.46333870e-04, -3.44462599e-03, 8.32232273e-04, - -1.63487093e-02], - [-8.04311081e-03, 6.72455897e-02, -1.63487093e-02, - 3.46011684e-01]]), scale=0.049981339324062825, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], - - [[0., 0., 0., 0.], - [0., 0., 0., 0.], - 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, - 183, 184]), model=ScalarModel(intercept=0.8806140250059065, linear_terms=array([ 0.00348081, 0.00103192, 0.00045499, -0.00120431]), square_terms=array([[ 7.93493802e-06, 1.82672879e-07, 1.06786659e-07, - 2.40577351e-04], - [ 1.82672879e-07, 1.04969220e-05, 4.04967602e-06, - -1.03166451e-03], - [ 1.06786659e-07, 4.04967602e-06, 1.68666246e-06, - -3.96649049e-04], - [ 2.40577351e-04, -1.03166451e-03, -3.96649049e-04, - 1.12239205e-01]]), scale=0.024990669662031412, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, - -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, - -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , - 0.09040456, 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2.92122061e+01, 9.24232050e-01]), index=185, x=array([1.42268091e+01, 4.11595541e+03, 2.92122061e+01, 9.24232050e-01]), fval=0.8889267485376284, rho=0.3128499910431618, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, - 183, 184]), old_indices_discarded=array([], dtype=int32), step_length=0.024992174326975865, relative_step_length=1.0000602090686166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42268091e+01, 4.11595541e+03, 2.92122061e+01, 9.24232050e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, - 184, 185]), model=ScalarModel(intercept=0.8804191957868968, linear_terms=array([ 0.00262124, -0.00038687, 0.00011566, -0.00743838]), square_terms=array([[ 1.53514229e-04, -1.70092698e-06, 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2.91861680e+01, 9.25856803e-01]), index=188, x=array([1.41081482e+01, 4.11586063e+03, 2.91861680e+01, 9.25856803e-01]), fval=0.8831177501278756, rho=0.6433964616125217, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([154, 171, 174, 175, 177, 178, 179, 180, 181, 182, 183, 184, 185, - 186, 187]), old_indices_discarded=array([163, 168, 172, 173, 176]), step_length=0.04998185651351309, relative_step_length=1.0000103476508886, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.41081482e+01, 4.11586063e+03, 2.91861680e+01, 9.25856803e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 168, 171, 174, 175, 178, 179, 180, 181, 182, 183, 185, 186, - 187, 188]), model=ScalarModel(intercept=0.8289860145213327, linear_terms=array([7.58494192e-05, 2.23407925e-02, 8.90899480e-02, 3.91983702e-03]), square_terms=array([[ 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2.91363397e+01, 9.25319713e-01]), index=190, x=array([1.41055555e+01, 4.11586221e+03, 2.91362378e+01, 9.26252295e-01]), fval=0.8827327947131193, rho=-0.04838258187938748, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([171, 187, 188, 189, 190]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.41055555e+01, 4.11586221e+03, 2.91362378e+01, 9.26252295e-01]), radius=0.012495334831015706, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, - 201, 202, 203]), model=ScalarModel(intercept=0.8819337429846763, linear_terms=array([ 0.0006271 , 0.00070911, 0.00036049, -0.0046377 ]), square_terms=array([[ 9.38940867e-06, -5.45262110e-06, -2.75044789e-06, - 5.01156031e-04], - [-5.45262110e-06, 4.00284949e-06, 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112.69145950023085, 113.88890310004354, 115.12849680008367, 116.31543600000441, 117.6343872002326, 118.81335589988157, 119.98585169995204, 121.1860096999444, 122.37323410017416, 123.57454850012437, 124.76921389997005, 126.4212179002352, 126.60760340001434, 126.79196709999815, 126.97290260018781, 127.15962800011039, 127.34788580005988, 127.53500150004402, 127.73213839996606, 127.93292480008677, 128.12795529980212, 128.32260590000078, 128.51027300022542, 129.75297969998792, 130.94742600014433, 132.1255693999119], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 8, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 20, 21, 22, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 36, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 87, 88, 89]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}, 'five_steps': {'relative_criterion_change': 0.001297727019196962, 'relative_params_change': 0.027894310200989765, 'absolute_criterion_change': 0.0001297727019196962, 'absolute_params_change': 113.76595754764212}}" + +multistart_info,"{'start_parameters': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.357791964783, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 6.019017903047592, 'BeqFac': 4094.4713174272265, 'BeqShift': 8.678234369345601, 'DiscFac': 0.9603698649490326}], 'local_optima': [Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.438e-06* 0.001161 +relative_params_change 5.434e-05 0.00258 +absolute_criterion_change 3.438e-07* 0.0001161 +absolute_params_change 0.00019 0.004757 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.703e-08* 1.469e-05 +relative_params_change 3.819e-07* 0.001782 +absolute_criterion_change 2.006e-08* 7.959e-06* +absolute_params_change 3.648e-06* 0.03719 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.0002068 0.02744 +relative_params_change 0.01485 0.1715 +absolute_criterion_change 2.068e-05 0.002744 +absolute_params_change 0.05016 0.2731 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 4 free parameters terminated. + +The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 0.00129 0.00488 +relative_params_change 0.001706 0.006966 +absolute_criterion_change 0.001137 0.004302 +absolute_params_change 0.02421 0.1485 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.28809908637635, 'BeqFac': 3985.3577919647823, 'BeqShift': 2.2275788765626143, 'DiscFac': 1.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375, 'DiscFac': 0.9875}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875, 'DiscFac': 0.8562500000000001}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375, 'DiscFac': 0.9312500000000001}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25, 'DiscFac': 0.875}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75, 'DiscFac': 0.7250000000000001}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125, 'DiscFac': 0.9125000000000001}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125, 'DiscFac': 0.6125}, {'CRRA': 16.1609375, 'BeqFac': 156.25, 'BeqShift': 66.71875, 'DiscFac': 0.9781250000000001}, {'CRRA': 15.274999999999999, 'BeqFac': 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'DiscFac': 1.0625}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'BeqShift': 32.8125, 'DiscFac': 0.89375}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'BeqShift': 39.375, 'DiscFac': 0.6875}, {'CRRA': 1.9859375, 'BeqFac': 2656.25, 'BeqShift': 49.21875, 'DiscFac': 0.828125}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'BeqShift': 15.3125, 'DiscFac': 1.0437500000000002}, {'CRRA': 18.5234375, 'BeqFac': 6406.25, 'BeqShift': 40.46875, 'DiscFac': 1.053125}, {'CRRA': 1.690625, 'BeqFac': 5312.5, 'BeqShift': 63.4375, 'DiscFac': 1.08125}], 'exploration_results': array([4.85268873e-02, 5.81756522e-01, 7.85434558e-01, 1.08501898e+00, + 1.09147168e+00, 1.15508374e+00, 1.35896335e+00, 1.47857950e+00, + 1.60399255e+00, 1.93344372e+00, 1.94383457e+00, 1.97559443e+00, + 2.00706025e+00, 2.09257083e+00, 2.11464344e+00, 2.13130763e+00, + 2.24401741e+00, 2.32809571e+00, 2.43093035e+00, 2.58864817e+00, + 2.64782870e+00, 2.75968951e+00, 2.76063416e+00, 2.87160809e+00, + 2.96402039e+00, 3.18404442e+00, 3.25640118e+00, 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candidate_x=array([1.31507572e+01, 3.94692938e+03, 7.00000000e+01, 6.81223719e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-0.003076041580485302, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=102.49222305696522, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=62.025760240591936, linear_terms=array([-174.60320912, 8.27773014, 1.75673439, 164.70357842]), square_terms=array([[ 2.48797993e+02, -1.17200748e+01, -2.72607667e+00, + 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9.80265374e-01])), candidate_index=16, candidate_x=array([5.77529085e+00, 4.06149904e+03, 4.05719276e+01, 1.10000000e+00]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-2.8961115549437415, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 3, 7, 11, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=25.623055764241304, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=26.49407321206659, linear_terms=array([-49.06494769, 9.79102818, 1.51086076, 57.27587634]), square_terms=array([[ 4.76594328e+01, -9.44277762e+00, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=12.811527882120652, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=36.484478434195694, linear_terms=array([-52.90415793, -4.33559743, 17.10456355, 79.00342384]), square_terms=array([[ 39.44693796, 3.09170401, -12.53562482, -56.43816351], + [ 3.09170401, 0.26120936, -1.01043316, -4.74528348], + [-12.53562482, -1.01043316, 4.02751365, 18.43138834], + [-56.43816351, -4.74528348, 18.43138834, 86.79993595]]), scale=array([7.40091372, 9.54747144, 5.96475664, 0.3 ]), shift=array([8.50091372e+00, 4.09968892e+03, 5.96475664e+00, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=30, candidate_x=array([1.11319430e+01, 4.10923639e+03, 0.00000000e+00, 6.76395240e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-0.01107643105638871, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([16]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=6.405763941060326, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30]), model=ScalarModel(intercept=41.718081219167644, linear_terms=array([-33.92226807, -4.49642876, 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4.09974713e+03, 2.33277881e+00, 1.03111738e+00]), index=75, x=array([6.29201645e+00, 4.09974713e+03, 2.33277881e+00, 1.03111738e+00]), fval=0.3880150372231467, rho=0.612492089426925, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), old_indices_discarded=array([47, 52, 53, 55, 58, 60]), step_length=0.11989908686500553, relative_step_length=1.1979120101778487, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29201645e+00, 4.09974713e+03, 2.33277881e+00, 1.03111738e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 60, 61, 62, 63, 64, 65, 66, 67, 70, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.6784397175461118, linear_terms=array([ 0.05917309, -0.09411167, 0.08046158, -1.61073661]), square_terms=array([[ 4.09804153e-03, -4.03800700e-03, 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51, 52, 53, 54, 55, 56, 57, 58, 59, 68, 69]), step_length=0.25874156763788225, relative_step_length=1.2925437528754322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 55, 59, 60, 61, 62, 64, 65, 67, 68, 70, 71, 73, 75, 76]), model=ScalarModel(intercept=0.32097874490276107, linear_terms=array([ 0.05330163, -0.04745503, -0.07450486, 0.04707809]), square_terms=array([[ 0.04450147, -0.0463324 , -0.06634309, -0.27074592], + [-0.0463324 , 0.0485459 , 0.06911336, 0.29211727], + [-0.06634309, 0.06911336, 0.09925331, 0.40416762], + [-0.27074592, 0.29211727, 0.40416762, 2.04202383]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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model=ScalarModel(intercept=0.3874021895959514, linear_terms=array([-1.11289514, -1.07248142, -0.06157134, -0.30876554]), square_terms=array([[6.85846414, 6.56737707, 0.304784 , 3.06229562], + [6.56737707, 6.28875786, 0.29204616, 2.9296343 ], + [0.304784 , 0.29204616, 0.01447866, 0.12773002], + [3.06229562, 2.9296343 , 0.12773002, 1.462907 ]]), scale=0.0500450307895338, shift=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=114, candidate_x=array([5.89553599e+00, 4.09993215e+03, 1.98226579e+00, 1.01756627e+00]), index=111, x=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), fval=0.30088852241782776, rho=-0.47013410940622297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 94, 95, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, + 111, 113]), old_indices_discarded=array([ 59, 96, 97, 98, 99, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92411952e+00, 4.09996773e+03, 1.96127057e+00, 1.02112845e+00]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 95, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125, 126]), 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117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_used=array([ 94, 95, 111, 114]), old_indices_discarded=array([], dtype=int32), step_length=0.02511249539583245, relative_step_length=1.0035959614629457, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.92278744e+00, 4.09994717e+03, 1.94692224e+00, 1.02144230e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), model=ScalarModel(intercept=0.2943051049928589, linear_terms=array([ 0.00564431, -0.00188183, 0.00320629, -0.09902661]), square_terms=array([[ 9.27890201e-05, 7.56993238e-06, 6.61699037e-06, + 5.86812035e-03], + [ 7.56993238e-06, 3.99076703e-05, -3.70323399e-05, + 8.89741428e-03], + [ 6.61699037e-06, -3.70323399e-05, 8.59295855e-05, + -8.43245723e-03], + [ 5.86812035e-03, 8.89741428e-03, 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State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), model=ScalarModel(intercept=0.2809513915508127, linear_terms=array([0.00283903, 0.01786394, 0.01405315, 0.08551073]), square_terms=array([[ 2.87201002e-03, -8.38100805e-03, -4.93196038e-03, + 1.13296034e-01], + [-8.38100805e-03, 2.61008372e-02, 1.54731491e-02, + -3.32316666e-01], + [-4.93196038e-03, 1.54731491e-02, 9.29788666e-03, + -1.97733985e-01], + [ 1.13296034e-01, -3.32316666e-01, -1.97733985e-01, + 4.54041405e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=129, candidate_x=array([5.80416113e+00, 4.09988374e+03, 1.85123275e+00, 1.01537731e+00]), index=128, x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), fval=0.2860214168257209, rho=-0.07512022159199228, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), old_indices_discarded=array([ 46, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, + 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, + 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, + 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), 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106, 107, 108, 109, + 110, 112, 116, 117, 118, 120, 121, 122, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), model=ScalarModel(intercept=0.6450590722968451, linear_terms=array([ 0.0687357 , 0.08298437, -0.06097559, -2.78513076]), square_terms=array([[ 4.13605440e-03, 4.60176141e-03, -3.54511454e-03, + -1.40984410e-01], + [ 4.60176141e-03, 6.36872983e-03, -5.14680157e-03, + -2.29612312e-01], + [-3.54511454e-03, -5.14680157e-03, 4.74541412e-03, + 1.85732745e-01], + [-1.40984410e-01, -2.29612312e-01, 1.85732745e-01, + 9.17763751e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11568783]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.84312166e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 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0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=135, candidate_x=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), index=135, x=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), fval=0.19084543631566314, rho=0.5530791700510971, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([129, 131, 132, 133, 134]), old_indices_discarded=array([], dtype=int32), step_length=0.12919783936681034, relative_step_length=1.2908158645176637, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 1.01421638e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, + 134, 135]), model=ScalarModel(intercept=0.6377265413639779, linear_terms=array([ 0.01280445, 0.03233605, 0.04550891, -2.87385806]), square_terms=array([[ 1.03672207e-03, 1.33278357e-04, 2.91938128e-04, + 1.62342101e-02], + [ 1.33278357e-04, 9.48774396e-04, 1.23085546e-03, + -8.83801170e-02], + [ 2.91938128e-04, 1.23085546e-03, 1.93567123e-03, + -1.18090773e-01], + [ 1.62342101e-02, -8.83801170e-02, -1.18090773e-01, + 9.26865853e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11748143]), shift=array([5.38702780e+00, 4.09967489e+03, 1.71388426e+00, 9.82518572e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 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old_indices_used=array([ 94, 114, 115, 123, 125, 126, 127, 128, 129, 130, 131, 132, 133, + 134, 135]), old_indices_discarded=array([ 46, 49, 54, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, + 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, + 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 124]), step_length=0.25839641740029984, relative_step_length=1.2908195545277783, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.23784856e+00, 4.09952572e+03, 1.56470502e+00, 1.01653381e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 114, 115, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, + 135, 136]), model=ScalarModel(intercept=3.3407898582211835, linear_terms=array([ 1.38155211e-01, -7.92390870e-03, 3.08910897e-01, -1.15990734e+01]), square_terms=array([[ 7.85599256e-03, 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9.26235080e-01, 9.54248156e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=-5.901698191343305, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([141, 145, 146, 147, 148, 149, 150, 151]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([146, 148, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, + 162, 163, 164]), model=ScalarModel(intercept=0.040408606819058814, linear_terms=array([ 1.79403686e-03, -1.26156607e-04, -3.91788853e-05, -1.94426398e-02]), square_terms=array([[ 1.03044977e-03, 2.02913221e-05, 3.65652688e-04, + -1.22326273e-02], + [ 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vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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1.53745960e+00, 9.76255422e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), model=ScalarModel(intercept=0.032130564354299555, linear_terms=array([-0.00156055, -0.00139965, 0.00036611, -0.025743 ]), square_terms=array([[ 4.05055456e-03, 1.48919579e-03, -6.07642697e-04, + 3.44846053e-02], + [ 1.48919579e-03, 1.66608590e-03, -7.81966055e-04, + 3.87222711e-02], + [-6.07642697e-04, -7.81966055e-04, 5.86968019e-04, + -1.75898376e-02], + [ 3.44846053e-02, 3.87222711e-02, -1.75898376e-02, + 9.06040243e-01]]), scale=0.1000900615790676, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 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candidate_x=array([4.15236900e+00, 4.09955913e+03, 1.56526359e+00, 9.74785320e-01]), index=179, x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), fval=0.03162745417502315, rho=-3.5033153040189933, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178, 179, 180]), model=ScalarModel(intercept=0.031652111625055504, linear_terms=array([ 4.80604637e-05, 1.39247104e-05, -2.39867534e-05, -1.89127269e-04]), square_terms=array([[ 3.90665224e-03, 1.27857855e-04, 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model=ScalarModel(intercept=0.03161885589197164, linear_terms=array([ 1.24235116e-05, 1.65270337e-05, -5.96983212e-07, -3.23007724e-04]), square_terms=array([[ 3.85807274e-03, 1.15384522e-04, 1.32757111e-03, + -5.48814365e-02], + [ 1.15384522e-04, 4.41711422e-06, 4.73125548e-05, + -2.03433014e-03], + [ 1.32757111e-03, 4.73125548e-05, 5.79485173e-04, + -2.24432493e-02], + [-5.48814365e-02, -2.03433014e-03, -2.24432493e-02, + 9.54134964e-01]]), scale=0.0500450307895338, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0250225153947669, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([175, 177, 179, 181, 182, 183]), model=ScalarModel(intercept=0.03159896733455111, linear_terms=array([ 5.72777496e-04, -4.79596153e-06, 1.29876317e-04, -7.46995187e-03]), square_terms=array([[ 1.16620241e-03, 2.24403583e-06, 3.92674408e-04, + -1.60307218e-02], + [ 2.24403583e-06, 7.30065521e-09, 8.86098589e-07, + -3.50743943e-05], + [ 3.92674408e-04, 8.86098589e-07, 1.60868867e-04, + -6.16883815e-03], + [-1.60307218e-02, -3.50743943e-05, -6.16883815e-03, + 2.56901239e-01]]), scale=0.0250225153947669, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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model_indices=array([179, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, + 194, 195, 196]), model=ScalarModel(intercept=0.03162939399086311, linear_terms=array([-2.05014239e-05, -4.40701544e-07, -7.03347564e-06, 4.85199295e-04]), square_terms=array([[ 3.49864962e-04, -2.65476555e-06, 1.06943866e-04, + -4.62038714e-03], + [-2.65476555e-06, 3.14059178e-08, -9.72077663e-07, + 4.40297209e-05], + [ 1.06943866e-04, -9.72077663e-07, 3.86168856e-05, + -1.57987714e-03], + [-4.62038714e-03, 4.40297209e-05, -1.57987714e-03, + 7.04100722e-02]]), scale=0.01251125769738345, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], 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1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-8.714421091041428, accepted=False, new_indices=array([185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196]), old_indices_used=array([179, 181, 183, 184]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.006255628848691725, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([181, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, + 196, 197]), model=ScalarModel(intercept=0.03163372180134707, linear_terms=array([-1.40416035e-05, -1.06245855e-05, -6.25328365e-06, 2.93425222e-04]), square_terms=array([[ 8.39288020e-05, 3.25915482e-06, 2.80895929e-05, + -1.12787517e-03], + [ 3.25915482e-06, 1.50804018e-07, 1.22778171e-06, + -5.07296461e-05], + [ 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State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0031278144243458623, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), model=ScalarModel(intercept=0.03163462823062035, linear_terms=array([-9.39533430e-06, -5.39540217e-06, 2.16599626e-07, 1.49312835e-04]), square_terms=array([[ 2.34139611e-05, 1.09806616e-06, 5.53797697e-06, + -3.00949207e-04], + [ 1.09806616e-06, 5.92778360e-08, 2.79762167e-07, + -1.59957868e-05], + [ 5.53797697e-06, 2.79762167e-07, 1.56644529e-06, + -7.74361413e-05], + [-3.00949207e-04, -1.59957868e-05, -7.74361413e-05, + 4.40255574e-03]]), scale=0.0031278144243458623, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 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1.00128795e+00]), index=90, x=array([4.20521015e+00, 3.98536472e+03, 2.20720870e+00, 9.92417556e-01]), fval=0.04041587683749358, rho=-9.015153548302399, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 64, 69, 73, 74, 75, 76, 79, 80, 81, 82, 85, 87, 89, 90]), old_indices_discarded=array([ 0, 59, 60, 62, 63, 65, 66, 67, 68, 70, 71, 72, 77, 78, 83, 84, 86, + 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.20521015e+00, 3.98536472e+03, 2.20720870e+00, 9.92417556e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 64, 69, 73, 74, 75, 76, 77, 78, 79, 84, 87, 88, 89, 90]), model=ScalarModel(intercept=0.040131573919175975, linear_terms=array([ 0.00323797, -0.0008344 , 0.0008575 , -0.01436487]), square_terms=array([[ 1.08500426e-03, -4.90024550e-03, 3.21182359e-04, + 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=93, candidate_x=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), index=93, x=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), fval=0.040204861958739575, rho=0.04694682324737547, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([64, 69, 73, 74, 75, 76, 77, 78, 79, 84, 87, 88, 89, 90, 92]), old_indices_discarded=array([ 0, 61, 67, 72, 80, 81, 82, 83, 85, 86, 91]), step_length=0.024864348162927093, relative_step_length=1.0221854638064007, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([73, 74, 90, 92, 93]), model=ScalarModel(intercept=0.04020486195873957, linear_terms=array([-0.00706289, -0.00175749, 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 90, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106]), model=ScalarModel(intercept=0.04013248724041306, linear_terms=array([ 7.02734078e-04, 4.25545025e-05, 1.68036148e-04, -8.04428384e-03]), square_terms=array([[ 1.09690480e-04, 1.40037264e-05, 6.92626716e-06, + -1.62751319e-03], + [ 1.40037264e-05, 2.06061675e-06, 7.87462498e-07, + -2.33069567e-04], + [ 6.92626716e-06, 7.87462498e-07, 1.26111162e-06, + -9.04427721e-05], + [-1.62751319e-03, -2.33069567e-04, -9.04427721e-05, + 2.79100169e-02]]), scale=0.006081173388618138, shift=array([4.19083419e+00, 3.98534699e+03, 2.19745030e+00, 9.91076059e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 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State(trustregion=Region(center=array([4.15700474e+00, 3.98535408e+03, 2.17086332e+00, 9.90922363e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 108, 109]), model=ScalarModel(intercept=0.03707324900316479, linear_terms=array([ 0.00134055, -0.00050342, 0.00101212, 0.00208648]), square_terms=array([[ 5.91854237e-03, 1.94274484e-03, 1.34141717e-03, + -9.61364422e-02], + [ 1.94274484e-03, 7.09489463e-04, 4.55910967e-04, + -3.55979605e-02], + [ 1.34141717e-03, 4.55910967e-04, 3.52856825e-04, + -2.30368734e-02], + [-9.61364422e-02, -3.55979605e-02, -2.30368734e-02, + 1.81990976e+00]]), scale=0.048649387108945105, shift=array([4.15700474e+00, 3.98535408e+03, 2.17086332e+00, 9.90922363e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 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3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=135, candidate_x=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), index=135, x=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), fval=0.03322208936021106, rho=0.6923368728093419, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([119, 126, 128, 129, 130, 131, 132, 133, 134]), old_indices_discarded=array([], dtype=int32), step_length=0.024497657933993316, relative_step_length=1.007110649888905, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 116, 119, 122, 125, 126, 127, 128, 129, 130, 131, 132, 133, + 134, 135]), model=ScalarModel(intercept=0.033351943337015584, 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116, 119, 122, 125, 126, 127, 128, 129, 130, 131, 132, 133, + 134, 135]), old_indices_discarded=array([ 60, 111, 114, 115, 117, 118, 120, 121, 123, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10102122e+00, 3.98544664e+03, 1.89832339e+00, 9.83143669e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([128, 130, 131, 132, 133, 134, 135, 136]), model=ScalarModel(intercept=0.03319114993036455, linear_terms=array([-5.23016113e-05, -9.59227897e-06, 1.63111360e-04, 1.22272274e-03]), square_terms=array([[ 1.65538480e-03, -1.07905373e-05, 4.49382646e-04, + -2.37818011e-02], + [-1.07905373e-05, 9.51781004e-08, -2.54406317e-06, + 1.39408687e-04], + [ 4.49382646e-04, -2.54406317e-06, 1.38176690e-04, + -7.08221157e-03], + [-2.37818011e-02, 1.39408687e-04, -7.08221157e-03, + 3.91873909e-01]]), 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upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 116, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, + 136, 137]), model=ScalarModel(intercept=0.033235684467905355, linear_terms=array([-2.53103045e-03, -1.23798078e-03, -6.59073825e-05, 2.22614798e-02]), square_terms=array([[ 2.43041668e-02, 8.79030260e-03, 4.02932575e-03, + -2.25980212e-01], + [ 8.79030260e-03, 3.26024408e-03, 1.47271211e-03, + -8.42717587e-02], + [ 4.02932575e-03, 1.47271211e-03, 7.11150827e-04, + -3.82927477e-02], + [-2.25980212e-01, -8.42717587e-02, -3.82927477e-02, + 2.18945052e+00]]), scale=0.048649387108945105, shift=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 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x=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01]), fval=0.033122729602777526, rho=-0.46260457325856585, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([113, 116, 119, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, + 136, 137]), old_indices_discarded=array([111, 114, 115, 117, 118, 120, 121, 122, 123, 124, 125]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10090535e+00, 3.98544804e+03, 1.87384893e+00, 9.82618156e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138]), model=ScalarModel(intercept=0.033088400599093445, linear_terms=array([-2.07963025e-04, 4.86452866e-05, 1.41008433e-04, 9.08365106e-05]), square_terms=array([[ 1.90554771e-03, -5.25048184e-05, 4.99344670e-04, + -2.51666427e-02], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 119, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), model=ScalarModel(intercept=0.0331182013788081, linear_terms=array([-1.97974768e-03, -1.06547349e-03, -5.51519758e-05, 1.81116415e-02]), square_terms=array([[ 2.38133820e-02, 8.51946412e-03, 4.28414957e-03, + -2.23713059e-01], + [ 8.51946412e-03, 3.13204553e-03, 1.55382928e-03, + -8.27366123e-02], + [ 4.28414957e-03, 1.55382928e-03, 8.16135432e-04, + -4.12818595e-02], + [-2.23713059e-01, -8.27366123e-02, -4.12818595e-02, + 2.19746582e+00]]), scale=0.048649387108945105, shift=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 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scale=398.5357791964783, shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=140, candidate_x=array([4.11871670e+00, 3.98548023e+03, 1.82440548e+00, 9.84089102e-01]), index=139, x=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), fval=0.033044655661740516, rho=-0.568130481594652, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([113, 119, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), old_indices_discarded=array([111, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01]), radius=0.024324693554472553, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140]), model=ScalarModel(intercept=0.032951013003172214, linear_terms=array([-1.03938213e-04, -3.52725894e-06, 1.07593804e-04, 1.86042680e-03]), square_terms=array([[ 1.75065726e-03, -3.94983661e-06, 4.88863565e-04, + -2.44687091e-02], + [-3.94983661e-06, 1.79124813e-08, -1.19360841e-06, + 6.43531025e-05], + [ 4.88863565e-04, -1.19360841e-06, 1.52931166e-04, + -7.43453721e-03], + [-2.44687091e-02, 6.43531025e-05, -7.43453721e-03, + 3.86356869e-01]]), scale=0.024324693554472553, shift=array([4.11526450e+00, 3.98544109e+03, 1.85318935e+00, 9.83206097e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140]), old_indices_discarded=array([], dtype=int32), step_length=0.024637350930881603, relative_step_length=1.0128534970320957, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11559640e+00, 3.98544181e+03, 1.82857121e+00, 9.82636343e-01]), radius=0.048649387108945105, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([113, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, + 140, 141]), model=ScalarModel(intercept=0.03290815908255805, linear_terms=array([-4.11171815e-04, -5.00685998e-04, -1.68988412e-05, 1.54742229e-02]), square_terms=array([[ 6.62320972e-03, 2.37316712e-03, 2.54362470e-03, + -1.24735278e-01], + [ 2.37316712e-03, 9.45809690e-04, 9.92127010e-04, + -5.01657639e-02], + [ 2.54362470e-03, 9.92127010e-04, 1.08295792e-03, + -5.27667377e-02], + 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candidate_x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=0.642468592930664, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([128, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, + 142, 143]), old_indices_discarded=array([ 0, 48, 49, 52, 53, 54, 56, 59, 60, 61, 62, 63, 64, + 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, + 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, + 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 129]), step_length=0.20552612749559135, relative_step_length=1.0561599010247422, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.38919509687156084, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144]), model=ScalarModel(intercept=0.5158401817968741, linear_terms=array([ 0.48374106, -0.21959826, -0.03034574, -2.45076283]), square_terms=array([[ 2.73512553e-01, -1.10681409e-01, -1.36794804e-02, + -1.22742522e+00], + [-1.10681409e-01, 5.00854227e-02, 7.37119045e-03, + 5.56398366e-01], + [-1.36794804e-02, 7.37119045e-03, 2.82983352e-03, + 7.80570977e-02], + [-1.22742522e+00, 5.56398366e-01, 7.80570977e-02, + 6.20455154e+00]]), scale=array([0.29003793, 0.29003793, 0.29003793, 0.20600944]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 8.93990557e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 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candidate_x=array([4.14231731e+00, 3.98555744e+03, 1.48330502e+00, 9.83650146e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-19.37116300456786, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 59, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144]), old_indices_discarded=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, + 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, + 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, + 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, + 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, + 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, + 124, 125, 126, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.19459754843578042, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, + 144, 145]), model=ScalarModel(intercept=0.034194805708801616, linear_terms=array([ 0.00897016, -0.00531212, 0.00118509, -0.10933048]), square_terms=array([[ 2.60943087e-02, -1.08818961e-02, 2.66055361e-03, + -2.23169348e-01], + [-1.08818961e-02, 6.33814787e-03, -1.14716135e-03, + 1.30275256e-01], + [ 2.66055361e-03, -1.14716135e-03, 6.17083790e-04, + -2.45327892e-02], + [-2.23169348e-01, 1.30275256e-01, -2.45327892e-02, + 2.68630749e+00]]), scale=array([0.14501897, 0.14501897, 0.14501897, 0.13349996]), shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.66500041e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, + 158, 159, 160]), model=ScalarModel(intercept=0.032556756784188255, linear_terms=array([-1.38069018e-03, -8.05850091e-05, -2.11446865e-04, 2.23013286e-02]), square_terms=array([[ 1.35249748e-03, 6.51569707e-05, 1.87461961e-04, + -1.84531760e-02], + [ 6.51569707e-05, 3.62304471e-06, 9.61360198e-06, + -1.02222512e-03], + [ 1.87461961e-04, 9.61360198e-06, 3.78176312e-05, + -2.78482419e-03], + [-1.84531760e-02, -1.02222512e-03, -2.78482419e-03, + 2.89607050e-01]]), scale=0.024324693554472553, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 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3.98567482e+03, 1.55173969e+00, 9.75875695e-01]), index=144, x=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), fval=0.03185233767614412, rho=-0.3377986236674669, accepted=False, new_indices=array([149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), old_indices_used=array([144, 145, 147, 148]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.012162346777236276, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, + 160, 161]), model=ScalarModel(intercept=0.032618873867381576, linear_terms=array([-0.00076276, -0.00023605, -0.00050889, 0.01153604]), square_terms=array([[ 3.73432785e-04, 9.98864964e-05, 2.43531496e-04, + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, + 161, 162]), model=ScalarModel(intercept=0.03252324635513784, linear_terms=array([-4.14065539e-04, -9.94093190e-05, -9.38607562e-05, 5.40904684e-03]), square_terms=array([[ 1.20853884e-04, 2.50219420e-05, 2.98370200e-05, + -1.40554138e-03], + [ 2.50219420e-05, 5.65327048e-06, 6.58419488e-06, + -3.17355619e-04], + [ 2.98370200e-05, 6.58419488e-06, 8.42877891e-06, + -3.71203286e-04], + [-1.40554138e-03, -3.17355619e-04, -3.71203286e-04, + 1.78905730e-02]]), scale=0.006081173388618138, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 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169, 170, 171, 172, 173, + 174, 175]), model=ScalarModel(intercept=0.03190626622045728, linear_terms=array([-5.00916898e-05, -1.60685558e-06, -3.41876508e-05, 8.86221374e-04]), square_terms=array([[ 2.04825629e-05, 2.61345798e-07, 7.17448985e-06, + -2.80536546e-04], + [ 2.61345798e-07, 2.02583758e-08, 6.64912984e-08, + -1.61121192e-06], + [ 7.17448985e-06, 6.64912984e-08, 3.00963453e-06, + -1.09754930e-04], + [-2.80536546e-04, -1.61121192e-06, -1.09754930e-04, + 4.34084080e-03]]), scale=0.003040586694309069, shift=array([4.12992939e+00, 3.98565047e+03, 1.55276547e+00, 9.78019048e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 0.04472352, 0.05087222, 0.05143555, -0.00068292, 0.11227828, + -0.11812791, -0.01940885, 0.06476132, 0.05389175, 0.03015647, + 0.00122987, -0.01762768]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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rho=0.7825513778471532, accepted=True, new_indices=array([164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), old_indices_used=array([144, 162, 163]), old_indices_discarded=array([], dtype=int32), step_length=0.00327686813422291, relative_step_length=1.077709160655106, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.006081173388618138, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, + 175, 176]), model=ScalarModel(intercept=0.031782193359979234, linear_terms=array([ 1.65934087e-05, -1.10658052e-05, -5.86797451e-05, 1.70650043e-05]), square_terms=array([[ 8.16385660e-05, 4.98651617e-07, 2.63205082e-05, + -1.11853097e-03], + [ 4.98651617e-07, 7.66151913e-08, 9.65330348e-08, + 7.67793318e-07], + [ 2.63205082e-05, 9.65330348e-08, 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State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, + 176, 177]), model=ScalarModel(intercept=0.03179776772837414, linear_terms=array([ 4.32340121e-06, -2.77572135e-06, -1.55094590e-05, 6.03814167e-06]), square_terms=array([[ 2.04941628e-05, 1.12208524e-07, 6.82404742e-06, + -2.79715394e-04], + [ 1.12208524e-07, 1.42062247e-08, 1.38323893e-08, + 1.90493930e-07], + [ 6.82404742e-06, 1.38323893e-08, 2.73772264e-06, + -1.05054792e-04], + [-2.79715394e-04, 1.90493930e-07, -1.05054792e-04, + 4.34256904e-03]]), scale=0.003040586694309069, shift=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01])), vector_model=VectorModel(intercepts=array([ 0.01801309, 0.0426184 , 0.02632857, 0.03840123, 0.041553 , + 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shift=array([4.28809909e+00, 3.98535779e+03, 2.22757888e+00, 1.00000000e+00])), candidate_index=178, candidate_x=array([4.12767420e+00, 3.98565133e+03, 1.55853065e+00, 9.77392943e-01]), index=176, x=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), fval=0.03177037559889115, rho=-0.606887945704526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([144, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, + 176, 177]), old_indices_discarded=array([162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12847123e+00, 3.98565079e+03, 1.55561076e+00, 9.77377950e-01]), radius=0.0015202933471545345, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, + 177, 178]), model=ScalarModel(intercept=0.03179737436356608, 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164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, + 177, 178]), old_indices_discarded=array([167]), step_length=0.001533153137050571, relative_step_length=1.0084587556211475, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.12778716e+00, 3.98565116e+03, 1.55693254e+00, 9.77361589e-01]), radius=0.003040586694309069, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([144, 164, 165, 166, 168, 169, 170, 171, 172, 174, 175, 176, 177, + 178, 179]), model=ScalarModel(intercept=0.031784323141796134, linear_terms=array([ 1.04203216e-05, -5.02575266e-06, -1.81791012e-05, 4.40512121e-07]), square_terms=array([[ 2.04146038e-05, 1.44018756e-07, 6.73868302e-06, + -2.78928952e-04], + [ 1.44018756e-07, 1.91750967e-08, 1.69350210e-08, + -1.86249381e-07], + [ 6.73868302e-06, 1.69350210e-08, 2.75520070e-06, + -1.04927558e-04], + [-2.78928952e-04, -1.86249381e-07, -1.04927558e-04, + 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State(trustregion=Region(center=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00]), radius=12.086955218773213, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 7, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=140.77239003523985, linear_terms=array([-172.90891621, -7.83111137, -2.15766299, 116.60174267]), square_terms=array([[ 1.07105586e+02, 4.80058830e+00, 1.23891759e+00, + -7.05566845e+01], + [ 4.80058830e+00, 2.17979398e-01, 6.07723262e-02, + -3.25273056e+00], + [ 1.23891759e+00, 6.07723262e-02, 2.72332254e-02, + -1.03206277e+00], + [-7.05566845e+01, -3.25273056e+00, -1.03206277e+00, + 5.01595469e+01]]), scale=array([7.03241927, 9.00750174, 9.00750174, 0.3 ]), shift=array([8.13241927e+00, 3.86782567e+03, 2.26532658e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 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candidate_index=63, candidate_x=array([6.64993454e+00, 3.86733307e+03, 2.20199258e+01, 9.63254363e-01]), index=46, x=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), fval=0.610030672784944, rho=-5.047496342917129, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 51, 55, 57, 59, 60, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.72030565e+00, 3.86726270e+03, 2.20902969e+01, 1.02151771e+00]), radius=0.04721466882333286, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.5645793736320908, linear_terms=array([ 0.02415958, -0.02485336, 0.02479561, -0.34196441]), square_terms=array([[ 1.39237450e-03, -1.62359879e-03, 1.62536290e-03, + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00]), radius=0.09442933764666572, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([46, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.51062010022679, linear_terms=array([ 0.01710986, -0.01628909, 0.0164215 , -0.04435784]), square_terms=array([[ 1.44592132e-03, -1.90647266e-03, 1.93858106e-03, + -5.80270672e-02], + [-1.90647266e-03, 2.93939927e-03, -3.00816915e-03, + 9.37944524e-02], + [ 1.93858106e-03, -3.00816915e-03, 3.08082456e-03, + -9.62367159e-02], + [-5.80270672e-02, 9.37944524e-02, -9.62367159e-02, + 3.30228437e+00]]), scale=array([0.07037111, 0.07037111, 0.07037111, 0.07037111]), shift=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 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model=ScalarModel(intercept=0.5106201002267896, linear_terms=array([ 0.01147966, -0.01092897, 0.01101781, -0.02976137]), square_terms=array([[ 6.50893276e-04, -8.58214219e-04, 8.72668080e-04, + -2.61213577e-02], + [-8.58214219e-04, 1.32319456e-03, -1.35415188e-03, + 4.22223379e-02], + [ 8.72668080e-04, -1.35415188e-03, 1.38685831e-03, + -4.33217428e-02], + [-2.61213577e-02, 4.22223379e-02, -4.33217428e-02, + 1.48655025e+00]]), scale=0.04721466882333286, shift=array([6.68822735e+00, 3.86729048e+03, 2.20628094e+01, 1.02919298e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-0.7561149550917772, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 83, 87, 91, 93, 94, 97, 98, 99, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=0.000184432300091144, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 133]), model=ScalarModel(intercept=0.5418053134335727, linear_terms=array([ 1.59107911e-06, 1.91604113e-06, -8.39570529e-07, 4.03636914e-06]), 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old_indices_discarded=array([115, 118, 119]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=9.2216150045572e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 134]), model=ScalarModel(intercept=0.5418036600266991, linear_terms=array([1.90610897e-06, 1.50974387e-06, 9.76797073e-08, 1.95253835e-06]), square_terms=array([[1.20515783e-09, 3.37366272e-11, 4.94787699e-11, 7.52437640e-08], + [3.37366272e-11, 1.02605967e-10, 3.57534723e-11, 1.62531668e-09], + [4.94787699e-11, 3.57534723e-11, 5.57431091e-11, 5.98222773e-09], + [7.52437640e-08, 1.62531668e-09, 5.98222773e-09, 7.49043871e-06]]), scale=9.2216150045572e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=386.7825670007428, shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=135, candidate_x=array([6.69783593e+00, 3.86727820e+03, 2.20808947e+01, 1.03483235e+00]), index=132, x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-2.447529842628543, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, + 132, 134]), old_indices_discarded=array([119, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=4.6108075022786e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([116, 120, 125, 129, 130, 131, 132, 134, 135]), model=ScalarModel(intercept=0.541801225592115, linear_terms=array([-3.85833151e-06, 1.66200121e-06, -7.39584646e-07, 5.43636323e-06]), square_terms=array([[ 5.05560336e-10, 1.28395863e-10, 5.46841377e-11, + 2.23605728e-08], + [ 1.28395863e-10, 9.20178070e-11, -7.88195222e-13, + 2.29617380e-09], + [ 5.46841377e-11, -7.88195222e-13, 3.43892202e-11, + 5.45528958e-09], + [ 2.23605728e-08, 2.29617380e-09, 5.45528958e-09, + 1.89032774e-06]]), scale=4.6108075022786e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=386.7825670007428, shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=136, candidate_x=array([6.69793775e+00, 3.86727825e+03, 2.20809041e+01, 1.03481767e+00]), index=132, x=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), fval=0.5417961421243523, rho=-2.670472262368463, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([116, 120, 125, 129, 130, 131, 132, 134, 135]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00]), radius=2.3054037511393e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 132, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, + 146, 147, 148]), model=ScalarModel(intercept=0.5417952329144413, linear_terms=array([ 9.66120026e-07, -1.00698628e-06, -2.74476935e-07, -9.79648793e-06]), square_terms=array([[3.32456620e-11, 1.25910740e-12, 3.31229126e-13, 2.67230800e-09], + [1.25910740e-12, 1.10109004e-11, 5.04392935e-12, 1.07277597e-09], + [3.31229126e-13, 5.04392935e-12, 2.52144478e-12, 3.73671865e-10], + [2.67230800e-09, 1.07277597e-09, 3.73671865e-10, 4.79025310e-07]]), scale=2.3054037511393e-05, shift=array([6.69790747e+00, 3.86727826e+03, 2.20808983e+01, 1.03484994e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 132, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, + 148, 149]), model=ScalarModel(intercept=0.5417877838400293, linear_terms=array([ 3.32286169e-07, -1.12576228e-06, -2.66782175e-07, -1.45832432e-05]), square_terms=array([[1.73587440e-10, 4.18888788e-12, 6.01676781e-12, 1.57992979e-08], + [4.18888788e-12, 6.23925265e-12, 3.51151103e-12, 3.47636777e-10], + [6.01676781e-12, 3.51151103e-12, 4.91855682e-12, 4.69589490e-10], + [1.57992979e-08, 3.47636777e-10, 4.69589490e-10, 1.89335649e-06]]), scale=4.6108075022786e-05, shift=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], 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x=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), fval=0.541790689077309, rho=-1.3326690558921785, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([125, 132, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, + 148, 149]), old_indices_discarded=array([116, 120, 128, 129, 130, 131, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69790464e+00, 3.86727826e+03, 2.20808995e+01, 1.03487271e+00]), radius=2.3054037511393e-05, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([132, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, + 149, 150]), model=ScalarModel(intercept=0.5417958106954883, linear_terms=array([-1.39628013e-06, 8.43762776e-07, -1.23740385e-07, 5.37052686e-07]), square_terms=array([[ 5.46236795e-11, -9.27306793e-12, -1.78917296e-12, + 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rho=-0.7375647236811499, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 169, + 170, 171]), old_indices_discarded=array([167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 158, 159, 160, 162, 163, 164, 165, 166, 169, 170, + 171, 172]), model=ScalarModel(intercept=0.5417883710603923, linear_terms=array([ 1.43992102e-07, -3.92495599e-08, -9.39836000e-08, -9.01031376e-08]), square_terms=array([[ 8.53994530e-14, -1.24453204e-14, -2.50307988e-14, + 4.70275038e-12], + [-1.24453204e-14, 1.24566358e-14, 1.43974985e-14, + 6.71297877e-13], + [-2.50307988e-14, 1.43974985e-14, 2.77619876e-14, + 1.47049227e-12], 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, + 172, 173]), model=ScalarModel(intercept=0.5417883675324741, linear_terms=array([ 1.43818395e-07, -3.70058572e-08, -9.73353633e-08, -8.86837355e-08]), square_terms=array([[ 8.52602282e-14, -1.20995611e-14, -2.52176727e-14, + 4.70638556e-12], + [-1.20995611e-14, 1.43089742e-14, 1.24025960e-14, + 6.47585103e-13], + [-2.52176727e-14, 1.24025960e-14, 2.84449624e-14, + 1.50365410e-12], + [ 4.70638556e-12, 6.47585103e-13, 1.50365410e-12, + 8.75615148e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), 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1.03486742e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.7821082957639802, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 166, 169, 170, 171, + 172, 173]), old_indices_discarded=array([158, 161, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 156, 157, 159, 160, 162, 163, 164, 165, 169, 170, 171, 172, + 173, 174]), model=ScalarModel(intercept=0.5417883646362189, linear_terms=array([ 1.39054002e-07, -3.67486274e-08, -9.76276358e-08, -9.03367442e-08]), square_terms=array([[ 8.31510649e-14, -1.19535743e-14, -2.39921551e-14, + 4.77544224e-12], + 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shift=array([6.15733679e+00, 3.86782567e+03, 2.26532658e+01, 1.00113435e+00])), candidate_index=176, candidate_x=array([6.69792315e+00, 3.86727825e+03, 2.20809018e+01, 1.03486746e+00]), index=151, x=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), fval=0.5417880312778971, rho=-0.8834804979032714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([151, 156, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, + 174, 175]), old_indices_discarded=array([158, 160, 161, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 157, 159, 162, 163, 164, 165, 169, 170, 171, 172, 173, 174, + 175, 176]), model=ScalarModel(intercept=0.5417882175351199, 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1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([151, 157, 163, 164, 165, 169, 170, 171, 172, 173, 174, 175, 176, + 177, 178]), model=ScalarModel(intercept=0.5417882076829452, linear_terms=array([-5.63158410e-09, -1.00512338e-08, -8.87137958e-08, 1.24139553e-08]), square_terms=array([[8.32424888e-14, 1.91902710e-15, 1.69446003e-14, 7.03679290e-12], + [1.91902710e-15, 3.00682277e-16, 2.65393612e-15, 1.50980635e-13], + [1.69446003e-14, 2.65393612e-15, 2.34246502e-14, 1.33333428e-12], + [7.03679290e-12, 1.50980635e-13, 1.33333428e-12, 8.72095518e-10]]), scale=1e-06, shift=array([6.69792371e+00, 3.86727825e+03, 2.20809012e+01, 1.03486698e+00])), vector_model=VectorModel(intercepts=array([ 0.02473786, 0.05916186, 0.05103045, 0.06783136, 0.0695652 , + 0.06270254, 0.04555533, -0.16182457, -0.31033459, -0.29822499, + -0.63415123, -0.62235824, 0.04878888, 0.06990258, 0.07736138, + 0.06837988, 0.04773419]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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1.03486684e+00]), fval=0.5417879962626828, rho=0.38593680496603683, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([151, 157, 163, 164, 165, 169, 170, 171, 172, 173, 174, 175, 176, + 177, 178]), old_indices_discarded=array([156, 158, 159, 160, 161, 162, 166, 167, 168]), step_length=1.0046483357496498e-06, relative_step_length=1.0046483357496498, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 180 entries., 'history': {'params': [{'CRRA': 6.15733678876515, 'BeqFac': 3867.825670007428, 'BeqShift': 22.653265765629147, 'DiscFac': 1.0011343468411}, {'CRRA': 1.9420465373776397, 'BeqFac': 3579.5856142194925, 'BeqShift': 63.884357717706486, 'DiscFac': 0.6683149691599166}, {'CRRA': 1.1, 'BeqFac': 4156.0657257953635, 'BeqShift': 70.0, 'DiscFac': 0.9091286558648171}, {'CRRA': 19.746753699543984, 'BeqFac': 3660.8959750636873, 'BeqShift': 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candidate_x=array([5.75763903e+00, 4.10028564e+03, 2.13437731e+00, 7.73678270e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-1.1311868352707046, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.27541175174579846, linear_terms=array([-0.06817834, -0.05276822, 0.126152 , -0.82843349]), square_terms=array([[ 0.05944259, 0.02280401, -0.0560511 , 0.53875948], + [ 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shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=60, candidate_x=array([6.20517676e+00, 4.09983810e+03, 2.23286259e+00, 9.36220917e-01]), index=0, x=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), fval=1.0675306002702436, rho=-4.1944986936569, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 47, 49, 51, 53, 55, 58, 60]), model=ScalarModel(intercept=0.2725232800460816, linear_terms=array([0.02014066, 0.01692965, 0.00516888, 0.19458607]), 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State(trustregion=Region(center=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 0, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.9761443032698459, linear_terms=array([ 0.0094557 , -0.02923194, 0.02583164, -0.9717343 ]), square_terms=array([[ 1.51163653e-04, -4.84525090e-05, 2.33633959e-06, + -2.54393388e-04], + [-4.84525090e-05, 6.22775993e-04, -5.30527383e-04, + 2.31310434e-02], + [ 2.33633959e-06, -5.30527383e-04, 5.40224484e-04, + -1.94176818e-02], + [-2.54393388e-04, 2.31310434e-02, -1.94176818e-02, + 8.94373433e-01]]), scale=0.0500450307895338, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, 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4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), fval=0.3874021895959502, rho=-4.0555723632570135, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 55, 59, 60, 61, 62, 64, 65, 67, 68, 70, 71, 73, 75, 76]), old_indices_discarded=array([49, 51, 63, 66, 69, 72, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.14283721e+00, 4.09989630e+03, 2.18359957e+00, 1.01755786e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([55, 59, 60, 76, 77]), model=ScalarModel(intercept=0.3874021895959514, linear_terms=array([-1.11289514, -1.07248142, -0.06157134, -0.30876554]), square_terms=array([[6.85846414, 6.56737707, 0.304784 , 3.06229562], + [6.56737707, 6.28875786, 0.29204616, 2.9296343 ], + [0.304784 , 0.29204616, 0.01447866, 0.12773002], + [3.06229562, 2.9296343 , 0.12773002, 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2.03586019e+00, 1.02534149e+00]), index=110, x=array([5.99870914e+00, 4.10004232e+03, 2.03586019e+00, 1.02534149e+00]), fval=0.31506887393988287, rho=1.4531169230617522, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 93, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, + 108, 109]), old_indices_discarded=array([59, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, + 92, 94, 95]), step_length=0.05004774724453496, relative_step_length=1.000054280214405, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.99870914e+00, 4.10004232e+03, 2.03586019e+00, 1.02534149e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 108, 110]), model=ScalarModel(intercept=0.3091099422488851, linear_terms=array([0.00887823, 0.01155048, 0.01058815, 0.06346417]), 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1.94692224e+00, 1.02144230e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), model=ScalarModel(intercept=0.2943051049928589, linear_terms=array([ 0.00564431, -0.00188183, 0.00320629, -0.09902661]), square_terms=array([[ 9.27890201e-05, 7.56993238e-06, 6.61699037e-06, + 5.86812035e-03], + [ 7.56993238e-06, 3.99076703e-05, -3.70323399e-05, + 8.89741428e-03], + [ 6.61699037e-06, -3.70323399e-05, 8.59295855e-05, + -8.43245723e-03], + [ 5.86812035e-03, 8.89741428e-03, -8.43245723e-03, + 2.25161796e+00]]), scale=0.0500450307895338, shift=array([5.92278744e+00, 4.09994717e+03, 1.94692224e+00, 1.02144230e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 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candidate_x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), index=128, x=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), fval=0.2860214168257209, rho=1.395194798708464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, + 126, 127]), old_indices_discarded=array([ 59, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 112, 113]), step_length=0.05013889760653118, relative_step_length=1.0018756471025494, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128]), old_indices_discarded=array([ 46, 59, 60, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, + 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, + 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, + 125, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.87875075e+00, 4.09995833e+03, 1.92582237e+00, 1.02362849e+00]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, + 127, 128]), model=ScalarModel(intercept=0.28601605918758766, linear_terms=array([ 0.00549346, -0.00187638, 0.00228974, -0.00413326]), square_terms=array([[ 9.45819260e-05, 2.05993647e-05, 2.22032641e-06, + 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118, 129]), step_length=0.0500760576419539, relative_step_length=1.0006199786858077, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([111, 114, 115, 116, 117, 119, 122, 123, 124, 125, 126, 127, 128, + 129, 130]), model=ScalarModel(intercept=0.27698506927654987, linear_terms=array([0.00886586, 0.00136488, 0.00678477, 0.02502439]), square_terms=array([[ 1.93358601e-04, 9.16030067e-06, 2.22153096e-05, + 9.68917980e-03], + [ 9.16030067e-06, 2.87649923e-05, 1.19928618e-04, + -1.05626890e-02], + [ 2.22153096e-05, 1.19928618e-04, 5.97417848e-04, + -4.71540074e-02], + [ 9.68917980e-03, -1.05626890e-02, -4.71540074e-02, + 5.10191499e+00]]), scale=array([0.07458962, 0.07458962, 0.07458962, 0.07458962]), shift=array([5.83456553e+00, 4.09997325e+03, 1.90758384e+00, 1.02375259e+00])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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State(trustregion=Region(center=array([5.75997591e+00, 4.09989866e+03, 1.83299422e+00, 1.02268458e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 114, 115, 117, 119, 123, 124, 125, 126, 127, 128, 129, + 130, 131]), model=ScalarModel(intercept=0.6958602300848257, linear_terms=array([ 0.00317513, 0.13661317, 0.00432578, -2.82344851]), square_terms=array([[ 8.92272971e-04, -9.23980154e-04, 1.92374401e-04, + 4.40514039e-02], + [-9.23980154e-04, 1.66500637e-02, -3.49295764e-04, + -3.72598035e-01], + [ 1.92374401e-04, -3.49295764e-04, 5.50846736e-04, + 6.72728739e-03], + [ 4.40514039e-02, -3.72598035e-01, 6.72728739e-03, + 9.12381886e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11324733]), shift=array([5.75997591e+00, 4.09989866e+03, 1.83299422e+00, 9.86752670e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=132, candidate_x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), index=132, x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), fval=0.22297505850584612, rho=0.8392169936307204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 114, 115, 117, 119, 123, 124, 125, 126, 127, 128, 129, + 130, 131]), old_indices_discarded=array([ 0, 46, 47, 49, 51, 53, 54, 55, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 112, 113, 116, 118, 120, 121, + 122]), step_length=0.25843212314368597, relative_step_length=1.2909979226036032, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.4003602463162704, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), model=ScalarModel(intercept=4.348347562426054, linear_terms=array([ 0.31927054, 0.46205338, -0.36145321, -14.31327039]), square_terms=array([[ 1.65442176e-02, 1.84070457e-02, -1.41804582e-02, + -4.63767949e-01], + [ 1.84070457e-02, 2.54749193e-02, -2.05872063e-02, + -7.55309262e-01], + [-1.41804582e-02, -2.05872063e-02, 1.89816565e-02, + 6.10967512e-01], + [-4.63767949e-01, -7.55309262e-01, 6.10967512e-01, + 2.48273398e+01]]), scale=array([0.29835848, 0.29835848, 0.29835848, 0.19027745]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.09722546e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, 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2.38204183e+00, 9.80265374e-01])), candidate_index=133, candidate_x=array([5.31243818e+00, 4.09945113e+03, 1.98217346e+00, 1.00539435e+00]), index=132, x=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), fval=0.22297505850584612, rho=-0.629024754891979, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 0, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, + 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, + 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, + 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, + 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 112, 116, 117, 118, 120, 121, 122, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 1.01780357e+00]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), model=ScalarModel(intercept=0.6450590722968451, linear_terms=array([ 0.0687357 , 0.08298437, -0.06097559, -2.78513076]), square_terms=array([[ 4.13605440e-03, 4.60176141e-03, -3.54511454e-03, + -1.40984410e-01], + [ 4.60176141e-03, 6.36872983e-03, -5.14680157e-03, + -2.29612312e-01], + [-3.54511454e-03, -5.14680157e-03, 4.74541412e-03, + 1.85732745e-01], + [-1.40984410e-01, -2.29612312e-01, 1.85732745e-01, + 9.17763751e+00]]), scale=array([0.14917924, 0.14917924, 0.14917924, 0.11568783]), shift=array([5.61079667e+00, 4.09974948e+03, 1.68381498e+00, 9.84312166e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 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candidate_x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), index=134, x=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), fval=0.21950785606017936, rho=0.08682007098967585, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 111, 113, 114, 115, 119, 123, 125, 126, 127, 128, 129, 130, + 131, 132]), old_indices_discarded=array([ 46, 49, 54, 55, 59, 60, 61, 62, 65, 70, 75, 76, 77, + 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, + 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 112, 116, 117, 118, 120, 121, 122, + 124, 133]), step_length=0.23555149344412082, relative_step_length=1.176697714677913, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.46161742e+00, 4.09960030e+03, 1.78847388e+00, 1.01309961e+00]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=140, candidate_x=array([4.04441463e+00, 4.09892900e+03, 7.09170793e-01, 9.87246327e-01]), index=139, x=array([4.64113160e+00, 4.09952572e+03, 9.67988057e-01, 9.80718762e-01]), fval=0.07568421749234616, rho=-97.55333914328999, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 94, 114, 125, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, + 138, 139]), old_indices_discarded=array([ 0, 35, 36, 37, 40, 42, 44, 45, 46, 47, 48, 49, 50, + 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, + 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, + 118, 119, 120, 121, 122, 123, 124, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, 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7.12517302e-01, 9.69291642e-01]), fval=0.04733711349557875, rho=-0.37241214656232297, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, + 143]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.34277312e+00, 4.09922736e+03, 7.12517302e-01, 9.69291642e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144]), model=ScalarModel(intercept=0.07189691058029209, linear_terms=array([ 0.0729615 , -0.01994005, -0.02510471, -0.67291357]), square_terms=array([[ 9.11672153e-02, -2.53027498e-02, -2.81155848e-02, + -8.77690304e-01], + [-2.53027498e-02, 7.33288652e-03, 8.17518993e-03, + 2.54924757e-01], + [-2.81155848e-02, 8.17518993e-03, 9.50928398e-03, + 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2.38204183e+00, 9.80265374e-01])), candidate_index=148, candidate_x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), index=148, x=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), fval=0.041423864822835596, rho=0.46328473374724694, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([138, 139, 140, 141, 144, 145, 146, 147]), old_indices_discarded=array([], dtype=int32), step_length=0.10031413807620175, relative_step_length=1.002238748718894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.23569943e+00, 4.09922711e+03, 8.81618923e-01, 9.61062476e-01]), radius=0.2001801231581352, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([137, 138, 139, 140, 141, 143, 144, 145, 146, 147, 148, 149]), model=ScalarModel(intercept=0.03951342557884055, linear_terms=array([ 0.00394695, 0.0005794 , -0.00109741, -0.02210072]), 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=175, candidate_x=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), index=175, x=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), fval=0.03196020635668493, rho=0.6511332348539848, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([169, 171, 172, 173, 174]), old_indices_discarded=array([], dtype=int32), step_length=0.05245185261086877, relative_step_length=1.0480931230007022, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175]), model=ScalarModel(intercept=0.03198863275603155, linear_terms=array([ 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176]), model=ScalarModel(intercept=0.03194809962562358, linear_terms=array([-8.08524576e-04, -1.08493288e-05, -5.49422714e-04, 1.75818827e-02]), square_terms=array([[ 3.50326784e-03, 3.48610273e-05, 1.24742847e-03, + -5.08202229e-02], + [ 3.48610273e-05, 4.48474855e-07, 1.38925490e-05, + -6.08176933e-04], + [ 1.24742847e-03, 1.38925490e-05, 5.71696697e-04, + -2.18658563e-02], + [-5.08202229e-02, -6.08176933e-04, -2.18658563e-02, + 9.21930745e-01]]), scale=0.0500450307895338, shift=array([4.14641789e+00, 4.09944333e+03, 1.44169648e+00, 9.75654332e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 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scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=177, candidate_x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), index=177, x=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), fval=0.03175931998394472, rho=0.6690416360198942, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176]), old_indices_discarded=array([], dtype=int32), step_length=0.050499371349656076, relative_step_length=1.0090786348405505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([150, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177]), model=ScalarModel(intercept=0.03248246414257855, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178]), model=ScalarModel(intercept=0.03172573143066533, linear_terms=array([-1.14059094e-04, -1.10807797e-05, -1.03748194e-04, 1.17775825e-03]), square_terms=array([[ 3.64736289e-03, 6.02428702e-05, 1.27706960e-03, + -5.24032340e-02], + [ 6.02428702e-05, 1.22643828e-06, 2.44981157e-05, + -1.05985180e-03], + [ 1.27706960e-03, 2.44981157e-05, 5.72112824e-04, + -2.20276660e-02], + [-5.24032340e-02, -1.05985180e-03, -2.20276660e-02, + 9.34368129e-01]]), scale=0.0500450307895338, shift=array([4.13372474e+00, 4.09944339e+03, 1.49057206e+00, 9.75159538e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + 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0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=179, candidate_x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), index=179, x=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), fval=0.03162745417502315, rho=2.510704045137157, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([171, 172, 173, 174, 175, 176, 177, 178]), old_indices_discarded=array([], dtype=int32), step_length=0.050325579243757135, relative_step_length=1.0056059203041197, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.1000900615790676, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), model=ScalarModel(intercept=0.032130564354299555, linear_terms=array([-0.00156055, -0.00139965, 0.00036611, -0.025743 ]), square_terms=array([[ 4.05055456e-03, 1.48919579e-03, -6.07642697e-04, + 3.44846053e-02], + [ 1.48919579e-03, 1.66608590e-03, -7.81966055e-04, + 3.87222711e-02], + [-6.07642697e-04, -7.81966055e-04, 5.86968019e-04, + -1.75898376e-02], + [ 3.44846053e-02, 3.87222711e-02, -1.75898376e-02, + 9.06040243e-01]]), scale=0.1000900615790676, shift=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13431204e+00, 4.09946163e+03, 1.53745960e+00, 9.76255422e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([171, 172, 173, 174, 175, 176, 177, 178, 179, 180]), model=ScalarModel(intercept=0.031652111625055504, linear_terms=array([ 4.80604637e-05, 1.39247104e-05, -2.39867534e-05, -1.89127269e-04]), square_terms=array([[ 3.90665224e-03, 1.27857855e-04, 1.36034221e-03, + -5.54542919e-02], + [ 1.27857855e-04, 5.35312567e-06, 5.28469976e-05, + -2.25099856e-03], + [ 1.36034221e-03, 5.28469976e-05, 5.95652314e-04, + -2.28246303e-02], + [-5.54542919e-02, -2.25099856e-03, -2.28246303e-02, + 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x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-0.04809068155083086, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, + 181]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), radius=0.0500450307895338, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), model=ScalarModel(intercept=0.03161885589197164, linear_terms=array([ 1.24235116e-05, 1.65270337e-05, -5.96983212e-07, -3.23007724e-04]), square_terms=array([[ 3.85807274e-03, 1.15384522e-04, 1.32757111e-03, + -5.48814365e-02], + [ 1.15384522e-04, 4.41711422e-06, 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model_indices=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), model=ScalarModel(intercept=0.03163462823062035, linear_terms=array([-9.39533430e-06, -5.39540217e-06, 2.16599626e-07, 1.49312835e-04]), square_terms=array([[ 2.34139611e-05, 1.09806616e-06, 5.53797697e-06, + -3.00949207e-04], + [ 1.09806616e-06, 5.92778360e-08, 2.79762167e-07, + -1.59957868e-05], + [ 5.53797697e-06, 2.79762167e-07, 1.56644529e-06, + -7.74361413e-05], + [-3.00949207e-04, -1.59957868e-05, -7.74361413e-05, + 4.40255574e-03]]), scale=0.0031278144243458623, shift=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01])), vector_model=VectorModel(intercepts=array([ 0.02076646, 0.04531076, 0.02641302, 0.03217015, 0.02428935, + 0.00692612, -0.01923886, -0.24709763, -0.3791683 , -0.33840917, + -0.63156409, -0.5726088 , 0.05335687, 0.06909317, 0.06438979, + 0.05047506, 0.04347886]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.96889222786086, shift=array([6.35435600e+00, 4.09968892e+03, 2.38204183e+00, 9.80265374e-01])), candidate_index=199, candidate_x=array([4.13001034e+00, 4.09942001e+03, 1.55905409e+00, 9.76337228e-01]), index=181, x=array([4.13026246e+00, 4.09941730e+03, 1.56057621e+00, 9.76477264e-01]), fval=0.03160677727045485, rho=-2.567463867080543, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([181, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, + 197, 198]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Maximum number of criterion evaluations reached.', 'tranquilo_history': History for least_squares function with 200 entries., 'history': {'params': [{'CRRA': 6.3543559970109325, 'BeqFac': 4099.6889222786085, 'BeqShift': 2.382041833192273, 'DiscFac': 0.9802653739222869}, {'CRRA': 1.5997660080756595, 'BeqFac': 3798.32770700145, 'BeqShift': 70.0, 'DiscFac': 0.7024407930795087}, {'CRRA': 1.1, 'BeqFac': 4399.8970420338355, 'BeqShift': 69.76434738104037, 'DiscFac': 0.9316481833606611}, {'CRRA': 17.36460836924718, 'BeqFac': 3912.562988977825, 'BeqShift': 70.0, 'DiscFac': 0.5294729131673447}, {'CRRA': 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9.60369865e-01])), candidate_index=46, candidate_x=array([1.71148115e+01, 4.11532983e+03, 2.95367480e+01, 5.98738718e-01]), index=33, x=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), fval=1.588023991113788, rho=-3.5378747881454817, accepted=False, new_indices=array([34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_used=array([20, 29, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.6348854061273638, linear_terms=array([0.02274535, 0.06472916, 0.06290409, 1.72847375]), square_terms=array([[6.13705874e-04, 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 34, 35, 37, 38, 39, 41, 42, 43, 44, 47]), model=ScalarModel(intercept=0.5832383280438845, linear_terms=array([-0.04366208, -0.07974917, 0.0121024 , -0.66338862]), square_terms=array([[ 1.24405099e-02, 1.84037162e-02, -3.21723600e-03, + 1.98213656e-01], + [ 1.84037162e-02, 2.76097603e-02, -4.77741170e-03, + 2.90439616e-01], + [-3.21723600e-03, -4.77741170e-03, 8.37149132e-04, + -5.12297952e-02], + [ 1.98213656e-01, 2.90439616e-01, -5.12297952e-02, + 3.19738903e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.65188540e+01, 4.11473387e+03, 2.89407905e+01, 6.97696283e-01])), 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46, 47, 48, 49]), model=ScalarModel(intercept=0.5792867438043003, linear_terms=array([ 0.0620906 , 0.05580846, -0.01892313, 1.03442734]), square_terms=array([[ 4.84671874e-02, 5.47513283e-02, -1.60884447e-02, + 6.51384141e-01], + [ 5.47513283e-02, 6.23780615e-02, -1.82999844e-02, + 7.33136697e-01], + [-1.60884447e-02, -1.82999844e-02, 5.38147700e-03, + -2.16952904e-01], + [ 6.51384141e-01, 7.33136697e-01, -2.16952904e-01, + 9.00073831e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.25071503]), shift=array([1.66678434e+01, 4.11488286e+03, 2.87918011e+01, 7.50715032e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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fval=1.2823500253026578, rho=0.4037813363749846, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([33, 37, 38, 44, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]), old_indices_discarded=array([34, 35, 39, 42]), step_length=0.2121197364289317, relative_step_length=1.0609946596949713, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.61338301e+01, 4.11492371e+03, 2.85465763e+01, 7.83158466e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([33, 37, 38, 44, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.616287359006102, linear_terms=array([ 0.1579405 , -0.06085977, -0.02629154, -0.26113511]), square_terms=array([[ 0.31070863, -0.13275671, -0.09956251, -1.66566784], + [-0.13275671, 0.05677437, 0.04270285, 0.71640616], + [-0.09956251, 0.04270285, 0.03313502, 0.55556134], + [-1.66566784, 0.71640616, 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candidate_x=array([1.53845479e+01, 4.11482259e+03, 2.79903380e+01, 8.64861786e-01]), index=108, x=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), fval=0.9585677871382879, rho=-0.7052809316351738, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 92, 93, 95, 96, 97, 100, 101, 102, 104, 105, 106, + 107, 108]), old_indices_discarded=array([ 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, + 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, + 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, + 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, + 87, 88, 90, 91, 94, 98, 99, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108]), model=ScalarModel(intercept=0.9577031847372262, linear_terms=array([ 0.03178872, 0.0310778 , -0.00399409, 0.28392775]), square_terms=array([[ 1.95437081e-03, 4.77684675e-03, -7.59671438e-04, + -4.27399704e-02], + [ 4.77684675e-03, 1.38624719e-02, -2.20752863e-03, + -1.50579650e-01], + [-7.59671438e-04, -2.20752863e-03, 3.56515716e-04, + 2.41971764e-02], + [-4.27399704e-02, -1.50579650e-01, 2.41971764e-02, + 1.98161272e+00]]), scale=0.1999253572962513, shift=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 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fval=0.9585677871382879, rho=-1.049281869869592, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 57, 89, 90, 93, 96, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108]), old_indices_discarded=array([ 37, 38, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, + 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, + 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 91, + 92, 94, 95, 97, 98, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.56825267e+01, 4.11512057e+03, 2.82883168e+01, 8.94314354e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 57, 89, 90, 93, 96, 99, 101, 102, 103, 104, 105, 106, 107, + 108, 110]), model=ScalarModel(intercept=0.9578113150011967, linear_terms=array([-0.00143314, -0.01180295, 0.00580005, 0.11249413]), square_terms=array([[ 2.42515718e-03, 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new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 130, 131, 135, 136, 138, 139, 141, 143, 144, 145, 146, + 147, 148]), old_indices_discarded=array([105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, + 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 132, 133, 134, + 137, 140, 142]), step_length=0.105280468337507, relative_step_length=1.0531977510136585, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54791737e+01, 4.11498376e+03, 2.83511980e+01, 8.94338923e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 137, 140, 141, 143, 144, 145, 146, 147, + 148, 149]), model=ScalarModel(intercept=0.9504811473000055, linear_terms=array([ 0.00337976, -0.00166767, 0.00065203, 0.00032394]), square_terms=array([[ 6.88080741e-05, 3.26320216e-05, -1.14612730e-05, + 4.04897732e-03], + [ 3.26320216e-05, 2.38843862e-05, -8.53007545e-06, + 2.37987944e-03], + [-1.14612730e-05, -8.53007545e-06, 3.08030290e-06, + -8.37502206e-04], + [ 4.04897732e-03, 2.37987944e-03, -8.37502206e-04, + 2.69451472e-01]]), scale=0.049981339324062825, shift=array([1.54791737e+01, 4.11498376e+03, 2.83511980e+01, 8.94338923e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 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+ [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=150, candidate_x=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), index=150, x=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), fval=0.9490921495752334, rho=0.48378050592219396, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 131, 135, 136, 137, 140, 141, 143, 144, 145, 146, 147, + 148, 149]), old_indices_discarded=array([113, 114, 116, 118, 119, 121, 122, 124, 125, 127, 128, 130, 132, + 133, 134, 138, 139, 142]), step_length=0.049982181792418395, relative_step_length=1.0000168556578708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, + 149, 150]), model=ScalarModel(intercept=0.9481669388008567, linear_terms=array([ 0.00491681, -0.00191175, 0.00090832, -0.00794232]), square_terms=array([[ 4.53197713e-04, 9.69734758e-05, -4.03223571e-05, + 2.17424813e-02], + [ 9.69734758e-05, 2.70238997e-05, -1.14028932e-05, + 5.09292923e-03], + [-4.03223571e-05, -1.14028932e-05, 4.97292295e-06, + -2.10313696e-03], + [ 2.17424813e-02, 5.09292923e-03, -2.10313696e-03, + 1.09787792e+00]]), scale=0.09996267864812565, shift=array([1.54350420e+01, 4.11500562e+03, 2.83426550e+01, 8.94717087e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]]), scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=151, candidate_x=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), index=151, x=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), fval=0.9449793990676408, rho=0.7464682068241998, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 129, 131, 135, 136, 140, 141, 143, 144, 145, 146, 147, 148, + 149, 150]), old_indices_discarded=array([106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 132, 133, 134, + 137, 138, 139, 142]), step_length=0.10016713827274805, relative_step_length=1.0020453596020782, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, + 150, 151]), model=ScalarModel(intercept=0.9447208950935511, linear_terms=array([ 0.00747774, -0.00115453, 0.00019389, -0.01790295]), square_terms=array([[ 2.32523138e-03, 1.54081854e-04, -2.97872562e-06, + 1.00174053e-01], + [ 1.54081854e-04, 1.28584982e-05, -4.90411666e-07, + 6.98051990e-03], + [-2.97872562e-06, -4.90411666e-07, 4.85734809e-07, + -6.45942014e-05], + [ 1.00174053e-01, 6.98051990e-03, -6.45942014e-05, + 4.46348961e+00]]), scale=0.1999253572962513, shift=array([1.53424000e+01, 4.11503994e+03, 2.83262993e+01, 8.97072705e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 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4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), index=152, x=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), fval=0.9352639178281498, rho=1.2203446548292984, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([110, 130, 131, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, + 150, 151]), old_indices_discarded=array([ 37, 38, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, + 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, + 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, + 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, + 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 132, 133, 134, + 137, 138, 139, 140, 142]), step_length=0.20002826060126488, relative_step_length=1.0005147086212836, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 9.02261230e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, + 151, 152]), model=ScalarModel(intercept=1.0726433496908907, linear_terms=array([-0.02343661, -0.00889048, -0.00275083, -1.36662877]), square_terms=array([[4.86288425e-03, 6.82588240e-04, 2.46055974e-04, 1.78667944e-01], + [6.82588240e-04, 1.16954213e-04, 3.75054128e-05, 2.65804533e-02], + [2.46055974e-04, 3.75054128e-05, 1.68608179e-05, 9.53055835e-03], + [1.78667944e-01, 2.65804533e-02, 9.53055835e-03, 6.81382039e+00]]), scale=array([0.29797877, 0.29797877, 0.29797877, 0.24785877]), shift=array([1.51445449e+01, 4.11506845e+03, 2.83213499e+01, 8.52141232e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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candidate_x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), index=153, x=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), fval=0.9223250761250302, rho=0.7752349469770148, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([109, 110, 135, 136, 141, 143, 144, 145, 146, 147, 148, 149, 150, + 151, 152]), old_indices_discarded=array([ 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 111, 112, 113, 114, 115, 116, + 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, + 130, 131, 132, 133, 134, 137, 138, 139, 140, 142]), step_length=0.5161364791949027, relative_step_length=1.2908229505627111, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 9.07039202e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, + 152, 153]), model=ScalarModel(intercept=1.8249779819419052, linear_terms=array([-1.76627203e-01, -2.47141400e-03, -2.38775460e-03, -4.85764149e+00]), square_terms=array([[ 2.34532115e-02, 1.92781711e-04, 1.57766385e-04, + 5.44396171e-01], + [ 1.92781711e-04, 6.31379450e-06, -3.02452352e-06, + 5.07453896e-03], + [ 1.57766385e-04, -3.02452352e-06, 9.75935474e-06, + 4.07694325e-03], + [ 5.44396171e-01, 5.07453896e-03, 4.07694325e-03, + 1.30477707e+01]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.48465661e+01, 4.11536643e+03, 2.86193287e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , 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scale=409.44713174272266, shift=array([6.01901790e+00, 4.09447132e+03, 8.67823437e+00, 9.60369865e-01])), candidate_index=154, candidate_x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), index=154, x=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), fval=0.8900718166078354, rho=1.1251274278087409, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([125, 136, 137, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, + 152, 153]), old_indices_discarded=array([ 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, + 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, + 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, + 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, + 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, + 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, + 139, 140, 147]), step_length=1.0323679882696024, relative_step_length=1.2909417822620541, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=1.5994028583700104, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), model=ScalarModel(intercept=1.1326265739323547, linear_terms=array([-0.09637569, -0.14379396, 0.09712327, -1.55727297]), square_terms=array([[ 0.04121845, 0.02313126, -0.01642407, 0.42608071], + [ 0.02313126, 0.01788942, -0.01256294, 0.26548283], + [-0.01642407, -0.01256294, 0.00885428, -0.18607463], + [ 0.42608071, 0.26548283, -0.18607463, 4.71648178]]), scale=array([1.19191507, 1.19191507, 1.19191507, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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120, + 121, 122, 123, 124, 126, 128, 129, 130, 131, 132, 133, 134, 135, + 137, 138, 139, 140, 141, 142, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.7997014291850052, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 144, 145, 146, 148, 149, 150, 151, 152, + 153, 154]), model=ScalarModel(intercept=1.1326265739323542, linear_terms=array([-0.04818785, -0.07189698, 0.04856163, -1.55727297]), square_terms=array([[ 1.03046127e-02, 5.78281380e-03, -4.10601701e-03, + 2.13040355e-01], + [ 5.78281380e-03, 4.47235416e-03, -3.14073444e-03, + 1.32741416e-01], + [-4.10601701e-03, -3.14073444e-03, 2.21357021e-03, + -9.30373154e-02], + [ 2.13040355e-01, 1.32741416e-01, -9.30373154e-02, + 4.71648178e+00]]), scale=array([0.59595753, 0.59595753, 0.59595753, 0.3 ]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 8.00000000e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + 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103, 104, 105, 106, 107, 108, 109, 110, 111, + 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, + 126, 128, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, + 141, 142, 147, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.3998507145925026, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([ 59, 125, 127, 136, 143, 145, 146, 148, 149, 150, 151, 152, 153, + 154, 156]), model=ScalarModel(intercept=0.9323612178144098, linear_terms=array([-0.01259834, 0.01279683, -0.02998382, -0.59640575]), square_terms=array([[ 4.02828891e-03, -1.26463784e-03, 2.65461049e-03, + 1.14730233e-01], + [-1.26463784e-03, 4.66105839e-04, -9.83258053e-04, + -3.67550589e-02], + [ 2.65461049e-03, -9.83258053e-04, 2.08826577e-03, + 7.82898537e-02], + [ 1.14730233e-01, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.1999253572962513, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, + 169]), model=ScalarModel(intercept=0.8621110105805507, linear_terms=array([ 0.05259407, 0.01444784, -0.04650949, 1.3449937 ]), square_terms=array([[ 1.06382832e-02, 1.77761267e-03, -1.34152433e-02, + 2.24240960e-01], + [ 1.77761267e-03, 3.91467956e-04, -2.20266751e-03, + 3.98071647e-02], + [-1.34152433e-02, -2.20266751e-03, 1.72375098e-02, + -2.79030554e-01], + [ 2.24240960e-01, 3.98071647e-02, -2.79030554e-01, + 4.86559567e+00]]), scale=array([0.14898938, 0.14898938, 0.14898938, 0.14898938]), shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], 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168, 169, + 170]), model=ScalarModel(intercept=0.8521861809194315, linear_terms=array([ 0.01363795, 0.00599446, -0.00829727, 0.87225482]), square_terms=array([[ 1.25459512e-04, 6.75203715e-05, -2.81566396e-05, + 4.82302182e-03], + [ 6.75203715e-05, 1.45256704e-04, 1.00392769e-04, + -6.60298780e-03], + [-2.81566396e-05, 1.00392769e-04, 3.70387367e-04, + -2.84734924e-02], + [ 4.82302182e-03, -6.60298780e-03, -2.84734924e-02, + 2.26757906e+00]]), scale=0.09996267864812565, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + 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new_indices=array([], dtype=int32), old_indices_used=array([154, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, + 170]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01]), radius=0.049981339324062825, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 158, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, + 181, 182, 183]), model=ScalarModel(intercept=0.8751612738683985, linear_terms=array([ 0.01069739, -0.0351117 , 0.00524044, 0.03150041]), square_terms=array([[ 2.80038988e-04, -1.92877967e-03, 4.46333870e-04, + -8.04311081e-03], + [-1.92877967e-03, 1.45965092e-02, -3.44462599e-03, + 6.72455897e-02], + [ 4.46333870e-04, -3.44462599e-03, 8.32232273e-04, + -1.63487093e-02], + [-8.04311081e-03, 6.72455897e-02, -1.63487093e-02, + 3.46011684e-01]]), scale=0.049981339324062825, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 0.05371988]), linear_terms=array([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]), square_terms=array([[[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]], + + [[0., 0., 0., 0.], + [0., 0., 0., 0.], + 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bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, + 183, 184]), model=ScalarModel(intercept=0.8806140250059065, linear_terms=array([ 0.00348081, 0.00103192, 0.00045499, -0.00120431]), square_terms=array([[ 7.93493802e-06, 1.82672879e-07, 1.06786659e-07, + 2.40577351e-04], + [ 1.82672879e-07, 1.04969220e-05, 4.04967602e-06, + -1.03166451e-03], + [ 1.06786659e-07, 4.04967602e-06, 1.68666246e-06, + -3.96649049e-04], + [ 2.40577351e-04, -1.03166451e-03, -3.96649049e-04, + 1.12239205e-01]]), scale=0.024990669662031412, shift=array([1.42506086e+01, 4.11596239e+03, 2.92152862e+01, 9.23995576e-01])), vector_model=VectorModel(intercepts=array([ 0.01362054, 0.02337808, -0.0118473 , -0.02328512, -0.04814505, + -0.08249999, -0.12599676, -0.41197179, -0.54908717, -0.50678141, + -0.79217548, -0.72078929, 0.12481253, 0.13659429, 0.1238582 , + 0.09040456, 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2.91861680e+01, 9.25856803e-01]), index=188, x=array([1.41081482e+01, 4.11586063e+03, 2.91861680e+01, 9.25856803e-01]), fval=0.8831177501278756, rho=0.6433964616125217, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([154, 171, 174, 175, 177, 178, 179, 180, 181, 182, 183, 184, 185, + 186, 187]), old_indices_discarded=array([163, 168, 172, 173, 176]), step_length=0.04998185651351309, relative_step_length=1.0000103476508886, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.41081482e+01, 4.11586063e+03, 2.91861680e+01, 9.25856803e-01]), radius=0.09996267864812565, bounds=Bounds(lower=array([1.1, 0. , 0. , 0.5]), upper=array([2.0e+01, 1.0e+04, 7.0e+01, 1.1e+00]))), model_indices=array([154, 168, 171, 174, 175, 178, 179, 180, 181, 182, 183, 185, 186, + 187, 188]), model=ScalarModel(intercept=0.8289860145213327, linear_terms=array([7.58494192e-05, 2.23407925e-02, 8.90899480e-02, 3.91983702e-03]), square_terms=array([[ 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4.92630064e+00, 5.00339546e+00, + 5.43544424e+00, 7.28251427e+00, 1.28207813e+01, 1.15051611e+03])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv index cb15f2c..7220a1d 100644 --- a/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv +++ b/src/estimark/content/tables/min/WarmGlowPortfolioShiftAlt_estimate_results.csv @@ -1,13571 +1,13590 @@ -CRRA,4.177600674290874 -BeqFac,2798.51512022846 -BeqShift,1.966935700692675 -time_to_estimate,213.91753792762756 -params,"{'CRRA': 4.177600674290874, 'BeqFac': 2798.51512022846, 'BeqShift': 1.966935700692675}" -criterion,0.050872538941762795 -start_criterion,0.149744421401998 -start_params,"{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,3 -message,Absolute criterion change smaller than tolerance. -success, 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26.400473299901932, 27.575066099874675, 28.895407499745488, 30.087074699811637, 31.28205809975043, 32.452069099992514, 33.63252570014447, 34.80818759975955, 35.983998800162226, 37.16741700004786, 38.50141609972343, 39.70189919974655, 40.88999589998275, 42.06016460014507, 43.238295500166714, 44.42306770011783, 45.59471370000392, 46.93225569976494, 48.113191300071776], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]}" -convergence_report,"{'one_step': {'relative_criterion_change': 0.018714300470562203, 'relative_params_change': 0.22317607955398075, 'absolute_criterion_change': 0.0018714300470562203, 'absolute_params_change': 561.5795603000294}, 'five_steps': {'relative_criterion_change': 0.018714300470562203, 'relative_params_change': 0.22317607955398075, 'absolute_criterion_change': 0.0018714300470562203, 'absolute_params_change': 561.5795603000294}}" -multistart_info,"{'start_parameters': [{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}, {'CRRA': 6.333842432535624, 'BeqFac': 3668.9001041779247, 'BeqShift': 6.573413466501883}, {'CRRA': 4.968360850653578, 'BeqFac': 3489.450001032764, 'BeqShift': 5.063898656927828}], 'local_optima': [Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 3.577e-07* 6.797e-06* -relative_params_change 0.0001525 0.0002161 -absolute_criterion_change 3.577e-08* 6.797e-07* -absolute_params_change 0.0007074 0.0007074 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 8.731e-08* 0.000407 -relative_params_change 0.001013 0.02664 -absolute_criterion_change 5.598e-08* 0.000261 -absolute_params_change 0.01402 0.3641 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 7.292e-06* 0.004464 -relative_params_change 0.02086 0.07591 -absolute_criterion_change 7.292e-07* 0.0004464 -absolute_params_change 0.04261 0.2097 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'BeqShift': 19.6875}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'BeqShift': 59.0625}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'BeqShift': 35.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'BeqShift': 50.3125}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'BeqShift': 21.875}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'BeqShift': 67.8125}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'BeqShift': 10.9375}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'BeqShift': 52.5}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'BeqShift': 6.5625}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'BeqShift': 2.1875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'BeqShift': 30.625}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'BeqShift': 41.5625}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'BeqShift': 65.625}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'BeqShift': 17.5}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'BeqShift': 37.1875}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'BeqShift': 26.25}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'BeqShift': 56.875}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'BeqShift': 32.8125}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'BeqShift': 15.3125}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'BeqShift': 24.0625}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'BeqShift': 39.375}], 'exploration_results': array([ 0.15183333, 0.64211538, 0.64842412, 0.66164015, 0.68191739, - 0.7044766 , 0.74714867, 0.84822968, 0.86260573, 0.97951322, - 0.98915247, 1.12431839, 1.18220644, 1.20662179, 1.30406801, - 1.52011014, 1.78024917, 1.78135807, 2.0971452 , 2.24437522, - 2.48116605, 2.85787595, 2.94379242, 3.4148927 , 3.4976294 , - 4.16035282, 4.94978648, 5.89491246, 7.00125067, 34.37799292])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.96836085, 3489.45000103, 5.06389866]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=[0], model=ScalarModel(intercept=1.181105432157004, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=0, candidate_x=array([ 4.96836085, 3489.45000103, 5.06389866]), index=0, x=array([ 4.96836085, 3489.45000103, 5.06389866]), fval=1.181105432157004, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.96836085, 3489.45000103, 5.06389866]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=101.00053527176014, linear_terms=array([-267.17329649, 80.95099224, 52.1467112 ]), square_terms=array([[ 361.29036327, -108.39796365, -69.65799497], - [-108.39796365, 32.72200507, 21.00295023], - [ -69.65799497, 21.00295023, 13.52711371]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3489.45000103, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=13, candidate_x=array([ 12.88097511, 3208.20173475, 0. ]), index=13, x=array([ 12.88097511, 3208.20173475, 0. ]), fval=1.1406131109915512, rho=0.00016464076262107032, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=281.40511690387655, relative_step_length=0.8064454765667648, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 12.88097511, 3208.20173475, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=30.184829913613466, linear_terms=array([-139.32389492, 29.36565848, 48.56265401]), square_terms=array([[ 337.66475622, -70.11282425, -115.6185002 ], - [ -70.11282425, 14.64456015, 24.13989901], - [-115.6185002 , 24.13989901, 39.85365245]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3208.20173475, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=14, candidate_x=array([ 9.25122599, 3067.57760161, 0. ]), index=14, x=array([ 9.25122599, 3067.57760161, 0. ]), fval=0.5545985836347647, rho=0.10620940220823766, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([5, 7]), step_length=140.67097035304008, relative_step_length=0.806264427410658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.25122599, 3067.57760161, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=5.307226904606869, linear_terms=array([-51.38753027, 26.65823128, 21.44983872]), square_terms=array([[ 305.31648211, -153.36036519, -122.65042123], - [-153.36036519, 77.5107252 , 61.97971585], - [-122.65042123, 61.97971585, 49.61959056]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model=ScalarModel(intercept=3.807942725574409, linear_terms=array([-39.58737235, 12.58807335, 13.34355021]), square_terms=array([[288.79762252, -88.45374867, -92.48750266], - [-88.45374867, 27.21485773, 28.46514941], - [-92.48750266, 28.46514941, 29.83245946]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), 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candidate_index=16, candidate_x=array([ 5.9246359 , 2926.95346847, 0. ]), index=16, x=array([ 5.9246359 , 2926.95346847, 0. ]), fval=0.23203665598876397, rho=0.570002726445654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([4, 5, 6, 7]), step_length=140.66347437444162, relative_step_length=0.8062214637424797, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 8, 9, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=3.9661830657942825, linear_terms=array([-28.12732145, 11.87395531, 18.45752875]), square_terms=array([[150.75705807, -60.44190282, -91.27776749], - [-60.44190282, 24.37668461, 36.9203391 ], - [-91.27776749, 36.9203391 , 56.05439485]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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old_indices_discarded=array([ 4, 5, 6, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 8, 9, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=9.571328999106925, linear_terms=array([-45.18448012, 9.36031969, 33.66340283]), square_terms=array([[133.09207534, -26.87423247, -93.65514891], - [-26.87423247, 5.44544862, 19.03151488], - [-93.65514891, 19.03151488, 66.85203657]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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model_indices=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=2.345400265444784, linear_terms=array([-9.54818498, 0.79259481, 6.47312741]), square_terms=array([[ 71.29706187, -5.72633659, -37.74299647], - [ -5.72633659, 0.46131347, 3.04307064], - [-37.74299647, 3.04307064, 20.58343169]]), scale=array([ 9.45 , 70.31206657, 35. ]), shift=array([ 10.55 , 2786.32933533, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20]), model=ScalarModel(intercept=16.004294311139557, linear_terms=array([70.66939 , 1.13700979, -8.50470948]), square_terms=array([[ 1.58368000e+02, 2.56906467e+00, -1.92507844e+01], - [ 2.56906467e+00, 4.19273077e-02, -3.14468097e-01], - [-1.92507844e+01, -3.14468097e-01, 2.36523947e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2786.32933533, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 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model=ScalarModel(intercept=2.9984363121302398, linear_terms=array([20.20379397, 0.14022481, -1.76089925]), square_terms=array([[ 7.28524130e+01, 5.11325102e-01, -6.48101817e+00], - [ 5.11325102e-01, 3.60021596e-03, -4.56833511e-02], - [-6.48101817e+00, -4.56833511e-02, 5.82826690e-01]]), scale=array([6.44462921, 8.78900832, 4.39450416]), shift=array([ 7.54462921, 2786.32933533, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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5.06389866])), candidate_index=22, candidate_x=array([ 5.16024118, 2790.95196969, 0. ]), index=22, x=array([ 5.16024118, 2790.95196969, 0. ]), fval=0.1628347112711602, rho=1.0555049921680508, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=4.622807489556225, relative_step_length=0.4239345444755384, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.16024118, 2790.95196969, 0. ]), radius=10.904531253227388, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22]), model=ScalarModel(intercept=3.006282375920816, linear_terms=array([20.16458994, 0.14100404, -1.76243727]), square_terms=array([[ 7.23456179e+01, 5.11502709e-01, -6.45362479e+00], - [ 5.11502709e-01, 3.62783851e-03, -4.58243177e-02], - [-6.45362479e+00, -4.58243177e-02, 5.81967789e-01]]), scale=array([6.42462475, 8.78900832, 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step_length=8.789122808311065, relative_step_length=0.806006476042679, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=15.956937372018299, linear_terms=array([68.87495584, 1.01962009, -8.11318965]), square_terms=array([[ 1.50828107e+02, 2.25090089e+00, -1.79474940e+01], - [ 2.25090089e+00, 3.37920630e-02, -2.69729608e-01], - [-1.79474940e+01, -2.69729608e-01, 2.15985383e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2799.74097801, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 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model=ScalarModel(intercept=3.0353080773001073, linear_terms=array([20.20386846, 0.10823129, -1.77493216]), square_terms=array([[ 7.18576918e+01, 3.89097471e-01, -6.44170944e+00], - [ 3.89097471e-01, 2.11348833e-03, -3.50355915e-02], - [-6.44170944e+00, -3.50355915e-02, 5.83744149e-01]]), scale=array([6.40219448, 8.78900832, 4.39450416]), shift=array([ 7.50219448, 2799.74097801, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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5.06389866])), candidate_index=25, candidate_x=array([ 5.09453133, 2808.27054949, 0. ]), index=23, x=array([ 5.11538064, 2799.74097801, 0. ]), fval=0.1625021910643016, rho=-0.257817250835792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=5.452265626613694, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.18941940373737348, linear_terms=array([-0.84702663, -0.00367806, 0.0743278 ]), square_terms=array([[ 3.09008276e+01, 1.15966532e-01, -2.10738689e+00], - [ 1.15966532e-01, 4.36576332e-04, -7.94409134e-03], - [-2.10738689e+00, -7.94409134e-03, 1.45288267e-01]]), scale=array([4.2049424 , 4.39450416, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26]), model=ScalarModel(intercept=0.040656194310767946, linear_terms=array([-0.04424986, -0.00023592, -0.08131239]), square_terms=array([[8.44848189e+00, 2.55963392e-02, 4.42498564e-02], - [2.55963392e-02, 7.77973037e-05, 2.35916270e-04], - [4.42498564e-02, 2.35916270e-04, 8.13123886e-02]]), scale=array([2.19725208, 2.19725208, 1.09862604]), shift=array([5.11538064e+00, 2.79974098e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model_indices=array([21, 23, 26, 27]), model=ScalarModel(intercept=0.143861477312966, linear_terms=array([ 1.39795372e-01, -1.67256636e-05, 9.44770003e-03]), square_terms=array([[ 4.72932788e-01, -1.19149158e-04, 1.43354432e-01], - [-1.19149158e-04, 3.31626099e-08, -4.20348131e-05], - [ 1.43354432e-01, -4.20348131e-05, 5.61768276e-02]]), scale=array([1.09862604, 1.09862604, 0.54931302]), shift=array([5.11538064e+00, 2.79974098e+03, 5.49313020e-01])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=28, candidate_x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), index=28, x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), fval=0.06529563513965841, rho=1.475519476069878, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=1.6872964496840501, relative_step_length=1.2378681195926988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=0.07730703358690492, linear_terms=array([-0.21420423, -0.00177457, -0.09323422]), square_terms=array([[7.19945275e+00, 3.82631907e-02, 1.99911955e+00], - [3.82631907e-02, 2.08378103e-04, 1.10096472e-02], - [1.99911955e+00, 1.10096472e-02, 6.63610242e-01]]), scale=array([2.19725208, 2.19725208, 1.64793906]), shift=array([4.45734496e+00, 2.79864235e+03, 1.64793906e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - 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rho=-0.24314171126774836, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29]), model=ScalarModel(intercept=0.06904406104722437, linear_terms=array([ 0.00492248, 0.00149362, -0.05074773]), square_terms=array([[ 6.12405684e-01, -2.44364302e-03, 3.09451673e-01], - [-2.44364302e-03, 4.14350874e-05, -2.29602578e-03], - [ 3.09451673e-01, -2.29602578e-03, 2.13954389e-01]]), scale=array([1.09862604, 1.09862604, 1.09862604]), shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30]), model=ScalarModel(intercept=0.08513789653811096, linear_terms=array([-0.05873789, -0.00640693, -0.05839653]), square_terms=array([[0.29594537, 0.02020028, 0.12960348], - [0.02020028, 0.00146583, 0.00964012], - [0.12960348, 0.00964012, 0.07540158]]), scale=0.6815332033267117, shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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x=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), fval=0.05415908459581581, rho=0.02787984735130772, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=1.4693394633669328, relative_step_length=1.0779661623195813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.06019280281239402, linear_terms=array([-0.05311338, -0.00664367, -0.01652794]), square_terms=array([[0.31168089, 0.01963004, 0.09342492], - [0.01963004, 0.00163636, 0.00734946], - [0.09342492, 0.00734946, 0.04083573]]), scale=0.6815332033267117, shift=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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relative_step_length=1.0130566200764606, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.05146650808405816, linear_terms=array([0.05615491, 0.00068073, 0.02123787]), square_terms=array([[1.36450351e+00, 9.46810979e-03, 6.16903067e-01], - [9.46810979e-03, 7.27379915e-05, 3.94676561e-03], - [6.16903067e-01, 3.94676561e-03, 3.53300245e-01]]), scale=1.3630664066534235, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.05177155574931393, linear_terms=array([ 2.24054327e-02, -6.65931684e-05, 7.36047043e-03]), square_terms=array([[0.29167665, 0.01447643, 0.13152612], - [0.01447643, 0.0008045 , 0.00750585], - [0.13152612, 0.00750585, 0.07813118]]), scale=0.6815332033267117, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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3489.45000103, 5.06389866])), candidate_index=35, candidate_x=array([4.02600282e+00, 2.79871386e+03, 2.27098711e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=-2.2415296614174687, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([28, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.05243167254991798, linear_terms=array([ 2.14835428e-02, -8.53058917e-05, 7.83429373e-03]), square_terms=array([[2.69010071e-01, 2.03422326e-04, 8.75369596e-02], - [2.03422326e-04, 6.23463910e-07, 8.57167056e-05], - [8.75369596e-02, 8.57167056e-05, 3.13897458e-02]]), scale=0.3407666016633559, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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old_indices_used=array([28, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.35386200358029707, relative_step_length=1.0384292411668858, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.0515876639767697, linear_terms=array([ 0.00588415, -0.00077363, -0.00241592]), square_terms=array([[0.31118702, 0.01242837, 0.13629499], - [0.01242837, 0.00054706, 0.00615461], - [0.13629499, 0.00615461, 0.07774976]]), scale=0.6815332033267117, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - 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radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=0.052532543329590564, linear_terms=array([-0.00426995, -0.00027655, -0.00095162]), square_terms=array([[9.08193291e-02, 1.45774993e-03, 2.59401890e-02], - [1.45774993e-03, 2.50681460e-05, 4.39732923e-04], - [2.59401890e-02, 4.39732923e-04, 1.01466646e-02]]), scale=0.3407666016633559, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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6.84781454e-02, -6.53727045e-05, 2.13071280e-02], - [-6.53727045e-05, 1.19864368e-07, -2.42922328e-05], - [ 2.13071280e-02, -2.42922328e-05, 7.24051619e-03]]), scale=0.17038330083167794, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - 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1.95671244e+00]), fval=0.05091436090216684, rho=1.1516416598700234, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.1759818526107779, relative_step_length=1.032858570950159, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.05221280899533157, linear_terms=array([-0.00115235, -0.00033548, -0.00127409]), square_terms=array([[8.81488941e-02, 2.02195870e-03, 2.52583882e-02], - [2.02195870e-03, 4.98103348e-05, 6.21228724e-04], - [2.52583882e-02, 6.21228724e-04, 9.98002687e-03]]), scale=0.3407666016633559, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.05090588732753695, linear_terms=array([ 2.00967052e-03, -4.51302094e-06, 7.70306669e-04]), square_terms=array([[ 6.02617840e-02, -3.14541700e-04, 1.76727179e-02], - [-3.14541700e-04, 1.67055199e-06, -9.60913876e-05], - [ 1.76727179e-02, -9.60913876e-05, 5.78378569e-03]]), scale=0.17038330083167794, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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model_indices=array([31, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=0.05095057563584681, linear_terms=array([ 1.26813383e-03, -6.52109364e-06, 4.29926422e-04]), square_terms=array([[1.02760072e-02, 1.58255954e-05, 2.97902836e-03], - [1.58255954e-05, 2.96488117e-08, 4.90049513e-06], - [2.97902836e-03, 4.90049513e-06, 1.01490054e-03]]), scale=0.08519165041583897, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), radius=0.08519165041583897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.0508813659118873, linear_terms=array([-1.44722476e-04, -3.19089966e-06, -5.21530523e-05]), square_terms=array([[1.06023461e-02, 7.49354229e-06, 3.10107179e-03], - [7.49354229e-06, 6.81249566e-09, 2.33378372e-06], - [3.10107179e-03, 2.33378372e-06, 1.05915929e-03]]), scale=0.08519165041583897, shift=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 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State(trustregion=Region(center=array([4.18776198e+00, 2.79860171e+03, 1.92864855e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.05089102600737241, linear_terms=array([-8.05219085e-04, 4.14561579e-06, -1.62643657e-04]), square_terms=array([[ 5.46071930e-02, -4.28787356e-04, 1.52566341e-02], - [-4.28787356e-04, 3.42160560e-06, -1.25140244e-04], - [ 1.52566341e-02, -1.25140244e-04, 4.84664124e-03]]), scale=0.17038330083167794, shift=array([4.18776198e+00, 2.79860171e+03, 1.92864855e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), 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44, 45]), model=ScalarModel(intercept=0.05090572581910401, linear_terms=array([-4.42619084e-04, 7.34434962e-06, -1.22305003e-04]), square_terms=array([[ 1.38215395e-02, -9.48643881e-05, 3.90966365e-03], - [-9.48643881e-05, 6.63904553e-07, -2.80605274e-05], - [ 3.90966365e-03, -2.80605274e-05, 1.25389961e-03]]), scale=0.08519165041583897, shift=array([4.18776198e+00, 2.79860171e+03, 1.92864855e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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old_indices_used=array([ 0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 5, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97205913, 2235.6686944 , 0. ]), radius=27.94585868000225, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 3, 8, 9, 14, 15]), model=ScalarModel(intercept=22.965240876566593, linear_terms=array([-54.08954502, -12.74951336, 33.1413111 ]), square_terms=array([[ 71.01168792, 15.97202572, -41.45842555], - [ 15.97202572, 3.66681836, -9.52191516], - [-41.45842555, -9.52191516, 24.73053157]]), scale=array([ 9.45 , 22.52424967, 11.26212484]), shift=array([ 10.55 , 2235.6686944 , 11.26212484])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, 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model_indices=array([49, 53, 55, 56, 57]), model=ScalarModel(intercept=0.05284048100725791, linear_terms=array([-5.75157948e-05, -1.60004012e-05, 1.95156603e-04]), square_terms=array([[1.98601191e-02, 4.22301104e-05, 5.77863749e-03], - [4.22301104e-05, 1.15750037e-07, 1.38137277e-05], - [5.77863749e-03, 1.38137277e-05, 1.95299639e-03]]), scale=0.10916351046875879, shift=array([4.10745549e+00, 2.23687992e+03, 1.79442393e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), 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candidate_x=array([4.13032949e+00, 2.23695798e+03, 1.71623236e+00]), index=55, x=array([4.10745549e+00, 2.23687992e+03, 1.79442393e+00]), fval=0.052775320400357525, rho=-0.10547897058319128, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([49, 53, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10745549e+00, 2.23687992e+03, 1.79442393e+00]), radius=0.054581755234379394, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([53, 55, 57, 58]), model=ScalarModel(intercept=0.05277532040035746, linear_terms=array([-1.64323783e-05, -3.01666074e-05, 8.63398572e-06]), square_terms=array([[ 5.85319673e-03, -2.28084159e-05, 1.72603213e-03], - [-2.28084159e-05, 1.44996872e-07, -6.90077769e-06], - [ 1.72603213e-03, -6.90077769e-06, 5.78855200e-04]]), scale=0.054581755234379394, 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relative_step_length=1.0094675417356582, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), radius=0.10916351046875879, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([49, 53, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.05281665458053994, linear_terms=array([ 6.69533060e-05, -1.59016211e-05, 2.13260623e-04]), square_terms=array([[2.01214560e-02, 3.75662561e-05, 5.84161452e-03], - [3.75662561e-05, 9.08192483e-08, 1.22889449e-05], - [5.84161452e-03, 1.22889449e-05, 1.96673699e-03]]), scale=0.10916351046875879, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model=ScalarModel(intercept=0.05274738642084201, linear_terms=array([-2.89095722e-03, -1.52762657e-05, -8.14429061e-04]), square_terms=array([[ 4.06012733e-03, -6.72244814e-06, 1.21120648e-03], - [-6.72244814e-06, 2.69342201e-08, -2.08679767e-06], - [ 1.21120648e-03, -2.08679767e-06, 4.31846954e-04]]), scale=0.054581755234379394, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 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1.76660455e+00]), index=59, x=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), fval=0.052755606205776504, rho=-1.1250970659194641, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), radius=0.027290877617189697, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([55, 58, 59, 60, 61]), model=ScalarModel(intercept=0.05276154511022461, linear_terms=array([-3.86556789e-05, -2.04771871e-06, 6.36275653e-07]), square_terms=array([[ 1.24535705e-03, -1.35375057e-07, 3.72209525e-04], - [-1.35375057e-07, 6.47746067e-10, -2.28047175e-08], - [ 3.72209525e-04, -2.28047175e-08, 1.28885229e-04]]), scale=0.027290877617189697, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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State(trustregion=Region(center=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00]), radius=0.013645438808594848, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([55, 59, 61, 62]), model=ScalarModel(intercept=0.05275560620577649, linear_terms=array([-1.97093175e-05, -5.42896838e-06, -1.19996937e-05]), square_terms=array([[3.10466342e-04, 1.05603109e-07, 9.24001766e-05], - [1.05603109e-07, 3.65759582e-09, 4.18438923e-08], - [9.24001766e-05, 4.18438923e-08, 3.17549202e-05]]), scale=0.013645438808594848, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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square_terms=array([[ 8.20272919e-05, -7.03115536e-08, 2.43677150e-05], - [-7.03115536e-08, 1.30003643e-09, -2.13391346e-08], - [ 2.43677150e-05, -2.13391346e-08, 8.30312530e-06]]), scale=0.006822719404297424, shift=array([4.10988644e+00, 2.23693452e+03, 1.78737883e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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rho=0.4983800116405342, accepted=True, new_indices=array([64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75]), old_indices_used=array([59, 62, 63]), old_indices_discarded=array([], dtype=int32), step_length=0.006920117064447186, relative_step_length=1.0142754896366417, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00]), radius=0.013645438808594848, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([59, 64, 65, 66, 67, 68, 70, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.05274921512205406, linear_terms=array([-2.32187448e-06, -3.03189290e-06, 1.22926474e-05]), square_terms=array([[3.28191192e-04, 2.38863375e-07, 9.75393216e-05], - [2.38863375e-07, 5.14401342e-09, 7.74187507e-08], - [9.75393216e-05, 7.74187507e-08, 3.33310299e-05]]), scale=0.013645438808594848, shift=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00]), radius=0.006822719404297424, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([59, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.05274737743387363, linear_terms=array([-3.08560817e-06, 1.00647640e-07, 8.72909747e-06]), square_terms=array([[8.19550727e-05, 4.78507252e-08, 2.42773470e-05], - [4.78507252e-08, 1.84716815e-09, 8.73766203e-09], - [2.42773470e-05, 8.73766203e-09, 8.24574337e-06]]), scale=0.006822719404297424, shift=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 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model=ScalarModel(intercept=0.05275084039469149, linear_terms=array([-9.80674453e-07, -4.73539110e-07, 2.45672083e-06]), square_terms=array([[2.05009973e-05, 9.48267824e-09, 6.05805690e-06], - [9.48267824e-09, 2.07251763e-10, 2.24783507e-09], - [6.05805690e-06, 2.24783507e-09, 2.05216978e-06]]), scale=0.003411359702148712, shift=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - 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1.77787754e+00]), index=79, x=array([4.11150945e+00, 2.23693233e+03, 1.77787754e+00]), fval=0.05274656188125212, rho=1.78595774700254, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([59, 65, 67, 68, 69, 70, 71, 72, 74, 75, 76, 78]), old_indices_discarded=array([64, 66, 73, 77]), step_length=0.0034420575444361447, relative_step_length=1.0089987116480554, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11150945e+00, 2.23693233e+03, 1.77787754e+00]), radius=0.006822719404297424, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([59, 67, 68, 69, 70, 71, 72, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=0.05275112021620292, linear_terms=array([-2.70754115e-06, -2.60641528e-06, 9.61700262e-07]), square_terms=array([[ 8.18217273e-05, -1.01517371e-07, 2.42634663e-05], - [-1.01517371e-07, 2.37295809e-09, -3.77829184e-08], - [ 2.42634663e-05, -3.77829184e-08, 8.25403428e-06]]), scale=0.006822719404297424, shift=array([4.11150945e+00, 2.23693233e+03, 1.77787754e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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66, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11150945e+00, 2.23693233e+03, 1.77787754e+00]), radius=0.003411359702148712, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([59, 68, 69, 70, 71, 72, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.0527518263392525, linear_terms=array([-1.50708000e-06, -2.58474681e-06, -1.20497330e-07]), square_terms=array([[ 2.04631968e-05, -4.61511085e-09, 6.05881213e-06], - [-4.61511085e-09, 1.93305890e-09, -3.59920561e-09], - [ 6.05881213e-06, -3.59920561e-09, 2.05813049e-06]]), scale=0.003411359702148712, shift=array([4.11150945e+00, 2.23693233e+03, 1.77787754e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, - 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, - -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, - 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.3719594 , 3687.3820962 , 10.89861347]), radius=0.716582051597251, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.6404926514152791, linear_terms=array([ 0.00985466, -0.00038215, -0.00076534]), square_terms=array([[ 4.62807937e-02, -8.53432130e-05, -1.30425185e-04], - [-8.53432130e-05, 4.67244373e-07, 8.14784874e-07], - [-1.30425185e-04, 8.14784874e-07, 1.55842322e-06]]), scale=0.716582051597251, shift=array([ 9.3719594 , 3687.3820962 , 10.89861347])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, - 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , - -0.62376469, -0.61159718, 0.03592713, 0.05727162, 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model_indices=array([20, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36]), model=ScalarModel(intercept=0.6404159621901686, linear_terms=array([-0.00235032, 0.0005279 , 0.00042805]), square_terms=array([[1.82912971e-01, 1.19176316e-04, 1.03107378e-04], - [1.19176316e-04, 1.03067442e-06, 6.43619009e-07], - [1.03107378e-04, 6.43619009e-07, 5.28724372e-07]]), scale=1.433164103194502, shift=array([ 9.22439025, 3687.7012643 , 11.54524388])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, - 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , - -0.62376469, -0.61159718, 0.03592713, 0.05727162, 0.065043 , - 0.05645612, 0.0379622 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), 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candidate_index=37, candidate_x=array([ 9.24396681, 3686.5883311 , 10.6423195 ]), index=36, x=array([ 9.22439025, 3687.7012643 , 11.54524388]), fval=0.6413804650211063, rho=-0.2440687245300182, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([20, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36]), old_indices_discarded=array([17, 21, 22, 23, 33, 35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.22439025, 3687.7012643 , 11.54524388]), radius=0.716582051597251, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([20, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36]), model=ScalarModel(intercept=0.6407072788899953, linear_terms=array([-0.00092301, 0.00023655, 0.00071886]), square_terms=array([[4.57691593e-02, 2.68093455e-05, 9.75075250e-05], - [2.68093455e-05, 2.29384837e-07, 4.35992514e-07], - [9.75075250e-05, 4.35992514e-07, 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old_indices_discarded=array([44, 56, 57]), step_length=0.011196594556213491, relative_step_length=1.0000000000005755, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2043489 , 3687.82812856, 11.41914483]), radius=0.022393189112414093, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([40, 45, 46, 48, 49, 50, 51, 53, 54, 56, 57, 58]), model=ScalarModel(intercept=0.6412653696397768, linear_terms=array([ 5.02347504e-05, -1.67843934e-05, 3.31896165e-06]), square_terms=array([[ 4.56781270e-05, 2.23686159e-09, 5.92586961e-09], - [ 2.23686159e-09, 7.69182274e-10, -1.26712821e-10], - [ 5.92586961e-09, -1.26712821e-10, 7.41689881e-11]]), scale=0.022393189112414093, shift=array([ 9.2043489 , 3687.82812856, 11.41914483])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, - 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , - -0.62376469, 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model_indices=array([40, 45, 46, 49, 50, 51, 53, 54, 56, 57, 58, 59]), model=ScalarModel(intercept=0.6412771044894154, linear_terms=array([ 6.01020178e-06, -1.39384897e-06, 5.24107505e-06]), square_terms=array([[1.13991454e-05, 4.35119389e-09, 2.64938593e-09], - [4.35119389e-09, 1.86028655e-10, 6.66030979e-11], - [2.64938593e-09, 6.66030979e-11, 9.58031972e-11]]), scale=0.011196594556207046, shift=array([ 9.2043489 , 3687.82812856, 11.41914483])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, - 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , - -0.62376469, -0.61159718, 0.03592713, 0.05727162, 0.065043 , - 0.05645612, 0.0379622 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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candidate_index=60, candidate_x=array([ 9.20032255, 3687.83107088, 11.40809601]), index=58, x=array([ 9.2043489 , 3687.82812856, 11.41914483]), fval=0.6412410861349671, rho=-6.999959129178455, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([40, 45, 46, 49, 50, 51, 53, 54, 56, 57, 58, 59]), old_indices_discarded=array([44, 47, 48, 52, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2043489 , 3687.82812856, 11.41914483]), radius=0.005598297278103523, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([40, 45, 46, 49, 50, 51, 53, 54, 57, 58, 59, 60]), model=ScalarModel(intercept=0.6412717155805557, linear_terms=array([-9.64669714e-06, -8.46619641e-06, 1.12964530e-06]), square_terms=array([[ 2.86249215e-06, 4.81129199e-09, 2.17636491e-09], - [ 4.81129199e-09, 1.43495830e-10, -9.14779665e-12], - [ 2.17636491e-09, -9.14779665e-12, 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step_length=0.005664266962718834, relative_step_length=1.011783883802194, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20814325, 3687.83229707, 11.41858768]), radius=0.011196594556207046, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([40, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.6412712637351203, linear_terms=array([1.04634331e-06, 3.39214140e-06, 3.94375277e-06]), square_terms=array([[1.13807753e-05, 1.64070890e-08, 7.06265242e-09], - [1.64070890e-08, 9.14207025e-10, 3.51357682e-10], - [7.06265242e-09, 3.51357682e-10, 1.65700957e-10]]), scale=0.011196594556207046, shift=array([ 9.20814325, 3687.83229707, 11.41858768])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, - 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , - -0.62376469, -0.61159718, 0.03592713, 0.05727162, 0.065043 , - 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model_indices=array([40, 45, 46, 48, 49, 51, 54, 57, 58, 60, 61, 62]), model=ScalarModel(intercept=0.6412653130751762, linear_terms=array([ 1.02424940e-05, -3.96493216e-05, 4.65584133e-06]), square_terms=array([[4.58894663e-05, 5.83168350e-08, 1.10057737e-08], - [5.83168350e-08, 3.50464895e-09, 1.59443535e-11], - [1.10057737e-08, 1.59443535e-11, 4.27344885e-10]]), scale=0.022393189112414093, shift=array([ 9.20744775, 3687.82500536, 11.41010724])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, - 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , - -0.62376469, -0.61159718, 0.03592713, 0.05727162, 0.065043 , - 0.05645612, 0.0379622 ]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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model=ScalarModel(intercept=101.00053527176014, linear_terms=array([-267.17329649, 80.95099224, 52.1467112 ]), square_terms=array([[ 361.29036327, -108.39796365, -69.65799497], - [-108.39796365, 32.72200507, 21.00295023], - [ -69.65799497, 21.00295023, 13.52711371]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3489.45000103, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=13, candidate_x=array([ 12.88097511, 3208.20173475, 0. ]), index=13, x=array([ 12.88097511, 3208.20173475, 0. ]), fval=1.1406131109915512, rho=0.00016464076262107032, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=281.40511690387655, relative_step_length=0.8064454765667648, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 12.88097511, 3208.20173475, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=30.184829913613466, linear_terms=array([-139.32389492, 29.36565848, 48.56265401]), square_terms=array([[ 337.66475622, -70.11282425, -115.6185002 ], - [ -70.11282425, 14.64456015, 24.13989901], - [-115.6185002 , 24.13989901, 39.85365245]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3208.20173475, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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step_length=140.67097035304008, relative_step_length=0.806264427410658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.25122599, 3067.57760161, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=5.307226904606869, linear_terms=array([-51.38753027, 26.65823128, 21.44983872]), square_terms=array([[ 305.31648211, -153.36036519, -122.65042123], - [-153.36036519, 77.5107252 , 61.97971585], - [-122.65042123, 61.97971585, 49.61959056]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model=ScalarModel(intercept=3.807942725574409, linear_terms=array([-39.58737235, 12.58807335, 13.34355021]), square_terms=array([[288.79762252, -88.45374867, -92.48750266], - [-88.45374867, 27.21485773, 28.46514941], - [-92.48750266, 28.46514941, 29.83245946]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=16, candidate_x=array([ 5.9246359 , 2926.95346847, 0. ]), index=16, x=array([ 5.9246359 , 2926.95346847, 0. ]), fval=0.23203665598876397, rho=0.570002726445654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([4, 5, 6, 7]), step_length=140.66347437444162, relative_step_length=0.8062214637424797, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 8, 9, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=3.9661830657942825, linear_terms=array([-28.12732145, 11.87395531, 18.45752875]), square_terms=array([[150.75705807, -60.44190282, -91.27776749], - [-60.44190282, 24.37668461, 36.9203391 ], - [-91.27776749, 36.9203391 , 56.05439485]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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old_indices_discarded=array([ 4, 5, 6, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 8, 9, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=9.571328999106925, linear_terms=array([-45.18448012, 9.36031969, 33.66340283]), square_terms=array([[133.09207534, -26.87423247, -93.65514891], - [-26.87423247, 5.44544862, 19.03151488], - [-93.65514891, 19.03151488, 66.85203657]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 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model_indices=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=2.345400265444784, linear_terms=array([-9.54818498, 0.79259481, 6.47312741]), square_terms=array([[ 71.29706187, -5.72633659, -37.74299647], - [ -5.72633659, 0.46131347, 3.04307064], - [-37.74299647, 3.04307064, 20.58343169]]), scale=array([ 9.45 , 70.31206657, 35. ]), shift=array([ 10.55 , 2786.32933533, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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17.57801664]), shift=array([ 10.55 , 2786.32933533, 17.57801664])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20]), model=ScalarModel(intercept=16.004294311139557, linear_terms=array([70.66939 , 1.13700979, -8.50470948]), square_terms=array([[ 1.58368000e+02, 2.56906467e+00, -1.92507844e+01], - [ 2.56906467e+00, 4.19273077e-02, -3.14468097e-01], - [-1.92507844e+01, -3.14468097e-01, 2.36523947e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2786.32933533, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 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model=ScalarModel(intercept=2.9984363121302398, linear_terms=array([20.20379397, 0.14022481, -1.76089925]), square_terms=array([[ 7.28524130e+01, 5.11325102e-01, -6.48101817e+00], - [ 5.11325102e-01, 3.60021596e-03, -4.56833511e-02], - [-6.48101817e+00, -4.56833511e-02, 5.82826690e-01]]), scale=array([6.44462921, 8.78900832, 4.39450416]), shift=array([ 7.54462921, 2786.32933533, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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step_length=8.789122808311065, relative_step_length=0.806006476042679, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=15.956937372018299, linear_terms=array([68.87495584, 1.01962009, -8.11318965]), square_terms=array([[ 1.50828107e+02, 2.25090089e+00, -1.79474940e+01], - [ 2.25090089e+00, 3.37920630e-02, -2.69729608e-01], - [-1.79474940e+01, -2.69729608e-01, 2.15985383e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2799.74097801, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], 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model=ScalarModel(intercept=3.0353080773001073, linear_terms=array([20.20386846, 0.10823129, -1.77493216]), square_terms=array([[ 7.18576918e+01, 3.89097471e-01, -6.44170944e+00], - [ 3.89097471e-01, 2.11348833e-03, -3.50355915e-02], - [-6.44170944e+00, -3.50355915e-02, 5.83744149e-01]]), scale=array([6.40219448, 8.78900832, 4.39450416]), shift=array([ 7.50219448, 2799.74097801, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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5.06389866])), candidate_index=25, candidate_x=array([ 5.09453133, 2808.27054949, 0. ]), index=23, x=array([ 5.11538064, 2799.74097801, 0. ]), fval=0.1625021910643016, rho=-0.257817250835792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=5.452265626613694, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.18941940373737348, linear_terms=array([-0.84702663, -0.00367806, 0.0743278 ]), square_terms=array([[ 3.09008276e+01, 1.15966532e-01, -2.10738689e+00], - [ 1.15966532e-01, 4.36576332e-04, -7.94409134e-03], - [-2.10738689e+00, -7.94409134e-03, 1.45288267e-01]]), scale=array([4.2049424 , 4.39450416, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26]), model=ScalarModel(intercept=0.040656194310767946, linear_terms=array([-0.04424986, -0.00023592, -0.08131239]), square_terms=array([[8.44848189e+00, 2.55963392e-02, 4.42498564e-02], - [2.55963392e-02, 7.77973037e-05, 2.35916270e-04], - [4.42498564e-02, 2.35916270e-04, 8.13123886e-02]]), scale=array([2.19725208, 2.19725208, 1.09862604]), shift=array([5.11538064e+00, 2.79974098e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, 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model_indices=array([21, 23, 26, 27]), model=ScalarModel(intercept=0.143861477312966, linear_terms=array([ 1.39795372e-01, -1.67256636e-05, 9.44770003e-03]), square_terms=array([[ 4.72932788e-01, -1.19149158e-04, 1.43354432e-01], - [-1.19149158e-04, 3.31626099e-08, -4.20348131e-05], - [ 1.43354432e-01, -4.20348131e-05, 5.61768276e-02]]), scale=array([1.09862604, 1.09862604, 0.54931302]), shift=array([5.11538064e+00, 2.79974098e+03, 5.49313020e-01])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=28, candidate_x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), index=28, x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), fval=0.06529563513965841, rho=1.475519476069878, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=1.6872964496840501, relative_step_length=1.2378681195926988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=0.07730703358690492, linear_terms=array([-0.21420423, -0.00177457, -0.09323422]), square_terms=array([[7.19945275e+00, 3.82631907e-02, 1.99911955e+00], - [3.82631907e-02, 2.08378103e-04, 1.10096472e-02], - [1.99911955e+00, 1.10096472e-02, 6.63610242e-01]]), scale=array([2.19725208, 2.19725208, 1.64793906]), shift=array([4.45734496e+00, 2.79864235e+03, 1.64793906e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - 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rho=-0.24314171126774836, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29]), model=ScalarModel(intercept=0.06904406104722437, linear_terms=array([ 0.00492248, 0.00149362, -0.05074773]), square_terms=array([[ 6.12405684e-01, -2.44364302e-03, 3.09451673e-01], - [-2.44364302e-03, 4.14350874e-05, -2.29602578e-03], - [ 3.09451673e-01, -2.29602578e-03, 2.13954389e-01]]), scale=array([1.09862604, 1.09862604, 1.09862604]), shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30]), model=ScalarModel(intercept=0.08513789653811096, linear_terms=array([-0.05873789, -0.00640693, -0.05839653]), square_terms=array([[0.29594537, 0.02020028, 0.12960348], - [0.02020028, 0.00146583, 0.00964012], - [0.12960348, 0.00964012, 0.07540158]]), scale=0.6815332033267117, shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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x=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), fval=0.05415908459581581, rho=0.02787984735130772, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=1.4693394633669328, relative_step_length=1.0779661623195813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.06019280281239402, linear_terms=array([-0.05311338, -0.00664367, -0.01652794]), square_terms=array([[0.31168089, 0.01963004, 0.09342492], - [0.01963004, 0.00163636, 0.00734946], - [0.09342492, 0.00734946, 0.04083573]]), scale=0.6815332033267117, shift=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=33, candidate_x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=0.18295227259122485, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([27, 28, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.6904317234320418, relative_step_length=1.0130566200764606, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.05146650808405816, linear_terms=array([0.05615491, 0.00068073, 0.02123787]), square_terms=array([[1.36450351e+00, 9.46810979e-03, 6.16903067e-01], - [9.46810979e-03, 7.27379915e-05, 3.94676561e-03], - [6.16903067e-01, 3.94676561e-03, 3.53300245e-01]]), scale=1.3630664066534235, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=34, candidate_x=array([4.05998931e+00, 2.79666699e+03, 2.28245373e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=-0.356779440889848, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.05177155574931393, linear_terms=array([ 2.24054327e-02, -6.65931684e-05, 7.36047043e-03]), square_terms=array([[0.29167665, 0.01447643, 0.13152612], - [0.01447643, 0.0008045 , 0.00750585], - [0.13152612, 0.00750585, 0.07813118]]), scale=0.6815332033267117, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=35, candidate_x=array([4.02600282e+00, 2.79871386e+03, 2.27098711e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=-2.2415296614174687, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([28, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.05243167254991798, linear_terms=array([ 2.14835428e-02, -8.53058917e-05, 7.83429373e-03]), square_terms=array([[2.69010071e-01, 2.03422326e-04, 8.75369596e-02], - [2.03422326e-04, 6.23463910e-07, 8.57167056e-05], - [8.75369596e-02, 8.57167056e-05, 3.13897458e-02]]), scale=0.3407666016633559, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=36, candidate_x=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), index=36, x=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), fval=0.05131896506402278, rho=1.3228515670897443, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([28, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.35386200358029707, relative_step_length=1.0384292411668858, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.0515876639767697, linear_terms=array([ 0.00588415, -0.00077363, -0.00241592]), square_terms=array([[0.31118702, 0.01242837, 0.13629499], - [0.01242837, 0.00054706, 0.00615461], - [0.13629499, 0.00615461, 0.07774976]]), scale=0.6815332033267117, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=37, candidate_x=array([4.03542102e+00, 2.79905700e+03, 2.26807283e+00]), index=36, x=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), fval=0.05131896506402278, rho=-3.102410944182445, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=0.052532543329590564, linear_terms=array([-0.00426995, -0.00027655, -0.00095162]), square_terms=array([[9.08193291e-02, 1.45774993e-03, 2.59401890e-02], - [1.45774993e-03, 2.50681460e-05, 4.39732923e-04], - [2.59401890e-02, 4.39732923e-04, 1.01466646e-02]]), scale=0.3407666016633559, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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[0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=38, candidate_x=array([4.16234285e+00, 2.79871204e+03, 2.08202831e+00]), index=36, x=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), fval=0.05131896506402278, rho=-0.325679194740959, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([27, 28, 30, 31, 32, 33, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([30, 31, 33, 35, 36, 37, 38]), model=ScalarModel(intercept=0.05135748588060747, linear_terms=array([ 1.00745392e-03, -3.03899194e-05, 9.57436526e-04]), square_terms=array([[ 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1.95671244e+00]), fval=0.05091436090216684, rho=1.1516416598700234, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.1759818526107779, relative_step_length=1.032858570950159, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.05221280899533157, linear_terms=array([-0.00115235, -0.00033548, -0.00127409]), square_terms=array([[8.81488941e-02, 2.02195870e-03, 2.52583882e-02], - [2.02195870e-03, 4.98103348e-05, 6.21228724e-04], - [2.52583882e-02, 6.21228724e-04, 9.98002687e-03]]), scale=0.3407666016633559, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.05090588732753695, linear_terms=array([ 2.00967052e-03, -4.51302094e-06, 7.70306669e-04]), square_terms=array([[ 6.02617840e-02, -3.14541700e-04, 1.76727179e-02], - [-3.14541700e-04, 1.67055199e-06, -9.60913876e-05], - [ 1.76727179e-02, -9.60913876e-05, 5.78378569e-03]]), scale=0.17038330083167794, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 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model_indices=array([31, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=0.05095057563584681, linear_terms=array([ 1.26813383e-03, -6.52109364e-06, 4.29926422e-04]), square_terms=array([[1.02760072e-02, 1.58255954e-05, 2.97902836e-03], - [1.58255954e-05, 2.96488117e-08, 4.90049513e-06], - [2.97902836e-03, 4.90049513e-06, 1.01490054e-03]]), scale=0.08519165041583897, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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4.96836085, 3489.45000103, 5.06389866])), candidate_index=42, candidate_x=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), index=42, x=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), fval=0.05088712976930869, rho=0.2733211260583337, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 36, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=0.09143628290487611, relative_step_length=1.073300992040367, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.0508972937795344, linear_terms=array([-1.27085852e-03, 7.63964281e-06, -3.16739400e-04]), square_terms=array([[ 5.60259685e-02, -4.42647317e-04, 1.59087832e-02], - [-4.42647317e-04, 3.55247313e-06, -1.31079791e-04], - [ 1.59087832e-02, -1.31079791e-04, 5.10730862e-03]]), scale=0.17038330083167794, shift=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), radius=0.08519165041583897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.0508813659118873, linear_terms=array([-1.44722476e-04, -3.19089966e-06, -5.21530523e-05]), square_terms=array([[1.06023461e-02, 7.49354229e-06, 3.10107179e-03], - [7.49354229e-06, 6.81249566e-09, 2.33378372e-06], - [3.10107179e-03, 2.33378372e-06, 1.05915929e-03]]), scale=0.08519165041583897, shift=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 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State(trustregion=Region(center=array([4.18776198e+00, 2.79860171e+03, 1.92864855e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.05089102600737241, linear_terms=array([-8.05219085e-04, 4.14561579e-06, -1.62643657e-04]), square_terms=array([[ 5.46071930e-02, -4.28787356e-04, 1.52566341e-02], - [-4.28787356e-04, 3.42160560e-06, -1.25140244e-04], - [ 1.52566341e-02, -1.25140244e-04, 4.84664124e-03]]), scale=0.17038330083167794, shift=array([4.18776198e+00, 2.79860171e+03, 1.92864855e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_discarded=array([33, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.19055071e+00, 2.79851652e+03, 1.92636431e+00]), radius=0.08519165041583897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.050883731876720184, linear_terms=array([ 2.37772026e-04, -7.63836759e-07, 6.40358355e-05]), square_terms=array([[1.06674285e-02, 5.35451639e-06, 3.12506976e-03], - [5.35451639e-06, 3.56049255e-09, 1.65543072e-06], - [3.12506976e-03, 1.65543072e-06, 1.06779579e-03]]), scale=0.08519165041583897, shift=array([4.19055071e+00, 2.79851652e+03, 1.92636431e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, - 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, - -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, - 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, - 2.24752360e-01]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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2.270987106425888}, {'CRRA': 4.142328704202798, 'BeqFac': 2798.371239838438, 'BeqShift': 2.115320067541048}, {'CRRA': 4.035421021891758, 'BeqFac': 2799.057000705553, 'BeqShift': 2.268072829011086}, {'CRRA': 4.162342849386828, 'BeqFac': 2798.7120382787884, 'BeqShift': 2.0820283126821564}, {'CRRA': 4.189180729803071, 'BeqFac': 2798.4313906514644, 'BeqShift': 1.9567124389800021}, {'CRRA': 4.157129843134547, 'BeqFac': 2798.773188323082, 'BeqShift': 2.0571220165181043}, {'CRRA': 4.197784149507958, 'BeqFac': 2798.2613053833006, 'BeqShift': 1.9049698616184068}, {'CRRA': 4.1882127035206445, 'BeqFac': 2798.516512627961, 'BeqShift': 1.9233372105334974}, {'CRRA': 4.196587606858762, 'BeqFac': 2798.6869627340325, 'BeqShift': 1.9121957926636612}, {'CRRA': 4.187761980105835, 'BeqFac': 2798.6017090857436, 'BeqShift': 1.9286485471909265}, {'CRRA': 4.196242132162183, 'BeqFac': 2798.7722084678076, 'BeqShift': 1.912079557485072}, {'CRRA': 4.190550713034702, 'BeqFac': 2798.5165199690787, 'BeqShift': 1.9263643061813749}, {'CRRA': 4.189871366285182, 'BeqFac': 2798.3461525835946, 'BeqShift': 1.923926585872323}, {'CRRA': 4.187709060456282, 'BeqFac': 2798.601712437453, 'BeqShift': 1.929437283451666}, {'CRRA': 4.177600674290874, 'BeqFac': 2798.51512022846, 'BeqShift': 1.966935700692675}], 'criterion': [1.181105432157004, 144.6645524000311, 3.4037821967481316, 8.319436275338557, 1.4593214606471996, 0.7011180013216807, 8.319436275338557, 1643.2967077523338, 1137.5807967428811, 8.319436275342273, 1.1697753502038721, 1668.6551408328123, 6.776870912396954, 1.1406131109915512, 0.5545985836347646, 2.2278811659484714, 0.232036655988764, 18.119098849415735, 0.16384370556335875, 0.2527959294336996, 0.6819421555415957, 0.1642991669381873, 0.1628347112711602, 0.1625021910643016, 0.16817866367514966, 0.1627923319728763, 0.16259635535169853, 0.42498980410057874, 0.06529563513965843, 0.08399250593750511, 0.10068669909272801, 0.05432022221654956, 0.05415908459581581, 0.05275688891668154, 0.05330674080076367, 0.057419127185494, 0.051318965064022795, 0.05612791206991445, 0.051424151471162374, 0.05091436090216683, 0.051068448210695803, 0.050914579506761565, 0.05088712976930869, 0.050908465316813346, 0.050875284135983245, 0.050910162881492554, 0.0508732681045439, 0.050878919485344336, 0.05087623002457618, 0.050872538941762795], 'runtime': [0.0, 1.504941400140524, 1.6768188998103142, 1.8667818000540137, 2.0520103997550905, 2.234589799772948, 2.4240341000258923, 2.6088847001083195, 2.7994983000680804, 2.995965300127864, 3.1924839997664094, 3.3760963999666274, 3.5580016998574138, 4.759761999826878, 5.9858081000857055, 7.170780099928379, 8.360401600133628, 9.697439599782228, 10.887631500139832, 12.083789399825037, 13.26669319998473, 14.449900799896568, 15.622420799918473, 16.798246500082314, 17.96437279973179, 19.288782899733633, 20.466819599736482, 21.645570000167936, 22.826812400016934, 24.006268199998885, 25.212331799790263, 26.400473299901932, 27.575066099874675, 28.895407499745488, 30.087074699811637, 31.28205809975043, 32.452069099992514, 33.63252570014447, 34.80818759975955, 35.983998800162226, 37.16741700004786, 38.50141609972343, 39.70189919974655, 40.88999589998275, 42.06016460014507, 43.238295500166714, 44.42306770011783, 45.59471370000392, 46.93225569976494, 48.113191300071776], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.018714300470562203, 'relative_params_change': 0.22317607955398075, 'absolute_criterion_change': 0.0018714300470562203, 'absolute_params_change': 561.5795603000294}, 'five_steps': {'relative_criterion_change': 0.018714300470562203, 'relative_params_change': 0.22317607955398075, 'absolute_criterion_change': 0.0018714300470562203, 'absolute_params_change': 561.5795603000294}}" + +multistart_info,"{'start_parameters': [{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}, {'CRRA': 6.333842432535624, 'BeqFac': 3668.9001041779247, 'BeqShift': 6.573413466501883}, {'CRRA': 4.968360850653578, 'BeqFac': 3489.450001032764, 'BeqShift': 5.063898656927828}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.577e-07* 6.797e-06* +relative_params_change 0.0001525 0.0002161 +absolute_criterion_change 3.577e-08* 6.797e-07* +absolute_params_change 0.0007074 0.0007074 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 8.731e-08* 0.000407 +relative_params_change 0.001013 0.02664 +absolute_criterion_change 5.598e-08* 0.000261 +absolute_params_change 0.01402 0.3641 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 7.292e-06* 0.004464 +relative_params_change 0.02086 0.07591 +absolute_criterion_change 7.292e-07* 0.0004464 +absolute_params_change 0.04261 0.2097 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018, 'BeqShift': 0.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0, 'BeqShift': 13.125}, {'CRRA': 8.778125, 'BeqFac': 9062.5, 'BeqShift': 19.6875}, {'CRRA': 9.959375, 'BeqFac': 937.5, 'BeqShift': 59.0625}, {'CRRA': 8.1875, 'BeqFac': 3750.0, 'BeqShift': 43.75}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0, 'BeqShift': 35.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5, 'BeqShift': 50.3125}, {'CRRA': 7.00625, 'BeqFac': 1875.0, 'BeqShift': 21.875}, {'CRRA': 11.73125, 'BeqFac': 4375.0, 'BeqShift': 4.375}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5, 'BeqShift': 67.8125}, {'CRRA': 6.415625, 'BeqFac': 2812.5, 'BeqShift': 10.9375}, {'CRRA': 12.9125, 'BeqFac': 1250.0, 'BeqShift': 61.25}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0, 'BeqShift': 52.5}, {'CRRA': 5.234375, 'BeqFac': 8437.5, 'BeqShift': 6.5625}, {'CRRA': 13.503124999999999, 'BeqFac': 6562.5, 'BeqShift': 2.1875}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0, 'BeqShift': 30.625}, {'CRRA': 14.684375, 'BeqFac': 3437.5, 'BeqShift': 41.5625}, {'CRRA': 4.64375, 'BeqFac': 3125.0, 'BeqShift': 65.625}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0, 'BeqShift': 17.5}, {'CRRA': 4.053125, 'BeqFac': 1562.5, 'BeqShift': 37.1875}, {'CRRA': 15.865624999999998, 'BeqFac': 7812.5, 'BeqShift': 45.9375}, {'CRRA': 3.4625, 'BeqFac': 6250.0, 'BeqShift': 26.25}, {'CRRA': 16.45625, 'BeqFac': 6875.0, 'BeqShift': 56.875}, {'CRRA': 2.871875, 'BeqFac': 4687.5, 'BeqShift': 32.8125}, {'CRRA': 17.046875, 'BeqFac': 2187.5, 'BeqShift': 15.3125}, {'CRRA': 17.6375, 'BeqFac': 8750.0, 'BeqShift': 8.75}, {'CRRA': 18.228125, 'BeqFac': 4062.5, 'BeqShift': 54.6875}, {'CRRA': 18.81875, 'BeqFac': 625.0, 'BeqShift': 48.125}, {'CRRA': 19.409375, 'BeqFac': 5937.5, 'BeqShift': 24.0625}, {'CRRA': 2.28125, 'BeqFac': 9375.0, 'BeqShift': 39.375}], 'exploration_results': array([ 0.15183333, 0.64211538, 0.64842412, 0.66164015, 0.68191739, + 0.7044766 , 0.74714867, 0.84822968, 0.86260573, 0.97951322, + 0.98915247, 1.12431839, 1.18220644, 1.20662179, 1.30406801, + 1.52011014, 1.78024917, 1.78135807, 2.0971452 , 2.24437522, + 2.48116605, 2.85787595, 2.94379242, 3.4148927 , 3.4976294 , + 4.16035282, 4.94978648, 5.89491246, 7.00125067, 34.37799292])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.96836085, 3489.45000103, 5.06389866]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=[0], model=ScalarModel(intercept=1.181105432157004, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=0, candidate_x=array([ 4.96836085, 3489.45000103, 5.06389866]), index=0, x=array([ 4.96836085, 3489.45000103, 5.06389866]), fval=1.181105432157004, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.96836085, 3489.45000103, 5.06389866]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=101.00053527176014, linear_terms=array([-267.17329649, 80.95099224, 52.1467112 ]), square_terms=array([[ 361.29036327, -108.39796365, -69.65799497], + [-108.39796365, 32.72200507, 21.00295023], + [ -69.65799497, 21.00295023, 13.52711371]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3489.45000103, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=13, candidate_x=array([ 12.88097511, 3208.20173475, 0. ]), index=13, x=array([ 12.88097511, 3208.20173475, 0. ]), fval=1.1406131109915512, rho=0.00016464076262107032, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=281.40511690387655, relative_step_length=0.8064454765667648, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 12.88097511, 3208.20173475, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=30.184829913613466, linear_terms=array([-139.32389492, 29.36565848, 48.56265401]), square_terms=array([[ 337.66475622, -70.11282425, -115.6185002 ], + [ -70.11282425, 14.64456015, 24.13989901], + [-115.6185002 , 24.13989901, 39.85365245]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3208.20173475, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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step_length=140.67097035304008, relative_step_length=0.806264427410658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.25122599, 3067.57760161, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=5.307226904606869, linear_terms=array([-51.38753027, 26.65823128, 21.44983872]), square_terms=array([[ 305.31648211, -153.36036519, -122.65042123], + [-153.36036519, 77.5107252 , 61.97971585], + [-122.65042123, 61.97971585, 49.61959056]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 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model=ScalarModel(intercept=3.807942725574409, linear_terms=array([-39.58737235, 12.58807335, 13.34355021]), square_terms=array([[288.79762252, -88.45374867, -92.48750266], + [-88.45374867, 27.21485773, 28.46514941], + [-92.48750266, 28.46514941, 29.83245946]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 3067.57760161, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=16, candidate_x=array([ 5.9246359 , 2926.95346847, 0. ]), index=16, x=array([ 5.9246359 , 2926.95346847, 0. ]), fval=0.23203665598876397, rho=0.570002726445654, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([4, 5, 6, 7]), step_length=140.66347437444162, relative_step_length=0.8062214637424797, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=348.9450001032764, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 3, 8, 9, 10, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=3.9661830657942825, linear_terms=array([-28.12732145, 11.87395531, 18.45752875]), square_terms=array([[150.75705807, -60.44190282, -91.27776749], + [-60.44190282, 24.37668461, 36.9203391 ], + [-91.27776749, 36.9203391 , 56.05439485]]), scale=array([ 9.45 , 281.24826628, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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old_indices_discarded=array([ 4, 5, 6, 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.9246359 , 2926.95346847, 0. ]), radius=174.4725000516382, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([ 0, 1, 2, 8, 9, 10, 12, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=9.571328999106925, linear_terms=array([-45.18448012, 9.36031969, 33.66340283]), square_terms=array([[133.09207534, -26.87423247, -93.65514891], + [-26.87423247, 5.44544862, 19.03151488], + [-93.65514891, 19.03151488, 66.85203657]]), scale=array([ 9.45 , 140.62413314, 35. ]), shift=array([ 10.55 , 2926.95346847, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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model_indices=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=2.345400265444784, linear_terms=array([-9.54818498, 0.79259481, 6.47312741]), square_terms=array([[ 71.29706187, -5.72633659, -37.74299647], + [ -5.72633659, 0.46131347, 3.04307064], + [-37.74299647, 3.04307064, 20.58343169]]), scale=array([ 9.45 , 70.31206657, 35. ]), shift=array([ 10.55 , 2786.32933533, 35. ])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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5.06389866])), candidate_index=19, candidate_x=array([ 6.05395429, 2716.01726876, 0. ]), index=18, x=array([ 5.2002501 , 2786.32933533, 0. ]), fval=0.1638437055633587, rho=-0.08459209004077528, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 2, 10, 12, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=43.61812501290955, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 16, 17, 18, 19]), model=ScalarModel(intercept=7.1526635561288225, linear_terms=array([ 54.6285074 , 0.69834955, -14.30532711]), square_terms=array([[ 2.09947491e+02, 2.68478398e+00, -5.46285074e+01], + [ 2.68478398e+00, 3.43920488e-02, -6.98349546e-01], + [-5.46285074e+01, -6.98349546e-01, 1.43053271e+01]]), scale=array([ 9.45 , 35.15603329, 17.57801664]), shift=array([ 10.55 , 2786.32933533, 17.57801664])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.2002501 , 2786.32933533, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20]), model=ScalarModel(intercept=16.004294311139557, linear_terms=array([70.66939 , 1.13700979, -8.50470948]), square_terms=array([[ 1.58368000e+02, 2.56906467e+00, -1.92507844e+01], + [ 2.56906467e+00, 4.19273077e-02, -3.14468097e-01], + [-1.92507844e+01, -3.14468097e-01, 2.36523947e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2786.32933533, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=2.9984363121302398, linear_terms=array([20.20379397, 0.14022481, -1.76089925]), square_terms=array([[ 7.28524130e+01, 5.11325102e-01, -6.48101817e+00], + [ 5.11325102e-01, 3.60021596e-03, -4.56833511e-02], + [-6.48101817e+00, -4.56833511e-02, 5.82826690e-01]]), scale=array([6.44462921, 8.78900832, 4.39450416]), shift=array([ 7.54462921, 2786.32933533, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=22, candidate_x=array([ 5.16024118, 2790.95196969, 0. ]), index=22, x=array([ 5.16024118, 2790.95196969, 0. ]), fval=0.1628347112711602, rho=1.0555049921680508, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=4.622807489556225, relative_step_length=0.4239345444755384, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.16024118, 2790.95196969, 0. ]), radius=10.904531253227388, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22]), model=ScalarModel(intercept=3.006282375920816, linear_terms=array([20.16458994, 0.14100404, -1.76243727]), square_terms=array([[ 7.23456179e+01, 5.11502709e-01, -6.45362479e+00], + [ 5.11502709e-01, 3.62783851e-03, -4.58243177e-02], + [-6.45362479e+00, -4.58243177e-02, 5.81967789e-01]]), scale=array([6.42462475, 8.78900832, 4.39450416]), shift=array([ 7.52462475, 2790.95196969, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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step_length=8.789122808311065, relative_step_length=0.806006476042679, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=21.809062506454776, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=15.956937372018299, linear_terms=array([68.87495584, 1.01962009, -8.11318965]), square_terms=array([[ 1.50828107e+02, 2.25090089e+00, -1.79474940e+01], + [ 2.25090089e+00, 3.37920630e-02, -2.69729608e-01], + [-1.79474940e+01, -2.69729608e-01, 2.15985383e+00]]), scale=array([ 9.45 , 17.57801664, 8.78900832]), shift=array([ 10.55 , 2799.74097801, 8.78900832])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 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model=ScalarModel(intercept=3.0353080773001073, linear_terms=array([20.20386846, 0.10823129, -1.77493216]), square_terms=array([[ 7.18576918e+01, 3.89097471e-01, -6.44170944e+00], + [ 3.89097471e-01, 2.11348833e-03, -3.50355915e-02], + [-6.44170944e+00, -3.50355915e-02, 5.83744149e-01]]), scale=array([6.40219448, 8.78900832, 4.39450416]), shift=array([ 7.50219448, 2799.74097801, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=25, candidate_x=array([ 5.09453133, 2808.27054949, 0. ]), index=23, x=array([ 5.11538064, 2799.74097801, 0. ]), fval=0.1625021910643016, rho=-0.257817250835792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=5.452265626613694, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.18941940373737348, linear_terms=array([-0.84702663, -0.00367806, 0.0743278 ]), square_terms=array([[ 3.09008276e+01, 1.15966532e-01, -2.10738689e+00], + [ 1.15966532e-01, 4.36576332e-04, -7.94409134e-03], + [-2.10738689e+00, -7.94409134e-03, 1.45288267e-01]]), scale=array([4.2049424 , 4.39450416, 2.19725208]), shift=array([5.30494240e+00, 2.79974098e+03, 2.19725208e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26]), model=ScalarModel(intercept=0.040656194310767946, linear_terms=array([-0.04424986, -0.00023592, -0.08131239]), square_terms=array([[8.44848189e+00, 2.55963392e-02, 4.42498564e-02], + [2.55963392e-02, 7.77973037e-05, 2.35916270e-04], + [4.42498564e-02, 2.35916270e-04, 8.13123886e-02]]), scale=array([2.19725208, 2.19725208, 1.09862604]), shift=array([5.11538064e+00, 2.79974098e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, 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scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=28, candidate_x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), index=28, x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), fval=0.06529563513965841, rho=1.475519476069878, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=1.6872964496840501, relative_step_length=1.2378681195926988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=0.07730703358690492, linear_terms=array([-0.21420423, -0.00177457, -0.09323422]), square_terms=array([[7.19945275e+00, 3.82631907e-02, 1.99911955e+00], + [3.82631907e-02, 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rho=-0.24314171126774836, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29]), model=ScalarModel(intercept=0.06904406104722437, linear_terms=array([ 0.00492248, 0.00149362, -0.05074773]), square_terms=array([[ 6.12405684e-01, -2.44364302e-03, 3.09451673e-01], + [-2.44364302e-03, 4.14350874e-05, -2.29602578e-03], + [ 3.09451673e-01, -2.29602578e-03, 2.13954389e-01]]), scale=array([1.09862604, 1.09862604, 1.09862604]), shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 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x=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), fval=0.05415908459581581, rho=0.02787984735130772, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=1.4693394633669328, relative_step_length=1.0779661623195813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.06019280281239402, linear_terms=array([-0.05311338, -0.00664367, -0.01652794]), square_terms=array([[0.31168089, 0.01963004, 0.09342492], + [0.01963004, 0.00163636, 0.00734946], + [0.09342492, 0.00734946, 0.04083573]]), scale=0.6815332033267117, shift=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.05177155574931393, linear_terms=array([ 2.24054327e-02, -6.65931684e-05, 7.36047043e-03]), square_terms=array([[0.29167665, 0.01447643, 0.13152612], + [0.01447643, 0.0008045 , 0.00750585], + [0.13152612, 0.00750585, 0.07813118]]), scale=0.6815332033267117, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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3489.45000103, 5.06389866])), candidate_index=35, candidate_x=array([4.02600282e+00, 2.79871386e+03, 2.27098711e+00]), index=33, x=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), fval=0.05275688891668154, rho=-2.2415296614174687, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([23, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([28, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.05243167254991798, linear_terms=array([ 2.14835428e-02, -8.53058917e-05, 7.83429373e-03]), square_terms=array([[2.69010071e-01, 2.03422326e-04, 8.75369596e-02], + [2.03422326e-04, 6.23463910e-07, 8.57167056e-05], + [8.75369596e-02, 8.57167056e-05, 3.13897458e-02]]), scale=0.3407666016633559, shift=array([4.13804573e+00, 2.79803113e+03, 2.21290949e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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old_indices_used=array([28, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.35386200358029707, relative_step_length=1.0384292411668858, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.0515876639767697, linear_terms=array([ 0.00588415, -0.00077363, -0.00241592]), square_terms=array([[0.31118702, 0.01242837, 0.13629499], + [0.01242837, 0.00054706, 0.00615461], + [0.13629499, 0.00615461, 0.07774976]]), scale=0.6815332033267117, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + 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radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37]), model=ScalarModel(intercept=0.052532543329590564, linear_terms=array([-0.00426995, -0.00027655, -0.00095162]), square_terms=array([[9.08193291e-02, 1.45774993e-03, 2.59401890e-02], + [1.45774993e-03, 2.50681460e-05, 4.39732923e-04], + [2.59401890e-02, 4.39732923e-04, 1.01466646e-02]]), scale=0.3407666016633559, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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6.84781454e-02, -6.53727045e-05, 2.13071280e-02], + [-6.53727045e-05, 1.19864368e-07, -2.42922328e-05], + [ 2.13071280e-02, -2.42922328e-05, 7.24051619e-03]]), scale=0.17038330083167794, shift=array([4.14232870e+00, 2.79837124e+03, 2.11532007e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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1.95671244e+00]), fval=0.05091436090216684, rho=1.1516416598700234, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.1759818526107779, relative_step_length=1.032858570950159, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.05221280899533157, linear_terms=array([-0.00115235, -0.00033548, -0.00127409]), square_terms=array([[8.81488941e-02, 2.02195870e-03, 2.52583882e-02], + [2.02195870e-03, 4.98103348e-05, 6.21228724e-04], + [2.52583882e-02, 6.21228724e-04, 9.98002687e-03]]), scale=0.3407666016633559, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.05090588732753695, linear_terms=array([ 2.00967052e-03, -4.51302094e-06, 7.70306669e-04]), square_terms=array([[ 6.02617840e-02, -3.14541700e-04, 1.76727179e-02], + [-3.14541700e-04, 1.67055199e-06, -9.60913876e-05], + [ 1.76727179e-02, -9.60913876e-05, 5.78378569e-03]]), scale=0.17038330083167794, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00]), radius=0.08519165041583897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.0508813659118873, linear_terms=array([-1.44722476e-04, -3.19089966e-06, -5.21530523e-05]), square_terms=array([[1.06023461e-02, 7.49354229e-06, 3.10107179e-03], + [7.49354229e-06, 6.81249566e-09, 2.33378372e-06], + [3.10107179e-03, 2.33378372e-06, 1.05915929e-03]]), scale=0.08519165041583897, shift=array([4.18821270e+00, 2.79851651e+03, 1.92333721e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 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model=ScalarModel(intercept=0.05275084039469149, linear_terms=array([-9.80674453e-07, -4.73539110e-07, 2.45672083e-06]), square_terms=array([[2.05009973e-05, 9.48267824e-09, 6.05805690e-06], + [9.48267824e-09, 2.07251763e-10, 2.24783507e-09], + [6.05805690e-06, 2.24783507e-09, 2.05216978e-06]]), scale=0.003411359702148712, shift=array([4.11051116e+00, 2.23693167e+03, 1.78110356e+00])), vector_model=VectorModel(intercepts=array([ 0.06291652, 0.11643581, 0.10823668, 0.12189314, 0.12343863, + 0.12357968, 0.12479589, 0.09434044, 0.01975806, 0.10844278, + -0.14820181, -0.07391777, -0.01698537, -0.01832682, -0.03672083, + -0.06411733, -0.08425918]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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candidate_index=85, candidate_x=array([4.11096719e+00, 2.23693585e+03, 1.77759273e+00]), index=81, x=array([4.11181375e+00, 2.23693575e+03, 1.77757132e+00]), fval=0.05274431641899499, rho=-0.5283398254295653, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([79, 80, 81, 83, 84]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.11181375e+00, 2.23693575e+03, 1.77757132e+00]), radius=0.000426419962768589, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=0.052744892341673844, linear_terms=array([-1.53029775e-06, 6.24435038e-07, 9.86295933e-07]), square_terms=array([[ 3.39968814e-07, -1.34328271e-09, 9.61921254e-08], + [-1.34328271e-09, 2.43509164e-11, -4.22033363e-10], + [ 9.61921254e-08, 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([65, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20680947, 3687.831003 , 11.41242438]), radius=0.0003498935798814702, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([65, 69, 71, 72]), model=ScalarModel(intercept=0.6411979640656267, linear_terms=array([-5.05084701e-06, 2.82872376e-06, 9.27467376e-07]), square_terms=array([[ 1.20197552e-08, -1.60511718e-10, -3.18686141e-11], + [-1.60511718e-10, 1.64022734e-11, 5.33471188e-12], + [-3.18686141e-11, 5.33471188e-12, 3.14758523e-12]]), scale=0.0003498935798814702, shift=array([ 9.20680947, 3687.831003 , 11.41242438])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, + 0.06945224, 0.05266735, -0.15458875, -0.30268525, 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model_indices=array([69, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.6411983788749992, linear_terms=array([ 1.68255446e-06, -5.57235139e-09, -7.04572418e-08]), square_terms=array([[2.81031505e-09, 1.30356265e-13, 1.96626650e-12], + [1.30356265e-13, 9.14800834e-17, 1.10316253e-15], + [1.96626650e-12, 1.10316253e-15, 1.44348922e-14]]), scale=0.0001749467899407351, shift=array([ 9.20680947, 3687.831003 , 11.41242438])), vector_model=VectorModel(intercepts=array([ 0.02576631, 0.06184162, 0.05506634, 0.07304972, 0.07539609, + 0.06945224, 0.05266735, -0.15458875, -0.30268525, -0.2894935 , + -0.62376469, -0.61159718, 0.03592713, 0.05727162, 0.065043 , + 0.05645612, 0.0379622 ]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=366.8900104177925, shift=array([ 6.33384243, 3668.90010418, 6.57341347])), candidate_index=86, candidate_x=array([ 9.20663468, 3687.83100372, 11.41243184]), index=86, x=array([ 9.20663468, 3687.83100372, 11.41243184]), fval=0.6411977764871175, rho=0.1114790501708937, accepted=True, new_indices=array([74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), old_indices_used=array([69, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.00017494678994204872, relative_step_length=1.0000000000075087, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 87 entries., 'history': {'params': [{'CRRA': 6.333842432535624, 'BeqFac': 3668.9001041779247, 'BeqShift': 6.573413466501883}, {'CRRA': 2.616981898670428, 'BeqFac': 3373.188231776675, 'BeqShift': 62.17867746421218}, {'CRRA': 18.050369850224293, 'BeqFac': 3373.188231776675, 'BeqShift': 61.4640779436021}, {'CRRA': 20.0, 'BeqFac': 3834.1986261245534, 'BeqShift': 65.26986123883711}, {'CRRA': 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model=ScalarModel(intercept=2.9984363121302398, linear_terms=array([20.20379397, 0.14022481, -1.76089925]), square_terms=array([[ 7.28524130e+01, 5.11325102e-01, -6.48101817e+00], + [ 5.11325102e-01, 3.60021596e-03, -4.56833511e-02], + [-6.48101817e+00, -4.56833511e-02, 5.82826690e-01]]), scale=array([6.44462921, 8.78900832, 4.39450416]), shift=array([ 7.54462921, 2786.32933533, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=22, candidate_x=array([ 5.16024118, 2790.95196969, 0. ]), index=22, x=array([ 5.16024118, 2790.95196969, 0. ]), fval=0.1628347112711602, rho=1.0555049921680508, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=4.622807489556225, relative_step_length=0.4239345444755384, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.16024118, 2790.95196969, 0. ]), radius=10.904531253227388, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22]), model=ScalarModel(intercept=3.006282375920816, linear_terms=array([20.16458994, 0.14100404, -1.76243727]), square_terms=array([[ 7.23456179e+01, 5.11502709e-01, -6.45362479e+00], + [ 5.11502709e-01, 3.62783851e-03, -4.58243177e-02], + [-6.45362479e+00, -4.58243177e-02, 5.81967789e-01]]), scale=array([6.42462475, 8.78900832, 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model=ScalarModel(intercept=3.0353080773001073, linear_terms=array([20.20386846, 0.10823129, -1.77493216]), square_terms=array([[ 7.18576918e+01, 3.89097471e-01, -6.44170944e+00], + [ 3.89097471e-01, 2.11348833e-03, -3.50355915e-02], + [-6.44170944e+00, -3.50355915e-02, 5.83744149e-01]]), scale=array([6.40219448, 8.78900832, 4.39450416]), shift=array([ 7.50219448, 2799.74097801, 4.39450416])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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5.06389866])), candidate_index=25, candidate_x=array([ 5.09453133, 2808.27054949, 0. ]), index=23, x=array([ 5.11538064, 2799.74097801, 0. ]), fval=0.1625021910643016, rho=-0.257817250835792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 20, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=5.452265626613694, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.18941940373737348, linear_terms=array([-0.84702663, -0.00367806, 0.0743278 ]), square_terms=array([[ 3.09008276e+01, 1.15966532e-01, -2.10738689e+00], + [ 1.15966532e-01, 4.36576332e-04, -7.94409134e-03], + [-2.10738689e+00, -7.94409134e-03, 1.45288267e-01]]), scale=array([4.2049424 , 4.39450416, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.11538064, 2799.74097801, 0. ]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26]), model=ScalarModel(intercept=0.040656194310767946, linear_terms=array([-0.04424986, -0.00023592, -0.08131239]), square_terms=array([[8.44848189e+00, 2.55963392e-02, 4.42498564e-02], + [2.55963392e-02, 7.77973037e-05, 2.35916270e-04], + [4.42498564e-02, 2.35916270e-04, 8.13123886e-02]]), scale=array([2.19725208, 2.19725208, 1.09862604]), shift=array([5.11538064e+00, 2.79974098e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, 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model_indices=array([21, 23, 26, 27]), model=ScalarModel(intercept=0.143861477312966, linear_terms=array([ 1.39795372e-01, -1.67256636e-05, 9.44770003e-03]), square_terms=array([[ 4.72932788e-01, -1.19149158e-04, 1.43354432e-01], + [-1.19149158e-04, 3.31626099e-08, -4.20348131e-05], + [ 1.43354432e-01, -4.20348131e-05, 5.61768276e-02]]), scale=array([1.09862604, 1.09862604, 0.54931302]), shift=array([5.11538064e+00, 2.79974098e+03, 5.49313020e-01])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=348.9450001032764, shift=array([ 4.96836085, 3489.45000103, 5.06389866])), candidate_index=28, candidate_x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), index=28, x=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), fval=0.06529563513965841, rho=1.475519476069878, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=1.6872964496840501, relative_step_length=1.2378681195926988, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=2.726132813306847, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=0.07730703358690492, linear_terms=array([-0.21420423, -0.00177457, -0.09323422]), square_terms=array([[7.19945275e+00, 3.82631907e-02, 1.99911955e+00], + [3.82631907e-02, 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rho=-0.24314171126774836, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([15, 18, 21, 22, 23, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=1.3630664066534235, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([21, 23, 26, 27, 28, 29]), model=ScalarModel(intercept=0.06904406104722437, linear_terms=array([ 0.00492248, 0.00149362, -0.05074773]), square_terms=array([[ 6.12405684e-01, -2.44364302e-03, 3.09451673e-01], + [-2.44364302e-03, 4.14350874e-05, -2.29602578e-03], + [ 3.09451673e-01, -2.29602578e-03, 2.13954389e-01]]), scale=array([1.09862604, 1.09862604, 1.09862604]), shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 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State(trustregion=Region(center=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([23, 27, 28, 29, 30]), model=ScalarModel(intercept=0.08513789653811096, linear_terms=array([-0.05873789, -0.00640693, -0.05839653]), square_terms=array([[0.29594537, 0.02020028, 0.12960348], + [0.02020028, 0.00146583, 0.00964012], + [0.12960348, 0.00964012, 0.07540158]]), scale=0.6815332033267117, shift=array([4.45734496e+00, 2.79864235e+03, 1.09862604e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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x=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), fval=0.05415908459581581, rho=0.02787984735130772, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([21, 23, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=1.4693394633669328, relative_step_length=1.0779661623195813, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00]), radius=0.6815332033267117, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32]), model=ScalarModel(intercept=0.06019280281239402, linear_terms=array([-0.05311338, -0.00664367, -0.01652794]), square_terms=array([[0.31168089, 0.01963004, 0.09342492], + [0.01963004, 0.00163636, 0.00734946], + [0.09342492, 0.00734946, 0.04083573]]), scale=0.6815332033267117, shift=array([4.05602518e+00, 2.79734636e+03, 2.24557407e+00])), 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1.95671244e+00]), fval=0.05091436090216684, rho=1.1516416598700234, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.1759818526107779, relative_step_length=1.032858570950159, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.3407666016633559, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([27, 28, 30, 31, 32, 33, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.05221280899533157, linear_terms=array([-0.00115235, -0.00033548, -0.00127409]), square_terms=array([[8.81488941e-02, 2.02195870e-03, 2.52583882e-02], + [2.02195870e-03, 4.98103348e-05, 6.21228724e-04], + [2.52583882e-02, 6.21228724e-04, 9.98002687e-03]]), scale=0.3407666016633559, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00]), radius=0.17038330083167794, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([ 20., 10000., 70.]))), model_indices=array([31, 33, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=0.05090588732753695, linear_terms=array([ 2.00967052e-03, -4.51302094e-06, 7.70306669e-04]), square_terms=array([[ 6.02617840e-02, -3.14541700e-04, 1.76727179e-02], + [-3.14541700e-04, 1.67055199e-06, -9.60913876e-05], + [ 1.76727179e-02, -9.60913876e-05, 5.78378569e-03]]), scale=0.17038330083167794, shift=array([4.18918073e+00, 2.79843139e+03, 1.95671244e+00])), vector_model=VectorModel(intercepts=array([ 1.57412309e-02, 3.56292054e-02, 1.46671126e-02, 2.00709899e-02, + 1.36314151e-02, 6.48338304e-06, -1.99307165e-02, -2.09559325e-01, + -3.40977225e-01, -3.07739315e-01, -6.19357811e-01, -5.84132142e-01, + 1.74722021e-01, 1.91181275e-01, 1.96930976e-01, 2.05706514e-01, + 2.24752360e-01]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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'exploration_results': array([ 0.15183333, 0.64211538, 0.64842412, 0.66164015, 0.68191739, + 0.7044766 , 0.74714867, 0.84822968, 0.86260573, 0.97951322, + 0.98915247, 1.12431839, 1.18220644, 1.20662179, 1.30406801, + 1.52011014, 1.78024917, 1.78135807, 2.0971452 , 2.24437522, + 2.48116605, 2.85787595, 2.94379242, 3.4148927 , 3.4976294 , + 4.16035282, 4.94978648, 5.89491246, 7.00125067, 34.37799292])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv index bdf8df1..3ff6990 100644 --- a/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv +++ b/src/estimark/content/tables/min/WarmGlowPortfolioShift_estimate_results.csv @@ -1,7577 +1,7596 @@ -CRRA,9.206764951619395 -BeqFac,51.48903871676966 -BeqShift,19.177911777729552 -time_to_estimate,169.86824083328247 -params,"{'CRRA': 9.206764951619395, 'BeqFac': 51.48903871676966, 'BeqShift': 19.177911777729552}" -criterion,0.6411981720246629 -start_criterion,3.9192779804696425 -start_params,"{'CRRA': 2.0, 'BeqFac': 1.0, 'BeqShift': 1.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,3 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 9.02557038439359, 'BeqFac': 50.30567549536045, 'BeqShift': 16.905833666158657}, {'CRRA': 10.430729201623546, 'BeqFac': 52.82847452758671, 'BeqShift': 12.786656125459029}, {'CRRA': 5.792976948374848, 'BeqFac': 52.659631320106804, 'BeqShift': 13.853647314869416}, {'CRRA': 4.750415983244248, 'BeqFac': 52.50140949121589, 'BeqShift': 18.391908003539935}, {'CRRA': 8.544902044207497, 'BeqFac': 45.298212588181066, 'BeqShift': 16.876117502031093}, {'CRRA': 8.272787894438386, 'BeqFac': 48.43106736325208, 'BeqShift': 21.512977330544985}, {'CRRA': 4.597416132398153, 'BeqFac': 47.95443005019735, 'BeqShift': 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29.419515199959278, 30.668996000196785, 31.875013300217688, 33.06283670011908, 34.25480200024322, 35.44796109991148, 36.63821780029684], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22]}" -convergence_report,"{'one_step': {'relative_criterion_change': 2.8119796241753443e-07, 'relative_params_change': 0.3073071893497542, 'absolute_criterion_change': 1.8030361947918294e-07, 'absolute_params_change': 11.624425850056147}, 'five_steps': {'relative_criterion_change': 2.8119796241753443e-07, 'relative_params_change': 0.3073071893497542, 'absolute_criterion_change': 1.8030361947918294e-07, 'absolute_params_change': 11.624425850056147}}" -multistart_info,"{'start_parameters': [{'CRRA': 9.36875, 'BeqFac': 39.375, 'BeqShift': 13.125}, {'CRRA': 9.02557038439359, 'BeqFac': 50.30567549536045, 'BeqShift': 16.905833666158657}, {'CRRA': 9.344871468725385, 'BeqFac': 43.24486525515954, 'BeqShift': 26.496870084805927}], 'local_optima': [Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.358e-07* 2.622e-05 -relative_params_change 6.705e-06* 0.003894 -absolute_criterion_change 8.706e-08* 1.681e-05 -absolute_params_change 0.0001056 0.08828 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.904e-07* 0.0007594 -relative_params_change 3.052e-05 0.1282 -absolute_criterion_change 1.221e-07* 0.0004869 -absolute_params_change 0.0006142 2.522 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 2.728e-05 0.000476 -relative_params_change 0.0002827 0.0766 -absolute_criterion_change 1.75e-05 0.0003053 -absolute_params_change 0.006458 2.233 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.368749999999999, 'BeqFac': 39.375, 'BeqShift': 13.125}, {'CRRA': 8.778125, 'BeqFac': 63.4375, 'BeqShift': 19.6875}, {'CRRA': 9.959375, 'BeqFac': 6.5625, 'BeqShift': 59.0625}, {'CRRA': 8.1875, 'BeqFac': 26.25, 'BeqShift': 43.75}, {'CRRA': 10.549999999999999, 'BeqFac': 35.0, 'BeqShift': 35.0}, {'CRRA': 7.596874999999999, 'BeqFac': 50.3125, 'BeqShift': 50.3125}, {'CRRA': 7.00625, 'BeqFac': 13.125, 'BeqShift': 21.875}, {'CRRA': 11.73125, 'BeqFac': 30.625, 'BeqShift': 4.375}, {'CRRA': 12.321874999999999, 'BeqFac': 67.8125, 'BeqShift': 67.8125}, {'CRRA': 6.415625, 'BeqFac': 19.6875, 'BeqShift': 10.9375}, {'CRRA': 12.9125, 'BeqFac': 8.75, 'BeqShift': 61.25}, {'CRRA': 5.824999999999999, 'BeqFac': 52.5, 'BeqShift': 52.5}, {'CRRA': 13.503124999999999, 'BeqFac': 45.9375, 'BeqShift': 2.1875}, {'CRRA': 5.234375, 'BeqFac': 59.0625, 'BeqShift': 6.5625}, {'CRRA': 14.093749999999998, 'BeqFac': 56.875, 'BeqShift': 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0.84823378, 0.86260589, 0.97951322, 0.98995977, - 1.12431839, 1.18221306, 1.30406647, 1.43719388, 1.52011014, - 1.78024917, 1.78140887, 2.0971452 , 2.2458042 , 2.48116605, - 2.89746811, 2.94379242, 3.4976294 , 3.77142778, 3.92475841, - 4.11872048, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.02557038, 50.3056755 , 16.90583367]), radius=5.030567549536045, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=0.642827121621846, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), scale=5.030567549536045, shift=array([ 9.02557038, 50.3056755 , 16.90583367])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - 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relative_step_length=0.9999999999983636, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20673 , 51.48925053, 19.17733633]), radius=0.002456331811296897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 49, 50, 51, 52, 53, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.6412082340781056, linear_terms=array([1.31850579e-05, 2.29695162e-06, 2.13180893e-06]), square_terms=array([[ 5.40420994e-07, -8.14514929e-11, -9.77509288e-10], - [-8.14514929e-11, 3.15361394e-11, 1.87438472e-11], - [-9.77509288e-10, 1.87438472e-11, 1.66932070e-11]]), scale=0.002456331811296897, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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model=ScalarModel(intercept=0.6412157536444247, linear_terms=array([9.00633808e-07, 3.16587415e-06, 1.03374985e-06]), square_terms=array([[ 1.36904318e-07, -3.84922396e-10, -1.62548009e-10], - [-3.84922396e-10, 1.84778132e-11, 7.00514318e-12], - [-1.62548009e-10, 7.00514318e-12, 3.33326143e-12]]), scale=0.0012281659056484484, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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index=63, x=array([ 9.20673 , 51.48925053, 19.17733633]), fval=0.6411982941369027, rho=-0.4736163965120041, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([16, 49, 50, 51, 52, 53, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20673 , 51.48925053, 19.17733633]), radius=0.0006140829528242242, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 62, 63, 64, 65]), model=ScalarModel(intercept=0.6412018772218984, linear_terms=array([-3.72388782e-06, 2.25369895e-05, -6.12278699e-05]), square_terms=array([[ 3.55640239e-08, -1.41336598e-09, 3.35637644e-09], - [-1.41336598e-09, 7.05971511e-10, -1.79557755e-09], - [ 3.35637644e-09, -1.79557755e-09, 4.68337624e-09]]), scale=0.0006140829528242242, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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'tranquilo_history': History for least_squares function with 67 entries., 'multistart_info': {'start_parameters': [array([ 9.36875, 39.375 , 13.125 ]), array([ 9.02557038, 50.3056755 , 16.90583367]), array([ 9.34487147, 43.24486526, 26.49687008])], 'local_optima': [{'solution_x': array([ 9.20671298, 40.69251276, 14.86951258]), 'solution_criterion': 0.6411983523282824, 'states': [State(trustregion=Region(center=array([ 9.36875, 39.375 , 13.125 ]), radius=3.9375, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=0.6421156352360291, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), scale=3.9375, shift=array([ 9.36875, 39.375 , 13.125 ])), vector_model=VectorModel(intercepts=array([ 0.04974919, 0.12690831, 0.15323823, 0.19937099, 0.22433814, - 0.23994656, 0.24229912, 0.07800181, -0.06876495, -0.05566806, - -0.39787992, -0.40698777, -0.12931719, -0.10261415, 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.2032825 , 40.56074605, 14.82995061]), radius=0.24609375, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 18, 19, 20]), model=ScalarModel(intercept=0.641263062739895, linear_terms=array([ 2.04833771e-03, 5.33557659e-05, -2.35441320e-05]), square_terms=array([[ 5.55352411e-03, 2.37091185e-06, -7.67351122e-07], - [ 2.37091185e-06, 2.52978013e-08, -1.07706734e-08], - [-7.67351122e-07, -1.07706734e-08, 4.63705684e-09]]), scale=0.24609375, shift=array([ 9.2032825 , 40.56074605, 14.82995061])), vector_model=VectorModel(intercepts=array([ 0.04974919, 0.12690831, 0.15323823, 0.19937099, 0.22433814, - 0.23994656, 0.24229912, 0.07800181, -0.06876495, -0.05566806, - -0.39787992, -0.40698777, -0.12931719, -0.10261415, -0.09327947, - -0.09658959, -0.10271731]), 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x=array([ 9.20683311, 40.69251307, 14.86951513]), fval=0.6411988675251178, rho=-0.12577706131837602, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([39, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72]), old_indices_discarded=array([61, 62, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683311, 40.69251307, 14.86951513]), radius=0.0001201629638671875, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([39, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.6411989739004574, linear_terms=array([1.25756181e-06, 2.57916717e-09, 2.59883266e-08]), square_terms=array([[ 1.32183800e-09, -4.80376959e-14, -4.83059862e-13], - [-4.80376959e-14, 1.90720908e-17, 1.91683790e-16], - [-4.83059862e-13, 1.91683790e-16, 1.92652730e-15]]), scale=0.0001201629638671875, shift=array([ 9.20683311, 40.69251307, 14.86951513])), vector_model=VectorModel(intercepts=array([ 0.04974919, 0.12690831, 0.15323823, 0.19937099, 0.22433814, - 0.23994656, 0.24229912, 0.07800181, -0.06876495, -0.05566806, - -0.39787992, -0.40698777, -0.12931719, -0.10261415, -0.09327947, - -0.09658959, -0.10271731]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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old_indices_discarded=array([ 1, 10, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.30406119, 52.12275648, 21.61704889]), radius=2.5152837747680223, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 15]), model=ScalarModel(intercept=0.6007211149366605, linear_terms=array([0.03580912, 0.00221802, 0.00871836]), square_terms=array([[6.55770014e-01, 1.19610905e-03, 9.83878349e-03], - [1.19610905e-03, 3.69913722e-05, 1.06326979e-04], - [9.83878349e-03, 1.06326979e-04, 3.82530099e-04]]), scale=2.5152837747680223, shift=array([ 9.30406119, 52.12275648, 21.61704889])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - -0.08549165, 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bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.6409807504214711, linear_terms=array([-5.31150435e-04, 7.59689962e-05, -2.38985242e-04]), square_terms=array([[ 8.94536013e-03, 3.93275859e-06, -7.33814659e-06], - [ 3.93275859e-06, 2.83430605e-08, -4.52241739e-08], - [-7.33814659e-06, -4.52241739e-08, 9.84248328e-08]]), scale=0.3144104718460028, shift=array([ 9.20569355, 51.48987239, 19.17755414])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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25, 26, 28, 29, 30]), old_indices_discarded=array([18, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20569355, 51.48987239, 19.17755414]), radius=0.0786026179615007, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 32]), model=ScalarModel(intercept=0.6411153314439284, linear_terms=array([-1.57124695e-04, 2.68280656e-05, 8.37357516e-05]), square_terms=array([[5.58654957e-04, 1.89676224e-07, 1.65767498e-06], - [1.89676224e-07, 2.03273476e-09, 9.75622664e-09], - [1.65767498e-06, 9.75622664e-09, 6.09058544e-08]]), scale=0.0786026179615007, shift=array([ 9.20569355, 51.48987239, 19.17755414])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, 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model=ScalarModel(intercept=0.641235124254665, linear_terms=array([-4.51519241e-05, 1.08975052e-05, -4.39512071e-06]), square_terms=array([[ 2.25133343e-06, -6.39470529e-09, 1.57142520e-09], - [-6.39470529e-09, 2.73716237e-10, 1.67175250e-11], - [ 1.57142520e-09, 1.67175250e-11, 6.56459959e-11]]), scale=0.004912663622593794, shift=array([ 9.20569355, 51.48987239, 19.17755414])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, - 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, - -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, - -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([14, 48, 52, 53, 54, 56, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.6415032205937842, linear_terms=array([ 4.58325703e-08, 2.01988168e-07, -4.06558004e-06]), square_terms=array([[ 3.79940847e-07, 7.24895548e-10, -1.51575087e-09], - [ 7.24895548e-10, 7.65644461e-12, -2.38274882e-11], - [-1.51575087e-09, -2.38274882e-11, 1.40563587e-10]]), scale=0.0021115656862870868, shift=array([ 9.27634526, 44.29435966, 28.46746867])), vector_model=VectorModel(intercepts=array([ 0.04954202, 0.12643175, 0.15249517, 0.19845787, 0.22322409, - 0.23873454, 0.24094459, 0.07618167, -0.07055715, -0.05748894, - -0.39965148, -0.40866339, -0.12848922, -0.10183191, -0.0925236 , - -0.09584653, -0.10195521]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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2.28125 , 65.625 , 39.375 ], - [17.6375 , 61.25 , 8.75 ], - [18.228125, 28.4375 , 54.6875 ], - [18.81875 , 4.375 , 48.125 ], - [19.409375, 41.5625 , 24.0625 ]]), 'exploration_results': array([0.64211564, 0.64842422, 0.66164015, 0.68191739, 0.7044766 , - 0.74714868, 0.84823378, 0.86260589, 0.97951322, 0.98995977, - 1.12431839, 1.18221306, 1.30406647, 1.43719388, 1.52011014, - 1.78024917, 1.78140887, 2.0971452 , 2.2458042 , 2.48116605, - 2.89746811, 2.94379242, 3.4976294 , 3.77142778, 3.92475841, - 4.11872048, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}}" +CRRA,9.206764951619395 + +BeqFac,51.48903871676966 + +BeqShift,19.177911777729552 + +time_to_estimate,169.86824083328247 + +params,"{'CRRA': 9.206764951619395, 'BeqFac': 51.48903871676966, 'BeqShift': 19.177911777729552}" + +criterion,0.6411981720246629 + +start_criterion,3.9192779804696425 + +start_params,"{'CRRA': 2.0, 'BeqFac': 1.0, 'BeqShift': 1.0}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,3 + 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12.863225900102407, 13.049990000203252, 13.235215300228447, 13.421385299880058, 13.612889300100505, 13.813482000026852, 14.012756899930537, 14.206071800086647, 14.40031210007146, 14.598113900050521, 15.857798700220883, 17.073550200089812, 18.39786330005154, 19.95549810025841, 20.136687899939716, 20.316136700101197, 20.503039999864995, 20.68812690023333, 20.880544799845666, 21.061436000280082, 21.258774400223047, 21.45859370008111, 21.63768619997427, 21.82981210015714, 22.024868600070477, 23.232584199868143, 24.429392700083554, 25.615990200079978, 27.17550570005551, 27.352922200225294, 27.533706800080836, 27.874167799949646, 28.064581600017846, 28.25611580023542, 28.4465796998702, 28.64204559987411, 28.83596420008689, 29.02757350029424, 29.219605300109833, 29.419515199959278, 30.668996000196785, 31.875013300217688, 33.06283670011908, 34.25480200024322, 35.44796109991148, 36.63821780029684], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 14, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 19, 20, 21, 22]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 2.8119796241753443e-07, 'relative_params_change': 0.3073071893497542, 'absolute_criterion_change': 1.8030361947918294e-07, 'absolute_params_change': 11.624425850056147}, 'five_steps': {'relative_criterion_change': 2.8119796241753443e-07, 'relative_params_change': 0.3073071893497542, 'absolute_criterion_change': 1.8030361947918294e-07, 'absolute_params_change': 11.624425850056147}}" + +multistart_info,"{'start_parameters': [{'CRRA': 9.36875, 'BeqFac': 39.375, 'BeqShift': 13.125}, {'CRRA': 9.02557038439359, 'BeqFac': 50.30567549536045, 'BeqShift': 16.905833666158657}, {'CRRA': 9.344871468725385, 'BeqFac': 43.24486525515954, 'BeqShift': 26.496870084805927}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.358e-07* 2.622e-05 +relative_params_change 6.705e-06* 0.003894 +absolute_criterion_change 8.706e-08* 1.681e-05 +absolute_params_change 0.0001056 0.08828 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.904e-07* 0.0007594 +relative_params_change 3.052e-05 0.1282 +absolute_criterion_change 1.221e-07* 0.0004869 +absolute_params_change 0.0006142 2.522 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 2.728e-05 0.000476 +relative_params_change 0.0002827 0.0766 +absolute_criterion_change 1.75e-05 0.0003053 +absolute_params_change 0.006458 2.233 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 9.368749999999999, 'BeqFac': 39.375, 'BeqShift': 13.125}, {'CRRA': 8.778125, 'BeqFac': 63.4375, 'BeqShift': 19.6875}, {'CRRA': 9.959375, 'BeqFac': 6.5625, 'BeqShift': 59.0625}, {'CRRA': 8.1875, 'BeqFac': 26.25, 'BeqShift': 43.75}, {'CRRA': 10.549999999999999, 'BeqFac': 35.0, 'BeqShift': 35.0}, {'CRRA': 7.596874999999999, 'BeqFac': 50.3125, 'BeqShift': 50.3125}, {'CRRA': 7.00625, 'BeqFac': 13.125, 'BeqShift': 21.875}, {'CRRA': 11.73125, 'BeqFac': 30.625, 'BeqShift': 4.375}, {'CRRA': 12.321874999999999, 'BeqFac': 67.8125, 'BeqShift': 67.8125}, {'CRRA': 6.415625, 'BeqFac': 19.6875, 'BeqShift': 10.9375}, {'CRRA': 12.9125, 'BeqFac': 8.75, 'BeqShift': 61.25}, {'CRRA': 5.824999999999999, 'BeqFac': 52.5, 'BeqShift': 52.5}, {'CRRA': 13.503124999999999, 'BeqFac': 45.9375, 'BeqShift': 2.1875}, {'CRRA': 5.234375, 'BeqFac': 59.0625, 'BeqShift': 6.5625}, {'CRRA': 14.093749999999998, 'BeqFac': 56.875, 'BeqShift': 30.625}, {'CRRA': 14.684375, 'BeqFac': 24.0625, 'BeqShift': 41.5625}, {'CRRA': 4.64375, 'BeqFac': 21.875, 'BeqShift': 65.625}, {'CRRA': 15.274999999999999, 'BeqFac': 17.5, 'BeqShift': 17.5}, {'CRRA': 4.053125, 'BeqFac': 10.9375, 'BeqShift': 37.1875}, {'CRRA': 15.865624999999998, 'BeqFac': 54.6875, 'BeqShift': 45.9375}, {'CRRA': 3.4625, 'BeqFac': 43.75, 'BeqShift': 26.25}, {'CRRA': 16.45625, 'BeqFac': 48.125, 'BeqShift': 56.875}, {'CRRA': 17.046875, 'BeqFac': 15.3125, 'BeqShift': 15.3125}, {'CRRA': 2.871875, 'BeqFac': 32.8125, 'BeqShift': 32.8125}, {'CRRA': 2.0, 'BeqFac': 1.0, 'BeqShift': 1.0}, {'CRRA': 2.28125, 'BeqFac': 65.625, 'BeqShift': 39.375}, {'CRRA': 17.6375, 'BeqFac': 61.25, 'BeqShift': 8.75}, {'CRRA': 18.228125, 'BeqFac': 28.4375, 'BeqShift': 54.6875}, {'CRRA': 18.81875, 'BeqFac': 4.375, 'BeqShift': 48.125}, {'CRRA': 19.409375, 'BeqFac': 41.5625, 'BeqShift': 24.0625}], 'exploration_results': array([0.64211564, 0.64842422, 0.66164015, 0.68191739, 0.7044766 , + 0.74714868, 0.84823378, 0.86260589, 0.97951322, 0.98995977, + 1.12431839, 1.18221306, 1.30406647, 1.43719388, 1.52011014, + 1.78024917, 1.78140887, 2.0971452 , 2.2458042 , 2.48116605, + 2.89746811, 2.94379242, 3.4976294 , 3.77142778, 3.92475841, + 4.11872048, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 9.02557038, 50.3056755 , 16.90583367]), radius=5.030567549536045, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=0.642827121621846, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=5.030567549536045, shift=array([ 9.02557038, 50.3056755 , 16.90583367])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + 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relative_step_length=0.9999999999983636, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20673 , 51.48925053, 19.17733633]), radius=0.002456331811296897, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 49, 50, 51, 52, 53, 57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.6412082340781056, linear_terms=array([1.31850579e-05, 2.29695162e-06, 2.13180893e-06]), square_terms=array([[ 5.40420994e-07, -8.14514929e-11, -9.77509288e-10], + [-8.14514929e-11, 3.15361394e-11, 1.87438472e-11], + [-9.77509288e-10, 1.87438472e-11, 1.66932070e-11]]), scale=0.002456331811296897, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.6412157536444247, linear_terms=array([9.00633808e-07, 3.16587415e-06, 1.03374985e-06]), square_terms=array([[ 1.36904318e-07, -3.84922396e-10, -1.62548009e-10], + [-3.84922396e-10, 1.84778132e-11, 7.00514318e-12], + [-1.62548009e-10, 7.00514318e-12, 3.33326143e-12]]), scale=0.0012281659056484484, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=63, x=array([ 9.20673 , 51.48925053, 19.17733633]), fval=0.6411982941369027, rho=-0.4736163965120041, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([16, 49, 50, 51, 52, 53, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20673 , 51.48925053, 19.17733633]), radius=0.0006140829528242242, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([16, 62, 63, 64, 65]), model=ScalarModel(intercept=0.6412018772218984, linear_terms=array([-3.72388782e-06, 2.25369895e-05, -6.12278699e-05]), square_terms=array([[ 3.55640239e-08, -1.41336598e-09, 3.35637644e-09], + [-1.41336598e-09, 7.05971511e-10, -1.79557755e-09], + [ 3.35637644e-09, -1.79557755e-09, 4.68337624e-09]]), scale=0.0006140829528242242, shift=array([ 9.20673 , 51.48925053, 19.17733633])), vector_model=VectorModel(intercepts=array([ 0.04710459, 0.11986993, 0.14282693, 0.18631824, 0.20833381, + 0.22221482, 0.22216466, 0.05252852, -0.09507962, -0.08224758, + -0.42404204, -0.4316247 , -0.11708532, -0.09095589, -0.08150493, + -0.08549165, -0.09152337]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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'tranquilo_history': History for least_squares function with 67 entries., 'multistart_info': {'start_parameters': [array([ 9.36875, 39.375 , 13.125 ]), array([ 9.02557038, 50.3056755 , 16.90583367]), array([ 9.34487147, 43.24486526, 26.49687008])], 'local_optima': [{'solution_x': array([ 9.20671298, 40.69251276, 14.86951258]), 'solution_criterion': 0.6411983523282824, 'states': [State(trustregion=Region(center=array([ 9.36875, 39.375 , 13.125 ]), radius=3.9375, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=0.6421156352360291, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=3.9375, shift=array([ 9.36875, 39.375 , 13.125 ])), vector_model=VectorModel(intercepts=array([ 0.04974919, 0.12690831, 0.15323823, 0.19937099, 0.22433814, + 0.23994656, 0.24229912, 0.07800181, -0.06876495, -0.05566806, + -0.39787992, -0.40698777, -0.12931719, -0.10261415, 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x=array([ 9.20683311, 40.69251307, 14.86951513]), fval=0.6411988675251178, rho=-0.12577706131837602, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([39, 59, 60, 63, 64, 65, 66, 68, 69, 70, 71, 72]), old_indices_discarded=array([61, 62, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 9.20683311, 40.69251307, 14.86951513]), radius=0.0001201629638671875, bounds=Bounds(lower=array([1.1, 0. , 0. ]), upper=array([20., 70., 70.]))), model_indices=array([39, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.6411989739004574, linear_terms=array([1.25756181e-06, 2.57916717e-09, 2.59883266e-08]), square_terms=array([[ 1.32183800e-09, -4.80376959e-14, -4.83059862e-13], + [-4.80376959e-14, 1.90720908e-17, 1.91683790e-16], + [-4.83059862e-13, 1.91683790e-16, 1.92652730e-15]]), scale=0.0001201629638671875, shift=array([ 9.20683311, 40.69251307, 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2.28125 , 65.625 , 39.375 ], + [17.6375 , 61.25 , 8.75 ], + [18.228125, 28.4375 , 54.6875 ], + [18.81875 , 4.375 , 48.125 ], + [19.409375, 41.5625 , 24.0625 ]]), 'exploration_results': array([0.64211564, 0.64842422, 0.66164015, 0.68191739, 0.7044766 , + 0.74714868, 0.84823378, 0.86260589, 0.97951322, 0.98995977, + 1.12431839, 1.18221306, 1.30406647, 1.43719388, 1.52011014, + 1.78024917, 1.78140887, 2.0971452 , 2.2458042 , 2.48116605, + 2.89746811, 2.94379242, 3.4976294 , 3.77142778, 3.92475841, + 4.11872048, 4.16035282, 4.94978648, 5.89491246, 7.00125067])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv index 196e1d5..27b3382 100644 --- a/src/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv +++ b/src/estimark/content/tables/min/WarmGlowPortfolioSub(Stock)_estimate_results.csv @@ -1,19323 +1,19341 @@ -CRRA,2.0 -BeqFac,1.0 -time_to_estimate,57.54076361656189 -params,"{'CRRA': 2.0, 'BeqFac': 1.0}" -criterion,0.8276471824376574 -start_criterion,0.8265504175460967 -start_params,"{'CRRA': 2.0, 'BeqFac': 1.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,2 -message, -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 2.116888877683535, 'BeqFac': 1.1622867532298418}, {'CRRA': 1.8776126026706215, 'BeqFac': 0.8418186958741936}, {'CRRA': 2.1528425841636905, 'BeqFac': 0.8710071921921023}, {'CRRA': 1.9202699787802653, 'BeqFac': 1.18342061965957}, {'CRRA': 1.8016623674015406, 'BeqFac': 1.0257329262859587}, {'CRRA': 1.8134776737972333, 'BeqFac': 0.9278236754336386}, {'CRRA': 1.9669947321437662, 'BeqFac': 0.802742167978713}, {'CRRA': 2.197271497523536, 'BeqFac': 0.9670765089211135}, {'CRRA': 1.8430981311476735, 'BeqFac': 1.124023399206147}, {'CRRA': 2.188253926053115, 'BeqFac': 1.0675311729913546}, {'CRRA': 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99, 100, 101]}" -convergence_report, -multistart_info,"{'start_parameters': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 4.069e-09** 7.731e-08* -relative_params_change 4.41e-06* 6.026e-05 -absolute_criterion_change 4.137e-07* 7.859e-06* -absolute_params_change 8.763e-06* 9.915e-05 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 2.28125, 'BeqFac': 9375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}], 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, - 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, - 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, - 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, - 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=0, candidate_x=array([2., 1.]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=86.15955603265024, linear_terms=array([ 0.05089577, -0.19533528]), square_terms=array([[ 1.51497851e-05, -5.81440784e-05], - [-5.81440784e-05, 2.23153914e-04]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-499.88400427494435, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.1, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), model=ScalarModel(intercept=69.9966297435316, linear_terms=array([ 5.52347875, 10.30766762]), square_terms=array([[0.22002817, 0.41060667], - [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=15, candidate_x=array([1.98543255, 0.95216916]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.192688611597553, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 3, 5, 6, 7, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.025, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], - [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=16, candidate_x=array([2.01033614, 1.0227644 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6632385320428, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.827647182437657, linear_terms=array([-45.37479674, 16.0066913 ]), square_terms=array([[ 6419.23080123, -2264.48759797], - [-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=17, candidate_x=array([2.004237 , 1.01176031]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7120258298457, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], - [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=18, candidate_x=array([1.99690325, 0.99457059]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6996277758512, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.003125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.8276471824376497, linear_terms=array([ 33.51528457, -14.29880894]), square_terms=array([[ 3502.17413253, -1494.15175942], - [-1494.15175942, 637.45816047]]), scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=19, candidate_x=array([1.9987484 , 0.99713645]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117679378963, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], - [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=20, candidate_x=array([2.00093264, 1.00125388]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7086956369039, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00078125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], - [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=21, candidate_x=array([2.00037411, 1.00068591]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117058020676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.000390625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.8276471824376602, linear_terms=array([ 10.88378502, -10.70264801]), square_terms=array([[ 369.32716463, -363.18052667], - [-363.18052667, 357.13618598]]), scale=0.000390625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], - [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], - [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=24, candidate_x=array([2.00008235, 1.00005249]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71151997705, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], - [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], - [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=26, candidate_x=array([1.99997671, 0.99999267]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711721703526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], - [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], - [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], - [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], - [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - 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1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], - [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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]), square_terms=array([[ 16.75517786, -19.70434065], - [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], - [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], - [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], - [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=37, candidate_x=array([2.00000097, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-19.954818815240166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=78.99436811656406, linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], - [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], - [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], - [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], - [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=41, candidate_x=array([2.00000087, 0.99999951]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.540882296216793, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), old_indices_discarded=array([29, 30, 31, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], - [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=45, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=46, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=47, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=48, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=51, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=53, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=56, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=58, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=61, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=62, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=76, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=81, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=82, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=83, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=84, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=85, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=86, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=87, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=91, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=107, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=109, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=110, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=111, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=112, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 113 entries., 'multistart_info': {'start_parameters': [array([2., 1.]), array([ 6.92609782, 183.76536854])], 'local_optima': [{'solution_x': array([2., 1.]), 'solution_criterion': 0.8276471824376574, 'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=0, candidate_x=array([2., 1.]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=86.15955603265024, linear_terms=array([ 0.05089577, -0.19533528]), square_terms=array([[ 1.51497851e-05, -5.81440784e-05], - [-5.81440784e-05, 2.23153914e-04]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-499.88400427494435, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.1, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), model=ScalarModel(intercept=69.9966297435316, linear_terms=array([ 5.52347875, 10.30766762]), square_terms=array([[0.22002817, 0.41060667], - [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], 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6, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=61.19193548717778, linear_terms=array([3.65115817, 8.68434653]), square_terms=array([[0.11012808, 0.26194167], - [0.26194167, 0.62303309]]), scale=0.05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], - [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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[-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=17, candidate_x=array([2.004237 , 1.01176031]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7120258298457, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], - [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=18, candidate_x=array([1.99690325, 0.99457059]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6996277758512, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.003125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.8276471824376497, linear_terms=array([ 33.51528457, -14.29880894]), square_terms=array([[ 3502.17413253, -1494.15175942], - [-1494.15175942, 637.45816047]]), scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=19, candidate_x=array([1.9987484 , 0.99713645]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117679378963, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], - [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=20, candidate_x=array([2.00093264, 1.00125388]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7086956369039, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00078125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], - [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=21, candidate_x=array([2.00037411, 1.00068591]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117058020676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.000390625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.8276471824376602, linear_terms=array([ 10.88378502, -10.70264801]), square_terms=array([[ 369.32716463, -363.18052667], - [-363.18052667, 357.13618598]]), scale=0.000390625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], - [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], - [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=24, candidate_x=array([2.00008235, 1.00005249]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71151997705, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], - [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], - [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=26, candidate_x=array([1.99997671, 0.99999267]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711721703526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], - [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], - [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=28, candidate_x=array([2.00000611, 0.99999986]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711677104554, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=3.0517578125e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.8276471824376628, linear_terms=array([-3.97279126, 9.21962533]), square_terms=array([[ 49.20880826, -114.19849305], - [-114.19849305, 265.01954175]]), scale=3.0517578125e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], - [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], - [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=31, candidate_x=array([2.00000074, 1.00000067]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116856529549, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], - [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=32, candidate_x=array([1.99999924, 0.99999934]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.746700442186407, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32]), model=ScalarModel(intercept=43.05839744142132, linear_terms=array([-37.69007116, 44.3240894 ]), square_terms=array([[ 16.75517786, -19.70434065], - [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=33, candidate_x=array([2.00000009, 0.999999 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-2.9522733070987055, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=56.93444143408821, linear_terms=array([ -6.96601288, -17.26411453]), square_terms=array([[0.43120475, 1.06866988], - [1.06866988, 2.64852213]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], - [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=35, candidate_x=array([2.00000098, 0.99999981]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-7.891442250906298, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], - [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], - [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=37, candidate_x=array([2.00000097, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-19.954818815240166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=78.99436811656406, linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], - [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], - [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], - [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], - [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=41, candidate_x=array([2.00000087, 0.99999951]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.540882296216793, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), old_indices_discarded=array([29, 30, 31, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], - [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=45, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], 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[-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=48, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=51, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=53, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=56, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=57, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=58, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=61, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=62, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=76, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=81, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=82, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=83, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=84, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=85, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=86, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=87, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=91, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, - 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, - 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, - 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=107, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=109, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=110, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=111, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], - [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, - 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, - -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, - 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=112, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, - 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, - 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, - 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, - 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 113 entries., 'history': {'params': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 2.116888877683535, 'BeqFac': 1.1622867532298418}, {'CRRA': 1.8776126026706215, 'BeqFac': 0.8418186958741936}, {'CRRA': 2.1528425841636905, 'BeqFac': 0.8710071921921023}, {'CRRA': 1.9202699787802653, 'BeqFac': 1.18342061965957}, {'CRRA': 1.8016623674015406, 'BeqFac': 1.0257329262859587}, {'CRRA': 1.8134776737972333, 'BeqFac': 0.9278236754336386}, {'CRRA': 1.9669947321437662, 'BeqFac': 0.802742167978713}, {'CRRA': 2.197271497523536, 'BeqFac': 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 6, 8, 11, 13, 14, 15]), model=ScalarModel(intercept=112.52716726953476, linear_terms=array([-5.49527902, 1.83435019]), square_terms=array([[87.85384367, 61.32646973], - [61.32646973, 42.95209287]]), scale=array([ 9.45 , 65.14312703]), shift=array([ 10.55 , 119.682045])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 6, 9, 10, 14, 15, 16]), model=ScalarModel(intercept=111.07641141436483, linear_terms=array([-11.32539952, -1.19335547]), square_terms=array([[37.72655582, 33.29962208], - [33.29962208, 29.74641185]]), scale=array([ 9.45 , 92.41258601]), shift=array([10.55 , 92.41258601])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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model=ScalarModel(intercept=118.5566856481718, linear_terms=array([16.30216759, -3.21271579]), square_terms=array([[23.7595385 , -7.01292519], - [-7.01292519, 2.0824903 ]]), scale=array([ 9.45 , 59.8410225]), shift=array([10.55 , 59.8410225])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - 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linear_terms=array([ 7.25637091, -2.42042422]), square_terms=array([[ 6.4002053 , -7.80908346], - [-7.80908346, 9.72016944]]), scale=array([ 9.45 , 65.14312703]), shift=array([10.55 , 65.14312703])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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-7.65738376], - [-7.65738376, 2.42416309]]), scale=array([ 9.45 , 32.57156351]), shift=array([10.55 , 32.57156351])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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shift=array([10.55 , 16.28578176])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=26, candidate_x=array([1.1 , 4.95638609]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.5089417781776332, accepted=False, new_indices=array([25]), old_indices_used=array([16, 17, 18, 20, 22, 23, 24]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=107.07780108961828, linear_terms=array([13.09888074, 0.39394958]), square_terms=array([[1.63256660e+01, 2.16792836e-02], - [2.16792836e-02, 7.25065498e-04]]), scale=array([4.15983125, 4.07144544]), shift=array([5.25983125, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=27, candidate_x=array([1.92771902, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=0.11582332012959147, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 20, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.650947401024822, relative_step_length=0.07084549240131369, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=104.55965217810557, linear_terms=array([8.8072047 , 0.73944899]), square_terms=array([[14.54343784, 0.6300655 ], - [ 0.6300655 , 0.02792488]]), scale=array([4.48530495, 4.07144544]), shift=array([5.58530495, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=29, candidate_x=array([3.06341409, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09805438131359735, accepted=False, new_indices=array([28]), old_indices_used=array([17, 18, 20, 22, 24, 25, 26, 27]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=4.594134213489858, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=101.7963759557694, linear_terms=array([0.88312694, 0.23121981]), square_terms=array([[4.43153858, 0.34430387], - [0.34430387, 0.02688032]]), scale=array([2.44958223, 2.03572272]), shift=array([3.54958223, 2.03572272])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=30, candidate_x=array([3.25174178, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09261502613162106, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=2.297067106744929, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=101.90622993713308, linear_terms=array([0.11164869, 0.06828134]), square_terms=array([[6.11612797e-05, 3.74045927e-05], - [3.74045927e-05, 2.28756423e-05]]), scale=array([1.43172087, 1.01786136]), shift=array([2.53172087, 1.01786136])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=31, candidate_x=array([1.1, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.5313962286424387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 26, 27, 28, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=1.1485335533724645, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=101.81701589262005, linear_terms=array([0.00903626, 0.02869081]), square_terms=array([[4.00983753e-07, 1.27315403e-06], - [1.27315403e-06, 4.04236127e-06]]), scale=array([0.92279019, 0.50893068]), shift=array([2.02279019, 0.50893068])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=32, candidate_x=array([1.1, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-4.229463057608299, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 26, 27, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=0.5742667766862323, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=25.447067538457258, linear_terms=array([ 2.26209655e-03, -5.08941351e+01]), square_terms=array([[ 1.00543625e-07, -2.26209655e-03], - [-2.26209655e-03, 5.08941351e+01]]), scale=array([0.50893068, 0.25446534]), shift=array([1.92771902, 0.25446534])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=33, candidate_x=array([1.92240301, 0.50893056]), index=33, x=array([1.92240301, 0.50893056]), fval=101.70281707849445, rho=0.0004965664827465271, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.5089583250388706, relative_step_length=0.8862750653551305, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92240301, 0.50893056]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=101.70281707849445, linear_terms=array([ 0.00255143, -0.04817497]), square_terms=array([[ 3.20040297e-08, -6.04285701e-07], - [-6.04285701e-07, 1.14098510e-05]]), scale=0.28713338834311614, shift=array([1.92240301, 0.50893056])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=34, candidate_x=array([1.9072569, 0.7956642]), index=34, x=array([1.9072569, 0.7956642]), fval=101.67554062166661, rho=0.565470242720224, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.28713338834311625, relative_step_length=1.0000000000000004, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9072569, 0.7956642]), radius=0.5742667766862323, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=101.6703406833042, linear_terms=array([ 0.00091326, -0.08127158]), square_terms=array([[ 4.10166532e-09, -3.65011616e-07], - [-3.65011616e-07, 3.24827770e-05]]), scale=0.5742667766862323, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=35, candidate_x=array([1.9009229 , 1.36989604]), index=34, x=array([1.9072569, 0.7956642]), fval=101.67554062166661, rho=-0.19509771603556256, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), old_indices_discarded=array([30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9072569, 0.7956642]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=101.7002045598023, linear_terms=array([-0.01763113, -0.01363475]), square_terms=array([[1.52829874e-06, 1.18188538e-06], - [1.18188538e-06, 9.13992149e-07]]), scale=0.28713338834311614, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=36, candidate_x=array([2.13447793, 0.9712096 ]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=0.38979121466463484, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 31, 32, 33, 34, 35]), old_indices_discarded=array([29]), step_length=0.2871333883431161, relative_step_length=0.9999999999999998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.5742667766862323, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 27, 29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=101.68718333350891, linear_terms=array([-3.30543881e-05, -5.13835828e-02]), square_terms=array([[5.37232193e-12, 8.35136165e-09], - [8.35136165e-09, 1.29823272e-05]]), scale=0.5742667766862323, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=37, candidate_x=array([2.13477466, 1.5454763 ]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-0.8259599057600131, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 27, 29, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([22, 24, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 27, 29, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=101.70477092673009, linear_terms=array([ 0.00172715, -0.01865006]), square_terms=array([[ 1.46652301e-08, -1.58357648e-07], - [-1.58357648e-07, 1.70997279e-06]]), scale=0.28713338834311614, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=38, candidate_x=array([2.10801746, 1.25712117]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-0.7547851367190244, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 27, 29, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([22, 24, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=101.68183255247968, linear_terms=array([-0.00546611, 0.00214313]), square_terms=array([[ 1.46920840e-07, -5.76042249e-08], - [-5.76042249e-08, 2.25852693e-08]]), scale=0.14356669417155807, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=39, candidate_x=array([2.26814041, 0.91880982]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-2.429463248081529, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39]), model=ScalarModel(intercept=101.6761802726613, linear_terms=array([0.0004533 , 0.00066536]), square_terms=array([[1.01045073e-09, 1.48316938e-09], - [1.48316938e-09, 2.17703977e-09]]), scale=0.07178334708577903, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=40, candidate_x=array([2.09406231, 0.91188484]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=1.0728799070913182, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577915, relative_step_length=1.0000000000000016, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=101.68098851485126, linear_terms=array([-0.0050226 , 0.00236335]), square_terms=array([[ 1.24047463e-07, -5.83696319e-08], - [-5.83696319e-08, 2.74654058e-08]]), scale=0.14356669417155807, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=41, candidate_x=array([2.22396921, 0.85076534]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=-2.526612521827417, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=101.67433941881659, linear_terms=array([0.00112428, 0.00054592]), square_terms=array([[6.21590003e-09, 3.01827897e-09], - [3.01827897e-09, 1.46559757e-09]]), scale=0.07178334708577903, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=42, candidate_x=array([2.02948871, 0.88053044]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=0.46666748151376614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577888, relative_step_length=0.9999999999999979, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=101.680416054201, linear_terms=array([-0.00475057, -0.00077785]), square_terms=array([[1.10974697e-07, 1.81707727e-08], - [1.81707727e-08, 2.97524560e-09]]), scale=0.14356669417155807, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=43, candidate_x=array([2.17116917, 0.90372619]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=-1.523359554750816, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=101.66905458361765, linear_terms=array([ 0.00365778, -0.00773732]), square_terms=array([[ 6.57986625e-08, -1.39184073e-07], - [-1.39184073e-07, 2.94416414e-07]]), scale=0.07178334708577903, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=44, candidate_x=array([1.99881359, 0.94542948]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=0.8000735183892913, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577906, relative_step_length=1.0000000000000004, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=101.66837110968486, linear_terms=array([0.00492682, 0.00116565]), square_terms=array([[1.19375993e-07, 2.82435251e-08], - [2.82435251e-08, 6.68222052e-09]]), scale=0.14356669417155807, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=45, candidate_x=array([1.85910309, 0.91237844]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=-2.1991263376100285, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([33, 35, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=101.66380899320433, linear_terms=array([ 0.00235297, -0.00665188]), square_terms=array([[ 2.72293463e-08, -7.69776254e-08], - [-7.69776254e-08, 2.17616492e-07]]), scale=0.07178334708577903, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=46, candidate_x=array([1.9748781 , 1.01310475]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=0.3720268111515636, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([38]), step_length=0.07178334708577894, relative_step_length=0.9999999999999987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=101.66834084528847, linear_terms=array([0.00105253, 0.00192198]), square_terms=array([[5.44817640e-09, 9.94871690e-09], - [9.94871690e-09, 1.81669903e-08]]), scale=0.14356669417155807, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=47, candidate_x=array([1.90592301, 0.88718179]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-5.4105642931805455, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), old_indices_discarded=array([33, 35, 37, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=101.65730356105429, linear_terms=array([ 0.00098995, -0.00629255]), square_terms=array([[ 4.82013494e-09, -3.06388241e-08], - [-3.06388241e-08, 1.94753374e-07]]), scale=0.07178334708577903, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=48, candidate_x=array([1.96372399, 1.0840162 ]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.9918406732109903, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([38, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.03589167354288952, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([36, 40, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=101.6617873815051, linear_terms=array([-0.00012068, -0.00134631]), square_terms=array([[7.16320381e-11, 7.99106267e-10], - [7.99106267e-10, 8.91459804e-09]]), scale=0.03589167354288952, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=49, candidate_x=array([1.97808243, 1.0488531 ]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-1.8650485155978065, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([36, 40, 42, 44, 45, 46, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49]), model=ScalarModel(intercept=101.65745624776355, linear_terms=array([0.00318564, 0.00116839]), square_terms=array([[4.99142631e-08, 1.83069319e-08], - [1.83069319e-08, 6.71438853e-09]]), scale=0.01794583677144476, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=50, candidate_x=array([1.9580296 , 1.00692568]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.4185964575065688, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 49, 50]), model=ScalarModel(intercept=101.65593428559325, linear_terms=array([-0.00102209, 0.00072439]), square_terms=array([[ 5.13823243e-09, -3.64162989e-09], - [-3.64162989e-09, 2.58093974e-09]]), scale=0.00897291838572238, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=51, candidate_x=array([1.98219895, 1.00791645]), index=51, x=array([1.98219895, 1.00791645]), fval=101.6550490329974, rho=0.7066458575122126, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722404, relative_step_length=1.0000000000000027, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98219895, 1.00791645]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51]), model=ScalarModel(intercept=101.65780983947639, linear_terms=array([0.00063274, 0.00063126]), square_terms=array([[1.96914913e-09, 1.96454291e-09], - [1.96454291e-09, 1.95994747e-09]]), scale=0.01794583677144476, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=52, candidate_x=array([1.96947888, 0.99525735]), index=51, x=array([1.98219895, 1.00791645]), fval=101.6550490329974, rho=-1.2760889741412444, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98219895, 1.00791645]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 49, 50, 51, 52]), model=ScalarModel(intercept=101.65521942995059, linear_terms=array([-0.00089145, 0.00057592]), square_terms=array([[ 3.90872913e-09, -2.52522797e-09], - [-2.52522797e-09, 1.63141934e-09]]), scale=0.00897291838572238, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=53, candidate_x=array([1.98973589, 1.00304738]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=0.8268176302277769, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.00897291838572237, relative_step_length=0.999999999999999, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=101.65686345501824, linear_terms=array([-9.30404245e-05, 5.65023341e-04]), square_terms=array([[ 4.25771576e-11, -2.58565972e-10], - [-2.58565972e-10, 1.57024014e-09]]), scale=0.01794583677144476, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=54, candidate_x=array([1.99265166, 0.98534 ]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=-0.993946834585867, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=101.65447794468584, linear_terms=array([-0.00081271, -0.0001358 ]), square_terms=array([[3.24870780e-09, 5.42864165e-10], - [5.42864165e-10, 9.07134529e-11]]), scale=0.00897291838572238, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=55, candidate_x=array([1.9985861 , 1.00452623]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=0.7239326168613196, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722428, relative_step_length=1.0000000000000053, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=101.65497283502123, linear_terms=array([-0.00108512, -0.00030579]), square_terms=array([[5.79156561e-09, 1.63209915e-09], - [1.63209915e-09, 4.59935676e-10]]), scale=0.01794583677144476, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=56, candidate_x=array([2.0158592 , 1.00939379]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-1.2143715762219611, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), old_indices_discarded=array([48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=101.65457751207414, linear_terms=array([-0.00046326, 0.00016734]), square_terms=array([[ 1.05559287e-09, -3.81311452e-10], - [-3.81311452e-10, 1.37741006e-10]]), scale=0.00897291838572238, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=57, candidate_x=array([2.0070253 , 1.00147777]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-0.49478560723833387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=101.65438327884436, linear_terms=array([-3.96566587e-05, 1.41729139e-05]), square_terms=array([[ 7.73528167e-12, -2.76451634e-12], - [-2.76451634e-12, 9.88011935e-13]]), scale=0.00448645919286119, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 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53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.004486459192861143, relative_step_length=0.9999999999999896, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=101.65419419312354, linear_terms=array([-0.00041313, 0.00011216]), square_terms=array([[ 8.39487554e-10, -2.27914839e-10], - [-2.27914839e-10, 6.18772410e-11]]), scale=0.00897291838572238, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=59, candidate_x=array([2.01147031, 1.00066538]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-1.7208326131972362, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=101.65422823536524, linear_terms=array([-5.03968696e-05, 6.48567467e-06]), square_terms=array([[ 1.24925668e-11, -1.60769358e-12], - [-1.60769358e-12, 2.06897326e-13]]), scale=0.00448645919286119, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=60, candidate_x=array([2.00726062, 1.0024437 ]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-6.962482671698848, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([51, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60]), model=ScalarModel(intercept=101.65364736975285, linear_terms=array([0.00018313, 0.0002147 ]), square_terms=array([[1.64949201e-10, 1.93390034e-10], - [1.93390034e-10, 2.26734687e-10]]), scale=0.002243229596430595, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=61, candidate_x=array([2.00135514, 1.0013096 ]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=0.793249003571856, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=0.0022432295964306204, relative_step_length=1.0000000000000113, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=101.65397564855002, linear_terms=array([ 0.00011335, -0.00016284]), square_terms=array([[ 6.31929509e-11, -9.07881463e-11], - [-9.07881463e-11, 1.30433654e-10]]), scale=0.00448645919286119, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=62, candidate_x=array([1.99879211, 1.00499188]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=-1.6387481559100683, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=101.65328749627801, linear_terms=array([0.00020435, 0.00031288]), square_terms=array([[2.05388937e-10, 3.14474469e-10], - [3.14474469e-10, 4.81497170e-10]]), scale=0.002243229596430595, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=63, candidate_x=array([2.0001285 , 0.99943145]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=0.3483172374666223, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.002243229596430552, relative_step_length=0.9999999999999809, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=101.65353276184649, linear_terms=array([6.33509679e-05, 2.13247340e-04]), square_terms=array([[1.97403131e-11, 6.64483812e-11], - [6.64483812e-11, 2.23673623e-10]]), scale=0.00448645919286119, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=64, candidate_x=array([1.99885087, 0.99513076]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-1.9869393097877708, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), old_indices_discarded=array([51, 54, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=101.65357133359919, linear_terms=array([-3.07662222e-05, 5.62449717e-05]), square_terms=array([[ 4.65581493e-12, -8.51148305e-12], - [-8.51148305e-12, 1.55601855e-11]]), scale=0.002243229596430595, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=65, candidate_x=array([2.00120502, 0.99746342]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-3.471200895305958, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 55, 57, 58, 60, 61, 62, 63, 64]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.0011216147982152974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 58, 61, 63, 64, 65]), model=ScalarModel(intercept=101.6534281159708, linear_terms=array([-3.92952930e-05, 6.94120144e-06]), square_terms=array([[ 7.59502203e-12, -1.34160032e-12], - [-1.34160032e-12, 2.36983043e-13]]), scale=0.0011216147982152974, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=66, candidate_x=array([2.00123302, 0.99923635]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-2.1738747036468844, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 58, 61, 63, 64, 65]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 65, 66]), model=ScalarModel(intercept=101.65315700952239, linear_terms=array([ 7.24065847e-05, -1.56138570e-05]), square_terms=array([[ 2.57872636e-11, -5.56080152e-12], - [-5.56080152e-12, 1.19913900e-12]]), scale=0.0005608073991076487, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=67, candidate_x=array([1.9995803 , 0.99954967]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-0.04414942354237646, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67]), model=ScalarModel(intercept=101.65315912374545, linear_terms=array([0.00012934, 0.00060757]), square_terms=array([[8.22898916e-11, 3.86540778e-10], - [3.86540778e-10, 1.81570021e-09]]), scale=0.00028040369955382435, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=68, candidate_x=array([2.00007012, 0.9991572 ]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-0.04231075182430718, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68]), model=ScalarModel(intercept=101.65315912374552, linear_terms=array([-3.56979582e-06, -1.26759543e-05]), square_terms=array([[6.26809943e-14, 2.22573351e-13], - [2.22573351e-13, 7.90333613e-13]]), scale=0.00014020184977691218, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=69, candidate_x=array([2.00016651, 0.99956641]), index=69, x=array([2.00016651, 0.99956641]), fval=101.65314728156433, rho=0.8992450217010943, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.0001402018497769173, relative_step_length=1.0000000000000366, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00016651, 0.99956641]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69]), model=ScalarModel(intercept=101.65316287886624, linear_terms=array([ 1.18463065e-05, -2.23664617e-05]), square_terms=array([[ 6.90263703e-13, -1.30325487e-12], - [-1.30325487e-12, 2.46061507e-12]]), scale=0.00028040369955382435, shift=array([2.00016651, 0.99956641])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=70, candidate_x=array([2.00003526, 0.9998142 ]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=1.087904744350226, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538049, relative_step_length=0.9999999999999306, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=101.6531539010588, linear_terms=array([ 5.98849665e-05, -1.62873618e-05]), square_terms=array([[ 1.76394390e-11, -4.79753001e-12], - [-4.79753001e-12, 1.30482008e-12]]), scale=0.0005608073991076487, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=71, candidate_x=array([1.99949411, 0.99996138]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=-0.5127930577353325, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=101.65314877678284, linear_terms=array([ 9.71223485e-06, -1.61205500e-05]), square_terms=array([[ 4.63967456e-13, -7.70101908e-13], - [-7.70101908e-13, 1.27822963e-12]]), scale=0.00028040369955382435, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=72, candidate_x=array([1.99989056, 1.00005438]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=0.35552372044377767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538801, relative_step_length=1.0000000000001987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=101.65315386439156, linear_terms=array([4.03977606e-05, 5.28856412e-06]), square_terms=array([[8.02719343e-12, 1.05085843e-12], - [1.05085843e-12, 1.37570304e-13]]), scale=0.0005608073991076487, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=73, candidate_x=array([1.9993345 , 0.99998159]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.334113586767923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=101.65311655752888, linear_terms=array([-1.71237318e-05, -2.37865075e-05]), square_terms=array([[1.44226858e-12, 2.00344952e-12], - [2.00344952e-12, 2.78298373e-12]]), scale=0.00028040369955382435, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=74, candidate_x=array([2.00005439, 1.00028195]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.5627670521816485, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=101.65313160034756, linear_terms=array([-5.77420288e-06, -6.18898976e-06]), square_terms=array([[1.63996024e-13, 1.75776594e-13], - [1.75776594e-13, 1.88403415e-13]]), scale=0.00014020184977691218, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=75, candidate_x=array([1.9999862, 1.0001569]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.41697664424331127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 69, 70, 71, 72, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75]), model=ScalarModel(intercept=101.65311148563875, linear_terms=array([5.50427867e-06, 9.82268200e-07]), square_terms=array([[1.49021920e-13, 2.65937649e-14], - [2.65937649e-14, 4.74580071e-15]]), scale=7.010092488845609e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=76, candidate_x=array([1.99982155, 1.00004207]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.0934080921860894, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76]), model=ScalarModel(intercept=101.65311305572646, linear_terms=array([-3.98365342e-06, 4.92341491e-06]), square_terms=array([[ 7.80571006e-14, -9.64711165e-14], - [-9.64711165e-14, 1.19229080e-13]]), scale=3.5050462444228044e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=77, candidate_x=array([1.99991261, 1.00002713]), index=77, x=array([1.99991261, 1.00002713]), fval=101.65310998586972, rho=0.4847239229932347, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([72, 75, 76]), old_indices_discarded=array([], dtype=int32), step_length=3.5050462444189934e-05, relative_step_length=0.9999999999989128, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75, 76, 77]), model=ScalarModel(intercept=101.65311599269691, linear_terms=array([2.73513590e-06, 1.10171467e-06]), square_terms=array([[3.67965523e-14, 1.48216772e-14], - [1.48216772e-14, 5.97018206e-15]]), scale=7.010092488845609e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=78, candidate_x=array([1.99984758, 1.00000094]), index=77, x=array([1.99991261, 1.00002713]), fval=101.65310998586972, rho=-2.0657227533912423, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75, 76, 77]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78]), model=ScalarModel(intercept=101.6531107725162, linear_terms=array([-2.89439098e-06, 2.97676454e-06]), square_terms=array([[ 4.12063098e-14, -4.23790299e-14], - [-4.23790299e-14, 4.35851253e-14]]), scale=3.5050462444228044e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - 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]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=101.65311560548442, linear_terms=array([2.27763281e-06, 1.31542912e-06]), square_terms=array([[2.55162430e-14, 1.47367077e-14], - [1.47367077e-14, 8.51107096e-15]]), scale=7.010092488845609e-05, shift=array([1.99993704, 1.000002 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=80, candidate_x=array([1.99987634, 0.99996694]), index=79, x=array([1.99993704, 1.000002 ]), fval=101.65310713409757, rho=-2.471821479499451, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75, 76, 77, 78, 79]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99993704, 1.000002 ]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=101.65310851811418, linear_terms=array([-2.55877294e-06, 2.22965678e-06]), square_terms=array([[ 3.22042240e-14, -2.80620313e-14], - [-2.80620313e-14, 2.44526185e-14]]), scale=3.5050462444228044e-05, shift=array([1.99993704, 1.000002 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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model_indices=array([70, 72, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=101.65311312451377, linear_terms=array([-7.55959616e-07, -6.02717625e-07]), square_terms=array([[2.81090723e-15, 2.24110295e-15], - [2.24110295e-15, 1.78680477e-15]]), scale=7.010092488845609e-05, shift=array([1.99996347, 0.99997898])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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79, 80, 81, 82]), model=ScalarModel(intercept=101.65310380914096, linear_terms=array([-1.03266467e-05, 8.97242802e-06]), square_terms=array([[ 5.24527182e-13, -4.55741590e-13], - [-4.55741590e-13, 3.95976422e-13]]), scale=0.00014020184977691218, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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model=ScalarModel(intercept=101.65311088628962, linear_terms=array([-3.79014379e-07, 1.04483358e-06]), square_terms=array([[ 7.06578965e-16, -1.94783489e-15], - [-1.94783489e-15, 5.36962031e-15]]), scale=7.010092488845609e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], 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linear_terms=array([1.46452562e-07, 8.40702212e-07]), square_terms=array([[1.05497769e-16, 6.05603664e-16], - [6.05603664e-16, 3.47643178e-15]]), scale=3.5050462444228044e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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scale=1.7525231222114022e-05, shift=array([2.00001226, 0.99998815])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], 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vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, - 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=89, candidate_x=array([2.00000352, 0.99998757]), index=89, x=array([2.00000352, 0.99998757]), fval=101.65310212694172, rho=0.30214250212998456, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([82, 84, 85, 87, 88]), old_indices_discarded=array([], dtype=int32), step_length=8.76261561092711e-06, relative_step_length=0.9999999999851755, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 90 entries., 'history': {'params': [{'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}, {'CRRA': 1.1, 'BeqFac': 168.99160456324418}, {'CRRA': 20.0, 'BeqFac': 181.63947671960705}, {'CRRA': 1.1, 'BeqFac': 196.1247826983854}, {'CRRA': 20.0, 'BeqFac': 198.68302674118215}, {'CRRA': 19.674724735180458, 'BeqFac': 167.47958678304056}, {'CRRA': 20.0, 'BeqFac': 167.52737770433455}, {'CRRA': 1.1, 'BeqFac': 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1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}, {'CRRA': 1.9999990044023963, 'BeqFac': 1.0000000937305267}], 'criterion': [0.8276471824376574, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 101.67310083509217, 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101.6531009350922], 'runtime': [0.0, 3.4025794002227485, 3.5323713002726436, 3.7029361999593675, 3.959222299978137, 4.147139200009406, 4.381295599974692, 4.54285839991644, 4.727524899877608, 4.90410690009594, 5.086126999929547, 5.262361200060695, 5.4208134999498725, 5.898215599823743, 6.125383300241083, 6.351102199871093, 6.574805700220168, 6.799138200003654, 7.023217299953103, 7.247513700276613, 7.4716604999266565, 7.695839799940586, 7.920229299925268, 8.144684699829668, 8.368564300239086, 8.591356500051916, 8.81424369988963, 9.037026800215244, 9.25977909984067, 9.482805599924177, 9.705878000240773, 9.9294539000839, 10.152522800024599, 10.377171100117266, 10.601050999946892, 10.825552000198513, 11.05199269996956, 11.422774800099432, 11.64544240012765, 11.868264500051737, 12.090906499885023, 12.31406190013513, 12.537094000261277, 12.759967099875212, 12.982686299830675, 13.205718399956822, 13.428489599842578, 13.651291300076991, 13.87483590003103, 14.097963999956846, 14.32136629987508, 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25.875632400158793, 26.0998467002064, 26.45643000025302, 26.680018399842083, 26.903818999882787, 27.127937300130725, 27.352323399856687, 27.57637930009514, 27.80031660012901, 28.02404379984364, 28.248444000259042, 28.4726595999673], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.069e-09** 7.731e-08* +relative_params_change 4.41e-06* 6.026e-05 +absolute_criterion_change 4.137e-07* 7.859e-06* +absolute_params_change 8.763e-06* 9.915e-05 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 2.0, 'BeqFac': 1.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 2.28125, 'BeqFac': 9375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}], 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, + 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, + 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, + 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, + 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=0, candidate_x=array([2., 1.]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=86.15955603265024, linear_terms=array([ 0.05089577, -0.19533528]), square_terms=array([[ 1.51497851e-05, -5.81440784e-05], + [-5.81440784e-05, 2.23153914e-04]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-499.88400427494435, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.1, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), model=ScalarModel(intercept=69.9966297435316, linear_terms=array([ 5.52347875, 10.30766762]), square_terms=array([[0.22002817, 0.41060667], + [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=14, candidate_x=array([1.9651098 , 0.90628408]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-9.081870753402008, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), old_indices_discarded=array([ 2, 3, 5, 6, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=61.19193548717778, linear_terms=array([3.65115817, 8.68434653]), square_terms=array([[0.11012808, 0.26194167], + [0.26194167, 0.62303309]]), scale=0.05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=15, candidate_x=array([1.98543255, 0.95216916]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.192688611597553, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 3, 5, 6, 7, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.025, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], + [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=16, candidate_x=array([2.01033614, 1.0227644 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6632385320428, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 15, 16]), model=ScalarModel(intercept=0.827647182437657, linear_terms=array([-45.37479674, 16.0066913 ]), square_terms=array([[ 6419.23080123, -2264.48759797], + [-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], + [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=19, candidate_x=array([1.9987484 , 0.99713645]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117679378963, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], + [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=20, candidate_x=array([2.00093264, 1.00125388]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7086956369039, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00078125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], + [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=21, candidate_x=array([2.00037411, 1.00068591]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117058020676, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.000390625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.8276471824376602, linear_terms=array([ 10.88378502, -10.70264801]), square_terms=array([[ 369.32716463, -363.18052667], + [-363.18052667, 357.13618598]]), scale=0.000390625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], + [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], + [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=24, candidate_x=array([2.00008235, 1.00005249]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71151997705, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], + [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], + [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=26, candidate_x=array([1.99997671, 0.99999267]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711721703526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], + [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], + [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], + [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], + [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], + [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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]), square_terms=array([[ 16.75517786, -19.70434065], + [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], + [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], + [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], + [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], + [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], + [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], + [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], + [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=41, candidate_x=array([2.00000087, 0.99999951]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.540882296216793, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), old_indices_discarded=array([29, 30, 31, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), model=ScalarModel(intercept=79.20315056997187, linear_terms=array([-4.18694583, -2.12920934]), square_terms=array([[0.11160833, 0.05675677], + [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=44, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=45, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=46, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=47, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=48, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=51, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=53, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=58, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 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36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=76, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=91, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=94, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=107, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=109, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=110, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=111, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=112, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 113 entries., 'multistart_info': {'start_parameters': [array([2., 1.]), array([ 6.92609782, 183.76536854])], 'local_optima': [{'solution_x': array([2., 1.]), 'solution_criterion': 0.8276471824376574, 'states': [State(trustregion=Region(center=array([2., 1.]), radius=0.2, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.8276471824376574, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.2, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=13, candidate_x=array([1.94980218, 1.19359798]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-499.88400427494435, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.1, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 7, 8, 9, 10, 11, 13]), model=ScalarModel(intercept=69.9966297435316, linear_terms=array([ 5.52347875, 10.30766762]), square_terms=array([[0.22002817, 0.41060667], + [0.41060667, 0.76625569]]), scale=0.1, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], 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6, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 4, 8, 9, 10, 11, 13, 14]), model=ScalarModel(intercept=61.19193548717778, linear_terms=array([3.65115817, 8.68434653]), square_terms=array([[0.11012808, 0.26194167], + [0.26194167, 0.62303309]]), scale=0.05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 14, 15]), model=ScalarModel(intercept=0.8276471824376403, linear_terms=array([-31.62005899, 14.00478571]), square_terms=array([[ 3117.28536415, -1380.6732394 ], + [-1380.6732394 , 611.51238059]]), scale=0.025, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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[-2264.48759797, 798.83466418]]), scale=0.0125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=17, candidate_x=array([2.004237 , 1.01176031]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7120258298457, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.00625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.8276471824376554, linear_terms=array([ 22.92925386, -12.7088243 ]), square_terms=array([[1639.19696011, -908.54564757], + [-908.54564757, 503.57291637]]), scale=0.00625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=18, candidate_x=array([1.99690325, 0.99457059]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.6996277758512, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.003125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.8276471824376497, linear_terms=array([ 33.51528457, -14.29880894]), square_terms=array([[ 3502.17413253, -1494.15175942], + [-1494.15175942, 637.45816047]]), scale=0.003125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0015625, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.8276471824376597, linear_terms=array([-16.19210931, 11.64397652]), square_terms=array([[ 817.44432411, -587.83592419], + [-587.83592419, 422.72123444]]), scale=0.0015625, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=0.8276471824376584, linear_terms=array([-24.44583172, 12.96813418]), square_terms=array([[1863.20708893, -988.40244714], + [-988.40244714, 524.33216002]]), scale=0.00078125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=22, candidate_x=array([1.99972026, 0.99972723]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7109492805971, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=0.0001953125, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.8276471824376606, linear_terms=array([ 17.44512723, -11.89823002]), square_terms=array([[ 948.85460568, -647.15437432], + [-647.15437432, 441.38351829]]), scale=0.0001953125, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=23, candidate_x=array([1.99988751, 0.99984033]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71171318721, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=9.765625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.8276471824376566, linear_terms=array([-6.61168567, 9.77707177]), square_terms=array([[ 136.29368845, -201.54515116], + [-201.54515116, 298.03616307]]), scale=9.765625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=24, candidate_x=array([2.00008235, 1.00005249]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.71151997705, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=4.8828125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.8276471824376598, linear_terms=array([-11.96730467, 10.99081158]), square_terms=array([[ 446.52322965, -410.08838858], + [-410.08838858, 376.62651187]]), scale=4.8828125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=25, candidate_x=array([2.00003373, 1.0000353 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711688055746, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=2.44140625e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.8276471824376532, linear_terms=array([ 3.08834797, -8.73857385]), square_terms=array([[ 29.73745547, -84.14302869], + [-84.14302869, 238.08524183]]), scale=2.44140625e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=26, candidate_x=array([1.99997671, 0.99999267]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711721703526, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.220703125e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.8276471824376521, linear_terms=array([ 7.57960308, -10.13596867]), square_terms=array([[ 179.12008348, -239.53174518], + [-239.53174518, 320.31839108]]), scale=1.220703125e-05, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=27, candidate_x=array([1.99999004, 0.99999294]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116874138196, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=6.103515625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.8276471824376579, linear_terms=array([-0.15165759, 7.44664151]), square_terms=array([[ 7.17099892e-02, -3.52108049e+00], + [-3.52108049e+00, 1.72890945e+02]]), scale=6.103515625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=28, candidate_x=array([2.00000611, 0.99999986]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.711677104554, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=3.0517578125e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.8276471824376628, linear_terms=array([-3.97279126, 9.21962533]), square_terms=array([[ 49.20880826, -114.19849305], + [-114.19849305, 265.01954175]]), scale=3.0517578125e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=29, candidate_x=array([1.99999724, 0.9999987 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7117397453109, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1.52587890625e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.827647182437653, linear_terms=array([-1.78151146, 13.64486064]), square_terms=array([[ 9.89529152, -75.78950624], + [-75.78950624, 580.48307582]]), scale=1.52587890625e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=30, candidate_x=array([1.99999849, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116873932954, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30]), model=ScalarModel(intercept=0.8276471824376591, linear_terms=array([ 6.78250872, -7.99625076]), square_terms=array([[ 143.4273832 , -169.09396935], + [-169.09396935, 199.3536369 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=31, candidate_x=array([2.00000074, 1.00000067]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-628.7116856529549, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31]), model=ScalarModel(intercept=10.049290130474787, linear_terms=array([-51.9281406 , 88.49493782]), square_terms=array([[ 143.70755383, -244.90364749], + [-244.90364749, 417.3600827 ]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=32, candidate_x=array([1.99999924, 0.99999934]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-10.746700442186407, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32]), model=ScalarModel(intercept=43.05839744142132, linear_terms=array([-37.69007116, 44.3240894 ]), square_terms=array([[ 16.75517786, -19.70434065], + [-19.70434065, 23.17260037]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=33, candidate_x=array([2.00000009, 0.999999 ]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-2.9522733070987055, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=56.93444143408821, linear_terms=array([ -6.96601288, -17.26411453]), square_terms=array([[0.43120475, 1.06866988], + [1.06866988, 2.64852213]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=34, candidate_x=array([2.00000019, 1.00000098]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-6.003128302627301, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=67.15977327832778, linear_terms=array([-12.95288056, 3.90965354]), square_terms=array([[ 1.26162446, -0.38080445], + [-0.38080445, 0.11494072]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=35, candidate_x=array([2.00000098, 0.99999981]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-7.891442250906298, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=74.40172512316786, linear_terms=array([-4.39913405, -4.74128575]), square_terms=array([[0.13123025, 0.14143696], + [0.14143696, 0.15243751]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=36, candidate_x=array([2.00000054, 1.00000084]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-16.18609474458335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=78.42574355713958, linear_terms=array([-4.95695165, 1.36114804]), square_terms=array([[ 0.15799804, -0.04338528], + [-0.04338528, 0.01191333]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=37, candidate_x=array([2.00000097, 0.99999977]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-19.954818815240166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=78.99436811656406, linear_terms=array([ 4.95171225, -0.92106365]), square_terms=array([[ 0.15651963, -0.02911408], + [-0.02911408, 0.00541548]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=38, candidate_x=array([1.99999901, 1.00000016]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-20.352438513412878, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=77.8470369868507, linear_terms=array([8.80696801, 1.13598751]), square_terms=array([[0.50248075, 0.06481366], + [0.06481366, 0.00836014]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=39, candidate_x=array([1.999999 , 0.99999991]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-11.699970979801405, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([29, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=79.64023815570656, linear_terms=array([ 0.29251479, -1.21077324]), square_terms=array([[ 0.00054174, -0.00224234], + [-0.00224234, 0.00928148]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=40, candidate_x=array([1.99999977, 1.00000097]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-81.26757484763098, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 36, 37, 38, 39]), old_indices_discarded=array([29, 30, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 36, 38, 39, 40]), model=ScalarModel(intercept=77.91642222342819, linear_terms=array([-7.61877194, 6.60055683]), square_terms=array([[ 0.3757044 , -0.32549317], + [-0.32549317, 0.28199245]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.05675677], + [0.05675677, 0.02886281]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=42, candidate_x=array([2.00000091, 1.00000041]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-21.818687823671702, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 38, 39, 40, 41]), old_indices_discarded=array([29, 30, 31, 36, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=43, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], 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old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=46, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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[-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=49, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=50, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=51, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=52, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=53, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=54, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=55, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=56, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=57, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=58, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=59, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=60, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=61, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=62, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=63, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=64, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=65, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=66, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=67, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=68, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=69, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=70, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=71, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=72, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=73, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=74, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=75, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=77, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=78, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=79, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=80, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=81, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=82, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=83, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=84, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=85, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=86, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=87, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=88, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=89, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=90, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=91, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=92, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=93, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=95, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=96, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=97, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=98, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=99, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=100, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, + 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, + 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, + 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=101, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=102, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=103, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=104, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=105, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=106, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=107, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.2, shift=array([2., 1.])), candidate_index=108, candidate_x=array([1.999999 , 1.00000009]), index=0, x=array([2., 1.]), fval=0.8276471824376574, rho=-18.910954574248176, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), old_indices_discarded=array([ 29, 30, 31, 36, 37, 38, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, + 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, + 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, + 102, 103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2., 1.]), radius=1e-06, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33, 34, 35, 39, 40, 41, 42]), model=ScalarModel(intercept=79.02245388255531, linear_terms=array([ 5.38941576, -0.6382063 ]), square_terms=array([[ 0.18534706, -0.02194851], + [-0.02194851, 0.00259911]]), scale=1e-06, shift=array([2., 1.])), vector_model=VectorModel(intercepts=array([ 0.01889705, 0.03737285, 0.0119039 , 0.01296309, 0.00703903, + 0.00334288, 0.00334819, -0.07072294, -0.20544166, -0.20714064, + -0.58726878, -0.61703582, 0.04720119, 0.05022993, 0.04425086, + 0.03692942, 0.04129992]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([ 7.25637091, -2.42042422]), square_terms=array([[ 6.4002053 , -7.80908346], + [-7.80908346, 9.72016944]]), scale=array([ 9.45 , 65.14312703]), shift=array([10.55 , 65.14312703])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=20, candidate_x=array([ 1.1 , 29.02913582]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-2.3760563129194923, accepted=False, new_indices=array([19]), old_indices_used=array([ 5, 11, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=73.50614741583773, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([13, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=117.7129776630629, linear_terms=array([25.2996436 , -4.89176169]), square_terms=array([[24.61806823, -7.65738376], + [-7.65738376, 2.42416309]]), scale=array([ 9.45 , 32.57156351]), shift=array([10.55 , 32.57156351])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=22, candidate_x=array([1.1, 0. ]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.06939648966118021, accepted=False, new_indices=array([21]), old_indices_used=array([13, 14, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=36.753073707918865, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([15, 16, 17, 18, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=124.40570480804853, linear_terms=array([49.3936391 , -9.48712516]), square_terms=array([[ 56.97724033, -12.61216539], + [-12.61216539, 2.80192327]]), scale=array([ 9.45 , 16.28578176]), shift=array([10.55 , 16.28578176])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=24, candidate_x=array([1.1, 0. ]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.10692730529367472, accepted=False, new_indices=array([23]), old_indices_used=array([15, 16, 17, 18, 19, 20, 21, 22]), old_indices_discarded=array([ 1, 2, 5, 6, 11, 13, 14]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=18.376536853959433, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([16, 17, 18, 20, 22, 23, 24, 25]), model=ScalarModel(intercept=117.57203468456304, linear_terms=array([30.73535329, -7.11397569]), square_terms=array([[30.73949783, -7.9121334 ], + [-7.9121334 , 2.03963642]]), scale=array([8.23127669, 8.14289088]), shift=array([9.33127669, 8.14289088])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=26, candidate_x=array([1.1 , 4.95638609]), index=18, x=array([1.27677162, 0. ]), fval=101.77651312344997, rho=-0.5089417781776332, accepted=False, new_indices=array([25]), old_indices_used=array([16, 17, 18, 20, 22, 23, 24]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.27677162, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=107.07780108961828, linear_terms=array([13.09888074, 0.39394958]), square_terms=array([[1.63256660e+01, 2.16792836e-02], + [2.16792836e-02, 7.25065498e-04]]), scale=array([4.15983125, 4.07144544]), shift=array([5.25983125, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=27, candidate_x=array([1.92771902, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=0.11582332012959147, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 20, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.650947401024822, relative_step_length=0.07084549240131369, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=9.188268426979716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 20, 22, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=104.55965217810557, linear_terms=array([8.8072047 , 0.73944899]), square_terms=array([[14.54343784, 0.6300655 ], + [ 0.6300655 , 0.02792488]]), scale=array([4.48530495, 4.07144544]), shift=array([5.58530495, 4.07144544])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=29, candidate_x=array([3.06341409, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09805438131359735, accepted=False, new_indices=array([28]), old_indices_used=array([17, 18, 20, 22, 24, 25, 26, 27]), old_indices_discarded=array([23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=4.594134213489858, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=101.7963759557694, linear_terms=array([0.88312694, 0.23121981]), square_terms=array([[4.43153858, 0.34430387], + [0.34430387, 0.02688032]]), scale=array([2.44958223, 2.03572272]), shift=array([3.54958223, 2.03572272])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=30, candidate_x=array([3.25174178, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.09261502613162106, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 22, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=2.297067106744929, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=101.90622993713308, linear_terms=array([0.11164869, 0.06828134]), square_terms=array([[6.11612797e-05, 3.74045927e-05], + [3.74045927e-05, 2.28756423e-05]]), scale=array([1.43172087, 1.01786136]), shift=array([2.53172087, 1.01786136])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=31, candidate_x=array([1.1, 0. ]), index=27, x=array([1.92771902, 0. ]), fval=101.75336172178959, rho=-0.5313962286424387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 26, 27, 28, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.92771902, 0. ]), radius=1.1485335533724645, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 26, 27, 29, 30, 31]), model=ScalarModel(intercept=101.81701589262005, linear_terms=array([0.00903626, 0.02869081]), square_terms=array([[4.00983753e-07, 1.27315403e-06], + [1.27315403e-06, 4.04236127e-06]]), scale=array([0.92279019, 0.50893068]), shift=array([2.02279019, 0.50893068])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32]), model=ScalarModel(intercept=25.447067538457258, linear_terms=array([ 2.26209655e-03, -5.08941351e+01]), square_terms=array([[ 1.00543625e-07, -2.26209655e-03], + [-2.26209655e-03, 5.08941351e+01]]), scale=array([0.50893068, 0.25446534]), shift=array([1.92771902, 0.25446534])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=101.70281707849445, linear_terms=array([ 0.00255143, -0.04817497]), square_terms=array([[ 3.20040297e-08, -6.04285701e-07], + [-6.04285701e-07, 1.14098510e-05]]), scale=0.28713338834311614, shift=array([1.92240301, 0.50893056])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=101.6703406833042, linear_terms=array([ 0.00091326, -0.08127158]), square_terms=array([[ 4.10166532e-09, -3.65011616e-07], + [-3.65011616e-07, 3.24827770e-05]]), scale=0.5742667766862323, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=35, candidate_x=array([1.9009229 , 1.36989604]), index=34, x=array([1.9072569, 0.7956642]), fval=101.67554062166661, rho=-0.19509771603556256, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 22, 24, 27, 29, 31, 32, 33, 34]), old_indices_discarded=array([30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9072569, 0.7956642]), radius=0.28713338834311614, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([18, 22, 24, 27, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=101.7002045598023, linear_terms=array([-0.01763113, -0.01363475]), square_terms=array([[1.52829874e-06, 1.18188538e-06], + [1.18188538e-06, 9.13992149e-07]]), scale=0.28713338834311614, shift=array([1.9072569, 0.7956642])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([-3.30543881e-05, -5.13835828e-02]), square_terms=array([[5.37232193e-12, 8.35136165e-09], + [8.35136165e-09, 1.29823272e-05]]), scale=0.5742667766862323, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.46652301e-08, -1.58357648e-07], + [-1.58357648e-07, 1.70997279e-06]]), scale=0.28713338834311614, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=38, candidate_x=array([2.10801746, 1.25712117]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-0.7547851367190244, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 27, 29, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([22, 24, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=101.68183255247968, linear_terms=array([-0.00546611, 0.00214313]), square_terms=array([[ 1.46920840e-07, -5.76042249e-08], + [-5.76042249e-08, 2.25852693e-08]]), scale=0.14356669417155807, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=39, candidate_x=array([2.26814041, 0.91880982]), index=36, x=array([2.13447793, 0.9712096 ]), fval=101.66685336190018, rho=-2.429463248081529, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.13447793, 0.9712096 ]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39]), model=ScalarModel(intercept=101.6761802726613, linear_terms=array([0.0004533 , 0.00066536]), square_terms=array([[1.01045073e-09, 1.48316938e-09], + [1.48316938e-09, 2.17703977e-09]]), scale=0.07178334708577903, shift=array([2.13447793, 0.9712096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=40, candidate_x=array([2.09406231, 0.91188484]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=1.0728799070913182, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577915, relative_step_length=1.0000000000000016, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 37, 38, 39, 40]), model=ScalarModel(intercept=101.68098851485126, linear_terms=array([-0.0050226 , 0.00236335]), square_terms=array([[ 1.24047463e-07, -5.83696319e-08], + [-5.83696319e-08, 2.74654058e-08]]), scale=0.14356669417155807, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=41, candidate_x=array([2.22396921, 0.85076534]), index=40, x=array([2.09406231, 0.91188484]), fval=101.66598958952493, rho=-2.526612521827417, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.09406231, 0.91188484]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41]), model=ScalarModel(intercept=101.67433941881659, linear_terms=array([0.00112428, 0.00054592]), square_terms=array([[6.21590003e-09, 3.01827897e-09], + [3.01827897e-09, 1.46559757e-09]]), scale=0.07178334708577903, shift=array([2.09406231, 0.91188484])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=42, candidate_x=array([2.02948871, 0.88053044]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=0.46666748151376614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577888, relative_step_length=0.9999999999999979, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=101.680416054201, linear_terms=array([-0.00475057, -0.00077785]), square_terms=array([[1.10974697e-07, 1.81707727e-08], + [1.81707727e-08, 2.97524560e-09]]), scale=0.14356669417155807, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=43, candidate_x=array([2.17116917, 0.90372619]), index=42, x=array([2.02948871, 0.88053044]), fval=101.66540634612655, rho=-1.523359554750816, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([33, 34, 35, 36, 38, 39, 40, 41, 42]), old_indices_discarded=array([37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.02948871, 0.88053044]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=101.66905458361765, linear_terms=array([ 0.00365778, -0.00773732]), square_terms=array([[ 6.57986625e-08, -1.39184073e-07], + [-1.39184073e-07, 2.94416414e-07]]), scale=0.07178334708577903, shift=array([2.02948871, 0.88053044])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=44, candidate_x=array([1.99881359, 0.94542948]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=0.8000735183892913, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.07178334708577906, relative_step_length=1.0000000000000004, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=101.66837110968486, linear_terms=array([0.00492682, 0.00116565]), square_terms=array([[1.19375993e-07, 2.82435251e-08], + [2.82435251e-08, 6.68222052e-09]]), scale=0.14356669417155807, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=45, candidate_x=array([1.85910309, 0.91237844]), index=44, x=array([1.99881359, 0.94542948]), fval=101.658559176471, rho=-2.1991263376100285, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([33, 35, 37]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99881359, 0.94542948]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=101.66380899320433, linear_terms=array([ 0.00235297, -0.00665188]), square_terms=array([[ 2.72293463e-08, -7.69776254e-08], + [-7.69776254e-08, 2.17616492e-07]]), scale=0.07178334708577903, shift=array([1.99881359, 0.94542948])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=46, candidate_x=array([1.9748781 , 1.01310475]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=0.3720268111515636, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([38]), step_length=0.07178334708577894, relative_step_length=0.9999999999999987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.14356669417155807, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=101.66834084528847, linear_terms=array([0.00105253, 0.00192198]), square_terms=array([[5.44817640e-09, 9.94871690e-09], + [9.94871690e-09, 1.81669903e-08]]), scale=0.14356669417155807, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=47, candidate_x=array([1.90592301, 0.88718179]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-5.4105642931805455, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 38, 40, 42, 43, 44, 45, 46]), old_indices_discarded=array([33, 35, 37, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.07178334708577903, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=101.65730356105429, linear_terms=array([ 0.00098995, -0.00629255]), square_terms=array([[ 4.82013494e-09, -3.06388241e-08], + [-3.06388241e-08, 1.94753374e-07]]), scale=0.07178334708577903, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=48, candidate_x=array([1.96372399, 1.0840162 ]), index=46, x=array([1.9748781 , 1.01310475]), fval=101.6559342855933, rho=-0.9918406732109903, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([34, 36, 40, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([38, 39, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9748781 , 1.01310475]), radius=0.03589167354288952, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([36, 40, 42, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=101.6617873815051, linear_terms=array([-0.00012068, -0.00134631]), square_terms=array([[7.16320381e-11, 7.99106267e-10], + [7.99106267e-10, 8.91459804e-09]]), scale=0.03589167354288952, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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10000.]))), model_indices=array([44, 46, 48, 49]), model=ScalarModel(intercept=101.65745624776355, linear_terms=array([0.00318564, 0.00116839]), square_terms=array([[4.99142631e-08, 1.83069319e-08], + [1.83069319e-08, 6.71438853e-09]]), scale=0.01794583677144476, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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linear_terms=array([-0.00102209, 0.00072439]), square_terms=array([[ 5.13823243e-09, -3.64162989e-09], + [-3.64162989e-09, 2.58093974e-09]]), scale=0.00897291838572238, shift=array([1.9748781 , 1.01310475])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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square_terms=array([[1.96914913e-09, 1.96454291e-09], + [1.96454291e-09, 1.95994747e-09]]), scale=0.01794583677144476, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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scale=0.00897291838572238, shift=array([1.98219895, 1.00791645])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=53, candidate_x=array([1.98973589, 1.00304738]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=0.8268176302277769, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 49, 50, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.00897291838572237, relative_step_length=0.999999999999999, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=101.65686345501824, linear_terms=array([-9.30404245e-05, 5.65023341e-04]), square_terms=array([[ 4.25771576e-11, -2.58565972e-10], + [-2.58565972e-10, 1.57024014e-09]]), scale=0.01794583677144476, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=54, candidate_x=array([1.99265166, 0.98534 ]), index=53, x=array([1.98973589, 1.00304738]), fval=101.65417152893576, rho=-0.993946834585867, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 48, 49, 50, 51, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.98973589, 1.00304738]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=101.65447794468584, linear_terms=array([-0.00081271, -0.0001358 ]), square_terms=array([[3.24870780e-09, 5.42864165e-10], + [5.42864165e-10, 9.07134529e-11]]), scale=0.00897291838572238, shift=array([1.98973589, 1.00304738])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=55, candidate_x=array([1.9985861 , 1.00452623]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=0.7239326168613196, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.008972918385722428, relative_step_length=1.0000000000000053, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.01794583677144476, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=101.65497283502123, linear_terms=array([-0.00108512, -0.00030579]), square_terms=array([[5.79156561e-09, 1.63209915e-09], + [1.63209915e-09, 4.59935676e-10]]), scale=0.01794583677144476, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=56, candidate_x=array([2.0158592 , 1.00939379]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-1.2143715762219611, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([44, 46, 49, 50, 51, 52, 53, 54, 55]), old_indices_discarded=array([48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=101.65457751207414, linear_terms=array([-0.00046326, 0.00016734]), square_terms=array([[ 1.05559287e-09, -3.81311452e-10], + [-3.81311452e-10, 1.37741006e-10]]), scale=0.00897291838572238, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=57, candidate_x=array([2.0070253 , 1.00147777]), index=55, x=array([1.9985861 , 1.00452623]), fval=101.65357502800954, rho=-0.49478560723833387, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 50, 51, 52, 53, 54, 55, 56]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.9985861 , 1.00452623]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=101.65438327884436, linear_terms=array([-3.96566587e-05, 1.41729139e-05]), square_terms=array([[ 7.73528167e-12, -2.76451634e-12], + [-2.76451634e-12, 9.88011935e-13]]), scale=0.00448645919286119, shift=array([1.9985861 , 1.00452623])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 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53, 54, 55, 56, 57]), old_indices_discarded=array([], dtype=int32), step_length=0.004486459192861143, relative_step_length=0.9999999999999896, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00897291838572238, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=101.65419419312354, linear_terms=array([-0.00041313, 0.00011216]), square_terms=array([[ 8.39487554e-10, -2.27914839e-10], + [-2.27914839e-10, 6.18772410e-11]]), scale=0.00897291838572238, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=59, candidate_x=array([2.01147031, 1.00066538]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-1.7208326131972362, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([46, 51, 52, 53, 54, 55, 56, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([51, 53, 54, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=101.65422823536524, linear_terms=array([-5.03968696e-05, 6.48567467e-06]), square_terms=array([[ 1.24925668e-11, -1.60769358e-12], + [-1.60769358e-12, 2.06897326e-13]]), scale=0.00448645919286119, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=60, candidate_x=array([2.00726062, 1.0024437 ]), index=58, x=array([2.00281085, 1.00301635]), fval=101.65351313645318, rho=-6.962482671698848, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([51, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00281085, 1.00301635]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60]), model=ScalarModel(intercept=101.65364736975285, linear_terms=array([0.00018313, 0.0002147 ]), square_terms=array([[1.64949201e-10, 1.93390034e-10], + [1.93390034e-10, 2.26734687e-10]]), scale=0.002243229596430595, shift=array([2.00281085, 1.00301635])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=61, candidate_x=array([2.00135514, 1.0013096 ]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=0.793249003571856, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=0.0022432295964306204, relative_step_length=1.0000000000000113, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=101.65397564855002, linear_terms=array([ 0.00011335, -0.00016284]), square_terms=array([[ 6.31929509e-11, -9.07881463e-11], + [-9.07881463e-11, 1.30433654e-10]]), scale=0.00448645919286119, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=62, candidate_x=array([1.99879211, 1.00499188]), index=61, x=array([2.00135514, 1.0013096 ]), fval=101.65328928827302, rho=-1.6387481559100683, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_discarded=array([51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00135514, 1.0013096 ]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 57, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=101.65328749627801, linear_terms=array([0.00020435, 0.00031288]), square_terms=array([[2.05388937e-10, 3.14474469e-10], + [3.14474469e-10, 4.81497170e-10]]), scale=0.002243229596430595, shift=array([2.00135514, 1.0013096 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=63, candidate_x=array([2.0001285 , 0.99943145]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=0.3483172374666223, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([55, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.002243229596430552, relative_step_length=0.9999999999999809, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00448645919286119, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=101.65353276184649, linear_terms=array([6.33509679e-05, 2.13247340e-04]), square_terms=array([[1.97403131e-11, 6.64483812e-11], + [6.64483812e-11, 2.23673623e-10]]), scale=0.00448645919286119, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=64, candidate_x=array([1.99885087, 0.99513076]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-1.9869393097877708, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 55, 57, 58, 59, 60, 61, 62, 63]), old_indices_discarded=array([51, 54, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.002243229596430595, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([53, 55, 57, 58, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=101.65357133359919, linear_terms=array([-3.07662222e-05, 5.62449717e-05]), square_terms=array([[ 4.65581493e-12, -8.51148305e-12], + [-8.51148305e-12, 1.55601855e-11]]), scale=0.002243229596430595, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=65, candidate_x=array([2.00120502, 0.99746342]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-3.471200895305958, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([53, 55, 57, 58, 60, 61, 62, 63, 64]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.0011216147982152974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([55, 58, 61, 63, 64, 65]), model=ScalarModel(intercept=101.6534281159708, linear_terms=array([-3.92952930e-05, 6.94120144e-06]), square_terms=array([[ 7.59502203e-12, -1.34160032e-12], + [-1.34160032e-12, 2.36983043e-13]]), scale=0.0011216147982152974, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=66, candidate_x=array([2.00123302, 0.99923635]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-2.1738747036468844, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([55, 58, 61, 63, 64, 65]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 65, 66]), model=ScalarModel(intercept=101.65315700952239, linear_terms=array([ 7.24065847e-05, -1.56138570e-05]), square_terms=array([[ 2.57872636e-11, -5.56080152e-12], + [-5.56080152e-12, 1.19913900e-12]]), scale=0.0005608073991076487, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=67, candidate_x=array([1.9995803 , 0.99954967]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-0.04414942354237646, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67]), model=ScalarModel(intercept=101.65315912374545, linear_terms=array([0.00012934, 0.00060757]), square_terms=array([[8.22898916e-11, 3.86540778e-10], + [3.86540778e-10, 1.81570021e-09]]), scale=0.00028040369955382435, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=68, candidate_x=array([2.00007012, 0.9991572 ]), index=63, x=array([2.0001285 , 0.99943145]), fval=101.65315912374548, rho=-0.04231075182430718, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.0001285 , 0.99943145]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68]), model=ScalarModel(intercept=101.65315912374552, linear_terms=array([-3.56979582e-06, -1.26759543e-05]), square_terms=array([[6.26809943e-14, 2.22573351e-13], + [2.22573351e-13, 7.90333613e-13]]), scale=0.00014020184977691218, shift=array([2.0001285 , 0.99943145])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=69, candidate_x=array([2.00016651, 0.99956641]), index=69, x=array([2.00016651, 0.99956641]), fval=101.65314728156433, rho=0.8992450217010943, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68]), old_indices_discarded=array([], dtype=int32), step_length=0.0001402018497769173, relative_step_length=1.0000000000000366, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00016651, 0.99956641]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69]), model=ScalarModel(intercept=101.65316287886624, linear_terms=array([ 1.18463065e-05, -2.23664617e-05]), square_terms=array([[ 6.90263703e-13, -1.30325487e-12], + [-1.30325487e-12, 2.46061507e-12]]), scale=0.00028040369955382435, shift=array([2.00016651, 0.99956641])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=70, candidate_x=array([2.00003526, 0.9998142 ]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=1.087904744350226, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538049, relative_step_length=0.9999999999999306, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 65, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=101.6531539010588, linear_terms=array([ 5.98849665e-05, -1.62873618e-05]), square_terms=array([[ 1.76394390e-11, -4.79753001e-12], + [-4.79753001e-12, 1.30482008e-12]]), scale=0.0005608073991076487, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=71, candidate_x=array([1.99949411, 0.99996138]), index=70, x=array([2.00003526, 0.9998142 ]), fval=101.65311974675102, rho=-0.5127930577353325, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([2.00003526, 0.9998142 ]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=101.65314877678284, linear_terms=array([ 9.71223485e-06, -1.61205500e-05]), square_terms=array([[ 4.63967456e-13, -7.70101908e-13], + [-7.70101908e-13, 1.27822963e-12]]), scale=0.00028040369955382435, shift=array([2.00003526, 0.9998142 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=72, candidate_x=array([1.99989056, 1.00005438]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=0.35552372044377767, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([63, 66, 67, 68, 69, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0002804036995538801, relative_step_length=1.0000000000001987, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.0005608073991076487, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=101.65315386439156, linear_terms=array([4.03977606e-05, 5.28856412e-06]), square_terms=array([[8.02719343e-12, 1.05085843e-12], + [1.05085843e-12, 1.37570304e-13]]), scale=0.0005608073991076487, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=73, candidate_x=array([1.9993345 , 0.99998159]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.334113586767923, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 63, 66, 67, 68, 69, 70, 71, 72]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00028040369955382435, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 68, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=101.65311655752888, linear_terms=array([-1.71237318e-05, -2.37865075e-05]), square_terms=array([[1.44226858e-12, 2.00344952e-12], + [2.00344952e-12, 2.78298373e-12]]), scale=0.00028040369955382435, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=74, candidate_x=array([2.00005439, 1.00028195]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.5627670521816485, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68, 69, 70, 71, 72, 73]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=0.00014020184977691218, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([63, 67, 69, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=101.65313160034756, linear_terms=array([-5.77420288e-06, -6.18898976e-06]), square_terms=array([[1.63996024e-13, 1.75776594e-13], + [1.75776594e-13, 1.88403415e-13]]), scale=0.00014020184977691218, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=75, candidate_x=array([1.9999862, 1.0001569]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-0.41697664424331127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 69, 70, 71, 72, 73, 74]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75]), model=ScalarModel(intercept=101.65311148563875, linear_terms=array([5.50427867e-06, 9.82268200e-07]), square_terms=array([[1.49021920e-13, 2.65937649e-14], + [2.65937649e-14, 4.74580071e-15]]), scale=7.010092488845609e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=76, candidate_x=array([1.99982155, 1.00004207]), index=72, x=array([1.99989056, 1.00005438]), fval=101.65311305572654, rho=-1.0934080921860894, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([70, 72, 74, 75]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99989056, 1.00005438]), radius=3.5050462444228044e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76]), model=ScalarModel(intercept=101.65311305572646, linear_terms=array([-3.98365342e-06, 4.92341491e-06]), square_terms=array([[ 7.80571006e-14, -9.64711165e-14], + [-9.64711165e-14, 1.19229080e-13]]), scale=3.5050462444228044e-05, shift=array([1.99989056, 1.00005438])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([1.99991261, 1.00002713]), radius=7.010092488845609e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([70, 72, 74, 75, 76, 77]), model=ScalarModel(intercept=101.65311599269691, linear_terms=array([2.73513590e-06, 1.10171467e-06]), square_terms=array([[3.67965523e-14, 1.48216772e-14], + [1.48216772e-14, 5.97018206e-15]]), scale=7.010092488845609e-05, shift=array([1.99991261, 1.00002713])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), 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upper=array([ 20., 10000.]))), model_indices=array([72, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=101.65310851811418, linear_terms=array([-2.55877294e-06, 2.22965678e-06]), square_terms=array([[ 3.22042240e-14, -2.80620313e-14], + [-2.80620313e-14, 2.44526185e-14]]), scale=3.5050462444228044e-05, shift=array([1.99993704, 1.000002 ])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model_indices=array([70, 72, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=101.65311312451377, linear_terms=array([-7.55959616e-07, -6.02717625e-07]), square_terms=array([[2.81090723e-15, 2.24110295e-15], + [2.24110295e-15, 1.78680477e-15]]), scale=7.010092488845609e-05, shift=array([1.99996347, 0.99997898])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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79, 80, 81, 82]), model=ScalarModel(intercept=101.65310380914096, linear_terms=array([-1.03266467e-05, 8.97242802e-06]), square_terms=array([[ 5.24527182e-13, -4.55741590e-13], + [-4.55741590e-13, 3.95976422e-13]]), scale=0.00014020184977691218, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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model=ScalarModel(intercept=101.65311088628962, linear_terms=array([-3.79014379e-07, 1.04483358e-06]), square_terms=array([[ 7.06578965e-16, -1.94783489e-15], + [-1.94783489e-15, 5.36962031e-15]]), scale=7.010092488845609e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([1.46452562e-07, 8.40702212e-07]), square_terms=array([[1.05497769e-16, 6.05603664e-16], + [6.05603664e-16, 3.47643178e-15]]), scale=3.5050462444228044e-05, shift=array([2.00001828, 1.00002268])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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scale=1.7525231222114022e-05, shift=array([2.00001226, 0.99998815])), vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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vector_model=VectorModel(intercepts=array([2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912, 2.65613912, 2.65613912, 2.65613912, + 2.65613912, 2.65613912]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=18.376536853959433, shift=array([ 6.92609782, 183.76536854])), candidate_index=89, candidate_x=array([2.00000352, 0.99998757]), index=89, x=array([2.00000352, 0.99998757]), fval=101.65310212694172, rho=0.30214250212998456, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([82, 84, 85, 87, 88]), old_indices_discarded=array([], dtype=int32), step_length=8.76261561092711e-06, relative_step_length=0.9999999999851755, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 90 entries., 'history': {'params': [{'CRRA': 6.926097823918753, 'BeqFac': 183.76536853959433}, {'CRRA': 1.1, 'BeqFac': 168.99160456324418}, {'CRRA': 20.0, 'BeqFac': 181.63947671960705}, {'CRRA': 1.1, 'BeqFac': 196.1247826983854}, {'CRRA': 20.0, 'BeqFac': 198.68302674118215}, {'CRRA': 19.674724735180458, 'BeqFac': 167.47958678304056}, {'CRRA': 20.0, 'BeqFac': 167.52737770433455}, {'CRRA': 1.1, 'BeqFac': 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101.65311974675102, 101.65315157086414, 101.65311305572654, 101.65316741078925, 101.65312954989032, 101.6531165851576, 101.65311916923056, 101.65310998586972, 101.65311607703549, 101.65310713409755, 101.65311363548847, 101.65310505004693, 101.65310374790073, 101.65311504895925, 101.6531068745941, 101.6531025406168, 101.65310749657145, 101.65310371105045, 101.65310429279558, 101.65310212694172], 'runtime': [0.0, 0.5039537996053696, 0.6895224996842444, 0.8690349999815226, 1.0506610996089876, 1.2314923000521958, 1.4181299996562302, 1.6065336000174284, 1.7943613999523222, 1.9807438999414444, 2.1682199998758733, 2.3564733997918665, 2.536936099641025, 3.9799724998883903, 4.208065299782902, 4.435462199617177, 4.663008799776435, 4.890417299699038, 6.057654699776322, 6.294685699976981, 6.520818299613893, 6.750279899686575, 8.044913499616086, 8.273229599930346, 8.49854000005871, 8.736742899753153, 8.962798699736595, 9.189374499954283, 9.416601500008255, 9.64259839989245, 9.866047400049865, 10.08863649982959, 10.31147739989683, 10.533809999935329, 10.757371000014246, 10.981160499621183, 11.20396799966693, 11.427139400038868, 11.64967789966613, 11.872440000064671, 12.095371199771762, 12.320008999668062, 12.543173499871045, 12.766807099804282, 12.990096800029278, 13.213486399967223, 13.437024199869484, 13.660355299711227, 13.883567699696869, 14.107257999945432, 14.330329000018537, 14.552937400061637, 14.91931949974969, 15.145517699886113, 15.37292360002175, 15.599727399647236, 15.82650229986757, 16.05304649984464, 16.28176339995116, 16.5043953997083, 16.727334199938923, 16.950124999973923, 17.17311149975285, 17.396053900010884, 17.618892499711365, 17.841556400060654, 18.064446199685335, 18.28782989969477, 18.510234999936074, 18.732881399802864, 18.95575479976833, 19.178739700000733, 19.401750299613923, 19.624973099678755, 19.847891699988395, 20.07073999987915, 20.294072199612856, 20.516933199949563, 20.73973249970004, 20.962752199731767, 21.186091699637473, 21.40902019990608, 21.63227880001068, 21.85612909961492, 22.079848199617118, 22.438155899755657, 22.660994099918753, 22.887149499729276, 23.114062799606472, 23.340816299896687], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]}}], 'exploration_sample': array([[2.0000000e+00, 1.0000000e+00], + [1.8818750e+01, 6.2500000e+02], + [1.2912500e+01, 1.2500000e+03], + [7.0062500e+00, 1.8750000e+03], + [1.7046875e+01, 2.1875000e+03], + [1.5275000e+01, 2.5000000e+03], + [4.6437500e+00, 3.1250000e+03], + [8.1875000e+00, 3.7500000e+03], + [1.1731250e+01, 4.3750000e+03], + [2.8718750e+00, 4.6875000e+03], + [1.0550000e+01, 5.0000000e+03], + [9.3687500e+00, 5.6250000e+03], + [3.4625000e+00, 6.2500000e+03], + [1.6456250e+01, 6.8750000e+03], + [7.5968750e+00, 7.1875000e+03], + [5.8250000e+00, 7.5000000e+03], + [1.4093750e+01, 8.1250000e+03], + [1.7637500e+01, 8.7500000e+03], + [2.2812500e+00, 9.3750000e+03], + [1.2321875e+01, 9.6875000e+03]]), 'exploration_results': array([8.33326931e-01, 1.64075763e+02, 2.26557868e+02, 2.89053770e+02, + 3.20308278e+02, 3.51556627e+02, 4.14053213e+02, 4.76553611e+02, + 5.39054183e+02, 5.70303109e+02, 6.01553832e+02, 6.64053584e+02, + 7.26553118e+02, 7.89054621e+02, 8.20303319e+02, 8.51553198e+02, + 9.14054001e+02, 9.76554498e+02, 1.03905310e+03, 1.07030365e+03])}}" diff --git a/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv b/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv index 34bcfc8..3dfdd53 100644 --- a/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv +++ b/src/estimark/content/tables/min/WarmGlowPortfolio_estimate_results.csv @@ -1,6625 +1,6643 @@ -CRRA,4.972059127341761 -BeqFac,2235.66869440018 -time_to_estimate,120.60259437561035 -params,"{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018}" -criterion,0.15183333483370898 -start_criterion,0.14974471998399175 -start_params,"{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,2 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974}, {'CRRA': 1.1, 'BeqFac': 2038.2167591428197}, {'CRRA': 20.0, 'BeqFac': 2200.680349896351}, {'CRRA': 1.1, 'BeqFac': 2429.9412094587697}, {'CRRA': 19.832484492263543, 'BeqFac': 2433.7996818884876}, {'CRRA': 19.93169977776446, 'BeqFac': 2037.53772188746}, {'CRRA': 20.0, 'BeqFac': 2064.4481322185475}, {'CRRA': 1.1, 'BeqFac': 2332.634198828065}, {'CRRA': 20.0, 'BeqFac': 2322.498125139562}, {'CRRA': 18.700329294200046, 'BeqFac': 2433.7996818884876}, {'CRRA': 1.1, 'BeqFac': 2430.302509845014}, {'CRRA': 7.813833384306793, 'BeqFac': 2037.53772188746}, {'CRRA': 8.624550632134087, 'BeqFac': 2433.7996818884876}, {'CRRA': 17.981702297455875, 'BeqFac': 2037.53772188746}, {'CRRA': 18.109298496140873, 'BeqFac': 2136.603211887717}, {'CRRA': 19.930536834777662, 'BeqFac': 2186.135956887845}, {'CRRA': 20.0, 'BeqFac': 2210.9023293879095}, {'CRRA': 4.079012994666991, 'BeqFac': 2248.051888138006}, {'CRRA': 3.121220401269943, 'BeqFac': 2241.86029501299}, {'CRRA': 4.8421373902763785, 'BeqFac': 2239.1595207269393}, {'CRRA': 4.935891618489661, 'BeqFac': 2237.414959439111}, {'CRRA': 4.960910699246413, 'BeqFac': 2234.79536779911}, {'CRRA': 4.975594073396457, 'BeqFac': 2236.1053544720567}, {'CRRA': 5.031762747602235, 'BeqFac': 2235.4503996289804}, {'CRRA': 4.95481938313597, 'BeqFac': 2235.5595356176655}, {'CRRA': 4.962658187223382, 'BeqFac': 2235.723260832982}, {'CRRA': 4.976595860461234, 'BeqFac': 2235.641409064533}, {'CRRA': 4.985643978352027, 'BeqFac': 2235.670829747999}, {'CRRA': 4.965480731774778, 'BeqFac': 2235.6673366817404}, {'CRRA': 4.9715471211216276, 'BeqFac': 2235.672056738335}, {'CRRA': 4.973852096731626, 'BeqFac': 2235.6684476620744}, {'CRRA': 4.9713764830158755, 'BeqFac': 2235.6683781025586}, {'CRRA': 4.972141953092093, 'BeqFac': 2235.669128923647}, {'CRRA': 4.972378447740231, 'BeqFac': 2235.6687118162295}, {'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018}], 'criterion': [0.15183336484019694, 1599.2256506697936, 8.33792735607635, 1628.996184127626, 7.942067008283367, 8.173798608673742, 8.33777080025052, 1622.3971740217662, 8.338047223703619, 5.70897681494281, 1629.0195228262494, 0.4459772753262474, 0.5044697780491717, 4.621902068591304, 4.800133525579624, 8.171500062212324, 8.337937651588279, 0.5669929910375328, 6.5167800463917995, 0.15533004766705152, 0.1521320528374574, 0.15187972532160543, 0.15186895370600006, 0.15287286421734658, 0.1519085924789972, 0.1518749282368537, 0.15186803665511617, 0.15194576604337082, 0.15186614554536748, 0.15183882851267255, 0.1518437390370875, 0.1518413460232434, 0.15183338142448646, 0.15183419903764392, 0.15183333483370898], 'runtime': [0.0, 3.234110999852419, 3.3567784996703267, 3.607193300034851, 3.782226899638772, 3.974268099758774, 4.16790989972651, 4.338716899976134, 4.589133599773049, 4.924178099725395, 5.076727400068194, 5.272234600037336, 5.437637999653816, 6.631617899984121, 7.839812099933624, 9.029853600077331, 10.224262000061572, 11.426363199949265, 12.594932300038636, 13.766675599850714, 14.943977899849415, 16.25537579972297, 17.437238699756563, 18.621162499766797, 19.853813599795103, 21.03928539995104, 22.241678999736905, 23.42911219969392, 24.757420799694955, 25.978194700088352, 27.192072699777782, 28.37384819984436, 29.543620300013572, 30.743656399659812, 31.96393799968064], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}" -convergence_report, -multistart_info,"{'start_parameters': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974}, {'CRRA': 5.221879724992187, 'BeqFac': 3777.5556353977354}], 'local_optima': [Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.976e-07* 1.976e-07* -relative_params_change 2.139e-05 2.139e-05 -absolute_criterion_change 3.001e-08* 3.001e-08* -absolute_params_change 0.0001066 0.0001066 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 2.639e-07* 0.0006443 -relative_params_change 2.12e-06* 0.002311 -absolute_criterion_change 4.695e-08* 0.0001146 -absolute_params_change 1.126e-05 6.64 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 2.28125, 'BeqFac': 9375.0}], 'exploration_results': array([1.51833365e-01, 2.18154716e-01, 2.63875842e-01, 3.77381203e-01, - 3.83108040e-01, 4.55042643e-01, 5.57986156e-01, 6.96479632e-01, - 8.83084679e-01, 1.01143958e+00, 1.14406241e+00, 1.54612379e+00, - 2.11582828e+00, 2.96340381e+00, 3.51474071e+00, 4.18178301e+00, - 5.91221015e+00, 7.72175220e+00, 2.55327213e+01, 2.20727523e+02])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=0, candidate_x=array([ 4.97216547, 2235.66870189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=196.12406005340955, linear_terms=array([-428.74914009, 49.95239728]), square_terms=array([[475.26532472, -54.98930881], - [-54.98930881, 6.38449896]]), scale=array([ 9.45 , 198.13098]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=13, candidate_x=array([ 17.9817023 , 2037.53772189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.008440716478759814, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=111.7834350943987, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), model=ScalarModel(intercept=242.07061318310053, linear_terms=array([-471.89691667, 101.1889206 ]), square_terms=array([[465.88374262, -99.22450662], - [-99.22450662, 21.20996444]]), scale=array([ 9.45 , 99.06549]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=14, candidate_x=array([ 18.1092985 , 2136.60321189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007755830036420791, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), old_indices_discarded=array([ 4, 5, 9, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), model=ScalarModel(intercept=298.87379830095927, linear_terms=array([-576.79607872, 19.05034093]), square_terms=array([[562.44777715, -18.48262992], - [-18.48262992, 0.60886349]]), scale=array([ 9.45 , 49.532745]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=15, candidate_x=array([ 19.93053683, 2186.13595689]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.010921826812679862, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), old_indices_discarded=array([ 4, 5, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=131.07369976203483, linear_terms=array([-204.19706036, 40.77897837]), square_terms=array([[163.90069793, -32.18422025], - [-32.18422025, 6.38059561]]), scale=array([ 9.45 , 24.7663725]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=16, candidate_x=array([ 20. , 2210.90232939]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.029581696796089584, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=13.972929386799837, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=0.9873239325348305, linear_terms=array([2.73907339, 0.01391224]), square_terms=array([[4.36743207e+00, 2.75767161e-02], - [2.75767161e-02, 2.41056737e-04]]), scale=array([ 8.12767586, 12.38318625]), shift=array([ 9.22767586, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=17, candidate_x=array([ 4.07901299, 2248.05188814]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-15.504804046764184, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.3431675848933713, linear_terms=array([1.33281827, 0.32943084]), square_terms=array([[4.36179571, 1.27691822], - [1.27691822, 0.39380953]]), scale=array([5.0318793 , 6.19159313]), shift=array([ 6.1318793 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=18, candidate_x=array([ 3.1212204 , 2241.86029501]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-100.94273410330236, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=3.4932323466999593, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.12431539, -0.0055439 ]), square_terms=array([[59.59210104, 2.34414471], - [ 2.34414471, 0.09228281]]), scale=3.4932323466999593, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=19, candidate_x=array([ 4.84213739, 2239.15952073]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.681921220989647, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.06103675, -0.00243685]), square_terms=array([[1.34115073e+01, 3.39655978e-01], - [3.39655978e-01, 8.60970305e-03]]), scale=1.7466161733499797, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=20, candidate_x=array([ 4.93589162, 2237.41495944]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.29114147897803394, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([0.00582774, 0.00013542]), square_terms=array([[3.87857288e-01, 8.27620950e-04], - [8.27620950e-04, 2.06651665e-06]]), scale=0.8733080866749898, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=21, candidate_x=array([ 4.9609107, 2234.7953678]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.27824291525666955, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.4366540433374949, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([-5.56309005e-04, -4.37454587e-06]), square_terms=array([[ 7.50279995e-02, -3.28466129e-05], - [-3.28466129e-05, 2.15202300e-08]]), scale=0.4366540433374949, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=22, candidate_x=array([ 4.97559407, 2236.10535447]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-5.330119577173148, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.15183336484019683, linear_terms=array([-4.45403999e-03, 5.16279055e-05]), square_terms=array([[1.62881565e-02, 6.77124384e-06], - [6.77124384e-06, 2.92278248e-08]]), scale=0.21832702166874746, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=23, candidate_x=array([ 5.03176275, 2235.45039963]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.5692018274741517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.1518333648401971, linear_terms=array([7.05546057e-04, 2.78738861e-06]), square_terms=array([[4.43307710e-03, 7.04906076e-07], - [7.04906076e-07, 5.61544409e-10]]), scale=0.10916351083437373, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=24, candidate_x=array([ 4.95481938, 2235.55953562]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.2789338326888193, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 2.00591466e-04, -4.01524035e-05]), square_terms=array([[1.12660455e-03, 2.72179550e-06], - [2.72179550e-06, 2.34943617e-08]]), scale=0.054581755417186864, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=25, candidate_x=array([ 4.96265819, 2235.72326083]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.7111299458870474, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-4.80890903e-05, 3.50155615e-06]), square_terms=array([[ 2.91958192e-04, -1.28086238e-07], - [-1.28086238e-07, 2.58764850e-09]]), scale=0.027290877708593432, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=26, candidate_x=array([ 4.97659586, 2235.64140906]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.660612278998031, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.013645438854296716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([-0.0017766 , -0.00030364]), square_terms=array([[4.26905240e-05, 1.06728380e-06], - [1.06728380e-06, 1.30381617e-06]]), scale=0.013645438854296716, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=27, candidate_x=array([ 4.98564398, 2235.67082975]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.06310400218678887, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.006822719427148358, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.1518333648401969, linear_terms=array([ 3.88679056e-05, -1.31679513e-06]), square_terms=array([[ 1.84940973e-05, -1.00210163e-07], - [-1.00210163e-07, 6.96346430e-10]]), scale=0.006822719427148358, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=28, candidate_x=array([ 4.96548073, 2235.66733668]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.131884231761533, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.003411359713574179, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 0.00012663, -0.00067966]), square_terms=array([[ 4.68892805e-06, -6.61282339e-07], - [-6.61282339e-07, 5.43609914e-06]]), scale=0.003411359713574179, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=29, candidate_x=array([ 4.97154712, 2235.67205674]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007935207946049206, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.0017056798567870895, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 28, 29]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-6.40650622e-06, 1.55695709e-06]), square_terms=array([[ 1.1627052e-06, -3.2284109e-09], - [-3.2284109e-09, 6.7517919e-11]]), scale=0.0017056798567870895, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], 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model=ScalarModel(intercept=0.151833364840197, linear_terms=array([5.29250449e-06, 2.34437075e-06]), square_terms=array([[3.10374558e-07, 1.18891884e-09], - [1.18891884e-09, 8.34845812e-11]]), scale=0.0008528399283935448, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - 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[1.44466078e-11, 7.36990793e-13]]), scale=0.0002132099820983862, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=34, candidate_x=array([ 4.97205913, 2235.6686944 ]), index=34, x=array([ 4.97205913, 2235.6686944 ]), fval=0.15183333483370898, rho=0.07308056702929565, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.00010660499104711433, relative_step_length=0.9999999999805003, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 35 entries., 'multistart_info': {'start_parameters': [array([ 4.97216547, 2235.66870189]), array([ 5.22187972, 3777.5556354 ])], 'local_optima': [{'solution_x': array([ 4.97205913, 2235.6686944 ]), 'solution_criterion': 0.15183333483370898, 'states': [State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - 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scale=array([ 9.45 , 198.13098]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - 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2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=14, candidate_x=array([ 18.1092985 , 2136.60321189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007755830036420791, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), old_indices_discarded=array([ 4, 5, 9, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), model=ScalarModel(intercept=298.87379830095927, linear_terms=array([-576.79607872, 19.05034093]), square_terms=array([[562.44777715, -18.48262992], - [-18.48262992, 0.60886349]]), scale=array([ 9.45 , 49.532745]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=15, candidate_x=array([ 19.93053683, 2186.13595689]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.010921826812679862, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), old_indices_discarded=array([ 4, 5, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=131.07369976203483, linear_terms=array([-204.19706036, 40.77897837]), square_terms=array([[163.90069793, -32.18422025], - [-32.18422025, 6.38059561]]), scale=array([ 9.45 , 24.7663725]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=16, candidate_x=array([ 20. , 2210.90232939]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.029581696796089584, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=13.972929386799837, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=0.9873239325348305, linear_terms=array([2.73907339, 0.01391224]), square_terms=array([[4.36743207e+00, 2.75767161e-02], - [2.75767161e-02, 2.41056737e-04]]), scale=array([ 8.12767586, 12.38318625]), shift=array([ 9.22767586, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=17, candidate_x=array([ 4.07901299, 2248.05188814]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-15.504804046764184, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.3431675848933713, linear_terms=array([1.33281827, 0.32943084]), square_terms=array([[4.36179571, 1.27691822], - [1.27691822, 0.39380953]]), scale=array([5.0318793 , 6.19159313]), shift=array([ 6.1318793 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=18, candidate_x=array([ 3.1212204 , 2241.86029501]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-100.94273410330236, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=3.4932323466999593, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.12431539, -0.0055439 ]), square_terms=array([[59.59210104, 2.34414471], - [ 2.34414471, 0.09228281]]), scale=3.4932323466999593, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=19, candidate_x=array([ 4.84213739, 2239.15952073]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.681921220989647, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.06103675, -0.00243685]), square_terms=array([[1.34115073e+01, 3.39655978e-01], - [3.39655978e-01, 8.60970305e-03]]), scale=1.7466161733499797, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=20, candidate_x=array([ 4.93589162, 2237.41495944]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.29114147897803394, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([0.00582774, 0.00013542]), square_terms=array([[3.87857288e-01, 8.27620950e-04], - [8.27620950e-04, 2.06651665e-06]]), scale=0.8733080866749898, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=21, candidate_x=array([ 4.9609107, 2234.7953678]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.27824291525666955, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.4366540433374949, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([-5.56309005e-04, -4.37454587e-06]), square_terms=array([[ 7.50279995e-02, -3.28466129e-05], - [-3.28466129e-05, 2.15202300e-08]]), scale=0.4366540433374949, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=22, candidate_x=array([ 4.97559407, 2236.10535447]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-5.330119577173148, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.15183336484019683, linear_terms=array([-4.45403999e-03, 5.16279055e-05]), square_terms=array([[1.62881565e-02, 6.77124384e-06], - [6.77124384e-06, 2.92278248e-08]]), scale=0.21832702166874746, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=23, candidate_x=array([ 5.03176275, 2235.45039963]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.5692018274741517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.1518333648401971, linear_terms=array([7.05546057e-04, 2.78738861e-06]), square_terms=array([[4.43307710e-03, 7.04906076e-07], - [7.04906076e-07, 5.61544409e-10]]), scale=0.10916351083437373, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=24, candidate_x=array([ 4.95481938, 2235.55953562]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.2789338326888193, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 2.00591466e-04, -4.01524035e-05]), square_terms=array([[1.12660455e-03, 2.72179550e-06], - [2.72179550e-06, 2.34943617e-08]]), scale=0.054581755417186864, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=25, candidate_x=array([ 4.96265819, 2235.72326083]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.7111299458870474, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-4.80890903e-05, 3.50155615e-06]), square_terms=array([[ 2.91958192e-04, -1.28086238e-07], - [-1.28086238e-07, 2.58764850e-09]]), scale=0.027290877708593432, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=26, candidate_x=array([ 4.97659586, 2235.64140906]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.660612278998031, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.013645438854296716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([-0.0017766 , -0.00030364]), square_terms=array([[4.26905240e-05, 1.06728380e-06], - [1.06728380e-06, 1.30381617e-06]]), scale=0.013645438854296716, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=27, candidate_x=array([ 4.98564398, 2235.67082975]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.06310400218678887, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.006822719427148358, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.1518333648401969, linear_terms=array([ 3.88679056e-05, -1.31679513e-06]), square_terms=array([[ 1.84940973e-05, -1.00210163e-07], - [-1.00210163e-07, 6.96346430e-10]]), scale=0.006822719427148358, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=28, candidate_x=array([ 4.96548073, 2235.66733668]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.131884231761533, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 27]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.003411359713574179, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 0.00012663, -0.00067966]), square_terms=array([[ 4.68892805e-06, -6.61282339e-07], - [-6.61282339e-07, 5.43609914e-06]]), scale=0.003411359713574179, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, - 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, - -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, - -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=29, candidate_x=array([ 4.97154712, 2235.67205674]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, 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candidate_x=array([ 18.85993617, 3442.77848375]), index=0, x=array([ 5.22187972, 3777.5556354 ]), fval=0.18001363343527702, rho=-0.012941179456654626, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=188.87778176988678, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 5, 7, 8, 9, 12]), model=ScalarModel(intercept=239.29760384959667, linear_terms=array([-485.98520657, -50.56057094]), square_terms=array([[499.68004973, 51.63444647], - [ 51.63444647, 5.35573357]]), scale=array([ 9.45 , 167.38857582]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=14, candidate_x=array([ 18.76448582, 3944.94421122]), index=0, x=array([ 5.22187972, 3777.5556354 ]), fval=0.18001363343527702, rho=-0.009561092191515052, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 5, 7, 8, 9, 12]), old_indices_discarded=array([ 4, 6, 10, 11, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=94.43889088494339, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 5, 7, 8, 9, 14]), model=ScalarModel(intercept=298.4276026191575, linear_terms=array([-576.46526059, 6.81414363]), square_terms=array([[ 5.62685577e+02, -6.61818552e+00], - [-6.61818552e+00, 7.80289630e-02]]), scale=array([ 9.45 , 83.69428791]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, 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fval=0.18001363343527702, rho=-0.011495911236178994, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 5, 7, 8, 9, 14]), old_indices_discarded=array([ 4, 6, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=47.219445442471695, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=125.45685337509687, linear_terms=array([-251.44927845, 30.0900477 ]), square_terms=array([[258.14181154, -30.52536684], - [-30.52536684, 3.63112862]]), scale=array([ 9.45 , 41.84714396]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=16, candidate_x=array([ 18.63753511, 3735.70849144]), index=0, x=array([ 5.22187972, 3777.5556354 ]), fval=0.18001363343527702, rho=-0.017757065060414894, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=23.609722721235848, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=1.0792632152960797, linear_terms=array([ 2.4820159 , -0.22529719]), square_terms=array([[ 3.14659679, -0.3739566 ], - [-0.3739566 , 0.08599544]]), scale=array([ 9.45 , 20.92357198]), shift=array([ 10.55 , 3777.5556354])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=25.62010487127418, linear_terms=array([117.78318548, 58.17307866]), square_terms=array([[272.64693372, 135.19717176], - [135.19717176, 67.23744252]]), scale=array([ 7.29183286, 10.46178599]), shift=array([ 8.39183286, 3777.5556354 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=8.66476096620099, linear_terms=array([145.36114645, -0.79163954]), square_terms=array([[ 1.24486907e+03, -6.74089224e+00], - [-6.74089224e+00, 3.66585907e-02]]), scale=array([4.67638636, 5.23089299]), shift=array([ 5.77638636, 3777.5556354 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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State(trustregion=Region(center=array([ 5.2556554 , 3782.78652839]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=0.4447192260530918, linear_terms=array([3.28591072, 0.7575156 ]), square_terms=array([[238.5254968 , 72.82153083], - [ 72.82153083, 22.31236539]]), scale=array([ 7.30872069, 10.46178599]), shift=array([ 8.40872069, 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - 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radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=2.0924308511078555, linear_terms=array([64.90826608, -1.00041399]), square_terms=array([[ 1.10459211e+03, -1.69795324e+01], - [-1.69795324e+01, 2.61125536e-01]]), scale=array([4.6932742 , 5.23089299]), shift=array([ 5.7932742 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19, 20, 21]), model=ScalarModel(intercept=0.60991129558898, linear_terms=array([-18.6614341 , 0.97529584]), square_terms=array([[405.20779142, -21.11615795], - [-21.11615795, 1.10050394]]), scale=2.951215340154481, shift=array([ 5.2556554 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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model=ScalarModel(intercept=0.41578081033668957, linear_terms=array([-7.17900081, 0.24007703]), square_terms=array([[108.78543013, -3.63193691], - [ -3.63193691, 0.12126679]]), scale=1.4756076700772405, shift=array([ 5.2556554 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], 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linear_terms=array([-5.28954140e-03, 1.23810918e-05]), square_terms=array([[ 1.91842376e-01, -2.99622408e-05], - [-2.99622408e-05, 9.50955359e-09]]), scale=0.7378038350386202, shift=array([ 5.30368819, 3781.308501 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], 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square_terms=array([[ 1.18917531e+02, -1.26160659e+00], - [-1.26160659e+00, 1.33856432e-02]]), scale=1.4756076700772405, shift=array([ 5.32391471, 3780.570694 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], 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[2.97857263e-07, 1.58135601e-09]]), scale=0.7378038350386202, shift=array([ 5.32391471, 3780.570694 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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scale=1.4756076700772405, shift=array([ 5.32396542, 3779.83289016])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=27, candidate_x=array([ 5.32393177, 3778.35728249]), index=27, x=array([ 5.32393177, 3778.35728249]), fval=0.17818383067605373, rho=1.4687207826788418, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=1.475607671493286, relative_step_length=1.0000000009596355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32393177, 3778.35728249]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.23681559100061073, linear_terms=array([ 7.52400864, -0.03697853]), square_terms=array([[ 4.81922118e+02, -2.37216170e+00], - [-2.37216170e+00, 1.16774340e-02]]), scale=2.951215340154481, shift=array([ 5.32393177, 3778.35728249])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=28, candidate_x=array([ 5.26333048, 3775.40633 ]), index=27, x=array([ 5.32393177, 3778.35728249]), fval=0.17818383067605373, rho=-0.008422541073735482, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32393177, 3778.35728249]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.17816465874227283, linear_terms=array([1.88966570e-04, 2.76343183e-05]), square_terms=array([[ 7.56470495e-01, -1.03969061e-04], - [-1.03969061e-04, 1.75995384e-08]]), scale=1.4756076700772405, shift=array([ 5.32393177, 3778.35728249])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=29, candidate_x=array([ 5.32336038, 3776.88167488]), index=29, x=array([ 5.32336038, 3776.88167488]), fval=0.17816777800820685, rho=0.5798905880546184, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=1.4756077161109993, relative_step_length=1.0000000311964756, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32336038, 3776.88167488]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.17814522233392394, linear_terms=array([1.70335715e-04, 5.34309034e-05]), square_terms=array([[ 3.02597147e+00, -4.39921704e-04], - [-4.39921704e-04, 7.75356986e-08]]), scale=2.951215340154481, shift=array([ 5.32336038, 3776.88167488])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=30, candidate_x=array([ 5.32276521, 3773.9304596 ]), index=30, x=array([ 5.32276521, 3773.9304596 ]), fval=0.1781338283759018, rho=0.6351225603827869, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([18, 19, 21]), step_length=2.9512153448295146, relative_step_length=1.0000000015841046, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32276521, 3773.9304596 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.22227385671774208, linear_terms=array([ 8.27516735e-01, -9.46109851e-05]), square_terms=array([[ 7.75302897e+00, -1.67049083e-03], - [-1.67049083e-03, 3.94188026e-07]]), scale=array([4.7268291 , 5.23089299]), shift=array([ 5.8268291, 3773.9304596])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=31, candidate_x=array([ 5.32129426, 3768.6995666 ]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=0.7154013322715937, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([17, 18, 19, 20, 21, 23]), step_length=5.230893201328401, relative_step_length=0.8862269604925865, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.5208780387285807, linear_terms=array([1.12839055, 0.01109011]), square_terms=array([[1.51827200e+00, 2.11161638e-02], - [2.11161638e-02, 4.80103617e-04]]), scale=array([ 7.34154012, 10.46178599]), shift=array([ 8.44154012, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=33, candidate_x=array([2.88314895e+00, 3.77916135e+03]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-248.64296126941807, accepted=False, new_indices=array([32]), old_indices_used=array([ 0, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([16, 17, 18, 19, 20, 21, 22, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 25, 27, 28, 29, 30, 31, 33]), model=ScalarModel(intercept=0.9998902148657269, linear_terms=array([15.98904319, 0.08221525]), square_terms=array([[1.55477573e+02, 8.04599038e-01], - [8.04599038e-01, 4.16601478e-03]]), scale=array([4.72609362, 5.23089299]), shift=array([ 5.82609362, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=34, candidate_x=array([ 5.31561276, 3773.9304596 ]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-0.031700914237791734, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 25, 27, 28, 29, 30, 31, 33]), old_indices_discarded=array([17, 18, 19, 20, 21, 23, 24, 26, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 27, 28, 29, 30, 31, 33, 34]), model=ScalarModel(intercept=0.17875368787838072, linear_terms=array([-0.35344824, -0.00160514]), square_terms=array([[6.02790036e+01, 2.32207919e-01], - [2.32207919e-01, 8.94985816e-04]]), scale=2.951215340154481, shift=array([ 5.32129426, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=35, candidate_x=array([ 5.3272299 , 3771.65082671]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-0.056632647014796866, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 27, 28, 29, 30, 31, 33, 34]), old_indices_discarded=array([17, 19, 22, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([28, 30, 31, 34, 35]), model=ScalarModel(intercept=0.1780820131294612, linear_terms=array([1.86739648e-03, 1.59075457e-05]), square_terms=array([[ 7.41957927e-01, -1.22029600e-04], - [-1.22029600e-04, 2.23518326e-08]]), scale=1.4756076700772405, shift=array([ 5.32129426, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=36, candidate_x=array([ 5.31733777, 3767.22395957]), index=36, x=array([ 5.31733777, 3767.22395957]), fval=0.17803012332934734, rho=2.3606102276820975, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([28, 30, 31, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=1.4756123434937174, relative_step_length=1.0000031671131642, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.31733777, 3767.22395957]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 28, 29, 30, 31, 34, 35, 36]), model=ScalarModel(intercept=0.18731061364611515, linear_terms=array([-2.78859371, -0.01533278]), square_terms=array([[4.21276635e+02, 2.31394055e+00], - [2.31394055e+00, 1.27109474e-02]]), scale=2.951215340154481, shift=array([ 5.31733777, 3767.22395957])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=37, candidate_x=array([ 5.32066257, 3770.17523664]), index=36, x=array([ 5.31733777, 3767.22395957]), fval=0.17803012332934734, rho=-0.007038929660613454, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 28, 29, 30, 31, 34, 35, 36]), old_indices_discarded=array([22, 23, 24, 25, 26, 27, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.31733777, 3767.22395957]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([30, 31, 34, 35, 36, 37]), model=ScalarModel(intercept=0.17803733457425416, linear_terms=array([4.67596486e-03, 1.97718973e-05]), square_terms=array([[ 6.86260197e-01, -1.28093076e-04], - [-1.28093076e-04, 2.65774935e-08]]), scale=1.4756076700772405, shift=array([ 5.31733777, 3767.22395957])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=38, candidate_x=array([ 5.30700832, 3765.7483538 ]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=1.3244637262751773, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=1.4756419211239096, relative_step_length=1.000023211485928, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 28, 30, 31, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.1853840653395654, linear_terms=array([-2.53255399, -0.00530967]), square_terms=array([[4.34594855e+02, 9.14605265e-01], - [9.14605265e-01, 1.92494707e-03]]), scale=2.951215340154481, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=39, candidate_x=array([ 5.33041694, 3762.7971812 ]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=-0.013094695227833497, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 28, 30, 31, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 0, 22, 25, 26, 27, 29, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.17798153266437608, linear_terms=array([2.67703532e-03, 1.84532996e-05]), square_terms=array([[ 6.87387208e-01, -1.22416627e-04], - [-1.22416627e-04, 2.49928337e-08]]), scale=1.4756076700772405, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=40, candidate_x=array([ 5.30099892, 3764.27274717]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=-1.7785842210147218, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 36, 38, 39, 40]), model=ScalarModel(intercept=0.17800937260106672, linear_terms=array([5.95288503e-06, 5.30714257e-06]), square_terms=array([[ 1.72058207e-01, -2.92041969e-05], - [-2.92041969e-05, 7.60304447e-09]]), scale=0.7378038350386202, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=41, candidate_x=array([ 5.30685756, 3765.01054998]), index=41, x=array([ 5.30685756, 3765.01054998]), fval=0.17796989499069643, rho=2.2212294285075673, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 36, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.7378038354799815, relative_step_length=1.0000000005982095, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30685756, 3765.01054998]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17799379519456676, linear_terms=array([9.04133766e-04, 1.68120998e-05]), square_terms=array([[ 6.87741442e-01, -1.22064373e-04], - [-1.22064373e-04, 2.53913004e-08]]), scale=1.4756076700772405, shift=array([ 5.30685756, 3765.01054998])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=42, candidate_x=array([ 5.30465582, 3763.53494268]), index=42, x=array([ 5.30465582, 3763.53494268]), fval=0.1779588833874623, rho=0.6269056309320292, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=1.4756089451393082, relative_step_length=1.0000008640928708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30465582, 3763.53494268]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 31, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.17947899803072848, linear_terms=array([-1.1742621 , 0.00126434]), square_terms=array([[ 4.44709273e+02, -4.57981892e-01], - [-4.57981892e-01, 4.71712559e-04]]), scale=2.951215340154481, shift=array([ 5.30465582, 3763.53494268])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=43, candidate_x=array([ 5.30940925, 3760.58372087]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=0.027004451558225083, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 31, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 28, 29, 30, 34, 35]), step_length=2.9512256285033613, relative_step_length=1.000003486139673, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.1783256875306959, linear_terms=array([-3.07287421e-01, -8.21318424e-05]), square_terms=array([[1.10555074e+02, 3.40646813e-02], - [3.40646813e-02, 1.05016887e-05]]), scale=1.4756076700772405, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=44, candidate_x=array([ 5.31396537, 3759.10811454]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.006804942702733265, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 36, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([39, 42, 43, 44]), model=ScalarModel(intercept=0.17792080695215554, linear_terms=array([2.06155976e-03, 1.11830329e-05]), square_terms=array([[ 1.70363006e-01, -3.82501682e-05], - [-3.82501682e-05, 1.24667902e-08]]), scale=0.7378038350386202, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=45, candidate_x=array([ 5.30031607, 3759.84591906]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-2.0310571357145926, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([39, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.3689019175193101, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 44, 45]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-1.17918083e-03, -3.42897503e-06]), square_terms=array([[ 4.43546197e-02, -8.57949761e-06], - [-8.57949761e-06, 4.04624924e-09]]), scale=0.3689019175193101, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=46, candidate_x=array([ 5.31928716, 3760.95262089]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-2.6149368631946275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.18445095875965506, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 45, 46]), model=ScalarModel(intercept=0.17791553246894204, linear_terms=array([ 1.84501111e-03, -3.17202089e-05]), square_terms=array([[ 1.06687234e-02, -2.43582255e-06], - [-2.43582255e-06, 7.19700602e-08]]), scale=0.18445095875965506, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.09222547937982753, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 46, 47]), model=ScalarModel(intercept=0.17791553246894176, linear_terms=array([-2.17795159e-04, 1.46733396e-05]), square_terms=array([[ 2.85029042e-03, -2.85696802e-06], - [-2.85696802e-06, 7.09340428e-09]]), scale=0.09222547937982753, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.046112739689913765, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 47, 48]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-3.39208594e-04, -3.23222752e-05]), square_terms=array([[7.54306993e-04, 2.98728975e-06], - [2.98728975e-06, 3.90562555e-08]]), scale=0.046112739689913765, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.023056369844956882, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 48, 49]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([6.09438346e-05, 1.13529285e-06]), square_terms=array([[ 1.66872702e-04, -1.24585747e-07], - [-1.24585747e-07, 1.17301795e-09]]), scale=0.023056369844956882, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 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radius=0.011528184922478441, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 49, 50]), model=ScalarModel(intercept=0.17791553246894215, linear_terms=array([ 0.00048585, -0.00019408]), square_terms=array([[ 5.31813583e-05, -5.75413054e-06], - [-5.75413054e-06, 2.79836435e-06]]), scale=0.011528184922478441, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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10000.]))), model_indices=array([43, 50, 51]), model=ScalarModel(intercept=0.17791553246894196, linear_terms=array([-4.88151251e-05, 9.00611126e-06]), square_terms=array([[ 1.09589251e-05, -5.41537073e-08], - [-5.41537073e-08, 1.87668792e-09]]), scale=0.005764092461239221, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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model=ScalarModel(intercept=0.17791553246894182, linear_terms=array([2.12658543e-05, 1.52919564e-04]), square_terms=array([[ 2.67941071e-06, -5.49830816e-08], - [-5.49830816e-08, 3.68065340e-07]]), scale=0.0028820462306196103, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=53, candidate_x=array([ 5.30901423, 3760.58084481]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.026380369819135392, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.0014410231153098052, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 52, 53]), model=ScalarModel(intercept=0.1779155324689421, linear_terms=array([ 5.01858339e-06, -2.73111673e-06]), square_terms=array([[ 6.71301844e-07, -6.77682479e-09], - [-6.77682479e-09, 2.94207799e-10]]), scale=0.0014410231153098052, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=54, candidate_x=array([ 5.30810199, 3760.58432719]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-1.11322964518744, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.0007205115576549026, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 53, 54]), model=ScalarModel(intercept=0.17791553246894193, linear_terms=array([-3.42548630e-06, -5.50440244e-07]), square_terms=array([[ 1.71589664e-07, -1.81884533e-09], - [-1.81884533e-09, 3.67816759e-11]]), scale=0.0007205115576549026, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=55, candidate_x=array([ 5.31011752, 3760.58385312]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-3.027754219035174, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.0003602557788274513, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 54, 55]), model=ScalarModel(intercept=0.17791553246894207, linear_terms=array([3.23278987e-06, 1.03878094e-05]), square_terms=array([[ 4.26477773e-08, -3.95820208e-10], - [-3.95820208e-10, 1.42364938e-09]]), scale=0.0003602557788274513, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=56, candidate_x=array([ 5.30930239, 3760.58337624]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.013594556601906208, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 54, 55]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.00018012788941372564, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 55, 56]), model=ScalarModel(intercept=0.17791553246894198, linear_terms=array([ 2.76028821e-06, -9.32486831e-07]), square_terms=array([[ 1.07776183e-08, -3.77632159e-10], - [-3.77632159e-10, 2.29706603e-11]]), scale=0.00018012788941372564, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=57, candidate_x=array([ 5.30923847, 3760.58377814]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.058405552122264226, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 55, 56]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=9.006394470686282e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 56, 57]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-9.09462086e-08, -1.01086542e-08]), square_terms=array([[2.14199013e-09, 4.89545745e-13], - [4.89545745e-13, 3.23794403e-15]]), scale=9.006394470686282e-05, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), 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0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=59, candidate_x=array([ 5.3093969 , 3760.58367756]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.05588336662878231, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 57, 58]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=2.2515986176715705e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 58, 59]), model=ScalarModel(intercept=0.17791553246894198, linear_terms=array([ 1.75952660e-07, -7.65912652e-08]), square_terms=array([[ 1.54804745e-10, -2.72364838e-12], - [-2.72364838e-12, 1.48003207e-13]]), scale=2.2515986176715705e-05, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, 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rho=-0.4501440029410156, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 58, 59]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=1.1257993088357853e-05, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 59, 60]), model=ScalarModel(intercept=0.17791553246894212, linear_terms=array([-4.69738998e-08, 1.72050290e-10]), square_terms=array([[ 3.44298654e-11, -7.24102661e-15], - [-7.24102661e-15, 1.66529426e-18]]), scale=1.1257993088357853e-05, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, - 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, - -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, - -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=61, candidate_x=array([ 5.30942051, 3760.58372084]), index=61, x=array([ 5.30942051, 3760.58372084]), fval=0.17791548552127476, rho=0.9998013126948817, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([43, 59, 60]), old_indices_discarded=array([], dtype=int32), step_length=1.1257993087530534e-05, relative_step_length=0.9999999999265128, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 62 entries., 'history': {'params': [{'CRRA': 5.221879724992187, 'BeqFac': 3777.5556353977354}, {'CRRA': 1.1, 'BeqFac': 3476.9035708167335}, {'CRRA': 20.0, 'BeqFac': 3688.8944111418195}, {'CRRA': 1.1, 'BeqFac': 4084.9216049500956}, {'CRRA': 19.360304400268685, 'BeqFac': 4112.332787046263}, {'CRRA': 20.0, 'BeqFac': 3463.4160733070307}, {'CRRA': 19.863685373375603, 'BeqFac': 3442.778483749208}, {'CRRA': 1.1, 'BeqFac': 3921.209349974723}, {'CRRA': 20.0, 'BeqFac': 3917.345783490104}, {'CRRA': 20.0, 'BeqFac': 4104.193678537616}, {'CRRA': 1.6231245796393248, 'BeqFac': 4112.332787046263}, {'CRRA': 7.312867713980317, 'BeqFac': 3442.778483749208}, {'CRRA': 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{'CRRA': 5.31928716076582, 'BeqFac': 3760.95262088828}, {'CRRA': 5.2776460433398364, 'BeqFac': 3760.768179047306}, {'CRRA': 5.316328813844509, 'BeqFac': 3760.4914884132822}, {'CRRA': 5.329152880451586, 'BeqFac': 3760.6299121589564}, {'CRRA': 5.3010308212525015, 'BeqFac': 3760.560670757597}, {'CRRA': 5.298390995427133, 'BeqFac': 3760.587111611849}, {'CRRA': 5.315158911543225, 'BeqFac': 3760.5833132448756}, {'CRRA': 5.309014234677713, 'BeqFac': 3760.5808448100856}, {'CRRA': 5.308101988325598, 'BeqFac': 3760.58432718544}, {'CRRA': 5.310117521888749, 'BeqFac': 3760.583853120034}, {'CRRA': 5.309302391284488, 'BeqFac': 3760.5833762400434}, {'CRRA': 5.3092384692778385, 'BeqFac': 3760.583778143919}, {'CRRA': 5.309498873803795, 'BeqFac': 3760.5837297748885}, {'CRRA': 5.309396904074384, 'BeqFac': 3760.5836775609505}, {'CRRA': 5.309388601727941, 'BeqFac': 3760.583729851879}, {'CRRA': 5.30942050863102, 'BeqFac': 3760.583720837601}], 'criterion': [0.180013633435277, 1678.4843881605434, 8.339129589465816, 1696.6097487115846, 6.9205174555809865, 8.338974655726659, 8.014586950358948, 1692.2691988842648, 8.339277422181112, 8.339396058648617, 786.7999683125798, 0.3784472776198116, 0.36659170900683713, 5.989658257990909, 5.821979609938599, 8.339132849159276, 5.606825202555598, 283.7342112939434, 317.5705638112315, 0.1790480946691564, 0.2372828546734973, 0.18885629086581307, 0.179481217471893, 0.17826845863091118, 0.17821555165650368, 0.17886569218031512, 0.17820437203599612, 0.17818383067605376, 0.17867899734016252, 0.17816777800820685, 0.17813382837590178, 0.17807394453160602, 2.4419459439323004, 20.410462034889765, 0.17812955485575382, 0.17814641058883646, 0.17803012332934734, 0.17809519611404184, 0.17798168290601363, 0.1780785718895836, 0.17802462026149418, 0.17796989499069646, 0.1779588833874623, 0.17791553246894196, 0.17791852392284568, 0.17796451623648468, 0.1779660799533308, 0.17819091933467376, 0.17793695510302382, 0.17803096549557537, 0.17796175066673592, 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36.06780719989911, 37.38965609995648, 38.56443569995463, 39.75320369983092, 40.928570999763906, 42.1049975999631, 43.29117719968781, 44.52116549992934, 45.709984899964184, 47.021010799799114, 48.199433099944144, 49.37279489962384, 50.552747999783605, 51.74405709980056, 52.95074549969286, 54.13156419992447, 55.446532499976456, 56.624599299859256, 57.809363099746406, 58.98130919970572, 60.15864789998159, 61.33934499975294, 62.50395749974996], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]}}], 'exploration_sample': array([[4.97216547e+00, 2.23566870e+03], - [5.82500000e+00, 7.50000000e+03], - [4.64375000e+00, 3.12500000e+03], - [7.59687500e+00, 7.18750000e+03], - [7.00625000e+00, 1.87500000e+03], - [8.18750000e+00, 3.75000000e+03], - [9.36875000e+00, 5.62500000e+03], - [1.05500000e+01, 5.00000000e+03], - [1.17312500e+01, 4.37500000e+03], - [1.23218750e+01, 9.68750000e+03], - [1.29125000e+01, 1.25000000e+03], - [1.40937500e+01, 8.12500000e+03], - [1.52750000e+01, 2.50000000e+03], - [1.64562500e+01, 6.87500000e+03], - [1.70468750e+01, 2.18750000e+03], - [1.76375000e+01, 8.75000000e+03], - [1.88187500e+01, 6.25000000e+02], - [3.46250000e+00, 6.25000000e+03], - [2.87187500e+00, 4.68750000e+03], - [2.28125000e+00, 9.37500000e+03]]), 'exploration_results': array([1.51833365e-01, 2.18154716e-01, 2.63875842e-01, 3.77381203e-01, - 3.83108040e-01, 4.55042643e-01, 5.57986156e-01, 6.96479632e-01, - 8.83084679e-01, 1.01143958e+00, 1.14406241e+00, 1.54612379e+00, - 2.11582828e+00, 2.96340381e+00, 3.51474071e+00, 4.18178301e+00, - 5.91221015e+00, 7.72175220e+00, 2.55327213e+01, 2.20727523e+02])}}" +CRRA,4.972059127341761 + +BeqFac,2235.66869440018 + +time_to_estimate,120.60259437561035 + +params,"{'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018}" + +criterion,0.15183333483370898 + +start_criterion,0.14974471998399175 + +start_params,"{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974}" + +algorithm,multistart_tranquilo_ls + +direction,minimize + +n_free,2 + +message,Absolute criterion change smaller than tolerance. + +success, + +n_criterion_evaluations, + +n_derivative_evaluations, + +n_iterations, + +history,"{'params': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974}, {'CRRA': 1.1, 'BeqFac': 2038.2167591428197}, {'CRRA': 20.0, 'BeqFac': 2200.680349896351}, {'CRRA': 1.1, 'BeqFac': 2429.9412094587697}, {'CRRA': 19.832484492263543, 'BeqFac': 2433.7996818884876}, {'CRRA': 19.93169977776446, 'BeqFac': 2037.53772188746}, {'CRRA': 20.0, 'BeqFac': 2064.4481322185475}, {'CRRA': 1.1, 'BeqFac': 2332.634198828065}, {'CRRA': 20.0, 'BeqFac': 2322.498125139562}, {'CRRA': 18.700329294200046, 'BeqFac': 2433.7996818884876}, {'CRRA': 1.1, 'BeqFac': 2430.302509845014}, {'CRRA': 7.813833384306793, 'BeqFac': 2037.53772188746}, {'CRRA': 8.624550632134087, 'BeqFac': 2433.7996818884876}, {'CRRA': 17.981702297455875, 'BeqFac': 2037.53772188746}, {'CRRA': 18.109298496140873, 'BeqFac': 2136.603211887717}, {'CRRA': 19.930536834777662, 'BeqFac': 2186.135956887845}, {'CRRA': 20.0, 'BeqFac': 2210.9023293879095}, {'CRRA': 4.079012994666991, 'BeqFac': 2248.051888138006}, {'CRRA': 3.121220401269943, 'BeqFac': 2241.86029501299}, {'CRRA': 4.8421373902763785, 'BeqFac': 2239.1595207269393}, {'CRRA': 4.935891618489661, 'BeqFac': 2237.414959439111}, {'CRRA': 4.960910699246413, 'BeqFac': 2234.79536779911}, {'CRRA': 4.975594073396457, 'BeqFac': 2236.1053544720567}, {'CRRA': 5.031762747602235, 'BeqFac': 2235.4503996289804}, {'CRRA': 4.95481938313597, 'BeqFac': 2235.5595356176655}, {'CRRA': 4.962658187223382, 'BeqFac': 2235.723260832982}, {'CRRA': 4.976595860461234, 'BeqFac': 2235.641409064533}, {'CRRA': 4.985643978352027, 'BeqFac': 2235.670829747999}, {'CRRA': 4.965480731774778, 'BeqFac': 2235.6673366817404}, {'CRRA': 4.9715471211216276, 'BeqFac': 2235.672056738335}, {'CRRA': 4.973852096731626, 'BeqFac': 2235.6684476620744}, {'CRRA': 4.9713764830158755, 'BeqFac': 2235.6683781025586}, {'CRRA': 4.972141953092093, 'BeqFac': 2235.669128923647}, {'CRRA': 4.972378447740231, 'BeqFac': 2235.6687118162295}, {'CRRA': 4.972059127341761, 'BeqFac': 2235.66869440018}], 'criterion': [0.15183336484019694, 1599.2256506697936, 8.33792735607635, 1628.996184127626, 7.942067008283367, 8.173798608673742, 8.33777080025052, 1622.3971740217662, 8.338047223703619, 5.70897681494281, 1629.0195228262494, 0.4459772753262474, 0.5044697780491717, 4.621902068591304, 4.800133525579624, 8.171500062212324, 8.337937651588279, 0.5669929910375328, 6.5167800463917995, 0.15533004766705152, 0.1521320528374574, 0.15187972532160543, 0.15186895370600006, 0.15287286421734658, 0.1519085924789972, 0.1518749282368537, 0.15186803665511617, 0.15194576604337082, 0.15186614554536748, 0.15183882851267255, 0.1518437390370875, 0.1518413460232434, 0.15183338142448646, 0.15183419903764392, 0.15183333483370898], 'runtime': [0.0, 3.234110999852419, 3.3567784996703267, 3.607193300034851, 3.782226899638772, 3.974268099758774, 4.16790989972651, 4.338716899976134, 4.589133599773049, 4.924178099725395, 5.076727400068194, 5.272234600037336, 5.437637999653816, 6.631617899984121, 7.839812099933624, 9.029853600077331, 10.224262000061572, 11.426363199949265, 12.594932300038636, 13.766675599850714, 14.943977899849415, 16.25537579972297, 17.437238699756563, 18.621162499766797, 19.853813599795103, 21.03928539995104, 22.241678999736905, 23.42911219969392, 24.757420799694955, 25.978194700088352, 27.192072699777782, 28.37384819984436, 29.543620300013572, 30.743656399659812, 31.96393799968064], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 4.972165469041287, 'BeqFac': 2235.668701887974}, {'CRRA': 5.221879724992187, 'BeqFac': 3777.5556353977354}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.976e-07* 1.976e-07* +relative_params_change 2.139e-05 2.139e-05 +absolute_criterion_change 3.001e-08* 3.001e-08* +absolute_params_change 0.0001066 0.0001066 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 2.639e-07* 0.0006443 +relative_params_change 2.12e-06* 0.002311 +absolute_criterion_change 4.695e-08* 0.0001146 +absolute_params_change 1.126e-05 6.64 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.972165469041286, 'BeqFac': 2235.668701887974}, {'CRRA': 5.824999999999999, 'BeqFac': 7500.0}, {'CRRA': 4.64375, 'BeqFac': 3125.0}, {'CRRA': 7.596874999999999, 'BeqFac': 7187.5}, {'CRRA': 7.00625, 'BeqFac': 1875.0}, {'CRRA': 8.1875, 'BeqFac': 3750.0}, {'CRRA': 9.368749999999999, 'BeqFac': 5625.0}, {'CRRA': 10.549999999999999, 'BeqFac': 5000.0}, {'CRRA': 11.73125, 'BeqFac': 4375.0}, {'CRRA': 12.321874999999999, 'BeqFac': 9687.5}, {'CRRA': 12.9125, 'BeqFac': 1250.0}, {'CRRA': 14.093749999999998, 'BeqFac': 8125.0}, {'CRRA': 15.274999999999999, 'BeqFac': 2500.0}, {'CRRA': 16.45625, 'BeqFac': 6875.0}, {'CRRA': 17.046875, 'BeqFac': 2187.5}, {'CRRA': 17.6375, 'BeqFac': 8750.0}, {'CRRA': 18.81875, 'BeqFac': 625.0}, {'CRRA': 3.4625, 'BeqFac': 6250.0}, {'CRRA': 2.871875, 'BeqFac': 4687.5}, {'CRRA': 2.28125, 'BeqFac': 9375.0}], 'exploration_results': array([1.51833365e-01, 2.18154716e-01, 2.63875842e-01, 3.77381203e-01, + 3.83108040e-01, 4.55042643e-01, 5.57986156e-01, 6.96479632e-01, + 8.83084679e-01, 1.01143958e+00, 1.14406241e+00, 1.54612379e+00, + 2.11582828e+00, 2.96340381e+00, 3.51474071e+00, 4.18178301e+00, + 5.91221015e+00, 7.72175220e+00, 2.55327213e+01, 2.20727523e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=[0], model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=0, candidate_x=array([ 4.97216547, 2235.66870189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=196.12406005340955, linear_terms=array([-428.74914009, 49.95239728]), square_terms=array([[475.26532472, -54.98930881], + [-54.98930881, 6.38449896]]), scale=array([ 9.45 , 198.13098]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=13, candidate_x=array([ 17.9817023 , 2037.53772189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.008440716478759814, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=111.7834350943987, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), model=ScalarModel(intercept=242.07061318310053, linear_terms=array([-471.89691667, 101.1889206 ]), square_terms=array([[465.88374262, -99.22450662], + [-99.22450662, 21.20996444]]), scale=array([ 9.45 , 99.06549]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=14, candidate_x=array([ 18.1092985 , 2136.60321189]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.007755830036420791, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 11]), old_indices_discarded=array([ 4, 5, 9, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=55.89171754719935, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), model=ScalarModel(intercept=298.87379830095927, linear_terms=array([-576.79607872, 19.05034093]), square_terms=array([[562.44777715, -18.48262992], + [-18.48262992, 0.60886349]]), scale=array([ 9.45 , 49.532745]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=15, candidate_x=array([ 19.93053683, 2186.13595689]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.010921826812679862, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 6, 7, 8, 10, 14]), old_indices_discarded=array([ 4, 5, 9, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=27.945858773599674, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 7, 8, 14, 15]), model=ScalarModel(intercept=131.07369976203483, linear_terms=array([-204.19706036, 40.77897837]), square_terms=array([[163.90069793, -32.18422025], + [-32.18422025, 6.38059561]]), scale=array([ 9.45 , 24.7663725]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=16, candidate_x=array([ 20. , 2210.90232939]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.029581696796089584, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=13.972929386799837, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=0.9873239325348305, linear_terms=array([2.73907339, 0.01391224]), square_terms=array([[4.36743207e+00, 2.75767161e-02], + [2.75767161e-02, 2.41056737e-04]]), scale=array([ 8.12767586, 12.38318625]), shift=array([ 9.22767586, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=17, candidate_x=array([ 4.07901299, 2248.05188814]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-15.504804046764184, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 15, 16]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=6.986464693399919, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17]), model=ScalarModel(intercept=0.3431675848933713, linear_terms=array([1.33281827, 0.32943084]), square_terms=array([[4.36179571, 1.27691822], + [1.27691822, 0.39380953]]), scale=array([5.0318793 , 6.19159313]), shift=array([ 6.1318793 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=18, candidate_x=array([ 3.1212204 , 2241.86029501]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-100.94273410330236, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=3.4932323466999593, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.12431539, -0.0055439 ]), square_terms=array([[59.59210104, 2.34414471], + [ 2.34414471, 0.09228281]]), scale=3.4932323466999593, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=19, candidate_x=array([ 4.84213739, 2239.15952073]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-4.681921220989647, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.06103675, -0.00243685]), square_terms=array([[1.34115073e+01, 3.39655978e-01], + [3.39655978e-01, 8.60970305e-03]]), scale=1.7466161733499797, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=20, candidate_x=array([ 4.93589162, 2237.41495944]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.29114147897803394, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([0.00582774, 0.00013542]), square_terms=array([[3.87857288e-01, 8.27620950e-04], + [8.27620950e-04, 2.06651665e-06]]), scale=0.8733080866749898, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=21, candidate_x=array([ 4.9609107, 2234.7953678]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.27824291525666955, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.4366540433374949, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([-5.56309005e-04, -4.37454587e-06]), square_terms=array([[ 7.50279995e-02, -3.28466129e-05], + [-3.28466129e-05, 2.15202300e-08]]), scale=0.4366540433374949, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=22, candidate_x=array([ 4.97559407, 2236.10535447]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-5.330119577173148, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.21832702166874746, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.15183336484019683, linear_terms=array([-4.45403999e-03, 5.16279055e-05]), square_terms=array([[1.62881565e-02, 6.77124384e-06], + [6.77124384e-06, 2.92278248e-08]]), scale=0.21832702166874746, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=23, candidate_x=array([ 5.03176275, 2235.45039963]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.5692018274741517, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 21, 22]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.10916351083437373, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23]), model=ScalarModel(intercept=0.1518333648401971, linear_terms=array([7.05546057e-04, 2.78738861e-06]), square_terms=array([[4.43307710e-03, 7.04906076e-07], + [7.04906076e-07, 5.61544409e-10]]), scale=0.10916351083437373, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=24, candidate_x=array([ 4.95481938, 2235.55953562]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-1.2789338326888193, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.054581755417186864, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 23, 24]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 2.00591466e-04, -4.01524035e-05]), square_terms=array([[1.12660455e-03, 2.72179550e-06], + [2.72179550e-06, 2.34943617e-08]]), scale=0.054581755417186864, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=25, candidate_x=array([ 4.96265819, 2235.72326083]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.7111299458870474, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-4.80890903e-05, 3.50155615e-06]), square_terms=array([[ 2.91958192e-04, -1.28086238e-07], + [-1.28086238e-07, 2.58764850e-09]]), scale=0.027290877708593432, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.013645438854296716, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([-0.0017766 , -0.00030364]), square_terms=array([[4.26905240e-05, 1.06728380e-06], + [1.06728380e-06, 1.30381617e-06]]), scale=0.013645438854296716, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.006822719427148358, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 26, 27]), model=ScalarModel(intercept=0.1518333648401969, linear_terms=array([ 3.88679056e-05, -1.31679513e-06]), square_terms=array([[ 1.84940973e-05, -1.00210163e-07], + [-1.00210163e-07, 6.96346430e-10]]), scale=0.006822719427148358, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 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radius=0.003411359713574179, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 27, 28]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([ 0.00012663, -0.00067966]), square_terms=array([[ 4.68892805e-06, -6.61282339e-07], + [-6.61282339e-07, 5.43609914e-06]]), scale=0.003411359713574179, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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model=ScalarModel(intercept=0.151833364840197, linear_terms=array([5.29250449e-06, 2.34437075e-06]), square_terms=array([[3.10374558e-07, 1.18891884e-09], + [1.18891884e-09, 8.34845812e-11]]), scale=0.0008528399283935448, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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[1.44466078e-11, 7.36990793e-13]]), scale=0.0002132099820983862, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=34, candidate_x=array([ 4.97205913, 2235.6686944 ]), index=34, x=array([ 4.97205913, 2235.6686944 ]), fval=0.15183333483370898, rho=0.07308056702929565, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.00010660499104711433, relative_step_length=0.9999999999805003, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 35 entries., 'multistart_info': {'start_parameters': [array([ 4.97216547, 2235.66870189]), array([ 5.22187972, 3777.5556354 ])], 'local_optima': [{'solution_x': array([ 4.97205913, 2235.6686944 ]), 'solution_criterion': 0.15183333483370898, 'states': [State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=223.5668701887974, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), 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scale=array([ 9.45 , 198.13098]), shift=array([ 10.55 , 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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20. , 2210.90232939]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.029581696796089584, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 2, 7, 8, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=13.972929386799837, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 2, 15, 16]), model=ScalarModel(intercept=0.9873239325348305, linear_terms=array([2.73907339, 0.01391224]), square_terms=array([[4.36743207e+00, 2.75767161e-02], + [2.75767161e-02, 2.41056737e-04]]), scale=array([ 8.12767586, 12.38318625]), shift=array([ 9.22767586, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + 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dtype=int32), old_indices_used=array([ 0, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=3.4932323466999593, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.12431539, -0.0055439 ]), square_terms=array([[59.59210104, 2.34414471], + [ 2.34414471, 0.09228281]]), scale=3.4932323466999593, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=1.7466161733499797, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19]), model=ScalarModel(intercept=0.15183336484019672, linear_terms=array([-0.06103675, -0.00243685]), square_terms=array([[1.34115073e+01, 3.39655978e-01], + [3.39655978e-01, 8.60970305e-03]]), scale=1.7466161733499797, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.8733080866749898, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 20]), model=ScalarModel(intercept=0.15183336484019688, linear_terms=array([0.00582774, 0.00013542]), square_terms=array([[3.87857288e-01, 8.27620950e-04], + [8.27620950e-04, 2.06651665e-06]]), scale=0.8733080866749898, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 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bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 20, 21]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([-5.56309005e-04, -4.37454587e-06]), square_terms=array([[ 7.50279995e-02, -3.28466129e-05], + [-3.28466129e-05, 2.15202300e-08]]), scale=0.4366540433374949, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model_indices=array([ 0, 21, 22]), model=ScalarModel(intercept=0.15183336484019683, linear_terms=array([-4.45403999e-03, 5.16279055e-05]), square_terms=array([[1.62881565e-02, 6.77124384e-06], + [6.77124384e-06, 2.92278248e-08]]), scale=0.21832702166874746, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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linear_terms=array([7.05546057e-04, 2.78738861e-06]), square_terms=array([[4.43307710e-03, 7.04906076e-07], + [7.04906076e-07, 5.61544409e-10]]), scale=0.10916351083437373, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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2.72179550e-06], + [2.72179550e-06, 2.34943617e-08]]), scale=0.054581755417186864, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=25, candidate_x=array([ 4.96265819, 2235.72326083]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.7111299458870474, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.027290877708593432, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 24, 25]), model=ScalarModel(intercept=0.15183336484019697, linear_terms=array([-4.80890903e-05, 3.50155615e-06]), square_terms=array([[ 2.91958192e-04, -1.28086238e-07], + [-1.28086238e-07, 2.58764850e-09]]), scale=0.027290877708593432, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=28, candidate_x=array([ 4.96548073, 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.0004264199641967724, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 30, 31]), model=ScalarModel(intercept=0.15183336484019694, linear_terms=array([ 6.65954541e-07, -1.19658346e-05]), square_terms=array([[ 7.65782868e-08, -2.91682815e-09], + [-2.91682815e-09, 2.06495167e-09]]), scale=0.0004264199641967724, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.0002132099820983862, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 31, 32]), model=ScalarModel(intercept=0.151833364840197, linear_terms=array([-2.07870134e-06, -1.06245181e-07]), square_terms=array([[1.86210252e-08, 1.44466078e-11], + [1.44466078e-11, 7.36990793e-13]]), scale=0.0002132099820983862, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=223.5668701887974, shift=array([ 4.97216547, 2235.66870189])), candidate_index=33, candidate_x=array([ 4.97237845, 2235.66871182]), index=0, x=array([ 4.97216547, 2235.66870189]), fval=0.15183336484019694, rho=-0.40258497004534627, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97216547, 2235.66870189]), radius=0.0001066049910491931, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 32, 33]), model=ScalarModel(intercept=0.15183336484019702, linear_terms=array([4.11915061e-07, 2.67955367e-08]), square_terms=array([[ 4.39274402e-09, -5.11390133e-12], + [-5.11390133e-12, 1.93132011e-14]]), scale=0.0001066049910491931, shift=array([ 4.97216547, 2235.66870189])), vector_model=VectorModel(intercepts=array([ 0.06291566, 0.1164343 , 0.10823532, 0.12189129, 0.12343732, + 0.12357637, 0.12479259, 0.09432371, 0.01973898, 0.10842372, + -0.14822489, -0.07394217, -0.01698702, -0.0183285 , -0.03672195, + -0.06411929, -0.08426122]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.22187972, 3777.5556354 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18]), model=ScalarModel(intercept=8.66476096620099, linear_terms=array([145.36114645, -0.79163954]), square_terms=array([[ 1.24486907e+03, -6.74089224e+00], + [-6.74089224e+00, 3.66585907e-02]]), scale=array([4.67638636, 5.23089299]), shift=array([ 5.77638636, 3777.5556354 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([ 5.2556554 , 3782.78652839]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 16, 17, 18, 19]), model=ScalarModel(intercept=0.4447192260530918, linear_terms=array([3.28591072, 0.7575156 ]), square_terms=array([[238.5254968 , 72.82153083], + [ 72.82153083, 22.31236539]]), scale=array([ 7.30872069, 10.46178599]), shift=array([ 8.40872069, 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + 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radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 18, 19, 20]), model=ScalarModel(intercept=2.0924308511078555, linear_terms=array([64.90826608, -1.00041399]), square_terms=array([[ 1.10459211e+03, -1.69795324e+01], + [-1.69795324e+01, 2.61125536e-01]]), scale=array([4.6932742 , 5.23089299]), shift=array([ 5.7932742 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19, 20, 21]), model=ScalarModel(intercept=0.60991129558898, linear_terms=array([-18.6614341 , 0.97529584]), square_terms=array([[405.20779142, -21.11615795], + [-21.11615795, 1.10050394]]), scale=2.951215340154481, shift=array([ 5.2556554 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=0.41578081033668957, linear_terms=array([-7.17900081, 0.24007703]), square_terms=array([[108.78543013, -3.63193691], + [ -3.63193691, 0.12126679]]), scale=1.4756076700772405, shift=array([ 5.2556554 , 3782.78652839])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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linear_terms=array([-5.28954140e-03, 1.23810918e-05]), square_terms=array([[ 1.91842376e-01, -2.99622408e-05], + [-2.99622408e-05, 9.50955359e-09]]), scale=0.7378038350386202, shift=array([ 5.30368819, 3781.308501 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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square_terms=array([[ 1.18917531e+02, -1.26160659e+00], + [-1.26160659e+00, 1.33856432e-02]]), scale=1.4756076700772405, shift=array([ 5.32391471, 3780.570694 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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[2.97857263e-07, 1.58135601e-09]]), scale=0.7378038350386202, shift=array([ 5.32391471, 3780.570694 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=26, candidate_x=array([ 5.32396542, 3779.83289016]), index=26, x=array([ 5.32396542, 3779.83289016]), fval=0.17820437203599612, rho=1.4728006799871363, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.7378038367024704, relative_step_length=1.000000002255139, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32396542, 3779.83289016]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.17819811285311968, linear_terms=array([3.3097250e-05, 1.3989285e-05]), square_terms=array([[7.55464798e-01, 1.58696940e-05], + [1.58696940e-05, 7.19280645e-09]]), scale=1.4756076700772405, shift=array([ 5.32396542, 3779.83289016])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=27, candidate_x=array([ 5.32393177, 3778.35728249]), index=27, x=array([ 5.32393177, 3778.35728249]), fval=0.17818383067605373, rho=1.4687207826788418, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int32), step_length=1.475607671493286, relative_step_length=1.0000000009596355, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32393177, 3778.35728249]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 18, 19, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.23681559100061073, linear_terms=array([ 7.52400864, -0.03697853]), square_terms=array([[ 4.81922118e+02, -2.37216170e+00], + [-2.37216170e+00, 1.16774340e-02]]), scale=2.951215340154481, shift=array([ 5.32393177, 3778.35728249])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=28, candidate_x=array([ 5.26333048, 3775.40633 ]), index=27, x=array([ 5.32393177, 3778.35728249]), fval=0.17818383067605373, rho=-0.008422541073735482, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 18, 19, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32393177, 3778.35728249]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 19, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.17816465874227283, linear_terms=array([1.88966570e-04, 2.76343183e-05]), square_terms=array([[ 7.56470495e-01, -1.03969061e-04], + [-1.03969061e-04, 1.75995384e-08]]), scale=1.4756076700772405, shift=array([ 5.32393177, 3778.35728249])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=29, candidate_x=array([ 5.32336038, 3776.88167488]), index=29, x=array([ 5.32336038, 3776.88167488]), fval=0.17816777800820685, rho=0.5798905880546184, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 19, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([], dtype=int32), step_length=1.4756077161109993, relative_step_length=1.0000000311964756, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32336038, 3776.88167488]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.17814522233392394, linear_terms=array([1.70335715e-04, 5.34309034e-05]), square_terms=array([[ 3.02597147e+00, -4.39921704e-04], + [-4.39921704e-04, 7.75356986e-08]]), scale=2.951215340154481, shift=array([ 5.32336038, 3776.88167488])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=30, candidate_x=array([ 5.32276521, 3773.9304596 ]), index=30, x=array([ 5.32276521, 3773.9304596 ]), fval=0.1781338283759018, rho=0.6351225603827869, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([18, 19, 21]), step_length=2.9512153448295146, relative_step_length=1.0000000015841046, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32276521, 3773.9304596 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.22227385671774208, linear_terms=array([ 8.27516735e-01, -9.46109851e-05]), square_terms=array([[ 7.75302897e+00, -1.67049083e-03], + [-1.67049083e-03, 3.94188026e-07]]), scale=array([4.7268291 , 5.23089299]), shift=array([ 5.8268291, 3773.9304596])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=31, candidate_x=array([ 5.32129426, 3768.6995666 ]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=0.7154013322715937, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([17, 18, 19, 20, 21, 23]), step_length=5.230893201328401, relative_step_length=0.8862269604925865, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=11.804861360617924, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.5208780387285807, linear_terms=array([1.12839055, 0.01109011]), square_terms=array([[1.51827200e+00, 2.11161638e-02], + [2.11161638e-02, 4.80103617e-04]]), scale=array([ 7.34154012, 10.46178599]), shift=array([ 8.44154012, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=33, candidate_x=array([2.88314895e+00, 3.77916135e+03]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-248.64296126941807, accepted=False, new_indices=array([32]), old_indices_used=array([ 0, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([16, 17, 18, 19, 20, 21, 22, 23, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=5.902430680308962, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 22, 25, 27, 28, 29, 30, 31, 33]), model=ScalarModel(intercept=0.9998902148657269, linear_terms=array([15.98904319, 0.08221525]), square_terms=array([[1.55477573e+02, 8.04599038e-01], + [8.04599038e-01, 4.16601478e-03]]), scale=array([4.72609362, 5.23089299]), shift=array([ 5.82609362, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=34, candidate_x=array([ 5.31561276, 3773.9304596 ]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-0.031700914237791734, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 22, 25, 27, 28, 29, 30, 31, 33]), old_indices_discarded=array([17, 18, 19, 20, 21, 23, 24, 26, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 25, 27, 28, 29, 30, 31, 33, 34]), model=ScalarModel(intercept=0.17875368787838072, linear_terms=array([-0.35344824, -0.00160514]), square_terms=array([[6.02790036e+01, 2.32207919e-01], + [2.32207919e-01, 8.94985816e-04]]), scale=2.951215340154481, shift=array([ 5.32129426, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=35, candidate_x=array([ 5.3272299 , 3771.65082671]), index=31, x=array([ 5.32129426, 3768.6995666 ]), fval=0.17807394453160602, rho=-0.056632647014796866, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 27, 28, 29, 30, 31, 33, 34]), old_indices_discarded=array([17, 19, 22, 23, 24, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.32129426, 3768.6995666 ]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([28, 30, 31, 34, 35]), model=ScalarModel(intercept=0.1780820131294612, linear_terms=array([1.86739648e-03, 1.59075457e-05]), square_terms=array([[ 7.41957927e-01, -1.22029600e-04], + [-1.22029600e-04, 2.23518326e-08]]), scale=1.4756076700772405, shift=array([ 5.32129426, 3768.6995666 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=36, candidate_x=array([ 5.31733777, 3767.22395957]), index=36, x=array([ 5.31733777, 3767.22395957]), fval=0.17803012332934734, rho=2.3606102276820975, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([28, 30, 31, 34, 35]), old_indices_discarded=array([], dtype=int32), step_length=1.4756123434937174, relative_step_length=1.0000031671131642, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.31733777, 3767.22395957]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([ 0, 17, 28, 29, 30, 31, 34, 35, 36]), model=ScalarModel(intercept=0.18731061364611515, linear_terms=array([-2.78859371, -0.01533278]), square_terms=array([[4.21276635e+02, 2.31394055e+00], + [2.31394055e+00, 1.27109474e-02]]), scale=2.951215340154481, shift=array([ 5.31733777, 3767.22395957])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=37, candidate_x=array([ 5.32066257, 3770.17523664]), index=36, x=array([ 5.31733777, 3767.22395957]), fval=0.17803012332934734, rho=-0.007038929660613454, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 28, 29, 30, 31, 34, 35, 36]), old_indices_discarded=array([22, 23, 24, 25, 26, 27, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.31733777, 3767.22395957]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([30, 31, 34, 35, 36, 37]), model=ScalarModel(intercept=0.17803733457425416, linear_terms=array([4.67596486e-03, 1.97718973e-05]), square_terms=array([[ 6.86260197e-01, -1.28093076e-04], + [-1.28093076e-04, 2.65774935e-08]]), scale=1.4756076700772405, shift=array([ 5.31733777, 3767.22395957])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=38, candidate_x=array([ 5.30700832, 3765.7483538 ]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=1.3244637262751773, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int32), step_length=1.4756419211239096, relative_step_length=1.000023211485928, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 28, 30, 31, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.1853840653395654, linear_terms=array([-2.53255399, -0.00530967]), square_terms=array([[4.34594855e+02, 9.14605265e-01], + [9.14605265e-01, 1.92494707e-03]]), scale=2.951215340154481, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=39, candidate_x=array([ 5.33041694, 3762.7971812 ]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=-0.013094695227833497, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 28, 30, 31, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 0, 22, 25, 26, 27, 29, 33]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.17798153266437608, linear_terms=array([2.67703532e-03, 1.84532996e-05]), square_terms=array([[ 6.87387208e-01, -1.22416627e-04], + [-1.22416627e-04, 2.49928337e-08]]), scale=1.4756076700772405, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=40, candidate_x=array([ 5.30099892, 3764.27274717]), index=38, x=array([ 5.30700832, 3765.7483538 ]), fval=0.17798168290601366, rho=-1.7785842210147218, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 37, 38, 39]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30700832, 3765.7483538 ]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 36, 38, 39, 40]), model=ScalarModel(intercept=0.17800937260106672, linear_terms=array([5.95288503e-06, 5.30714257e-06]), square_terms=array([[ 1.72058207e-01, -2.92041969e-05], + [-2.92041969e-05, 7.60304447e-09]]), scale=0.7378038350386202, shift=array([ 5.30700832, 3765.7483538 ])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=41, candidate_x=array([ 5.30685756, 3765.01054998]), index=41, x=array([ 5.30685756, 3765.01054998]), fval=0.17796989499069643, rho=2.2212294285075673, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 36, 38, 39, 40]), old_indices_discarded=array([], dtype=int32), step_length=0.7378038354799815, relative_step_length=1.0000000005982095, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30685756, 3765.01054998]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([31, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17799379519456676, linear_terms=array([9.04133766e-04, 1.68120998e-05]), square_terms=array([[ 6.87741442e-01, -1.22064373e-04], + [-1.22064373e-04, 2.53913004e-08]]), scale=1.4756076700772405, shift=array([ 5.30685756, 3765.01054998])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=42, candidate_x=array([ 5.30465582, 3763.53494268]), index=42, x=array([ 5.30465582, 3763.53494268]), fval=0.1779588833874623, rho=0.6269056309320292, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([31, 35, 36, 37, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int32), step_length=1.4756089451393082, relative_step_length=1.0000008640928708, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30465582, 3763.53494268]), radius=2.951215340154481, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 31, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.17947899803072848, linear_terms=array([-1.1742621 , 0.00126434]), square_terms=array([[ 4.44709273e+02, -4.57981892e-01], + [-4.57981892e-01, 4.71712559e-04]]), scale=2.951215340154481, shift=array([ 5.30465582, 3763.53494268])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=43, candidate_x=array([ 5.30940925, 3760.58372087]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=0.027004451558225083, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 31, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 28, 29, 30, 34, 35]), step_length=2.9512256285033613, relative_step_length=1.000003486139673, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=1.4756076700772405, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([17, 36, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.1783256875306959, linear_terms=array([-3.07287421e-01, -8.21318424e-05]), square_terms=array([[1.10555074e+02, 3.40646813e-02], + [3.40646813e-02, 1.05016887e-05]]), scale=1.4756076700772405, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=44, candidate_x=array([ 5.31396537, 3759.10811454]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.006804942702733265, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 36, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.7378038350386202, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([39, 42, 43, 44]), model=ScalarModel(intercept=0.17792080695215554, linear_terms=array([2.06155976e-03, 1.11830329e-05]), square_terms=array([[ 1.70363006e-01, -3.82501682e-05], + [-3.82501682e-05, 1.24667902e-08]]), scale=0.7378038350386202, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=45, candidate_x=array([ 5.30031607, 3759.84591906]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-2.0310571357145926, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([39, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.3689019175193101, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 44, 45]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-1.17918083e-03, -3.42897503e-06]), square_terms=array([[ 4.43546197e-02, -8.57949761e-06], + [-8.57949761e-06, 4.04624924e-09]]), scale=0.3689019175193101, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=46, candidate_x=array([ 5.31928716, 3760.95262089]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-2.6149368631946275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.18445095875965506, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 45, 46]), model=ScalarModel(intercept=0.17791553246894204, linear_terms=array([ 1.84501111e-03, -3.17202089e-05]), square_terms=array([[ 1.06687234e-02, -2.43582255e-06], + [-2.43582255e-06, 7.19700602e-08]]), scale=0.18445095875965506, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.09222547937982753, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 46, 47]), model=ScalarModel(intercept=0.17791553246894176, linear_terms=array([-2.17795159e-04, 1.46733396e-05]), square_terms=array([[ 2.85029042e-03, -2.85696802e-06], + [-2.85696802e-06, 7.09340428e-09]]), scale=0.09222547937982753, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.046112739689913765, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 47, 48]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([-3.39208594e-04, -3.23222752e-05]), square_terms=array([[7.54306993e-04, 2.98728975e-06], + [2.98728975e-06, 3.90562555e-08]]), scale=0.046112739689913765, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.023056369844956882, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 48, 49]), model=ScalarModel(intercept=0.17791553246894187, linear_terms=array([6.09438346e-05, 1.13529285e-06]), square_terms=array([[ 1.66872702e-04, -1.24585747e-07], + [-1.24585747e-07, 1.17301795e-09]]), scale=0.023056369844956882, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=50, candidate_x=array([ 5.30103082, 3760.56067076]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-3.755110924764953, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 48, 49]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.011528184922478441, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 49, 50]), model=ScalarModel(intercept=0.17791553246894215, linear_terms=array([ 0.00048585, -0.00019408]), square_terms=array([[ 5.31813583e-05, -5.75413054e-06], + [-5.75413054e-06, 2.79836435e-06]]), scale=0.011528184922478441, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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10000.]))), model_indices=array([43, 50, 51]), model=ScalarModel(intercept=0.17791553246894196, linear_terms=array([-4.88151251e-05, 9.00611126e-06]), square_terms=array([[ 1.09589251e-05, -5.41537073e-08], + [-5.41537073e-08, 1.87668792e-09]]), scale=0.005764092461239221, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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model=ScalarModel(intercept=0.17791553246894182, linear_terms=array([2.12658543e-05, 1.52919564e-04]), square_terms=array([[ 2.67941071e-06, -5.49830816e-08], + [-5.49830816e-08, 3.68065340e-07]]), scale=0.0028820462306196103, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=53, candidate_x=array([ 5.30901423, 3760.58084481]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-0.026380369819135392, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.0014410231153098052, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 52, 53]), model=ScalarModel(intercept=0.1779155324689421, linear_terms=array([ 5.01858339e-06, -2.73111673e-06]), square_terms=array([[ 6.71301844e-07, -6.77682479e-09], + [-6.77682479e-09, 2.94207799e-10]]), scale=0.0014410231153098052, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=54, candidate_x=array([ 5.30810199, 3760.58432719]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-1.11322964518744, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.0007205115576549026, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 53, 54]), model=ScalarModel(intercept=0.17791553246894193, linear_terms=array([-3.42548630e-06, -5.50440244e-07]), square_terms=array([[ 1.71589664e-07, -1.81884533e-09], + [-1.81884533e-09, 3.67816759e-11]]), scale=0.0007205115576549026, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=377.75556353977356, shift=array([ 5.22187972, 3777.5556354 ])), candidate_index=55, candidate_x=array([ 5.31011752, 3760.58385312]), index=43, x=array([ 5.30940925, 3760.58372087]), fval=0.17791553246894198, rho=-3.027754219035174, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.30940925, 3760.58372087]), radius=0.0003602557788274513, bounds=Bounds(lower=array([1.1, 0. ]), upper=array([ 20., 10000.]))), model_indices=array([43, 54, 55]), model=ScalarModel(intercept=0.17791553246894207, linear_terms=array([3.23278987e-06, 1.03878094e-05]), square_terms=array([[ 4.26477773e-08, -3.95820208e-10], + [-3.95820208e-10, 1.42364938e-09]]), scale=0.0003602557788274513, shift=array([ 5.30940925, 3760.58372087])), vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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vector_model=VectorModel(intercepts=array([ 0.06685046, 0.12384426, 0.11810388, 0.133374 , 0.13650311, + 0.13812835, 0.14087628, 0.11546522, 0.042301 , 0.13300251, + -0.12212488, -0.04643257, -0.03720919, -0.03626661, -0.05256766, + -0.07828796, -0.09714704]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), 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5.00000000e+03], + [1.17312500e+01, 4.37500000e+03], + [1.23218750e+01, 9.68750000e+03], + [1.29125000e+01, 1.25000000e+03], + [1.40937500e+01, 8.12500000e+03], + [1.52750000e+01, 2.50000000e+03], + [1.64562500e+01, 6.87500000e+03], + [1.70468750e+01, 2.18750000e+03], + [1.76375000e+01, 8.75000000e+03], + [1.88187500e+01, 6.25000000e+02], + [3.46250000e+00, 6.25000000e+03], + [2.87187500e+00, 4.68750000e+03], + [2.28125000e+00, 9.37500000e+03]]), 'exploration_results': array([1.51833365e-01, 2.18154716e-01, 2.63875842e-01, 3.77381203e-01, + 3.83108040e-01, 4.55042643e-01, 5.57986156e-01, 6.96479632e-01, + 8.83084679e-01, 1.01143958e+00, 1.14406241e+00, 1.54612379e+00, + 2.11582828e+00, 2.96340381e+00, 3.51474071e+00, 4.18178301e+00, + 5.91221015e+00, 7.72175220e+00, 2.55327213e+01, 2.20727523e+02])}}" diff --git a/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv b/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv index 8c13be2..1662749 100644 --- a/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv +++ b/src/estimark/content/tables/min/WealthPortfolioBeta_estimate_results.csv @@ -1,19638 +1,19657 @@ -CRRA,4.844436801414261 -WealthShare,0.3460128282561451 -DiscFac,0.9323589063936805 -time_to_estimate,217.95789170265198 -params,"{'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451, 'DiscFac': 0.9323589063936805}" -criterion,0.17254703939389457 -start_criterion,0.23891445185117913 -start_params,"{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'DiscFac': 1.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,3 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}, {'CRRA': 4.877555010789218, 'WealthShare': 0.01, 'DiscFac': 1.0004206381225442}, {'CRRA': 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58.923332899808884, 58.96633999980986, 59.00750799989328, 59.05656659975648, 59.08834679983556, 59.132794899865985, 60.22101899981499, 61.250512999948114, 62.25834989966825, 63.258237699978054, 64.29787159990519, 65.34936949983239, 66.36967970011756, 67.51696019992232, 68.52077089995146, 69.51590960007161, 70.63079519988969, 70.66824259981513, 70.70960419997573, 70.75069979997352, 70.79406809993088, 70.83469379972667, 70.87575369980186, 70.91594929993153, 70.95578130008653, 70.99587279977277, 71.0371133997105, 71.07846489967778, 72.16604539984837], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 52, 53, 54, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68]}" -convergence_report,"{'one_step': {'relative_criterion_change': 0.003307242007129943, 'relative_params_change': 0.03956657811621874, 'absolute_criterion_change': 0.0005706548168893932, 'absolute_params_change': 0.19167597945625867}, 'five_steps': {'relative_criterion_change': 0.014922806335674835, 'relative_params_change': 0.0806704377224931, 'absolute_criterion_change': 0.0025748860526691453, 'absolute_params_change': 0.36818918796590866}}" -multistart_info,"{'start_parameters': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'DiscFac': 1.0}, {'CRRA': 4.472854104495062, 'WealthShare': 0.457530286489798, 'DiscFac': 0.8472036933750238}, {'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}], 'local_optima': [Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 6.023e-05 0.0008401 -relative_params_change 0.0007429 0.01168 -absolute_criterion_change 1.055e-05 0.0001471 -absolute_params_change 0.0002607 0.005784 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 4.685e-07* 0.0001119 -relative_params_change 2.549e-06* 0.0008924 -absolute_criterion_change 8.11e-08* 1.938e-05 -absolute_params_change 5.392e-06* 0.0004772 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.731e-05 0.0001779 -relative_params_change 8.502e-05 0.003461 -absolute_criterion_change 2.988e-06* 3.07e-05 -absolute_params_change 3.236e-05 0.00824 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'DiscFac': 1.0}, {'CRRA': 3.4625, 'WealthShare': 0.6225, 'DiscFac': 0.7250000000000001}, {'CRRA': 8.1875, 'WealthShare': 0.3775, 'DiscFac': 0.875}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5, 'DiscFac': 0.8}, {'CRRA': 14.684375, 'WealthShare': 0.346875, 'DiscFac': 0.8562500000000001}, {'CRRA': 9.959375, 'WealthShare': 0.101875, 'DiscFac': 1.00625}, {'CRRA': 19.409375, 'WealthShare': 0.591875, 'DiscFac': 0.70625}, {'CRRA': 13.503124999999999, 'WealthShare': 0.653125, 'DiscFac': 0.51875}, {'CRRA': 2.871875, 'WealthShare': 0.469375, 'DiscFac': 0.78125}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125, 'DiscFac': 0.6125}, {'CRRA': 18.81875, 'WealthShare': 0.07125, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'WealthShare': 0.224375, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'WealthShare': 0.1325, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'WealthShare': 0.43875, 'DiscFac': 0.5375}, {'CRRA': 18.228125, 'WealthShare': 0.408125, 'DiscFac': 0.96875}, {'CRRA': 7.00625, 'WealthShare': 0.19375, 'DiscFac': 0.6875}, {'CRRA': 6.415625, 'WealthShare': 0.285625, 'DiscFac': 0.59375}, {'CRRA': 4.053125, 'WealthShare': 0.16312500000000002, 'DiscFac': 0.8187500000000001}, {'CRRA': 5.234375, 'WealthShare': 0.836875, 'DiscFac': 0.55625}, {'CRRA': 16.45625, 'WealthShare': 0.68375, 'DiscFac': 0.9875}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625, 'DiscFac': 0.7625000000000001}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003, 'DiscFac': 1.0625}, {'CRRA': 15.865624999999998, 'WealthShare': 0.775625, 'DiscFac': 0.89375}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999, 'DiscFac': 0.575}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745, 'DiscFac': 0.9500000000000001}, {'CRRA': 8.778125, 'WealthShare': 0.898125, 'DiscFac': 0.66875}, {'CRRA': 2.28125, 'WealthShare': 0.92875, 'DiscFac': 0.8375}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375, 'DiscFac': 1.08125}], 'exploration_results': array([2.42222292e-01, 3.03201302e-01, 3.75385332e-01, 3.98577685e-01, - 4.64539853e-01, 6.05168236e-01, 6.05724112e-01, 7.88383230e-01, - 8.39981988e-01, 1.04605604e+00, 1.22782446e+00, 1.48069565e+00, - 1.56893759e+00, 1.77192100e+00, 1.95139780e+00, 2.44243013e+00, - 2.75221387e+00, 2.82714102e+00, 3.02109383e+00, 6.88603970e+00, - 8.55954606e+00, 9.85239944e+00, 1.14488917e+01, 1.20953777e+01, - 1.29729655e+01, 1.58640798e+01, 3.65763661e+01, 5.66287991e+01, - 4.39985749e+02, 5.91890647e+02])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.5301397603427972, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=[0], model=ScalarModel(intercept=0.18834408422290103, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 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old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.5301397603427972, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.973876171490377, linear_terms=array([ 0.72542682, 14.35685233, 14.83307135]), square_terms=array([[ 0.11413661, 1.93082302, 1.94450878], - [ 1.93082302, 36.39381604, 37.51674902], - [ 1.94450878, 37.51674902, 38.95260216]]), scale=array([0.42729051, 0.38454047, 0.3 ]), shift=array([5.3013976 , 0.39454047, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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linear_terms=array([-1.30558411, 10.89377008, 12.35168828]), square_terms=array([[ 0.13964041, -1.14158086, -1.299604 ], - [-1.14158086, 9.51269818, 10.71520355], - [-1.299604 , 10.71520355, 12.18421669]]), scale=array([0.21364526, 0.21364526, 0.1959726 ]), shift=array([5.3013976 , 0.35179043, 0.9040274 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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fval=0.18834408422290103, rho=-0.29383208425185714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13]), old_indices_discarded=array([ 6, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.1325349400856993, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=6.9052436115388875, linear_terms=array([-0.69117403, 7.16500457, 9.13260536]), square_terms=array([[ 0.03812681, -0.36969498, -0.4746088 ], - [-0.36969498, 3.7854496 , 4.79829205], - [-0.4746088 , 4.79829205, 6.13906768]]), scale=0.1325349400856993, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16542657032258398, linear_terms=array([0.01630779, 0.01475797, 0.02073112]), square_terms=array([[ 0.05951927, -0.12255125, -0.23582986], - [-0.12255125, 0.26987927, 0.50459186], - [-0.23582986, 0.50459186, 0.96563619]]), scale=0.06626747004284965, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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candidate_index=28, candidate_x=array([5.23292383, 0.33864579, 0.91062871]), index=0, x=array([5.3013976 , 0.35179043, 0.92170005]), fval=0.18834408422290103, rho=-4.217700932825573, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16782671230459395, linear_terms=array([ 0.0009187 , -0.00784055, -0.02621131]), square_terms=array([[4.57181704e-05, 1.25022351e-03, 2.32119020e-03], - [1.25022351e-03, 9.27555996e-02, 1.94331965e-01], - [2.32119020e-03, 1.94331965e-01, 4.14037852e-01]]), scale=0.033133735021424825, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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relative_step_length=1.000149803912295, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.29121744, 0.32407875, 0.9367533 ]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 21, 22, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.16770628802381632, linear_terms=array([-0.00491028, -0.01260532, -0.02110318]), square_terms=array([[1.33508452e-03, 2.20746795e-02, 4.37252047e-02], - [2.20746795e-02, 3.94713894e-01, 7.95682111e-01], - [4.37252047e-02, 7.95682111e-01, 1.63156707e+00]]), scale=0.06626747004284965, shift=array([5.29121744, 0.32407875, 0.9367533 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 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linear_terms=array([ 0.00059146, -0.004534 , -0.01522606]), square_terms=array([[3.03577735e-05, 5.05858062e-04, 7.12748102e-04], - [5.05858062e-04, 9.30360296e-02, 1.93666384e-01], - [7.12748102e-04, 1.93666384e-01, 4.10425108e-01]]), scale=0.033133735021424825, shift=array([5.29121744, 0.32407875, 0.9367533 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - 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fval=0.1799906266171576, rho=1.161952729888368, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 20, 21, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([15, 16, 19, 22, 23]), step_length=0.03324491388144948, relative_step_length=1.0033554581139965, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.17004437609421225, linear_terms=array([-0.00047394, -0.007927 , -0.01239616]), square_terms=array([[3.94062801e-04, 1.07905924e-02, 2.16812211e-02], - [1.07905924e-02, 3.74115243e-01, 7.88602520e-01], - [2.16812211e-02, 7.88602520e-01, 1.69226096e+00]]), scale=0.06626747004284965, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.1684737651459072, linear_terms=array([0.00169024, 0.00402826, 0.00597484]), square_terms=array([[ 4.51971473e-04, -5.93887978e-03, -1.50514931e-02], - [-5.93887978e-03, 9.28119628e-02, 2.24673825e-01], - [-1.50514931e-02, 2.24673825e-01, 5.53588350e-01]]), scale=0.033133735021424825, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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2.85834005e-05, -7.58751720e-04, -1.71197263e-03], - [-7.58751720e-04, 2.93707342e-02, 6.18391893e-02], - [-1.71197263e-03, 6.18391893e-02, 1.32214577e-01]]), scale=0.016566867510712412, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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dtype=int32), old_indices_used=array([ 0, 17, 24, 25, 26, 28, 29, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.01739885473916772, relative_step_length=1.0502199482139474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27530111, 0.31274546, 0.94445306]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.17197721637129393, linear_terms=array([-0.0002372 , -0.00370266, -0.00638111]), square_terms=array([[ 3.15094268e-05, -3.42643248e-04, -1.18023772e-03], - [-3.42643248e-04, 1.05602531e-01, 2.35178611e-01], - [-1.18023772e-03, 2.35178611e-01, 5.32242456e-01]]), scale=0.033133735021424825, shift=array([5.27530111, 0.31274546, 0.94445306])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.17237609478517008, linear_terms=array([ 0.00518006, -0.00474084, -0.0125618 ]), square_terms=array([[ 2.10162976e-03, -2.78797705e-02, -6.43090686e-02], - [-2.78797705e-02, 4.31690458e-01, 9.58890944e-01], - [-6.43090686e-02, 9.58890944e-01, 2.16354079e+00]]), scale=0.06626747004284965, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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candidate_index=42, candidate_x=array([5.28676541, 0.32788588, 0.93995208]), index=42, x=array([5.28676541, 0.32788588, 0.93995208]), fval=0.17632410901347453, rho=1.690269630156808, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40, 41]), old_indices_discarded=array([17, 20, 22, 24, 25, 27, 28, 30, 33, 36]), step_length=0.016703174494972207, relative_step_length=1.0082276860228196, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.28676541, 0.32788588, 0.93995208]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 29, 31, 34, 35, 36, 37, 38, 39, 40, 42]), model=ScalarModel(intercept=0.17584179736570787, linear_terms=array([ 0.00072293, -0.00395619, -0.0056097 ]), square_terms=array([[ 2.77942804e-05, -1.36149379e-04, -6.48042884e-04], - [-1.36149379e-04, 1.20187024e-01, 2.58595324e-01], - [-6.48042884e-04, 2.58595324e-01, 5.64442351e-01]]), scale=0.033133735021424825, shift=array([5.28676541, 0.32788588, 0.93995208])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], 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old_indices_discarded=array([15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 32, 33, 41]), step_length=0.03319197686050779, relative_step_length=1.001757780674147, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 21, 24, 28, 31, 35, 36, 37, 38, 43]), model=ScalarModel(intercept=0.17516083815911454, linear_terms=array([0.00333895, 0.02345551, 0.04006676]), square_terms=array([[ 9.17825370e-04, -1.49978117e-02, -3.32074698e-02], - [-1.49978117e-02, 3.29043755e-01, 6.85571868e-01], - [-3.32074698e-02, 6.85571868e-01, 1.45766388e+00]]), scale=0.06626747004284965, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), model=ScalarModel(intercept=0.17500252339842898, linear_terms=array([0.00143805, 0.00318419, 0.00584766]), square_terms=array([[ 2.32402708e-04, -4.32320857e-03, -9.89988047e-03], - [-4.32320857e-03, 1.00521560e-01, 2.19588637e-01], - [-9.89988047e-03, 2.19588637e-01, 4.87595223e-01]]), scale=0.033133735021424825, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=45, candidate_x=array([5.22598818, 0.34124333, 0.9330266 ]), index=43, x=array([5.25969725, 0.34546622, 0.93220909]), fval=0.17587143240730305, rho=-0.04773487920840637, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), old_indices_discarded=array([15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 38, - 41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), model=ScalarModel(intercept=0.1751974222542535, linear_terms=array([0.00059828, 0.00104291, 0.00174938]), square_terms=array([[ 2.32452897e-05, 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old_indices_used=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), old_indices_discarded=array([19, 21, 24, 25, 26, 31, 33, 35, 38, 40, 41, 44]), step_length=0.017343162853838502, relative_step_length=1.0468583057493592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.24271155, 0.34212944, 0.93327545]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 29, 34, 36, 37, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.1748745633782247, linear_terms=array([ 0.00075962, -0.00172199, -0.00445794]), square_terms=array([[ 3.54305914e-05, -8.55387782e-04, -2.21218821e-03], - [-8.55387782e-04, 1.02537349e-01, 2.22590300e-01], - [-2.21218821e-03, 2.22590300e-01, 4.91024973e-01]]), scale=0.033133735021424825, shift=array([5.24271155, 0.34212944, 0.93327545])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=47, candidate_x=array([5.20917798, 0.34014316, 0.93432412]), index=47, x=array([5.20917798, 0.34014316, 0.93432412]), fval=0.1752985258377397, rho=0.5865083498507621, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 28, 29, 34, 36, 37, 42, 43, 44, 45, 46]), old_indices_discarded=array([ 0, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, - 38, 39, 40, 41]), step_length=0.03360870947755719, relative_step_length=1.0143350713653392, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 37, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.17493421876270587, linear_terms=array([-0.00156353, -0.00842537, -0.01190066]), square_terms=array([[0.00281928, 0.03109626, 0.0629877 ], - [0.03109626, 0.34695273, 0.70455773], - [0.0629877 , 0.70455773, 1.45975502]]), scale=0.06626747004284965, shift=array([5.20917798, 0.34014316, 0.93432412])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=48, candidate_x=array([5.27749183, 0.35670252, 0.9239295 ]), index=47, x=array([5.20917798, 0.34014316, 0.93432412]), fval=0.1752985258377397, rho=-2.6662319197648348, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 21, 28, 33, 36, 37, 43, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 14, 15, 16, 19, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, - 35, 38, 39, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.17451083881940163, linear_terms=array([ 0.00045657, -0.00270283, -0.00529828]), square_terms=array([[4.56076655e-05, 1.60381216e-03, 3.26261075e-03], - [1.60381216e-03, 9.80067681e-02, 2.16994269e-01], - [3.26261075e-03, 2.16994269e-01, 4.88889862e-01]]), scale=0.033133735021424825, shift=array([5.20917798, 0.34014316, 0.93432412])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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rho=0.563659202220171, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, - 35, 38, 39, 40, 41, 42]), step_length=0.033441763641147204, relative_step_length=1.0092965257168625, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 43, 44, 45, 46, 47, 49]), model=ScalarModel(intercept=0.17564926316541823, linear_terms=array([-0.00274284, -0.01384935, -0.02471199]), square_terms=array([[0.0035693 , 0.03481689, 0.07133062], - [0.03481689, 0.34263536, 0.7019492 ], - [0.07133062, 0.7019492 , 1.46757845]]), scale=0.06626747004284965, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 36, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.1762502933918212, linear_terms=array([-0.00155691, -0.00561339, -0.01308175]), square_terms=array([[0.00079825, 0.00800458, 0.01674233], - [0.00800458, 0.08167794, 0.17110631], - [0.01674233, 0.17110631, 0.36563479]]), scale=0.033133735021424825, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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linear_terms=array([0.00019334, 0.00068168, 0.002236 ]), square_terms=array([[6.30086486e-06, 1.31120182e-04, 1.92914840e-04], - [1.31120182e-04, 2.30933740e-02, 4.65644949e-02], - [1.92914840e-04, 4.65644949e-02, 9.56283946e-02]]), scale=0.016566867510712412, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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fval=0.1749771962623699, rho=-0.48116939358057886, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52]), model=ScalarModel(intercept=0.17496972296461294, linear_terms=array([ 6.97863248e-05, -8.21475281e-05, -4.56887150e-04]), square_terms=array([[1.84361126e-06, 2.04013067e-05, 2.19735979e-05], - [2.04013067e-05, 5.26300747e-03, 1.10824804e-02], - [2.19735979e-05, 1.10824804e-02, 2.38107760e-02]]), scale=0.008283433755356206, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.1749826973068216, linear_terms=array([0.0001074 , 0.00038707, 0.00171986]), square_terms=array([[7.58231429e-06, 2.29836559e-04, 3.96776253e-04], - [2.29836559e-04, 2.29465979e-02, 4.65350783e-02], - [3.96776253e-04, 4.65350783e-02, 9.61235255e-02]]), scale=0.016566867510712412, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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old_indices_discarded=array([], dtype=int32), step_length=0.00829691933846346, relative_step_length=1.0016280184649915, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.16274229, 0.3394025 , 0.93558336]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([28, 36, 44, 45, 46, 47, 49, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.17484837066121958, linear_terms=array([0.00018133, 0.00104454, 0.00262711]), square_terms=array([[ 6.95365975e-06, -7.57386741e-06, -1.02111471e-04], - [-7.57386741e-06, 2.04612522e-02, 4.30911992e-02], - [-1.02111471e-04, 4.30911992e-02, 9.26639337e-02]]), scale=0.016566867510712412, shift=array([5.16274229, 0.3394025 , 0.93558336])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, 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model_indices=array([49, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.17457404964764986, linear_terms=array([ 0.00013515, -0.00194552, -0.00421915]), square_terms=array([[ 1.71429278e-06, 8.55431370e-06, -6.69374131e-07], - [ 8.55431370e-06, 6.91060946e-03, 1.45165898e-02], - [-6.69374131e-07, 1.45165898e-02, 3.09731721e-02]]), scale=0.008283433755356206, shift=array([5.14672647, 0.34473233, 0.9326238 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 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State(trustregion=Region(center=array([5.14420282, 0.34178406, 0.93435027]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([49, 52, 53, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.17448263024954638, linear_terms=array([8.45063966e-05, 2.93674559e-04, 4.11365018e-04]), square_terms=array([[1.72519621e-06, 1.48187759e-05, 1.09062891e-05], - [1.48187759e-05, 6.27266360e-03, 1.32612134e-02], - [1.09062891e-05, 1.32612134e-02, 2.85049653e-02]]), scale=0.008283433755356206, shift=array([5.14420282, 0.34178406, 0.93435027])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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State(trustregion=Region(center=array([5.08741985, 0.34221541, 0.93430985]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.17399871546423445, linear_terms=array([5.54960169e-04, 2.77020491e-05, 2.46039514e-04]), square_terms=array([[1.12760714e-04, 1.09146716e-03, 9.82283047e-04], - [1.09146716e-03, 3.54762285e-01, 7.53825721e-01], - [9.82283047e-04, 7.53825721e-01, 1.63251500e+00]]), scale=0.06626747004284965, shift=array([5.08741985, 0.34221541, 0.93430985])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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candidate_x=array([4.85934517, 0.34730674, 0.93082827]), index=67, x=array([4.85934517, 0.34730674, 0.93082827]), fval=0.17278415445032944, rho=0.24429294106502478, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.033133735021424575, relative_step_length=0.9999999999999925, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.17800589377398301, linear_terms=array([-0.00030888, -0.11314456, -0.23802422]), square_terms=array([[ 1.44537530e-04, -2.97382575e-03, -7.90103681e-03], - [-2.97382575e-03, 1.94242721e+00, 4.15401254e+00], - [-7.90103681e-03, 4.15401254e+00, 8.91430260e+00]]), scale=0.06626747004284965, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.17242592156362221, linear_terms=array([-3.95014403e-05, 7.38523177e-03, 1.66472760e-02]), square_terms=array([[ 3.41520113e-05, -4.66917848e-04, -1.38426097e-03], - [-4.66917848e-04, 2.96431160e-01, 6.38285543e-01], - [-1.38426097e-03, 6.38285543e-01, 1.38203197e+00]]), scale=0.033133735021424825, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], 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square_terms=array([[7.38828522e-06, 1.36373870e-04, 2.09192264e-04], - [1.36373870e-04, 2.30290804e-02, 4.72040978e-02], - [2.09192264e-04, 4.72040978e-02, 9.83617297e-02]]), scale=0.016566867510712412, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([63, 67, 69, 70]), model=ScalarModel(intercept=0.17278415445032952, linear_terms=array([-0.0002103 , 0.00306398, 0.00684224]), square_terms=array([[ 5.08646918e-06, -1.24755432e-04, -3.02817812e-04], - [-1.24755432e-04, 1.06839840e-02, 2.29501959e-02], - [-3.02817812e-04, 2.29501959e-02, 4.98109499e-02]]), scale=0.008283433755356206, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 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77, 78, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.17275901380787767, linear_terms=array([ 5.13124779e-05, -7.09231772e-04, -1.50668997e-03]), square_terms=array([[ 5.24206101e-07, 2.61469482e-06, -1.23800127e-07], - [ 2.61469482e-06, 1.62536068e-03, 3.42956073e-03], - [-1.23800127e-07, 3.42956073e-03, 7.35873402e-03]]), scale=0.004141716877678103, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 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scale=0.008283433755356206, shift=array([4.855221 , 0.34798078, 0.93135837])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.855221 , 0.34798078, 0.93135837]), radius=0.004141716877678103, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 75, 76, 77, 78, 79, 80, 81, 83, 84]), model=ScalarModel(intercept=0.17260619326375592, linear_terms=array([ 2.78770574e-05, 3.99370799e-05, -3.58102897e-05]), square_terms=array([[6.23527341e-07, 1.30746129e-05, 2.28943703e-05], - [1.30746129e-05, 1.62671678e-03, 3.42953962e-03], - [2.28943703e-05, 3.42953962e-03, 7.34898344e-03]]), scale=0.004141716877678103, shift=array([4.855221 , 0.34798078, 0.93135837])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 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model=ScalarModel(intercept=0.17259055555259742, linear_terms=array([3.92597809e-05, 5.10311101e-06, 2.28646135e-05]), square_terms=array([[2.47326484e-06, 5.53761478e-05, 9.73115792e-05], - [5.53761478e-05, 6.56831938e-03, 1.37867518e-02], - [9.73115792e-05, 1.37867518e-02, 2.94101730e-02]]), scale=0.008283433755356206, shift=array([4.8525976 , 0.34498062, 0.93277891])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - 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x=array([4.84436454, 0.3458171 , 0.93240807]), fval=0.1725720095711579, rho=0.09077331695760522, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 85, 86]), old_indices_discarded=array([63, 69, 70, 71, 74, 79, 82, 83]), step_length=0.008283749415294117, relative_step_length=1.0000381073775964, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.004141716877678103, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), model=ScalarModel(intercept=0.1725764987304485, linear_terms=array([7.05977087e-06, 2.29884404e-05, 1.21152777e-05]), square_terms=array([[5.86956811e-07, 1.16384872e-05, 1.94432612e-05], - [1.16384872e-05, 1.63380924e-03, 3.44140991e-03], - [1.94432612e-05, 3.44140991e-03, 7.36782486e-03]]), scale=0.004141716877678103, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, - 99, 100]), model=ScalarModel(intercept=0.17258738194234413, linear_terms=array([ 3.33838120e-06, -1.97151771e-08, 5.27903701e-06]), square_terms=array([[ 1.36507244e-07, 3.44142448e-07, -7.87178649e-07], - [ 3.44142448e-07, 4.10506238e-04, 8.69151483e-04], - [-7.87178649e-07, 8.69151483e-04, 1.87129773e-03]]), scale=0.0020708584388390515, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], 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linear_terms=array([ 2.41233698e-05, -2.61891003e-06, 5.11296505e-06]), square_terms=array([[ 4.37386430e-08, -5.79574560e-07, -1.57888647e-06], - [-5.79574560e-07, 1.02556710e-04, 2.17206204e-04], - [-1.57888647e-06, 2.17206204e-04, 4.67922792e-04]]), scale=0.0010354292194195258, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 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0.93232094]), fval=0.1725670999317009, rho=0.19934711308216232, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), old_indices_discarded=array([ 88, 100, 101]), step_length=0.001038810734087629, relative_step_length=1.0032658095837772, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84334274, 0.34598281, 0.93232094]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102]), model=ScalarModel(intercept=0.17258247058638926, linear_terms=array([-2.68495297e-05, -2.10095588e-06, -2.07300030e-05]), square_terms=array([[1.91163962e-07, 3.32105210e-06, 5.86912190e-06], - [3.32105210e-06, 4.07207519e-04, 8.64705423e-04], - [5.86912190e-06, 8.64705423e-04, 1.86822036e-03]]), scale=0.0020708584388390515, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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State(trustregion=Region(center=array([4.84334274, 0.34598281, 0.93232094]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.17258275627245578, linear_terms=array([-1.33291997e-05, -4.54612468e-06, -9.95213586e-06]), square_terms=array([[4.81840647e-08, 1.07426513e-06, 1.94899377e-06], - [1.07426513e-06, 1.02515718e-04, 2.17008681e-04], - [1.94899377e-06, 2.17008681e-04, 4.67269204e-04]]), scale=0.0010354292194195258, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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linear_terms=array([ 2.41019710e-05, -4.48842471e-06, -4.27936350e-05]), square_terms=array([[1.56434406e-07, 3.33318421e-06, 6.03774428e-06], - [3.33318421e-06, 4.15831904e-04, 8.74354178e-04], - [6.03774428e-06, 8.74354178e-04, 1.86706420e-03]]), scale=0.0020708584388390515, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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0.93234197]), fval=0.1725502344785951, rho=-1.01272054792213, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), old_indices_discarded=array([86, 88, 91, 93, 94, 95, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.17258372705737116, linear_terms=array([ 4.09478695e-06, -3.55795689e-06, -4.27096698e-05]), square_terms=array([[3.35585618e-08, 2.15061082e-07, 1.37358651e-07], - [2.15061082e-07, 1.03219001e-04, 2.16664028e-04], - [1.37358651e-07, 2.16664028e-04, 4.61793255e-04]]), scale=0.0010354292194195258, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0005177146097097629, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 96, 97, 98, 100, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.1725840937366451, linear_terms=array([ 3.96813424e-06, -3.12252958e-06, -1.95411424e-05]), square_terms=array([[ 8.35946962e-09, 7.31232339e-09, -6.36196010e-08], - [ 7.31232339e-09, 2.56886777e-05, 5.40554793e-05], - [-6.36196010e-08, 5.40554793e-05, 1.15524825e-04]]), scale=0.0005177146097097629, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 104, 108, 109]), model=ScalarModel(intercept=0.17255023447859502, linear_terms=array([-3.73002754e-05, -6.01751396e-06, -3.13820183e-06]), square_terms=array([[ 3.36899890e-08, -2.15013771e-08, -5.98360204e-08], - [-2.15013771e-08, 4.08581107e-07, 8.66434307e-07], - [-5.98360204e-08, 8.66434307e-07, 1.87671962e-06]]), scale=6.471432621372036e-05, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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old_indices_used=array([104, 110]), old_indices_discarded=array([], dtype=int32), step_length=3.2357163106865805e-05, relative_step_length=1.0000000000001739, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 124 entries., 'multistart_info': {'start_parameters': [array([5.33878077, 0.17065529, 1. ]), array([4.4728541 , 0.45753029, 0.84720369]), array([5.3013976 , 0.35179043, 0.92170005])], 'local_optima': [{'solution_x': array([5.21248464, 0.33676407, 0.93666301]), 'solution_criterion': 0.17512192544656371, 'states': [State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=[0], model=ScalarModel(intercept=0.24222229239256646, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 1. ])), 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candidate_index=13, candidate_x=array([5.76908436, 0.22960637, 0.83623674]), index=0, x=array([5.33878077, 0.17065529, 1. ]), fval=0.24222229239256646, rho=-0.6890593411620649, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.2669390387240524, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13]), model=ScalarModel(intercept=0.9034009752440392, linear_terms=array([-0.07604809, 2.69028608, 3.69106229]), square_terms=array([[ 4.61599959e-03, -1.10384595e-01, -1.63381782e-01], - [-1.10384595e-01, 4.67692138e+00, 6.22592464e+00], - [-1.63381782e-01, 6.22592464e+00, 8.42706648e+00]]), scale=array([0.21515179, 0.18790354, 0.1575759 ]), shift=array([5.33878077, 0.19790354, 0.9424241 ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=1.8208078161679868, linear_terms=array([-0.19004525, 2.49267386, 3.44430864]), square_terms=array([[ 0.01155517, -0.14231501, -0.19433776], - [-0.14231501, 1.88777533, 2.52140739], - [-0.19433776, 2.52140739, 3.42876704]]), scale=array([0.1075759 , 0.1075759 , 0.10378795]), shift=array([5.33878077, 0.17065529, 0.99621205])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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0.615266 , 1.31192301], - [-0.54318936, 1.31192301, 2.8728991 ]]), scale=0.0667347596810131, shift=array([5.33878077, 0.17065529, 1. ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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x=array([5.30968909, 0.25995847, 0.96850838]), fval=0.18645615650369046, rho=1.932188396931057, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29]), old_indices_discarded=array([14, 15, 17, 24, 26]), step_length=0.07028379860830133, relative_step_length=1.0531812648198984, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30968909, 0.25995847, 0.96850838]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), model=ScalarModel(intercept=0.1877934505138043, linear_terms=array([-0.03082531, -0.06829227, -0.20801109]), square_terms=array([[ 0.02060883, 0.09482843, 0.45039114], - [ 0.09482843, 0.47240082, 2.15885408], - [ 0.45039114, 2.15885408, 10.7559403 ]]), scale=array([0.1075759, 0.1075759, 0.1075759]), shift=array([5.30968909, 0.25995847, 0.96850838])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30968909, 0.25995847, 0.96850838]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 28, 29, 30, 31]), model=ScalarModel(intercept=0.18732156451899337, linear_terms=array([-0.00503334, 0.01007561, 0.10159589]), square_terms=array([[0.00372373, 0.03082621, 0.11068236], - [0.03082621, 0.26932161, 0.95090676], - [0.11068236, 0.95090676, 3.51609679]]), scale=0.0667347596810131, shift=array([5.30968909, 0.25995847, 0.96850838])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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0.21124739]), square_terms=array([[ 0.02086028, 0.15586295, 0.51678677], - [ 0.15586295, 1.20259849, 3.90266093], - [ 0.51678677, 3.90266093, 13.13777487]]), scale=0.1334695193620262, shift=array([5.35620118, 0.30725103, 0.95237438])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, - 24, 25, 26, 27]), step_length=0.1362670918270145, relative_step_length=1.0209603846508213, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.49059454, 0.32570596, 0.93946857]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 19, 20, 22, 23, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.180667786417314, linear_terms=array([ 0.00742114, -0.04161736, -0.07431271]), square_terms=array([[1.78983921e-03, 2.53213785e-02, 7.69037706e-02], - [2.53213785e-02, 4.62056121e-01, 1.34550576e+00], - [7.69037706e-02, 1.34550576e+00, 4.02143592e+00]]), scale=0.0667347596810131, shift=array([5.49059454, 0.32570596, 0.93946857])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 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radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([16, 31, 32, 33, 34]), model=ScalarModel(intercept=0.17885448842070745, linear_terms=array([ 0.00036015, -0.00617824, -0.01541375]), square_terms=array([[ 4.69844786e-05, -5.76980455e-04, -1.75264843e-03], - [-5.76980455e-04, 7.91846870e-02, 1.77376377e-01], - [-1.75264843e-03, 1.77376377e-01, 4.05791772e-01]]), scale=0.03336737984050655, shift=array([5.49059454, 0.32570596, 0.93946857])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 1. ])), candidate_index=35, candidate_x=array([5.45605167, 0.31937954, 0.94335016]), index=35, x=array([5.45605167, 0.31937954, 0.94335016]), fval=0.17865352146923588, rho=1.7560661361636971, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([16, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.03533129183788615, relative_step_length=1.0588572434145846, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.45605167, 0.31937954, 0.94335016]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([16, 19, 22, 23, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.17878573888910204, linear_terms=array([0.00314262, 0.0122901 , 0.05451291]), square_terms=array([[2.48587668e-03, 3.36638168e-02, 9.88750525e-02], - [3.36638168e-02, 4.62230398e-01, 1.35093786e+00], - [9.88750525e-02, 1.35093786e+00, 4.02709990e+00]]), scale=0.0667347596810131, shift=array([5.45605167, 0.31937954, 0.94335016])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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old_indices_discarded=array([ 0, 15, 17, 18, 20, 21, 24, 25, 26, 27]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.45605167, 0.31937954, 0.94335016]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([16, 22, 23, 28, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.1790323519378867, linear_terms=array([0.0013272 , 0.00989131, 0.0356542 ]), square_terms=array([[3.55389452e-04, 6.44027461e-03, 1.85296295e-02], - [6.44027461e-03, 1.18577376e-01, 3.42723014e-01], - [1.85296295e-02, 3.42723014e-01, 1.00879983e+00]]), scale=0.03336737984050655, shift=array([5.45605167, 0.31937954, 0.94335016])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, 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30, 31, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.17700172025913433, linear_terms=array([0.00175806, 0.00225572, 0.01143802]), square_terms=array([[1.47745785e-03, 2.63476322e-02, 7.55629276e-02], - [2.63476322e-02, 4.77095203e-01, 1.37390372e+00], - [7.55629276e-02, 1.37390372e+00, 4.02874190e+00]]), scale=0.0667347596810131, shift=array([5.43271536, 0.34232811, 0.93481237])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], 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index=37, x=array([5.43271536, 0.34232811, 0.93481237]), fval=0.1783330759349926, rho=-1.1757297921299623, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([16, 22, 23, 28, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.43271536, 0.34232811, 0.93481237]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([16, 23, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.17796437166095838, linear_terms=array([0.00053793, 0.01010837, 0.02505791]), square_terms=array([[ 2.91945200e-05, -2.80425639e-04, -1.06624623e-03], - [-2.80425639e-04, 8.52256537e-02, 1.96627340e-01], - [-1.06624623e-03, 1.96627340e-01, 4.63387728e-01]]), scale=0.03336737984050655, shift=array([5.43271536, 0.34232811, 0.93481237])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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State(trustregion=Region(center=array([5.43271536, 0.34232811, 0.93481237]), radius=0.016683689920253274, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([31, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.17859393839701726, linear_terms=array([0.00025769, 0.00346171, 0.00673753]), square_terms=array([[5.45007628e-06, 1.44712556e-04, 2.48310922e-04], - [1.44712556e-04, 2.68411053e-02, 5.93478659e-02], - [2.48310922e-04, 5.93478659e-02, 1.33139624e-01]]), scale=0.016683689920253274, shift=array([5.43271536, 0.34232811, 0.93481237])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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old_indices_used=array([28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([16, 22, 23, 29]), step_length=0.033843016758019415, relative_step_length=1.0142545479982659, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.38728963, 0.34094095, 0.93379815]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17833091404398044, linear_terms=array([ 0.00023398, -0.00934546, -0.02018432]), square_terms=array([[ 1.84541465e-04, -3.68424553e-03, -1.03268272e-02], - [-3.68424553e-03, 3.76260371e-01, 8.61424538e-01], - [-1.03268272e-02, 8.61424538e-01, 2.00874749e+00]]), scale=0.0667347596810131, shift=array([5.38728963, 0.34094095, 0.93379815])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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0.92835645]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([28, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.1780148058238638, linear_terms=array([-0.00063323, 0.00975489, 0.01812727]), square_terms=array([[ 3.82472761e-04, -3.14587621e-03, -1.38016696e-02], - [-3.14587621e-03, 1.40349253e+00, 3.23544311e+00], - [-1.38016696e-02, 3.23544311e+00, 7.61272179e+00]]), scale=0.1334695193620262, shift=array([5.32187633, 0.35441087, 0.92835645])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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[0., 0., 0.]]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 1. ])), candidate_index=43, candidate_x=array([5.45360088, 0.33279398, 0.9374639 ]), index=42, x=array([5.32187633, 0.35441087, 0.92835645]), fval=0.17729627717476631, rho=-1.495239835103236, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([28, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 33, 35]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.32187633, 0.35441087, 0.92835645]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.17765432457403693, linear_terms=array([ 0.00037676, -0.00182863, -0.00374153]), square_terms=array([[ 1.50428947e-04, -2.69288377e-03, -7.97567575e-03], - [-2.69288377e-03, 3.75575586e-01, 8.63048473e-01], - [-7.97567575e-03, 8.63048473e-01, 2.02064605e+00]]), scale=0.0667347596810131, shift=array([5.32187633, 0.35441087, 0.92835645])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 1. ])), candidate_index=44, candidate_x=array([5.25577068, 0.36291503, 0.92458747]), index=44, x=array([5.25577068, 0.36291503, 0.92458747]), fval=0.1767393872279913, rho=1.518722789411614, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 0, 5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, - 29, 33, 35]), step_length=0.0667568953608683, relative_step_length=1.0003316964047075, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([23, 28, 30, 31, 32, 36, 38, 39, 40, 41, 42, 44]), model=ScalarModel(intercept=0.17756581846293762, linear_terms=array([-0.00064376, 0.02147547, 0.0472283 ]), square_terms=array([[ 6.33696631e-04, -1.27178250e-02, -3.69738106e-02], - [-1.27178250e-02, 1.40423713e+00, 3.24790628e+00], - [-3.69738106e-02, 3.24790628e+00, 7.66915637e+00]]), scale=0.1334695193620262, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([23, 28, 30, 32, 36, 38, 39, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.1766790596574145, linear_terms=array([0.00015191, 0.01298194, 0.03144833]), square_terms=array([[ 1.77245269e-04, -3.57687440e-03, -1.01635519e-02], - [-3.57687440e-03, 3.48864307e-01, 8.05273892e-01], - [-1.01635519e-02, 8.05273892e-01, 1.89795527e+00]]), scale=0.0667347596810131, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 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model=ScalarModel(intercept=0.17525877839750087, linear_terms=array([-0.00010129, -0.00030159, 0.00010143]), square_terms=array([[ 7.27975974e-07, -3.44039770e-06, -1.62287222e-05], - [-3.44039770e-06, 1.59357350e-03, 3.51627807e-03], - [-1.62287222e-05, 3.51627807e-03, 7.91154768e-03]]), scale=0.004170922480063319, shift=array([5.21614051, 0.34083281, 0.93444849])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - 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x=array([5.21614051, 0.34083281, 0.93444849]), fval=0.17525877839750098, rho=-0.24091601228165707, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.21614051, 0.34083281, 0.93444849]), radius=0.0020854612400316593, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([66, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.1752716135829646, linear_terms=array([ 2.83868377e-05, -1.19751994e-04, -3.17911081e-04]), square_terms=array([[1.11944911e-07, 1.15337902e-06, 1.26187859e-06], - [1.15337902e-06, 3.96183939e-04, 8.51195967e-04], - [1.26187859e-06, 8.51195967e-04, 1.85994556e-03]]), scale=0.0020854612400316593, shift=array([5.21614051, 0.34083281, 0.93444849])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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State(trustregion=Region(center=array([5.21445938, 0.33986937, 0.93523224]), radius=0.004170922480063319, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([66, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 83]), model=ScalarModel(intercept=0.17518669448557733, linear_terms=array([ 7.78751734e-05, 7.17819525e-05, -3.14079467e-05]), square_terms=array([[ 4.44838423e-07, 1.98588662e-06, -9.88468833e-07], - [ 1.98588662e-06, 1.57281816e-03, 3.39350199e-03], - [-9.88468833e-07, 3.39350199e-03, 7.44451235e-03]]), scale=0.004170922480063319, shift=array([5.21445938, 0.33986937, 0.93523224])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([66, 71, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83]), old_indices_discarded=array([69, 70, 72, 78, 84]), step_length=0.0020864655983197176, relative_step_length=1.0004816000742565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2128509 , 0.338664 , 0.93579187]), radius=0.004170922480063319, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([66, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.17516697035176923, linear_terms=array([5.83865581e-05, 4.63782474e-05, 1.68217449e-05]), square_terms=array([[4.67576709e-07, 8.05449658e-06, 1.20770208e-05], - [8.05449658e-06, 1.57585820e-03, 3.39676314e-03], - [1.20770208e-05, 3.39676314e-03, 7.44531749e-03]]), scale=0.004170922480063319, shift=array([5.2128509 , 0.338664 , 0.93579187])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 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scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=13, candidate_x=array([4.83336435, 0.4575753 , 0.65009533]), index=0, x=array([4.4728541 , 0.45753029, 0.84720369]), fval=0.2599824045161018, rho=-0.12411983528344617, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.4728541 , 0.45753029, 0.84720369]), radius=0.22364270522475313, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13]), model=ScalarModel(intercept=13.32075376929986, linear_terms=array([-0.77855773, 22.15052524, 31.25464247]), square_terms=array([[ 3.62123655e-02, -5.69173819e-01, -8.75044366e-01], - [-5.69173819e-01, 1.90363765e+01, 2.64619666e+01], - 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1, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.4728541 , 0.45753029, 0.84720369]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14]), model=ScalarModel(intercept=11.15823156654076, linear_terms=array([-2.20441623, 7.91443486, 12.3102787 ]), square_terms=array([[ 0.22591551, -0.80305612, -1.23945487], - [-0.80305612, 2.88610994, 4.42245511], - [-1.23945487, 4.42245511, 6.86786803]]), scale=0.11182135261237656, shift=array([4.4728541 , 0.45753029, 0.84720369])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 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new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.05679485397543904, relative_step_length=1.0158141115018653, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.50720674, 0.43612797, 0.88704705]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28]), model=ScalarModel(intercept=0.18327143599497123, linear_terms=array([-5.41772218e-05, 9.48399680e-02, 8.15930570e-02]), square_terms=array([[1.64107302e-03, 3.85060065e-02, 5.19063677e-02], - [3.85060065e-02, 1.24556355e+00, 1.71851894e+00], - [5.19063677e-02, 1.71851894e+00, 2.40787942e+00]]), scale=0.11182135261237656, shift=array([4.50720674, 0.43612797, 0.88704705])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.17579528910570869, linear_terms=array([ 0.00021618, -0.01105215, -0.01976729]), square_terms=array([[ 1.96121450e-04, -3.38913019e-03, -7.33697269e-03], - [-3.38913019e-03, 2.06463498e-01, 3.77049739e-01], - [-7.33697269e-03, 3.77049739e-01, 7.04976367e-01]]), scale=0.05591067630618828, shift=array([4.60129558, 0.3319619 , 0.93616111])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=36, candidate_x=array([4.54190424, 0.34416472, 0.93058415]), index=36, x=array([4.54190424, 0.34416472, 0.93058415]), fval=0.17557093407600716, rho=0.20005246667450538, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 0, 15, 16, 17, 18, 19, 21, 22, 23, 24]), step_length=0.0608879546199208, relative_step_length=1.0890219657954956, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.54190424, 0.34416472, 0.93058415]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.17568176927667278, linear_terms=array([-0.00016853, -0.01916852, -0.03813775]), square_terms=array([[ 5.84778824e-04, -9.07741680e-03, 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old_indices_used=array([20, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 8, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, - 23, 24, 27]), step_length=0.11188469579144777, relative_step_length=1.00056646765212, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65201452, 0.32727883, 0.94101379]), radius=0.22364270522475313, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 28, 29, 30, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.19282537449314455, linear_terms=array([-0.00075052, -0.3155938 , -0.55999152]), square_terms=array([[2.25983121e-03, 6.61141972e-02, 1.06324274e-01], - [6.61141972e-02, 3.24859534e+00, 5.56048612e+00], - [1.06324274e-01, 5.56048612e+00, 9.66322356e+00]]), scale=array([0.18025512, 0.18025512, 0.16962067]), shift=array([4.65201452, 0.32727883, 0.93037933])), vector_model=VectorModel(intercepts=array([ 0.06583231, 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.65201452, 0.32727883, 0.94101379]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 26, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.1755053447924909, linear_terms=array([0.0009879 , 0.02059209, 0.0322827 ]), square_terms=array([[4.69289255e-04, 6.84432041e-03, 9.11727164e-03], - [6.84432041e-03, 1.07829645e+00, 2.03285155e+00], - [9.11727164e-03, 2.03285155e+00, 3.90203103e+00]]), scale=0.11182135261237656, shift=array([4.65201452, 0.32727883, 0.94101379])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=39, candidate_x=array([4.53916448, 0.31780007, 0.9452902 ]), index=37, x=array([4.65201452, 0.32727883, 0.94101379]), fval=0.17479029841995192, rho=-3.0403572413217264, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([20, 26, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 0, 2, 3, 6, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, - 24, 25, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65201452, 0.32727883, 0.94101379]), radius=0.05591067630618828, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.17535921159705348, linear_terms=array([4.38472954e-05, 7.95540383e-03, 1.29641548e-02]), square_terms=array([[1.48495897e-04, 3.66230666e-03, 5.98532157e-03], - [3.66230666e-03, 3.24058063e-01, 6.11393607e-01], - [5.98532157e-03, 6.11393607e-01, 1.17098510e+00]]), scale=0.05591067630618828, shift=array([4.65201452, 0.32727883, 0.94101379])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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State(trustregion=Region(center=array([4.65564547, 0.35033578, 0.93001783]), radius=0.006988834538273535, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58]), model=ScalarModel(intercept=0.17320449006214028, linear_terms=array([-3.05104415e-05, 1.99630776e-05, -1.99729776e-04]), square_terms=array([[ 1.82853803e-06, -1.07295862e-06, -1.93551509e-05], - [-1.07295862e-06, 4.95414255e-03, 1.02579296e-02], - [-1.93551509e-05, 1.02579296e-02, 2.15599033e-02]]), scale=0.006988834538273535, shift=array([4.65564547, 0.35033578, 0.93001783])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=59, candidate_x=array([4.65947059, 0.34493224, 0.93264892]), index=59, x=array([4.65947059, 0.34493224, 0.93264892]), fval=0.17319386240144724, rho=0.5556773242862739, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58]), old_indices_discarded=array([37, 43, 44, 45, 49, 55]), step_length=0.0071240740929125876, relative_step_length=1.0193508021828859, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65947059, 0.34493224, 0.93264892]), radius=0.01397766907654707, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 59]), model=ScalarModel(intercept=0.17316220625202705, linear_terms=array([-3.50632174e-05, 1.39156316e-04, 1.47834988e-04]), square_terms=array([[7.28393196e-06, 7.01493967e-05, 8.02193747e-05], - [7.01493967e-05, 1.96925095e-02, 4.09319734e-02], - [8.02193747e-05, 4.09319734e-02, 8.63772645e-02]]), scale=0.01397766907654707, shift=array([4.65947059, 0.34493224, 0.93264892])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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fval=0.17319386240144724, rho=-2.368503752157372, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 59]), old_indices_discarded=array([33, 34, 37, 40, 42, 43, 44, 45, 49, 55, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65947059, 0.34493224, 0.93264892]), radius=0.006988834538273535, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59]), model=ScalarModel(intercept=0.17322335454301357, linear_terms=array([5.84657279e-05, 5.61600646e-05, 1.11287704e-04]), square_terms=array([[ 1.73166366e-06, -5.21647152e-06, -2.70055797e-05], - [-5.21647152e-06, 4.91675196e-03, 1.02038442e-02], - [-2.70055797e-05, 1.02038442e-02, 2.15010081e-02]]), scale=0.006988834538273535, shift=array([4.65947059, 0.34493224, 0.93264892])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.01397766907654707, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 45, 47, 48, 50, 51, 53, 54, 56, 57, 59, 61]), model=ScalarModel(intercept=0.17319833291851128, linear_terms=array([-1.51384574e-04, -7.53489850e-06, -1.33953022e-04]), square_terms=array([[7.88278598e-06, 6.56801117e-05, 6.75420704e-05], - [6.56801117e-05, 1.96414914e-02, 4.08747995e-02], - [6.75420704e-05, 4.08747995e-02, 8.63573595e-02]]), scale=0.01397766907654707, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 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model=ScalarModel(intercept=0.1731417369534366, linear_terms=array([8.53555441e-05, 3.94898724e-05, 4.21614549e-05]), square_terms=array([[ 1.87710384e-06, -1.89887381e-05, -5.59774665e-05], - [-1.89887381e-05, 4.91019732e-03, 1.02046258e-02], - [-5.59774665e-05, 1.02046258e-02, 2.15316921e-02]]), scale=0.006988834538273535, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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index=61, x=array([4.65246302, 0.34513124, 0.93250988]), fval=0.17314745134607396, rho=-0.7767331150668283, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 48, 49, 50, 51, 54, 55, 56, 58, 59, 61]), old_indices_discarded=array([37, 44, 45, 47, 52, 53, 57, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.0034944172691367677, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 54, 56, 58, 59, 61, 63]), model=ScalarModel(intercept=0.17318840104590158, linear_terms=array([8.40920959e-06, 1.60644298e-06, 1.78650732e-05]), square_terms=array([[ 4.56677469e-07, -1.61383200e-06, -7.65761701e-06], - [-1.61383200e-06, 1.22943708e-03, 2.55528223e-03], - [-7.65761701e-06, 2.55528223e-03, 5.39279040e-03]]), scale=0.0034944172691367677, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.0017472086345683838, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 56, 58, 59, 61, 63, 64]), model=ScalarModel(intercept=0.1731833699424632, linear_terms=array([-1.55880845e-06, 2.34892542e-05, 3.50350099e-05]), square_terms=array([[1.14286970e-07, 5.95276640e-07, 1.79303603e-07], - [5.95276640e-07, 3.00448801e-04, 6.27473167e-04], - [1.79303603e-07, 6.27473167e-04, 1.33116839e-03]]), scale=0.0017472086345683838, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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model=ScalarModel(intercept=0.17320359100698507, linear_terms=array([-1.04740719e-05, -4.15791794e-05, 1.53390193e-05]), square_terms=array([[ 3.35637081e-08, 1.29446597e-07, -5.58028919e-08], - [ 1.29446597e-07, 7.70487534e-05, 1.63752882e-04], - [-5.58028919e-08, 1.63752882e-04, 3.53849647e-04]]), scale=0.0008736043172841919, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=78, candidate_x=array([4.65266665, 0.34590517, 0.93215796]), index=78, x=array([4.65266665, 0.34590517, 0.93215796]), fval=0.17313846350417914, rho=0.20010394985498053, accepted=True, new_indices=array([66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), old_indices_used=array([61, 64, 65]), old_indices_discarded=array([], dtype=int32), step_length=0.0008742235274298533, relative_step_length=1.0007087993195665, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.0017472086345683838, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78]), model=ScalarModel(intercept=0.17316117071960616, linear_terms=array([ 1.17846159e-06, -7.30897908e-05, 3.56834929e-05]), square_terms=array([[ 1.26266358e-07, -2.95552318e-07, -1.85714932e-06], - [-2.95552318e-07, 3.06633677e-04, 6.53705727e-04], - [-1.85714932e-06, 6.53705727e-04, 1.41658448e-03]]), scale=0.0017472086345683838, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.0008736043172841919, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78]), model=ScalarModel(intercept=0.17315497758754703, linear_terms=array([-1.03031559e-05, -4.11416984e-05, 1.56495230e-05]), square_terms=array([[ 3.02714973e-08, 8.04834807e-08, -1.41448123e-07], - [ 8.04834807e-08, 7.72107726e-05, 1.63938593e-04], - [-1.41448123e-07, 1.63938593e-04, 3.53626006e-04]]), scale=0.0008736043172841919, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), 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model=ScalarModel(intercept=0.17317273550338308, linear_terms=array([ 4.91859051e-07, -1.11539853e-05, -3.77793814e-06]), square_terms=array([[ 7.22202027e-09, 2.91274101e-08, -7.91097523e-09], - [ 2.91274101e-08, 1.93478699e-05, 4.10231377e-05], - [-7.91097523e-09, 4.10231377e-05, 8.82835907e-05]]), scale=0.00043680215864209596, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 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index=78, x=array([4.65266665, 0.34590517, 0.93215796]), fval=0.17313846350417914, rho=-2.0772563083934017, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 80]), old_indices_discarded=array([68, 70, 76, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.00021840107932104798, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 67, 69, 74, 75, 78, 80, 81]), model=ScalarModel(intercept=0.1731737579944651, linear_terms=array([-3.79844970e-06, -3.69441907e-06, 9.93700675e-07]), square_terms=array([[2.70592783e-09, 4.30842813e-08, 7.31011322e-08], - [4.30842813e-08, 4.85108303e-06, 1.02966777e-05], - [7.31011322e-08, 1.02966777e-05, 2.21791802e-05]]), scale=0.00021840107932104798, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.00010920053966052399, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 74, 78, 81, 82]), model=ScalarModel(intercept=0.17315192460300521, linear_terms=array([-2.81303010e-06, -3.86667778e-06, -1.04055075e-05]), square_terms=array([[ 2.19586773e-09, -3.23204802e-08, -7.89353277e-08], - [-3.23204802e-08, 1.26964622e-06, 2.77090607e-06], - [-7.89353277e-08, 2.77090607e-06, 6.13234159e-06]]), scale=0.00010920053966052399, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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square_terms=array([[2.64398046e-09, 3.49469833e-08, 5.44401595e-08], - [3.49469833e-08, 4.84395791e-06, 1.02903559e-05], - [5.44401595e-08, 1.02903559e-05, 2.21879286e-05]]), scale=0.00021840107932104798, shift=array([4.65274023, 0.34596235, 0.93221488])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, - -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, - -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([61, 66, 67, 69, 74, 75, 78, 80, 81, 82, 83]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65274023, 0.34596235, 0.93221488]), radius=0.00010920053966052399, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 74, 78, 81, 82, 83, 84]), model=ScalarModel(intercept=0.17313324975295558, linear_terms=array([ 1.13548705e-07, -7.38513649e-06, -1.90247819e-05]), square_terms=array([[ 9.59722961e-10, -1.16741064e-08, -3.13545253e-08], - [-1.16741064e-08, 1.20937518e-06, 2.62532345e-06], - [-3.13545253e-08, 2.62532345e-06, 5.78198005e-06]]), scale=0.00010920053966052399, shift=array([4.65274023, 0.34596235, 0.93221488])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, - 0.06536156, 0.03646807, 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3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.2650698801713986, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13]), model=ScalarModel(intercept=6.371470325591758, linear_terms=array([-1.30558411, 10.89377008, 12.35168828]), square_terms=array([[ 0.13964041, -1.14158086, -1.299604 ], - [-1.14158086, 9.51269818, 10.71520355], - [-1.299604 , 10.71520355, 12.18421669]]), scale=array([0.21364526, 0.21364526, 0.1959726 ]), shift=array([5.3013976 , 0.35179043, 0.9040274 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , 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10, 12, 13, 14]), model=ScalarModel(intercept=6.9052436115388875, linear_terms=array([-0.69117403, 7.16500457, 9.13260536]), square_terms=array([[ 0.03812681, -0.36969498, -0.4746088 ], - [-0.36969498, 3.7854496 , 4.79829205], - [-0.4746088 , 4.79829205, 6.13906768]]), scale=0.1325349400856993, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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0.35179043, 0.92170005]), fval=0.18834408422290103, rho=-0.21724111958860234, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14]), old_indices_discarded=array([6, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16542657032258398, linear_terms=array([0.01630779, 0.01475797, 0.02073112]), square_terms=array([[ 0.05951927, -0.12255125, -0.23582986], - [-0.12255125, 0.26987927, 0.50459186], - [-0.23582986, 0.50459186, 0.96563619]]), scale=0.06626747004284965, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 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22, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([14, 15, 16, 19, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.29121744, 0.32407875, 0.9367533 ]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 21, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.1685046981505246, linear_terms=array([ 0.00059146, -0.004534 , -0.01522606]), square_terms=array([[3.03577735e-05, 5.05858062e-04, 7.12748102e-04], - [5.05858062e-04, 9.30360296e-02, 1.93666384e-01], - [7.12748102e-04, 1.93666384e-01, 4.10425108e-01]]), scale=0.033133735021424825, shift=array([5.29121744, 0.32407875, 0.9367533 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, 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model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.17004437609421225, linear_terms=array([-0.00047394, -0.007927 , -0.01239616]), square_terms=array([[3.94062801e-04, 1.07905924e-02, 2.16812211e-02], - [1.07905924e-02, 3.74115243e-01, 7.88602520e-01], - [2.16812211e-02, 7.88602520e-01, 1.69226096e+00]]), scale=0.06626747004284965, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), 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candidate_x=array([5.34476989, 0.3113389 , 0.94369486]), index=31, x=array([5.27834936, 0.29684139, 0.95081613]), fval=0.1799906266171576, rho=-2.1259132820697104, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([14, 15, 16, 19, 21, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.1684737651459072, linear_terms=array([0.00169024, 0.00402826, 0.00597484]), square_terms=array([[ 4.51971473e-04, -5.93887978e-03, -1.50514931e-02], - [-5.93887978e-03, 9.28119628e-02, 2.24673825e-01], - [-1.50514931e-02, 2.24673825e-01, 5.53588350e-01]]), 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fval=0.17755692096608, rho=1.8988432161216091, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([15, 16, 19, 20, 21, 22, 23, 27, 30]), step_length=0.033281691430788476, relative_step_length=1.004465431055931, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30566766, 0.32537105, 0.9393405 ]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.17237609478517008, linear_terms=array([ 0.00518006, -0.00474084, -0.0125618 ]), square_terms=array([[ 2.10162976e-03, -2.78797705e-02, -6.43090686e-02], - [-2.78797705e-02, 4.31690458e-01, 9.58890944e-01], - [-6.43090686e-02, 9.58890944e-01, 2.16354079e+00]]), scale=0.06626747004284965, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.30566766, 0.32537105, 0.9393405 ]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.1723760947851703, linear_terms=array([ 0.00259003, -0.00237042, -0.0062809 ]), square_terms=array([[ 5.25407439e-04, -6.96994262e-03, -1.60772672e-02], - [-6.96994262e-03, 1.07922614e-01, 2.39722736e-01], - [-1.60772672e-02, 2.39722736e-01, 5.40885198e-01]]), scale=0.033133735021424825, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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linear_terms=array([-0.00039037, -0.0076903 , -0.01984056]), square_terms=array([[1.68083117e-05, 5.99857721e-04, 1.30035997e-03], - [5.99857721e-04, 3.07066045e-02, 7.08873570e-02], - [1.30035997e-03, 7.08873570e-02, 1.65894328e-01]]), scale=0.016566867510712412, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - 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fval=0.17733908278986127, rho=0.12075915604171515, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 20, 25, 26, 29, 30, 31, 32, 34, 35, 37]), old_indices_discarded=array([22, 24, 27, 28, 33, 36]), step_length=0.01691501274286895, relative_step_length=1.021014547978453, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31420845, 0.31272412, 0.94663622]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 20, 25, 26, 29, 30, 31, 32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.17451053131095257, linear_terms=array([ 0.00026766, -0.00034627, -0.00190087]), square_terms=array([[ 2.82594589e-05, -3.98670431e-04, -1.30601253e-03], - [-3.98670431e-04, 1.28325976e-01, 2.92377009e-01], - [-1.30601253e-03, 2.92377009e-01, 6.74940821e-01]]), scale=0.033133735021424825, shift=array([5.31420845, 0.31272412, 0.94663622])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.31420845, 0.31272412, 0.94663622]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 20, 26, 29, 30, 31, 32, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.1771813911491111, linear_terms=array([ 1.44498627e-05, -7.05759415e-04, -8.71906755e-04]), square_terms=array([[6.32366784e-06, 6.97090062e-05, 6.24448079e-05], - [6.97090062e-05, 2.21772805e-02, 4.87945740e-02], - [6.24448079e-05, 4.87945740e-02, 1.09441144e-01]]), scale=0.016566867510712412, shift=array([5.31420845, 0.31272412, 0.94663622])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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linear_terms=array([-8.72839083e-04, 6.46911097e-05, -1.15041490e-03]), square_terms=array([[1.69827888e-04, 4.48946449e-03, 9.85596140e-03], - [4.48946449e-03, 1.31226175e-01, 2.95972451e-01], - [9.85596140e-03, 2.95972451e-01, 6.76192041e-01]]), scale=0.033133735021424825, shift=array([5.30300313, 0.32433431, 0.9416009 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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fval=0.1766329595168218, rho=-0.9795538799146807, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40]), old_indices_discarded=array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30300313, 0.32433431, 0.9416009 ]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17721715662436963, linear_terms=array([0.00016892, 0.00010182, 0.00044048]), square_terms=array([[6.47390632e-06, 4.70823827e-05, 1.17452629e-05], - [4.70823827e-05, 2.18434219e-02, 4.79874666e-02], - [1.17452629e-05, 4.79874666e-02, 1.07501169e-01]]), scale=0.016566867510712412, shift=array([5.30300313, 0.32433431, 0.9416009 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.28676541, 0.32788588, 0.93995208]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 29, 31, 34, 35, 36, 37, 38, 39, 40, 42]), model=ScalarModel(intercept=0.17584179736570787, linear_terms=array([ 0.00072293, -0.00395619, -0.0056097 ]), square_terms=array([[ 2.77942804e-05, -1.36149379e-04, -6.48042884e-04], - [-1.36149379e-04, 1.20187024e-01, 2.58595324e-01], - [-6.48042884e-04, 2.58595324e-01, 5.64442351e-01]]), scale=0.033133735021424825, shift=array([5.28676541, 0.32788588, 0.93995208])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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model=ScalarModel(intercept=0.17516083815911454, linear_terms=array([0.00333895, 0.02345551, 0.04006676]), square_terms=array([[ 9.17825370e-04, -1.49978117e-02, -3.32074698e-02], - [-1.49978117e-02, 3.29043755e-01, 6.85571868e-01], - [-3.32074698e-02, 6.85571868e-01, 1.45766388e+00]]), scale=0.06626747004284965, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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x=array([5.25969725, 0.34546622, 0.93220909]), fval=0.17587143240730305, rho=-0.2929960708109051, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 21, 24, 28, 31, 35, 36, 37, 38, 43]), old_indices_discarded=array([14, 15, 16, 19, 20, 22, 23, 25, 26, 27, 29, 30, 32, 33, 34, 39, 40, - 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), model=ScalarModel(intercept=0.17500252339842898, linear_terms=array([0.00143805, 0.00318419, 0.00584766]), square_terms=array([[ 2.32402708e-04, -4.32320857e-03, -9.89988047e-03], - [-4.32320857e-03, 1.00521560e-01, 2.19588637e-01], - [-9.89988047e-03, 2.19588637e-01, 4.87595223e-01]]), scale=0.033133735021424825, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), model=ScalarModel(intercept=0.1751974222542535, linear_terms=array([0.00059828, 0.00104291, 0.00174938]), square_terms=array([[ 2.32452897e-05, -6.04410229e-04, -1.41547067e-03], - [-6.04410229e-04, 2.52698536e-02, 5.50888216e-02], - [-1.41547067e-03, 5.50888216e-02, 1.22064179e-01]]), scale=0.016566867510712412, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), 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27, 29, 30, 31, 32, 34, - 35, 38, 39, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=0.17451083881940163, linear_terms=array([ 0.00045657, -0.00270283, -0.00529828]), square_terms=array([[4.56076655e-05, 1.60381216e-03, 3.26261075e-03], - [1.60381216e-03, 9.80067681e-02, 2.16994269e-01], - [3.26261075e-03, 2.16994269e-01, 4.88889862e-01]]), scale=0.033133735021424825, shift=array([5.20917798, 0.34014316, 0.93432412])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - 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24, 25, 26, 29, 31, 33, 34, 35, 37, 38, 39, 40, 41, - 42, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51]), model=ScalarModel(intercept=0.17468169315856466, linear_terms=array([0.00019334, 0.00068168, 0.002236 ]), square_terms=array([[6.30086486e-06, 1.31120182e-04, 1.92914840e-04], - [1.31120182e-04, 2.30933740e-02, 4.65644949e-02], - [1.92914840e-04, 4.65644949e-02, 9.56283946e-02]]), scale=0.016566867510712412, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - 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model=ScalarModel(intercept=0.17496972296461294, linear_terms=array([ 6.97863248e-05, -8.21475281e-05, -4.56887150e-04]), square_terms=array([[1.84361126e-06, 2.04013067e-05, 2.19735979e-05], - [2.04013067e-05, 5.26300747e-03, 1.10824804e-02], - [2.19735979e-05, 1.10824804e-02, 2.38107760e-02]]), scale=0.008283433755356206, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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index=53, x=array([5.17030197, 0.34249393, 0.93412279]), fval=0.17478348430580953, rho=1.7260767313376242, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([44, 47, 49, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.008414173637941258, relative_step_length=1.0157832954842565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.1749826973068216, linear_terms=array([0.0001074 , 0.00038707, 0.00171986]), square_terms=array([[7.58231429e-06, 2.29836559e-04, 3.96776253e-04], - [2.29836559e-04, 2.29465979e-02, 4.65350783e-02], - [3.96776253e-04, 4.65350783e-02, 9.61235255e-02]]), scale=0.016566867510712412, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=0.1747885766485168, linear_terms=array([ 7.81846919e-05, 3.99579562e-05, -9.60174366e-05]), square_terms=array([[1.77118649e-06, 1.19372381e-05, 4.26127615e-06], - [1.19372381e-05, 5.37799886e-03, 1.13872076e-02], - [4.26127615e-06, 1.13872076e-02, 2.45888809e-02]]), scale=0.008283433755356206, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.17800589377398301, linear_terms=array([-0.00030888, -0.11314456, -0.23802422]), square_terms=array([[ 1.44537530e-04, -2.97382575e-03, -7.90103681e-03], - [-2.97382575e-03, 1.94242721e+00, 4.15401254e+00], - [-7.90103681e-03, 4.15401254e+00, 8.91430260e+00]]), scale=0.06626747004284965, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([63, 67, 68, 69]), model=ScalarModel(intercept=0.1727841544503295, linear_terms=array([-7.91356495e-05, -4.65438901e-04, -1.53490121e-03]), square_terms=array([[7.38828522e-06, 1.36373870e-04, 2.09192264e-04], - [1.36373870e-04, 2.30290804e-02, 4.72040978e-02], - [2.09192264e-04, 4.72040978e-02, 9.83617297e-02]]), scale=0.016566867510712412, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84]), model=ScalarModel(intercept=0.17265527071731143, linear_terms=array([-7.78798603e-05, 6.36909620e-05, -1.62218247e-04]), square_terms=array([[2.54965658e-06, 3.42416705e-05, 4.82468529e-05], - [3.42416705e-05, 6.54926335e-03, 1.37980536e-02], - [4.82468529e-05, 1.37980536e-02, 2.95393721e-02]]), scale=0.008283433755356206, shift=array([4.855221 , 0.34798078, 0.93135837])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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0.35179043, 0.92170005])), candidate_index=85, candidate_x=array([4.86133568, 0.34289827, 0.93375881]), index=84, x=array([4.855221 , 0.34798078, 0.93135837]), fval=0.17268283509914406, rho=-0.03949268461742166, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84]), old_indices_discarded=array([63, 69, 70, 71, 75, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.855221 , 0.34798078, 0.93135837]), radius=0.004141716877678103, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 75, 76, 77, 78, 79, 80, 81, 83, 84]), model=ScalarModel(intercept=0.17260619326375592, linear_terms=array([ 2.78770574e-05, 3.99370799e-05, -3.58102897e-05]), square_terms=array([[6.23527341e-07, 1.30746129e-05, 2.28943703e-05], - [1.30746129e-05, 1.62671678e-03, 3.42953962e-03], - [2.28943703e-05, 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82, 85]), step_length=0.004230967300558663, relative_step_length=1.0215491366301683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.8525976 , 0.34498062, 0.93277891]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 85, 86]), model=ScalarModel(intercept=0.17259055555259742, linear_terms=array([3.92597809e-05, 5.10311101e-06, 2.28646135e-05]), square_terms=array([[2.47326484e-06, 5.53761478e-05, 9.73115792e-05], - [5.53761478e-05, 6.56831938e-03, 1.37867518e-02], - [9.73115792e-05, 1.37867518e-02, 2.94101730e-02]]), scale=0.008283433755356206, shift=array([4.8525976 , 0.34498062, 0.93277891])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - 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model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), model=ScalarModel(intercept=0.1725764987304485, linear_terms=array([7.05977087e-06, 2.29884404e-05, 1.21152777e-05]), square_terms=array([[5.86956811e-07, 1.16384872e-05, 1.94432612e-05], - [1.16384872e-05, 1.63380924e-03, 3.44140991e-03], - [1.94432612e-05, 3.44140991e-03, 7.36782486e-03]]), scale=0.004141716877678103, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=88, candidate_x=array([4.84072397, 0.34397348, 0.93327128]), index=87, x=array([4.84436454, 0.3458171 , 0.93240807]), fval=0.1725720095711579, rho=-1.0641290402854422, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), old_indices_discarded=array([74, 79, 82, 83, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, - 99, 100]), model=ScalarModel(intercept=0.17258738194234413, linear_terms=array([ 3.33838120e-06, -1.97151771e-08, 5.27903701e-06]), square_terms=array([[ 1.36507244e-07, 3.44142448e-07, -7.87178649e-07], - [ 3.44142448e-07, 4.10506238e-04, 8.69151483e-04], - [-7.87178649e-07, 8.69151483e-04, 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.1725830437240501, linear_terms=array([ 2.41233698e-05, -2.61891003e-06, 5.11296505e-06]), square_terms=array([[ 4.37386430e-08, -5.79574560e-07, -1.57888647e-06], - [-5.79574560e-07, 1.02556710e-04, 2.17206204e-04], - [-1.57888647e-06, 2.17206204e-04, 4.67922792e-04]]), scale=0.0010354292194195258, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), 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100, 101, 102]), model=ScalarModel(intercept=0.17258247058638926, linear_terms=array([-2.68495297e-05, -2.10095588e-06, -2.07300030e-05]), square_terms=array([[1.91163962e-07, 3.32105210e-06, 5.86912190e-06], - [3.32105210e-06, 4.07207519e-04, 8.64705423e-04], - [5.86912190e-06, 8.64705423e-04, 1.86822036e-03]]), scale=0.0020708584388390515, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 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relative_step_length=1.0043585886444073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.17259245410531784, linear_terms=array([ 2.41019710e-05, -4.48842471e-06, -4.27936350e-05]), square_terms=array([[1.56434406e-07, 3.33318421e-06, 6.03774428e-06], - [3.33318421e-06, 4.15831904e-04, 8.74354178e-04], - [6.03774428e-06, 8.74354178e-04, 1.86706420e-03]]), scale=0.0020708584388390515, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), 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105]), model=ScalarModel(intercept=0.17258372705737116, linear_terms=array([ 4.09478695e-06, -3.55795689e-06, -4.27096698e-05]), square_terms=array([[3.35585618e-08, 2.15061082e-07, 1.37358651e-07], - [2.15061082e-07, 1.03219001e-04, 2.16664028e-04], - [1.37358651e-07, 2.16664028e-04, 4.61793255e-04]]), scale=0.0010354292194195258, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, - 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , - -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, - -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 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scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=123, candidate_x=array([4.8444368 , 0.34601283, 0.93235891]), index=123, x=array([4.8444368 , 0.34601283, 0.93235891]), fval=0.17254703939389457, rho=0.23884848773610878, accepted=True, new_indices=array([111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_used=array([104, 110]), old_indices_discarded=array([], dtype=int32), step_length=3.2357163106865805e-05, relative_step_length=1.0000000000001739, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 124 entries., 'history': {'params': [{'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}, {'CRRA': 4.877555010789218, 'WealthShare': 0.01, 'DiscFac': 1.0004206381225442}, {'CRRA': 5.726445053953837, 'WealthShare': 0.038959478718889294, 'DiscFac': 1.1}, {'CRRA': 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47.70846509980038, 48.712551299948245, 49.71067059971392, 50.85821390012279, 51.968732699751854, 52.000046600122005, 52.05437640007585, 52.089332200121135, 52.12897969968617, 52.171603799797595, 52.21189439995214, 52.25841079978272, 52.291212500073016, 52.33110769977793, 52.37101669982076, 52.410584500059485, 53.5000541000627, 54.49080609995872, 55.507327899802476, 56.50876809982583, 57.50562849966809, 58.68333930009976, 58.724851000122726, 58.7636810997501, 58.79772430006415, 58.8442971999757, 58.891991399694234, 58.923332899808884, 58.96633999980986, 59.00750799989328, 59.05656659975648, 59.08834679983556, 59.132794899865985, 60.22101899981499, 61.250512999948114, 62.25834989966825, 63.258237699978054, 64.29787159990519, 65.34936949983239, 66.36967970011756, 67.51696019992232, 68.52077089995146, 69.51590960007161, 70.63079519988969, 70.66824259981513, 70.70960419997573, 70.75069979997352, 70.79406809993088, 70.83469379972667, 70.87575369980186, 70.91594929993153, 70.95578130008653, 70.99587279977277, 71.0371133997105, 71.07846489967778, 72.16604539984837], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 52, 53, 54, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68]}" + +convergence_report,"{'one_step': {'relative_criterion_change': 0.003307242007129943, 'relative_params_change': 0.03956657811621874, 'absolute_criterion_change': 0.0005706548168893932, 'absolute_params_change': 0.19167597945625867}, 'five_steps': {'relative_criterion_change': 0.014922806335674835, 'relative_params_change': 0.0806704377224931, 'absolute_criterion_change': 0.0025748860526691453, 'absolute_params_change': 0.36818918796590866}}" + +multistart_info,"{'start_parameters': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'DiscFac': 1.0}, {'CRRA': 4.472854104495062, 'WealthShare': 0.457530286489798, 'DiscFac': 0.8472036933750238}, {'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 6.023e-05 0.0008401 +relative_params_change 0.0007429 0.01168 +absolute_criterion_change 1.055e-05 0.0001471 +absolute_params_change 0.0002607 0.005784 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 4.685e-07* 0.0001119 +relative_params_change 2.549e-06* 0.0008924 +absolute_criterion_change 8.11e-08* 1.938e-05 +absolute_params_change 5.392e-06* 0.0004772 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.731e-05 0.0001779 +relative_params_change 8.502e-05 0.003461 +absolute_criterion_change 2.988e-06* 3.07e-05 +absolute_params_change 3.236e-05 0.00824 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'DiscFac': 1.0}, {'CRRA': 3.4625, 'WealthShare': 0.6225, 'DiscFac': 0.7250000000000001}, {'CRRA': 8.1875, 'WealthShare': 0.3775, 'DiscFac': 0.875}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5, 'DiscFac': 0.8}, {'CRRA': 14.684375, 'WealthShare': 0.346875, 'DiscFac': 0.8562500000000001}, {'CRRA': 9.959375, 'WealthShare': 0.101875, 'DiscFac': 1.00625}, {'CRRA': 19.409375, 'WealthShare': 0.591875, 'DiscFac': 0.70625}, {'CRRA': 13.503124999999999, 'WealthShare': 0.653125, 'DiscFac': 0.51875}, {'CRRA': 2.871875, 'WealthShare': 0.469375, 'DiscFac': 0.78125}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125, 'DiscFac': 0.6125}, {'CRRA': 18.81875, 'WealthShare': 0.07125, 'DiscFac': 0.9125000000000001}, {'CRRA': 17.046875, 'WealthShare': 0.224375, 'DiscFac': 0.6312500000000001}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255, 'DiscFac': 0.65}, {'CRRA': 12.9125, 'WealthShare': 0.1325, 'DiscFac': 1.0250000000000001}, {'CRRA': 11.73125, 'WealthShare': 0.43875, 'DiscFac': 0.5375}, {'CRRA': 18.228125, 'WealthShare': 0.408125, 'DiscFac': 0.96875}, {'CRRA': 7.00625, 'WealthShare': 0.19375, 'DiscFac': 0.6875}, {'CRRA': 6.415625, 'WealthShare': 0.285625, 'DiscFac': 0.59375}, {'CRRA': 4.053125, 'WealthShare': 0.16312500000000002, 'DiscFac': 0.8187500000000001}, {'CRRA': 5.234375, 'WealthShare': 0.836875, 'DiscFac': 0.55625}, {'CRRA': 16.45625, 'WealthShare': 0.68375, 'DiscFac': 0.9875}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625, 'DiscFac': 0.7625000000000001}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003, 'DiscFac': 1.0625}, {'CRRA': 15.865624999999998, 'WealthShare': 0.775625, 'DiscFac': 0.89375}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999, 'DiscFac': 0.575}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375, 'DiscFac': 0.9312500000000001}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745, 'DiscFac': 0.9500000000000001}, {'CRRA': 8.778125, 'WealthShare': 0.898125, 'DiscFac': 0.66875}, {'CRRA': 2.28125, 'WealthShare': 0.92875, 'DiscFac': 0.8375}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375, 'DiscFac': 1.08125}], 'exploration_results': array([2.42222292e-01, 3.03201302e-01, 3.75385332e-01, 3.98577685e-01, + 4.64539853e-01, 6.05168236e-01, 6.05724112e-01, 7.88383230e-01, + 8.39981988e-01, 1.04605604e+00, 1.22782446e+00, 1.48069565e+00, + 1.56893759e+00, 1.77192100e+00, 1.95139780e+00, 2.44243013e+00, + 2.75221387e+00, 2.82714102e+00, 3.02109383e+00, 6.88603970e+00, + 8.55954606e+00, 9.85239944e+00, 1.14488917e+01, 1.20953777e+01, + 1.29729655e+01, 1.58640798e+01, 3.65763661e+01, 5.66287991e+01, + 4.39985749e+02, 5.91890647e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.5301397603427972, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=[0], model=ScalarModel(intercept=0.18834408422290103, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.5301397603427972, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.973876171490377, linear_terms=array([ 0.72542682, 14.35685233, 14.83307135]), square_terms=array([[ 0.11413661, 1.93082302, 1.94450878], + [ 1.93082302, 36.39381604, 37.51674902], + [ 1.94450878, 37.51674902, 38.95260216]]), scale=array([0.42729051, 0.38454047, 0.3 ]), shift=array([5.3013976 , 0.39454047, 0.8 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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linear_terms=array([-1.30558411, 10.89377008, 12.35168828]), square_terms=array([[ 0.13964041, -1.14158086, -1.299604 ], + [-1.14158086, 9.51269818, 10.71520355], + [-1.299604 , 10.71520355, 12.18421669]]), scale=array([0.21364526, 0.21364526, 0.1959726 ]), shift=array([5.3013976 , 0.35179043, 0.9040274 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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fval=0.18834408422290103, rho=-0.29383208425185714, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13]), old_indices_discarded=array([ 6, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.1325349400856993, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=6.9052436115388875, linear_terms=array([-0.69117403, 7.16500457, 9.13260536]), square_terms=array([[ 0.03812681, -0.36969498, -0.4746088 ], + [-0.36969498, 3.7854496 , 4.79829205], + [-0.4746088 , 4.79829205, 6.13906768]]), scale=0.1325349400856993, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16542657032258398, linear_terms=array([0.01630779, 0.01475797, 0.02073112]), square_terms=array([[ 0.05951927, -0.12255125, -0.23582986], + [-0.12255125, 0.26987927, 0.50459186], + [-0.23582986, 0.50459186, 0.96563619]]), scale=0.06626747004284965, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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candidate_index=28, candidate_x=array([5.23292383, 0.33864579, 0.91062871]), index=0, x=array([5.3013976 , 0.35179043, 0.92170005]), fval=0.18834408422290103, rho=-4.217700932825573, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.3013976 , 0.35179043, 0.92170005]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.16782671230459395, linear_terms=array([ 0.0009187 , -0.00784055, -0.02621131]), square_terms=array([[4.57181704e-05, 1.25022351e-03, 2.32119020e-03], + [1.25022351e-03, 9.27555996e-02, 1.94331965e-01], + [2.32119020e-03, 1.94331965e-01, 4.14037852e-01]]), scale=0.033133735021424825, shift=array([5.3013976 , 0.35179043, 0.92170005])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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linear_terms=array([ 0.00059146, -0.004534 , -0.01522606]), square_terms=array([[3.03577735e-05, 5.05858062e-04, 7.12748102e-04], + [5.05858062e-04, 9.30360296e-02, 1.93666384e-01], + [7.12748102e-04, 1.93666384e-01, 4.10425108e-01]]), scale=0.033133735021424825, shift=array([5.29121744, 0.32407875, 0.9367533 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=0.1799906266171576, rho=1.161952729888368, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 20, 21, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([15, 16, 19, 22, 23]), step_length=0.03324491388144948, relative_step_length=1.0033554581139965, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.17004437609421225, linear_terms=array([-0.00047394, -0.007927 , -0.01239616]), square_terms=array([[3.94062801e-04, 1.07905924e-02, 2.16812211e-02], + [1.07905924e-02, 3.74115243e-01, 7.88602520e-01], + [2.16812211e-02, 7.88602520e-01, 1.69226096e+00]]), scale=0.06626747004284965, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([5.27834936, 0.29684139, 0.95081613]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.1684737651459072, linear_terms=array([0.00169024, 0.00402826, 0.00597484]), square_terms=array([[ 4.51971473e-04, -5.93887978e-03, -1.50514931e-02], + [-5.93887978e-03, 9.28119628e-02, 2.24673825e-01], + [-1.50514931e-02, 2.24673825e-01, 5.53588350e-01]]), scale=0.033133735021424825, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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dtype=int32), old_indices_used=array([ 0, 17, 24, 25, 26, 28, 29, 31, 32, 33]), old_indices_discarded=array([], dtype=int32), step_length=0.01739885473916772, relative_step_length=1.0502199482139474, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.27530111, 0.31274546, 0.94445306]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.17197721637129393, linear_terms=array([-0.0002372 , -0.00370266, -0.00638111]), square_terms=array([[ 3.15094268e-05, -3.42643248e-04, -1.18023772e-03], + [-3.42643248e-04, 1.05602531e-01, 2.35178611e-01], + [-1.18023772e-03, 2.35178611e-01, 5.32242456e-01]]), scale=0.033133735021424825, shift=array([5.27530111, 0.31274546, 0.94445306])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 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radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.17237609478517008, linear_terms=array([ 0.00518006, -0.00474084, -0.0125618 ]), square_terms=array([[ 2.10162976e-03, -2.78797705e-02, -6.43090686e-02], + [-2.78797705e-02, 4.31690458e-01, 9.58890944e-01], + [-6.43090686e-02, 9.58890944e-01, 2.16354079e+00]]), scale=0.06626747004284965, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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old_indices_discarded=array([15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 32, 33, 41]), step_length=0.03319197686050779, relative_step_length=1.001757780674147, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 21, 24, 28, 31, 35, 36, 37, 38, 43]), model=ScalarModel(intercept=0.17516083815911454, linear_terms=array([0.00333895, 0.02345551, 0.04006676]), square_terms=array([[ 9.17825370e-04, -1.49978117e-02, -3.32074698e-02], + [-1.49978117e-02, 3.29043755e-01, 6.85571868e-01], + [-3.32074698e-02, 6.85571868e-01, 1.45766388e+00]]), scale=0.06626747004284965, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 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scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=45, candidate_x=array([5.22598818, 0.34124333, 0.9330266 ]), index=43, x=array([5.25969725, 0.34546622, 0.93220909]), fval=0.17587143240730305, rho=-0.04773487920840637, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), old_indices_discarded=array([15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 38, + 41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), model=ScalarModel(intercept=0.1751974222542535, linear_terms=array([0.00059828, 0.00104291, 0.00174938]), square_terms=array([[ 2.32452897e-05, 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old_indices_used=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), old_indices_discarded=array([19, 21, 24, 25, 26, 31, 33, 35, 38, 40, 41, 44]), step_length=0.017343162853838502, relative_step_length=1.0468583057493592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.24271155, 0.34212944, 0.93327545]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 28, 29, 34, 36, 37, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.1748745633782247, linear_terms=array([ 0.00075962, -0.00172199, -0.00445794]), square_terms=array([[ 3.54305914e-05, -8.55387782e-04, -2.21218821e-03], + [-8.55387782e-04, 1.02537349e-01, 2.22590300e-01], + [-2.21218821e-03, 2.22590300e-01, 4.91024973e-01]]), scale=0.033133735021424825, shift=array([5.24271155, 0.34212944, 0.93327545])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], 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State(trustregion=Region(center=array([5.20917798, 0.34014316, 0.93432412]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 37, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.17493421876270587, linear_terms=array([-0.00156353, -0.00842537, -0.01190066]), square_terms=array([[0.00281928, 0.03109626, 0.0629877 ], + [0.03109626, 0.34695273, 0.70455773], + [0.0629877 , 0.70455773, 1.45975502]]), scale=0.06626747004284965, shift=array([5.20917798, 0.34014316, 0.93432412])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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rho=0.563659202220171, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 28, 33, 36, 37, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, + 35, 38, 39, 40, 41, 42]), step_length=0.033441763641147204, relative_step_length=1.0092965257168625, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 33, 36, 43, 44, 45, 46, 47, 49]), model=ScalarModel(intercept=0.17564926316541823, linear_terms=array([-0.00274284, -0.01384935, -0.02471199]), square_terms=array([[0.0035693 , 0.03481689, 0.07133062], + [0.03481689, 0.34263536, 0.7019492 ], + [0.07133062, 0.7019492 , 1.46757845]]), scale=0.06626747004284965, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([17, 18, 21, 28, 36, 43, 44, 45, 46, 47, 49, 50]), model=ScalarModel(intercept=0.1762502933918212, linear_terms=array([-0.00155691, -0.00561339, -0.01308175]), square_terms=array([[0.00079825, 0.00800458, 0.01674233], + [0.00800458, 0.08167794, 0.17110631], + [0.01674233, 0.17110631, 0.36563479]]), scale=0.033133735021424825, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([0.00019334, 0.00068168, 0.002236 ]), square_terms=array([[6.30086486e-06, 1.31120182e-04, 1.92914840e-04], + [1.31120182e-04, 2.30933740e-02, 4.65644949e-02], + [1.92914840e-04, 4.65644949e-02, 9.56283946e-02]]), scale=0.016566867510712412, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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fval=0.1749771962623699, rho=-0.48116939358057886, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52]), model=ScalarModel(intercept=0.17496972296461294, linear_terms=array([ 6.97863248e-05, -8.21475281e-05, -4.56887150e-04]), square_terms=array([[1.84361126e-06, 2.04013067e-05, 2.19735979e-05], + [2.04013067e-05, 5.26300747e-03, 1.10824804e-02], + [2.19735979e-05, 1.10824804e-02, 2.38107760e-02]]), scale=0.008283433755356206, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 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radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.1749826973068216, linear_terms=array([0.0001074 , 0.00038707, 0.00171986]), square_terms=array([[7.58231429e-06, 2.29836559e-04, 3.96776253e-04], + [2.29836559e-04, 2.29465979e-02, 4.65350783e-02], + [3.96776253e-04, 4.65350783e-02, 9.61235255e-02]]), scale=0.016566867510712412, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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model_indices=array([49, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.17457404964764986, linear_terms=array([ 0.00013515, -0.00194552, -0.00421915]), square_terms=array([[ 1.71429278e-06, 8.55431370e-06, -6.69374131e-07], + [ 8.55431370e-06, 6.91060946e-03, 1.45165898e-02], + [-6.69374131e-07, 1.45165898e-02, 3.09731721e-02]]), scale=0.008283433755356206, shift=array([5.14672647, 0.34473233, 0.9326238 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 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27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, + 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.88958301, 0.35997157, 0.92601999]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.17542324482018204, linear_terms=array([-0.00082497, -0.05406882, -0.10601177]), square_terms=array([[1.43516091e-04, 1.15577625e-03, 8.44733923e-04], + [1.15577625e-03, 1.97661939e+00, 4.19325940e+00], + [8.44733923e-04, 4.19325940e+00, 8.92777781e+00]]), scale=0.06626747004284965, shift=array([4.88958301, 0.35997157, 0.92601999])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + 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0.99, 1.1 ]))), model_indices=array([62, 63, 65, 66]), model=ScalarModel(intercept=0.17321977271534045, linear_terms=array([3.28707734e-05, 4.59008902e-02, 9.65184277e-02]), square_terms=array([[ 3.32187843e-05, -6.21575226e-04, -1.69036788e-03], + [-6.21575226e-04, 6.40009370e-01, 1.36558381e+00], + [-1.69036788e-03, 1.36558381e+00, 2.92112120e+00]]), scale=0.033133735021424825, shift=array([4.88958301, 0.35997157, 0.92601999])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([4.85934517, 0.34730674, 0.93082827]), index=67, x=array([4.85934517, 0.34730674, 0.93082827]), fval=0.17278415445032944, rho=0.24429294106502478, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([62, 63, 65, 66]), old_indices_discarded=array([], dtype=int32), step_length=0.033133735021424575, relative_step_length=0.9999999999999925, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.17800589377398301, linear_terms=array([-0.00030888, -0.11314456, -0.23802422]), square_terms=array([[ 1.44537530e-04, -2.97382575e-03, -7.90103681e-03], + [-2.97382575e-03, 1.94242721e+00, 4.15401254e+00], + [-7.90103681e-03, 4.15401254e+00, 8.91430260e+00]]), scale=0.06626747004284965, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([62, 63, 65, 66, 67, 68]), model=ScalarModel(intercept=0.17242592156362221, linear_terms=array([-3.95014403e-05, 7.38523177e-03, 1.66472760e-02]), square_terms=array([[ 3.41520113e-05, -4.66917848e-04, -1.38426097e-03], + [-4.66917848e-04, 2.96431160e-01, 6.38285543e-01], + [-1.38426097e-03, 6.38285543e-01, 1.38203197e+00]]), scale=0.033133735021424825, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], 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square_terms=array([[7.38828522e-06, 1.36373870e-04, 2.09192264e-04], + [1.36373870e-04, 2.30290804e-02, 4.72040978e-02], + [2.09192264e-04, 4.72040978e-02, 9.83617297e-02]]), scale=0.016566867510712412, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([63, 67, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.85934517, 0.34730674, 0.93082827]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([63, 67, 69, 70]), model=ScalarModel(intercept=0.17278415445032952, linear_terms=array([-0.0002103 , 0.00306398, 0.00684224]), square_terms=array([[ 5.08646918e-06, -1.24755432e-04, -3.02817812e-04], + [-1.24755432e-04, 1.06839840e-02, 2.29501959e-02], + [-3.02817812e-04, 2.29501959e-02, 4.98109499e-02]]), scale=0.008283433755356206, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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77, 78, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.17275901380787767, linear_terms=array([ 5.13124779e-05, -7.09231772e-04, -1.50668997e-03]), square_terms=array([[ 5.24206101e-07, 2.61469482e-06, -1.23800127e-07], + [ 2.61469482e-06, 1.62536068e-03, 3.42956073e-03], + [-1.23800127e-07, 3.42956073e-03, 7.35873402e-03]]), scale=0.004141716877678103, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 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0.34798078, 0.93135837]), index=84, x=array([4.855221 , 0.34798078, 0.93135837]), fval=0.17268283509914406, rho=0.49118937685797404, accepted=True, new_indices=array([72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), old_indices_used=array([67, 70, 71]), old_indices_discarded=array([], dtype=int32), step_length=0.0042123718047314095, relative_step_length=1.0170593329143531, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.855221 , 0.34798078, 0.93135837]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84]), model=ScalarModel(intercept=0.17265527071731143, linear_terms=array([-7.78798603e-05, 6.36909620e-05, -1.62218247e-04]), square_terms=array([[2.54965658e-06, 3.42416705e-05, 4.82468529e-05], + [3.42416705e-05, 6.54926335e-03, 1.37980536e-02], + [4.82468529e-05, 1.37980536e-02, 2.95393721e-02]]), scale=0.008283433755356206, shift=array([4.855221 , 0.34798078, 0.93135837])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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x=array([4.84436454, 0.3458171 , 0.93240807]), fval=0.1725720095711579, rho=0.09077331695760522, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 85, 86]), old_indices_discarded=array([63, 69, 70, 71, 74, 79, 82, 83]), step_length=0.008283749415294117, relative_step_length=1.0000381073775964, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.004141716877678103, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), model=ScalarModel(intercept=0.1725764987304485, linear_terms=array([7.05977087e-06, 2.29884404e-05, 1.21152777e-05]), square_terms=array([[5.86956811e-07, 1.16384872e-05, 1.94432612e-05], + [1.16384872e-05, 1.63380924e-03, 3.44140991e-03], + [1.94432612e-05, 3.44140991e-03, 7.36782486e-03]]), scale=0.004141716877678103, 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vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([4.84334274, 0.34598281, 0.93232094]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.17258275627245578, linear_terms=array([-1.33291997e-05, -4.54612468e-06, -9.95213586e-06]), square_terms=array([[4.81840647e-08, 1.07426513e-06, 1.94899377e-06], + [1.07426513e-06, 1.02515718e-04, 2.17008681e-04], + [1.94899377e-06, 2.17008681e-04, 4.67269204e-04]]), scale=0.0010354292194195258, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([ 2.41019710e-05, -4.48842471e-06, -4.27936350e-05]), square_terms=array([[1.56434406e-07, 3.33318421e-06, 6.03774428e-06], + [3.33318421e-06, 4.15831904e-04, 8.74354178e-04], + [6.03774428e-06, 8.74354178e-04, 1.86706420e-03]]), scale=0.0020708584388390515, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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0.93234197]), fval=0.1725502344785951, rho=-1.01272054792213, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), old_indices_discarded=array([86, 88, 91, 93, 94, 95, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.17258372705737116, linear_terms=array([ 4.09478695e-06, -3.55795689e-06, -4.27096698e-05]), square_terms=array([[3.35585618e-08, 2.15061082e-07, 1.37358651e-07], + [2.15061082e-07, 1.03219001e-04, 2.16664028e-04], + [1.37358651e-07, 2.16664028e-04, 4.61793255e-04]]), scale=0.0010354292194195258, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0005177146097097629, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 90, 92, 96, 97, 98, 100, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.1725840937366451, linear_terms=array([ 3.96813424e-06, -3.12252958e-06, -1.95411424e-05]), square_terms=array([[ 8.35946962e-09, 7.31232339e-09, -6.36196010e-08], + [ 7.31232339e-09, 2.56886777e-05, 5.40554793e-05], + [-6.36196010e-08, 5.40554793e-05, 1.15524825e-04]]), scale=0.0005177146097097629, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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candidate_index=13, candidate_x=array([5.76908436, 0.22960637, 0.83623674]), index=0, x=array([5.33878077, 0.17065529, 1. ]), fval=0.24222229239256646, rho=-0.6890593411620649, accepted=False, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.2669390387240524, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13]), model=ScalarModel(intercept=0.9034009752440392, linear_terms=array([-0.07604809, 2.69028608, 3.69106229]), square_terms=array([[ 4.61599959e-03, -1.10384595e-01, -1.63381782e-01], + [-1.10384595e-01, 4.67692138e+00, 6.22592464e+00], + [-1.63381782e-01, 6.22592464e+00, 8.42706648e+00]]), scale=array([0.21515179, 0.18790354, 0.1575759 ]), 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State(trustregion=Region(center=array([5.33878077, 0.17065529, 1. ]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 13, 14]), model=ScalarModel(intercept=1.8208078161679868, linear_terms=array([-0.19004525, 2.49267386, 3.44430864]), square_terms=array([[ 0.01155517, -0.14231501, -0.19433776], + [-0.14231501, 1.88777533, 2.52140739], + [-0.19433776, 2.52140739, 3.42876704]]), scale=array([0.1075759 , 0.1075759 , 0.10378795]), shift=array([5.33878077, 0.17065529, 0.99621205])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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x=array([5.30968909, 0.25995847, 0.96850838]), fval=0.18645615650369046, rho=1.932188396931057, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29]), old_indices_discarded=array([14, 15, 17, 24, 26]), step_length=0.07028379860830133, relative_step_length=1.0531812648198984, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30968909, 0.25995847, 0.96850838]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), model=ScalarModel(intercept=0.1877934505138043, linear_terms=array([-0.03082531, -0.06829227, -0.20801109]), square_terms=array([[ 0.02060883, 0.09482843, 0.45039114], + [ 0.09482843, 0.47240082, 2.15885408], + [ 0.45039114, 2.15885408, 10.7559403 ]]), scale=array([0.1075759, 0.1075759, 0.1075759]), shift=array([5.30968909, 0.25995847, 0.96850838])), 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old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 31, 32]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, + 24, 25, 26, 27]), step_length=0.1362670918270145, relative_step_length=1.0209603846508213, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.49059454, 0.32570596, 0.93946857]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 16, 19, 20, 22, 23, 28, 29, 30, 31, 32, 33]), model=ScalarModel(intercept=0.180667786417314, linear_terms=array([ 0.00742114, -0.04161736, -0.07431271]), square_terms=array([[1.78983921e-03, 2.53213785e-02, 7.69037706e-02], + [2.53213785e-02, 4.62056121e-01, 1.34550576e+00], + [7.69037706e-02, 1.34550576e+00, 4.02143592e+00]]), scale=0.0667347596810131, shift=array([5.49059454, 0.32570596, 0.93946857])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 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radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([16, 31, 32, 33, 34]), model=ScalarModel(intercept=0.17885448842070745, linear_terms=array([ 0.00036015, -0.00617824, -0.01541375]), square_terms=array([[ 4.69844786e-05, -5.76980455e-04, -1.75264843e-03], + [-5.76980455e-04, 7.91846870e-02, 1.77376377e-01], + [-1.75264843e-03, 1.77376377e-01, 4.05791772e-01]]), scale=0.03336737984050655, shift=array([5.49059454, 0.32570596, 0.93946857])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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index=37, x=array([5.43271536, 0.34232811, 0.93481237]), fval=0.1783330759349926, rho=-1.1757297921299623, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([16, 22, 23, 28, 30, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([ 0, 15, 17, 18, 19, 20, 21, 24, 25, 26, 27, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.43271536, 0.34232811, 0.93481237]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([16, 23, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.17796437166095838, linear_terms=array([0.00053793, 0.01010837, 0.02505791]), square_terms=array([[ 2.91945200e-05, -2.80425639e-04, -1.06624623e-03], + [-2.80425639e-04, 8.52256537e-02, 1.96627340e-01], + [-1.06624623e-03, 1.96627340e-01, 4.63387728e-01]]), scale=0.03336737984050655, shift=array([5.43271536, 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old_indices_used=array([28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([16, 22, 23, 29]), step_length=0.033843016758019415, relative_step_length=1.0142545479982659, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.38728963, 0.34094095, 0.93379815]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17833091404398044, linear_terms=array([ 0.00023398, -0.00934546, -0.02018432]), square_terms=array([[ 1.84541465e-04, -3.68424553e-03, -1.03268272e-02], + [-3.68424553e-03, 3.76260371e-01, 8.61424538e-01], + [-1.03268272e-02, 8.61424538e-01, 2.00874749e+00]]), scale=0.0667347596810131, shift=array([5.38728963, 0.34094095, 0.93379815])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + 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0.92835645]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([28, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.1780148058238638, linear_terms=array([-0.00063323, 0.00975489, 0.01812727]), square_terms=array([[ 3.82472761e-04, -3.14587621e-03, -1.38016696e-02], + [-3.14587621e-03, 1.40349253e+00, 3.23544311e+00], + [-1.38016696e-02, 3.23544311e+00, 7.61272179e+00]]), scale=0.1334695193620262, shift=array([5.32187633, 0.35441087, 0.92835645])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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square_terms=array([[ 1.50428947e-04, -2.69288377e-03, -7.97567575e-03], + [-2.69288377e-03, 3.75575586e-01, 8.63048473e-01], + [-7.97567575e-03, 8.63048473e-01, 2.02064605e+00]]), scale=0.0667347596810131, shift=array([5.32187633, 0.35441087, 0.92835645])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 0, 5, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, + 29, 33, 35]), step_length=0.0667568953608683, relative_step_length=1.0003316964047075, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([23, 28, 30, 31, 32, 36, 38, 39, 40, 41, 42, 44]), model=ScalarModel(intercept=0.17756581846293762, linear_terms=array([-0.00064376, 0.02147547, 0.0472283 ]), square_terms=array([[ 6.33696631e-04, -1.27178250e-02, -3.69738106e-02], + [-1.27178250e-02, 1.40423713e+00, 3.24790628e+00], + [-3.69738106e-02, 3.24790628e+00, 7.66915637e+00]]), scale=0.1334695193620262, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25577068, 0.36291503, 0.92458747]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([23, 28, 30, 32, 36, 38, 39, 40, 41, 42, 44, 45]), model=ScalarModel(intercept=0.1766790596574145, linear_terms=array([0.00015191, 0.01298194, 0.03144833]), square_terms=array([[ 1.77245269e-04, -3.57687440e-03, -1.01635519e-02], + [-3.57687440e-03, 3.48864307e-01, 8.05273892e-01], + [-1.01635519e-02, 8.05273892e-01, 1.89795527e+00]]), scale=0.0667347596810131, shift=array([5.25577068, 0.36291503, 0.92458747])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 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x=array([5.21614051, 0.34083281, 0.93444849]), fval=0.17525877839750098, rho=-0.24091601228165707, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([64, 66, 68, 69]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.21614051, 0.34083281, 0.93444849]), radius=0.0020854612400316593, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([66, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.1752716135829646, linear_terms=array([ 2.83868377e-05, -1.19751994e-04, -3.17911081e-04]), square_terms=array([[1.11944911e-07, 1.15337902e-06, 1.26187859e-06], + [1.15337902e-06, 3.96183939e-04, 8.51195967e-04], + [1.26187859e-06, 8.51195967e-04, 1.85994556e-03]]), scale=0.0020854612400316593, shift=array([5.21614051, 0.34083281, 0.93444849])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([66, 71, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83]), old_indices_discarded=array([69, 70, 72, 78, 84]), step_length=0.0020864655983197176, relative_step_length=1.0004816000742565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2128509 , 0.338664 , 0.93579187]), radius=0.004170922480063319, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([66, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.17516697035176923, linear_terms=array([5.83865581e-05, 4.63782474e-05, 1.68217449e-05]), square_terms=array([[4.67576709e-07, 8.05449658e-06, 1.20770208e-05], + [8.05449658e-06, 1.57585820e-03, 3.39676314e-03], + [1.20770208e-05, 3.39676314e-03, 7.44531749e-03]]), scale=0.004170922480063319, shift=array([5.2128509 , 0.338664 , 0.93579187])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 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bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.17579528910570869, linear_terms=array([ 0.00021618, -0.01105215, -0.01976729]), square_terms=array([[ 1.96121450e-04, -3.38913019e-03, -7.33697269e-03], + [-3.38913019e-03, 2.06463498e-01, 3.77049739e-01], + [-7.33697269e-03, 3.77049739e-01, 7.04976367e-01]]), scale=0.05591067630618828, shift=array([4.60129558, 0.3319619 , 0.93616111])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=36, candidate_x=array([4.54190424, 0.34416472, 0.93058415]), index=36, x=array([4.54190424, 0.34416472, 0.93058415]), fval=0.17557093407600716, rho=0.20005246667450538, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([ 0, 15, 16, 17, 18, 19, 21, 22, 23, 24]), step_length=0.0608879546199208, relative_step_length=1.0890219657954956, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.54190424, 0.34416472, 0.93058415]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.17568176927667278, linear_terms=array([-0.00016853, -0.01916852, -0.03813775]), square_terms=array([[ 5.84778824e-04, -9.07741680e-03, 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old_indices_used=array([20, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 8, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, + 23, 24, 27]), step_length=0.11188469579144777, relative_step_length=1.00056646765212, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65201452, 0.32727883, 0.94101379]), radius=0.22364270522475313, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 25, 26, 28, 29, 30, 32, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.19282537449314455, linear_terms=array([-0.00075052, -0.3155938 , -0.55999152]), square_terms=array([[2.25983121e-03, 6.61141972e-02, 1.06324274e-01], + [6.61141972e-02, 3.24859534e+00, 5.56048612e+00], + [1.06324274e-01, 5.56048612e+00, 9.66322356e+00]]), scale=array([0.18025512, 0.18025512, 0.16962067]), shift=array([4.65201452, 0.32727883, 0.93037933])), vector_model=VectorModel(intercepts=array([ 0.06583231, 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n_evals_acceptance=1), State(trustregion=Region(center=array([4.65201452, 0.32727883, 0.94101379]), radius=0.11182135261237656, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 26, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.1755053447924909, linear_terms=array([0.0009879 , 0.02059209, 0.0322827 ]), square_terms=array([[4.69289255e-04, 6.84432041e-03, 9.11727164e-03], + [6.84432041e-03, 1.07829645e+00, 2.03285155e+00], + [9.11727164e-03, 2.03285155e+00, 3.90203103e+00]]), scale=0.11182135261237656, shift=array([4.65201452, 0.32727883, 0.94101379])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.44728541044950626, shift=array([4.4728541 , 0.45753029, 0.84720369])), candidate_index=39, candidate_x=array([4.53916448, 0.31780007, 0.9452902 ]), index=37, x=array([4.65201452, 0.32727883, 0.94101379]), fval=0.17479029841995192, rho=-3.0403572413217264, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([20, 26, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38]), old_indices_discarded=array([ 0, 2, 3, 6, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, + 24, 25, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65201452, 0.32727883, 0.94101379]), radius=0.05591067630618828, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([20, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.17535921159705348, linear_terms=array([4.38472954e-05, 7.95540383e-03, 1.29641548e-02]), square_terms=array([[1.48495897e-04, 3.66230666e-03, 5.98532157e-03], + [3.66230666e-03, 3.24058063e-01, 6.11393607e-01], + [5.98532157e-03, 6.11393607e-01, 1.17098510e+00]]), scale=0.05591067630618828, shift=array([4.65201452, 0.32727883, 0.94101379])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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State(trustregion=Region(center=array([4.65564547, 0.35033578, 0.93001783]), radius=0.006988834538273535, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58]), model=ScalarModel(intercept=0.17320449006214028, linear_terms=array([-3.05104415e-05, 1.99630776e-05, -1.99729776e-04]), square_terms=array([[ 1.82853803e-06, -1.07295862e-06, -1.93551509e-05], + [-1.07295862e-06, 4.95414255e-03, 1.02579296e-02], + [-1.93551509e-05, 1.02579296e-02, 2.15599033e-02]]), scale=0.006988834538273535, shift=array([4.65564547, 0.35033578, 0.93001783])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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linear_terms=array([-3.50632174e-05, 1.39156316e-04, 1.47834988e-04]), square_terms=array([[7.28393196e-06, 7.01493967e-05, 8.02193747e-05], + [7.01493967e-05, 1.96925095e-02, 4.09319734e-02], + [8.02193747e-05, 4.09319734e-02, 8.63772645e-02]]), scale=0.01397766907654707, shift=array([4.65947059, 0.34493224, 0.93264892])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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fval=0.17319386240144724, rho=-2.368503752157372, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 59]), old_indices_discarded=array([33, 34, 37, 40, 42, 43, 44, 45, 49, 55, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65947059, 0.34493224, 0.93264892]), radius=0.006988834538273535, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59]), model=ScalarModel(intercept=0.17322335454301357, linear_terms=array([5.84657279e-05, 5.61600646e-05, 1.11287704e-04]), square_terms=array([[ 1.73166366e-06, -5.21647152e-06, -2.70055797e-05], + [-5.21647152e-06, 4.91675196e-03, 1.02038442e-02], + [-2.70055797e-05, 1.02038442e-02, 2.15010081e-02]]), scale=0.006988834538273535, shift=array([4.65947059, 0.34493224, 0.93264892])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.01397766907654707, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 45, 47, 48, 50, 51, 53, 54, 56, 57, 59, 61]), model=ScalarModel(intercept=0.17319833291851128, linear_terms=array([-1.51384574e-04, -7.53489850e-06, -1.33953022e-04]), square_terms=array([[7.88278598e-06, 6.56801117e-05, 6.75420704e-05], + [6.56801117e-05, 1.96414914e-02, 4.08747995e-02], + [6.75420704e-05, 4.08747995e-02, 8.63573595e-02]]), scale=0.01397766907654707, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 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model=ScalarModel(intercept=0.1731417369534366, linear_terms=array([8.53555441e-05, 3.94898724e-05, 4.21614549e-05]), square_terms=array([[ 1.87710384e-06, -1.89887381e-05, -5.59774665e-05], + [-1.89887381e-05, 4.91019732e-03, 1.02046258e-02], + [-5.59774665e-05, 1.02046258e-02, 2.15316921e-02]]), scale=0.006988834538273535, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=61, x=array([4.65246302, 0.34513124, 0.93250988]), fval=0.17314745134607396, rho=-0.7767331150668283, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([41, 46, 48, 49, 50, 51, 54, 55, 56, 58, 59, 61]), old_indices_discarded=array([37, 44, 45, 47, 52, 53, 57, 60, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.0034944172691367677, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 54, 56, 58, 59, 61, 63]), model=ScalarModel(intercept=0.17318840104590158, linear_terms=array([8.40920959e-06, 1.60644298e-06, 1.78650732e-05]), square_terms=array([[ 4.56677469e-07, -1.61383200e-06, -7.65761701e-06], + [-1.61383200e-06, 1.22943708e-03, 2.55528223e-03], + [-7.65761701e-06, 2.55528223e-03, 5.39279040e-03]]), scale=0.0034944172691367677, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65246302, 0.34513124, 0.93250988]), radius=0.0017472086345683838, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([41, 46, 48, 49, 50, 51, 56, 58, 59, 61, 63, 64]), model=ScalarModel(intercept=0.1731833699424632, linear_terms=array([-1.55880845e-06, 2.34892542e-05, 3.50350099e-05]), square_terms=array([[1.14286970e-07, 5.95276640e-07, 1.79303603e-07], + [5.95276640e-07, 3.00448801e-04, 6.27473167e-04], + [1.79303603e-07, 6.27473167e-04, 1.33116839e-03]]), scale=0.0017472086345683838, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 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model=ScalarModel(intercept=0.17320359100698507, linear_terms=array([-1.04740719e-05, -4.15791794e-05, 1.53390193e-05]), square_terms=array([[ 3.35637081e-08, 1.29446597e-07, -5.58028919e-08], + [ 1.29446597e-07, 7.70487534e-05, 1.63752882e-04], + [-5.58028919e-08, 1.63752882e-04, 3.53849647e-04]]), scale=0.0008736043172841919, shift=array([4.65246302, 0.34513124, 0.93250988])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=78, x=array([4.65266665, 0.34590517, 0.93215796]), fval=0.17313846350417914, rho=0.20010394985498053, accepted=True, new_indices=array([66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), old_indices_used=array([61, 64, 65]), old_indices_discarded=array([], dtype=int32), step_length=0.0008742235274298533, relative_step_length=1.0007087993195665, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.0017472086345683838, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78]), model=ScalarModel(intercept=0.17316117071960616, linear_terms=array([ 1.17846159e-06, -7.30897908e-05, 3.56834929e-05]), square_terms=array([[ 1.26266358e-07, -2.95552318e-07, -1.85714932e-06], + [-2.95552318e-07, 3.06633677e-04, 6.53705727e-04], + [-1.85714932e-06, 6.53705727e-04, 1.41658448e-03]]), scale=0.0017472086345683838, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.0008736043172841919, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 67, 69, 70, 71, 72, 73, 74, 75, 77, 78]), model=ScalarModel(intercept=0.17315497758754703, linear_terms=array([-1.03031559e-05, -4.11416984e-05, 1.56495230e-05]), square_terms=array([[ 3.02714973e-08, 8.04834807e-08, -1.41448123e-07], + [ 8.04834807e-08, 7.72107726e-05, 1.63938593e-04], + [-1.41448123e-07, 1.63938593e-04, 3.53626006e-04]]), scale=0.0008736043172841919, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), 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index=78, x=array([4.65266665, 0.34590517, 0.93215796]), fval=0.17313846350417914, rho=-2.0772563083934017, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([61, 66, 67, 69, 71, 72, 73, 74, 75, 77, 78, 80]), old_indices_discarded=array([68, 70, 76, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.00021840107932104798, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([61, 66, 67, 69, 74, 75, 78, 80, 81]), model=ScalarModel(intercept=0.1731737579944651, linear_terms=array([-3.79844970e-06, -3.69441907e-06, 9.93700675e-07]), square_terms=array([[2.70592783e-09, 4.30842813e-08, 7.31011322e-08], + [4.30842813e-08, 4.85108303e-06, 1.02966777e-05], + [7.31011322e-08, 1.02966777e-05, 2.21791802e-05]]), scale=0.00021840107932104798, shift=array([4.65266665, 0.34590517, 0.93215796])), 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State(trustregion=Region(center=array([4.65266665, 0.34590517, 0.93215796]), radius=0.00010920053966052399, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 74, 78, 81, 82]), model=ScalarModel(intercept=0.17315192460300521, linear_terms=array([-2.81303010e-06, -3.86667778e-06, -1.04055075e-05]), square_terms=array([[ 2.19586773e-09, -3.23204802e-08, -7.89353277e-08], + [-3.23204802e-08, 1.26964622e-06, 2.77090607e-06], + [-7.89353277e-08, 2.77090607e-06, 6.13234159e-06]]), scale=0.00010920053966052399, shift=array([4.65266665, 0.34590517, 0.93215796])), vector_model=VectorModel(intercepts=array([ 0.06583231, 0.12997867, 0.12006044, 0.11918703, 0.09509912, + 0.06536156, 0.03646807, -0.03599738, -0.10970519, -0.02523239, + -0.301317 , -0.26498249, -0.08962614, -0.06116485, -0.05206693, + -0.0551122 , -0.05468477]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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model_indices=array([ 0, 17, 18, 20, 24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.17004437609421225, linear_terms=array([-0.00047394, -0.007927 , -0.01239616]), square_terms=array([[3.94062801e-04, 1.07905924e-02, 2.16812211e-02], + [1.07905924e-02, 3.74115243e-01, 7.88602520e-01], + [2.16812211e-02, 7.88602520e-01, 1.69226096e+00]]), scale=0.06626747004284965, shift=array([5.27834936, 0.29684139, 0.95081613])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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fval=0.17755692096608, rho=1.8988432161216091, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 24, 25, 26, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([15, 16, 19, 20, 21, 22, 23, 27, 30]), step_length=0.033281691430788476, relative_step_length=1.004465431055931, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30566766, 0.32537105, 0.9393405 ]), radius=0.06626747004284965, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.17237609478517008, linear_terms=array([ 0.00518006, -0.00474084, -0.0125618 ]), square_terms=array([[ 2.10162976e-03, -2.78797705e-02, -6.43090686e-02], + [-2.78797705e-02, 4.31690458e-01, 9.58890944e-01], + [-6.43090686e-02, 9.58890944e-01, 2.16354079e+00]]), scale=0.06626747004284965, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.30566766, 0.32537105, 0.9393405 ]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 20, 24, 25, 26, 29, 30, 31, 32, 34, 35]), model=ScalarModel(intercept=0.1723760947851703, linear_terms=array([ 0.00259003, -0.00237042, -0.0062809 ]), square_terms=array([[ 5.25407439e-04, -6.96994262e-03, -1.60772672e-02], + [-6.96994262e-03, 1.07922614e-01, 2.39722736e-01], + [-1.60772672e-02, 2.39722736e-01, 5.40885198e-01]]), scale=0.033133735021424825, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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linear_terms=array([-0.00039037, -0.0076903 , -0.01984056]), square_terms=array([[1.68083117e-05, 5.99857721e-04, 1.30035997e-03], + [5.99857721e-04, 3.07066045e-02, 7.08873570e-02], + [1.30035997e-03, 7.08873570e-02, 1.65894328e-01]]), scale=0.016566867510712412, shift=array([5.30566766, 0.32537105, 0.9393405 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + 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fval=0.17733908278986127, rho=0.12075915604171515, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 20, 25, 26, 29, 30, 31, 32, 34, 35, 37]), old_indices_discarded=array([22, 24, 27, 28, 33, 36]), step_length=0.01691501274286895, relative_step_length=1.021014547978453, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31420845, 0.31272412, 0.94663622]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 20, 25, 26, 29, 30, 31, 32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.17451053131095257, linear_terms=array([ 0.00026766, -0.00034627, -0.00190087]), square_terms=array([[ 2.82594589e-05, -3.98670431e-04, -1.30601253e-03], + [-3.98670431e-04, 1.28325976e-01, 2.92377009e-01], + [-1.30601253e-03, 2.92377009e-01, 6.74940821e-01]]), scale=0.033133735021424825, shift=array([5.31420845, 0.31272412, 0.94663622])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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linear_terms=array([-8.72839083e-04, 6.46911097e-05, -1.15041490e-03]), square_terms=array([[1.69827888e-04, 4.48946449e-03, 9.85596140e-03], + [4.48946449e-03, 1.31226175e-01, 2.95972451e-01], + [9.85596140e-03, 2.95972451e-01, 6.76192041e-01]]), scale=0.033133735021424825, shift=array([5.30300313, 0.32433431, 0.9416009 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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fval=0.1766329595168218, rho=-0.9795538799146807, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 25, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40]), old_indices_discarded=array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 30, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.30300313, 0.32433431, 0.9416009 ]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 26, 29, 31, 32, 34, 35, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.17721715662436963, linear_terms=array([0.00016892, 0.00010182, 0.00044048]), square_terms=array([[6.47390632e-06, 4.70823827e-05, 1.17452629e-05], + [4.70823827e-05, 2.18434219e-02, 4.79874666e-02], + [1.17452629e-05, 4.79874666e-02, 1.07501169e-01]]), scale=0.016566867510712412, shift=array([5.30300313, 0.32433431, 0.9416009 ])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.28676541, 0.32788588, 0.93995208]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 29, 31, 34, 35, 36, 37, 38, 39, 40, 42]), model=ScalarModel(intercept=0.17584179736570787, linear_terms=array([ 0.00072293, -0.00395619, -0.0056097 ]), square_terms=array([[ 2.77942804e-05, -1.36149379e-04, -6.48042884e-04], + [-1.36149379e-04, 1.20187024e-01, 2.58595324e-01], + [-6.48042884e-04, 2.58595324e-01, 5.64442351e-01]]), scale=0.033133735021424825, shift=array([5.28676541, 0.32788588, 0.93995208])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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model=ScalarModel(intercept=0.17516083815911454, linear_terms=array([0.00333895, 0.02345551, 0.04006676]), square_terms=array([[ 9.17825370e-04, -1.49978117e-02, -3.32074698e-02], + [-1.49978117e-02, 3.29043755e-01, 6.85571868e-01], + [-3.32074698e-02, 6.85571868e-01, 1.45766388e+00]]), scale=0.06626747004284965, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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x=array([5.25969725, 0.34546622, 0.93220909]), fval=0.17587143240730305, rho=-0.2929960708109051, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 17, 18, 21, 24, 28, 31, 35, 36, 37, 38, 43]), old_indices_discarded=array([14, 15, 16, 19, 20, 22, 23, 25, 26, 27, 29, 30, 32, 33, 34, 39, 40, + 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.033133735021424825, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 40, 42, 43]), model=ScalarModel(intercept=0.17500252339842898, linear_terms=array([0.00143805, 0.00318419, 0.00584766]), square_terms=array([[ 2.32402708e-04, -4.32320857e-03, -9.89988047e-03], + [-4.32320857e-03, 1.00521560e-01, 2.19588637e-01], + [-9.89988047e-03, 2.19588637e-01, 4.87595223e-01]]), scale=0.033133735021424825, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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41, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25969725, 0.34546622, 0.93220909]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 0, 17, 18, 28, 29, 34, 36, 37, 39, 42, 43, 45]), model=ScalarModel(intercept=0.1751974222542535, linear_terms=array([0.00059828, 0.00104291, 0.00174938]), square_terms=array([[ 2.32452897e-05, -6.04410229e-04, -1.41547067e-03], + [-6.04410229e-04, 2.52698536e-02, 5.50888216e-02], + [-1.41547067e-03, 5.50888216e-02, 1.22064179e-01]]), scale=0.016566867510712412, shift=array([5.25969725, 0.34546622, 0.93220909])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), 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24, 25, 26, 29, 31, 33, 34, 35, 37, 38, 39, 40, 41, + 42, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17665032, 0.34743911, 0.93166475]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51]), model=ScalarModel(intercept=0.17468169315856466, linear_terms=array([0.00019334, 0.00068168, 0.002236 ]), square_terms=array([[6.30086486e-06, 1.31120182e-04, 1.92914840e-04], + [1.31120182e-04, 2.30933740e-02, 4.65644949e-02], + [1.92914840e-04, 4.65644949e-02, 9.56283946e-02]]), scale=0.016566867510712412, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + 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model=ScalarModel(intercept=0.17496972296461294, linear_terms=array([ 6.97863248e-05, -8.21475281e-05, -4.56887150e-04]), square_terms=array([[1.84361126e-06, 2.04013067e-05, 2.19735979e-05], + [2.04013067e-05, 5.26300747e-03, 1.10824804e-02], + [2.19735979e-05, 1.10824804e-02, 2.38107760e-02]]), scale=0.008283433755356206, shift=array([5.17665032, 0.34743911, 0.93166475])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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index=53, x=array([5.17030197, 0.34249393, 0.93412279]), fval=0.17478348430580953, rho=1.7260767313376242, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([44, 47, 49, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.008414173637941258, relative_step_length=1.0157832954842565, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.016566867510712412, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([18, 28, 36, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.1749826973068216, linear_terms=array([0.0001074 , 0.00038707, 0.00171986]), square_terms=array([[7.58231429e-06, 2.29836559e-04, 3.96776253e-04], + [2.29836559e-04, 2.29465979e-02, 4.65350783e-02], + [3.96776253e-04, 4.65350783e-02, 9.61235255e-02]]), scale=0.016566867510712412, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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State(trustregion=Region(center=array([5.17030197, 0.34249393, 0.93412279]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([44, 47, 49, 51, 52, 53, 54]), model=ScalarModel(intercept=0.1747885766485168, linear_terms=array([ 7.81846919e-05, 3.99579562e-05, -9.60174366e-05]), square_terms=array([[1.77118649e-06, 1.19372381e-05, 4.26127615e-06], + [1.19372381e-05, 5.37799886e-03, 1.13872076e-02], + [4.26127615e-06, 1.13872076e-02, 2.45888809e-02]]), scale=0.008283433755356206, shift=array([5.17030197, 0.34249393, 0.93412279])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 8, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.17800589377398301, linear_terms=array([-0.00030888, -0.11314456, -0.23802422]), square_terms=array([[ 1.44537530e-04, -2.97382575e-03, -7.90103681e-03], + [-2.97382575e-03, 1.94242721e+00, 4.15401254e+00], + [-7.90103681e-03, 4.15401254e+00, 8.91430260e+00]]), scale=0.06626747004284965, shift=array([4.85934517, 0.34730674, 0.93082827])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84]), model=ScalarModel(intercept=0.17265527071731143, linear_terms=array([-7.78798603e-05, 6.36909620e-05, -1.62218247e-04]), square_terms=array([[2.54965658e-06, 3.42416705e-05, 4.82468529e-05], + [3.42416705e-05, 6.54926335e-03, 1.37980536e-02], + [4.82468529e-05, 1.37980536e-02, 2.95393721e-02]]), scale=0.008283433755356206, shift=array([4.855221 , 0.34798078, 0.93135837])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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82, 85]), step_length=0.004230967300558663, relative_step_length=1.0215491366301683, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.8525976 , 0.34498062, 0.93277891]), radius=0.008283433755356206, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 85, 86]), model=ScalarModel(intercept=0.17259055555259742, linear_terms=array([3.92597809e-05, 5.10311101e-06, 2.28646135e-05]), square_terms=array([[2.47326484e-06, 5.53761478e-05, 9.73115792e-05], + [5.53761478e-05, 6.56831938e-03, 1.37867518e-02], + [9.73115792e-05, 1.37867518e-02, 2.94101730e-02]]), scale=0.008283433755356206, shift=array([4.8525976 , 0.34498062, 0.93277891])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + 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model_indices=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), model=ScalarModel(intercept=0.1725764987304485, linear_terms=array([7.05977087e-06, 2.29884404e-05, 1.21152777e-05]), square_terms=array([[5.86956811e-07, 1.16384872e-05, 1.94432612e-05], + [1.16384872e-05, 1.63380924e-03, 3.44140991e-03], + [1.94432612e-05, 3.44140991e-03, 7.36782486e-03]]), scale=0.004141716877678103, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([4.84072397, 0.34397348, 0.93327128]), index=87, x=array([4.84436454, 0.3458171 , 0.93240807]), fval=0.1725720095711579, rho=-1.0641290402854422, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([67, 72, 73, 75, 76, 77, 78, 80, 81, 84, 86, 87]), old_indices_discarded=array([74, 79, 82, 83, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, + 99, 100]), model=ScalarModel(intercept=0.17258738194234413, linear_terms=array([ 3.33838120e-06, -1.97151771e-08, 5.27903701e-06]), square_terms=array([[ 1.36507244e-07, 3.44142448e-07, -7.87178649e-07], + [ 3.44142448e-07, 4.10506238e-04, 8.69151483e-04], + [-7.87178649e-07, 8.69151483e-04, 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84436454, 0.3458171 , 0.93240807]), radius=0.0010354292194195258, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.1725830437240501, linear_terms=array([ 2.41233698e-05, -2.61891003e-06, 5.11296505e-06]), square_terms=array([[ 4.37386430e-08, -5.79574560e-07, -1.57888647e-06], + [-5.79574560e-07, 1.02556710e-04, 2.17206204e-04], + [-1.57888647e-06, 2.17206204e-04, 4.67922792e-04]]), scale=0.0010354292194195258, shift=array([4.84436454, 0.3458171 , 0.93240807])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), 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100, 101, 102]), model=ScalarModel(intercept=0.17258247058638926, linear_terms=array([-2.68495297e-05, -2.10095588e-06, -2.07300030e-05]), square_terms=array([[1.91163962e-07, 3.32105210e-06, 5.86912190e-06], + [3.32105210e-06, 4.07207519e-04, 8.64705423e-04], + [5.86912190e-06, 8.64705423e-04, 1.86822036e-03]]), scale=0.0020708584388390515, shift=array([4.84334274, 0.34598281, 0.93232094])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 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relative_step_length=1.0043585886444073, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([4.84438244, 0.34597441, 0.93234197]), radius=0.0020708584388390515, bounds=Bounds(lower=array([1.1 , 0.01, 0.5 ]), upper=array([20. , 0.99, 1.1 ]))), model_indices=array([ 87, 89, 90, 92, 96, 97, 98, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.17259245410531784, linear_terms=array([ 2.41019710e-05, -4.48842471e-06, -4.27936350e-05]), square_terms=array([[1.56434406e-07, 3.33318421e-06, 6.03774428e-06], + [3.33318421e-06, 4.15831904e-04, 8.74354178e-04], + [6.03774428e-06, 8.74354178e-04, 1.86706420e-03]]), scale=0.0020708584388390515, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), 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105]), model=ScalarModel(intercept=0.17258372705737116, linear_terms=array([ 4.09478695e-06, -3.55795689e-06, -4.27096698e-05]), square_terms=array([[3.35585618e-08, 2.15061082e-07, 1.37358651e-07], + [2.15061082e-07, 1.03219001e-04, 2.16664028e-04], + [1.37358651e-07, 2.16664028e-04, 4.61793255e-04]]), scale=0.0010354292194195258, shift=array([4.84438244, 0.34597441, 0.93234197])), vector_model=VectorModel(intercepts=array([ 0.05267501, 0.10545582, 0.09377041, 0.09722369, 0.08350715, + 0.06843123, 0.05931804, 0.03774492, -0.03016214, 0.0549344 , + -0.23227729, -0.2172487 , -0.10547152, -0.08018271, -0.07300715, + -0.0770617 , -0.07552639]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 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scale=0.5301397603427972, shift=array([5.3013976 , 0.35179043, 0.92170005])), candidate_index=123, candidate_x=array([4.8444368 , 0.34601283, 0.93235891]), index=123, x=array([4.8444368 , 0.34601283, 0.93235891]), fval=0.17254703939389457, rho=0.23884848773610878, accepted=True, new_indices=array([111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_used=array([104, 110]), old_indices_discarded=array([], dtype=int32), step_length=3.2357163106865805e-05, relative_step_length=1.0000000000001739, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 124 entries., 'history': {'params': [{'CRRA': 5.301397603427972, 'WealthShare': 0.35179042920828707, 'DiscFac': 0.9217000493025207}, {'CRRA': 4.877555010789218, 'WealthShare': 0.01, 'DiscFac': 1.0004206381225442}, {'CRRA': 5.726445053953837, 'WealthShare': 0.038959478718889294, 'DiscFac': 1.1}, {'CRRA': 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0.95 ], + [ 8.778125 , 0.898125 , 0.66875 ], + [ 2.28125 , 0.92875 , 0.8375 ], + [12.321875 , 0.959375 , 1.08125 ]]), 'exploration_results': array([2.42222292e-01, 3.03201302e-01, 3.75385332e-01, 3.98577685e-01, + 4.64539853e-01, 6.05168236e-01, 6.05724112e-01, 7.88383230e-01, + 8.39981988e-01, 1.04605604e+00, 1.22782446e+00, 1.48069565e+00, + 1.56893759e+00, 1.77192100e+00, 1.95139780e+00, 2.44243013e+00, + 2.75221387e+00, 2.82714102e+00, 3.02109383e+00, 6.88603970e+00, + 8.55954606e+00, 9.85239944e+00, 1.14488917e+01, 1.20953777e+01, + 1.29729655e+01, 1.58640798e+01, 3.65763661e+01, 5.66287991e+01, + 4.39985749e+02, 5.91890647e+02])}}" diff --git a/src/estimark/content/tables/min/WealthPortfolioShift_estimate_results.csv b/src/estimark/content/tables/min/WealthPortfolioShift_estimate_results.csv index 618fa4d..b227b6b 100644 --- a/src/estimark/content/tables/min/WealthPortfolioShift_estimate_results.csv +++ b/src/estimark/content/tables/min/WealthPortfolioShift_estimate_results.csv @@ -1,18242 +1,18261 @@ -CRRA,5.371266391432918 -WealthShare,0.1943943314688513 -WealthShift,0.679443956662166 -time_to_estimate,224.97277808189392 -params,"{'CRRA': 5.371266391432918, 'WealthShare': 0.1943943314688513, 'WealthShift': 0.679443956662166}" -criterion,0.2295381734816966 -start_criterion,0.23891445185117913 -start_params,"{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'WealthShift': 0.0}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,3 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'WealthShift': 0.0}, {'CRRA': 4.9314527326473225, 'WealthShare': 0.01, 'WealthShift': 0.35816358982006313}, {'CRRA': 5.608800970109241, 'WealthShare': 0.01, 'WealthShift': 0.41982482970398044}, {'CRRA': 5.76908435711711, 'WealthShare': 0.5158471609068197, 'WealthShift': 0.3981698683104749}, {'CRRA': 5.390261879203018, 'WealthShare': 0.6009588706847881, 'WealthShift': 0.003082036998847681}, {'CRRA': 5.0949845924943, 'WealthShare': 0.6009588706847881, 'WealthShift': 0.41866388994943565}, {'CRRA': 5.76908435711711, 'WealthShare': 0.5976536067789384, 'WealthShift': 0.41827443823708427}, {'CRRA': 4.908477191844984, 'WealthShare': 0.5384571464024298, 'WealthShift': 0.19135832558451127}, {'CRRA': 4.975000750048482, 'WealthShare': 0.18477605865660418, 'WealthShift': 0.4303035826360633}, {'CRRA': 5.763402303522219, 'WealthShare': 0.5343447437326452, 'WealthShift': 0.0}, {'CRRA': 5.379677063585084, 'WealthShare': 0.01, 'WealthShift': 0.031234005334165788}, {'CRRA': 4.913899279994994, 'WealthShare': 0.5975644386843522, 'WealthShift': 0.0}, {'CRRA': 5.76908435711711, 'WealthShare': 0.054512958460777576, 'WealthShift': 0.3504038911118758}, {'CRRA': 5.76908435711711, 'WealthShare': 0.16644423783469894, 'WealthShift': 0.4303035826360633}, {'CRRA': 5.553932565799078, 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0.17463134398088948, 'WealthShift': 0.23524340411523165}, {'CRRA': 5.325541830496868, 'WealthShare': 0.17974301931737452, 'WealthShift': 0.2299299334463044}, {'CRRA': 5.3161806178264435, 'WealthShare': 0.1825356923200299, 'WealthShift': 0.2959461118791828}, {'CRRA': 5.314585164275839, 'WealthShare': 0.18690146408804456, 'WealthShift': 0.4356758535670983}, {'CRRA': 5.5297369555938705, 'WealthShare': 0.18472896613551854, 'WealthShift': 0.65082764488513}, {'CRRA': 5.3329175805976, 'WealthShare': 0.18962821019558507, 'WealthShift': 0.5690827203694488}, {'CRRA': 5.548069371915632, 'WealthShare': 0.1888356302424437, 'WealthShift': 0.7842345116874805}, {'CRRA': 5.416426465391631, 'WealthShare': 0.1875544749657896, 'WealthShift': 0.6839451706228246}, {'CRRA': 5.284031408983628, 'WealthShare': 0.19177281457806364, 'WealthShift': 0.6149219146576859}, {'CRRA': 5.358343089593194, 'WealthShare': 0.19969139305379582, 'WealthShift': 0.5885916997749058}, {'CRRA': 5.3477071926390485, 'WealthShare': 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0.1944841827989484, 'WealthShift': 0.6946412707076063}, {'CRRA': 5.362530858243278, 'WealthShare': 0.19371235845028822, 'WealthShift': 0.67917279093371}, {'CRRA': 5.371252044414068, 'WealthShare': 0.19515406179895806, 'WealthShift': 0.6748494653651586}, {'CRRA': 5.371040357003462, 'WealthShare': 0.19512570949497798, 'WealthShift': 0.6804574712649102}, {'CRRA': 5.370362032234467, 'WealthShare': 0.19301496853268316, 'WealthShift': 0.6791361950445876}, {'CRRA': 5.370417235247351, 'WealthShare': 0.1946836539449308, 'WealthShift': 0.6789290949944624}, {'CRRA': 5.371700118481283, 'WealthShare': 0.19407592989865619, 'WealthShift': 0.679466131617694}, {'CRRA': 5.370996809733228, 'WealthShare': 0.1932546349189789, 'WealthShift': 0.6779038972651502}, {'CRRA': 5.371282416198062, 'WealthShare': 0.1928112110018877, 'WealthShift': 0.6787645124805319}, {'CRRA': 5.3710903886459365, 'WealthShare': 0.1934205187933144, 'WealthShift': 0.6797526277529055}, {'CRRA': 5.3704769790314755, 'WealthShare': 0.19429995636141595, 'WealthShift': 0.6780101915875183}, {'CRRA': 5.371545682473069, 'WealthShare': 0.19411655863874636, 'WealthShift': 0.6779925063945972}, {'CRRA': 5.370195456559467, 'WealthShare': 0.19397745856737997, 'WealthShift': 0.6794840078126839}, {'CRRA': 5.370050397316498, 'WealthShare': 0.19350353913218477, 'WealthShift': 0.6783649794283761}, {'CRRA': 5.371962513757795, 'WealthShare': 0.19354151125781988, 'WealthShift': 0.6787284059664366}, {'CRRA': 5.37138823145291, 'WealthShare': 0.19474863411072363, 'WealthShift': 0.6787693935361694}, {'CRRA': 5.3710792065342154, 'WealthShare': 0.19443913068868013, 'WealthShift': 0.679621235613119}, {'CRRA': 5.3704034370938185, 'WealthShare': 0.19458142919799615, 'WealthShift': 0.6776431911313305}, {'CRRA': 5.371320550989001, 'WealthShare': 0.19446077911052448, 'WealthShift': 0.678607008167103}, {'CRRA': 5.3713519822030475, 'WealthShare': 0.1944618633719279, 'WealthShift': 0.6791772691529462}, {'CRRA': 5.371266391432918, 'WealthShare': 0.1943943314688513, 'WealthShift': 0.679443956662166}], 'criterion': [0.24222229239256646, 1.478112872055069, 1.1738993056575204, 7.66561489336726, 18.738700547401976, 19.22108795656615, 15.506705787934324, 12.31515961934518, 0.23576937639679463, 9.707223917852335, 1.2482439718131173, 20.90388989527211, 0.7443512934453255, 0.24479444524730848, 0.2614718904910609, 0.270639050852218, 0.24095668076641172, 0.2399553333009884, 0.2377361837426363, 0.2568729292948785, 0.23880933776883706, 0.2357308733203971, 0.23672808947597423, 0.23462018780083854, 0.23526585311770365, 0.23386060852077306, 0.23281729277673047, 0.23109088667954464, 0.23231088686731985, 0.22984874056451765, 0.2321432347041099, 0.2311825152488482, 0.2298724029405645, 0.23262807844020333, 0.23060109795757228, 0.2333646350605909, 0.22998277512898735, 0.23771152142310734, 0.23273062652367485, 0.23024047517833396, 0.23539451131722727, 0.23520968963219913, 0.23708932322781573, 0.23014224071617864, 0.24191165473844944, 0.23307446207864127, 0.22981068942013977, 0.22970924468774562, 0.22961013657146467, 0.22981789832329552, 0.22973138822808242, 0.22962474605065475, 0.22963112451216366, 0.22958113184400364, 0.22959160086907093, 0.22960873266061366, 0.22963318479728462, 0.22961335501541685, 0.22966650476648243, 0.22957347491137076, 0.22954657256751673, 0.2296255367268235, 0.2297114465837029, 0.22962539796630416, 0.2295392655379997, 0.2295439897979808, 0.22956976961891207, 0.22960795862685532, 0.22960143762896604, 0.2295819805696318, 0.22953900680686223, 0.22956535348444274, 0.2295484781109926, 0.2295457973718279, 0.22953817348169658], 'runtime': [0.0, 1.078445800114423, 1.1149003999307752, 1.1535559999756515, 1.1922693997621536, 1.2300137002021074, 1.2728542000986636, 1.3101439997553825, 1.3493002001196146, 1.3861357998102903, 1.4237115997821093, 1.4617324001155794, 1.5002863998524845, 2.6419434999115765, 3.6615598001517355, 4.674209500197321, 5.7083767000585794, 6.739310999866575, 7.906286799814552, 8.925660899840295, 9.94611269980669, 10.963380999863148, 11.982361000031233, 13.00331459986046, 14.01534829987213, 15.032428000122309, 16.052119500003755, 17.067473100032657, 18.08538859989494, 19.095149700064212, 20.1070157000795, 21.11918029980734, 22.27491270005703, 23.287277800031006, 24.42046120017767, 24.446721899788827, 24.48540070001036, 24.524533899966627, 24.56690760003403, 24.604305800050497, 24.64112879987806, 24.678917500190437, 24.717683599796146, 24.75592500017956, 24.79437270015478, 24.83348789997399, 25.926029299851507, 26.96925489977002, 28.003000599797815, 29.01732419990003, 30.056552299764007, 31.073117999825627, 32.104939199984074, 33.1367243998684, 34.14407660020515, 35.153611599933356, 36.16626589978114, 37.30677030002698, 38.441172500140965, 38.47760089999065, 38.51622289977968, 38.55528979981318, 38.59801559988409, 38.63672780012712, 38.67566310008988, 38.71459549991414, 38.75300749996677, 38.79171779984608, 38.83782930020243, 38.870700700208545, 39.96831429982558, 40.98857139982283, 42.03050069976598, 43.07035129982978, 44.10055600013584], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 38, 39, 40, 41]}" -convergence_report, -multistart_info,"{'start_parameters': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'WealthShift': 0.0}, {'CRRA': 5.812664276213293, 'WealthShare': 0.23295294896519178, 'WealthShift': 6.996179195190264}, {'CRRA': 5.6712914737321025, 'WealthShare': 0.19427609444130123, 'WealthShift': 6.289245513555226}], 'local_optima': [Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 3.63e-06* 0.0001872 -relative_params_change 0.0003499 0.003202 -absolute_criterion_change 8.333e-07* 4.296e-05 -absolute_params_change 0.0002617 0.001639 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.133e-05 7.917e-05 -relative_params_change 0.0002288 0.001211 -absolute_criterion_change 2.602e-06* 1.818e-05 -absolute_params_change 8.54e-05 0.001737 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 2.534e-06* 0.001255 -relative_params_change 5.552e-05 0.0427 -absolute_criterion_change 5.817e-07* 0.000288 -absolute_params_change 8.297e-05 0.08045 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'WealthShift': 0.0}, {'CRRA': 6.415625, 'WealthShare': 0.285625, 'WealthShift': 15.625}, {'CRRA': 7.00625, 'WealthShare': 0.19375, 'WealthShift': 31.25}, {'CRRA': 9.959375, 'WealthShare': 0.101875, 'WealthShift': 84.375}, {'CRRA': 8.1875, 'WealthShare': 0.3775, 'WealthShift': 62.5}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003, 'WealthShift': 93.75}, {'CRRA': 12.9125, 'WealthShare': 0.1325, 'WealthShift': 87.5}, {'CRRA': 4.053125, 'WealthShare': 0.16312500000000002, 'WealthShift': 53.125}, {'CRRA': 2.871875, 'WealthShare': 0.469375, 'WealthShift': 46.875}, {'CRRA': 18.81875, 'WealthShare': 0.07125, 'WealthShift': 68.75}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255, 'WealthShift': 25.0}, {'CRRA': 11.73125, 'WealthShare': 0.43875, 'WealthShift': 6.25}, {'CRRA': 17.046875, 'WealthShare': 0.224375, 'WealthShift': 21.875}, {'CRRA': 3.4625, 'WealthShare': 0.6225, 'WealthShift': 37.5}, {'CRRA': 14.684375, 'WealthShare': 0.346875, 'WealthShift': 59.375}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5, 'WealthShift': 50.0}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125, 'WealthShift': 18.75}, {'CRRA': 13.503124999999999, 'WealthShare': 0.653125, 'WealthShift': 3.125}, {'CRRA': 18.228125, 'WealthShare': 0.408125, 'WealthShift': 78.125}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745, 'WealthShift': 75.0}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375, 'WealthShift': 71.875}, {'CRRA': 19.409375, 'WealthShare': 0.591875, 'WealthShift': 34.375}, {'CRRA': 16.45625, 'WealthShare': 0.68375, 'WealthShift': 81.25}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999, 'WealthShift': 12.5}, {'CRRA': 15.865624999999998, 'WealthShare': 0.775625, 'WealthShift': 65.625}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625, 'WealthShift': 43.75}, {'CRRA': 5.234375, 'WealthShare': 0.836875, 'WealthShift': 9.375}, {'CRRA': 8.778125, 'WealthShare': 0.898125, 'WealthShift': 28.125}, {'CRRA': 2.28125, 'WealthShare': 0.92875, 'WealthShift': 56.25}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375, 'WealthShift': 96.875}], 'exploration_results': array([2.42222292e-01, 4.68695243e-01, 6.55725054e-01, 7.71100519e-01, - 1.19425789e+00, 1.58158215e+00, 1.58170972e+00, 2.10988168e+00, - 2.53770191e+00, 3.19288105e+00, 3.47314230e+00, 3.48315006e+00, - 3.92293459e+00, 4.56511870e+00, 5.33390087e+00, 5.34621006e+00, - 6.48994604e+00, 9.32970623e+00, 1.17999718e+01, 1.57570577e+01, - 1.66647063e+01, 2.65180559e+01, 4.77377487e+01, 8.29741042e+01, - 8.62618718e+01, 8.81150175e+01, 1.31509063e+02, 1.97370096e+02, - 6.42774449e+02, 7.33084130e+02])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=[0], 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State(trustregion=Region(center=array([5.36850386, 0.16748761, 0.07694641]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 10, 14, 15, 16, 17]), model=ScalarModel(intercept=0.23890976132589972, linear_terms=array([ 0.00395884, 0.00060464, -0.00501012]), square_terms=array([[ 3.88386854e-04, 5.41430235e-03, -3.24823409e-04], - [ 5.41430235e-03, 3.71867735e-01, -1.63306975e-02], - [-3.24823409e-04, -1.63306975e-02, 8.40524718e-04]]), scale=0.0667347596810131, shift=array([5.36850386, 0.16748761, 0.07694641])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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x=array([5.33002354, 0.17880899, 0.16290546]), fval=0.2357308733203971, rho=0.520588290268742, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.03414988324909559, relative_step_length=1.023451149365918, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33002354, 0.17880899, 0.16290546]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 10, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.24317221807426376, linear_terms=array([ 0.003688 , 0.04308027, -0.00169941]), square_terms=array([[ 3.13655766e-04, 5.35056446e-03, -1.68322512e-04], - [ 5.35056446e-03, 3.71736068e-01, -1.42374292e-02], - [-1.68322512e-04, -1.42374292e-02, 6.15338931e-04]]), scale=0.0667347596810131, shift=array([5.33002354, 0.17880899, 0.16290546])), 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State(trustregion=Region(center=array([5.33002354, 0.17880899, 0.16290546]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2353621409779833, linear_terms=array([-8.14409317e-06, 3.93183005e-03, -1.42315911e-03]), square_terms=array([[ 3.70635713e-05, 8.65491261e-04, -3.88139805e-05], - [ 8.65491261e-04, 9.48655967e-02, -3.68813140e-03], - [-3.88139805e-05, -3.68813140e-03, 1.59862148e-04]]), scale=0.03336737984050655, shift=array([5.33002354, 0.17880899, 0.16290546])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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candidate_index=26, candidate_x=array([5.31618062, 0.18253569, 0.29594611]), index=26, x=array([5.31618062, 0.18253569, 0.29594611]), fval=0.23281729277673047, rho=0.48733419085251684, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([10, 14]), step_length=0.06673505180969229, relative_step_length=1.0000043774590721, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31618062, 0.18253569, 0.29594611]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.23204324546921717, linear_terms=array([ 0.00025096, 0.00865537, -0.00418553]), square_terms=array([[ 6.18617997e-04, 1.60769042e-02, -6.94988440e-04], - [ 1.60769042e-02, 1.55222925e+00, -5.66918990e-02], - [-6.94988440e-04, -5.66918990e-02, 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candidate_x=array([5.28403141, 0.19177281, 0.61492191]), index=29, x=array([5.33291758, 0.18962821, 0.56908272]), fval=0.22984874056451765, rho=-0.02856672672872867, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33291758, 0.18962821, 0.56908272]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([27, 29, 31, 32]), model=ScalarModel(intercept=0.22984874056451784, linear_terms=array([-0.0007202 , -0.0225488 , 0.00026261]), square_terms=array([[ 3.86082847e-05, 6.55960813e-04, -2.54355074e-05], - [ 6.55960813e-04, 7.67687434e-02, -2.23118856e-03], - [-2.54355074e-05, -2.23118856e-03, 6.84392300e-05]]), scale=0.03336737984050655, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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0.18962821, 0.56908272]), radius=0.016683689920253274, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.22990493283945845, linear_terms=array([-1.03738771e-04, -1.68226271e-03, -6.74238130e-05]), square_terms=array([[ 9.10673381e-06, 2.28039022e-04, -8.01399215e-06], - [ 2.28039022e-04, 2.35834730e-02, -6.68846684e-04], - [-8.01399215e-06, -6.68846684e-04, 1.97600773e-05]]), scale=0.016683689920253274, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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square_terms=array([[ 3.96994755e-05, 8.87767492e-04, -3.15974520e-05], - [ 8.87767492e-04, 9.42786791e-02, -2.68571082e-03], - [-3.15974520e-05, -2.68571082e-03, 7.96443473e-05]]), scale=0.03336737984050655, shift=array([5.34360311, 0.19111935, 0.58363527])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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new_indices=array([], dtype=int32), old_indices_used=array([29, 33, 34, 35, 36, 37, 38, 41, 42, 44, 45, 46]), old_indices_discarded=array([27, 31, 32, 39, 40, 43]), step_length=0.03400453221467406, relative_step_length=1.0190950676143302, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35700667, 0.19188293, 0.61487738]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 33, 34, 36, 37, 38, 41, 42, 43, 44, 45, 47]), model=ScalarModel(intercept=0.22961161556149873, linear_terms=array([-1.08762044e-04, 6.20777750e-06, -2.55001393e-04]), square_terms=array([[ 1.55766467e-04, 3.42812737e-03, -1.19175418e-04], - [ 3.42812737e-03, 3.76975356e-01, -1.06993808e-02], - [-1.19175418e-04, -1.06993808e-02, 3.14652536e-04]]), scale=0.0667347596810131, shift=array([5.35700667, 0.19188293, 0.61487738])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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State(trustregion=Region(center=array([5.37932077, 0.19348564, 0.67857404]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 31, 32, 33, 36, 41, 42, 43, 44, 45, 47, 48]), model=ScalarModel(intercept=0.22970735119178143, linear_terms=array([ 3.43934507e-05, 1.22525304e-03, -1.31321997e-04]), square_terms=array([[ 6.10000508e-04, 1.59844518e-02, -5.32347729e-04], - [ 1.59844518e-02, 1.50185560e+00, -4.16673928e-02], - [-5.32347729e-04, -4.16673928e-02, 1.19704831e-03]]), scale=0.1334695193620262, shift=array([5.37932077, 0.19348564, 0.67857404])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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model=ScalarModel(intercept=0.22974098770427356, linear_terms=array([-1.09021468e-05, 4.08548402e-04, 5.20757068e-06]), square_terms=array([[ 1.53966444e-04, 3.96473554e-03, -1.32542901e-04], - [ 3.96473554e-03, 3.75251867e-01, -1.03635926e-02], - [-1.32542901e-04, -1.03635926e-02, 2.96619546e-04]]), scale=0.0667347596810131, shift=array([5.37932077, 0.19348564, 0.67857404])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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x=array([5.37095446, 0.19380066, 0.67879122]), fval=0.22958113184400364, rho=-1.5380154974499307, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([48, 51, 52, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37095446, 0.19380066, 0.67879122]), radius=0.004170922480063319, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([48, 53, 54, 55]), model=ScalarModel(intercept=0.22958113184400375, linear_terms=array([-6.02315281e-06, -5.19466449e-04, 2.24761048e-05]), square_terms=array([[ 6.02720198e-07, 1.50364181e-05, -5.22986429e-07], - [ 1.50364181e-05, 1.46988707e-03, -4.09273676e-05], - [-5.22986429e-07, -4.09273676e-05, 1.18598493e-06]]), scale=0.004170922480063319, shift=array([5.37095446, 0.19380066, 0.67879122])), vector_model=VectorModel(intercepts=array([ 0.03980461, 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0.67962124])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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candidate_x=array([5.33878077, 0.17065529, 0. ]), index=0, x=array([5.33878077, 0.17065529, 0. ]), fval=0.24222229239256646, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.225040705891025, linear_terms=array([-0.41697775, 7.48428914, -0.17091496]), square_terms=array([[ 4.61705353e-02, -7.15556597e-01, 1.51914511e-02], - [-7.15556597e-01, 1.37527722e+01, -2.97488982e-01], - [ 1.51914511e-02, -2.97488982e-01, 6.91651527e-03]]), scale=array([0.43030358, 0.29547944, 0.21515179]), shift=array([5.33878077, 0.30547944, 0.21515179])), vector_model=VectorModel(intercepts=array([ 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13]), model=ScalarModel(intercept=0.425381512628019, linear_terms=array([-0.05492339, 1.6515207 , -0.01207703]), square_terms=array([[ 8.17527296e-03, -1.66195924e-01, 1.66474843e-03], - [-1.66195924e-01, 5.78105326e+00, -4.38301566e-02], - [ 1.66474843e-03, -4.38301566e-02, 4.71218119e-04]]), scale=array([0.21515179, 0.18790354, 0.1075759 ]), shift=array([5.33878077, 0.19790354, 0.1075759 ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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State(trustregion=Region(center=array([5.32798039, 0.17024985, 0.12990877]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.23750709163817948, linear_terms=array([-0.00028041, -0.02014843, -0.00082547]), square_terms=array([[ 3.87878112e-05, 8.30222831e-04, -3.48271727e-05], - [ 8.30222831e-04, 9.10354447e-02, -3.71220454e-03], - [-3.48271727e-05, -3.71220454e-03, 1.74706201e-04]]), scale=0.03336737984050655, shift=array([5.32798039, 0.17024985, 0.12990877])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.32554183, 0.17974302, 0.22992993]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.23352532372101198, linear_terms=array([ 0.00032407, -0.00134543, -0.00209466]), square_terms=array([[ 1.62460581e-04, 4.46079029e-03, -1.94367438e-04], - [ 4.46079029e-03, 3.73787533e-01, -1.39088251e-02], - [-1.94367438e-04, -1.39088251e-02, 5.70449352e-04]]), scale=0.0667347596810131, shift=array([5.32554183, 0.17974302, 0.22992993])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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0.00865537, -0.00418553]), square_terms=array([[ 6.18617997e-04, 1.60769042e-02, -6.94988440e-04], - [ 1.60769042e-02, 1.55222925e+00, -5.66918990e-02], - [-6.94988440e-04, -5.66918990e-02, 2.25978715e-03]]), scale=0.1334695193620262, shift=array([5.31618062, 0.18253569, 0.29594611])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - 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accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]), step_length=0.13980703182363227, relative_step_length=1.047482844711653, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31458516, 0.18690146, 0.43567585]), radius=0.2669390387240524, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 2, 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.24889256895749373, linear_terms=array([ 0.00121002, 0.32434574, -0.015755 ]), square_terms=array([[ 1.70973791e-03, 4.47870922e-02, -1.80519470e-03], - [ 4.47870922e-02, 2.63148581e+00, -8.32303744e-02], - [-1.80519470e-03, -8.32303744e-02, 2.99733583e-03]]), scale=array([0.21515179, 0.19602663, 0.21515179]), shift=array([5.31458516, 0.20602663, 0.43567585])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.31458516, 0.18690146, 0.43567585]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.23229852733260054, linear_terms=array([ 2.12256197e-05, 1.30675034e-02, -1.76767877e-03]), square_terms=array([[ 6.85846279e-04, 1.62602126e-02, -6.27129917e-04], - [ 1.62602126e-02, 1.52275966e+00, -4.64589216e-02], - [-6.27129917e-04, -4.64589216e-02, 1.49949621e-03]]), scale=0.1334695193620262, shift=array([5.31458516, 0.18690146, 0.43567585])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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model=ScalarModel(intercept=0.2472192748047931, linear_terms=array([ 0.00288894, 0.29428743, -0.01173496]), square_terms=array([[ 1.76702756e-03, 4.10334146e-02, -1.66808852e-03], - [ 4.10334146e-02, 2.64561735e+00, -8.66366920e-02], - [-1.66808852e-03, -8.66366920e-02, 3.03240965e-03]]), scale=array([0.21515179, 0.19739 , 0.21515179]), shift=array([5.33291758, 0.20739 , 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - 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index=29, x=array([5.33291758, 0.18962821, 0.56908272]), fval=0.22984874056451765, rho=-0.5690444447139047, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 2, 8, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, - 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33291758, 0.18962821, 0.56908272]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 2, 8, 19, 21, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.23259268781303988, linear_terms=array([-0.00018402, 0.03913342, -0.00193276]), square_terms=array([[ 6.90417570e-04, 1.72993592e-02, -6.33886092e-04], - [ 1.72993592e-02, 1.20998840e+00, -3.61884661e-02], - [-6.33886092e-04, -3.61884661e-02, 1.14819645e-03]]), scale=0.1334695193620262, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, - 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, - -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, - -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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State(trustregion=Region(center=array([5.68911594, 0.27992637, 6.77848331]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 18, 21, 23, 24, 27, 28, 29, 31, 32, 33]), model=ScalarModel(intercept=0.31490091077750826, linear_terms=array([0.00056326, 0.00239357, 0.00169179]), square_terms=array([[ 5.75896146e-04, 1.04942751e-02, -1.87864830e-04], - [ 1.04942751e-02, 3.55429643e-01, -5.22363671e-03], - [-1.87864830e-04, -5.22363671e-03, 8.85281587e-05]]), scale=0.0874522399398783, shift=array([5.68911594, 0.27992637, 6.77848331])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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rho=2.303521247100925, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 21, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34]), old_indices_discarded=array([ 0, 4, 7, 9, 10, 11, 14, 15, 16, 17, 19, 20, 22, 25, 26]), step_length=0.17526838550868756, relative_step_length=1.0020805963871315, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.63387982, 0.27999515, 6.52104392]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 21, 23, 24, 27, 28, 29, 30, 32, 33, 34, 35]), model=ScalarModel(intercept=0.30866554196660845, linear_terms=array([0.00936286, 0.19190386, 0.00628487]), square_terms=array([[ 3.36823188e-03, 3.92947433e-02, -1.80589971e-03], - [ 3.92947433e-02, 2.40659516e+00, -1.01216488e-01], - [-1.80589971e-03, -1.01216488e-01, 4.78566658e-03]]), scale=array([0.28194461, 0.27596988, 0.28194461]), shift=array([5.63387982, 0.28596988, 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model=ScalarModel(intercept=0.3087550370612184, linear_terms=array([-0.00016978, -0.00018205, 0.00062361]), square_terms=array([[ 1.72439008e-04, 3.54038359e-03, -4.56529466e-05], - [ 3.54038359e-03, 1.22124851e-01, -1.30875293e-03], - [-4.56529466e-05, -1.30875293e-03, 1.58649032e-05]]), scale=0.04372611996993915, shift=array([5.66198183, 0.28019625, 6.43594289])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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x=array([5.67220534, 0.2794848 , 6.39080358]), fval=0.3078435872229235, rho=1.1650054937337413, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.04628805319266375, relative_step_length=1.0585904540463658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67220534, 0.2794848 , 6.39080358]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 30, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.312469012000744, linear_terms=array([0.0006424 , 0.02844622, 0.0008161 ]), square_terms=array([[ 5.62722956e-04, 7.63491905e-03, -8.19951703e-05], - [ 7.63491905e-03, 2.06477523e-01, -1.47782624e-03], - [-8.19951703e-05, -1.47782624e-03, 1.63030415e-05]]), scale=0.0874522399398783, shift=array([5.67220534, 0.2794848 , 6.39080358])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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State(trustregion=Region(center=array([5.67220534, 0.2794848 , 6.39080358]), radius=0.04372611996993915, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.30810133617053853, linear_terms=array([-5.37601995e-05, -7.00924716e-04, 6.13829298e-04]), square_terms=array([[ 1.69906512e-04, 3.48813268e-03, -4.47825909e-05], - [ 3.48813268e-03, 1.22865562e-01, -1.30475046e-03], - [-4.47825909e-05, -1.30475046e-03, 1.56904743e-05]]), scale=0.04372611996993915, shift=array([5.67220534, 0.2794848 , 6.39080358])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.6996179195190264, shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=45, candidate_x=array([5.67390089, 0.27921825, 6.34662353]), index=45, x=array([5.67390089, 0.27921825, 6.34662353]), fval=0.3070622532156878, rho=1.2710816575871142, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), old_indices_discarded=array([], dtype=int32), step_length=0.04421337799866002, relative_step_length=1.0111434087693087, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67390089, 0.27921825, 6.34662353]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([30, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.3072429396986354, linear_terms=array([ 0.00049808, -0.0032087 , 0.00153465]), square_terms=array([[ 6.88807865e-04, 1.42287963e-02, -1.79323342e-04], - [ 1.42287963e-02, 4.89895284e-01, -5.14535676e-03], - [-1.79323342e-04, -5.14535676e-03, 6.29284246e-05]]), scale=0.0874522399398783, shift=array([5.67390089, 0.27921825, 6.34662353])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.6996179195190264, shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=46, candidate_x=array([5.6442248 , 0.2797616 , 6.26191928]), index=46, x=array([5.6442248 , 0.2797616 , 6.26191928]), fval=0.30527087889581456, rho=1.0825812593103086, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([10, 33, 34]), step_length=0.08975397172291417, relative_step_length=1.0263198722481925, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6442248 , 0.2797616 , 6.26191928]), radius=0.1749044798797566, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.30567622969623337, linear_terms=array([0.00043556, 0.00433094, 0.00285119]), square_terms=array([[ 2.64696146e-03, 5.47994591e-02, -7.40327662e-04], - [ 5.47994591e-02, 1.99195970e+00, -2.25935972e-02], - [-7.40327662e-04, -2.25935972e-02, 2.87765901e-04]]), scale=0.1749044798797566, shift=array([5.6442248 , 0.2797616 , 6.26191928])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , 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State(trustregion=Region(center=array([5.62536876, 0.27793055, 6.08804292]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.3040948590093949, linear_terms=array([0.0042244 , 0.12687991, 0.00336446]), square_terms=array([[ 6.74129095e-03, 1.36794923e-01, -1.95067616e-03], - [ 1.36794923e-01, 4.93983470e+00, -5.88435839e-02], - [-1.95067616e-03, -5.88435839e-02, 7.90272898e-04]]), scale=array([0.28194461, 0.27493758, 0.28194461]), shift=array([5.62536876, 0.28493758, 6.08804292])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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41, 42, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0215763649772467, linear_terms=array([ 0.14375631, 4.04635095, -0.05668364]), square_terms=array([[ 2.61048432e-02, 4.02728139e-01, -7.94395225e-03], - [ 4.02728139e-01, 1.13099345e+01, -1.85248296e-01], - [-7.94395225e-03, -1.85248296e-01, 3.39233697e-03]]), scale=array([0.56388923, 0.41557528, 0.56388923]), shift=array([5.52684234, 0.42557528, 5.80609831])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.6996179195190264, shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=49, candidate_x=array([5.47795249, 0.27137126, 5.24220908]), index=49, x=array([5.47795249, 0.27137126, 5.24220908]), fval=0.2889275687162242, rho=0.9414379695286359, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 40]), step_length=0.5660353098175912, relative_step_length=0.8090634816883041, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47795249, 0.27137126, 5.24220908]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 10, 11, 30, 35, 37, 41, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=8.401379808041456, linear_terms=array([20.48337965, 36.87386893, -0.97232092]), square_terms=array([[ 2.73718279e+01, 4.74699169e+01, 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old_indices_used=array([ 4, 10, 11, 30, 35, 37, 41, 44, 46, 47, 48, 49]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 39, - 40, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47795249, 0.27137126, 5.24220908]), radius=0.6996179195190264, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=1.0328048943255177, linear_terms=array([ 0.14706337, 4.05118743, -0.06028908]), square_terms=array([[ 2.63006632e-02, 3.99779202e-01, -8.22079292e-03], - [ 3.99779202e-01, 1.10294613e+01, -1.88747720e-01], - [-8.22079292e-03, -1.88747720e-01, 3.58770611e-03]]), scale=array([0.56388923, 0.41263024, 0.56388923]), shift=array([5.47795249, 0.42263024, 5.24220908])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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- 34, 35, 39, 40, 50]), step_length=0.569074661100749, relative_step_length=0.8134077833397645, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.4014905 , 0.26603536, 4.67831985]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=1.629273584089034, linear_terms=array([ 0.61497725, 5.59543793, -0.20069807]), square_terms=array([[ 0.18910149, 1.29985268, -0.05669145], - [ 1.29985268, 11.59567242, -0.45769195], - [-0.05669145, -0.45769195, 0.01956068]]), scale=array([1.12777846, 0.49 , 1.12777846]), shift=array([5.4014905 , 0.5 , 4.67831985])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - 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radius=2.7984716780761056, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=1.488299231526368, linear_terms=array([ 0.68708864, 4.68077783, -0.25965598]), square_terms=array([[ 0.36780394, 1.27824646, -0.10258544], - [ 1.27824646, 8.94190812, -0.55744366], - [-0.10258544, -0.55744366, 0.04079265]]), scale=array([2.25555691, 0.49 , 2.25555691]), shift=array([5.58019624, 0.5 , 3.55054139])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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scale=0.6996179195190264, shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=53, candidate_x=array([5.08211349, 0.22842295, 1.29498448]), index=53, x=array([5.08211349, 0.22842295, 1.29498448]), fval=0.24778697688048176, rho=0.5404450920215796, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([37, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 38, 39, 40, 41]), step_length=2.309907709026206, relative_step_length=0.8254175760014203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=5.596943356152211, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 9, 11, 14, 29, 30, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=11.714007670319425, linear_terms=array([24.50039079, 41.60470357, -4.28313711]), square_terms=array([[27.62848504, 45.16147148, -4.78880059], - [45.16147148, 75.49315113, -7.88257189], - [-4.78880059, -7.88257189, 0.83480515]]), scale=array([4.24661366, 0.49 , 2.90304915]), shift=array([5.34661366, 0.5 , 2.90304915])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 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rho=-2.6229548840900434, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 4, 9, 11, 14, 29, 30, 47, 49, 50, 51, 52, 53]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20, - 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, 45, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=2.7984716780761056, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 42, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=1.7564665441870613, linear_terms=array([ 0.48912219, 5.07900078, -0.29740787]), square_terms=array([[ 0.1529226 , 0.60855853, -0.04226286], - [ 0.60855853, 8.41899891, -0.52187489], - [-0.04226286, -0.52187489, 0.03505869]]), scale=array([2.25555691, 0.49 , 1.7752707 ]), shift=array([5.08211349, 0.5 , 1.7752707 ])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - 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24, 25, 26, 27, 28, 29, 30, 31, 32, 33, - 34, 35, 36, 38, 39, 40, 41, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=1.413470861366983, linear_terms=array([-0.20579534, 3.45109869, -0.17983883]), square_terms=array([[ 0.08818994, -0.14324532, 0.0077864 ], - [-0.14324532, 5.59046525, -0.27774519], - [ 0.0077864 , -0.27774519, 0.01448073]]), scale=array([1.12777846, 0.49 , 1.12777846]), shift=array([5.08211349, 0.5 , 1.29498448])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 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radius=0.6996179195190264, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.8726934072149458, linear_terms=array([-0.0783212 , 2.00091325, -0.09456309]), square_terms=array([[ 0.0203444 , -0.03857019, 0.00397376], - [-0.03857019, 3.83246439, -0.14592643], - [ 0.00397376, -0.14592643, 0.00645507]]), scale=array([0.56388923, 0.39115609, 0.56388923]), shift=array([5.08211349, 0.40115609, 1.29498448])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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relative_step_length=1.0600922623051872, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33869833, 0.20073019, 0.94626119]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 57, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.34007729667017145, linear_terms=array([ 0.02037288, 1.09201706, -0.02759842]), square_terms=array([[ 3.67439486e-03, 9.39073880e-02, -3.03941614e-03], - [ 9.39073880e-02, 5.48630084e+00, -1.48255420e-01], - [-3.03941614e-03, -1.48255420e-01, 4.16295796e-03]]), scale=array([0.28194461, 0.2363374 , 0.28194461]), shift=array([5.33869833, 0.2463374 , 0.94626119])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), 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x=array([5.37701979, 0.19637249, 0.77552158]), fval=0.22975800522408243, rho=0.8228733999861161, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([53, 60, 62, 64, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.17504153263478317, relative_step_length=1.0007835863044834, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37701979, 0.19637249, 0.77552158]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 57, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.32928826166731434, linear_terms=array([ 0.01408983, 0.97330408, -0.02555177]), square_terms=array([[ 3.33165512e-03, 6.78396125e-02, -2.45923055e-03], - [ 6.78396125e-02, 4.77687728e+00, -1.33384029e-01], - [-2.45923055e-03, -1.33384029e-01, 3.89493474e-03]]), scale=array([0.28194461, 0.23415855, 0.28194461]), shift=array([5.37701979, 0.24415855, 0.77552158])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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relative_step_length=0.07369657850734845, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37273054, 0.19594589, 0.75010477]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.33015631732120326, linear_terms=array([ 0.01200808, 0.97883005, -0.02664989]), square_terms=array([[ 3.15748987e-03, 6.07783540e-02, -2.29315594e-03], - [ 6.07783540e-02, 4.77347700e+00, -1.36482392e-01], - [-2.29315594e-03, -1.36482392e-01, 4.07018190e-03]]), scale=array([0.28194461, 0.23394525, 0.28194461]), shift=array([5.37273054, 0.24394525, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), 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model=ScalarModel(intercept=0.23011185928296796, linear_terms=array([-0.00054442, 0.0073945 , 0.00026198]), square_terms=array([[ 1.16376582e-03, 2.55336284e-02, -8.48105173e-04], - [ 2.55336284e-02, 2.77348932e+00, -6.94401574e-02], - [-8.48105173e-04, -6.94401574e-02, 1.80580668e-03]]), scale=0.1749044798797566, shift=array([5.37273054, 0.19594589, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , - 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, - -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, - 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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model_indices=array([18, 21, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.29826041062417086, linear_terms=array([1.26644753e-04, 2.40778277e-05, 3.09071996e-04]), square_terms=array([[ 1.09948334e-04, 2.20679348e-03, -9.94800662e-06], - [ 2.20679348e-03, 8.64431746e-02, -2.48315302e-04], - [-9.94800662e-06, -2.48315302e-04, 1.72749999e-06]]), scale=0.03930778445972016, shift=array([5.60812207, 0.27408211, 5.7920935 ])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.6289245513555226, shift=array([5.67129147, 0.19427609, 6.28924551])), candidate_index=35, candidate_x=array([5.5943578 , 0.27431124, 5.75363516]), index=35, x=array([5.5943578 , 0.27431124, 5.75363516]), fval=0.29668989844277605, rho=2.3974430325385168, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 21, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.04084790948344249, relative_step_length=1.0391811709790089, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.5943578 , 0.27431124, 5.75363516]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([18, 19, 21, 23, 24, 28, 29, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2995054829562912, linear_terms=array([1.59717607e-03, 8.40278740e-03, 2.79653900e-05]), square_terms=array([[ 2.92809658e-04, 4.55607833e-03, -1.77568617e-05], - [ 4.55607833e-03, 2.75571107e-01, 8.33702661e-05], - [-1.77568617e-05, 8.33702661e-05, 5.08567335e-06]]), scale=0.07861556891944033, shift=array([5.5943578 , 0.27431124, 5.75363516])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.5943578 , 0.27431124, 5.75363516]), radius=0.03930778445972016, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.2968263734417504, linear_terms=array([-0.00013262, -0.00109787, 0.00071038]), square_terms=array([[ 1.17780580e-04, 2.56942060e-03, -3.82135280e-05], - [ 2.56942060e-03, 1.01712962e-01, -1.29146121e-03], - [-3.82135280e-05, -1.29146121e-03, 1.79763784e-05]]), scale=0.03930778445972016, shift=array([5.5943578 , 0.27431124, 5.75363516])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 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linear_terms=array([0.00063314, 0.00259505, 0.00077565]), square_terms=array([[ 4.13948227e-04, 8.30470975e-03, -6.37384456e-05], - [ 8.30470975e-03, 3.46745608e-01, -1.95444378e-03], - [-6.37384456e-05, -1.95444378e-03, 1.51370328e-05]]), scale=0.07861556891944033, shift=array([5.59967088, 0.27409353, 5.71352946])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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fval=0.29502502944321474, rho=1.0288115202626666, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 21, 23, 28, 29, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([10, 15, 16, 17, 19, 20, 22, 24, 25, 26, 27, 30]), step_length=0.0786238847613481, relative_step_length=1.0001057785629752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55934649, 0.27409052, 5.64603383]), radius=0.15723113783888065, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 21, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.3048638716861844, linear_terms=array([ 0.00790086, 0.02682295, -0.0070564 ]), square_terms=array([[ 1.11247314e-03, -4.07052752e-03, -3.87937482e-04], - [-4.07052752e-03, 7.45474880e-01, 2.67385638e-02], - [-3.87937482e-04, 2.67385638e-02, 1.19103427e-03]]), scale=0.15723113783888065, shift=array([5.55934649, 0.27409052, 5.64603383])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55934649, 0.27409052, 5.64603383]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([21, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.29531157794914653, linear_terms=array([-0.00079777, 0.00028918, 0.00139716]), square_terms=array([[ 6.60562106e-04, 1.23198516e-02, -1.27064144e-04], - [ 1.23198516e-02, 3.59764181e-01, -3.16811387e-03], - [-1.27064144e-04, -3.16811387e-03, 3.32377356e-05]]), scale=0.07861556891944033, shift=array([5.55934649, 0.27409052, 5.64603383])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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model=ScalarModel(intercept=0.29355339341176606, linear_terms=array([-0.00019797, -0.00071454, 0.00299404]), square_terms=array([[ 2.02380756e-03, 4.26701579e-02, -6.18734942e-04], - [ 4.26701579e-02, 1.62234158e+00, -2.01145634e-02], - [-6.18734942e-04, -2.01145634e-02, 2.76548771e-04]]), scale=0.15723113783888065, shift=array([5.59413123, 0.27222346, 5.57554581])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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x=array([5.59773281, 0.27023777, 5.41715529]), fval=0.29147518333688877, rho=0.8159702169964561, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([ 0, 4, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, - 26, 27, 28]), step_length=0.15844390380935744, relative_step_length=1.0077132684221846, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.59773281, 0.27023777, 5.41715529]), radius=0.3144622756777613, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.2911350818408353, linear_terms=array([2.41630387e-05, 7.16611760e-04, 4.34790153e-03]), square_terms=array([[ 5.27549003e-03, 1.10723873e-01, -1.61625725e-03], - [ 1.10723873e-01, 4.21106621e+00, -5.16302574e-02], - [-1.61625725e-03, -5.16302574e-02, 7.03695581e-04]]), scale=array([0.25345533, 0.25345533, 0.25345533]), shift=array([5.59773281, 0.27023777, 5.41715529])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 34]), step_length=0.25504244694828304, relative_step_length=0.8110430619971487, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56942636, 0.2678314 , 5.16369996]), radius=0.6289245513555226, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 30, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.7970330746584172, linear_terms=array([ 0.10907961, 3.12734191, -0.04329691]), square_terms=array([[ 2.11126557e-02, 3.34560090e-01, -6.62328982e-03], - [ 3.34560090e-01, 9.59024924e+00, -1.58399852e-01], - [-6.62328982e-03, -1.58399852e-01, 2.90681443e-03]]), scale=array([0.50691066, 0.38237103, 0.50691066]), shift=array([5.56942636, 0.39237103, 5.16369996])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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State(trustregion=Region(center=array([5.51152948, 0.26288937, 4.6567893 ]), radius=1.2578491027110452, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 10, 24, 28, 29, 30, 31, 34, 40, 41, 42, 43]), model=ScalarModel(intercept=5.024125827775805, linear_terms=array([ 6.42333612, 21.1370778 , 1.00694802]), square_terms=array([[ 4.65892503, 14.70300577, 0.73508329], - [14.70300577, 47.22213909, 2.32311952], - [ 0.73508329, 2.32311952, 0.12514989]]), scale=array([1.01382132, 0.49 , 1.01382132]), shift=array([5.51152948, 0.5 , 4.6567893 ])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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linear_terms=array([ 0.11039741, 3.19266153, -0.04690528]), square_terms=array([[ 2.07035863e-02, 3.28080415e-01, -6.81650621e-03], - [ 3.28080415e-01, 9.51207209e+00, -1.64188087e-01], - [-6.81650621e-03, -1.64188087e-01, 3.14212476e-03]]), scale=array([0.50691066, 0.37990001, 0.50691066]), shift=array([5.51152948, 0.38990001, 4.6567893 ])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], 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4.14987865]), fval=0.27060072197678664, rho=1.0027114346640618, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 34, 44]), step_length=0.5128070669439712, relative_step_length=0.8153713602668505, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.43415317, 0.25783182, 4.14987865]), radius=1.2578491027110452, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([30, 31, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45]), model=ScalarModel(intercept=2.2313260222378055, linear_terms=array([ 0.39940297, 7.9100079 , -0.20216257]), square_terms=array([[ 7.68147957e-02, 8.04452650e-01, -2.69071098e-02], - [ 8.04452650e-01, 1.59550602e+01, -4.40323323e-01], - [-2.69071098e-02, -4.40323323e-01, 1.34308096e-02]]), scale=array([1.01382132, 0.49 , 1.01382132]), shift=array([5.43415317, 0.5 , 4.14987865])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 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7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 44]), step_length=1.0462520903512822, relative_step_length=0.8317786991271787, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.17672719, 0.2345587 , 3.13605733]), radius=2.5156982054220904, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([30, 31, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46]), model=ScalarModel(intercept=2.4157379520152538, linear_terms=array([ 0.63641679, 8.35461052, -0.44234772]), square_terms=array([[ 0.2316333 , 1.26480581, -0.09114979], - [ 1.26480581, 16.17103872, -0.91725459], - [-0.09114979, -0.91725459, 0.05713252]]), scale=array([2.02764263, 0.49 , 2.02764263]), shift=array([5.17672719, 0.5 , 3.13605733])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 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State(trustregion=Region(center=array([5.14044679, 0.21973818, 1.1084147 ]), radius=5.031396410844181, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 9, 11, 14, 30, 34, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=7.578123065462952, linear_terms=array([ 8.77370694, 27.83603808, -1.43728114]), square_terms=array([[ 5.96236428, 16.97550627, -0.9684297 ], - [16.97550627, 52.89432326, -2.79183279], - [-0.9684297 , -2.79183279, 0.16071254]]), scale=array([4.04786603, 0.49 , 2.58184998]), shift=array([5.14786603, 0.5 , 2.58184998])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]]), scale=0.6289245513555226, shift=array([5.67129147, 0.19427609, 6.28924551])), candidate_index=48, candidate_x=array([5.8352042 , 0.18956831, 0. ]), index=47, x=array([5.14044679, 0.21973818, 1.1084147 ]), fval=0.24008569037689945, rho=-0.9720376955975245, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 4, 9, 11, 14, 30, 34, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20, - 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39, - 40, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.14044679, 0.21973818, 1.1084147 ]), radius=2.5156982054220904, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([30, 31, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=2.578020554580501, linear_terms=array([ 1.21626104, 8.06658535, -0.46481816]), square_terms=array([[ 0.45089658, 2.05574383, -0.14024761], - [ 2.05574383, 13.89162144, -0.83270766], - [-0.14024761, -0.83270766, 0.05320328]]), scale=array([2.02764263, 0.49 , 1.56802866]), shift=array([5.14044679, 0.5 , 1.56802866])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], 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]), fval=0.24008569037689945, rho=-3.6573442288989653, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 37, 38, 40, 41, 42, 43, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, - 36, 39, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.14044679, 0.21973818, 1.1084147 ]), radius=1.2578491027110452, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([30, 31, 38, 40, 41, 42, 43, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=3.0012010152202784, linear_terms=array([ 0.1340118 , 8.78430767, -0.34977956]), square_terms=array([[ 0.03494041, 0.24697248, -0.01571295], - [ 0.24697248, 14.01479366, -0.57430277], - [-0.01571295, -0.57430277, 0.02492346]]), scale=array([1.01382132, 0.49 , 1.01382132]), 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22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, - 36, 37, 39, 44]), step_length=1.1331834061322807, relative_step_length=0.9008897837506326, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.64407971, 0.16850485, 0.09459338]), radius=0.6289245513555226, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([46, 47, 48, 49, 50]), model=ScalarModel(intercept=1.096043505482867, linear_terms=array([-2.20009474e-03, 3.49841131e+00, -8.40941933e-02]), square_terms=array([[ 7.85560010e-03, 1.04506254e-02, -1.04238360e-03], - [ 1.04506254e-02, 7.15764907e+00, -1.68023293e-01], - [-1.04238360e-03, -1.68023293e-01, 4.13540543e-03]]), scale=array([0.50691066, 0.33270775, 0.30075202]), shift=array([5.64407971, 0.34270775, 0.30075202])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - 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State(trustregion=Region(center=array([5.74647636, 0.17698886, 0.34804871]), radius=0.3144622756777613, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 48, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.3304714024432466, linear_terms=array([ 0.00672234, 0.88757699, -0.03510327]), square_terms=array([[ 1.78234494e-03, 3.00255688e-02, -1.41070675e-03], - [ 3.00255688e-02, 4.26064366e+00, -1.52651251e-01], - [-1.41070675e-03, -1.52651251e-01, 5.70379274e-03]]), scale=array([0.25345533, 0.21022209, 0.25345533]), shift=array([5.74647636, 0.22022209, 0.34804871])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 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vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.25872555, 0.19502188, 0.69471579]), radius=0.3144622756777613, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.2843406451397853, linear_terms=array([ 0.00864362, 0.66880793, -0.02153665]), square_terms=array([[ 2.02864798e-03, 5.20711154e-02, -2.00548145e-03], - [ 5.20711154e-02, 4.18844306e+00, -1.31694121e-01], - [-2.00548145e-03, -1.31694121e-01, 4.32121474e-03]]), scale=array([0.25345533, 0.2192386 , 0.25345533]), shift=array([5.25872555, 0.2292386 , 0.69471579])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], 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rho=-0.02524571622235216, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([47, 50, 52, 54, 56, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25872555, 0.19502188, 0.69471579]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.22994912955767116, linear_terms=array([-0.00025941, -0.00706267, 0.00039407]), square_terms=array([[ 2.32468966e-04, 5.95108709e-03, -1.95902884e-04], - [ 5.95108709e-03, 5.00411141e-01, -1.30922256e-02], - [-1.95902884e-04, -1.30922256e-02, 3.57697068e-04]]), scale=0.07861556891944033, shift=array([5.25872555, 0.19502188, 0.69471579])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 52, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.2300853167775167, linear_terms=array([ 0.00078659, 0.02760188, -0.00044154]), square_terms=array([[ 9.43727230e-04, 2.94358721e-02, -9.17315300e-04], - [ 2.94358721e-02, 2.40966772e+00, -6.27058201e-02], - [-9.17315300e-04, -6.27058201e-02, 1.69122066e-03]]), scale=0.15723113783888065, shift=array([5.28926298, 0.19386526, 0.62196173])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 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scale=0.6289245513555226, shift=array([5.67129147, 0.19427609, 6.28924551])), candidate_index=65, candidate_x=array([5.19299093, 0.18968233, 0.4852126 ]), index=64, x=array([5.28926298, 0.19386526, 0.62196173]), fval=0.2298513003202055, rho=-2.223906397897434, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([47, 52, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64]), old_indices_discarded=array([50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.28926298, 0.19386526, 0.62196173]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([57, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.22983273501407558, linear_terms=array([-0.0006163 , -0.02327892, 0.00053352]), square_terms=array([[ 2.08817020e-04, 4.32654330e-03, -1.56441413e-04], - [ 4.32654330e-03, 4.14588316e-01, -1.13336809e-02], - 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100. ]))), model_indices=array([71, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 89]), model=ScalarModel(intercept=0.22964083720898745, linear_terms=array([ 5.36375221e-06, 6.49233965e-05, -5.72473971e-06]), square_terms=array([[ 5.30815741e-08, 1.38912698e-06, -4.95079236e-08], - [ 1.38912698e-06, 1.26489377e-04, -3.87973280e-06], - [-4.95079236e-08, -3.87973280e-06, 1.22649858e-07]]), scale=0.001228368264366255, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - 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candidate_index=90, candidate_x=array([5.37411718, 0.19399202, 0.68831849]), index=71, x=array([5.37502587, 0.19456137, 0.68757298]), fval=0.22954649382876313, rho=-2.773013561970068, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([71, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 89]), old_indices_discarded=array([79, 86, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37502587, 0.19456137, 0.68757298]), radius=0.0006141841321831275, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 71, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, - 101, 102]), model=ScalarModel(intercept=0.22957543584367357, linear_terms=array([-8.13901107e-07, -6.75905236e-06, 3.70842400e-06]), square_terms=array([[ 1.24784163e-08, 3.17885746e-07, -1.10122031e-08], - [ 3.17885746e-07, 3.19810599e-05, -8.54961251e-07], - [-1.10122031e-08, 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37502587, 0.19456137, 0.68757298]), radius=0.0003070920660915638, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 71, 91, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102]), model=ScalarModel(intercept=0.22957245176877, linear_terms=array([-2.22206342e-06, -6.14397943e-06, 6.57765597e-07]), square_terms=array([[ 3.28312122e-09, 8.88488490e-08, -3.02227643e-09], - [ 8.88488490e-08, 8.01014176e-06, -2.18461658e-07], - [-3.02227643e-09, -2.18461658e-07, 6.26317057e-09]]), scale=0.0003070920660915638, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 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model=ScalarModel(intercept=0.22957307152698875, linear_terms=array([-6.64279536e-07, -3.17186565e-06, 9.39653938e-07]), square_terms=array([[ 8.30162512e-10, 2.27090598e-08, -7.70023749e-10], - [ 2.27090598e-08, 2.00284035e-06, -5.43072997e-08], - [-7.70023749e-10, -5.43072997e-08, 1.55195044e-09]]), scale=0.0001535460330457819, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 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index=71, x=array([5.37502587, 0.19456137, 0.68757298]), fval=0.22954649382876313, rho=-2.030261445123912, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 71, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 104]), old_indices_discarded=array([ 91, 92, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37502587, 0.19456137, 0.68757298]), radius=7.677301652289094e-05, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 71, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, - 116, 117]), model=ScalarModel(intercept=0.22954896050293003, linear_terms=array([ 3.28680984e-06, -4.77347360e-07, 1.37109067e-07]), square_terms=array([[ 2.55761258e-10, 4.01597052e-09, -1.56505450e-10], - [ 4.01597052e-09, 5.03977931e-07, -1.41568103e-08], - [-1.56505450e-10, -1.41568103e-08, 4.17172147e-10]]), scale=7.677301652289094e-05, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, - 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, - -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , - 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 0.], - [0., 0., 0.], - [0., 0., 0.]], - - [[0., 0., 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26.96925489977002, 28.003000599797815, 29.01732419990003, 30.056552299764007, 31.073117999825627, 32.104939199984074, 33.1367243998684, 34.14407660020515, 35.153611599933356, 36.16626589978114, 37.30677030002698, 38.441172500140965, 38.47760089999065, 38.51622289977968, 38.55528979981318, 38.59801559988409, 38.63672780012712, 38.67566310008988, 38.71459549991414, 38.75300749996677, 38.79171779984608, 38.83782930020243, 38.870700700208545, 39.96831429982558, 40.98857139982283, 42.03050069976598, 43.07035129982978, 44.10055600013584], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 38, 39, 40, 41]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872487, 'WealthShift': 0.0}, {'CRRA': 5.812664276213293, 'WealthShare': 0.23295294896519178, 'WealthShift': 6.996179195190264}, {'CRRA': 5.6712914737321025, 'WealthShare': 0.19427609444130123, 'WealthShift': 6.289245513555226}], 'local_optima': [Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.63e-06* 0.0001872 +relative_params_change 0.0003499 0.003202 +absolute_criterion_change 8.333e-07* 4.296e-05 +absolute_params_change 0.0002617 0.001639 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.133e-05 7.917e-05 +relative_params_change 0.0002288 0.001211 +absolute_criterion_change 2.602e-06* 1.818e-05 +absolute_params_change 8.54e-05 0.001737 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 2.534e-06* 0.001255 +relative_params_change 5.552e-05 0.0427 +absolute_criterion_change 5.817e-07* 0.000288 +absolute_params_change 8.297e-05 0.08045 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485, 'WealthShift': 0.0}, {'CRRA': 6.415625, 'WealthShare': 0.285625, 'WealthShift': 15.625}, {'CRRA': 7.00625, 'WealthShare': 0.19375, 'WealthShift': 31.25}, {'CRRA': 9.959375, 'WealthShare': 0.101875, 'WealthShift': 84.375}, {'CRRA': 8.1875, 'WealthShare': 0.3775, 'WealthShift': 62.5}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003, 'WealthShift': 93.75}, {'CRRA': 12.9125, 'WealthShare': 0.1325, 'WealthShift': 87.5}, {'CRRA': 4.053125, 'WealthShare': 0.16312500000000002, 'WealthShift': 53.125}, {'CRRA': 2.871875, 'WealthShare': 0.469375, 'WealthShift': 46.875}, {'CRRA': 18.81875, 'WealthShare': 0.07125, 'WealthShift': 68.75}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255, 'WealthShift': 25.0}, {'CRRA': 11.73125, 'WealthShare': 0.43875, 'WealthShift': 6.25}, {'CRRA': 17.046875, 'WealthShare': 0.224375, 'WealthShift': 21.875}, {'CRRA': 3.4625, 'WealthShare': 0.6225, 'WealthShift': 37.5}, {'CRRA': 14.684375, 'WealthShare': 0.346875, 'WealthShift': 59.375}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5, 'WealthShift': 50.0}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125, 'WealthShift': 18.75}, {'CRRA': 13.503124999999999, 'WealthShare': 0.653125, 'WealthShift': 3.125}, {'CRRA': 18.228125, 'WealthShare': 0.408125, 'WealthShift': 78.125}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745, 'WealthShift': 75.0}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375, 'WealthShift': 71.875}, {'CRRA': 19.409375, 'WealthShare': 0.591875, 'WealthShift': 34.375}, {'CRRA': 16.45625, 'WealthShare': 0.68375, 'WealthShift': 81.25}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999, 'WealthShift': 12.5}, {'CRRA': 15.865624999999998, 'WealthShare': 0.775625, 'WealthShift': 65.625}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625, 'WealthShift': 43.75}, {'CRRA': 5.234375, 'WealthShare': 0.836875, 'WealthShift': 9.375}, {'CRRA': 8.778125, 'WealthShare': 0.898125, 'WealthShift': 28.125}, {'CRRA': 2.28125, 'WealthShare': 0.92875, 'WealthShift': 56.25}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375, 'WealthShift': 96.875}], 'exploration_results': array([2.42222292e-01, 4.68695243e-01, 6.55725054e-01, 7.71100519e-01, + 1.19425789e+00, 1.58158215e+00, 1.58170972e+00, 2.10988168e+00, + 2.53770191e+00, 3.19288105e+00, 3.47314230e+00, 3.48315006e+00, + 3.92293459e+00, 4.56511870e+00, 5.33390087e+00, 5.34621006e+00, + 6.48994604e+00, 9.32970623e+00, 1.17999718e+01, 1.57570577e+01, + 1.66647063e+01, 2.65180559e+01, 4.77377487e+01, 8.29741042e+01, + 8.62618718e+01, 8.81150175e+01, 1.31509063e+02, 1.97370096e+02, + 6.42774449e+02, 7.33084130e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=[0], model=ScalarModel(intercept=0.24222229239256646, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 0. ])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.5338780774481048, shift=array([5.33878077, 0.17065529, 0. ])), candidate_index=0, candidate_x=array([5.33878077, 0.17065529, 0. ]), index=0, x=array([5.33878077, 0.17065529, 0. ]), fval=0.24222229239256646, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.5338780774481048, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=2.225040705891025, linear_terms=array([-0.41697775, 7.48428914, -0.17091496]), square_terms=array([[ 4.61705353e-02, -7.15556597e-01, 1.51914511e-02], + [-7.15556597e-01, 1.37527722e+01, -2.97488982e-01], + [ 1.51914511e-02, -2.97488982e-01, 6.91651527e-03]]), scale=array([0.43030358, 0.29547944, 0.21515179]), shift=array([5.33878077, 0.30547944, 0.21515179])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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fval=0.24222229239256646, rho=-0.2762680450576785, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13]), old_indices_discarded=array([3, 6]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33878077, 0.17065529, 0. ]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.25159110698009957, linear_terms=array([-0.01524465, 0.47423814, -0.00627023]), square_terms=array([[ 2.17792939e-03, -4.77186682e-02, 7.80957785e-04], + [-4.77186682e-02, 1.83079477e+00, -2.76050226e-02], + [ 7.80957785e-04, -2.76050226e-02, 4.40535462e-04]]), scale=array([0.1075759 , 0.1075759 , 0.05378795]), shift=array([5.33878077, 0.17065529, 0.05378795])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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x=array([5.33002354, 0.17880899, 0.16290546]), fval=0.2357308733203971, rho=0.520588290268742, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.03414988324909559, relative_step_length=1.023451149365918, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33002354, 0.17880899, 0.16290546]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 10, 14, 15, 16, 17, 18, 19, 20, 21]), model=ScalarModel(intercept=0.24317221807426376, linear_terms=array([ 0.003688 , 0.04308027, -0.00169941]), square_terms=array([[ 3.13655766e-04, 5.35056446e-03, -1.68322512e-04], + [ 5.35056446e-03, 3.71736068e-01, -1.42374292e-02], + [-1.68322512e-04, -1.42374292e-02, 6.15338931e-04]]), scale=0.0667347596810131, shift=array([5.33002354, 0.17880899, 0.16290546])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([5.33002354, 0.17880899, 0.16290546]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2353621409779833, linear_terms=array([-8.14409317e-06, 3.93183005e-03, -1.42315911e-03]), square_terms=array([[ 3.70635713e-05, 8.65491261e-04, -3.88139805e-05], + [ 8.65491261e-04, 9.48655967e-02, -3.68813140e-03], + [-3.88139805e-05, -3.68813140e-03, 1.59862148e-04]]), scale=0.03336737984050655, shift=array([5.33002354, 0.17880899, 0.16290546])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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dtype=int32), old_indices_used=array([ 0, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33133588, 0.17874369, 0.19708448]), radius=0.03336737984050655, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24]), model=ScalarModel(intercept=0.2347506284146446, linear_terms=array([ 0.00018161, 0.00068761, -0.00095169]), square_terms=array([[ 3.81618863e-05, 1.04534847e-03, -4.58629133e-05], + [ 1.04534847e-03, 9.51162615e-02, -3.43629229e-03], + [-4.58629133e-05, -3.43629229e-03, 1.35226170e-04]]), scale=0.03336737984050655, shift=array([5.33133588, 0.17874369, 0.19708448])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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20. , 0.99, 100. ]))), model_indices=array([ 0, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]), model=ScalarModel(intercept=0.23352532372101198, linear_terms=array([ 0.00032407, -0.00134543, -0.00209466]), square_terms=array([[ 1.62460581e-04, 4.46079029e-03, -1.94367438e-04], + [ 4.46079029e-03, 3.73787533e-01, -1.39088251e-02], + [-1.94367438e-04, -1.39088251e-02, 5.70449352e-04]]), scale=0.0667347596810131, shift=array([5.32554183, 0.17974302, 0.22992993])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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candidate_index=26, candidate_x=array([5.31618062, 0.18253569, 0.29594611]), index=26, x=array([5.31618062, 0.18253569, 0.29594611]), fval=0.23281729277673047, rho=0.48733419085251684, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]), old_indices_discarded=array([10, 14]), step_length=0.06673505180969229, relative_step_length=1.0000043774590721, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31618062, 0.18253569, 0.29594611]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=0.23204324546921717, linear_terms=array([ 0.00025096, 0.00865537, -0.00418553]), square_terms=array([[ 6.18617997e-04, 1.60769042e-02, -6.94988440e-04], + [ 1.60769042e-02, 1.55222925e+00, -5.66918990e-02], + [-6.94988440e-04, -5.66918990e-02, 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accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]), step_length=0.13980703182363227, relative_step_length=1.047482844711653, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31458516, 0.18690146, 0.43567585]), radius=0.2669390387240524, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 2, 8, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=0.24889256895749373, linear_terms=array([ 0.00121002, 0.32434574, -0.015755 ]), square_terms=array([[ 1.70973791e-03, 4.47870922e-02, -1.80519470e-03], + [ 4.47870922e-02, 2.63148581e+00, -8.32303744e-02], + [-1.80519470e-03, -8.32303744e-02, 2.99733583e-03]]), scale=array([0.21515179, 0.19602663, 0.21515179]), shift=array([5.31458516, 0.20602663, 0.43567585])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model=ScalarModel(intercept=0.2472192748047931, linear_terms=array([ 0.00288894, 0.29428743, -0.01173496]), square_terms=array([[ 1.76702756e-03, 4.10334146e-02, -1.66808852e-03], + [ 4.10334146e-02, 2.64561735e+00, -8.66366920e-02], + [-1.66808852e-03, -8.66366920e-02, 3.03240965e-03]]), scale=array([0.21515179, 0.19739 , 0.21515179]), shift=array([5.33291758, 0.20739 , 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + 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index=29, x=array([5.33291758, 0.18962821, 0.56908272]), fval=0.22984874056451765, rho=-0.5690444447139047, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 2, 8, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([ 0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33291758, 0.18962821, 0.56908272]), radius=0.1334695193620262, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 2, 8, 19, 21, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.23259268781303988, linear_terms=array([-0.00018402, 0.03913342, -0.00193276]), square_terms=array([[ 6.90417570e-04, 1.72993592e-02, -6.33886092e-04], + [ 1.72993592e-02, 1.20998840e+00, -3.61884661e-02], + [-6.33886092e-04, -3.61884661e-02, 1.14819645e-03]]), scale=0.1334695193620262, 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33291758, 0.18962821, 0.56908272]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.23020148167955468, linear_terms=array([ 0.00065502, -0.00077507, -0.00054048]), square_terms=array([[ 1.82914785e-04, 5.47852986e-03, -1.90103684e-04], + [ 5.47852986e-03, 3.99384620e-01, -1.17504731e-02], + [-1.90103684e-04, -1.17504731e-02, 3.60523943e-04]]), scale=0.0667347596810131, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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old_indices_used=array([27, 29, 31, 32]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33291758, 0.18962821, 0.56908272]), radius=0.016683689920253274, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), model=ScalarModel(intercept=0.22990493283945845, linear_terms=array([-1.03738771e-04, -1.68226271e-03, -6.74238130e-05]), square_terms=array([[ 9.10673381e-06, 2.28039022e-04, -8.01399215e-06], + [ 2.28039022e-04, 2.35834730e-02, -6.68846684e-04], + [-8.01399215e-06, -6.68846684e-04, 1.97600773e-05]]), scale=0.016683689920253274, shift=array([5.33291758, 0.18962821, 0.56908272])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + 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0.99, 100. ]))), model_indices=array([29, 33, 34, 35, 36, 37, 38, 41, 42, 44, 45, 46]), model=ScalarModel(intercept=0.22970166870174738, linear_terms=array([-1.06584794e-04, -5.12343108e-06, -2.33290567e-04]), square_terms=array([[ 3.96994755e-05, 8.87767492e-04, -3.15974520e-05], + [ 8.87767492e-04, 9.42786791e-02, -2.68571082e-03], + [-3.15974520e-05, -2.68571082e-03, 7.96443473e-05]]), scale=0.03336737984050655, shift=array([5.34360311, 0.19111935, 0.58363527])), vector_model=VectorModel(intercepts=array([ 0.03980461, 0.08715954, 0.08449923, 0.10710153, 0.11905802, + 0.13160042, 0.14768033, 0.15905648, 0.07367938, 0.12505611, + -0.21199057, -0.25057516, -0.05680053, -0.03662718, -0.03009585, + -0.03160325, -0.02367163]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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candidate_index=47, candidate_x=array([5.35700667, 0.19188293, 0.61487738]), index=47, x=array([5.35700667, 0.19188293, 0.61487738]), fval=0.22970924468774562, rho=0.39042346819671714, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 33, 34, 35, 36, 37, 38, 41, 42, 44, 45, 46]), old_indices_discarded=array([27, 31, 32, 39, 40, 43]), step_length=0.03400453221467406, relative_step_length=1.0190950676143302, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.35700667, 0.19188293, 0.61487738]), radius=0.0667347596810131, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 33, 34, 36, 37, 38, 41, 42, 43, 44, 45, 47]), model=ScalarModel(intercept=0.22961161556149873, linear_terms=array([-1.08762044e-04, 6.20777750e-06, -2.55001393e-04]), square_terms=array([[ 1.55766467e-04, 3.42812737e-03, -1.19175418e-04], + [ 3.42812737e-03, 3.76975356e-01, -1.06993808e-02], + 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x=array([5.67220534, 0.2794848 , 6.39080358]), fval=0.3078435872229235, rho=1.1650054937337413, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int32), step_length=0.04628805319266375, relative_step_length=1.0585904540463658, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.67220534, 0.2794848 , 6.39080358]), radius=0.0874522399398783, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 30, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.312469012000744, linear_terms=array([0.0006424 , 0.02844622, 0.0008161 ]), square_terms=array([[ 5.62722956e-04, 7.63491905e-03, -8.19951703e-05], + [ 7.63491905e-03, 2.06477523e-01, -1.47782624e-03], + [-8.19951703e-05, -1.47782624e-03, 1.63030415e-05]]), scale=0.0874522399398783, shift=array([5.67220534, 0.2794848 , 6.39080358])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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State(trustregion=Region(center=array([5.67220534, 0.2794848 , 6.39080358]), radius=0.04372611996993915, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), model=ScalarModel(intercept=0.30810133617053853, linear_terms=array([-5.37601995e-05, -7.00924716e-04, 6.13829298e-04]), square_terms=array([[ 1.69906512e-04, 3.48813268e-03, -4.47825909e-05], + [ 3.48813268e-03, 1.22865562e-01, -1.30475046e-03], + [-4.47825909e-05, -1.30475046e-03, 1.56904743e-05]]), scale=0.04372611996993915, shift=array([5.67220534, 0.2794848 , 6.39080358])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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new_indices=array([], dtype=int32), old_indices_used=array([30, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]), old_indices_discarded=array([10, 33, 34]), step_length=0.08975397172291417, relative_step_length=1.0263198722481925, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6442248 , 0.2797616 , 6.26191928]), radius=0.1749044798797566, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=0.30567622969623337, linear_terms=array([0.00043556, 0.00433094, 0.00285119]), square_terms=array([[ 2.64696146e-03, 5.47994591e-02, -7.40327662e-04], + [ 5.47994591e-02, 1.99195970e+00, -2.25935972e-02], + [-7.40327662e-04, -2.25935972e-02, 2.87765901e-04]]), scale=0.1749044798797566, shift=array([5.6442248 , 0.2797616 , 6.26191928])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , 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State(trustregion=Region(center=array([5.62536876, 0.27793055, 6.08804292]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=0.3040948590093949, linear_terms=array([0.0042244 , 0.12687991, 0.00336446]), square_terms=array([[ 6.74129095e-03, 1.36794923e-01, -1.95067616e-03], + [ 1.36794923e-01, 4.93983470e+00, -5.88435839e-02], + [-1.95067616e-03, -5.88435839e-02, 7.90272898e-04]]), scale=array([0.28194461, 0.27493758, 0.28194461]), shift=array([5.62536876, 0.28493758, 6.08804292])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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41, 42, 43, 44, 45, 46, 47, 48]), model=ScalarModel(intercept=1.0215763649772467, linear_terms=array([ 0.14375631, 4.04635095, -0.05668364]), square_terms=array([[ 2.61048432e-02, 4.02728139e-01, -7.94395225e-03], + [ 4.02728139e-01, 1.13099345e+01, -1.85248296e-01], + [-7.94395225e-03, -1.85248296e-01, 3.39233697e-03]]), scale=array([0.56388923, 0.41557528, 0.56388923]), shift=array([5.52684234, 0.42557528, 5.80609831])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([5.47795249, 0.27137126, 5.24220908]), index=49, x=array([5.47795249, 0.27137126, 5.24220908]), fval=0.2889275687162242, rho=0.9414379695286359, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 40]), step_length=0.5660353098175912, relative_step_length=0.8090634816883041, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47795249, 0.27137126, 5.24220908]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 10, 11, 30, 35, 37, 41, 44, 46, 47, 48, 49]), model=ScalarModel(intercept=8.401379808041456, linear_terms=array([20.48337965, 36.87386893, -0.97232092]), square_terms=array([[ 2.73718279e+01, 4.74699169e+01, 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old_indices_used=array([ 4, 10, 11, 30, 35, 37, 41, 44, 46, 47, 48, 49]), old_indices_discarded=array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 38, 39, + 40, 42, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.47795249, 0.27137126, 5.24220908]), radius=0.6996179195190264, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=1.0328048943255177, linear_terms=array([ 0.14706337, 4.05118743, -0.06028908]), square_terms=array([[ 2.63006632e-02, 3.99779202e-01, -8.22079292e-03], + [ 3.99779202e-01, 1.10294613e+01, -1.88747720e-01], + [-8.22079292e-03, -1.88747720e-01, 3.58770611e-03]]), scale=array([0.56388923, 0.41263024, 0.56388923]), shift=array([5.47795249, 0.42263024, 5.24220908])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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+ 34, 35, 39, 40, 50]), step_length=0.569074661100749, relative_step_length=0.8134077833397645, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.4014905 , 0.26603536, 4.67831985]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=1.629273584089034, linear_terms=array([ 0.61497725, 5.59543793, -0.20069807]), square_terms=array([[ 0.18910149, 1.29985268, -0.05669145], + [ 1.29985268, 11.59567242, -0.45769195], + [-0.05669145, -0.45769195, 0.01956068]]), scale=array([1.12777846, 0.49 , 1.12777846]), shift=array([5.4014905 , 0.5 , 4.67831985])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + 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radius=2.7984716780761056, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=1.488299231526368, linear_terms=array([ 0.68708864, 4.68077783, -0.25965598]), square_terms=array([[ 0.36780394, 1.27824646, -0.10258544], + [ 1.27824646, 8.94190812, -0.55744366], + [-0.10258544, -0.55744366, 0.04079265]]), scale=array([2.25555691, 0.49 , 2.25555691]), shift=array([5.58019624, 0.5 , 3.55054139])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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scale=0.6996179195190264, shift=array([5.81266428, 0.23295295, 6.9961792 ])), candidate_index=53, candidate_x=array([5.08211349, 0.22842295, 1.29498448]), index=53, x=array([5.08211349, 0.22842295, 1.29498448]), fval=0.24778697688048176, rho=0.5404450920215796, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([37, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 38, 39, 40, 41]), step_length=2.309907709026206, relative_step_length=0.8254175760014203, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=5.596943356152211, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 9, 11, 14, 29, 30, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=11.714007670319425, 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24, 25, 26, 27, 28, 29, 30, 31, 32, 33, + 34, 35, 36, 38, 39, 40, 41, 43, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.08211349, 0.22842295, 1.29498448]), radius=1.3992358390380528, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([37, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=1.413470861366983, linear_terms=array([-0.20579534, 3.45109869, -0.17983883]), square_terms=array([[ 0.08818994, -0.14324532, 0.0077864 ], + [-0.14324532, 5.59046525, -0.27774519], + [ 0.0077864 , -0.27774519, 0.01448073]]), scale=array([1.12777846, 0.49 , 1.12777846]), shift=array([5.08211349, 0.5 , 1.29498448])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 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radius=0.6996179195190264, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([50, 51, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.8726934072149458, linear_terms=array([-0.0783212 , 2.00091325, -0.09456309]), square_terms=array([[ 0.0203444 , -0.03857019, 0.00397376], + [-0.03857019, 3.83246439, -0.14592643], + [ 0.00397376, -0.14592643, 0.00645507]]), scale=array([0.56388923, 0.39115609, 0.56388923]), shift=array([5.08211349, 0.40115609, 1.29498448])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 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x=array([5.37701979, 0.19637249, 0.77552158]), fval=0.22975800522408243, rho=0.8228733999861161, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([53, 60, 62, 64, 65, 66, 67, 68, 69, 70]), old_indices_discarded=array([], dtype=int32), step_length=0.17504153263478317, relative_step_length=1.0007835863044834, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37701979, 0.19637249, 0.77552158]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 57, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.32928826166731434, linear_terms=array([ 0.01408983, 0.97330408, -0.02555177]), square_terms=array([[ 3.33165512e-03, 6.78396125e-02, -2.45923055e-03], + [ 6.78396125e-02, 4.77687728e+00, -1.33384029e-01], + [-2.45923055e-03, -1.33384029e-01, 3.89493474e-03]]), scale=array([0.28194461, 0.23415855, 0.28194461]), 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relative_step_length=0.07369657850734845, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37273054, 0.19594589, 0.75010477]), radius=0.3498089597595132, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([53, 60, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.33015631732120326, linear_terms=array([ 0.01200808, 0.97883005, -0.02664989]), square_terms=array([[ 3.15748987e-03, 6.07783540e-02, -2.29315594e-03], + [ 6.07783540e-02, 4.77347700e+00, -1.36482392e-01], + [-2.29315594e-03, -1.36482392e-01, 4.07018190e-03]]), scale=array([0.28194461, 0.23394525, 0.28194461]), shift=array([5.37273054, 0.24394525, 0.75010477])), vector_model=VectorModel(intercepts=array([ 0.02515583, 0.06381521, 0.06386806, 0.09321649, 0.1132282 , + 0.13241041, 0.15213805, 0.11382721, -0.00122978, 0.01828718, + -0.34841965, -0.40948853, -0.01328219, 0.01123573, 0.02522056, + 0.04033998, 0.06941292]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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model_indices=array([18, 21, 28, 29, 31, 32, 33, 34]), model=ScalarModel(intercept=0.29826041062417086, linear_terms=array([1.26644753e-04, 2.40778277e-05, 3.09071996e-04]), square_terms=array([[ 1.09948334e-04, 2.20679348e-03, -9.94800662e-06], + [ 2.20679348e-03, 8.64431746e-02, -2.48315302e-04], + [-9.94800662e-06, -2.48315302e-04, 1.72749999e-06]]), scale=0.03930778445972016, shift=array([5.60812207, 0.27408211, 5.7920935 ])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), 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candidate_x=array([5.5943578 , 0.27431124, 5.75363516]), index=35, x=array([5.5943578 , 0.27431124, 5.75363516]), fval=0.29668989844277605, rho=2.3974430325385168, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 21, 28, 29, 31, 32, 33, 34]), old_indices_discarded=array([], dtype=int32), step_length=0.04084790948344249, relative_step_length=1.0391811709790089, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.5943578 , 0.27431124, 5.75363516]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([18, 19, 21, 23, 24, 28, 29, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2995054829562912, linear_terms=array([1.59717607e-03, 8.40278740e-03, 2.79653900e-05]), square_terms=array([[ 2.92809658e-04, 4.55607833e-03, -1.77568617e-05], + [ 4.55607833e-03, 2.75571107e-01, 8.33702661e-05], + [-1.77568617e-05, 8.33702661e-05, 5.08567335e-06]]), scale=0.07861556891944033, shift=array([5.5943578 , 0.27431124, 5.75363516])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 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step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.5943578 , 0.27431124, 5.75363516]), radius=0.03930778445972016, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.2968263734417504, linear_terms=array([-0.00013262, -0.00109787, 0.00071038]), square_terms=array([[ 1.17780580e-04, 2.56942060e-03, -3.82135280e-05], + [ 2.56942060e-03, 1.01712962e-01, -1.29146121e-03], + [-3.82135280e-05, -1.29146121e-03, 1.79763784e-05]]), scale=0.03930778445972016, shift=array([5.5943578 , 0.27431124, 5.75363516])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 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fval=0.29502502944321474, rho=1.0288115202626666, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([18, 21, 23, 28, 29, 31, 32, 33, 34, 35, 36, 37]), old_indices_discarded=array([10, 15, 16, 17, 19, 20, 22, 24, 25, 26, 27, 30]), step_length=0.0786238847613481, relative_step_length=1.0001057785629752, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55934649, 0.27409052, 5.64603383]), radius=0.15723113783888065, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([10, 21, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]), model=ScalarModel(intercept=0.3048638716861844, linear_terms=array([ 0.00790086, 0.02682295, -0.0070564 ]), square_terms=array([[ 1.11247314e-03, -4.07052752e-03, -3.87937482e-04], + [-4.07052752e-03, 7.45474880e-01, 2.67385638e-02], + [-3.87937482e-04, 2.67385638e-02, 1.19103427e-03]]), scale=0.15723113783888065, shift=array([5.55934649, 0.27409052, 5.64603383])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.55934649, 0.27409052, 5.64603383]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([21, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]), model=ScalarModel(intercept=0.29531157794914653, linear_terms=array([-0.00079777, 0.00028918, 0.00139716]), square_terms=array([[ 6.60562106e-04, 1.23198516e-02, -1.27064144e-04], + [ 1.23198516e-02, 3.59764181e-01, -3.16811387e-03], + [-1.27064144e-04, -3.16811387e-03, 3.32377356e-05]]), scale=0.07861556891944033, shift=array([5.55934649, 0.27409052, 5.64603383])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + 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model=ScalarModel(intercept=0.29355339341176606, linear_terms=array([-0.00019797, -0.00071454, 0.00299404]), square_terms=array([[ 2.02380756e-03, 4.26701579e-02, -6.18734942e-04], + [ 4.26701579e-02, 1.62234158e+00, -2.01145634e-02], + [-6.18734942e-04, -2.01145634e-02, 2.76548771e-04]]), scale=0.15723113783888065, shift=array([5.59413123, 0.27222346, 5.57554581])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 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x=array([5.59773281, 0.27023777, 5.41715529]), fval=0.29147518333688877, rho=0.8159702169964561, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]), old_indices_discarded=array([ 0, 4, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, + 26, 27, 28]), step_length=0.15844390380935744, relative_step_length=1.0077132684221846, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.59773281, 0.27023777, 5.41715529]), radius=0.3144622756777613, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41]), model=ScalarModel(intercept=0.2911350818408353, linear_terms=array([2.41630387e-05, 7.16611760e-04, 4.34790153e-03]), square_terms=array([[ 5.27549003e-03, 1.10723873e-01, -1.61625725e-03], + [ 1.10723873e-01, 4.21106621e+00, -5.16302574e-02], + [-1.61625725e-03, -5.16302574e-02, 7.03695581e-04]]), scale=array([0.25345533, 0.25345533, 0.25345533]), shift=array([5.59773281, 0.27023777, 5.41715529])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 34]), step_length=0.25504244694828304, relative_step_length=0.8110430619971487, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.56942636, 0.2678314 , 5.16369996]), radius=0.6289245513555226, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([29, 30, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.7970330746584172, linear_terms=array([ 0.10907961, 3.12734191, -0.04329691]), square_terms=array([[ 2.11126557e-02, 3.34560090e-01, -6.62328982e-03], + [ 3.34560090e-01, 9.59024924e+00, -1.58399852e-01], + [-6.62328982e-03, -1.58399852e-01, 2.90681443e-03]]), scale=array([0.50691066, 0.38237103, 0.50691066]), shift=array([5.56942636, 0.39237103, 5.16369996])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]]), scale=0.6289245513555226, shift=array([5.67129147, 0.19427609, 6.28924551])), candidate_index=43, candidate_x=array([5.51152948, 0.26288937, 4.6567893 ]), index=43, x=array([5.51152948, 0.26288937, 4.6567893 ]), fval=0.2787897629404394, rho=1.030446066706609, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([29, 30, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 32, 34]), step_length=0.5102302296784873, relative_step_length=0.8112741481927949, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.51152948, 0.26288937, 4.6567893 ]), radius=1.2578491027110452, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 4, 10, 24, 28, 29, 30, 31, 34, 40, 41, 42, 43]), model=ScalarModel(intercept=5.024125827775805, linear_terms=array([ 6.42333612, 21.1370778 , 1.00694802]), square_terms=array([[ 4.65892503, 14.70300577, 0.73508329], + [14.70300577, 47.22213909, 2.32311952], + [ 0.73508329, 2.32311952, 0.12514989]]), scale=array([1.01382132, 0.49 , 1.01382132]), shift=array([5.51152948, 0.5 , 4.6567893 ])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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model=ScalarModel(intercept=2.578020554580501, linear_terms=array([ 1.21626104, 8.06658535, -0.46481816]), square_terms=array([[ 0.45089658, 2.05574383, -0.14024761], + [ 2.05574383, 13.89162144, -0.83270766], + [-0.14024761, -0.83270766, 0.05320328]]), scale=array([2.02764263, 0.49 , 1.56802866]), shift=array([5.14044679, 0.5 , 1.56802866])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], 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0.97543478]), index=59, x=array([5.42539954, 0.19222483, 0.72197945]), fval=0.23014612373591264, rho=-1.0953230954897188, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), old_indices_discarded=array([49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.42539954, 0.19222483, 0.72197945]), radius=0.15723113783888065, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]), model=ScalarModel(intercept=0.23153502885898644, linear_terms=array([ 0.00075427, -0.02088725, 0.00047796]), square_terms=array([[ 7.96578936e-04, 2.33779148e-02, -7.53075969e-04], + [ 2.33779148e-02, 2.04660391e+00, -5.33667251e-02], + [-7.53075969e-04, -5.33667251e-02, 1.45482726e-03]]), scale=0.15723113783888065, shift=array([5.42539954, 0.19222483, 0.72197945])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.25872555, 0.19502188, 0.69471579]), radius=0.3144622756777613, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.2843406451397853, linear_terms=array([ 0.00864362, 0.66880793, -0.02153665]), square_terms=array([[ 2.02864798e-03, 5.20711154e-02, -2.00548145e-03], + [ 5.20711154e-02, 4.18844306e+00, -1.31694121e-01], + [-2.00548145e-03, -1.31694121e-01, 4.32121474e-03]]), scale=array([0.25345533, 0.2192386 , 0.25345533]), shift=array([5.25872555, 0.2292386 , 0.69471579])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], 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1.61093111e-02, -1.02340711e-03]), square_terms=array([[ 8.94621017e-04, 2.68768407e-02, -9.00350011e-04], + [ 2.68768407e-02, 2.38980257e+00, -6.71920674e-02], + [-9.00350011e-04, -6.71920674e-02, 1.96128863e-03]]), scale=0.15723113783888065, shift=array([5.25872555, 0.19502188, 0.69471579])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 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rho=-0.02524571622235216, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([47, 50, 52, 54, 56, 57, 58, 59, 60, 61, 62]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.25872555, 0.19502188, 0.69471579]), radius=0.07861556891944033, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([57, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.22994912955767116, linear_terms=array([-0.00025941, -0.00706267, 0.00039407]), square_terms=array([[ 2.32468966e-04, 5.95108709e-03, -1.95902884e-04], + [ 5.95108709e-03, 5.00411141e-01, -1.30922256e-02], + [-1.95902884e-04, -1.30922256e-02, 3.57697068e-04]]), scale=0.07861556891944033, shift=array([5.25872555, 0.19502188, 0.69471579])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 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bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([47, 52, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.2300853167775167, linear_terms=array([ 0.00078659, 0.02760188, -0.00044154]), square_terms=array([[ 9.43727230e-04, 2.94358721e-02, -9.17315300e-04], + [ 2.94358721e-02, 2.40966772e+00, -6.27058201e-02], + [-9.17315300e-04, -6.27058201e-02, 1.69122066e-03]]), scale=0.15723113783888065, shift=array([5.28926298, 0.19386526, 0.62196173])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 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index=71, x=array([5.37502587, 0.19456137, 0.68757298]), fval=0.22954649382876313, rho=-2.030261445123912, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 71, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 104]), old_indices_discarded=array([ 91, 92, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.37502587, 0.19456137, 0.68757298]), radius=7.677301652289094e-05, bounds=Bounds(lower=array([1.1 , 0.01, 0. ]), upper=array([ 20. , 0.99, 100. ]))), model_indices=array([ 71, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, + 116, 117]), model=ScalarModel(intercept=0.22954896050293003, linear_terms=array([ 3.28680984e-06, -4.77347360e-07, 1.37109067e-07]), square_terms=array([[ 2.55761258e-10, 4.01597052e-09, -1.56505450e-10], + [ 4.01597052e-09, 5.03977931e-07, -1.41568103e-08], + [-1.56505450e-10, -1.41568103e-08, 4.17172147e-10]]), scale=7.677301652289094e-05, shift=array([5.37502587, 0.19456137, 0.68757298])), vector_model=VectorModel(intercepts=array([ 0.02354116, 0.05893189, 0.05511322, 0.08007337, 0.09513781, + 0.10839155, 0.1198678 , 0.04749388, -0.0746656 , -0.05874137, + -0.42316514, -0.47259966, 0.00921586, 0.03275234, 0.0475013 , + 0.0622027 , 0.08758365]), linear_terms=array([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]]), square_terms=array([[[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], + + [[0., 0., 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6.55725054e-01, 7.71100519e-01, + 1.19425789e+00, 1.58158215e+00, 1.58170972e+00, 2.10988168e+00, + 2.53770191e+00, 3.19288105e+00, 3.47314230e+00, 3.48315006e+00, + 3.92293459e+00, 4.56511870e+00, 5.33390087e+00, 5.34621006e+00, + 6.48994604e+00, 9.32970623e+00, 1.17999718e+01, 1.57570577e+01, + 1.66647063e+01, 2.65180559e+01, 4.77377487e+01, 8.29741042e+01, + 8.62618718e+01, 8.81150175e+01, 1.31509063e+02, 1.97370096e+02, + 6.42774449e+02, 7.33084130e+02])}}" diff --git a/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv b/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv index d2043bc..d8b4f14 100644 --- a/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv +++ b/src/estimark/content/tables/min/WealthPortfolio_estimate_results.csv @@ -1,7041 +1,7059 @@ -CRRA,5.338780774481047 -WealthShare,0.17065528804872485 -time_to_estimate,87.05302119255066 -params,"{'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485}" -criterion,0.24222229239256732 -start_criterion,2.0450859311174514 -start_params,"{'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451}" -algorithm,multistart_tranquilo_ls -direction,minimize -n_free,2 -message,Absolute criterion change smaller than tolerance. -success, -n_criterion_evaluations, -n_derivative_evaluations, -n_iterations, -history,"{'params': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 6.385337260354661, 'WealthShare': 0.0589657827107465}, {'CRRA': 7.627162739645338, 'WealthShare': 0.2909337034881926}, {'CRRA': 6.385337260354661, 'WealthShare': 0.4422314188083436}, {'CRRA': 7.627162739645338, 'WealthShare': 0.6717516331999257}, {'CRRA': 7.627162739645338, 'WealthShare': 0.010000476301985028}, {'CRRA': 7.556316959078161, 'WealthShare': 0.01}, {'CRRA': 6.385337260354661, 'WealthShare': 0.4716198511344087}, {'CRRA': 7.627162739645338, 'WealthShare': 0.751444172509113}, {'CRRA': 7.435546062237209, 'WealthShare': 0.8146627396453386}, {'CRRA': 6.407783701100443, 'WealthShare': 0.8146627396453386}, {'CRRA': 6.6651666175265545, 'WealthShare': 0.01}, {'CRRA': 6.48493214278919, 'WealthShare': 0.8146627396453386}, {'CRRA': 6.385337260354661, 'WealthShare': 0.1237617248187634}, {'CRRA': 6.0748808905319915, 'WealthShare': 0.20052322401594325}, {'CRRA': 6.2532427503011325, 'WealthShare': 0.19438789449563343}, {'CRRA': 6.3382227136719385, 'WealthShare': 0.1578819528196836}, {'CRRA': 6.493450898583273, 'WealthShare': 0.13740842870128234}, {'CRRA': 6.4256753849363895, 'WealthShare': 0.15294566470705245}, {'CRRA': 6.294459741840304, 'WealthShare': 0.16000090800258274}, {'CRRA': 6.382143835462875, 'WealthShare': 0.16046020191691993}, {'CRRA': 6.250679533322458, 'WealthShare': 0.16091446504625906}, {'CRRA': 6.163102752485386, 'WealthShare': 0.16148208803528766}, {'CRRA': 6.007874567574052, 'WealthShare': 0.16384849824047343}, {'CRRA': 5.697418197751382, 'WealthShare': 0.17249887954427012}, {'CRRA': 5.236146802438669, 'WealthShare': 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5.230029569186239, 'WealthShare': 0.16984189173088587}, {'CRRA': 5.361391583162623, 'WealthShare': 0.16869150649947118}, {'CRRA': 5.339508482161973, 'WealthShare': 0.17080481516331386}, {'CRRA': 5.368957473427081, 'WealthShare': 0.1704982684436756}, {'CRRA': 5.3283746442860265, 'WealthShare': 0.170903000375076}, {'CRRA': 5.328563448185613, 'WealthShare': 0.17111248155527758}, {'CRRA': 5.334036845737762, 'WealthShare': 0.17107114051345143}, {'CRRA': 5.342235745297582, 'WealthShare': 0.1697818067119096}, {'CRRA': 5.3408803356615, 'WealthShare': 0.17109939320809675}, {'CRRA': 5.338823528524706, 'WealthShare': 0.17065422827949905}, {'CRRA': 5.340193277829363, 'WealthShare': 0.17078798862632172}, {'CRRA': 5.338140970895526, 'WealthShare': 0.17083226059338175}, {'CRRA': 5.338518273170905, 'WealthShare': 0.17049811328240777}, {'CRRA': 5.338781916239757, 'WealthShare': 0.1708201405184341}, {'CRRA': 5.338915212045181, 'WealthShare': 0.17067448290561968}, {'CRRA': 5.338780774481047, 'WealthShare': 0.17065528804872485}], 'criterion': [0.3273843758368489, 0.5699740200328935, 0.842575503738206, 3.810658336120194, 20.623483705368287, 0.6910521757749679, 0.6995225156927494, 4.9395441503578565, 42.99134020288109, 86.6456059148058, 105.27402392078089, 0.8474994237274145, 103.66743022564344, 0.30690596890028216, 0.29210255832128873, 0.28576369985789285, 0.26145704727673624, 0.28242718110045806, 0.26615570282764395, 0.2594527871161228, 0.26221365404602937, 0.2577870645585467, 0.25523533119834385, 0.25064066883423725, 0.24497093468209197, 0.24406000199271466, 0.24743835677691237, 0.24950377050231112, 6.8114635382632684, 0.244474337584774, 0.24364090572406066, 0.24383728429031426, 0.2434506325404963, 0.24360441582812653, 0.5131462702699118, 0.24274413357026287, 0.24315059044862256, 0.2425520875633037, 0.24267761171614233, 0.24262874555716651, 0.24241985946629965, 0.24303623087126985, 0.24256813594664275, 0.24222716621340298, 0.24232942620846887, 0.24226459021544208, 0.24228552258028713, 0.2422627008915168, 0.24227972390907074, 0.24224781333659, 0.24222315394360727, 0.2422366702780749, 0.24222456968889083, 0.2422369639832645, 0.24222420706924647, 0.2422233576238524, 0.2422222923925673], 'runtime': [0.0, 3.0856837001629174, 3.092791900038719, 3.145945300348103, 3.198699700180441, 3.1877071000635624, 3.257912500295788, 3.279559100046754, 3.320520000066608, 3.3423851002007723, 3.399885100312531, 3.4487606999464333, 3.469572300091386, 4.5710685001686215, 5.594150300137699, 6.609584300313145, 7.625805400311947, 8.643067300319672, 9.653973900247365, 10.668333900161088, 11.684152700006962, 12.696534700226039, 13.830191600136459, 14.843372600153089, 15.85343430005014, 16.867383300326765, 17.886160700116307, 18.908616400323808, 19.927985700313002, 20.95058420021087, 21.96936420025304, 22.98745770007372, 24.00510139996186, 25.026439000386745, 26.052888700272888, 27.192030200269073, 28.2193304002285, 29.282562700100243, 30.321563200093806, 31.38996300008148, 32.42062650015578, 33.43918160023168, 34.45591269992292, 35.487439500167966, 36.49907220015302, 37.50863410020247, 38.52129440009594, 39.53660099999979, 40.67266120016575, 41.69906500028446, 42.72594740008935, 43.75388570036739, 44.77801220025867, 45.799775400198996, 46.81848719995469, 47.845374200027436, 48.87321070022881], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" -convergence_report, -multistart_info,"{'start_parameters': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 7.557071776832622, 'WealthShare': 0.15947986291737937}], 'local_optima': [Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 3.557e-06* 0.002154 -relative_params_change 1.013e-05 0.02634 -absolute_criterion_change 8.616e-07* 0.0005218 -absolute_params_change 4.277e-05 0.1102 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. - -The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. - -Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: - - one_step five_steps -relative_criterion_change 1.266e-06* 0.2375 -relative_params_change 0.006515 0.369 -absolute_criterion_change 3.068e-07* 0.05754 -absolute_params_change 0.02362 1.523 - -(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, - 2.03621762e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, - 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, - 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, - 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" -algorithm_output,"{'states': [State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.3273843758368489, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.700625, shift=array([7.00625, 0.19375])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=0, candidate_x=array([7.00625, 0.19375]), index=0, x=array([7.00625, 0.19375]), fval=0.32738437583684893, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=11.224951550903995, linear_terms=array([-1.37625354, 32.5154691 ]), square_terms=array([[ 0.09351784, -2.07104056], - [-2.07104056, 48.22141916]]), scale=array([0.62091274, 0.40233137]), shift=array([7.00625 , 0.41233137])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=13, candidate_x=array([6.38533726, 0.12376172]), index=13, x=array([6.38533726, 0.12376172]), fval=0.30690596890028216, rho=0.04742895556261184, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.6248447718567584, relative_step_length=0.8918391034530002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.38533726, 0.12376172]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=0.7313551856043823, linear_terms=array([0.5632349 , 4.14900217]), square_terms=array([[ 0.31889798, 2.36106382], - [ 2.36106382, 17.56884109]]), scale=array([0.31045637, 0.21210905]), shift=array([6.38533726, 0.22210905])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=14, candidate_x=array([6.07488089, 0.20052322]), index=14, x=array([6.07488089, 0.20052322]), fval=0.2921025583212886, rho=0.03221406908218355, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), old_indices_discarded=array([2, 4, 5, 8, 9]), step_length=0.3198053866376689, relative_step_length=0.9129145738095811, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.07488089, 0.20052322]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5857130929401587, linear_terms=array([-0.66733221, -2.49758335]), square_terms=array([[ 0.71346148, 2.85346922], - [ 2.85346922, 11.61097172]]), scale=0.17515625, shift=array([6.07488089, 0.20052322])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=15, candidate_x=array([6.25324275, 0.19438789]), index=15, x=array([6.25324275, 0.19438789]), fval=0.28576369985789285, rho=0.020008399324012414, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int32), step_length=0.17846735076374473, relative_step_length=1.0189036974914953, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25324275, 0.19438789]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15]), model=ScalarModel(intercept=0.2909351370686484, linear_terms=array([0.01016955, 0.239779 ]), square_terms=array([[0.00441972, 0.04993243], - [0.04993243, 0.684917 ]]), scale=0.087578125, shift=array([6.25324275, 0.19438789])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=16, candidate_x=array([6.33822271, 0.15788195]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=0.4991794966519752, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 13, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0924893396676145, relative_step_length=1.0560780978996123, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=0.2600470730558603, linear_terms=array([-0.10978373, -0.38806688]), square_terms=array([[0.34292001, 1.58579951], - [1.58579951, 7.51750345]]), scale=array([0.15522818, 0.15155507]), shift=array([6.33822271, 0.16155507])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=17, candidate_x=array([6.4934509 , 0.13740843]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.4623888253617076, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), old_indices_discarded=array([ 0, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.24686579583420382, linear_terms=array([-0.00725813, -0.0120927 ]), square_terms=array([[0.00459974, 0.05116398], - [0.05116398, 0.68633371]]), scale=0.087578125, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=18, candidate_x=array([6.42567538, 0.15294566]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.7750579998984433, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2588317231036329, linear_terms=array([ 0.00363168, -0.00423961]), square_terms=array([[1.17813939e-04, 1.72772939e-03], - [1.72772939e-03, 1.19696569e-01]]), scale=0.0437890625, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=19, candidate_x=array([6.29445974, 0.16000091]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=0.5388880366161921, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.04381424054578142, relative_step_length=1.0005749848099947, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.2541677101008941, linear_terms=array([-0.00368238, -0.03323817]), square_terms=array([[0.00145555, 0.02969193], - [0.02969193, 0.6672799 ]]), scale=0.087578125, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=20, candidate_x=array([6.38214384, 0.1604602 ]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=-0.930670096284453, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.25622931078220434, linear_terms=array([ 0.0032415 , -0.00073259]), square_terms=array([[1.03290011e-04, 1.82810922e-03], - [1.82810922e-03, 1.19546137e-01]]), scale=0.0437890625, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=21, candidate_x=array([6.25067953, 0.16091447]), index=21, x=array([6.25067953, 0.16091447]), fval=0.2577870645585467, rho=0.517848980651204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.043789739030257874, relative_step_length=1.0000154497543279, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25067953, 0.16091447]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.25500561017058476, linear_terms=array([0.00514188, 0.00157206]), square_terms=array([[3.29759763e-04, 4.68476327e-03], - [4.68476327e-03, 4.75402508e-01]]), scale=0.087578125, shift=array([6.25067953, 0.16091447])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=22, candidate_x=array([6.16310275, 0.16148209]), index=22, x=array([6.16310275, 0.16148209]), fval=0.2552353311983439, rho=0.5116649557870464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), old_indices_discarded=array([ 3, 7, 17]), step_length=0.08757862032278296, relative_step_length=1.0000056557819998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.16310275, 0.16148209]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2515880836028085, linear_terms=array([0.00754998, 0.01225116]), square_terms=array([[8.67053730e-04, 1.69602107e-02], - [1.69602107e-02, 1.46374627e+00]]), scale=array([0.15522818, 0.15335514]), shift=array([6.16310275, 0.16335514])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=23, candidate_x=array([6.00787457, 0.1638485 ]), index=23, x=array([6.00787457, 0.1638485 ]), fval=0.25064066883423725, rho=0.6486363276282296, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 3, 7, 10, 11, 12, 17, 18]), step_length=0.1552462214938793, relative_step_length=0.8863298996974375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.00787457, 0.1638485 ]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.43332719746500753, linear_terms=array([0.03146291, 1.14425379]), square_terms=array([[2.68439891e-03, 7.19387302e-02], - [7.19387302e-02, 3.57398202e+00]]), scale=array([0.31045637, 0.23215243]), shift=array([6.00787457, 0.24215243])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=24, candidate_x=array([5.6974182 , 0.17249888]), index=24, x=array([5.6974182 , 0.17249888]), fval=0.244970934682092, rho=0.6800504779079048, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18]), step_length=0.3105768611152018, relative_step_length=0.8865708791870166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6974182 , 0.17249888]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), model=ScalarModel(intercept=1.9359689768705584, linear_terms=array([0.14431675, 5.7192291 ]), square_terms=array([[0.01061289, 0.22387827], - [0.22387827, 9.66222181]]), scale=array([0.62091274, 0.39170581]), shift=array([5.6974182 , 0.40170581])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=25, candidate_x=array([5.2361468 , 0.17615356]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=0.12172165982234218, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 20, 21]), step_length=0.46128587321697967, relative_step_length=0.6583919689091592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=1.40125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), model=ScalarModel(intercept=19.556928495798605, linear_terms=array([ 2.66108959, 60.1251518 ]), square_terms=array([[ 0.19711181, 4.14595444], - [ 4.14595444, 93.60232406]]), scale=array([1.24182548, 0.49 ]), shift=array([5.2361468, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=26, candidate_x=array([5.42516995, 0.18194647]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-0.2074474380333275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 15, 18, 19, 20, 21, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=2.023039226033913, linear_terms=array([0.11112348, 6.16604764]), square_terms=array([[9.99812684e-03, 1.87643817e-01], - [1.87643817e-01, 1.06798417e+01]]), scale=array([0.62091274, 0.39353315]), shift=array([5.2361468 , 0.40353315])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=27, candidate_x=array([4.97795012, 0.17920049]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-9.379349103371986, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, - 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.5871067495424316, linear_terms=array([0.02508987, 2.27718757]), square_terms=array([[2.83476082e-03, 8.32114910e-02], - [8.32114910e-02, 7.59032634e+00]]), scale=array([0.31045637, 0.23830497]), shift=array([5.2361468 , 0.24830497])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=29, candidate_x=array([5.21588983, 0.17698112]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-12.575485760554423, accepted=False, new_indices=array([28]), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24537978825844767, linear_terms=array([-0.00017886, -0.00847909]), square_terms=array([[6.40294020e-04, 1.45475512e-02], - [1.45475512e-02, 3.24873183e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2361468 , 0.17615356])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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old_indices_discarded=array([], dtype=int32), step_length=0.03802522196097743, relative_step_length=0.21709314946499153, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24506470035985778, linear_terms=array([5.54445372e-05, 8.93470786e-05]), square_terms=array([[6.38751471e-04, 1.45480845e-02], - [1.45480845e-02, 3.24873994e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24474192813793477, linear_terms=array([-0.00048722, -0.00034972]), square_terms=array([[2.32194110e-04, 6.38817516e-03], - [6.38817516e-03, 1.03554328e+00]]), scale=0.087578125, shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=32, candidate_x=array([5.36174794, 0.1758779 ]), index=32, x=array([5.36174794, 0.1758779 ]), fval=0.24345063254049631, rho=0.4894780781881887, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([24, 25, 26, 27, 28, 29, 30, 31]), old_indices_discarded=array([], dtype=int32), step_length=0.08757812830780477, relative_step_length=1.00000003776976, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36174794, 0.1758779 ]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.2442299698961134, linear_terms=array([-0.00054183, 0.00016305]), square_terms=array([[7.30553468e-04, 2.00849094e-02], - [2.00849094e-02, 3.25315131e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=33, candidate_x=array([5.50067057, 0.17501241]), index=32, x=array([5.36174794, 0.1758779 ]), fval=0.24345063254049631, rho=-0.6330830612424933, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([24, 25, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([14, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36174794, 0.1758779 ]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.2435599842214549, linear_terms=array([0.00032528, 0.05273933]), square_terms=array([[2.32924566e-04, 6.28242993e-03], - [6.28242993e-03, 6.44662821e-01]]), scale=0.087578125, shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=35, candidate_x=array([5.44896581, 0.16786346]), index=35, x=array([5.44896581, 0.16786346]), fval=0.24274413357026287, rho=0.3126050424887087, accepted=True, new_indices=array([34]), old_indices_used=array([24, 25, 26, 29, 30, 31, 32, 33]), old_indices_discarded=array([27, 28]), step_length=0.0875853150013436, relative_step_length=1.0000820981420142, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.44896581, 0.16786346]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 29, 30, 31, 32, 33, 34, 35]), model=ScalarModel(intercept=0.2411702136529787, linear_terms=array([-0.00029148, -0.00161231]), square_terms=array([[7.59338494e-04, 1.81333427e-02], - [1.81333427e-02, 2.02348592e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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35, 36]), model=ScalarModel(intercept=0.24163478548943507, linear_terms=array([ 0.00016458, -0.00055621]), square_terms=array([[2.57908296e-04, 6.00198503e-03], - [6.00198503e-03, 6.44118485e-01]]), scale=0.087578125, shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], 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linear_terms=array([-0.00018881, 0.00035398]), square_terms=array([[8.05230298e-04, 1.86970634e-02], - [1.86970634e-02, 2.02386702e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], 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square_terms=array([[2.60518703e-04, 5.70133267e-03], - [5.70133267e-03, 6.43763482e-01]]), scale=0.087578125, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 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1.60944325e-01]]), scale=0.0437890625, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=41, candidate_x=array([5.23002957, 0.16984189]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-6.146531737571127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), old_indices_discarded=array([24, 26, 27, 28, 29, 33, 35, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), model=ScalarModel(intercept=0.24213302949529256, linear_terms=array([-3.78622622e-05, 2.41519221e-04]), square_terms=array([[6.90966248e-05, 1.48098964e-03], - [1.48098964e-03, 1.60965844e-01]]), scale=0.0437890625, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=42, candidate_x=array([5.36139158, 0.16869151]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-11.833264403894546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), old_indices_discarded=array([26, 29, 33, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.24265765523257948, linear_terms=array([-0.00010296, -0.00348716]), square_terms=array([[1.52341867e-05, 3.50476207e-04], - [3.50476207e-04, 4.17971723e-02]]), scale=0.02189453125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=43, candidate_x=array([5.33950848, 0.17080482]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=0.9043831615651715, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), old_indices_discarded=array([26, 29, 34, 39]), step_length=0.021964765363485655, relative_step_length=1.0032078381895322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), model=ScalarModel(intercept=0.2423976703093635, linear_terms=array([-3.01430010e-05, 2.34992933e-04]), square_terms=array([[5.91513482e-05, 1.37667035e-03], - [1.37667035e-03, 1.65820314e-01]]), scale=0.0437890625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=44, candidate_x=array([5.36895747, 0.17049827]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-9.331642519757773, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), old_indices_discarded=array([25, 26, 29, 33, 34, 35, 36, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), model=ScalarModel(intercept=0.24235348524537714, linear_terms=array([ 5.95198861e-06, -8.48814584e-06]), square_terms=array([[1.47776329e-05, 3.48481891e-04], - [3.48481891e-04, 4.14093403e-02]]), scale=0.02189453125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=45, candidate_x=array([5.32837464, 0.170903 ]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-24.42199537270614, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), old_indices_discarded=array([25, 26, 31, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.010947265625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=0.2422806237585476, linear_terms=array([ 2.67171372e-05, -2.05431480e-04]), square_terms=array([[3.68279483e-06, 8.49740419e-05], - [8.49740419e-05, 1.03070513e-02]]), scale=0.010947265625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 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old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0054736328125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 45, 46]), model=ScalarModel(intercept=0.24229025426011608, linear_terms=array([ 1.48827190e-05, -1.05452116e-04]), square_terms=array([[9.09604685e-07, 2.07560472e-05], - [2.07560472e-05, 2.57873989e-03]]), scale=0.0054736328125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=47, candidate_x=array([5.33403685, 0.17107114]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.029303716626684, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.00273681640625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 45, 46, 47]), model=ScalarModel(intercept=0.24222624543591792, linear_terms=array([-7.33525171e-06, 2.20337847e-04]), square_terms=array([[2.41748426e-07, 5.48721051e-06], - [5.48721051e-06, 5.94912648e-04]]), scale=0.00273681640625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=48, candidate_x=array([5.34223575, 0.16978181]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-1.050453647514022, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 45, 46, 47]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.001368408203125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 47, 48]), model=ScalarModel(intercept=0.24222716621340284, linear_terms=array([-1.02551120e-05, -3.95148592e-05]), square_terms=array([[6.98218857e-08, 1.88148453e-06], - [1.88148453e-06, 1.65041401e-04]]), scale=0.001368408203125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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48, 49]), model=ScalarModel(intercept=0.24222716621340307, linear_terms=array([6.14670909e-06, 1.02989082e-05]), square_terms=array([[1.43372945e-08, 1.96028127e-07], - [1.96028127e-07, 3.98197561e-05]]), scale=0.0006842041015625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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linear_terms=array([-3.89600052e-06, -1.76546459e-05]), square_terms=array([[6.25029901e-08, 1.54407834e-06], - [1.54407834e-06, 1.61121526e-04]]), scale=0.001368408203125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=51, candidate_x=array([5.34019328, 0.17078799]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-2.8921828205303095, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 47, 48, 49, 50]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.0006842041015625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 49, 50, 51]), model=ScalarModel(intercept=0.24222219917408055, linear_terms=array([ 7.93230033e-06, -1.17711940e-05]), square_terms=array([[1.62310723e-08, 3.63400305e-07], - 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[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=52, candidate_x=array([5.33814097, 0.17083226]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.14512140450342684, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.00034210205078125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 50, 51, 52]), model=ScalarModel(intercept=0.24222319841032375, linear_terms=array([1.99763146e-06, 5.57738554e-06]), square_terms=array([[3.72373755e-09, 9.11821416e-08], - [9.11821416e-08, 9.85528901e-06]]), scale=0.00034210205078125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=53, candidate_x=array([5.33851827, 0.17049811]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-4.232464647796792, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 50, 51, 52]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.000171051025390625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 50, 52, 53]), model=ScalarModel(intercept=0.24222851970904374, linear_terms=array([ 9.20804052e-08, -5.32837905e-06]), square_terms=array([[9.45290458e-10, 2.30234760e-08], - [2.30234760e-08, 2.43134636e-06]]), scale=0.000171051025390625, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=54, candidate_x=array([5.33878192, 0.17082014]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.2598782806832573, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 50, 52, 53]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=8.55255126953125e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 53, 54]), model=ScalarModel(intercept=0.2422231539436072, linear_terms=array([-3.14907959e-06, -8.42234292e-07]), square_terms=array([[3.63526659e-10, 1.13163660e-09], - [1.13163660e-09, 6.14271368e-07]]), scale=8.55255126953125e-05, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=55, candidate_x=array([5.33891521, 0.17067448]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.05725279410002452, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([50, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=4.276275634765625e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 54, 55]), model=ScalarModel(intercept=0.24222315394360722, linear_terms=array([ 8.70513659e-08, -4.37510681e-09]), square_terms=array([[8.02169837e-11, 1.60425228e-09], - [1.60425228e-09, 1.54231322e-07]]), scale=4.276275634765625e-05, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 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step_length=4.276717619883001e-05, relative_step_length=1.000103357490285, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 57 entries., 'multistart_info': {'start_parameters': [array([7.00625, 0.19375]), array([7.55707178, 0.15947986])], 'local_optima': [{'solution_x': array([5.33878077, 0.17065529]), 'solution_criterion': 0.24222229239256732, 'states': [State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.3273843758368489, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], - [0., 0.]]), scale=0.700625, shift=array([7.00625, 0.19375])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, 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step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=11.224951550903995, linear_terms=array([-1.37625354, 32.5154691 ]), square_terms=array([[ 0.09351784, -2.07104056], - [-2.07104056, 48.22141916]]), scale=array([0.62091274, 0.40233137]), shift=array([7.00625 , 0.41233137])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.38533726, 0.12376172]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=0.7313551856043823, linear_terms=array([0.5632349 , 4.14900217]), square_terms=array([[ 0.31889798, 2.36106382], - [ 2.36106382, 17.56884109]]), scale=array([0.31045637, 0.21210905]), shift=array([6.38533726, 0.22210905])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], 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0.20052322]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5857130929401587, linear_terms=array([-0.66733221, -2.49758335]), square_terms=array([[ 0.71346148, 2.85346922], - [ 2.85346922, 11.61097172]]), scale=0.17515625, shift=array([6.07488089, 0.20052322])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15]), model=ScalarModel(intercept=0.2909351370686484, linear_terms=array([0.01016955, 0.239779 ]), square_terms=array([[0.00441972, 0.04993243], - [0.04993243, 0.684917 ]]), scale=0.087578125, shift=array([6.25324275, 0.19438789])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=16, candidate_x=array([6.33822271, 0.15788195]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=0.4991794966519752, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 13, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0924893396676145, relative_step_length=1.0560780978996123, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=0.2600470730558603, linear_terms=array([-0.10978373, -0.38806688]), square_terms=array([[0.34292001, 1.58579951], - [1.58579951, 7.51750345]]), scale=array([0.15522818, 0.15155507]), shift=array([6.33822271, 0.16155507])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], 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0.68633371]]), scale=0.087578125, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=18, candidate_x=array([6.42567538, 0.15294566]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.7750579998984433, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2588317231036329, linear_terms=array([ 0.00363168, -0.00423961]), square_terms=array([[1.17813939e-04, 1.72772939e-03], - [1.72772939e-03, 1.19696569e-01]]), scale=0.0437890625, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=19, candidate_x=array([6.29445974, 0.16000091]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=0.5388880366161921, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.04381424054578142, relative_step_length=1.0005749848099947, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.2541677101008941, linear_terms=array([-0.00368238, -0.03323817]), square_terms=array([[0.00145555, 0.02969193], - [0.02969193, 0.6672799 ]]), scale=0.087578125, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=20, candidate_x=array([6.38214384, 0.1604602 ]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=-0.930670096284453, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.25622931078220434, linear_terms=array([ 0.0032415 , -0.00073259]), square_terms=array([[1.03290011e-04, 1.82810922e-03], - [1.82810922e-03, 1.19546137e-01]]), scale=0.0437890625, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=21, candidate_x=array([6.25067953, 0.16091447]), index=21, x=array([6.25067953, 0.16091447]), fval=0.2577870645585467, rho=0.517848980651204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.043789739030257874, relative_step_length=1.0000154497543279, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25067953, 0.16091447]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.25500561017058476, linear_terms=array([0.00514188, 0.00157206]), square_terms=array([[3.29759763e-04, 4.68476327e-03], - [4.68476327e-03, 4.75402508e-01]]), scale=0.087578125, shift=array([6.25067953, 0.16091447])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=22, candidate_x=array([6.16310275, 0.16148209]), index=22, x=array([6.16310275, 0.16148209]), fval=0.2552353311983439, rho=0.5116649557870464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), old_indices_discarded=array([ 3, 7, 17]), step_length=0.08757862032278296, relative_step_length=1.0000056557819998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.16310275, 0.16148209]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2515880836028085, linear_terms=array([0.00754998, 0.01225116]), square_terms=array([[8.67053730e-04, 1.69602107e-02], - [1.69602107e-02, 1.46374627e+00]]), scale=array([0.15522818, 0.15335514]), shift=array([6.16310275, 0.16335514])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 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22]), old_indices_discarded=array([ 0, 3, 7, 10, 11, 12, 17, 18]), step_length=0.1552462214938793, relative_step_length=0.8863298996974375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.00787457, 0.1638485 ]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.43332719746500753, linear_terms=array([0.03146291, 1.14425379]), square_terms=array([[2.68439891e-03, 7.19387302e-02], - [7.19387302e-02, 3.57398202e+00]]), scale=array([0.31045637, 0.23215243]), shift=array([6.00787457, 0.24215243])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18]), step_length=0.3105768611152018, relative_step_length=0.8865708791870166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6974182 , 0.17249888]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), model=ScalarModel(intercept=1.9359689768705584, linear_terms=array([0.14431675, 5.7192291 ]), square_terms=array([[0.01061289, 0.22387827], - [0.22387827, 9.66222181]]), scale=array([0.62091274, 0.39170581]), shift=array([5.6974182 , 0.40170581])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=25, candidate_x=array([5.2361468 , 0.17615356]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=0.12172165982234218, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 20, 21]), step_length=0.46128587321697967, relative_step_length=0.6583919689091592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=1.40125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), model=ScalarModel(intercept=19.556928495798605, linear_terms=array([ 2.66108959, 60.1251518 ]), square_terms=array([[ 0.19711181, 4.14595444], - [ 4.14595444, 93.60232406]]), scale=array([1.24182548, 0.49 ]), shift=array([5.2361468, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=26, candidate_x=array([5.42516995, 0.18194647]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-0.2074474380333275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 15, 18, 19, 20, 21, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=2.023039226033913, linear_terms=array([0.11112348, 6.16604764]), square_terms=array([[9.99812684e-03, 1.87643817e-01], - [1.87643817e-01, 1.06798417e+01]]), scale=array([0.62091274, 0.39353315]), shift=array([5.2361468 , 0.40353315])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=27, candidate_x=array([4.97795012, 0.17920049]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-9.379349103371986, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, - 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.5871067495424316, linear_terms=array([0.02508987, 2.27718757]), square_terms=array([[2.83476082e-03, 8.32114910e-02], - [8.32114910e-02, 7.59032634e+00]]), scale=array([0.31045637, 0.23830497]), shift=array([5.2361468 , 0.24830497])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=29, candidate_x=array([5.21588983, 0.17698112]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-12.575485760554423, accepted=False, new_indices=array([28]), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24537978825844767, linear_terms=array([-0.00017886, -0.00847909]), square_terms=array([[6.40294020e-04, 1.45475512e-02], - [1.45475512e-02, 3.24873183e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2361468 , 0.17615356])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=30, candidate_x=array([5.2741713 , 0.17638843]), index=30, x=array([5.2741713 , 0.17638843]), fval=0.24364090572406066, rho=14.798075799096576, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([14, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([], dtype=int32), step_length=0.03802522196097743, relative_step_length=0.21709314946499153, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24506470035985778, linear_terms=array([5.54445372e-05, 8.93470786e-05]), square_terms=array([[6.38751471e-04, 1.45480845e-02], - [1.45480845e-02, 3.24873994e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=31, candidate_x=array([5.25927523, 0.17645087]), index=30, x=array([5.2741713 , 0.17638843]), fval=0.24364090572406066, rho=-74.32031418400335, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24474192813793477, linear_terms=array([-0.00048722, -0.00034972]), square_terms=array([[2.32194110e-04, 6.38817516e-03], - [6.38817516e-03, 1.03554328e+00]]), scale=0.087578125, shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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31, 32]), model=ScalarModel(intercept=0.2442299698961134, linear_terms=array([-0.00054183, 0.00016305]), square_terms=array([[7.30553468e-04, 2.00849094e-02], - [2.00849094e-02, 3.25315131e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 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linear_terms=array([0.00032528, 0.05273933]), square_terms=array([[2.32924566e-04, 6.28242993e-03], - [6.28242993e-03, 6.44662821e-01]]), scale=0.087578125, shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - 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1.81333427e-02], - [1.81333427e-02, 2.02348592e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=36, candidate_x=array([5.52101795, 0.16734146]), index=35, x=array([5.44896581, 0.16786346]), fval=0.24274413357026287, rho=-6.259192132860256, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 29, 30, 31, 32, 33, 34, 35]), old_indices_discarded=array([14, 15, 19, 21, 22, 23, 24, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.44896581, 0.16786346]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 30, 31, 32, 33, 34, 35, 36]), model=ScalarModel(intercept=0.24163478548943507, linear_terms=array([ 0.00016458, -0.00055621]), square_terms=array([[2.57908296e-04, 6.00198503e-03], - [6.00198503e-03, 6.44118485e-01]]), scale=0.087578125, shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=37, candidate_x=array([5.36139226, 0.16875945]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=2.7825937304103108, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([24, 28, 29]), step_length=0.08757812500000042, relative_step_length=1.0000000000000047, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 29, 31, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.2417800168998051, linear_terms=array([-0.00018881, 0.00035398]), square_terms=array([[8.05230298e-04, 1.86970634e-02], - [1.86970634e-02, 2.02386702e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=38, candidate_x=array([5.40853278, 0.1682968 ]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=-4.2991999497290685, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 29, 31, 33, 34, 35, 36, 37]), old_indices_discarded=array([14, 22, 23, 24, 27, 28, 30, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 30, 31, 32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.24170158357350824, linear_terms=array([-0.00012888, -0.00012381]), square_terms=array([[2.60518703e-04, 5.70133267e-03], - [5.70133267e-03, 6.43763482e-01]]), scale=0.087578125, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=39, candidate_x=array([5.41467648, 0.16830439]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=-1.9714007957034316, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 30, 31, 32, 34, 35, 37, 38]), old_indices_discarded=array([24, 27, 28, 29, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.2417553910728286, linear_terms=array([ 4.54049262e-05, -4.56249946e-05]), square_terms=array([[6.76405313e-05, 1.43316796e-03], - [1.43316796e-03, 1.60944325e-01]]), scale=0.0437890625, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=40, candidate_x=array([5.31760507, 0.16916422]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=7.194700985609317, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), old_indices_discarded=array([25, 29, 33, 36]), step_length=0.043789062500000274, relative_step_length=1.0000000000000062, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24205486457802278, linear_terms=array([0.00023275, 0.000779 ]), square_terms=array([[3.03100394e-04, 5.73493197e-03], - [5.73493197e-03, 6.43806788e-01]]), scale=0.087578125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, 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accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), old_indices_discarded=array([24, 26, 27, 28, 29, 33, 35, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), model=ScalarModel(intercept=0.24213302949529256, linear_terms=array([-3.78622622e-05, 2.41519221e-04]), square_terms=array([[6.90966248e-05, 1.48098964e-03], - [1.48098964e-03, 1.60965844e-01]]), scale=0.0437890625, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=42, candidate_x=array([5.36139158, 0.16869151]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-11.833264403894546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), old_indices_discarded=array([26, 29, 33, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.24265765523257948, linear_terms=array([-0.00010296, -0.00348716]), square_terms=array([[1.52341867e-05, 3.50476207e-04], - [3.50476207e-04, 4.17971723e-02]]), scale=0.02189453125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=43, candidate_x=array([5.33950848, 0.17080482]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=0.9043831615651715, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), old_indices_discarded=array([26, 29, 34, 39]), step_length=0.021964765363485655, relative_step_length=1.0032078381895322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), model=ScalarModel(intercept=0.2423976703093635, linear_terms=array([-3.01430010e-05, 2.34992933e-04]), square_terms=array([[5.91513482e-05, 1.37667035e-03], - [1.37667035e-03, 1.65820314e-01]]), scale=0.0437890625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), model=ScalarModel(intercept=0.24235348524537714, linear_terms=array([ 5.95198861e-06, -8.48814584e-06]), square_terms=array([[1.47776329e-05, 3.48481891e-04], - [3.48481891e-04, 4.14093403e-02]]), scale=0.02189453125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.010947265625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=0.2422806237585476, linear_terms=array([ 2.67171372e-05, -2.05431480e-04]), square_terms=array([[3.68279483e-06, 8.49740419e-05], - [8.49740419e-05, 1.03070513e-02]]), scale=0.010947265625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=46, candidate_x=array([5.32856345, 0.17111248]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.0149510031231594, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0054736328125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 45, 46]), model=ScalarModel(intercept=0.24229025426011608, linear_terms=array([ 1.48827190e-05, -1.05452116e-04]), square_terms=array([[9.09604685e-07, 2.07560472e-05], - [2.07560472e-05, 2.57873989e-03]]), scale=0.0054736328125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=47, candidate_x=array([5.33403685, 0.17107114]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.029303716626684, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.00273681640625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=8.55255126953125e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 53, 54]), model=ScalarModel(intercept=0.2422231539436072, linear_terms=array([-3.14907959e-06, -8.42234292e-07]), square_terms=array([[3.63526659e-10, 1.13163660e-09], - [1.13163660e-09, 6.14271368e-07]]), scale=8.55255126953125e-05, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, - 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, - -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, - -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], 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0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=16, candidate_x=array([4.35588121, 0.19138224]), index=14, x=array([5.54788763, 0.13840761]), fval=0.281460321662095, rho=-0.12925950467134442, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 12, 13, 14, 15]), old_indices_discarded=array([0, 2, 4, 5, 6, 8, 9]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.54788763, 0.13840761]), radius=0.7557071776832622, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=8.996740109761234, linear_terms=array([ 0.78693981, 28.2331618 ]), square_terms=array([[4.13131262e-02, 1.26605832e+00], - [1.26605832e+00, 4.54928341e+01]]), scale=array([0.66972805, 0.39906783]), shift=array([5.54788763, 0.40906783])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=17, candidate_x=array([5.41398251, 0.16362415]), index=17, x=array([5.41398251, 0.16362415]), fval=0.24430617695746665, rho=0.49111647332124986, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 12]), step_length=0.13625877882159818, relative_step_length=0.18030631816852744, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.41398251, 0.16362415]), radius=0.7557071776832622, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=10.490606167255109, linear_terms=array([ 0.06290294, 33.4368883 ]), square_terms=array([[7.62487103e-03, 9.95079480e-02], - [9.95079480e-02, 5.45079084e+01]]), scale=array([0.66972805, 0.4116761 ]), shift=array([5.41398251, 0.4216761 ])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=18, candidate_x=array([5.24647936, 0.16932883]), index=18, x=array([5.24647936, 0.16932883]), fval=0.24296186338597675, rho=0.2622049237607477, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 11, 12]), step_length=0.1676002645957951, relative_step_length=0.22177937373785406, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.24647936, 0.16932883]), radius=0.7557071776832622, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=2.8790620949739303, linear_terms=array([0.26988009, 8.74126763]), square_terms=array([[ 0.01986858, 0.45368432], - [ 0.45368432, 14.46207235]]), scale=array([0.66972805, 0.41452844]), shift=array([5.24647936, 0.42452844])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=19, candidate_x=array([5.76206313, 0.16396522]), index=18, x=array([5.24647936, 0.16932883]), fval=0.24296186338597675, rho=-1.3215682344113984, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 13, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 0, 2, 4, 5, 6, 8, 9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.24647936, 0.16932883]), radius=0.3778535888416311, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.5133453029704279, linear_terms=array([0.0412064 , 1.66429691]), square_terms=array([[4.79896476e-03, 1.29982477e-01], - [1.29982477e-01, 5.02450715e+00]]), scale=array([0.33486402, 0.24709642]), shift=array([5.24647936, 0.25709642])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=20, candidate_x=array([5.58134339, 0.16885692]), index=18, x=array([5.24647936, 0.16932883]), fval=0.24296186338597675, rho=-0.060409566917049635, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 10]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.24647936, 0.16932883]), radius=0.18892679442081556, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.24991953890636176, linear_terms=array([-0.00132101, 0.11413806]), square_terms=array([[1.14227713e-03, 1.94139880e-02], - [1.94139880e-02, 2.74832485e+00]]), scale=array([0.16743201, 0.16338042]), shift=array([5.24647936, 0.17338042])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - 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model=ScalarModel(intercept=0.24280392898324044, linear_terms=array([-0.00042791, -0.02448472]), square_terms=array([[2.79194455e-04, 7.31380916e-03], - [7.31380916e-03, 7.13962361e-01]]), scale=0.09446339721040778, shift=array([5.24647936, 0.16932883])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=22, candidate_x=array([5.34091551, 0.17159741]), index=22, x=array([5.34091551, 0.17159741]), fval=0.2423262078217145, rho=1.2846773231722988, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([14, 17, 18, 20, 21]), old_indices_discarded=array([], dtype=int32), step_length=0.09446339721040756, relative_step_length=0.9999999999999977, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.34091551, 0.17159741]), radius=0.18892679442081556, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 17, 18, 19, 20, 21, 22]), model=ScalarModel(intercept=0.24312778092414664, linear_terms=array([0.00244388, 0.06648202]), square_terms=array([[1.19336081e-03, 3.24541529e-02], - [3.24541529e-02, 2.37275707e+00]]), scale=array([0.16743201, 0.16451471]), shift=array([5.34091551, 0.17451471])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, - 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , - -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, - -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.], - [0., 0.]]), square_terms=array([[[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], - - [[0., 0.], - [0., 0.]], 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3.198699700180441, 3.1877071000635624, 3.257912500295788, 3.279559100046754, 3.320520000066608, 3.3423851002007723, 3.399885100312531, 3.4487606999464333, 3.469572300091386, 4.5710685001686215, 5.594150300137699, 6.609584300313145, 7.625805400311947, 8.643067300319672, 9.653973900247365, 10.668333900161088, 11.684152700006962, 12.696534700226039, 13.830191600136459, 14.843372600153089, 15.85343430005014, 16.867383300326765, 17.886160700116307, 18.908616400323808, 19.927985700313002, 20.95058420021087, 21.96936420025304, 22.98745770007372, 24.00510139996186, 25.026439000386745, 26.052888700272888, 27.192030200269073, 28.2193304002285, 29.282562700100243, 30.321563200093806, 31.38996300008148, 32.42062650015578, 33.43918160023168, 34.45591269992292, 35.487439500167966, 36.49907220015302, 37.50863410020247, 38.52129440009594, 39.53660099999979, 40.67266120016575, 41.69906500028446, 42.72594740008935, 43.75388570036739, 44.77801220025867, 45.799775400198996, 46.81848719995469, 47.845374200027436, 48.87321070022881], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]}" + +convergence_report, + +multistart_info,"{'start_parameters': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 7.557071776832622, 'WealthShare': 0.15947986291737937}], 'local_optima': [Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 3.557e-06* 0.002154 +relative_params_change 1.013e-05 0.02634 +absolute_criterion_change 8.616e-07* 0.0005218 +absolute_params_change 4.277e-05 0.1102 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated. + +The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance. + +Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria: + + one_step five_steps +relative_criterion_change 1.266e-06* 0.2375 +relative_params_change 0.006515 0.369 +absolute_criterion_change 3.068e-07* 0.05754 +absolute_params_change 0.02362 1.523 + +(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 7.00625, 'WealthShare': 0.19375}, {'CRRA': 12.9125, 'WealthShare': 0.1325}, {'CRRA': 4.64375, 'WealthShare': 0.31625000000000003}, {'CRRA': 8.1875, 'WealthShare': 0.3775}, {'CRRA': 4.844436801414261, 'WealthShare': 0.3460128282561451}, {'CRRA': 15.274999999999999, 'WealthShare': 0.255}, {'CRRA': 17.046875, 'WealthShare': 0.224375}, {'CRRA': 11.73125, 'WealthShare': 0.43875}, {'CRRA': 18.81875, 'WealthShare': 0.07125}, {'CRRA': 10.549999999999999, 'WealthShare': 0.5}, {'CRRA': 9.368749999999999, 'WealthShare': 0.56125}, {'CRRA': 16.45625, 'WealthShare': 0.68375}, {'CRRA': 2.871875, 'WealthShare': 0.469375}, {'CRRA': 7.596874999999999, 'WealthShare': 0.714375}, {'CRRA': 14.093749999999998, 'WealthShare': 0.80625}, {'CRRA': 3.4625, 'WealthShare': 0.6225}, {'CRRA': 17.6375, 'WealthShare': 0.8674999999999999}, {'CRRA': 5.824999999999999, 'WealthShare': 0.745}, {'CRRA': 12.321874999999999, 'WealthShare': 0.959375}, {'CRRA': 2.28125, 'WealthShare': 0.92875}], 'exploration_results': array([3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, + 2.03621762e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, + 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, + 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}" + +algorithm_output,"{'states': [State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.3273843758368489, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.700625, shift=array([7.00625, 0.19375])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=0, candidate_x=array([7.00625, 0.19375]), index=0, x=array([7.00625, 0.19375]), fval=0.32738437583684893, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=11.224951550903995, linear_terms=array([-1.37625354, 32.5154691 ]), square_terms=array([[ 0.09351784, -2.07104056], + [-2.07104056, 48.22141916]]), scale=array([0.62091274, 0.40233137]), shift=array([7.00625 , 0.41233137])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=13, candidate_x=array([6.38533726, 0.12376172]), index=13, x=array([6.38533726, 0.12376172]), fval=0.30690596890028216, rho=0.04742895556261184, accepted=True, new_indices=array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int32), step_length=0.6248447718567584, relative_step_length=0.8918391034530002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.38533726, 0.12376172]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=0.7313551856043823, linear_terms=array([0.5632349 , 4.14900217]), square_terms=array([[ 0.31889798, 2.36106382], + [ 2.36106382, 17.56884109]]), scale=array([0.31045637, 0.21210905]), shift=array([6.38533726, 0.22210905])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=14, candidate_x=array([6.07488089, 0.20052322]), index=14, x=array([6.07488089, 0.20052322]), fval=0.2921025583212886, rho=0.03221406908218355, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), old_indices_discarded=array([2, 4, 5, 8, 9]), step_length=0.3198053866376689, relative_step_length=0.9129145738095811, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.07488089, 0.20052322]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5857130929401587, linear_terms=array([-0.66733221, -2.49758335]), square_terms=array([[ 0.71346148, 2.85346922], + [ 2.85346922, 11.61097172]]), scale=0.17515625, shift=array([6.07488089, 0.20052322])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=15, candidate_x=array([6.25324275, 0.19438789]), index=15, x=array([6.25324275, 0.19438789]), fval=0.28576369985789285, rho=0.020008399324012414, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int32), step_length=0.17846735076374473, relative_step_length=1.0189036974914953, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25324275, 0.19438789]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15]), model=ScalarModel(intercept=0.2909351370686484, linear_terms=array([0.01016955, 0.239779 ]), square_terms=array([[0.00441972, 0.04993243], + [0.04993243, 0.684917 ]]), scale=0.087578125, shift=array([6.25324275, 0.19438789])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=16, candidate_x=array([6.33822271, 0.15788195]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=0.4991794966519752, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 13, 14, 15]), old_indices_discarded=array([], dtype=int32), step_length=0.0924893396676145, relative_step_length=1.0560780978996123, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=0.2600470730558603, linear_terms=array([-0.10978373, -0.38806688]), square_terms=array([[0.34292001, 1.58579951], + [1.58579951, 7.51750345]]), scale=array([0.15522818, 0.15155507]), shift=array([6.33822271, 0.16155507])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=17, candidate_x=array([6.4934509 , 0.13740843]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.4623888253617076, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), old_indices_discarded=array([ 0, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.24686579583420382, linear_terms=array([-0.00725813, -0.0120927 ]), square_terms=array([[0.00459974, 0.05116398], + [0.05116398, 0.68633371]]), scale=0.087578125, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=18, candidate_x=array([6.42567538, 0.15294566]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.7750579998984433, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2588317231036329, linear_terms=array([ 0.00363168, -0.00423961]), square_terms=array([[1.17813939e-04, 1.72772939e-03], + [1.72772939e-03, 1.19696569e-01]]), scale=0.0437890625, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=19, candidate_x=array([6.29445974, 0.16000091]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=0.5388880366161921, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.04381424054578142, relative_step_length=1.0005749848099947, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.2541677101008941, linear_terms=array([-0.00368238, -0.03323817]), square_terms=array([[0.00145555, 0.02969193], + [0.02969193, 0.6672799 ]]), scale=0.087578125, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=20, candidate_x=array([6.38214384, 0.1604602 ]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=-0.930670096284453, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.25622931078220434, linear_terms=array([ 0.0032415 , -0.00073259]), square_terms=array([[1.03290011e-04, 1.82810922e-03], + [1.82810922e-03, 1.19546137e-01]]), scale=0.0437890625, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=21, candidate_x=array([6.25067953, 0.16091447]), index=21, x=array([6.25067953, 0.16091447]), fval=0.2577870645585467, rho=0.517848980651204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.043789739030257874, relative_step_length=1.0000154497543279, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25067953, 0.16091447]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.25500561017058476, linear_terms=array([0.00514188, 0.00157206]), square_terms=array([[3.29759763e-04, 4.68476327e-03], + [4.68476327e-03, 4.75402508e-01]]), scale=0.087578125, shift=array([6.25067953, 0.16091447])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=22, candidate_x=array([6.16310275, 0.16148209]), index=22, x=array([6.16310275, 0.16148209]), fval=0.2552353311983439, rho=0.5116649557870464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), old_indices_discarded=array([ 3, 7, 17]), step_length=0.08757862032278296, relative_step_length=1.0000056557819998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.16310275, 0.16148209]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2515880836028085, linear_terms=array([0.00754998, 0.01225116]), square_terms=array([[8.67053730e-04, 1.69602107e-02], + [1.69602107e-02, 1.46374627e+00]]), scale=array([0.15522818, 0.15335514]), shift=array([6.16310275, 0.16335514])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=23, candidate_x=array([6.00787457, 0.1638485 ]), index=23, x=array([6.00787457, 0.1638485 ]), fval=0.25064066883423725, rho=0.6486363276282296, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), old_indices_discarded=array([ 0, 3, 7, 10, 11, 12, 17, 18]), step_length=0.1552462214938793, relative_step_length=0.8863298996974375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.00787457, 0.1638485 ]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.43332719746500753, linear_terms=array([0.03146291, 1.14425379]), square_terms=array([[2.68439891e-03, 7.19387302e-02], + [7.19387302e-02, 3.57398202e+00]]), scale=array([0.31045637, 0.23215243]), shift=array([6.00787457, 0.24215243])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=24, candidate_x=array([5.6974182 , 0.17249888]), index=24, x=array([5.6974182 , 0.17249888]), fval=0.244970934682092, rho=0.6800504779079048, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18]), step_length=0.3105768611152018, relative_step_length=0.8865708791870166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6974182 , 0.17249888]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), model=ScalarModel(intercept=1.9359689768705584, linear_terms=array([0.14431675, 5.7192291 ]), square_terms=array([[0.01061289, 0.22387827], + [0.22387827, 9.66222181]]), scale=array([0.62091274, 0.39170581]), shift=array([5.6974182 , 0.40170581])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=25, candidate_x=array([5.2361468 , 0.17615356]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=0.12172165982234218, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 20, 21]), step_length=0.46128587321697967, relative_step_length=0.6583919689091592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=1.40125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), model=ScalarModel(intercept=19.556928495798605, linear_terms=array([ 2.66108959, 60.1251518 ]), square_terms=array([[ 0.19711181, 4.14595444], + [ 4.14595444, 93.60232406]]), scale=array([1.24182548, 0.49 ]), shift=array([5.2361468, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=26, candidate_x=array([5.42516995, 0.18194647]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-0.2074474380333275, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 15, 18, 19, 20, 21, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=2.023039226033913, linear_terms=array([0.11112348, 6.16604764]), square_terms=array([[9.99812684e-03, 1.87643817e-01], + [1.87643817e-01, 1.06798417e+01]]), scale=array([0.62091274, 0.39353315]), shift=array([5.2361468 , 0.40353315])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=27, candidate_x=array([4.97795012, 0.17920049]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-9.379349103371986, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, + 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.5871067495424316, linear_terms=array([0.02508987, 2.27718757]), square_terms=array([[2.83476082e-03, 8.32114910e-02], + [8.32114910e-02, 7.59032634e+00]]), scale=array([0.31045637, 0.23830497]), shift=array([5.2361468 , 0.24830497])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=29, candidate_x=array([5.21588983, 0.17698112]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-12.575485760554423, accepted=False, new_indices=array([28]), old_indices_used=array([14, 21, 22, 23, 24, 25, 26, 27]), old_indices_discarded=array([ 1, 3, 7, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24537978825844767, linear_terms=array([-0.00017886, -0.00847909]), square_terms=array([[6.40294020e-04, 1.45475512e-02], + [1.45475512e-02, 3.24873183e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2361468 , 0.17615356])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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old_indices_discarded=array([], dtype=int32), step_length=0.03802522196097743, relative_step_length=0.21709314946499153, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24506470035985778, linear_terms=array([5.54445372e-05, 8.93470786e-05]), square_terms=array([[6.38751471e-04, 1.45480845e-02], + [1.45480845e-02, 3.24873994e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2741713 , 0.17638843]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24474192813793477, linear_terms=array([-0.00048722, -0.00034972]), square_terms=array([[2.32194110e-04, 6.38817516e-03], + [6.38817516e-03, 1.03554328e+00]]), scale=0.087578125, shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.36174794, 0.1758779 ]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31, 32]), model=ScalarModel(intercept=0.2442299698961134, linear_terms=array([-0.00054183, 0.00016305]), square_terms=array([[7.30553468e-04, 2.00849094e-02], + [2.00849094e-02, 3.25315131e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=33, candidate_x=array([5.50067057, 0.17501241]), index=32, x=array([5.36174794, 0.1758779 ]), fval=0.24345063254049631, rho=-0.6330830612424933, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([24, 25, 26, 27, 28, 29, 30, 31, 32]), old_indices_discarded=array([14, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36174794, 0.1758779 ]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 29, 30, 31, 32, 33, 34]), model=ScalarModel(intercept=0.2435599842214549, linear_terms=array([0.00032528, 0.05273933]), square_terms=array([[2.32924566e-04, 6.28242993e-03], + [6.28242993e-03, 6.44662821e-01]]), scale=0.087578125, shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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35, 36]), model=ScalarModel(intercept=0.24163478548943507, linear_terms=array([ 0.00016458, -0.00055621]), square_terms=array([[2.57908296e-04, 6.00198503e-03], + [6.00198503e-03, 6.44118485e-01]]), scale=0.087578125, shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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linear_terms=array([-0.00018881, 0.00035398]), square_terms=array([[8.05230298e-04, 1.86970634e-02], + [1.86970634e-02, 2.02386702e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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square_terms=array([[2.60518703e-04, 5.70133267e-03], + [5.70133267e-03, 6.43763482e-01]]), scale=0.087578125, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 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1.60944325e-01]]), scale=0.0437890625, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=40, candidate_x=array([5.31760507, 0.16916422]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=7.194700985609317, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), old_indices_discarded=array([25, 29, 33, 36]), step_length=0.043789062500000274, relative_step_length=1.0000000000000062, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24205486457802278, linear_terms=array([0.00023275, 0.000779 ]), square_terms=array([[3.03100394e-04, 5.73493197e-03], + [5.73493197e-03, 6.43806788e-01]]), scale=0.087578125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=41, candidate_x=array([5.23002957, 0.16984189]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-6.146531737571127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), old_indices_discarded=array([24, 26, 27, 28, 29, 33, 35, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), model=ScalarModel(intercept=0.24213302949529256, linear_terms=array([-3.78622622e-05, 2.41519221e-04]), square_terms=array([[6.90966248e-05, 1.48098964e-03], + [1.48098964e-03, 1.60965844e-01]]), scale=0.0437890625, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=42, candidate_x=array([5.36139158, 0.16869151]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-11.833264403894546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), old_indices_discarded=array([26, 29, 33, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.24265765523257948, linear_terms=array([-0.00010296, -0.00348716]), square_terms=array([[1.52341867e-05, 3.50476207e-04], + [3.50476207e-04, 4.17971723e-02]]), scale=0.02189453125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=43, candidate_x=array([5.33950848, 0.17080482]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=0.9043831615651715, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), old_indices_discarded=array([26, 29, 34, 39]), step_length=0.021964765363485655, relative_step_length=1.0032078381895322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), model=ScalarModel(intercept=0.2423976703093635, linear_terms=array([-3.01430010e-05, 2.34992933e-04]), square_terms=array([[5.91513482e-05, 1.37667035e-03], + [1.37667035e-03, 1.65820314e-01]]), scale=0.0437890625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=44, candidate_x=array([5.36895747, 0.17049827]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-9.331642519757773, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), old_indices_discarded=array([25, 26, 29, 33, 34, 35, 36, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), model=ScalarModel(intercept=0.24235348524537714, linear_terms=array([ 5.95198861e-06, -8.48814584e-06]), square_terms=array([[1.47776329e-05, 3.48481891e-04], + [3.48481891e-04, 4.14093403e-02]]), scale=0.02189453125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=45, candidate_x=array([5.32837464, 0.170903 ]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-24.42199537270614, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), old_indices_discarded=array([25, 26, 31, 34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.010947265625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=0.2422806237585476, linear_terms=array([ 2.67171372e-05, -2.05431480e-04]), square_terms=array([[3.68279483e-06, 8.49740419e-05], + [8.49740419e-05, 1.03070513e-02]]), scale=0.010947265625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=46, candidate_x=array([5.32856345, 0.17111248]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.0149510031231594, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0054736328125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 45, 46]), model=ScalarModel(intercept=0.24229025426011608, linear_terms=array([ 1.48827190e-05, -1.05452116e-04]), square_terms=array([[9.09604685e-07, 2.07560472e-05], + [2.07560472e-05, 2.57873989e-03]]), scale=0.0054736328125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=47, candidate_x=array([5.33403685, 0.17107114]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.029303716626684, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 45, 46]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.00273681640625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 45, 46, 47]), model=ScalarModel(intercept=0.24222624543591792, linear_terms=array([-7.33525171e-06, 2.20337847e-04]), square_terms=array([[2.41748426e-07, 5.48721051e-06], + [5.48721051e-06, 5.94912648e-04]]), scale=0.00273681640625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=48, candidate_x=array([5.34223575, 0.16978181]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-1.050453647514022, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 45, 46, 47]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.001368408203125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 47, 48]), model=ScalarModel(intercept=0.24222716621340284, linear_terms=array([-1.02551120e-05, -3.95148592e-05]), square_terms=array([[6.98218857e-08, 1.88148453e-06], + [1.88148453e-06, 1.65041401e-04]]), scale=0.001368408203125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=49, candidate_x=array([5.34088034, 0.17109939]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-1.4217767819138583, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 47, 48]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0006842041015625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 48, 49]), model=ScalarModel(intercept=0.24222716621340307, linear_terms=array([6.14670909e-06, 1.02989082e-05]), square_terms=array([[1.43372945e-08, 1.96028127e-07], + [1.96028127e-07, 3.98197561e-05]]), scale=0.0006842041015625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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linear_terms=array([-3.89600052e-06, -1.76546459e-05]), square_terms=array([[6.25029901e-08, 1.54407834e-06], + [1.54407834e-06, 1.61121526e-04]]), scale=0.001368408203125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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new_indices=array([], dtype=int32), old_indices_used=array([50, 53, 54]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=4.276275634765625e-05, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([50, 54, 55]), model=ScalarModel(intercept=0.24222315394360722, linear_terms=array([ 8.70513659e-08, -4.37510681e-09]), square_terms=array([[8.02169837e-11, 1.60425228e-09], + [1.60425228e-09, 1.54231322e-07]]), scale=4.276275634765625e-05, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 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step_length=4.276717619883001e-05, relative_step_length=1.000103357490285, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 57 entries., 'multistart_info': {'start_parameters': [array([7.00625, 0.19375]), array([7.55707178, 0.15947986])], 'local_optima': [{'solution_x': array([5.33878077, 0.17065529]), 'solution_criterion': 0.24222229239256732, 'states': [State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=[0], model=ScalarModel(intercept=0.3273843758368489, linear_terms=array([0., 0.]), square_terms=array([[0., 0.], + [0., 0.]]), scale=0.700625, shift=array([7.00625, 0.19375])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, 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step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([7.00625, 0.19375]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), model=ScalarModel(intercept=11.224951550903995, linear_terms=array([-1.37625354, 32.5154691 ]), square_terms=array([[ 0.09351784, -2.07104056], + [-2.07104056, 48.22141916]]), scale=array([0.62091274, 0.40233137]), shift=array([7.00625 , 0.41233137])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([6.38533726, 0.12376172]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 0, 1, 3, 6, 7, 10, 11, 12, 13]), model=ScalarModel(intercept=0.7313551856043823, linear_terms=array([0.5632349 , 4.14900217]), square_terms=array([[ 0.31889798, 2.36106382], + [ 2.36106382, 17.56884109]]), scale=array([0.31045637, 0.21210905]), shift=array([6.38533726, 0.22210905])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], 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0.20052322]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=0.5857130929401587, linear_terms=array([-0.66733221, -2.49758335]), square_terms=array([[ 0.71346148, 2.85346922], + [ 2.85346922, 11.61097172]]), scale=0.17515625, shift=array([6.07488089, 0.20052322])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=15, candidate_x=array([6.25324275, 0.19438789]), index=15, x=array([6.25324275, 0.19438789]), fval=0.28576369985789285, rho=0.020008399324012414, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 10, 11, 12, 13, 14]), old_indices_discarded=array([], dtype=int32), step_length=0.17846735076374473, relative_step_length=1.0189036974914953, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25324275, 0.19438789]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15]), model=ScalarModel(intercept=0.2909351370686484, linear_terms=array([0.01016955, 0.239779 ]), square_terms=array([[0.00441972, 0.04993243], + [0.04993243, 0.684917 ]]), scale=0.087578125, shift=array([6.25324275, 0.19438789])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-0.10978373, -0.38806688]), square_terms=array([[0.34292001, 1.58579951], + [1.58579951, 7.51750345]]), scale=array([0.15522818, 0.15155507]), shift=array([6.33822271, 0.16155507])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], 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0.68633371]]), scale=0.087578125, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=18, candidate_x=array([6.42567538, 0.15294566]), index=16, x=array([6.33822271, 0.15788195]), fval=0.2614570472767362, rho=-0.7750579998984433, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 7, 11, 13, 14, 15, 16, 17]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.33822271, 0.15788195]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18]), model=ScalarModel(intercept=0.2588317231036329, linear_terms=array([ 0.00363168, -0.00423961]), square_terms=array([[1.17813939e-04, 1.72772939e-03], + [1.72772939e-03, 1.19696569e-01]]), scale=0.0437890625, shift=array([6.33822271, 0.15788195])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=19, candidate_x=array([6.29445974, 0.16000091]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=0.5388880366161921, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18]), old_indices_discarded=array([], dtype=int32), step_length=0.04381424054578142, relative_step_length=1.0005749848099947, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.2541677101008941, linear_terms=array([-0.00368238, -0.03323817]), square_terms=array([[0.00145555, 0.02969193], + [0.02969193, 0.6672799 ]]), scale=0.087578125, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=20, candidate_x=array([6.38214384, 0.1604602 ]), index=19, x=array([6.29445974, 0.16000091]), fval=0.2594527871161228, rho=-0.930670096284453, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), old_indices_discarded=array([ 7, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29445974, 0.16000091]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.25622931078220434, linear_terms=array([ 0.0032415 , -0.00073259]), square_terms=array([[1.03290011e-04, 1.82810922e-03], + [1.82810922e-03, 1.19546137e-01]]), scale=0.0437890625, shift=array([6.29445974, 0.16000091])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=21, candidate_x=array([6.25067953, 0.16091447]), index=21, x=array([6.25067953, 0.16091447]), fval=0.2577870645585467, rho=0.517848980651204, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 15, 16, 17, 18, 19, 20]), old_indices_discarded=array([], dtype=int32), step_length=0.043789739030257874, relative_step_length=1.0000154497543279, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.25067953, 0.16091447]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), model=ScalarModel(intercept=0.25500561017058476, linear_terms=array([0.00514188, 0.00157206]), square_terms=array([[3.29759763e-04, 4.68476327e-03], + [4.68476327e-03, 4.75402508e-01]]), scale=0.087578125, shift=array([6.25067953, 0.16091447])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=22, candidate_x=array([6.16310275, 0.16148209]), index=22, x=array([6.16310275, 0.16148209]), fval=0.2552353311983439, rho=0.5116649557870464, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 18, 19, 20, 21]), old_indices_discarded=array([ 3, 7, 17]), step_length=0.08757862032278296, relative_step_length=1.0000056557819998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.16310275, 0.16148209]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 20, 21, 22]), model=ScalarModel(intercept=0.2515880836028085, linear_terms=array([0.00754998, 0.01225116]), square_terms=array([[8.67053730e-04, 1.69602107e-02], + [1.69602107e-02, 1.46374627e+00]]), scale=array([0.15522818, 0.15335514]), shift=array([6.16310275, 0.16335514])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 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22]), old_indices_discarded=array([ 0, 3, 7, 10, 11, 12, 17, 18]), step_length=0.1552462214938793, relative_step_length=0.8863298996974375, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.00787457, 0.1638485 ]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), model=ScalarModel(intercept=0.43332719746500753, linear_terms=array([0.03146291, 1.14425379]), square_terms=array([[2.68439891e-03, 7.19387302e-02], + [7.19387302e-02, 3.57398202e+00]]), scale=array([0.31045637, 0.23215243]), shift=array([6.00787457, 0.24215243])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=24, candidate_x=array([5.6974182 , 0.17249888]), index=24, x=array([5.6974182 , 0.17249888]), fval=0.244970934682092, rho=0.6800504779079048, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([13, 14, 15, 16, 19, 20, 21, 22, 23]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18]), step_length=0.3105768611152018, relative_step_length=0.8865708791870166, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.6974182 , 0.17249888]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), model=ScalarModel(intercept=1.9359689768705584, linear_terms=array([0.14431675, 5.7192291 ]), square_terms=array([[0.01061289, 0.22387827], + [0.22387827, 9.66222181]]), scale=array([0.62091274, 0.39170581]), shift=array([5.6974182 , 0.40170581])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=25, candidate_x=array([5.2361468 , 0.17615356]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=0.12172165982234218, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([ 1, 13, 14, 15, 16, 19, 22, 23, 24]), old_indices_discarded=array([ 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 20, 21]), step_length=0.46128587321697967, relative_step_length=0.6583919689091592, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=1.40125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 7, 10, 14, 16, 17, 22, 24, 25]), model=ScalarModel(intercept=19.556928495798605, linear_terms=array([ 2.66108959, 60.1251518 ]), square_terms=array([[ 0.19711181, 4.14595444], + [ 4.14595444, 93.60232406]]), scale=array([1.24182548, 0.49 ]), shift=array([5.2361468, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.700625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), model=ScalarModel(intercept=2.023039226033913, linear_terms=array([0.11112348, 6.16604764]), square_terms=array([[9.99812684e-03, 1.87643817e-01], + [1.87643817e-01, 1.06798417e+01]]), scale=array([0.62091274, 0.39353315]), shift=array([5.2361468 , 0.40353315])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=27, candidate_x=array([4.97795012, 0.17920049]), index=25, x=array([5.2361468 , 0.17615356]), fval=0.2440600019927147, rho=-9.379349103371986, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 15, 19, 21, 22, 23, 24, 25, 26]), old_indices_discarded=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, + 20]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.3503125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=0.5871067495424316, linear_terms=array([0.02508987, 2.27718757]), square_terms=array([[2.83476082e-03, 8.32114910e-02], + [8.32114910e-02, 7.59032634e+00]]), scale=array([0.31045637, 0.23830497]), shift=array([5.2361468 , 0.24830497])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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State(trustregion=Region(center=array([5.2361468 , 0.17615356]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=0.24537978825844767, linear_terms=array([-0.00017886, -0.00847909]), square_terms=array([[6.40294020e-04, 1.45475512e-02], + [1.45475512e-02, 3.24873183e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2361468 , 0.17615356])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + 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0.17638843]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 23, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=0.24506470035985778, linear_terms=array([5.54445372e-05, 8.93470786e-05]), square_terms=array([[6.38751471e-04, 1.45480845e-02], + [1.45480845e-02, 3.24873994e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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upper=array([20. , 0.99]))), model_indices=array([24, 25, 26, 27, 28, 29, 30, 31]), model=ScalarModel(intercept=0.24474192813793477, linear_terms=array([-0.00048722, -0.00034972]), square_terms=array([[2.32194110e-04, 6.38817516e-03], + [6.38817516e-03, 1.03554328e+00]]), scale=0.087578125, shift=array([5.2741713 , 0.17638843])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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31, 32]), model=ScalarModel(intercept=0.2442299698961134, linear_terms=array([-0.00054183, 0.00016305]), square_terms=array([[7.30553468e-04, 2.00849094e-02], + [2.00849094e-02, 3.25315131e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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linear_terms=array([0.00032528, 0.05273933]), square_terms=array([[2.32924566e-04, 6.28242993e-03], + [6.28242993e-03, 6.44662821e-01]]), scale=0.087578125, shift=array([5.36174794, 0.1758779 ])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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1.81333427e-02], + [1.81333427e-02, 2.02348592e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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scale=0.087578125, shift=array([5.44896581, 0.16786346])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=37, candidate_x=array([5.36139226, 0.16875945]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=2.7825937304103108, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 30, 31, 32, 33, 34, 35, 36]), old_indices_discarded=array([24, 28, 29]), step_length=0.08757812500000042, relative_step_length=1.0000000000000047, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.17515625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 29, 31, 33, 34, 35, 36, 37]), model=ScalarModel(intercept=0.2417800168998051, linear_terms=array([-0.00018881, 0.00035398]), square_terms=array([[8.05230298e-04, 1.86970634e-02], + [1.86970634e-02, 2.02386702e+00]]), scale=array([0.15522818, 0.15522818]), shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=38, candidate_x=array([5.40853278, 0.1682968 ]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=-4.2991999497290685, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 29, 31, 33, 34, 35, 36, 37]), old_indices_discarded=array([14, 22, 23, 24, 27, 28, 30, 32]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 26, 30, 31, 32, 34, 35, 37, 38]), model=ScalarModel(intercept=0.24170158357350824, linear_terms=array([-0.00012888, -0.00012381]), square_terms=array([[2.60518703e-04, 5.70133267e-03], + [5.70133267e-03, 6.43763482e-01]]), scale=0.087578125, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=39, candidate_x=array([5.41467648, 0.16830439]), index=37, x=array([5.36139226, 0.16875945]), fval=0.24255208756330368, rho=-1.9714007957034316, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 26, 30, 31, 32, 34, 35, 37, 38]), old_indices_discarded=array([24, 27, 28, 29, 33, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.36139226, 0.16875945]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), model=ScalarModel(intercept=0.2417553910728286, linear_terms=array([ 4.54049262e-05, -4.56249946e-05]), square_terms=array([[6.76405313e-05, 1.43316796e-03], + [1.43316796e-03, 1.60944325e-01]]), scale=0.0437890625, shift=array([5.36139226, 0.16875945])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=40, candidate_x=array([5.31760507, 0.16916422]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=7.194700985609317, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([26, 30, 31, 32, 34, 35, 37, 38, 39]), old_indices_discarded=array([25, 29, 33, 36]), step_length=0.043789062500000274, relative_step_length=1.0000000000000062, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.087578125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), model=ScalarModel(intercept=0.24205486457802278, linear_terms=array([0.00023275, 0.000779 ]), square_terms=array([[3.03100394e-04, 5.73493197e-03], + [5.73493197e-03, 6.43806788e-01]]), scale=0.087578125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=41, candidate_x=array([5.23002957, 0.16984189]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-6.146531737571127, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 39, 40]), old_indices_discarded=array([24, 26, 27, 28, 29, 33, 35, 36]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), model=ScalarModel(intercept=0.24213302949529256, linear_terms=array([-3.78622622e-05, 2.41519221e-04]), square_terms=array([[6.90966248e-05, 1.48098964e-03], + [1.48098964e-03, 1.60965844e-01]]), scale=0.0437890625, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=42, candidate_x=array([5.36139158, 0.16869151]), index=40, x=array([5.31760507, 0.16916422]), fval=0.24241985946629963, rho=-11.833264403894546, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([25, 30, 31, 32, 34, 37, 38, 40, 41]), old_indices_discarded=array([26, 29, 33, 35, 36, 39]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.31760507, 0.16916422]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([25, 30, 31, 32, 37, 38, 40, 41, 42]), model=ScalarModel(intercept=0.24265765523257948, linear_terms=array([-0.00010296, -0.00348716]), square_terms=array([[1.52341867e-05, 3.50476207e-04], + [3.50476207e-04, 4.17971723e-02]]), scale=0.02189453125, shift=array([5.31760507, 0.16916422])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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old_indices_discarded=array([26, 29, 34, 39]), step_length=0.021964765363485655, relative_step_length=1.0032078381895322, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0437890625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 31, 32, 37, 38, 39, 40, 42, 43]), model=ScalarModel(intercept=0.2423976703093635, linear_terms=array([-3.01430010e-05, 2.34992933e-04]), square_terms=array([[5.91513482e-05, 1.37667035e-03], + [1.37667035e-03, 1.65820314e-01]]), scale=0.0437890625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], 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41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.02189453125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([30, 32, 37, 38, 39, 40, 42, 43, 44]), model=ScalarModel(intercept=0.24235348524537714, linear_terms=array([ 5.95198861e-06, -8.48814584e-06]), square_terms=array([[1.47776329e-05, 3.48481891e-04], + [3.48481891e-04, 4.14093403e-02]]), scale=0.02189453125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.010947265625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=0.2422806237585476, linear_terms=array([ 2.67171372e-05, -2.05431480e-04]), square_terms=array([[3.68279483e-06, 8.49740419e-05], + [8.49740419e-05, 1.03070513e-02]]), scale=0.010947265625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=46, candidate_x=array([5.32856345, 0.17111248]), index=43, x=array([5.33950848, 0.17080482]), fval=0.24222716621340298, rho=-2.0149510031231594, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([32, 37, 40, 42, 43, 44, 45]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33950848, 0.17080482]), radius=0.0054736328125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([32, 37, 40, 42, 43, 45, 46]), model=ScalarModel(intercept=0.24229025426011608, linear_terms=array([ 1.48827190e-05, -1.05452116e-04]), square_terms=array([[9.09604685e-07, 2.07560472e-05], + [2.07560472e-05, 2.57873989e-03]]), scale=0.0054736328125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 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0.99]))), model_indices=array([43, 45, 46, 47]), model=ScalarModel(intercept=0.24222624543591792, linear_terms=array([-7.33525171e-06, 2.20337847e-04]), square_terms=array([[2.41748426e-07, 5.48721051e-06], + [5.48721051e-06, 5.94912648e-04]]), scale=0.00273681640625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([-1.02551120e-05, -3.95148592e-05]), square_terms=array([[6.98218857e-08, 1.88148453e-06], + [1.88148453e-06, 1.65041401e-04]]), scale=0.001368408203125, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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3.98197561e-05]]), scale=0.0006842041015625, shift=array([5.33950848, 0.17080482])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=51, candidate_x=array([5.34019328, 0.17078799]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-2.8921828205303095, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 47, 48, 49, 50]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.0006842041015625, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 49, 50, 51]), model=ScalarModel(intercept=0.24222219917408055, linear_terms=array([ 7.93230033e-06, -1.17711940e-05]), square_terms=array([[1.62310723e-08, 3.63400305e-07], + [3.63400305e-07, 3.86016620e-05]]), scale=0.0006842041015625, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=52, candidate_x=array([5.33814097, 0.17083226]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-0.14512140450342684, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([43, 49, 50, 51]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.33882353, 0.17065423]), radius=0.00034210205078125, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([43, 50, 51, 52]), model=ScalarModel(intercept=0.24222319841032375, linear_terms=array([1.99763146e-06, 5.57738554e-06]), square_terms=array([[3.72373755e-09, 9.11821416e-08], + [9.11821416e-08, 9.85528901e-06]]), scale=0.00034210205078125, shift=array([5.33882353, 0.17065423])), vector_model=VectorModel(intercepts=array([ 0.04424533, 0.09706756, 0.09964179, 0.12817482, 0.14600609, + 0.16462579, 0.18727309, 0.2198423 , 0.14621231, 0.21589468, + -0.09902459, -0.11677074, -0.13794235, -0.11385871, -0.10645744, + -0.10854051, -0.10625494]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.700625, shift=array([7.00625, 0.19375])), candidate_index=53, candidate_x=array([5.33851827, 0.17049811]), index=50, x=array([5.33882353, 0.17065423]), fval=0.24222315394360727, rho=-4.232464647796792, accepted=False, 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relative_step_length=0.8862422839798872, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.54788763, 0.13840761]), radius=3.022828710733049, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 2, 5, 7, 9, 10, 11, 12, 14]), model=ScalarModel(intercept=25.012358556527673, linear_terms=array([-0.61430887, 64.51324305]), square_terms=array([[ 8.11644928e-02, -8.27104967e-01], + [-8.27104967e-01, 8.39641444e+01]]), scale=array([2.67891219, 0.49 ]), shift=array([5.54788763, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + 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n_evals_acceptance=1), State(trustregion=Region(center=array([5.54788763, 0.13840761]), radius=1.5114143553665245, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 12, 13, 14, 15]), model=ScalarModel(intercept=18.794379657430337, linear_terms=array([ 3.28885102, 54.38386836]), square_terms=array([[ 0.31274504, 4.77989803], + [ 4.77989803, 79.59287121]]), scale=array([1.3394561, 0.49 ]), shift=array([5.54788763, 0.5 ])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 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0.13840761]), radius=0.7557071776832622, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=8.996740109761234, linear_terms=array([ 0.78693981, 28.2331618 ]), square_terms=array([[4.13131262e-02, 1.26605832e+00], + [1.26605832e+00, 4.54928341e+01]]), scale=array([0.66972805, 0.39906783]), shift=array([5.54788763, 0.40906783])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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radius=0.7557071776832622, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 10, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=10.490606167255109, linear_terms=array([ 0.06290294, 33.4368883 ]), square_terms=array([[7.62487103e-03, 9.95079480e-02], + [9.95079480e-02, 5.45079084e+01]]), scale=array([0.66972805, 0.4116761 ]), shift=array([5.41398251, 0.4216761 ])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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radius=0.7557071776832622, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 7, 13, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=2.8790620949739303, linear_terms=array([0.26988009, 8.74126763]), square_terms=array([[ 0.01986858, 0.45368432], + [ 0.45368432, 14.46207235]]), scale=array([0.66972805, 0.41452844]), shift=array([5.24647936, 0.42452844])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + 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bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([ 1, 3, 13, 14, 15, 16, 17, 18, 19]), model=ScalarModel(intercept=0.5133453029704279, linear_terms=array([0.0412064 , 1.66429691]), square_terms=array([[4.79896476e-03, 1.29982477e-01], + [1.29982477e-01, 5.02450715e+00]]), scale=array([0.33486402, 0.24709642]), shift=array([5.24647936, 0.25709642])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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0.99]))), model_indices=array([14, 15, 16, 17, 18, 19, 20]), model=ScalarModel(intercept=0.24991953890636176, linear_terms=array([-0.00132101, 0.11413806]), square_terms=array([[1.14227713e-03, 1.94139880e-02], + [1.94139880e-02, 2.74832485e+00]]), scale=array([0.16743201, 0.16338042]), shift=array([5.24647936, 0.17338042])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + 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model=ScalarModel(intercept=0.24280392898324044, linear_terms=array([-0.00042791, -0.02448472]), square_terms=array([[2.79194455e-04, 7.31380916e-03], + [7.31380916e-03, 7.13962361e-01]]), scale=0.09446339721040778, shift=array([5.24647936, 0.16932883])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + 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linear_terms=array([0.00244388, 0.06648202]), square_terms=array([[1.19336081e-03, 3.24541529e-02], + [3.24541529e-02, 2.37275707e+00]]), scale=array([0.16743201, 0.16451471]), shift=array([5.34091551, 0.17451471])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], 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square_terms=array([[2.79537451e-04, 7.66237713e-03], + [7.66237713e-03, 7.24390446e-01]]), scale=0.09446339721040778, shift=array([5.34091551, 0.17159741])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=24, candidate_x=array([5.24354407, 0.17223973]), index=22, x=array([5.34091551, 0.17159741]), fval=0.2423262078217145, rho=-5.7434868783001045, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 17, 18, 19, 20, 21, 22, 23]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.34091551, 0.17159741]), radius=0.04723169860520389, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([14, 17, 18, 21, 22, 23, 24]), model=ScalarModel(intercept=0.24222407360091366, linear_terms=array([-0.00017893, -0.00118479]), square_terms=array([[7.40043144e-05, 1.60823102e-03], + [1.60823102e-03, 1.76208417e-01]]), scale=0.04723169860520389, shift=array([5.34091551, 0.17159741])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=25, candidate_x=array([5.38814784, 0.17148398]), index=22, x=array([5.34091551, 0.17159741]), fval=0.2423262078217145, rho=-0.24046453118083538, accepted=False, new_indices=array([], dtype=int32), old_indices_used=array([14, 17, 18, 21, 22, 23, 24]), old_indices_discarded=array([], dtype=int32), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([5.34091551, 0.17159741]), radius=0.023615849302601945, bounds=Bounds(lower=array([1.1 , 0.01]), upper=array([20. , 0.99]))), model_indices=array([17, 18, 21, 22, 24, 25]), model=ScalarModel(intercept=0.24243549485529609, linear_terms=array([-4.62482233e-05, 1.22755071e-03]), square_terms=array([[1.80610011e-05, 4.38168169e-04], + [4.38168169e-04, 4.79017603e-02]]), scale=0.023615849302601945, shift=array([5.34091551, 0.17159741])), vector_model=VectorModel(intercepts=array([ 0.04372593, 0.09723622, 0.10188573, 0.13262325, 0.15154936, + 0.16923117, 0.18861608, 0.18401939, 0.09682264, 0.1552954 , + -0.16489717, -0.1798028 , -0.14271083, -0.11811719, -0.10957416, + -0.11108474, -0.1079118 ]), linear_terms=array([[0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.], + [0., 0.]]), square_terms=array([[[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]], + + [[0., 0.], + [0., 0.]]]), scale=0.7557071776832622, shift=array([7.55707178, 0.15947986])), candidate_index=26, candidate_x=array([5.36452368, 0.17077702]), index=26, x=array([5.36452368, 0.17077702]), fval=0.24232590102301266, rho=0.004636863515290328, accepted=True, new_indices=array([], dtype=int32), old_indices_used=array([17, 18, 21, 22, 24, 25]), old_indices_discarded=array([], dtype=int32), step_length=0.023622419090119764, relative_step_length=1.0002781939973293, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 27 entries., 'history': {'params': [{'CRRA': 7.557071776832622, 'WealthShare': 0.15947986291737937}, {'CRRA': 6.887343728211803, 'WealthShare': 0.013137857270913678}, {'CRRA': 8.22679982545344, 'WealthShare': 0.33278788031954126}, {'CRRA': 6.887343728211803, 'WealthShare': 0.4927195085070417}, {'CRRA': 8.22679982545344, 'WealthShare': 0.8233666446704474}, {'CRRA': 7.6877492399818115, 'WealthShare': 0.01}, {'CRRA': 8.22679982545344, 'WealthShare': 0.014431776924461504}, {'CRRA': 6.887343728211803, 'WealthShare': 0.5044867019135671}, {'CRRA': 8.22679982545344, 'WealthShare': 0.6439347881014327}, {'CRRA': 8.22679982545344, 'WealthShare': 0.8286676871687395}, {'CRRA': 6.888144129782968, 'WealthShare': 0.829207911538198}, {'CRRA': 7.292700445703663, 'WealthShare': 0.01}, {'CRRA': 7.3755390376972425, 'WealthShare': 0.829207911538198}, {'CRRA': 6.887343728211803, 'WealthShare': 0.13052177573508408}, {'CRRA': 5.547887630970166, 'WealthShare': 0.13840761057808626}, {'CRRA': 4.770403559848555, 'WealthShare': 0.12211118058865711}, {'CRRA': 4.355881206682243, 'WealthShare': 0.19138224232966042}, {'CRRA': 5.413982512136979, 'WealthShare': 0.16362415189967155}, {'CRRA': 5.246479361728566, 'WealthShare': 0.16932882505745808}, {'CRRA': 5.762063127158538, 'WealthShare': 0.1639652195341865}, {'CRRA': 5.581343386038975, 'WealthShare': 0.16885692016159012}, {'CRRA': 5.292164748029701, 'WealthShare': 0.16625553267531312}, {'CRRA': 5.340915514376363, 'WealthShare': 0.17159741175699056}, {'CRRA': 5.199939503168527, 'WealthShare': 0.1717711380449}, {'CRRA': 5.243544072816682, 'WealthShare': 0.17223972982564176}, {'CRRA': 5.388147840073009, 'WealthShare': 0.17148397634254275}, {'CRRA': 5.364523683205526, 'WealthShare': 0.17077701660648517}], 'criterion': [0.3237027602159698, 0.7812967907209172, 1.2463667430775796, 5.3731601742222, 83.85576746268217, 0.6845161962193543, 0.6204394025802298, 5.923680424712876, 14.623295430433139, 89.28677844528951, 112.97532984686313, 0.7343607833893859, 103.50996033506941, 0.2998665188430385, 0.281460321662095, 0.37742949555588207, 0.2981488331322679, 0.24430617695746668, 0.24296186338597675, 0.24637026776611792, 0.24311726730149846, 0.24366070482725372, 0.24232620782171452, 0.24322374032290844, 0.2429668792738361, 0.24236046007028705, 0.24232590102301269], 'runtime': [0.0, 1.0775652001611888, 1.1048381002619863, 1.1430752002634108, 1.181620600167662, 1.2234753998927772, 1.2626859000883996, 1.3012720001861453, 1.3398684998974204, 1.3782349000684917, 1.4174270001240075, 1.4558502002619207, 1.4946371000260115, 2.5929303001612425, 3.6385490000247955, 4.827053700108081, 5.851515099871904, 6.857261700090021, 7.868444700259715, 8.889939799904823, 9.900565400253981, 10.905832400079817, 11.924034600146115, 12.952283100225031, 13.976015199907124, 14.989212099928409, 16.00469780014828], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]}}], 'exploration_sample': array([[ 7.00625 , 0.19375 ], + [12.9125 , 0.1325 ], + [ 4.64375 , 0.31625 ], + [ 8.1875 , 0.3775 ], + [ 4.8444368 , 0.34601283], + [15.275 , 0.255 ], + [17.046875 , 0.224375 ], + [11.73125 , 0.43875 ], + [18.81875 , 0.07125 ], + [10.55 , 0.5 ], + [ 9.36875 , 0.56125 ], + [16.45625 , 0.68375 ], + [ 2.871875 , 0.469375 ], + [ 7.596875 , 0.714375 ], + [14.09375 , 0.80625 ], + [ 3.4625 , 0.6225 ], + [17.6375 , 0.8675 ], + [ 5.825 , 0.745 ], + [12.321875 , 0.959375 ], + [ 2.28125 , 0.92875 ]]), 'exploration_results': array([3.27384376e-01, 1.14034055e+00, 1.50312179e+00, 1.79325436e+00, + 2.03621762e+00, 2.08879824e+00, 2.54067782e+00, 2.64114350e+00, + 2.73814230e+00, 3.83228381e+00, 6.46248546e+00, 9.30118040e+00, + 1.59966394e+01, 3.03715662e+01, 3.38771067e+01, 4.26926083e+01, + 5.84656546e+01, 5.90505617e+01, 5.22500282e+02, 9.01924413e+02])}}" diff --git a/src/estimark/estimation.py b/src/estimark/estimation.py index 47be7b6..ad8bef3 100644 --- a/src/estimark/estimation.py +++ b/src/estimark/estimation.py @@ -8,6 +8,8 @@ income as defined in ConsIndShockModel. """ +from __future__ import annotations + import csv from pathlib import Path from time import time @@ -880,7 +882,7 @@ def estimate( # Set booleans to determine which tasks should be done # Which agent type to estimate ("IndShock" or "Portfolio") local_agent_name = "WealthPortfolio" - local_params_to_estimate = ["CRRA", "WealthShare","WealthShift"] + local_params_to_estimate = ["CRRA", "WealthShare", "WealthShift"] local_estimate_model = True # Whether to estimate the model # Whether to get standard errors via bootstrap local_compute_se_bootstrap = False diff --git a/src/estimark/options.py b/src/estimark/options.py index c09594b..5c6f690 100644 --- a/src/estimark/options.py +++ b/src/estimark/options.py @@ -1,5 +1,6 @@ # Define settings for "main()" function in StructuralEstiamtion.py based on # resource requirements: +from __future__ import annotations params_to_estimate = ["CRRA", "BeqShift", "BeqFac", "WealthShare"] diff --git a/src/estimark/parameters.py b/src/estimark/parameters.py index 7527eea..c5c4b88 100644 --- a/src/estimark/parameters.py +++ b/src/estimark/parameters.py @@ -5,6 +5,7 @@ # Discount Factor of 1.0 always # income uncertainty doubles at retirement # only estimate CRRA, Bequest params +from __future__ import annotations import warnings @@ -14,10 +15,6 @@ from HARK.Calibration.Income.IncomeTools import Cagetti_income, parse_income_spec from HARK.Calibration.life_tables.us_ssa.SSATools import parse_ssa_life_table from HARK.distribution import DiscreteDistribution -from HARK.ConsumptionSaving.ConsPortfolioModel import ( - PortfolioConsumerType_constructors_default, -) -from HARK.ConsumptionSaving.ConsWealthPortfolioModel import make_ChiFromOmega_function # --------------------------------------------------------------------------------- # - Define all of the model parameters for EstimatingMicroDSOPs and ConsumerExamples - diff --git a/src/estimark/scf.py b/src/estimark/scf.py index b7b6cde..08ca84d 100644 --- a/src/estimark/scf.py +++ b/src/estimark/scf.py @@ -1,5 +1,7 @@ """Sets up the SCF data for use in the EstimatingMicroDSOPs estimation.""" +from __future__ import annotations + from pathlib import Path import pandas as pd diff --git a/src/estimark/snp.py b/src/estimark/snp.py index 2d7e594..798f818 100644 --- a/src/estimark/snp.py +++ b/src/estimark/snp.py @@ -1,5 +1,7 @@ """Sets up the S&P data for use in the EstimatingMicroDSOPs estimation.""" +from __future__ import annotations + from pathlib import Path import pandas as pd diff --git a/src/notebooks/Model_Comparisons.ipynb b/src/notebooks/Model_Comparisons.ipynb index 8f0a67d..4a51861 100644 --- a/src/notebooks/Model_Comparisons.ipynb +++ b/src/notebooks/Model_Comparisons.ipynb @@ -6,6 +6,8 @@ "metadata": {}, "outputs": [], "source": [ + "from __future__ import annotations\n", + "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", diff --git a/src/notebooks/WarmGlow.ipynb b/src/notebooks/WarmGlow.ipynb index 3cb128b..a2c9e42 100644 --- a/src/notebooks/WarmGlow.ipynb +++ b/src/notebooks/WarmGlow.ipynb @@ -175,4 +175,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/src/notebooks/median_share.svg b/src/notebooks/median_share.svg index 04dfe9e..17b2ce5 100644 --- a/src/notebooks/median_share.svg +++ b/src/notebooks/median_share.svg @@ -21,33 +21,33 @@ - - - - @@ -58,35 +58,35 @@ L 0 3.5 - - @@ -97,8 +97,8 @@ z - @@ -110,29 +110,29 @@ L 129.024 41.472 - @@ -143,8 +143,8 @@ z - @@ -156,43 +156,43 @@ L 200.448 41.472 - @@ -203,8 +203,8 @@ z - @@ -222,8 +222,8 @@ L 271.872 41.472 - @@ -235,34 +235,34 @@ L 343.296 41.472 - @@ -273,8 +273,8 @@ z - @@ -294,79 +294,79 @@ L 414.72 41.472 - - - @@ -379,14 +379,14 @@ z - - @@ -397,11 +397,11 @@ L -3.5 0 - @@ -413,8 +413,8 @@ z - @@ -426,28 +426,28 @@ L 414.72 254.3616 - @@ -459,8 +459,8 @@ z - @@ -472,23 +472,23 @@ L 414.72 201.1392 - @@ -500,8 +500,8 @@ z - @@ -513,34 +513,34 @@ L 414.72 147.9168 - @@ -552,8 +552,8 @@ z - @@ -572,8 +572,8 @@ L 414.72 94.6944 - @@ -585,18 +585,18 @@ L 414.72 41.472 - @@ -610,299 +610,299 @@ z - - - - - - - - - - - - - - @@ -931,236 +931,236 @@ z - - - - - - - - - - - @@ -1212,84 +1212,84 @@ z - - - - - - @@ -1316,100 +1316,100 @@ z - - - - - @@ -1435,24 +1435,24 @@ z - - @@ -1472,45 +1472,45 @@ z - - - diff --git a/src/notebooks/median_wealth.svg b/src/notebooks/median_wealth.svg index 3042812..b41a049 100644 --- a/src/notebooks/median_wealth.svg +++ b/src/notebooks/median_wealth.svg @@ -21,33 +21,33 @@ - - - - @@ -58,57 +58,57 @@ L 0 3.5 - - @@ -119,8 +119,8 @@ z - @@ -132,23 +132,23 @@ L 134.125714 41.472 - @@ -159,8 +159,8 @@ z - @@ -172,29 +172,29 @@ L 185.142857 41.472 - @@ -205,8 +205,8 @@ z - @@ -218,34 +218,34 @@ L 236.16 41.472 - @@ -256,8 +256,8 @@ z - @@ -269,14 +269,14 @@ L 287.177143 41.472 - @@ -287,8 +287,8 @@ z - @@ -300,43 +300,43 @@ L 338.194286 41.472 - @@ -347,8 +347,8 @@ z - @@ -360,34 +360,34 @@ L 389.211429 41.472 - @@ -400,79 +400,79 @@ z - - - @@ -485,14 +485,14 @@ z - - @@ -508,8 +508,8 @@ L -3.5 0 - @@ -521,28 +521,28 @@ L 414.72 263.232 - @@ -552,8 +552,8 @@ z - @@ -570,8 +570,8 @@ L 414.72 218.88 - @@ -588,8 +588,8 @@ L 414.72 174.528 - @@ -606,8 +606,8 @@ L 414.72 130.176 - @@ -619,18 +619,18 @@ L 414.72 85.824 - @@ -641,8 +641,8 @@ z - @@ -662,240 +662,240 @@ L 414.72 41.472 - - - - - - - - - - - - @@ -925,423 +925,423 @@ z - - - - - - - - - - - - - - @@ -1384,80 +1384,80 @@ z - - - - - - @@ -1484,54 +1484,54 @@ z - - - @@ -1557,24 +1557,24 @@ z - - @@ -1594,57 +1594,57 @@ z - - - diff --git a/src/run_all.py b/src/run_all.py index be5e974..39dda9d 100644 --- a/src/run_all.py +++ b/src/run_all.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from estimark.estimation import estimate from estimark.options import low_resource diff --git a/src/run_all_msm.py b/src/run_all_msm.py index 8e48a66..ae35064 100644 --- a/src/run_all_msm.py +++ b/src/run_all_msm.py @@ -1,7 +1,10 @@ +from __future__ import annotations + import itertools import dask from dask.distributed import Client + from estimark.estimation import estimate, get_empirical_moments, get_moments_cov from estimark.options import low_resource diff --git a/src/stata/AppendDataUsingSCF1992_2007.do b/src/stata/AppendDataUsingSCF1992_2007.do index 8eec271..91c8f5c 100644 --- a/src/stata/AppendDataUsingSCF1992_2007.do +++ b/src/stata/AppendDataUsingSCF1992_2007.do @@ -1,6 +1,6 @@ * AppendDataUsingSCF1992_2007.do -* This file gives selected varaibles of the Population -clear +* This file gives selected varaibles of the Population +clear cd $basePath/$logPath cap log close diff --git a/src/stata/ReadMe.txt b/src/stata/ReadMe.txt index a2d392d..5875f83 100644 --- a/src/stata/ReadMe.txt +++ b/src/stata/ReadMe.txt @@ -1,13 +1,13 @@ In order for the files in this directory to work properly, the Federal -Reserve's SCF datasets +Reserve's SCF datasets that the programs use must be located in -appropriate directories that are accessible to the +appropriate directories that are accessible to the programs. For example, the 1992 SCF needs to be located at - + ../../../Downloads/SCF/1992 @@ -16,7 +16,7 @@ example, the 1992 SCF needs to be located at An all the required SCF datasets in the appropriate directory structure can be downloaded from - + ftp://llorracc.net/VaultPub/Data/SCF @@ -25,31 +25,31 @@ ftp://llorracc.net/VaultPub/Data/SCF or the entire set of SCF's (warning: it is very large) from - + ftp://llorracc.net/VaultPub/Data/SCF.zip -which has SCF files downloaded on 2011/08/06. +which has SCF files downloaded on 2011/08/06. -Alternatively, the latest versions of the individual +Alternatively, the latest versions of the individual files can be obtained directly from the Fed's website: - + http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html -but if you download them one-by-one you need to make sure that you put them in a directory structure -corresponding -to the structure at +but if you download them one-by-one you need to make sure that you put them in a directory structure +corresponding +to the structure at ftp://llorracc.net/VaultPub/Data/SCF -(you don't need to download the extra files like codebooks etc; all that is needed for the programs to +(you don't need to download the extra files like codebooks etc; all that is needed for the programs to work is the Stata scf files, like scf92.dta for the 1992 SCF, which should be in a directory 1992/scf92.dta) @@ -59,10 +59,9 @@ doAll.do file runs all the programs. In Particular: -1) SelectVarsUsingSCFXXXX.do: Selects the variables from the SCF raw data and construct the Permanent income, +1) SelectVarsUsingSCFXXXX.do: Selects the variables from the SCF raw data and construct the Permanent income, wealth and the weights of each household in the population for the year XXXX. 2) AppendDataUsingSCF1992_2007.do: Appends the outcomes of the SelectVarsUsingSCFXXXX. -3) WIRatioPopulation.do: Constructs the Wealth to after tax permanent income ratio of each households. And save the - output "SCFdata.txt" in the folder "./Code/Mathematica/StructuralEstimation" which +3) WIRatioPopulation.do: Constructs the Wealth to after tax permanent income ratio of each households. And save the + output "SCFdata.txt" in the folder "./Code/Mathematica/StructuralEstimation" which is used by the Mathematica programs to estimate the structural parameters. - diff --git a/src/stata/SelectVarsUsingSCF1992.do b/src/stata/SelectVarsUsingSCF1992.do index 94ab72d..6ef4de5 100644 --- a/src/stata/SelectVarsUsingSCF1992.do +++ b/src/stata/SelectVarsUsingSCF1992.do @@ -1,12 +1,12 @@ * This file selects variables, using SCF92 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) -clear +clear -** Set memeory -set memory 32m +** Set memeory +set memory 32m global startDir "`c(pwd)'" global scfFldr 1992 @@ -50,7 +50,7 @@ use y1 x42000 x42001 x5729 x5751 x7650 /// using "scf92.dta" -** Generate variables +** Generate variables * ID gen ID = y1 /* ID # */ gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ @@ -64,17 +64,17 @@ gen INCOME = x5729 /* Income before tax */ scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ scalar CPIADJ = CPIBASE/2116 /* Adjust with CPI (adjusted to 1992$ price) */ scalar CPILAG = 2103/2051 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ keep if D_NORMINC == 1 /* Keep if inc level is normal */ -* Asset +* Asset gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) + +max(0,x3529)*(x3527==5) gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) + +max(0,x3816)+max(0,x3818) gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// @@ -85,7 +85,7 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(11<=x9131 & x9131<=13) /// +max(0,x3711)*(11<=x9132 & x9132<=13) /// +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) + +max(0,x3718)*(11<=x9133 & x9133<=13) gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// @@ -96,25 +96,25 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(x9131<11|x9131>13) /// +max(0,x3711)*(x9132<11|x9132>13) /// +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// @@ -126,7 +126,7 @@ gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen PMOP = x4436 replace PMOP = 0 if x4436<=0 replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP replace PMOP = x5036 replace PMOP = 0 if x5036<=0 replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 @@ -139,17 +139,17 @@ gen CASHLI = max(0,x4006) gen COTHMA = 0 replace ROTHMA = x3942 if x3947==1 | x3947==3 replace SOTHMA = x3942 if x3947==2 | x3947==7 - replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 + replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 gen OTHMA = ROTHMA+SOTHMA+COTHMA gen OTHFIN = x4018+x4022*(x4020==61|x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74) /// +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74) /// - +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74) + +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74) gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168) /// +max(0,x2422)+max(0,x2506)+max(0,x2606)+max(0,x2623) replace x507 = 9000 if x507 > 9000 -gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) +gen HOUSES = (x604+x614+x623+x716) + ((10000-x507)/10000)*(x513+x526) gen ORESRE = max(x1405,x1409)+max(x1505,x1509)+max(x1605,x1609)+max(0,x1619) /// +(x1703==12|x1703==14|x1703==21|x1703==22|x1703==25|x1703==40|x1703==41|x1703==42|x1703==43|x1703==44|x1703==49|x1703==50|x1703==52|x1703==999) /// *max(0,x1706)*(x1705/10000) /// @@ -189,7 +189,7 @@ gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN gen ASSET = FIN+NFIN /* Total asset */ -* Debt +* Debt gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 @@ -206,11 +206,11 @@ replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2 +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 gen FLAG67 = (ORESRE>0) replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) gen INSTALL = x2218+x2318+x2418+x2424+x2519+x2619+x2625+x7824 /// +x7847+x7870+x7924+x7947+x7970+x1044+x1215+x1219 @@ -230,12 +230,12 @@ gen CALLDBT = max(0,x3932) gen ODEBT = max(0,x4032) gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ -* Net worth +* Net worth gen NETW = ASSET-DEBT replace NETW = NETW*CPIADJ -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME * Demographic vars @@ -249,7 +249,7 @@ replace EDUC = 2 if x5902==1 /* High school deg */ replace EDUC = 3 if x5904==1 /* College deg */ * keep if EDUC == 3 /* Keep college graduates only */ -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) drop if INCOME<=0 replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) @@ -261,7 +261,7 @@ keep HHID YEAR WGT INCOME NETW WIRATIO AGE cd "$startDir" cd ../../Data/Constructed ** Save data -save "./SCF$scfFldr$SuffixForConstructedFile", replace +save "./SCF$scfFldr$SuffixForConstructedFile", replace ** End in the same directory you started from cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF1995.do b/src/stata/SelectVarsUsingSCF1995.do index 2331f69..bb9aea2 100644 --- a/src/stata/SelectVarsUsingSCF1995.do +++ b/src/stata/SelectVarsUsingSCF1995.do @@ -1,11 +1,11 @@ * This file selects variables, using SCF95 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) -clear +clear -** Set memeory -set memory 32m +** Set memeory +set memory 32m global startDir "`c(pwd)'" global scfFldr 1995 @@ -21,7 +21,7 @@ if _rc~=0 { } - + ** Load data and pick up necessary vars from original data use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// @@ -47,7 +47,7 @@ use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// using "scf95.dta" -** Generate variables +** Generate variables * ID gen ID = y1 /* ID # */ gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ @@ -62,15 +62,15 @@ replace INCOME = x7362 if x7650!=3 scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ scalar CPIADJ = CPIBASE/2265 /* Adjust with CPI (adjusted to 1992$ price) */ scalar CPILAG = 2254/2201 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ -* Asset +* Asset gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) + +max(0,x3529)*(x3527==5) gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) + +max(0,x3816)+max(0,x3818) gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// @@ -81,7 +81,7 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(11<=x9131 & x9131<=13) /// +max(0,x3711)*(11<=x9132 & x9132<=13) /// +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) + +max(0,x3718)*(11<=x9133 & x9133<=13) gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// @@ -92,25 +92,25 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(x9131<11|x9131>13) /// +max(0,x3711)*(x9132<11|x9132>13) /// +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// @@ -122,7 +122,7 @@ gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen PMOP = x4436 replace PMOP = 0 if x4436<=0 replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP replace PMOP = x5036 replace PMOP = 0 if x5036<=0 replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 @@ -135,11 +135,11 @@ gen CASHLI = max(0,x4006) gen COTHMA = 0 replace ROTHMA = x3942 if x3947==1 | x3947==3 replace SOTHMA = x3942 if x3947==2 | x3947==7 - replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 + replace COTHMA = x3942 if x3947==5 | x3947==6 | x3947==8 | x3947==9 | x3947==-7 gen OTHMA = ROTHMA+SOTHMA+COTHMA gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// - +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) + +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// @@ -186,7 +186,7 @@ gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN gen ASSET = FIN+NFIN /* Total asset */ -* Debt +* Debt gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 @@ -203,11 +203,11 @@ replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2 +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 gen FLAG67 = (ORESRE>0) replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 @@ -227,12 +227,12 @@ gen CALLDBT = max(0,x3932)*(x7194==5) gen ODEBT = max(0,x4032) gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ -* Net worth +* Net worth gen NETW = ASSET-DEBT replace NETW = NETW*CPIADJ -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME * Demographic vars @@ -246,7 +246,7 @@ replace EDUC = 2 if /* RTORESP==1 & */ x5902==1 /* High school deg */ replace EDUC = 3 if /* RTORESP==1 & */ x5904==1 /* College deg */ * keep if EDUC == 3 /* Keep college graduates only */ -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) drop if INCOME<=0 replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) @@ -258,7 +258,7 @@ keep HHID YEAR WGT INCOME NETW WIRATIO AGE cd "$startDir" cd ../../Data/Constructed ** Save data -save "./SCF$scfFldr$SuffixForConstructedFile", replace +save "./SCF$scfFldr$SuffixForConstructedFile", replace ** End in the same directory you started from cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF1998.do b/src/stata/SelectVarsUsingSCF1998.do index a226443..ad81634 100644 --- a/src/stata/SelectVarsUsingSCF1998.do +++ b/src/stata/SelectVarsUsingSCF1998.do @@ -1,11 +1,11 @@ * This file selects variables, using SCF98 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) -clear +clear -** Set memeory -set memory 32m +** Set memeory +set memory 32m global startDir "`c(pwd)'" global scfFldr 1998 @@ -20,7 +20,7 @@ if _rc~=0 { exit } - + ** Load data and pick up necessary vars from original data use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// @@ -34,7 +34,7 @@ use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// x8166 x8167 x8168 x8188 x2422 x2506 x2606 x2623 /// x507 x604 x614 x623 x716 x513 x526 x7134 x716 x701 x7133 /// x1405 x1409 x1505 x1509 x1605 x1609 x1619 x1703 x1706 x1705 x1803 x1806 x1805 x1903 x1906 x1905 x2002 x2012 x1715 x1815 x1915 x2016 x2723 x2710 x2740 x2727 x2823 x2810 x2840 x2827 x2923 x2910 x2940 x2927 /// - x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// + x3129 x3124 x3126 x3127 x3121 x3122 x3122 x3229 x3224 x3226 x3227 x3221 x3222 x3222 x3329 x3324 x3326 x3327 x3321 x3322 x3322 x3335 x507 x513 x526 x3408 x3412 x3416 x3420 x3424 x3428 /// x4022 x4026 x4030 /// x805 x905 x1005 x1108 x1103 x1119 x1114 x1130 x1125 x1136 /// x1417 x1517 x1617 x1621 x2006 /// @@ -46,7 +46,7 @@ use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// using "scf98.dta" -** Generate variables +** Generate variables * ID gen ID = y1 /* ID # */ gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ @@ -61,15 +61,15 @@ replace INCOME = x7362 if x7650!=3 scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ scalar CPIADJ = CPIBASE/2405 /* Adjust with CPI (adjusted to 1992$ price) */ scalar CPILAG = 2397/2364 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ -* Asset +* Asset gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) + +max(0,x3529)*(x3527==5) gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) + +max(0,x3816)+max(0,x3818) gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// @@ -80,7 +80,7 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(11<=x9131 & x9131<=13) /// +max(0,x3711)*(11<=x9132 & x9132<=13) /// +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) + +max(0,x3718)*(11<=x9133 & x9133<=13) gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// @@ -91,25 +91,25 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(x9131<11|x9131>13) /// +max(0,x3711)*(x9132<11|x9132>13) /// +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// @@ -121,7 +121,7 @@ gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen PMOP = x4436 replace PMOP = 0 if x4436<=0 replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP replace PMOP = x5036 replace PMOP = 0 if x5036<=0 replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 @@ -144,10 +144,10 @@ gen CASHLI = max(0,x4006) gen ROTHMA = max(0,(RANNUIT + RTRUST)) gen SOTHMA = max(0,(SANNUIT + STRUST)) gen COTHMA = max(0,(CANNUIT + CTRUST)) -gen OTHMA = ROTHMA+SOTHMA+COTHMA +gen OTHMA = ROTHMA+SOTHMA+COTHMA gen OTHFIN = x4018+x4022*(x4020==62|x4020==63|x4020==64|x4020==66|x4020==71|x4020==73|x4020==74|x4020==-7) /// +x4026*(x4024==62|x4024==63|x4024==64|x4024==66|x4024==71|x4024==73|x4024==74|x4024==-7) /// - +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) + +x4030*(x4028==62|x4028==63|x4028==64|x4028==66|x4028==71|x4028==73|x4028==74|x4028==-7) gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// @@ -186,7 +186,7 @@ gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN gen ASSET = FIN+NFIN /* Total asset */ -* Debt +* Debt gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 @@ -203,11 +203,11 @@ replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2 +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 gen FLAG67 = (ORESRE>0) replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 @@ -227,12 +227,12 @@ gen CALLDBT = max(0,x3932) gen ODEBT = max(0,x4032) gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ -* Net worth +* Net worth gen NETW = ASSET-DEBT replace NETW = NETW*CPIADJ -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME * Demographic vars @@ -246,7 +246,7 @@ replace EDUC = 2 if x5902==1 /* High school deg */ replace EDUC = 3 if x5904==1 /* College deg */ * keep if EDUC == 3 /* Keep college graduates only */ -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) drop if INCOME<=0 replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) @@ -259,7 +259,7 @@ keep HHID YEAR WGT INCOME NETW WIRATIO AGE cd "$startDir" cd ../../Data/Constructed ** Save data -save "./SCF$scfFldr$SuffixForConstructedFile", replace +save "./SCF$scfFldr$SuffixForConstructedFile", replace ** End in the same directory you started from cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF2001.do b/src/stata/SelectVarsUsingSCF2001.do index e4ec3c2..f90f244 100644 --- a/src/stata/SelectVarsUsingSCF2001.do +++ b/src/stata/SelectVarsUsingSCF2001.do @@ -1,11 +1,11 @@ * This file selects variables, using SCF2001 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) -clear +clear -** Set memeory -set memory 32m +** Set memeory +set memory 32m global startDir "`c(pwd)'" global scfFldr 2001 @@ -45,7 +45,7 @@ use y1 x42000 x42001 x5729 x7362 x5751 x7650 /// x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// using "scf2001.dta" -** Generate variables +** Generate variables * ID gen ID = y1 /* ID # */ gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ @@ -60,15 +60,15 @@ replace INCOME = x7362 if x7650!=3 scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ scalar CPIADJ = CPIBASE/2618 /* Adjust with CPI (adjusted to 1992$ price) */ scalar CPILAG = 2600/2529 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ -* Asset +* Asset gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) + +max(0,x3529)*(x3527==5) gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// - +max(0,x3816)+max(0,x3818) + +max(0,x3816)+max(0,x3818) gen MMDA = max(0,x3506)*((x3507==1)*(11<=x9113 & x9113<=13)) /// +max(0,x3510)*((x3511==1)*(11<=x9114 & x9114<=13)) /// +max(0,x3514)*((x3515==1)*(11<=x9115 & x9115<=13)) /// @@ -79,7 +79,7 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(11<=x9131 & x9131<=13) /// +max(0,x3711)*(11<=x9132 & x9132<=13) /// +max(0,x3716)*(11<=x9133 & x9133<=13) /// - +max(0,x3718)*(11<=x9133 & x9133<=13) + +max(0,x3718)*(11<=x9133 & x9133<=13) gen MMMF = max(0,x3506)*(x3507==1)*(x9113<11|x9113>13) /// +max(0,x3510)*(x3511==1)*(x9114<11|x9114>13) /// +max(0,x3514)*(x3515==1)*(x9115<11|x9115>13) /// @@ -90,25 +90,25 @@ gen SAVING = max(0,x3804)+max(0,x3807)+max(0,x3810)+max(0,x3813) /// +max(0,x3706)*(x9131<11|x9131>13) /// +max(0,x3711)*(x9132<11|x9132>13) /// +max(0,x3716)*(x9133<11|x9133>13) /// - +max(0,x3718)*(x9133<11|x9133>13) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL - -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) -gen NMMF = SNMMF + RNMMF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 + +max(0,x3718)*(x9133<11|x9133>13) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL + +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) +gen NMMF = SNMMF + RNMMF +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen IRAKH = max(0,x3610)+max(0,x3620)+max(0,x3630) gen THRIFT = max(0,x4226)*(x4216==1|x4216==2|x4227==1|x4231==1) /// @@ -120,7 +120,7 @@ gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen PMOP = x4436 replace PMOP = 0 if x4436<=0 replace PMOP = 0 if x4216!=0 & x4316!=0 & x4416!=0 & x4231!=0 & x4331!=0 & x4431!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP replace PMOP = x5036 replace PMOP = 0 if x5036<=0 replace PMOP = 0 if x4816!=0 & x4916!=0 & x5016!=0 & x4831!=0 & x4931!=0 & x5031!=0 @@ -137,7 +137,7 @@ gen OTHFIN = x4018 /// +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// - x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// @@ -176,7 +176,7 @@ gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN gen ASSET = FIN+NFIN /* Total asset */ -* Debt +* Debt gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 @@ -193,11 +193,11 @@ replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2 +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 gen FLAG67 = (ORESRE>0) replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 @@ -217,12 +217,12 @@ gen CALLDBT = max(0,x3932) gen ODEBT = max(0,x4032) gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ -* Net worth +* Net worth gen NETW = ASSET-DEBT replace NETW = NETW*CPIADJ -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME * Demographic vars @@ -236,19 +236,19 @@ replace EDUC = 2 if x5902==1 /* High school deg */ replace EDUC = 3 if x5904==1 /* College deg */ * keep if EDUC == 3 /* Keep college graduates only */ -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) drop if INCOME<=0 replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) ** Keep necessary vars -keep HHID YEAR WGT INCOME NETW WIRATIO AGE +keep HHID YEAR WGT INCOME NETW WIRATIO AGE ** Save data cd "$startDir" cd ../../Data/Constructed -save "./SCF$scfFldr$SuffixForConstructedFile", replace +save "./SCF$scfFldr$SuffixForConstructedFile", replace ** End in the same directory you started from cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF2004.do b/src/stata/SelectVarsUsingSCF2004.do index cf10063..b4730f4 100644 --- a/src/stata/SelectVarsUsingSCF2004.do +++ b/src/stata/SelectVarsUsingSCF2004.do @@ -1,11 +1,11 @@ * This file selects variables, using SCF2004 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) -clear +clear -** Set memeory -set memory 32m +** Set memeory +set memory 32m global startDir "`c(pwd)'" global scfFldr 2004 @@ -20,7 +20,7 @@ if _rc~=0 { exit } - + ** Load data and pick up necessary vars from original data use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x6965 x6971 x6977 x6983 x6989 x6995 x7362 x5751 x7650 /// x3506 x3507 x3510 x3511 x3514 x3515 x3518 x3519 x3522 x3523 x3526 x3527 x3529 /// @@ -48,7 +48,7 @@ use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// using "scf2004.dta" -** Generate variables +** Generate variables * ID gen ID = y1 /* ID # */ gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ @@ -66,16 +66,16 @@ replace INCOME = x7362 if x7650!=3 scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ scalar CPIADJ = CPIBASE/2788 /* Adjust with CPI (adjusted to 1992$ price) */ scalar CPILAG = 2774/2701 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ * gen INCOMEAT = x5751 /* After tax income */ * gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ * keep if D_NORMINC == 1 /* Keep if inc level is normal */ -* Asset +* Asset gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) + +max(0,x3529)*(x3527==5) gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// +max(0,x3736*(x3738!=4 & x3738!=30)) /// +max(0,x3742*(x3744!=4 & x3744!=30)) /// @@ -107,42 +107,42 @@ gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// - +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL + +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) gen OMUTF = (x7785==1)*max(0,x7787) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) gen NMMF = SNMMF + RNMMF + OMUTF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 - gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// + gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// - +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// + +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// - +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) + +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) gen PMOP = x11259 replace PMOP = 0 if x11259<=0 replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP replace PMOP = x11559 replace PMOP = 0 if x11559<=0 replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP gen RETQLIQ = IRAKH+THRIFT gen SAVBND = x3902 gen CASHLI = max(0,x4006) @@ -155,7 +155,7 @@ gen OTHFIN = x4018 /// +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// - x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// @@ -194,7 +194,7 @@ gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN gen ASSET = FIN+NFIN /* Total asset */ -* Debt +* Debt gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 @@ -211,11 +211,11 @@ replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2 +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 gen FLAG67 = (ORESRE>0) replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 @@ -235,12 +235,12 @@ gen CALLDBT = max(0,x3932) gen ODEBT = max(0,x4032) gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ -* Net worth +* Net worth gen NETW = ASSET-DEBT replace NETW = NETW*CPIADJ -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME * Demographic vars @@ -254,7 +254,7 @@ replace EDUC = 2 if x5902==1 /* High school deg */ replace EDUC = 3 if x5904==1 /* College deg */ * keep if EDUC == 3 /* Keep college graduates only */ -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) drop if INCOME<=0 replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) @@ -266,7 +266,7 @@ keep HHID YEAR WGT INCOME NETW WIRATIO AGE ** Save data cd "$startDir" cd ../../Data/Constructed -save "./SCF$scfFldr$SuffixForConstructedFile", replace +save "./SCF$scfFldr$SuffixForConstructedFile", replace ** End in the same directory you started from cd "$startDir" diff --git a/src/stata/SelectVarsUsingSCF2007.do b/src/stata/SelectVarsUsingSCF2007.do index e8e61cc..705a20d 100644 --- a/src/stata/SelectVarsUsingSCF2007.do +++ b/src/stata/SelectVarsUsingSCF2007.do @@ -1,11 +1,11 @@ * This file selects variables, using SCF2007 -* This file closely follows codes written for SAS which create summary variables. -* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) +* This file closely follows codes written for SAS which create summary variables. +* (These codes are available at http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt) -clear +clear -** Set memeory -set memory 32m +** Set memeory +set memory 32m global startDir "`c(pwd)'" global scfFldr 2007 @@ -47,7 +47,7 @@ use y1 x42000 x42001 x5729 x6558 x6566 x6574 x6464 x6469 x6474 x6479 x6484 x6489 x14 x19 x8020 x8023 x5902 x5904 x6102 x6104 /// using "scf2007.dta" -** Generate variables +** Generate variables * ID gen ID = y1 /* ID # */ gen HHID = (y1-mod(y1,10))/10 /* HH ID # */ @@ -65,16 +65,16 @@ replace INCOME = x7362 if x7650!=3 scalar CPIBASE = 2116 /* September 1992 consumer price index level, the numbers can be found in http://www.federalreserve.gov/pubs/oss/oss2/bulletin.macro.txt*/ scalar CPIADJ = CPIBASE/3062 /* Adjust with CPI (adjusted to 1992$ price) */ scalar CPILAG = 3045/2961 /* Income is the previous year's income level, CPILAG adjust income to survey year */ -replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ +replace INCOME = INCOME*CPILAG*CPIADJ /* Adjust with CPI (adjusted to 1992$ price) */ * gen INCOMEAT = x5751 /* After tax income */ * gen D_NORMINC = (x7650==3) /* D_NORMINC = 1 if inc level is normal */ * keep if D_NORMINC == 1 /* Keep if inc level is normal */ -* Asset +* Asset gen CHECKING = max(0,x3506)*(x3507==5)+max(0,x3510)*(x3511==5) /// +max(0,x3514)*(x3515==5)+max(0,x3518)*(x3519==5) /// +max(0,x3522)*(x3523==5)+max(0,x3526)*(x3527==5) /// - +max(0,x3529)*(x3527==5) + +max(0,x3529)*(x3527==5) gen SAVING = max(0,x3730*(x3732!=4 & x3732!=30)) /// +max(0,x3736*(x3738!=4 & x3738!=30)) /// +max(0,x3742*(x3744!=4 & x3744!=30)) /// @@ -106,42 +106,42 @@ gen MMMF = max(0,x3506)*((x3507==1)*(x9113<11 | x9113>13)) /// +max(0,x3742*(x3744==4|x3744==30)*(x9261<11 | x9261>13)) /// +max(0,x3748*(x3750==4|x3750==30)*(x9262<11 | x9262>13)) /// +max(0,x3754*(x3756==4|x3756==30)*(x9263<11 | x9263>13)) /// - +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) -gen MMA = MMDA+MMMF -gen CALL = max(0,x3930) -gen LIQ = CHECKING+SAVING+MMA+CALL + +max(0,x3760*(x3762==4|x3762==30)*(x9264<11 | x9264>13)) +gen MMA = MMDA+MMMF +gen CALL = max(0,x3930) +gen LIQ = CHECKING+SAVING+MMA+CALL -gen CDS = max(0,x3721) - gen STMUTF = (x3821==1)*max(0,x3822) - gen TFBMUTF = (x3823==1)*max(0,x3824) - gen GBMUTF = (x3825==1)*max(0,x3826) - gen OBMUTF = (x3827==1)*max(0,x3828) - gen COMUTF = (x3829==1)*max(0,x3830) +gen CDS = max(0,x3721) + gen STMUTF = (x3821==1)*max(0,x3822) + gen TFBMUTF = (x3823==1)*max(0,x3824) + gen GBMUTF = (x3825==1)*max(0,x3826) + gen OBMUTF = (x3827==1)*max(0,x3828) + gen COMUTF = (x3829==1)*max(0,x3830) gen OMUTF = (x7785==1)*max(0,x7787) - gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) - gen RNMMF = STMUTF + (.5*(COMUTF)) + gen SNMMF = TFBMUTF+GBMUTF+OBMUTF+(.5*(COMUTF)) + gen RNMMF = STMUTF + (.5*(COMUTF)) gen NMMF = SNMMF + RNMMF + OMUTF -gen STOCKS = max(0,x3915) - gen NOTXBND = x3910 - gen MORTBND = x3906 - gen GOVTBND = x3908 - gen OBND = x7634+x7633 +gen STOCKS = max(0,x3915) + gen NOTXBND = x3910 + gen MORTBND = x3906 + gen GOVTBND = x3908 + gen OBND = x7634+x7633 gen BOND = NOTXBND + MORTBND + GOVTBND + OBND gen IRAKH = x6551+x6559+x6567+x6552+x6560+x6568+x6553+x6561+x6569+x6554+x6562+x6570 - gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// + gen THRIFT = max(0,x11032)*(x11000==5|x11000==6|x11000==10|x11001==2|x11001==3|x11001==4|x11001==6|x11025==1|x11031==1) /// +max(0,x11132)*(x11100==5|x11100==6|x11100==10|x11101==2|x11101==3|x11101==4|x11101==6|x11125==1|x11131==1) /// - +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// + +max(0,x11232)*(x11200==5|x11200==6|x11200==10|x11201==2|x11201==3|x11201==4|x11201==6|x11225==1|x11231==1) /// +max(0,x11332)*(x11300==5|x11300==6|x11300==10|x11301==2|x11301==3|x11301==4|x11301==6|x11325==1|x11331==1) /// +max(0,x11432)*(x11400==5|x11400==6|x11400==10|x11401==2|x11401==3|x11401==4|x11401==6|x11425==1|x11431==1) /// - +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) + +max(0,x11532)*(x11500==5|x11500==6|x11500==10|x11501==2|x11501==3|x11501==4|x11501==6|x11525==1|x11531==1) gen PMOP = x11259 replace PMOP = 0 if x11259<=0 replace PMOP = 0 if x11000!=0 & x11100!=0 & x11200!=0 & x11031!=0 & x11131!=0 & x11231!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP replace PMOP = x11559 replace PMOP = 0 if x11559<=0 replace PMOP = 0 if x11300!=0 & x11400!=0 & x11500!=0 & x11331!=0 & x11431!=0 & x11531!=0 - replace THRIFT = THRIFT + PMOP + replace THRIFT = THRIFT + PMOP gen RETQLIQ = IRAKH+THRIFT gen SAVBND = x3902 gen CASHLI = max(0,x4006) @@ -154,7 +154,7 @@ gen OTHFIN = x4018 /// +x4026*(x4024==61|x4024==62|x4024==63|x4024==64|x4024==65|x4024==66|x4024==71| /// x4024==72|x4024==73|x4024==74|x4024==77|x4024==80|x4024==81|x4024==-7) /// +x4030*(x4028==61|x4028==62|x4028==63|x4028==64|x4028==65|x4028==66|x4028==71| /// - x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) + x4028==72|x4028==73|x4028==74|x4028==77|x4028==80|x4028==81|x4028==-7) gen FIN = LIQ+CDS+NMMF+STOCKS+BOND+RETQLIQ+SAVBND+CASHLI+OTHMA+OTHFIN /* Total fin asset */ gen VEHIC = max(0,x8166)+max(0,x8167)+max(0,x8168)+max(0,x8188) /// @@ -193,7 +193,7 @@ gen OTHNFIN = x4022 + x4026 + x4030 - OTHFIN + x4018 gen NFIN = VEHIC+HOUSES+ORESRE+NNRESRE+BUS+OTHNFIN gen ASSET = FIN+NFIN /* Total asset */ -* Debt +* Debt gen MRTHEL = x805+x905+x1005+x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1)+max(0,x1136)*(x1108*(x1103==1)+x1119*(x1114==1) /// +x1130*(x1125==1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 @@ -210,11 +210,11 @@ replace RESDBT = RESDBT+x2723*(x2710==78)+x2740*(x2727==78)+x2823*(x2810==78)+x2 +x2923*(x2910==78)+x2940*(x2927==78) if FLAG781!=1 & ORESRE>0 gen FLAG67 = (ORESRE>0) replace RESDBT= RESDBT+x2723*(x2710==67)+x2740*(x2727==67)+x2823*(x2810==67)+x2840*(x2827==67) /// - +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 + +x2923*(x2910==67)+x2940*(x2927==67) if ORESRE>0 gen OTHLOC = x1108*(x1103!=1)+x1119*(x1114!=1)+x1130*(x1125!=1) /// +max(0,x1136)*(x1108*(x1103!=1)+x1119*(x1114!=1) /// +x1130*(x1125!=1))/(x1108+x1119+x1130) if (x1108+x1119+x1130)>=1 -replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 +replace OTHLOC = .5*(max(0,x1136)) if (x1108+x1119+x1130)<1 gen CCBAL = max(0,x427)+max(0,x413)+max(0,x421)+max(0,x430)+max(0,x424)+max(0,x7575) gen INSTALL = x2218+x2318+x2418+x7169+x2424+x2519+x2619+x2625+x7183 /// +x7824+x7847+x7870+x7924+x7947+x7970+x7179+x1044+x1215+x1219 @@ -234,12 +234,12 @@ gen CALLDBT = max(0,x3932) gen ODEBT = max(0,x4032) gen DEBT = MRTHEL+RESDBT+OTHLOC+CCBAL+INSTALL+PENDBT+CASHLIDB+CALLDBT+ODEBT /* Total debt */ -* Net worth +* Net worth gen NETW = ASSET-DEBT replace NETW = NETW*CPIADJ -* Ratio of net worth to income -gen WIRATIO = NETW/INCOME +* Ratio of net worth to income +gen WIRATIO = NETW/INCOME * Demographic vars @@ -253,7 +253,7 @@ replace EDUC = 2 if x5902==1 /* High school deg */ replace EDUC = 3 if x5904==1 /* College deg */ * keep if EDUC == 3 /* Keep college graduates only */ -* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) +* Correct time effects. The base is set at 25 yrs old in 1980 (0 yrs old in 1955) replace INCOME = INCOME/exp(0.016*(YEAR-1955-AGE)) drop if INCOME<=0 replace NETW = NETW/exp(0.016*(YEAR-1955-AGE)) @@ -265,7 +265,7 @@ keep HHID YEAR WGT INCOME NETW WIRATIO AGE ** Save data cd "$startDir" cd ../../Data/Constructed -save "./SCF$scfFldr$SuffixForConstructedFile", replace +save "./SCF$scfFldr$SuffixForConstructedFile", replace ** End in the same directory you started from cd "$startDir" diff --git a/src/stata/WIRatioPopulation.do b/src/stata/WIRatioPopulation.do index 8c4fb3e..9b9c810 100644 --- a/src/stata/WIRatioPopulation.do +++ b/src/stata/WIRatioPopulation.do @@ -1,6 +1,6 @@ -/* This program gives the Summary statistics for Income, Net Worth and -Wealth/Income Ratio of the Married Households whose ages are between -31 and 55. +/* This program gives the Summary statistics for Income, Net Worth and +Wealth/Income Ratio of the Married Households whose ages are between +31 and 55. The program builts on the results obtained by doAll.do file, so run this file after running the doAll.do file. */ @@ -10,12 +10,12 @@ cd $basePath/$stataPath cd ../../Data/Constructed *************************************************************************************************** -/* Specifies the list of percentiles of INCOME, NETW and WIRATIO. p50 represents 50th percentile: median. +/* Specifies the list of percentiles of INCOME, NETW and WIRATIO. p50 represents 50th percentile: median. Modify the list if you want to obtain results for different percentiles.*/ -global percentiles = "p50" +global percentiles = "p50" -scalar AgeRange1 = `"26-30"' -scalar AgeRange2 = `"31-35"' +scalar AgeRange1 = `"26-30"' +scalar AgeRange2 = `"31-35"' scalar AgeRange3 = `"36-40"' scalar AgeRange4 = `"41-45"' scalar AgeRange5 = `"46-50"' @@ -45,7 +45,7 @@ gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income xtsum HHID keep if OBS==1 -drop OBS +drop OBS keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ gen AGEID = int((AGE-26)/5)+1 @@ -55,7 +55,7 @@ sort AGEID HHID ***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** /* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) which is based on after tax income.) @@ -63,29 +63,29 @@ sort AGEID HHID /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) svmat RawMat rename RawMat1 RAWIRATIO -rename RawMat2 RAWI +rename RawMat2 RAWI -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ local N=_N qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ replace RAWI = AVGINC[`i'] in 10 ipolate RAWIRATIO RAWI, gen(TEMP) epolate replace TXIRATIO = TEMP[10] in `i' - drop TEMP + drop TEMP } replace RAWI =. in 10 replace TXIRATIO = 1 if TXIRATIO < 1 -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** qui: sum AGEID, d local size=r(max) /* size is used as an index number in the following loops*/ @@ -96,30 +96,30 @@ gen OBSBYAGE = . qui foreach p in $percentiles { gen `p'INCBYAGE = . qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d replace AVGINCBYAGE = r(mean) if AGEID==`k' replace `p'INCBYAGE = r(`p') if AGEID==`k' replace OBSBYAGE = r(N) if AGEID==`k' } - } - + } + gen AVGNETWBYAGE = . qui foreach p in $percentiles { gen `p'NETWBYAGE = . qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d replace AVGNETWBYAGE = r(mean) if AGEID==`k' replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - + } + } + gen AVGWIRATIOBYAGE = . qui foreach p in $percentiles { gen `p'WIRATIOBYAGE = . qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' } @@ -131,7 +131,7 @@ qui forvalues k=1/`size' { } by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All1992", replace +save "$basePath/Data/Constructed/All1992", replace ************************************* 1995 Survey Summary ****************************************** @@ -156,7 +156,7 @@ gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income xtsum HHID keep if OBS==1 -drop OBS +drop OBS keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ gen AGEID = int((AGE-26)/5)+1 @@ -166,7 +166,7 @@ sort AGEID HHID ***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** /* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) which is based on after tax income.) @@ -174,29 +174,29 @@ sort AGEID HHID /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) svmat RawMat rename RawMat1 RAWIRATIO -rename RawMat2 RAWI +rename RawMat2 RAWI -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ local N=_N qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ replace RAWI = AVGINC[`i'] in 10 ipolate RAWIRATIO RAWI, gen(TEMP) epolate replace TXIRATIO = TEMP[10] in `i' - drop TEMP + drop TEMP } replace RAWI =. in 10 replace TXIRATIO = 1 if TXIRATIO < 1 -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** +************************************************************************************************** qui: sum AGEID, d local size=r(max) /* size is used as an index number in the following loops*/ @@ -207,30 +207,30 @@ gen OBSBYAGE = . qui foreach p in $percentiles { gen `p'INCBYAGE = . qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d replace AVGINCBYAGE = r(mean) if AGEID==`k' replace `p'INCBYAGE = r(`p') if AGEID==`k' replace OBSBYAGE = r(N) if AGEID==`k' } - } - + } + gen AVGNETWBYAGE = . qui foreach p in $percentiles { gen `p'NETWBYAGE = . qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d replace AVGNETWBYAGE = r(mean) if AGEID==`k' replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - + } + } + gen AVGWIRATIOBYAGE = . qui foreach p in $percentiles { gen `p'WIRATIOBYAGE = . qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' } @@ -242,7 +242,7 @@ qui forvalues k=1/`size' { } by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All1995", replace +save "$basePath/Data/Constructed/All1995", replace ************************************* 1998 Survey Summary ****************************************** @@ -267,7 +267,7 @@ gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income xtsum HHID keep if OBS==1 -drop OBS +drop OBS keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ gen AGEID = int((AGE-26)/5)+1 @@ -277,7 +277,7 @@ sort AGEID HHID ***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** /* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) which is based on after tax income.) @@ -285,29 +285,29 @@ sort AGEID HHID /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) svmat RawMat rename RawMat1 RAWIRATIO -rename RawMat2 RAWI +rename RawMat2 RAWI -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ local N=_N qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ replace RAWI = AVGINC[`i'] in 10 ipolate RAWIRATIO RAWI, gen(TEMP) epolate replace TXIRATIO = TEMP[10] in `i' - drop TEMP + drop TEMP } replace RAWI =. in 10 replace TXIRATIO = 1 if TXIRATIO < 1 -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** qui: sum AGEID, d local size=r(max) /* size is used as an index number in the following loops*/ @@ -318,30 +318,30 @@ gen OBSBYAGE = . qui foreach p in $percentiles { gen `p'INCBYAGE = . qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d replace AVGINCBYAGE = r(mean) if AGEID==`k' replace `p'INCBYAGE = r(`p') if AGEID==`k' replace OBSBYAGE = r(N) if AGEID==`k' } - } - + } + gen AVGNETWBYAGE = . qui foreach p in $percentiles { gen `p'NETWBYAGE = . qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d replace AVGNETWBYAGE = r(mean) if AGEID==`k' replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - + } + } + gen AVGWIRATIOBYAGE = . qui foreach p in $percentiles { gen `p'WIRATIOBYAGE = . qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' } @@ -353,7 +353,7 @@ qui forvalues k=1/`size' { } by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All1998", replace +save "$basePath/Data/Constructed/All1998", replace ************************************* 2001 Survey Summary ****************************************** @@ -378,7 +378,7 @@ gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income xtsum HHID keep if OBS==1 -drop OBS +drop OBS keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ gen AGEID = int((AGE-26)/5)+1 @@ -388,7 +388,7 @@ sort AGEID HHID ***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** /* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) which is based on after tax income.) @@ -396,29 +396,29 @@ sort AGEID HHID /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) svmat RawMat rename RawMat1 RAWIRATIO -rename RawMat2 RAWI +rename RawMat2 RAWI -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ local N=_N qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ replace RAWI = AVGINC[`i'] in 10 ipolate RAWIRATIO RAWI, gen(TEMP) epolate replace TXIRATIO = TEMP[10] in `i' - drop TEMP + drop TEMP } replace RAWI =. in 10 replace TXIRATIO = 1 if TXIRATIO < 1 -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ + +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** +************************************************************************************************** qui: sum AGEID, d local size=r(max) /* size is used as an index number in the following loops*/ @@ -429,30 +429,30 @@ gen OBSBYAGE = . qui foreach p in $percentiles { gen `p'INCBYAGE = . qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d replace AVGINCBYAGE = r(mean) if AGEID==`k' replace `p'INCBYAGE = r(`p') if AGEID==`k' replace OBSBYAGE = r(N) if AGEID==`k' } - } - + } + gen AVGNETWBYAGE = . qui foreach p in $percentiles { gen `p'NETWBYAGE = . qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d replace AVGNETWBYAGE = r(mean) if AGEID==`k' replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - + } + } + gen AVGWIRATIOBYAGE = . qui foreach p in $percentiles { gen `p'WIRATIOBYAGE = . qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' } @@ -464,7 +464,7 @@ qui forvalues k=1/`size' { } by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All2001", replace +save "$basePath/Data/Constructed/All2001", replace ************************************* 2004 Survey Summary ****************************************** @@ -489,7 +489,7 @@ gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income xtsum HHID keep if OBS==1 -drop OBS +drop OBS keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ gen AGEID = int((AGE-26)/5)+1 @@ -499,7 +499,7 @@ sort AGEID HHID ***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** /* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) which is based on after tax income.) @@ -507,29 +507,29 @@ sort AGEID HHID /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) svmat RawMat rename RawMat1 RAWIRATIO -rename RawMat2 RAWI +rename RawMat2 RAWI -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ local N=_N qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ replace RAWI = AVGINC[`i'] in 10 ipolate RAWIRATIO RAWI, gen(TEMP) epolate replace TXIRATIO = TEMP[10] in `i' - drop TEMP + drop TEMP } replace RAWI =. in 10 replace TXIRATIO = 1 if TXIRATIO < 1 -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** qui: sum AGEID, d local size=r(max) /* size is used as an index number in the following loops*/ @@ -540,42 +540,42 @@ gen OBSBYAGE = . qui foreach p in $percentiles { gen `p'INCBYAGE = . qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d replace AVGINCBYAGE = r(mean) if AGEID==`k' replace `p'INCBYAGE = r(`p') if AGEID==`k' replace OBSBYAGE = r(N) if AGEID==`k' } - } - + } + gen AVGNETWBYAGE = . qui foreach p in $percentiles { gen `p'NETWBYAGE = . qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d replace AVGNETWBYAGE = r(mean) if AGEID==`k' replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - + } + } + gen AVGWIRATIOBYAGE = . qui foreach p in $percentiles { gen `p'WIRATIOBYAGE = . qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' } } - + gen AGERANGE= `""' qui forvalues k=1/`size' { replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ } by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All2004", replace +save "$basePath/Data/Constructed/All2004", replace ************************************* 2007 Survey Summary ****************************************** @@ -600,7 +600,7 @@ gen AVGWIRATIO = AVGNETW/AVGINC /* Generates the Average Wealth/Income xtsum HHID keep if OBS==1 -drop OBS +drop OBS keep if AGE >= 26 & AGE <= 60 /* Constructs 5 year period age groups: 31-35, ...., 51-55. */ gen AGEID = int((AGE-26)/5)+1 @@ -610,7 +610,7 @@ sort AGEID HHID ***************** Before Tax Permanent Income / After Tax Permanent Income RATIO ***************** /* This section gives the ratio: Before Tax Permanent Income / After Tax Permanent Income - This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained + This adjustment is necessary; we need to rescale WIRATIO properly, since WIRATIO obtained using STATA is the ratio of wealth to before tax permanent income, not to after tax permanent income. (Note that the work in the MICRODSOP lecture notes takes parameters from Cagetti (2003) which is based on after tax income.) @@ -618,29 +618,29 @@ sort AGEID HHID /* Income and IncomeRatio are calculated using data in Cagetti (2003) and SCF data */ matrix input RawMat = (1.1758, 39497 \ 1.221, 49262 \ 1.2874, 61057 \ 1.2594, 68224 \ 1.4432, 86353 /// - \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) + \ 1.5055, 96983 \ 1.5509, 98786 \ 1.5663, 1.0223e+005 \ 1.5663, 1e+010 ) svmat RawMat rename RawMat1 RAWIRATIO -rename RawMat2 RAWI +rename RawMat2 RAWI -gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ +gen TXIRATIO =. /* := Before Tax Permanent Income / After Tax Permanent Income */ local N=_N qui forvalues i=1/`N' { /* Gives the Before Tax Permanent Income / After Tax Permanent Income RATIO */ replace RAWI = AVGINC[`i'] in 10 ipolate RAWIRATIO RAWI, gen(TEMP) epolate replace TXIRATIO = TEMP[10] in `i' - drop TEMP + drop TEMP } replace RAWI =. in 10 replace TXIRATIO = 1 if TXIRATIO < 1 -/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, -thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ +/* AfterTax adjustment of Wealth/Income Ratio: AVGWIRATIO represents Net Worth/Before Tax Permanent Income for each HH, +thus multiplying AVGWIRATIO by TXIRATIO (Bef. Tax Inc / After Tax Inc) gives AVGWIRATIO= Net Worth/After Tax Inc, which is desired. */ -replace AVGWIRATIO = AVGWIRATIO*TXIRATIO - -************************************************************************************************** +replace AVGWIRATIO = AVGWIRATIO*TXIRATIO + +************************************************************************************************** qui: sum AGEID, d local size=r(max) /* size is used as an index number in the following loops*/ @@ -651,42 +651,42 @@ gen OBSBYAGE = . qui foreach p in $percentiles { gen `p'INCBYAGE = . qui forvalues k = 1/`size' { /* Generates the Average Income for each Age Group defined by AGEID */ - sum AVGINC [aweight = AVGWGT] if AGEID==`k', d + sum AVGINC [aweight = AVGWGT] if AGEID==`k', d replace AVGINCBYAGE = r(mean) if AGEID==`k' replace `p'INCBYAGE = r(`p') if AGEID==`k' replace OBSBYAGE = r(N) if AGEID==`k' } - } - + } + gen AVGNETWBYAGE = . qui foreach p in $percentiles { gen `p'NETWBYAGE = . qui forvalues k = 1/`size' { /* Generates the Net Worth for each Age Group defined by AGEID */ - sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d + sum AVGNETW [aweight = AVGWGT] if AGEID==`k', d replace AVGNETWBYAGE = r(mean) if AGEID==`k' replace `p'NETWBYAGE = r(`p') if AGEID==`k' - } - } - + } + } + gen AVGWIRATIOBYAGE = . qui foreach p in $percentiles { gen `p'WIRATIOBYAGE = . qui forvalues k = 1/`size' { /* Generates the Wealth/Income Ratio for each Age Group defined by AGEID */ - sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d + sum AVGWIRATIO [aweight = AVGWGT] if AGEID==`k', d replace AVGWIRATIOBYAGE = r(mean) if AGEID==`k' replace `p'WIRATIOBYAGE = r(`p') if AGEID==`k' } } - + gen AGERANGE= `""' qui forvalues k=1/`size' { replace AGERANGE = AgeRange`k' if AGEID==`k' /* Generates string values for each Age Group which are used in graphs for illustrative purposes */ } by AGEID: gen OBS=_n -save "$basePath/Data/Constructed/All2007", replace +save "$basePath/Data/Constructed/All2007", replace ************************************ 2001-2007 Population WIRATIO , AGEID and WEIGHT ******************************* cd $basePath/Data/Constructed/ @@ -699,11 +699,11 @@ append using All1995 append using All1992 keep HHID YEAR AGEID AGERANGE AVGWIRATIO AVGWGT -sort AGEID AVGWIRATIO +sort AGEID AVGWIRATIO bysort AGEID: gen N=_N egen SUMAVGWGT = sum(AVGWGT), by(AGEID) -gen WGTPOP = (AVGWGT/SUMAVGWGT)*N +gen WGTPOP = (AVGWGT/SUMAVGWGT)*N gen WIRATIOPOP = AVGWIRATIO order WIRATIOPOP AGEID WGTPOP diff --git a/src/stata/doAll.do b/src/stata/doAll.do index 9d363a7..ab395bc 100644 --- a/src/stata/doAll.do +++ b/src/stata/doAll.do @@ -5,10 +5,9 @@ set linesize 200 * make paths global basePath "/Volumes/Data/Notes/NumericalMethods/EstimatingMicroDSOPs/Latest" global stataPath "Code/Stata" -global logPath "Code/Stata" +global logPath "Code/Stata" cd $basePath/$stataPath do AppendDataUsingSCF1992_2007.do do WIRatioPopulation.do - diff --git a/src/tests.py b/src/tests.py index 5b6fc26..56493be 100644 --- a/src/tests.py +++ b/src/tests.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from estimark.min import estimate_min from estimark.options import low_resource, medium_resource From c607e4575aa49d2833e272ae8a256455ffd2912a Mon Sep 17 00:00:00 2001 From: Alan Lujan Date: Fri, 20 Sep 2024 16:16:51 -0400 Subject: [PATCH 6/7] restore readme --- README.md | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ff45258..1ab3d54 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,40 @@ -# estimark +# EstimatingMicroDSOPs (estimark) + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/EstimatingMicroDSOPs/HEAD) + +To reproduces all the results in the repository first clone this repository locally: + +``` +# Clone this repository +$ git clone https://github.com/econ-ark/EstimatingMicroDSOPs + +# Change working directory to EstimatingMicroDSOPs +$ cd EstimatingMicroDSOPs +``` + +Then you can either use a local virtual env(conda) or [nbreproduce](https://github.com/econ-ark/nbreproduce) to +reproduce to the results. + +#### A local conda environment and execute the do_all.py file. + +``` +$ conda env create -f environment.yml +$ conda activate estimatingmicrodsops +# execute the script, select the appropriate option and use it to reproduce the data and figures. +$ python do_all.py +``` + +#### [nbreproduce](https://github.com/econ-ark/nbreproduce) (requires Docker to be installed on the machine). + +``` +# Install nbreproduce +$ pip install nbreproduce + +# Reproduce all results using nbreproduce +$ nbreproduce +``` + +## References [![Actions Status][actions-badge]][actions-link] [![Documentation Status][rtd-badge]][rtd-link] From d6b54e9415b6b88796d2f95e05672ed15b3dcac1 Mon Sep 17 00:00:00 2001 From: Alan Lujan Date: Fri, 20 Sep 2024 16:18:41 -0400 Subject: [PATCH 7/7] pre-commit --- README.md | 8 +++++--- content/paper/01-paper.md | 2 +- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 1ab3d54..5423cce 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,8 @@ [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/EstimatingMicroDSOPs/HEAD) -To reproduces all the results in the repository first clone this repository locally: +To reproduces all the results in the repository first clone this repository +locally: ``` # Clone this repository @@ -12,8 +13,9 @@ $ git clone https://github.com/econ-ark/EstimatingMicroDSOPs $ cd EstimatingMicroDSOPs ``` -Then you can either use a local virtual env(conda) or [nbreproduce](https://github.com/econ-ark/nbreproduce) to -reproduce to the results. +Then you can either use a local virtual env(conda) or +[nbreproduce](https://github.com/econ-ark/nbreproduce) to reproduce to the +results. #### A local conda environment and execute the do_all.py file. diff --git a/content/paper/01-paper.md b/content/paper/01-paper.md index cf23f04..fe19684 100644 --- a/content/paper/01-paper.md +++ b/content/paper/01-paper.md @@ -292,7 +292,7 @@ complicated and may not be invertible in terms of optimal consumption. Consider the first order condition for an optimal combination of consumption and savings, denoted by $^*$: -\begin{equation} \uFunc*{c}'(\cNrm^*, \aNrm^*) - \uFunc*{a}'(\cNrm^_, \aNrm^_) = +\begin{equation} \uFunc*{c}'(\cNrm^*, \aNrm^_) - \uFunc_{a}'(\cNrm^_, \aNrm^_) = \DiscFac \wFunc'(\aNrm^\*) \end{equation} If the utility of consumption and wealth is additively separable, then the Euler