From 89ab9cbd9fd548919c89b72b375e2fec14278d1f Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Fri, 27 Oct 2017 15:11:42 -0700 Subject: [PATCH 1/7] Updated example notebook and added sorted to spectra loading for consistency. --- esp/spec_utils.py | 2 +- examples/esp_example.ipynb | 321 +++++++++++++++---------------------- 2 files changed, 133 insertions(+), 190 deletions(-) diff --git a/esp/spec_utils.py b/esp/spec_utils.py index ec708d4..f60f5f2 100644 --- a/esp/spec_utils.py +++ b/esp/spec_utils.py @@ -28,7 +28,7 @@ def load_spectra(self, directory): for root, dirs, files in os.walk(directory): file_total = len(files) file_on = 1 - for name in files: + for name in sorted(files): if file_on % 100 == 0: print(str("File On " + str(file_on) + " out of " + str(file_total))) diff --git a/examples/esp_example.ipynb b/examples/esp_example.ipynb index f1456b9..dfc64c8 100644 --- a/examples/esp_example.ipynb +++ b/examples/esp_example.ipynb @@ -32,9 +32,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import esp\n", @@ -56,9 +54,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -87,15 +83,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "image/png": 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mX0AZr3FtPEXRgn8ej/Yn3Lp5oURF68G+qKhKB7lMATNf0NKYhWjoQCzfeh6y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\nQLfjgscyszFX/R7n6luC++ITMP2GetuODxMRiWnWApJTvX3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JtHdfehJfhWCf+8YLYY/t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KW7d+W1pz4jhdUu++KMNJn3Zsgs/Ezamyxt3SDrthE+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dlcjBRdbwb5qWbTEXX+t/XjiPn532RZmHJSIiIiIiIiJyNESE+oKXXHIJnTt3Zvz48bz99tvE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\nIvkAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -119,9 +113,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "os.mkdir('results')\n", @@ -149,9 +141,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "new_pca_obj = esp.pcaSED()\n", @@ -172,9 +162,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "bandpass_dir = '../data/lsst_bandpasses/'\n", @@ -193,10 +181,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -210,7 +196,7 @@ ], "source": [ "colors = new_pca_obj.calc_colors(bandpass_dict, 10)\n", - "print colors[0:3]" + "print(colors[0:3])" ] }, { @@ -222,10 +208,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", @@ -245,10 +229,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, sample_cat_colors)" @@ -256,10 +238,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "nn_spec = nn_obj.nn_predict(1)" @@ -267,10 +247,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -291,7 +269,7 @@ ], "source": [ "nn_colors = nn_spec.calc_colors(bandpass_dict, 10)\n", - "print nn_colors" + "print(nn_colors)" ] }, { @@ -303,10 +281,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "nn_spec = nn_obj.nn_predict(5, knr_args=dict(weights='distance'))" @@ -314,10 +290,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -338,7 +312,7 @@ ], "source": [ "nn_colors = nn_spec.calc_colors(bandpass_dict, 10)\n", - "print nn_colors" + "print(nn_colors)" ] }, { @@ -352,10 +326,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, sample_cat_colors)" @@ -370,13 +342,11 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ - "gp_kernel = gp_obj.define_kernel('exp', 1.0e-3, 1.0e-3)" + "gp_kernel = gp_obj.define_kernel('exp', 1.0e-3, 1.0e-3, n_dim=len(sample_cat_colors[0]))" ] }, { @@ -388,30 +358,11 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n" - ] - } - ], + "execution_count": 20, + "metadata": {}, + "outputs": [], "source": [ - "gp_spec = gp_obj.gp_predict(gp_kernel)" + "gp_spec = gp_obj.gp_predict(gp_kernel, opt_bandpass_dict=bandpass_dict)" ] }, { @@ -423,41 +374,39 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 1.48259565e+00 6.34273717e-01 3.51637041e-01 1.97321028e-01\n", - " 2.06638566e-01]\n", - " [ 1.20720703e+00 5.24873542e-01 3.04177878e-01 1.73014997e-01\n", - " 1.81074863e-01]\n", - " [ 2.23148027e-01 -8.10239054e-02 -2.72291043e-02 -1.44996039e-02\n", - " -2.40196438e-02]\n", - " [ 3.16087419e-01 -4.57000039e-02 -6.75145850e-03 -3.33506373e-03\n", - " -2.06629463e-02]\n", - " [ 1.92674918e+00 7.88706699e-01 4.17094266e-01 2.30498389e-01\n", - " 2.40935565e-01]\n", - " [ -1.87715688e-01 -3.62335147e-01 -2.51756701e-01 -1.73704523e-01\n", - " -1.44281105e-01]\n", - " [ 7.67671808e-01 2.62621809e-01 1.82086524e-01 1.09516237e-01\n", - " 1.09411657e-01]\n", - " [ 1.32756032e+00 5.86262668e-01 3.34829134e-01 1.89706629e-01\n", - " 1.99054981e-01]\n", - " [ 1.19639924e-01 -1.58376044e-01 -8.73754682e-02 -5.52563335e-02\n", - " -6.74647498e-02]\n", - " [ 3.25796145e-01 -4.18719758e-02 -4.04103510e-04 2.33542088e-03\n", - " -1.36647545e-02]]\n" + "[[ 1.48259547e+00 6.34273658e-01 3.51637246e-01 1.97321181e-01\n", + " 2.06638718e-01]\n", + " [ 1.20720686e+00 5.24873317e-01 3.04178207e-01 1.73015286e-01\n", + " 1.81075159e-01]\n", + " [ 2.23148004e-01 -8.10239454e-02 -2.72290631e-02 -1.44995997e-02\n", + " -2.40196514e-02]\n", + " [ 3.16087269e-01 -4.56999514e-02 -6.75139348e-03 -3.33504716e-03\n", + " -2.06629060e-02]\n", + " [ 1.92675148e+00 7.88706703e-01 4.17093961e-01 2.30498172e-01\n", + " 2.40935345e-01]\n", + " [ -1.87715089e-01 -3.62335529e-01 -2.51757218e-01 -1.73704598e-01\n", + " -1.44281324e-01]\n", + " [ 7.67671533e-01 2.62621700e-01 1.82086743e-01 1.09516467e-01\n", + " 1.09411940e-01]\n", + " [ 1.32756024e+00 5.86262576e-01 3.34829321e-01 1.89706780e-01\n", + " 1.99055131e-01]\n", + " [ 1.19639982e-01 -1.58376073e-01 -8.73753082e-02 -5.52564616e-02\n", + " -6.74650616e-02]\n", + " [ 3.25795909e-01 -4.18717282e-02 -4.04201270e-04 2.33524196e-03\n", + " -1.36649579e-02]]\n" ] } ], "source": [ "gp_colors = gp_spec.calc_colors(bandpass_dict, 10)\n", - "print gp_colors" + "print(gp_colors)" ] }, { @@ -469,57 +418,55 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ -5.29392211e-03, 3.97502760e-04, -5.97803844e-05,\n", - " 5.96910317e-07, 1.24569224e-09, -5.29031044e-18,\n", - " 8.35792815e-27, -1.54863411e-27, 4.43354059e-28,\n", - " 2.30928677e-63],\n", - " [ -4.03598243e-03, 3.87658390e-05, -1.04272225e-04,\n", - " -5.26424343e-08, -2.43779358e-12, 1.31558089e-17,\n", - " -5.13274147e-29, 5.61151679e-32, 2.62751572e-30,\n", - " 3.29888947e-65],\n", - " [ 4.39784968e-03, -1.25272081e-04, 5.18402542e-06,\n", - " 1.73387747e-06, -1.06190594e-13, 4.35922786e-23,\n", - " -2.17210606e-37, -9.39048947e-37, -2.25057184e-37,\n", - " 6.40151736e-73],\n", - " [ 3.64618000e-03, -5.37071792e-04, 2.38330581e-05,\n", - " 9.62796067e-07, 3.14543529e-11, -5.09440836e-19,\n", - " 5.71694290e-31, 4.06877332e-30, 8.49295963e-34,\n", - " -2.11298331e-67],\n", - " [ -6.79193645e-03, 1.07672793e-03, 6.55271114e-05,\n", - " 3.56056066e-07, -7.07914054e-11, 1.51207927e-19,\n", - " 4.91073749e-32, 1.69016214e-32, -3.55693314e-34,\n", - " 5.67232482e-68],\n", - " [ 8.98958600e-03, 1.90483599e-03, -4.24401312e-05,\n", - " -8.86750257e-06, -9.75072413e-10, -1.67226273e-15,\n", - " 1.54902996e-23, -1.60431247e-23, -4.53279478e-24,\n", - " -2.42855179e-58],\n", - " [ -8.48227699e-04, -7.00850840e-04, -4.30934033e-05,\n", - " -6.80800041e-07, -1.56286188e-11, -4.42770892e-21,\n", - " 3.61001856e-35, -2.29911520e-35, -3.73601497e-35,\n", - " 2.81711706e-71],\n", - " [ -4.71439361e-03, 2.86615443e-04, -5.48707779e-05,\n", - " 1.69009624e-07, 9.11059766e-11, 9.28905135e-21,\n", - " 1.89975046e-31, -3.50558448e-32, 1.00692902e-32,\n", - " 5.20057710e-68],\n", - " [ 5.61204111e-03, 1.97930012e-04, 3.58950160e-06,\n", - " 1.02629446e-05, -1.25800175e-10, 9.81633092e-18,\n", - " -1.67654462e-27, -7.29737080e-27, -1.71285359e-27,\n", - " 4.89895611e-63],\n", - " [ 3.55049033e-03, -5.71426370e-04, 4.65960163e-05,\n", - " -4.41035924e-07, 3.09208710e-11, 2.83273054e-20,\n", - " -9.20270336e-34, -1.08824707e-33, -6.24099295e-35,\n", - " -8.71720985e-70]])" + "array([[ -5.29392215e-003, 3.97502947e-004, -5.97781593e-005,\n", + " 5.96706672e-007, 1.24511164e-009, -5.31084407e-018,\n", + " 2.73158832e-044, -5.49073909e-066, 1.13984502e-077,\n", + " 7.09408740e-035],\n", + " [ -4.03598209e-003, 3.87650771e-005, -1.04268173e-004,\n", + " -5.26242933e-008, -2.43345797e-012, 1.32022480e-017,\n", + " -4.03094999e-048, 7.32808913e-074, 1.73176545e-084,\n", + " 4.79352253e-035],\n", + " [ 4.39785002e-003, -1.25271918e-004, 5.18427065e-006,\n", + " 1.73318955e-006, -1.06136653e-013, 4.38088631e-023,\n", + " -3.92054757e-062, -3.68202725e-091, -1.49349738e-107,\n", + " -1.10947387e-035],\n", + " [ 3.64618029e-003, -5.37070365e-004, 2.38328629e-005,\n", + " 9.62296439e-007, 3.14418639e-011, -5.11444263e-019,\n", + " 5.28709379e-051, 3.87384952e-074, -4.84215160e-088,\n", + " 5.37050541e-035],\n", + " [ -6.79193809e-003, 1.07672557e-003, 6.55241057e-005,\n", + " 3.55927310e-007, -7.07629719e-011, 1.51831129e-019,\n", + " 2.59380307e-053, 2.78449109e-079, -1.63859334e-094,\n", + " -4.63134552e-036],\n", + " [ 8.98958521e-003, 1.90482837e-003, -4.24383914e-005,\n", + " -8.86460804e-006, -9.74548806e-010, -1.67577985e-015,\n", + " 2.10224689e-037, -5.17758443e-054, -2.12933541e-063,\n", + " -1.73017950e-034],\n", + " [ -8.48227096e-004, -7.00850247e-004, -4.30913196e-005,\n", + " -6.80546016e-007, -1.56230362e-011, -4.44879773e-021,\n", + " 8.40882003e-059, -2.50721322e-087, -3.60800756e-102,\n", + " 5.81971540e-035],\n", + " [ -4.71439363e-003, 2.86615210e-004, -5.48685834e-005,\n", + " 1.68948124e-007, 9.10692955e-011, 9.32699925e-021,\n", + " 1.29724366e-052, -1.01580300e-078, 9.60769758e-093,\n", + " 4.98645390e-035],\n", + " [ 5.61204119e-003, 1.97930435e-004, 3.58971157e-006,\n", + " 1.02594316e-005, -1.25739590e-010, 9.84513591e-018,\n", + " -2.01215393e-044, -4.55548655e-064, -1.77260145e-075,\n", + " -1.11069026e-034],\n", + " [ 3.55049048e-003, -5.71423682e-004, 4.65939729e-005,\n", + " -4.40927913e-007, 3.09092821e-011, 2.84535233e-020,\n", + " -2.79983690e-056, -3.45519241e-083, -8.25992654e-099,\n", + " 4.40119727e-035]])" ] }, - "execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -530,30 +477,28 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.48352090e-05, 4.52598350e-05, 4.71502913e-05, ...,\n", - " 1.21002058e-04, 1.22372724e-04, 1.18107400e-04],\n", - " [ 8.45857994e-05, 8.49526678e-05, 8.59582976e-05, ...,\n", - " 1.04685496e-04, 1.05444673e-04, 1.01982254e-04],\n", - " [ 4.87095267e-04, 4.79709153e-04, 4.71544985e-04, ...,\n", - " 3.07910763e-05, 3.02259189e-05, 2.96785954e-05],\n", + "array([[ 4.48352213e-05, 4.52598543e-05, 4.71503073e-05, ...,\n", + " 1.21002111e-04, 1.22372783e-04, 1.18107455e-04],\n", + " [ 8.45857546e-05, 8.49526397e-05, 8.59582642e-05, ...,\n", + " 1.04685580e-04, 1.05444766e-04, 1.01982340e-04],\n", + " [ 4.87095327e-04, 4.79709213e-04, 4.71545044e-04, ...,\n", + " 3.07910768e-05, 3.02259190e-05, 2.96785957e-05],\n", " ..., \n", - " [ 6.64109050e-05, 6.66839038e-05, 6.80662174e-05, ...,\n", - " 1.14709706e-04, 1.15902935e-04, 1.11920220e-04],\n", - " [ 5.65995756e-04, 5.56072606e-04, 5.46200066e-04, ...,\n", - " 2.37984266e-05, 2.32903612e-05, 2.29166694e-05],\n", - " [ 4.18165055e-04, 4.13550829e-04, 4.06782713e-04, ...,\n", - " 3.40956785e-05, 3.34186501e-05, 3.28448311e-05]])" + " [ 6.64108858e-05, 6.66838931e-05, 6.80662039e-05, ...,\n", + " 1.14709755e-04, 1.15902988e-04, 1.11920270e-04],\n", + " [ 5.65995958e-04, 5.56072808e-04, 5.46200268e-04, ...,\n", + " 2.37984143e-05, 2.32903439e-05, 2.29166552e-05],\n", + " [ 4.18165229e-04, 4.13550985e-04, 4.06782868e-04, ...,\n", + " 3.40956608e-05, 3.34186298e-05, 3.28448122e-05]])" ] }, - "execution_count": 19, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -571,27 +516,25 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 6.46474508e-05, 1.00654859e+02],\n", - " [ 2.46361195e-06, 1.58661503e+00],\n", - " [ 2.03176438e-07, 3.22550082e-02],\n", - " [ 2.47913142e-08, 8.46513490e-03],\n", - " [ 7.90147130e-09, 8.84631510e-04],\n", - " [ 3.75381235e-09, 1.63991239e-04],\n", - " [ 2.43902383e-09, 4.73493594e-05],\n", - " [ 8.03498342e-10, 4.73493514e-05],\n", - " [ 1.88471192e-10, 4.73493506e-05],\n", - " [ 9.79633677e-48, 4.73493506e-05]])" + "array([[ 1.29296503e-05, 1.00658054e+02],\n", + " [ 4.92717212e-07, 1.58662586e+00],\n", + " [ 4.06353554e-08, 3.22545915e-02],\n", + " [ 4.95798649e-09, 8.46472158e-03],\n", + " [ 1.58004090e-09, 8.84679628e-04],\n", + " [ 7.50512800e-10, 1.64048257e-04],\n", + " [ 4.87554603e-10, 1.47458472e-05],\n", + " [ 1.60449696e-10, 6.30120257e-06],\n", + " [ 3.74442361e-11, 4.49575522e-06],\n", + " [ 4.26342055e-29, 4.60469283e-02]])" ] }, - "execution_count": 20, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -613,21 +556,21 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.0" } }, "nbformat": 4, From 8ee948281a6e3a3838e9f08f1bbaa48e074f7b43 Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Fri, 27 Oct 2017 15:11:42 -0700 Subject: [PATCH 2/7] Updated example notebook and added sorted to spectra loading for consistency. --- esp/spec_utils.py | 2 +- examples/esp_example.ipynb | 321 +++++++++++++++---------------------- 2 files changed, 133 insertions(+), 190 deletions(-) diff --git a/esp/spec_utils.py b/esp/spec_utils.py index ec708d4..f60f5f2 100644 --- a/esp/spec_utils.py +++ b/esp/spec_utils.py @@ -28,7 +28,7 @@ def load_spectra(self, directory): for root, dirs, files in os.walk(directory): file_total = len(files) file_on = 1 - for name in files: + for name in sorted(files): if file_on % 100 == 0: print(str("File On " + str(file_on) + " out of " + str(file_total))) diff --git a/examples/esp_example.ipynb b/examples/esp_example.ipynb index f1456b9..dfc64c8 100644 --- a/examples/esp_example.ipynb +++ b/examples/esp_example.ipynb @@ -32,9 +32,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import esp\n", @@ -56,9 +54,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -87,15 +83,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "image/png": 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mX0AZr3FtPEXRgn8ej/Yn3Lp5oURF68G+qKhKB7lMATNf0NKYhWjoQCzfeh6y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\nQLfjgscyszFX/R7n6luC++ITMP2GetuODxMRiWnWApJTvX3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JtHdfehJfhWCf+8YLYY/t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KW7d+W1pz4jhdUu++KMNJn3Zsgs/Ezamyxt3SDrthE+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dlcjBRdbwb5qWbTEXX+t/XjiPn532RZmHJSIiIiIiIiJyNESE+oKXXHIJnTt3Zvz48bz99tvE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\nIvkAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -119,9 +113,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "os.mkdir('results')\n", @@ -149,9 +141,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "new_pca_obj = esp.pcaSED()\n", @@ -172,9 +162,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "bandpass_dir = '../data/lsst_bandpasses/'\n", @@ -193,10 +181,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -210,7 +196,7 @@ ], "source": [ "colors = new_pca_obj.calc_colors(bandpass_dict, 10)\n", - "print colors[0:3]" + "print(colors[0:3])" ] }, { @@ -222,10 +208,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", @@ -245,10 +229,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, sample_cat_colors)" @@ -256,10 +238,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "nn_spec = nn_obj.nn_predict(1)" @@ -267,10 +247,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -291,7 +269,7 @@ ], "source": [ "nn_colors = nn_spec.calc_colors(bandpass_dict, 10)\n", - "print nn_colors" + "print(nn_colors)" ] }, { @@ -303,10 +281,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "nn_spec = nn_obj.nn_predict(5, knr_args=dict(weights='distance'))" @@ -314,10 +290,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -338,7 +312,7 @@ ], "source": [ "nn_colors = nn_spec.calc_colors(bandpass_dict, 10)\n", - "print nn_colors" + "print(nn_colors)" ] }, { @@ -352,10 +326,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, sample_cat_colors)" @@ -370,13 +342,11 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ - "gp_kernel = gp_obj.define_kernel('exp', 1.0e-3, 1.0e-3)" + "gp_kernel = gp_obj.define_kernel('exp', 1.0e-3, 1.0e-3, n_dim=len(sample_cat_colors[0]))" ] }, { @@ -388,30 +358,11 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n", - "Optimization terminated successfully.\n" - ] - } - ], + "execution_count": 20, + "metadata": {}, + "outputs": [], "source": [ - "gp_spec = gp_obj.gp_predict(gp_kernel)" + "gp_spec = gp_obj.gp_predict(gp_kernel, opt_bandpass_dict=bandpass_dict)" ] }, { @@ -423,41 +374,39 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 1.48259565e+00 6.34273717e-01 3.51637041e-01 1.97321028e-01\n", - " 2.06638566e-01]\n", - " [ 1.20720703e+00 5.24873542e-01 3.04177878e-01 1.73014997e-01\n", - " 1.81074863e-01]\n", - " [ 2.23148027e-01 -8.10239054e-02 -2.72291043e-02 -1.44996039e-02\n", - " -2.40196438e-02]\n", - " [ 3.16087419e-01 -4.57000039e-02 -6.75145850e-03 -3.33506373e-03\n", - " -2.06629463e-02]\n", - " [ 1.92674918e+00 7.88706699e-01 4.17094266e-01 2.30498389e-01\n", - " 2.40935565e-01]\n", - " [ -1.87715688e-01 -3.62335147e-01 -2.51756701e-01 -1.73704523e-01\n", - " -1.44281105e-01]\n", - " [ 7.67671808e-01 2.62621809e-01 1.82086524e-01 1.09516237e-01\n", - " 1.09411657e-01]\n", - " [ 1.32756032e+00 5.86262668e-01 3.34829134e-01 1.89706629e-01\n", - " 1.99054981e-01]\n", - " [ 1.19639924e-01 -1.58376044e-01 -8.73754682e-02 -5.52563335e-02\n", - " -6.74647498e-02]\n", - " [ 3.25796145e-01 -4.18719758e-02 -4.04103510e-04 2.33542088e-03\n", - " -1.36647545e-02]]\n" + "[[ 1.48259547e+00 6.34273658e-01 3.51637246e-01 1.97321181e-01\n", + " 2.06638718e-01]\n", + " [ 1.20720686e+00 5.24873317e-01 3.04178207e-01 1.73015286e-01\n", + " 1.81075159e-01]\n", + " [ 2.23148004e-01 -8.10239454e-02 -2.72290631e-02 -1.44995997e-02\n", + " -2.40196514e-02]\n", + " [ 3.16087269e-01 -4.56999514e-02 -6.75139348e-03 -3.33504716e-03\n", + " -2.06629060e-02]\n", + " [ 1.92675148e+00 7.88706703e-01 4.17093961e-01 2.30498172e-01\n", + " 2.40935345e-01]\n", + " [ -1.87715089e-01 -3.62335529e-01 -2.51757218e-01 -1.73704598e-01\n", + " -1.44281324e-01]\n", + " [ 7.67671533e-01 2.62621700e-01 1.82086743e-01 1.09516467e-01\n", + " 1.09411940e-01]\n", + " [ 1.32756024e+00 5.86262576e-01 3.34829321e-01 1.89706780e-01\n", + " 1.99055131e-01]\n", + " [ 1.19639982e-01 -1.58376073e-01 -8.73753082e-02 -5.52564616e-02\n", + " -6.74650616e-02]\n", + " [ 3.25795909e-01 -4.18717282e-02 -4.04201270e-04 2.33524196e-03\n", + " -1.36649579e-02]]\n" ] } ], "source": [ "gp_colors = gp_spec.calc_colors(bandpass_dict, 10)\n", - "print gp_colors" + "print(gp_colors)" ] }, { @@ -469,57 +418,55 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ -5.29392211e-03, 3.97502760e-04, -5.97803844e-05,\n", - " 5.96910317e-07, 1.24569224e-09, -5.29031044e-18,\n", - " 8.35792815e-27, -1.54863411e-27, 4.43354059e-28,\n", - " 2.30928677e-63],\n", - " [ -4.03598243e-03, 3.87658390e-05, -1.04272225e-04,\n", - " -5.26424343e-08, -2.43779358e-12, 1.31558089e-17,\n", - " -5.13274147e-29, 5.61151679e-32, 2.62751572e-30,\n", - " 3.29888947e-65],\n", - " [ 4.39784968e-03, -1.25272081e-04, 5.18402542e-06,\n", - " 1.73387747e-06, -1.06190594e-13, 4.35922786e-23,\n", - " -2.17210606e-37, -9.39048947e-37, -2.25057184e-37,\n", - " 6.40151736e-73],\n", - " [ 3.64618000e-03, -5.37071792e-04, 2.38330581e-05,\n", - " 9.62796067e-07, 3.14543529e-11, -5.09440836e-19,\n", - " 5.71694290e-31, 4.06877332e-30, 8.49295963e-34,\n", - " -2.11298331e-67],\n", - " [ -6.79193645e-03, 1.07672793e-03, 6.55271114e-05,\n", - " 3.56056066e-07, -7.07914054e-11, 1.51207927e-19,\n", - " 4.91073749e-32, 1.69016214e-32, -3.55693314e-34,\n", - " 5.67232482e-68],\n", - " [ 8.98958600e-03, 1.90483599e-03, -4.24401312e-05,\n", - " -8.86750257e-06, -9.75072413e-10, -1.67226273e-15,\n", - " 1.54902996e-23, -1.60431247e-23, -4.53279478e-24,\n", - " -2.42855179e-58],\n", - " [ -8.48227699e-04, -7.00850840e-04, -4.30934033e-05,\n", - " -6.80800041e-07, -1.56286188e-11, -4.42770892e-21,\n", - " 3.61001856e-35, -2.29911520e-35, -3.73601497e-35,\n", - " 2.81711706e-71],\n", - " [ -4.71439361e-03, 2.86615443e-04, -5.48707779e-05,\n", - " 1.69009624e-07, 9.11059766e-11, 9.28905135e-21,\n", - " 1.89975046e-31, -3.50558448e-32, 1.00692902e-32,\n", - " 5.20057710e-68],\n", - " [ 5.61204111e-03, 1.97930012e-04, 3.58950160e-06,\n", - " 1.02629446e-05, -1.25800175e-10, 9.81633092e-18,\n", - " -1.67654462e-27, -7.29737080e-27, -1.71285359e-27,\n", - " 4.89895611e-63],\n", - " [ 3.55049033e-03, -5.71426370e-04, 4.65960163e-05,\n", - " -4.41035924e-07, 3.09208710e-11, 2.83273054e-20,\n", - " -9.20270336e-34, -1.08824707e-33, -6.24099295e-35,\n", - " -8.71720985e-70]])" + "array([[ -5.29392215e-003, 3.97502947e-004, -5.97781593e-005,\n", + " 5.96706672e-007, 1.24511164e-009, -5.31084407e-018,\n", + " 2.73158832e-044, -5.49073909e-066, 1.13984502e-077,\n", + " 7.09408740e-035],\n", + " [ -4.03598209e-003, 3.87650771e-005, -1.04268173e-004,\n", + " -5.26242933e-008, -2.43345797e-012, 1.32022480e-017,\n", + " -4.03094999e-048, 7.32808913e-074, 1.73176545e-084,\n", + " 4.79352253e-035],\n", + " [ 4.39785002e-003, -1.25271918e-004, 5.18427065e-006,\n", + " 1.73318955e-006, -1.06136653e-013, 4.38088631e-023,\n", + " -3.92054757e-062, -3.68202725e-091, -1.49349738e-107,\n", + " -1.10947387e-035],\n", + " [ 3.64618029e-003, -5.37070365e-004, 2.38328629e-005,\n", + " 9.62296439e-007, 3.14418639e-011, -5.11444263e-019,\n", + " 5.28709379e-051, 3.87384952e-074, -4.84215160e-088,\n", + " 5.37050541e-035],\n", + " [ -6.79193809e-003, 1.07672557e-003, 6.55241057e-005,\n", + " 3.55927310e-007, -7.07629719e-011, 1.51831129e-019,\n", + " 2.59380307e-053, 2.78449109e-079, -1.63859334e-094,\n", + " -4.63134552e-036],\n", + " [ 8.98958521e-003, 1.90482837e-003, -4.24383914e-005,\n", + " -8.86460804e-006, -9.74548806e-010, -1.67577985e-015,\n", + " 2.10224689e-037, -5.17758443e-054, -2.12933541e-063,\n", + " -1.73017950e-034],\n", + " [ -8.48227096e-004, -7.00850247e-004, -4.30913196e-005,\n", + " -6.80546016e-007, -1.56230362e-011, -4.44879773e-021,\n", + " 8.40882003e-059, -2.50721322e-087, -3.60800756e-102,\n", + " 5.81971540e-035],\n", + " [ -4.71439363e-003, 2.86615210e-004, -5.48685834e-005,\n", + " 1.68948124e-007, 9.10692955e-011, 9.32699925e-021,\n", + " 1.29724366e-052, -1.01580300e-078, 9.60769758e-093,\n", + " 4.98645390e-035],\n", + " [ 5.61204119e-003, 1.97930435e-004, 3.58971157e-006,\n", + " 1.02594316e-005, -1.25739590e-010, 9.84513591e-018,\n", + " -2.01215393e-044, -4.55548655e-064, -1.77260145e-075,\n", + " -1.11069026e-034],\n", + " [ 3.55049048e-003, -5.71423682e-004, 4.65939729e-005,\n", + " -4.40927913e-007, 3.09092821e-011, 2.84535233e-020,\n", + " -2.79983690e-056, -3.45519241e-083, -8.25992654e-099,\n", + " 4.40119727e-035]])" ] }, - "execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -530,30 +477,28 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 4.48352090e-05, 4.52598350e-05, 4.71502913e-05, ...,\n", - " 1.21002058e-04, 1.22372724e-04, 1.18107400e-04],\n", - " [ 8.45857994e-05, 8.49526678e-05, 8.59582976e-05, ...,\n", - " 1.04685496e-04, 1.05444673e-04, 1.01982254e-04],\n", - " [ 4.87095267e-04, 4.79709153e-04, 4.71544985e-04, ...,\n", - " 3.07910763e-05, 3.02259189e-05, 2.96785954e-05],\n", + "array([[ 4.48352213e-05, 4.52598543e-05, 4.71503073e-05, ...,\n", + " 1.21002111e-04, 1.22372783e-04, 1.18107455e-04],\n", + " [ 8.45857546e-05, 8.49526397e-05, 8.59582642e-05, ...,\n", + " 1.04685580e-04, 1.05444766e-04, 1.01982340e-04],\n", + " [ 4.87095327e-04, 4.79709213e-04, 4.71545044e-04, ...,\n", + " 3.07910768e-05, 3.02259190e-05, 2.96785957e-05],\n", " ..., \n", - " [ 6.64109050e-05, 6.66839038e-05, 6.80662174e-05, ...,\n", - " 1.14709706e-04, 1.15902935e-04, 1.11920220e-04],\n", - " [ 5.65995756e-04, 5.56072606e-04, 5.46200066e-04, ...,\n", - " 2.37984266e-05, 2.32903612e-05, 2.29166694e-05],\n", - " [ 4.18165055e-04, 4.13550829e-04, 4.06782713e-04, ...,\n", - " 3.40956785e-05, 3.34186501e-05, 3.28448311e-05]])" + " [ 6.64108858e-05, 6.66838931e-05, 6.80662039e-05, ...,\n", + " 1.14709755e-04, 1.15902988e-04, 1.11920270e-04],\n", + " [ 5.65995958e-04, 5.56072808e-04, 5.46200268e-04, ...,\n", + " 2.37984143e-05, 2.32903439e-05, 2.29166552e-05],\n", + " [ 4.18165229e-04, 4.13550985e-04, 4.06782868e-04, ...,\n", + " 3.40956608e-05, 3.34186298e-05, 3.28448122e-05]])" ] }, - "execution_count": 19, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -571,27 +516,25 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 6.46474508e-05, 1.00654859e+02],\n", - " [ 2.46361195e-06, 1.58661503e+00],\n", - " [ 2.03176438e-07, 3.22550082e-02],\n", - " [ 2.47913142e-08, 8.46513490e-03],\n", - " [ 7.90147130e-09, 8.84631510e-04],\n", - " [ 3.75381235e-09, 1.63991239e-04],\n", - " [ 2.43902383e-09, 4.73493594e-05],\n", - " [ 8.03498342e-10, 4.73493514e-05],\n", - " [ 1.88471192e-10, 4.73493506e-05],\n", - " [ 9.79633677e-48, 4.73493506e-05]])" + "array([[ 1.29296503e-05, 1.00658054e+02],\n", + " [ 4.92717212e-07, 1.58662586e+00],\n", + " [ 4.06353554e-08, 3.22545915e-02],\n", + " [ 4.95798649e-09, 8.46472158e-03],\n", + " [ 1.58004090e-09, 8.84679628e-04],\n", + " [ 7.50512800e-10, 1.64048257e-04],\n", + " [ 4.87554603e-10, 1.47458472e-05],\n", + " [ 1.60449696e-10, 6.30120257e-06],\n", + " [ 3.74442361e-11, 4.49575522e-06],\n", + " [ 4.26342055e-29, 4.60469283e-02]])" ] }, - "execution_count": 20, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -613,21 +556,21 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.0" } }, "nbformat": 4, From ce62ff6a08c69208346b54d1691f0689297538d9 Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Sat, 28 Oct 2017 11:13:02 -0700 Subject: [PATCH 3/7] Added paper notebook. --- examples/esp_paper_plots.ipynb | 2697 ++++++++++++++++++++++++++++++++ 1 file changed, 2697 insertions(+) create mode 100644 examples/esp_paper_plots.ipynb diff --git a/examples/esp_paper_plots.ipynb b/examples/esp_paper_plots.ipynb new file mode 100644 index 0000000..cd361ed --- /dev/null +++ b/examples/esp_paper_plots.ipynb @@ -0,0 +1,2697 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ESP Paper\n", + "\n", + "Here is all the code that went into the paper and everything required to create the plots as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import esp\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import shutil\n", + "import pandas as pd\n", + "from esp.lsst_utils import Bandpass\n", + "from esp.lsst_utils import BandpassDict\n", + "from esp.lsst_utils import Sed\n", + "from sklearn.neighbors import NearestNeighbors\n", + "from scipy.stats import binned_statistic, trim_mean\n", + "from sklearn.decomposition import PCA as sklPCA\n", + "from esp.spec_utils import specUtils\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load `sims_sed_library` and remove duplicate SEDs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "home_dir = os.getenv('HOME')\n", + "galaxy_dir = '%s/lsst/DarwinX86/sims_sed_library/2016.01.26/galaxySED/' % home_dir\n", + "pca_obj = esp.pcaSED()\n", + "pca_obj.load_full_spectra(galaxy_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "new_spec_list = []\n", + "i = 0\n", + "for spec_obj in pca_obj.spec_list_orig:\n", + " if i % 100 == 0:\n", + " print('On Spectrum %i out of %i' % (i, len(pca_obj.spec_list_orig)))\n", + " j = 0\n", + " keep = True\n", + " for new_spec_obj in new_spec_list:\n", + " spec_flux = new_spec_obj.flambda\n", + " if np.array_equal(np.array(spec_obj.flambda), spec_flux):\n", + " #print(i,j) ## Uncomment to see which SEDs match to one another\n", + " keep = False\n", + " elif len(np.where(np.isclose(np.array(spec_obj.flambda), \n", + " spec_flux, atol=0., rtol=1e-5) == True)[0]) >= (0.9*6900):\n", + " #print(i,j) ## Uncomment to see which SEDs match to one another\n", + " keep = False\n", + " else:\n", + " j+=1\n", + " \n", + " if keep is True:\n", + " new_spec_list.append(spec_obj)\n", + " i += 1\n", + "print(len(new_spec_list))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the resolution of the spectra and what do they look like?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resolution = new_spec_list[0].wavelen[1:] - new_spec_list[0].wavelen[:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(new_spec_list[190].name)\n", + "print(new_spec_list[240].name)\n", + "print(new_spec_list[360].name)\n", + "print(new_spec_list[686].name)\n", + "print(np.where(new_spec_list[0].wavelen <= 2400.))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mpl.rc('xtick', labelsize=22) \n", + "mpl.rc('ytick', labelsize=22) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(14,12))\n", + "\n", + "fig.text(0.55, -0.02, 'Wavelength (nm)', ha='center', size=32)\n", + "\n", + "ax[0].plot(new_spec_list[0].wavelen[:6561], \n", + " (new_spec_list[190].flambda/np.max(new_spec_list[190].flambda))[:6561], lw=4,\n", + " label='Burst SF, 5.0E9 Years, Z=Z_sun')\n", + "ax[0].plot(new_spec_list[0].wavelen[:6561], \n", + " (new_spec_list[240].flambda/np.max(new_spec_list[240].flambda))[:6561], lw=4,\n", + " label='Constant SF, 1.0E9 Years, Z=0.005*Z_sun')\n", + "ax[0].plot(new_spec_list[0].wavelen[:6561], \n", + " (new_spec_list[360].flambda/np.max(new_spec_list[360].flambda))[:6561], lw=4,\n", + " label='Exponential SF, 1.5E6 Years, Z=0.02*Z_sun')\n", + "ax[0].plot(new_spec_list[0].wavelen[:6561], \n", + " (new_spec_list[686].flambda/np.max(new_spec_list[686].flambda))[:6561], lw=4,\n", + " label='Instantaneous SF, 3.2E8 Years, Z=2.5*Z_sun')\n", + "ax[0].set_ylabel('Scaled Flux', size=24)\n", + "ax[0].set_title('Example Spectra', size=24)\n", + "ax[0].legend(fontsize=18)\n", + "\n", + "\n", + "\n", + "ax[1].plot(new_spec_list[0].wavelen[:6561], resolution[:6561], label='Resolution', lw=6)\n", + "ax[1].set_title('Resolution', size=24)\n", + "ax[1].set_ylabel('Resolution (nm)', size=24)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_0.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Bandpasses and Create Artificial Bandpasses beyond the ugrizy of LSST" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bandpass_dir = '%s/lsst/DarwinX86/throughputs/2016.12.13/baseline/' % home_dir\n", + "filters = ['u', 'g', 'r', 'i', 'z', 'y']\n", + "bandpass_dict = BandpassDict.loadTotalBandpassesFromFiles(bandpassNames = filters,\n", + " bandpassDir = bandpass_dir,\n", + " bandpassRoot = 'total_')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "blue_sb_1 = np.zeros(13000)\n", + "blue_sb_1[0:500] += 1.\n", + "#blue_sb_1[:100] += 1.\n", + "blue_sb_2 = np.zeros(13000)\n", + "blue_sb_2[500:1000] += 1.\n", + "#blue_sb_2[100:200] += 1.\n", + "\n", + "blue_sb_3 = np.zeros(13000)\n", + "blue_sb_3[1000:1500] += 1.\n", + "blue_sb_4 = np.zeros(13000)\n", + "blue_sb_4[1500:2000] += 1.\n", + "\n", + "red_sb_1 = np.zeros(13000)\n", + "red_sb_1[11000:11500] += 1.\n", + "#red_sb_1[1100:1200] += 1.\n", + "red_sb_2 = np.zeros(13000)\n", + "red_sb_2[11500:12000] += 1.\n", + "#red_sb_2[1200:] += 1.\n", + "\n", + "red_sb_3 = np.zeros(13000)\n", + "red_sb_3[12000:12500] += 1.\n", + "red_sb_4 = np.zeros(13000)\n", + "red_sb_4[12500:13000] += 1.\n", + "\n", + "blue_bandpass_1 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=blue_sb_1)\n", + "blue_bandpass_2 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=blue_sb_2)\n", + "blue_bandpass_3 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=blue_sb_3)\n", + "blue_bandpass_4 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=blue_sb_4)\n", + "red_bandpass_1 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=red_sb_1)\n", + "red_bandpass_2 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=red_sb_2)\n", + "red_bandpass_3 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=red_sb_3)\n", + "red_bandpass_4 = Bandpass(wavelen=np.arange(100, 1400, .1), sb=red_sb_4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_bandpass_dict = BandpassDict([\n", + " blue_bandpass_1, blue_bandpass_3, \n", + " #blue_bandpass_3, blue_bandpass_4,\n", + " bandpass_dict['u'], bandpass_dict['g'], \n", + " bandpass_dict['r'], bandpass_dict['i'], bandpass_dict['z'], \n", + " bandpass_dict['y'], #red_bandpass_1, red_bandpass_2, \n", + " red_bandpass_2, red_bandpass_4\n", + " ],\n", + " ['blue_1', #'blue_2', \n", + " 'blue_3', #'blue_4', \n", + " 'u', 'g', 'r', 'i', 'z', 'y', \n", + " 'red_2',#'red_2', \n", + " 'red_4', #'red_4'\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pca_obj.spec_list_orig = new_spec_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define functions for later" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def choose_training_and_test_set(pca_obj, bandpass_dict, min_wavelen, max_wavelen, \n", + " rand_print=False):\n", + " rand_choice = np.random.choice(np.arange(len(pca_obj.spec_list_orig)), size=60, replace=False)\n", + " if rand_print is True:\n", + " print(rand_choice)\n", + " sed_list = []\n", + " names = []\n", + " for row in rand_choice:\n", + " #print row\n", + " sed_list.append(pca_obj.spec_list_orig[row])\n", + " names.append(sed_list[-1].name)\n", + " training_list = sed_list[:10]\n", + " training_names = names[:10]\n", + " test_list = sed_list[10:]\n", + " test_names = names[10:]\n", + " \n", + " training_colors = []\n", + " test_colors = []\n", + " \n", + " for train_sed in training_list:\n", + " \n", + " train_mags = bandpass_dict.magListForSed(train_sed)\n", + " \n", + " colors = [train_mags[x] - train_mags[x+1] for x in range(len(train_mags)-1)]\n", + " training_colors.append(colors) \n", + " \n", + " \n", + " min_idx = np.where(test_list[0].wavelen < min_wavelen)[0][-1] + 1\n", + " max_idx = np.where(test_list[0].wavelen > max_wavelen)[0][0]\n", + " test_fluxes = []\n", + " \n", + " for test_sed in test_list:\n", + " \n", + " test_fluxes.append(pca_obj.scale_spectrum(test_sed.flambda[min_idx:max_idx]))\n", + " \n", + " test_mags = bandpass_dict.magListForSed(test_sed)\n", + " \n", + " colors = [test_mags[x] - test_mags[x+1] for x in range(len(test_mags)-1)]\n", + " test_colors.append(colors)\n", + " test_colors = np.array(test_colors)\n", + " test_fluxes = np.array(test_fluxes)\n", + " \n", + " return training_colors, training_list, training_names, test_list, test_names, test_fluxes, test_colors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "def linear_interpolation_spectra(training_colors, test_colors, training_spectra, \n", + " bandpass_dict, min_wavelen, max_wavelen):\n", + " lin_reg = LinearRegression()\n", + " su = specUtils()\n", + " \n", + " training_fluxes = []\n", + " for train_spec in training_spectra:\n", + " training_fluxes.append(train_spec.flambda)\n", + " \n", + " min_idx = np.where(training_spectra[0].wavelen < min_wavelen)[0][-1] + 1\n", + " max_idx = np.where(training_spectra[0].wavelen > max_wavelen)[0][0]\n", + " \n", + " lin_reg.fit(training_colors, training_fluxes)\n", + " test_list = lin_reg.predict(test_colors)\n", + " test_fluxes = []\n", + " test_colors = []\n", + " for test_flux in test_list:\n", + " \n", + " test_sed = Sed()\n", + " for test_flux_bin in range(len(test_flux)):\n", + " if test_flux[test_flux_bin] < 0.:\n", + " test_flux[test_flux_bin] = 0.\n", + " \n", + " test_sed.setSED(wavelen=training_spectra[0].wavelen[min_idx:max_idx], flambda=test_flux[min_idx:max_idx])\n", + " \n", + " test_fluxes.append(su.scale_spectrum(test_sed.flambda))\n", + " \n", + " test_mags = bandpass_dict.magListForSed(test_sed)\n", + " \n", + " colors = [test_mags[x] - test_mags[x+1] for x in range(len(test_mags)-1)]\n", + " test_colors.append(colors)\n", + " \n", + " return test_fluxes, test_colors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_dist_bins(distances, results, n_bins):\n", + " #bin_vals = [(float(i)/n_bins)*np.max(distances) for i in range(n_bins)]\n", + " bin_vals = [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, \n", + " 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6]\n", + " bin_vals.append(float(np.max(distances)))\n", + " idx_sort = distances.argsort()\n", + " dist_sort = distances[idx_sort]\n", + " #z_true_sort = z_true[idx_sort]\n", + " results_sort = results[idx_sort]\n", + " idx_bins = dist_sort.searchsorted(bin_vals)\n", + " #print idx_bins\n", + " results_binned = [results_sort[idx_bins[i]:idx_bins[i+1]] for i in range(len(bin_vals)-1)]\n", + " return bin_vals, results_binned" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def mean_dist_results(results_binned):\n", + " mean_results = [np.mean(x) for x in results_binned]\n", + " return np.array(mean_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def median_dist_results(results_binned):\n", + " median_results = [np.median(x) for x in results_binned]\n", + " return np.array(median_results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test 1: Optical Wavelengths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(2314)\n", + "\n", + "li_flux_results = np.zeros((25000, 6071))\n", + "nn_flux_u_results = np.zeros((25000, 6071))\n", + "#nn_flux_2u_results = np.zeros((25000, 6071))\n", + "nn_flux_2d_results = np.zeros((25000, 6071))\n", + "#nn_flux_4u_results = np.zeros((25000, 6071))\n", + "#nn_flux_4d_results = np.zeros((25000, 6071))\n", + "gp_exp_flux_results = np.zeros((25000, 6071))\n", + "gp_sq_exp_flux_results = np.zeros((25000, 6071))\n", + "gp_matern_32_flux_results = np.zeros((25000, 6071))\n", + "gp_matern_52_flux_results = np.zeros((25000, 6071))\n", + "\n", + "training_colors_full = []\n", + "training_coeffs_full = []\n", + "training_eigenspectra = []\n", + "training_meanspec = []\n", + "\n", + "test_colors_full = []\n", + "\n", + "test_exp_params = []\n", + "test_sq_exp_params = []\n", + "test_matern_32_params = []\n", + "test_matern_52_params = []\n", + "\n", + "distances_all = []\n", + "\n", + "test_flux_orig = []\n", + "flux_errors_full = []\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 299.\n", + "max_wavelen = 1200.\n", + "n_colors = 5\n", + "n_comps = 9\n", + "\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + "\n", + " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", + " distance, idx = nbrs.kneighbors(test_colors)\n", + " distances_all.append(np.ravel(distance))\n", + " \n", + " #print 'Linear Interp'\n", + " li_spec, li_colors = linear_interpolation_spectra(colors, test_colors, new_pca_obj.spec_list_orig, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " li_flux_results[i*50:(i+1)*50] = np.abs(np.array((li_spec - test_fluxes)/test_fluxes))\n", + " \n", + " #print 'Nearest Neighbor Results'\n", + " nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " nn_spec = nn_obj.nn_predict(1)\n", + " nn_flux_u_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " #nn_spec = nn_obj.nn_predict(2)\n", + " #nn_flux_2u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", + " nn_spec = nn_obj.nn_predict(2, knr_args=dict(weights='distance'))\n", + " nn_flux_2d_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " #nn_spec = nn_obj.nn_predict(4)\n", + " #nn_flux_4u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", + " #nn_spec = nn_obj.nn_predict(4, knr_args=dict(weights='distance'))\n", + " \n", + " #print 'Gaussian Process Results'\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", + " test_exp_params.append(gp_spec.params)\n", + " \n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", + " test_sq_exp_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_32_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_52_params.append(gp_spec.params)\n", + " \n", + " training_colors_full.append(colors)\n", + " test_colors_full.append(test_colors)\n", + "\n", + "\n", + "test_colors_full = np.array(test_colors_full)\n", + "\n", + "training_colors_full = np.array(training_colors_full)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating overall means\")\n", + "\n", + "gp_exp_flux_mean = np.mean(gp_exp_flux_results)\n", + "nn_flux_u_mean = np.mean(nn_flux_u_results)\n", + "gp_sq_exp_flux_mean = np.mean(gp_sq_exp_flux_results)\n", + "nn_flux_2d_mean = np.mean(nn_flux_2d_results)\n", + "#nn_flux_2u_mean = np.mean(nn_flux_2u_results)\n", + "#nn_flux_4d_mean = np.mean(nn_flux_4d_results)\n", + "#nn_flux_4u_mean = np.mean(nn_flux_4u_results)\n", + "li_flux_mean = np.mean(li_flux_results)\n", + "gp_matern_32_flux_mean = np.mean(gp_matern_32_flux_results)\n", + "gp_matern_52_flux_mean = np.mean(gp_matern_52_flux_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_spec = np.mean(gp_exp_flux_results, axis=0)\n", + "gp_sq_exp_flux_mean_spec = np.mean(gp_sq_exp_flux_results, axis=0)\n", + "nn_flux_u_mean_spec = np.mean(nn_flux_u_results, axis=0)\n", + "nn_flux_2d_mean_spec = np.mean(nn_flux_2d_results, axis=0)\n", + "#nn_flux_2u_mean_spec = np.mean(nn_flux_2u_results, axis=0)\n", + "#nn_flux_4d_mean_spec = np.mean(nn_flux_4d_results, axis=0)\n", + "#nn_flux_4u_mean_spec = np.mean(nn_flux_4u_results, axis=0)\n", + "li_flux_mean_spec = np.mean(li_flux_results, axis=0)\n", + "gp_matern_32_flux_mean_spec = np.mean(gp_matern_32_flux_results, axis=0)\n", + "gp_matern_52_flux_mean_spec = np.mean(gp_matern_52_flux_results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_dist = np.mean(gp_exp_flux_results, axis=1)\n", + "gp_sq_exp_flux_mean_dist = np.mean(gp_sq_exp_flux_results, axis=1)\n", + "gp_matern_32_flux_mean_dist = np.mean(gp_matern_32_flux_results, axis=1)\n", + "gp_matern_52_flux_mean_dist = np.mean(gp_matern_52_flux_results, axis=1)\n", + "nn_flux_u_mean_dist = np.mean(nn_flux_u_results, axis=1)\n", + "nn_flux_2d_mean_dist = np.mean(nn_flux_2d_results, axis=1)\n", + "li_flux_mean_dist = np.mean(li_flux_results, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Exponential Kernel 1st 3\n", + "print('Exponential')\n", + "print(np.mean(np.array(np.exp(test_exp_params))[:, 0], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_exp_params))[:, 1], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_exp_params))[:, 2], axis=0)*np.array([5., 1.]))\n", + "#Squared Exponential Kernel 1st 3\n", + "print('Squared Exponential')\n", + "print(np.mean(np.array(np.exp(test_sq_exp_params))[:, 0], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_sq_exp_params))[:, 1], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_sq_exp_params))[:, 2], axis=0)*np.array([5., 1.]))\n", + "#Matern 3/2 Kernel 1st 3\n", + "print('Matern 3/2')\n", + "print(np.mean(np.array(np.exp(test_matern_32_params))[:, 0], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_matern_32_params))[:, 1], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_matern_32_params))[:, 2], axis=0)*np.array([5., 1.]))\n", + "#Matern 5/2 Kernel 1st 3\n", + "print('Matern 5/2')\n", + "print(np.mean(np.array(np.exp(test_matern_52_params))[:, 0], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_matern_52_params))[:, 1], axis=0)*np.array([5., 1.]))\n", + "print(np.mean(np.array(np.exp(test_matern_52_params))[:, 2], axis=0)*np.array([5., 1.]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(gp_exp_flux_mean)\n", + "print(gp_sq_exp_flux_mean)\n", + "print(gp_matern_32_flux_mean)\n", + "print(gp_matern_52_flux_mean)\n", + "print(nn_flux_u_mean)\n", + "print(nn_flux_2d_mean)\n", + "print(li_flux_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist)\n", + "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_exp_flux_mean_dist[gp_exp_trim_25_idx]))\n", + "\n", + "gp_sq_exp_argsort = np.argsort(gp_sq_exp_flux_mean_dist)\n", + "gp_sq_exp_trim_25_idx = gp_sq_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_sq_exp_flux_mean_dist[gp_sq_exp_trim_25_idx]))\n", + "\n", + "gp_matern_32_argsort = np.argsort(gp_matern_32_flux_mean_dist)\n", + "gp_matern_32_trim_25_idx = gp_matern_32_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_32_flux_mean_dist[gp_exp_trim_25_idx]))\n", + "\n", + "gp_matern_52_argsort = np.argsort(gp_matern_52_flux_mean_dist)\n", + "gp_matern_52_trim_25_idx = gp_matern_52_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_52_flux_mean_dist[gp_matern_52_trim_25_idx]))\n", + "\n", + "nn_flux_u_argsort = np.argsort(nn_flux_u_mean_dist)\n", + "nn_flux_u_trim_25_idx = nn_flux_u_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_u_mean_dist[nn_flux_u_trim_25_idx]))\n", + "\n", + "nn_flux_2d_argsort = np.argsort(nn_flux_2d_mean_dist)\n", + "nn_flux_2d_trim_25_idx = nn_flux_2d_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_2d_mean_dist[nn_flux_2d_trim_25_idx]))\n", + "\n", + "li_flux_argsort = np.argsort(li_flux_mean_dist)\n", + "li_flux_trim_25_idx = li_flux_argsort[6250:-6250]\n", + "print(np.mean(li_flux_mean_dist[li_flux_trim_25_idx]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(14, 6))\n", + "\n", + "ax = plt.gca()\n", + "ax.set_xlabel('Wavelength (nm)', size=32)\n", + "mpl.rc('xtick', labelsize=22) \n", + "mpl.rc('ytick', labelsize=22) \n", + "\n", + "fig.add_subplot(1,1,1)\n", + "plt.plot(new_pca_obj.wavelengths, nn_flux_u_mean_spec, label='Nearest')\n", + "plt.plot(new_pca_obj.wavelengths, nn_flux_2d_mean_spec, label='2 Nearest, Distance')\n", + "plt.plot(new_pca_obj.wavelengths, li_flux_mean_spec, label='Linear Estimation')\n", + "plt.plot(new_pca_obj.wavelengths, gp_sq_exp_flux_mean_spec, label='GP w/ Sq. Exp. Kernel')\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_52_flux_mean_spec, label='GP w/ Matern-5/2 Kernel')\n", + "\n", + "\n", + "plt.legend(fontsize=22)\n", + "plt.title('Mean Fractional Flux Residuals', size=24)\n", + "plt.ylabel('Mean Fractional Residuals', size=22)\n", + "plt.xlim(300, 1200)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_2.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(14, 6))\n", + "\n", + "ax = plt.gca()\n", + "ax.set_xlabel('Wavelength (nm)', size=32)\n", + "mpl.rc('xtick', labelsize=22) \n", + "mpl.rc('ytick', labelsize=22)\n", + "\n", + "fig.add_subplot(1,1,1)\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_52_flux_mean_spec/nn_flux_2d_mean_spec, label='GP with Matern-5/2 / 2 NN')\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_52_flux_mean_spec/li_flux_mean_spec, label='GP with Matern-5/2 / Linear')\n", + "plt.legend(fontsize=22)\n", + "plt.title('Ratio of Mean Fractional Residuals', size=24)\n", + "plt.ylabel('Ratio of Mean Frac. Resid.', size=22)\n", + "plt.hlines(1.0, 300, 1200, colors='k', linestyles='dashed', lw=6)\n", + "plt.xlim(300, 1200)\n", + "plt.ylim(0.1, 1.3)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_3.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(14,12))\n", + "\n", + "fig.text(0.55, -0.02, 'Wavelength (nm)', ha='center', size=32)\n", + "fig.text(-0.02, 0.5, 'Flux (scaled)', va='center', rotation='vertical', size=32)\n", + "\n", + "ax[0].plot(new_pca_obj.wavelengths, new_pca_obj.mean_spec, label='Mean Spectrum')\n", + "ax[0].set_title('Mean Spectrum', size=24)\n", + "\n", + "ax[1].plot(new_pca_obj.wavelengths, new_pca_obj.eigenspectra[0], label='1st Eigenspectrum')\n", + "\n", + "ax[1].plot(new_pca_obj.wavelengths, new_pca_obj.eigenspectra[1], label='2nd Eigenspectrum')\n", + "\n", + "ax[1].plot(new_pca_obj.wavelengths, new_pca_obj.eigenspectra[2], label='3rd Eigenspectrum')\n", + "plt.legend(fontsize=24)\n", + "plt.title('1st 3 Eigenspectra', size=24)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_1.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test with larger part of spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(2314)\n", + "\n", + "li_flux_results = np.zeros((25000, 6431))\n", + "nn_flux_u_results = np.zeros((25000, 6431))\n", + "nn_flux_2d_results = np.zeros((25000, 6431))\n", + "gp_exp_flux_results = np.zeros((25000, 6431))\n", + "gp_sq_exp_flux_results = np.zeros((25000, 6431))\n", + "gp_matern_32_flux_results = np.zeros((25000, 6431))\n", + "gp_matern_52_flux_results = np.zeros((25000, 6431))\n", + "\n", + "training_colors_full = []\n", + "training_coeffs_full = []\n", + "training_eigenspectra = []\n", + "training_meanspec = []\n", + "\n", + "test_colors_full = []\n", + "test_exp_params = []\n", + "test_sq_exp_params = []\n", + "test_matern_32_params = []\n", + "test_matern_52_params = []\n", + "distances_all = []\n", + "\n", + "test_flux_orig = []\n", + "flux_errors_full = []\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 99.\n", + "max_wavelen = 2400.\n", + "n_colors = 5\n", + "n_comps = 9\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + " \n", + " #print 'Linear Interp'\n", + " li_spec, li_colors = linear_interpolation_spectra(colors, test_colors, new_pca_obj.spec_list_orig, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " li_flux_results[i*50:(i+1)*50] = np.abs(np.array((li_spec - test_fluxes)/test_fluxes))\n", + " \n", + " #print 'Nearest Neighbor Results'\n", + " nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " nn_spec = nn_obj.nn_predict(1)\n", + " nn_flux_u_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " nn_spec = nn_obj.nn_predict(2, knr_args=dict(weights='distance'))\n", + " nn_flux_2d_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + " \n", + " #print 'Gaussian Process Results'\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", + " test_exp_params.append(gp_spec.params)\n", + " \n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", + " test_sq_exp_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_32_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_52_params.append(gp_spec.params)\n", + " \n", + " training_colors_full.append(colors)\n", + " test_colors_full.append(test_colors)\n", + "\n", + "\n", + "test_colors_full = np.array(test_colors_full)\n", + "\n", + "training_colors_full = np.array(training_colors_full)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating overall means\")\n", + "\n", + "gp_exp_flux_mean = np.mean(gp_exp_flux_results)\n", + "nn_flux_u_mean = np.mean(nn_flux_u_results)\n", + "gp_sq_exp_flux_mean = np.mean(gp_sq_exp_flux_results)\n", + "nn_flux_2d_mean = np.mean(nn_flux_2d_results)\n", + "li_flux_mean = np.mean(li_flux_results)\n", + "gp_matern_32_flux_mean = np.mean(gp_matern_32_flux_results)\n", + "gp_matern_52_flux_mean = np.mean(gp_matern_52_flux_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(gp_exp_flux_mean)\n", + "print(gp_sq_exp_flux_mean)\n", + "print(gp_matern_32_flux_mean)\n", + "print(gp_matern_52_flux_mean)\n", + "print(nn_flux_u_mean)\n", + "print(nn_flux_2d_mean)\n", + "print(li_flux_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_dist_2 = np.mean(gp_exp_flux_results, axis=1)\n", + "gp_sq_exp_flux_mean_dist_2 = np.mean(gp_sq_exp_flux_results, axis=1)\n", + "nn_flux_u_mean_dist_2 = np.mean(nn_flux_u_results, axis=1)\n", + "nn_flux_2d_mean_dist_2 = np.mean(nn_flux_2d_results, axis=1)\n", + "li_flux_mean_dist_2 = np.mean(li_flux_results, axis=1)\n", + "gp_matern_32_flux_mean_dist_2 = np.mean(gp_matern_32_flux_results, axis=1)\n", + "gp_matern_52_flux_mean_dist_2 = np.mean(gp_matern_52_flux_results, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist_2)\n", + "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_exp_flux_mean_dist_2[gp_exp_trim_25_idx]))\n", + "\n", + "gp_sq_exp_argsort = np.argsort(gp_sq_exp_flux_mean_dist_2)\n", + "gp_sq_exp_trim_25_idx = gp_sq_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_sq_exp_flux_mean_dist_2[gp_sq_exp_trim_25_idx]))\n", + "\n", + "gp_matern_32_argsort = np.argsort(gp_matern_32_flux_mean_dist_2)\n", + "gp_matern_32_trim_25_idx = gp_matern_32_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_32_flux_mean_dist_2[gp_exp_trim_25_idx]))\n", + "\n", + "gp_matern_52_argsort = np.argsort(gp_matern_52_flux_mean_dist_2)\n", + "gp_matern_52_trim_25_idx = gp_matern_52_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_52_flux_mean_dist_2[gp_matern_52_trim_25_idx]))\n", + "\n", + "nn_flux_u_argsort = np.argsort(nn_flux_u_mean_dist_2)\n", + "nn_flux_u_trim_25_idx = nn_flux_u_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_u_mean_dist_2[nn_flux_u_trim_25_idx]))\n", + "\n", + "nn_flux_2d_argsort = np.argsort(nn_flux_2d_mean_dist_2)\n", + "nn_flux_2d_trim_25_idx = nn_flux_2d_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_2d_mean_dist_2[nn_flux_2d_trim_25_idx]))\n", + "\n", + "li_flux_argsort = np.argsort(li_flux_mean_dist_2)\n", + "li_flux_trim_25_idx = li_flux_argsort[6250:-6250]\n", + "print(np.mean(li_flux_mean_dist_2[li_flux_trim_25_idx]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_spec_2 = np.mean(gp_exp_flux_results, axis=0)\n", + "gp_sq_exp_flux_mean_spec_2 = np.mean(gp_sq_exp_flux_results, axis=0)\n", + "nn_flux_u_mean_spec_2 = np.mean(nn_flux_u_results, axis=0)\n", + "nn_flux_2d_mean_spec_2 = np.mean(nn_flux_2d_results, axis=0)\n", + "li_flux_mean_spec_2 = np.mean(li_flux_results, axis=0)\n", + "gp_matern_32_flux_mean_spec_2 = np.mean(gp_matern_32_flux_results, axis=0)\n", + "gp_matern_52_flux_mean_spec_2 = np.mean(gp_matern_52_flux_results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idx_300 = np.where(new_pca_obj.wavelengths >= 299.)[0][0]\n", + "idx_1200 = np.where(new_pca_obj.wavelengths <= 1200.)[0][-1]+1\n", + "print(np.mean(gp_exp_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(gp_sq_exp_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(gp_matern_32_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(gp_matern_52_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(nn_flux_u_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(nn_flux_2d_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(li_flux_mean_spec_2[idx_300:idx_1200]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test with artificial filters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "np.random.seed(2314)\n", + "\n", + "gp_exp_flux_results = np.zeros((25000, 6431))\n", + "gp_sq_exp_flux_results = np.zeros((25000, 6431))\n", + "gp_matern_32_flux_results = np.zeros((25000, 6431))\n", + "gp_matern_52_flux_results = np.zeros((25000, 6431))\n", + "\n", + "training_colors_full = []\n", + "training_coeffs_full = []\n", + "training_eigenspectra = []\n", + "training_meanspec = []\n", + "\n", + "test_colors_full = []\n", + "test_spectra_full = []\n", + "test_coeffs_full = []\n", + "test_exp_params = []\n", + "test_sq_exp_params = []\n", + "test_matern_32_params = []\n", + "test_matern_52_params = []\n", + "distances_all = []\n", + "\n", + "test_flux_orig = []\n", + "flux_errors_full = []\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 99.\n", + "max_wavelen = 2400.\n", + "n_colors = 9\n", + "n_comps = 9\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + "\n", + " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", + " distance, idx = nbrs.kneighbors(test_colors)\n", + " distances_all.append(np.ravel(distance))\n", + " \n", + " #print 'Gaussian Process Results'\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, new_bandpass_dict)\n", + " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", + " test_exp_params.append(gp_spec.params)\n", + " \n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, new_bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", + " test_sq_exp_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, new_bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_32_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, new_bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_52_params.append(gp_spec.params)\n", + " \n", + " training_colors_full.append(colors)\n", + " test_colors_full.append(test_colors)\n", + "\n", + "test_colors_full = np.array(test_colors_full)\n", + "\n", + "training_colors_full = np.array(training_colors_full)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating overall means\")\n", + "\n", + "gp_exp_flux_mean = np.mean(gp_exp_flux_results)\n", + "#nn_flux_u_mean = np.mean(nn_flux_u_results)\n", + "gp_sq_exp_flux_mean = np.mean(gp_sq_exp_flux_results)\n", + "#nn_flux_2d_mean = np.mean(nn_flux_2d_results)\n", + "#nn_flux_2u_mean = np.mean(nn_flux_2u_results)\n", + "#nn_flux_4d_mean = np.mean(nn_flux_4d_results)\n", + "#nn_flux_4u_mean = np.mean(nn_flux_4u_results)\n", + "#li_flux_mean = np.mean(li_flux_results)\n", + "gp_matern_32_flux_mean = np.mean(gp_matern_32_flux_results)\n", + "gp_matern_52_flux_mean = np.mean(gp_matern_52_flux_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(gp_sq_exp_flux_mean)\n", + "print(gp_exp_flux_mean)\n", + "#print(nn_flux_u_mean)\n", + "#print(nn_flux_2d_mean)\n", + "#print(li_flux_mean)\n", + "print(gp_matern_32_flux_mean)\n", + "print(gp_matern_52_flux_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_spec_3 = np.mean(gp_exp_flux_results, axis=0)\n", + "gp_sq_exp_flux_mean_spec_3 = np.mean(gp_sq_exp_flux_results, axis=0)\n", + "gp_matern_32_flux_mean_spec_3 = np.mean(gp_matern_32_flux_results, axis=0)\n", + "gp_matern_52_flux_mean_spec_3 = np.mean(gp_matern_52_flux_results, axis=0)\n", + "nn_flux_u_mean_spec_3 = np.mean(nn_flux_u_results, axis=0)\n", + "li_flux_mean_spec_3 = np.mean(li_flux_results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "idx_300 = np.where(new_pca_obj.wavelengths >= 299.)[0][0]\n", + "idx_1200 = np.where(new_pca_obj.wavelengths <= 1200.)[0][-1]+1\n", + "print(np.mean(gp_exp_flux_mean_spec_3[idx_300:idx_1200]))\n", + "print(np.mean(gp_sq_exp_flux_mean_spec_3[idx_300:idx_1200]))\n", + "print(np.mean(gp_matern_32_flux_mean_spec_3[idx_300:idx_1200]))\n", + "print(np.mean(gp_matern_52_flux_mean_spec_3[idx_300:idx_1200]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_dist_3 = np.mean(gp_exp_flux_results, axis=1)\n", + "gp_sq_exp_flux_mean_dist_3 = np.mean(gp_sq_exp_flux_results, axis=1)\n", + "nn_flux_u_mean_dist_3 = np.mean(nn_flux_u_results, axis=1)\n", + "#nn_flux_2d_mean_dist_3 = np.mean(nn_flux_2d_results, axis=1)\n", + "li_flux_mean_dist_3 = np.mean(li_flux_results, axis=1)\n", + "gp_matern_32_flux_mean_dist_3 = np.mean(gp_matern_32_flux_results, axis=1)\n", + "gp_matern_52_flux_mean_dist_3 = np.mean(gp_matern_52_flux_results, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist_3)\n", + "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_exp_flux_mean_dist_3[gp_exp_trim_25_idx]))\n", + "\n", + "gp_sq_exp_argsort = np.argsort(gp_sq_exp_flux_mean_dist_3)\n", + "gp_sq_exp_trim_25_idx = gp_sq_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_sq_exp_flux_mean_dist_3[gp_sq_exp_trim_25_idx]))\n", + "\n", + "gp_matern_32_argsort = np.argsort(gp_matern_32_flux_mean_dist_3)\n", + "gp_matern_32_trim_25_idx = gp_matern_32_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_32_flux_mean_dist_3[gp_exp_trim_25_idx]))\n", + "\n", + "gp_matern_52_argsort = np.argsort(gp_matern_52_flux_mean_dist_3)\n", + "gp_matern_52_trim_25_idx = gp_matern_52_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_52_flux_mean_dist_3[gp_matern_52_trim_25_idx]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(14, 12))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "ax.spines['top'].set_color('none')\n", + "ax.spines['bottom'].set_color('none')\n", + "ax.spines['left'].set_color('none')\n", + "ax.spines['right'].set_color('none')\n", + "ax.set_axis_bgcolor('white') \n", + "ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')\n", + "ax.set_xlabel('Wavelength (nm)', size=32)\n", + "ax.set_ylabel('Ratio of Mean Fractional Residuals', size=32)\n", + "mpl.rc('xtick', labelsize=22) \n", + "mpl.rc('ytick', labelsize=22)\n", + "\n", + "fig.add_subplot(2,1,1)\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_32_flux_mean_spec_2/nn_flux_u_mean_spec_3, label='GP with Matern-3/2 / NN', lw=4)\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_32_flux_mean_spec_2/li_flux_mean_spec_3, label='GP with Matern-3/2 / Linear', lw=4)\n", + "plt.legend(fontsize=22)\n", + "plt.title('Only training with LSST filters', size=24)\n", + "plt.hlines(1.0, 99, 2400, colors='k', linestyles='dashed', lw=6)\n", + "plt.xlim(99, 2400)\n", + "\n", + "fig.add_subplot(2,1,2)\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_32_flux_mean_spec_3/nn_flux_u_mean_spec_3, label='GP with Matern-3/2 / NN', lw=4)\n", + "plt.plot(new_pca_obj.wavelengths, gp_matern_32_flux_mean_spec_3/li_flux_mean_spec_3, label='GP with Matern-3/2 / Linear', lw=4)\n", + "plt.legend(fontsize=22)\n", + "plt.title('With additional training filters', size=24)\n", + "plt.hlines(1.0, 99, 2400, colors='k', linestyles='dashed', lw=6)\n", + "plt.xlim(99, 2400)\n", + "plt.ylim(0, 2)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_6.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Look at distances from nearest neighbor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(2314)\n", + "\n", + "distances_all = []\n", + "\n", + "test_flux_orig = []\n", + "flux_errors_full = []\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 99.\n", + "max_wavelen = 2400.\n", + "n_colors = 9\n", + "n_comps = 9\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + "\n", + " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", + " distance, idx = nbrs.kneighbors(test_colors)\n", + " distances_all.append(np.ravel(distance))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "use_distances = np.ravel(distances_all)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_bins = 25\n", + "bin_vals, gp_matern_52_dist_results = calc_dist_bins(use_distances, gp_matern_52_flux_mean_dist, n_bins)\n", + "bin_vals, nn_u_dist_results = calc_dist_bins(use_distances, nn_flux_u_mean_dist, n_bins)\n", + "bin_vals, nn_2d_dist_results = calc_dist_bins(use_distances, nn_flux_2d_mean_dist, n_bins)\n", + "bin_vals, li_dist_results = calc_dist_bins(use_distances, li_flux_mean_dist, n_bins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_bins = 25\n", + "bin_vals, gp_matern_32_dist_results_3 = calc_dist_bins(use_distances, gp_matern_32_flux_mean_dist_3, n_bins)\n", + "bin_vals, nn_u_dist_results_3 = calc_dist_bins(use_distances, nn_flux_u_mean_dist_3, n_bins)\n", + "bin_vals, li_dist_results_3 = calc_dist_bins(use_distances, li_flux_mean_dist_3, n_bins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(18,12))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "ax.spines['top'].set_color('none')\n", + "ax.spines['bottom'].set_color('none')\n", + "ax.spines['left'].set_color('none')\n", + "ax.spines['right'].set_color('none')\n", + "ax.set_axis_bgcolor('white') \n", + "ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')\n", + "ax.set_ylabel('Ratio of Mean Fractional Residuals', size=32)\n", + "ax.set_xlabel('Distance in mags to nearest neighbor', size=32)\n", + "mpl.rc('xtick', labelsize=22) \n", + "mpl.rc('ytick', labelsize=22)\n", + "\n", + "fig.add_subplot(2,1,1)\n", + "\n", + "plt.plot(bin_vals[:-1], mean_dist_results(gp_matern_52_dist_results)/mean_dist_results(nn_2d_dist_results), label='GP with Matern-5/2 / 2 NN', lw=8)\n", + "plt.plot(bin_vals[:-1], mean_dist_results(gp_matern_52_dist_results)/mean_dist_results(li_dist_results), label='GP with Matern-5/2 / LI', lw=8)\n", + "plt.xlim(0, 0.6)\n", + "plt.ylim(0, 2.)\n", + "plt.hlines(1.0, 0.0, 0.6, linestyles='dashed', lw=6)\n", + "plt.legend(fontsize=22)\n", + "plt.title('Ratio of Residual Errors in Test 1', size=32)\n", + "\n", + "fig.add_subplot(2,1,2)\n", + "\n", + "plt.plot(bin_vals[:-1], mean_dist_results(gp_matern_32_dist_results_3)/mean_dist_results(nn_u_dist_results_3), label='GP with Matern-3/2 / NN', lw=8)\n", + "plt.plot(bin_vals[:-1], mean_dist_results(gp_matern_32_dist_results_3)/mean_dist_results(li_dist_results_3), label='GP with Matern-3/2 / LI', lw=8)\n", + "plt.xlim(0, 0.6)\n", + "plt.ylim(0, 2.)\n", + "plt.hlines(1.0, 0.0, 0.6, linestyles='dashed', lw=6)\n", + "plt.legend(fontsize=22)\n", + "plt.title('Ratio of Residual Errors in Test 3', size=32)\n", + "\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_7.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n, bins = np.histogram(use_distances, bins=bin_vals)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.sum(n[:12])/np.sum(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Narrowband filters test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filters = ['F344N', 'F502N', 'F658N', 'F892N']\n", + "narrow_band_dict = {}\n", + "total_wavelengths = []\n", + "for filt_name in filters:\n", + " narrow_band = np.genfromtxt('narrowband_filters/HST_ACS_HRC.%s.dat' % filt_name, unpack=True)\n", + " narrow_band[0] /= 10. # Convert to nanometers\n", + " total_wavelengths.append(narrow_band[0])\n", + " narrow_band_dict[filt_name] = narrow_band" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_wavelengths = [x for y in total_wavelengths for x in y]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "narrow_bandpasses = []\n", + "for filt_name in filters:\n", + " bandpass = np.zeros(len(total_wavelengths))\n", + " min_idx = np.where(total_wavelengths == narrow_band_dict[filt_name][0][0])[0][0]\n", + " max_idx = np.where(total_wavelengths == narrow_band_dict[filt_name][0][-1])[0][0]\n", + " bandpass[min_idx:max_idx+1] = narrow_band_dict[filt_name][1]\n", + " narrow_bandpasses.append(Bandpass(wavelen=np.array(total_wavelengths), sb=bandpass))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "narrow_bandpass_dict = BandpassDict(narrow_bandpasses, filters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(2314)\n", + "\n", + "li_flux_results = np.zeros((25000, 6071))\n", + "nn_flux_u_results = np.zeros((25000, 6071))\n", + "#nn_flux_2u_results = np.zeros((25000, 6071))\n", + "nn_flux_2d_results = np.zeros((25000, 6071))\n", + "#nn_flux_4u_results = np.zeros((25000, 6071))\n", + "#nn_flux_4d_results = np.zeros((25000, 6071))\n", + "gp_exp_flux_results = np.zeros((25000, 6071))\n", + "gp_sq_exp_flux_results = np.zeros((25000, 6071))\n", + "gp_matern_32_flux_results = np.zeros((25000, 6071))\n", + "gp_matern_52_flux_results = np.zeros((25000, 6071))\n", + "\n", + "training_colors_full = []\n", + "training_coeffs_full = []\n", + "training_eigenspectra = []\n", + "training_meanspec = []\n", + "\n", + "test_colors_full = []\n", + "test_exp_params = []\n", + "test_sq_exp_params = []\n", + "test_matern_32_params = []\n", + "test_matern_52_params = []\n", + "distances_all = []\n", + "\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 299.\n", + "max_wavelen = 1200.\n", + "n_colors = 3\n", + "n_comps = 9\n", + "\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + "\n", + " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", + " distance, idx = nbrs.kneighbors(test_colors)\n", + " distances_all.append(np.ravel(distance))\n", + " \n", + " #print 'Linear Interp'\n", + " li_spec, li_colors = linear_interpolation_spectra(colors, test_colors, new_pca_obj.spec_list_orig, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " li_flux_results[i*50:(i+1)*50] = np.abs(np.array((li_spec - test_fluxes)/test_fluxes))\n", + " \n", + " #print 'Nearest Neighbor Results'\n", + " nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " nn_spec = nn_obj.nn_predict(1)\n", + " nn_flux_u_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " #nn_spec = nn_obj.nn_predict(2)\n", + " #nn_flux_2u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", + " nn_spec = nn_obj.nn_predict(2, knr_args=dict(weights='distance'))\n", + " nn_flux_2d_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " #nn_spec = nn_obj.nn_predict(4)\n", + " #nn_flux_4u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", + " #nn_spec = nn_obj.nn_predict(4, knr_args=dict(weights='distance'))\n", + " \n", + " #print 'Gaussian Process Results'\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", + " test_exp_params.append(gp_spec.params)\n", + " \n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", + " test_sq_exp_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_32_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_52_params.append(gp_spec.params)\n", + " \n", + " training_colors_full.append(colors)\n", + " test_colors_full.append(test_colors)\n", + "\n", + "\n", + "test_colors_full = np.array(test_colors_full)\n", + "\n", + "training_colors_full = np.array(training_colors_full)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating overall means\")\n", + "\n", + "gp_exp_flux_mean_nb = np.mean(gp_exp_flux_results)\n", + "nn_flux_u_mean_nb = np.mean(nn_flux_u_results)\n", + "gp_sq_exp_flux_mean_nb = np.mean(gp_sq_exp_flux_results)\n", + "nn_flux_2d_mean_nb = np.mean(nn_flux_2d_results)\n", + "li_flux_mean_nb = np.mean(li_flux_results)\n", + "gp_matern_32_flux_mean_nb = np.mean(gp_matern_32_flux_results)\n", + "gp_matern_52_flux_mean_nb = np.mean(gp_matern_52_flux_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(gp_exp_flux_mean_nb)\n", + "print(gp_sq_exp_flux_mean_nb)\n", + "print(gp_matern_32_flux_mean_nb)\n", + "print(gp_matern_52_flux_mean_nb)\n", + "print(nn_flux_u_mean_nb)\n", + "print(nn_flux_2d_mean_nb)\n", + "print(li_flux_mean_nb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_dist_nb = np.mean(gp_exp_flux_results, axis=1)\n", + "gp_sq_exp_flux_mean_dist_nb = np.mean(gp_sq_exp_flux_results, axis=1)\n", + "nn_flux_u_mean_dist_nb = np.mean(nn_flux_u_results, axis=1)\n", + "nn_flux_2d_mean_dist_nb = np.mean(nn_flux_2d_results, axis=1)\n", + "li_flux_mean_dist_nb = np.mean(li_flux_results, axis=1)\n", + "gp_matern_32_flux_mean_dist_nb = np.mean(gp_matern_32_flux_results, axis=1)\n", + "gp_matern_52_flux_mean_dist_nb = np.mean(gp_matern_52_flux_results, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist_nb)\n", + "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_exp_flux_mean_dist_nb[gp_exp_trim_25_idx]))\n", + "\n", + "gp_sq_exp_argsort = np.argsort(gp_sq_exp_flux_mean_dist_nb)\n", + "gp_sq_exp_trim_25_idx = gp_sq_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_sq_exp_flux_mean_dist_nb[gp_sq_exp_trim_25_idx]))\n", + "\n", + "gp_matern_32_argsort = np.argsort(gp_matern_32_flux_mean_dist_nb)\n", + "gp_matern_32_trim_25_idx = gp_matern_32_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_32_flux_mean_dist_nb[gp_exp_trim_25_idx]))\n", + "\n", + "gp_matern_52_argsort = np.argsort(gp_matern_52_flux_mean_dist_nb)\n", + "gp_matern_52_trim_25_idx = gp_matern_52_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_52_flux_mean_dist_nb[gp_matern_52_trim_25_idx]))\n", + "\n", + "nn_flux_u_argsort = np.argsort(nn_flux_u_mean_dist_nb)\n", + "nn_flux_u_trim_25_idx = nn_flux_u_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_u_mean_dist_nb[nn_flux_u_trim_25_idx]))\n", + "\n", + "nn_flux_2d_argsort = np.argsort(nn_flux_2d_mean_dist_nb)\n", + "nn_flux_2d_trim_25_idx = nn_flux_2d_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_2d_mean_dist_nb[nn_flux_2d_trim_25_idx]))\n", + "\n", + "li_flux_argsort = np.argsort(li_flux_mean_dist_nb)\n", + "li_flux_trim_25_idx = li_flux_argsort[6250:-6250]\n", + "print(np.mean(li_flux_mean_dist_nb[li_flux_trim_25_idx]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "narrow_bandpasses.insert(0, blue_bandpass_3)\n", + "narrow_bandpasses.insert(0, blue_bandpass_1)\n", + "narrow_bandpasses.append(bandpass_dict['y'])\n", + "narrow_bandpasses.append(red_bandpass_2)\n", + "narrow_bandpasses.append(red_bandpass_4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "narrow_bandpass_dict_training = BandpassDict(narrow_bandpasses, ['blue_1', 'blue_3', filters[0], filters[1],\n", + " filters[2], filters[3], 'y', 'red_2', 'red_4'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "np.random.seed(2314)\n", + "\n", + "li_flux_results = np.zeros((25000, 6431))\n", + "nn_flux_u_results = np.zeros((25000, 6431))\n", + "nn_flux_2d_results = np.zeros((25000, 6431))\n", + "gp_exp_flux_results = np.zeros((25000, 6431))\n", + "gp_sq_exp_flux_results = np.zeros((25000, 6431))\n", + "gp_matern_32_flux_results = np.zeros((25000, 6431))\n", + "gp_matern_52_flux_results = np.zeros((25000, 6431))\n", + "\n", + "training_colors_full = []\n", + "training_coeffs_full = []\n", + "training_eigenspectra = []\n", + "training_meanspec = []\n", + "\n", + "test_colors_full = []\n", + "test_spectra_full = []\n", + "test_coeffs_full = []\n", + "test_exp_params = []\n", + "test_sq_exp_params = []\n", + "test_matern_32_params = []\n", + "test_matern_52_params = []\n", + "test_var = []\n", + "distances_all = []\n", + "\n", + "test_flux_orig = []\n", + "flux_errors_full = []\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 99.\n", + "max_wavelen = 2400.\n", + "n_colors = 8\n", + "n_comps = 9\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + "\n", + " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", + " distance, idx = nbrs.kneighbors(test_colors)\n", + " distances_all.append(np.ravel(distance))\n", + " \n", + " #print 'Gaussian Process Results'\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", + " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", + " test_exp_params.append(gp_spec.params)\n", + " \n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", + " test_sq_exp_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_32_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + " test_matern_52_params.append(gp_spec.params)\n", + " \n", + " training_colors_full.append(colors)\n", + " test_colors_full.append(test_colors)\n", + "\n", + "test_colors_full = np.array(test_colors_full)\n", + "\n", + "training_colors_full = np.array(training_colors_full)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating overall means\")\n", + "\n", + "gp_exp_flux_mean_nb_2 = np.mean(gp_exp_flux_results)\n", + "nn_flux_u_mean_nb_2 = np.mean(nn_flux_u_results)\n", + "gp_sq_exp_flux_mean_nb_2 = np.mean(gp_sq_exp_flux_results)\n", + "nn_flux_2d_mean_nb_2 = np.mean(nn_flux_2d_results)\n", + "li_flux_mean_nb_2 = np.mean(li_flux_results)\n", + "gp_matern_32_flux_mean_nb_2 = np.mean(gp_matern_32_flux_results)\n", + "gp_matern_52_flux_mean_nb_2 = np.mean(gp_matern_52_flux_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "print(gp_exp_flux_mean_nb_2)\n", + "print(gp_sq_exp_flux_mean_nb_2)\n", + "print(gp_matern_32_flux_mean_nb_2)\n", + "print(gp_matern_52_flux_mean_nb_2)\n", + "print(nn_flux_u_mean_nb_2)\n", + "print(nn_flux_2d_mean_nb_2)\n", + "print(li_flux_mean_nb_2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_spec_2 = np.mean(gp_exp_flux_results, axis=0)\n", + "gp_sq_exp_flux_mean_spec_2 = np.mean(gp_sq_exp_flux_results, axis=0)\n", + "nn_flux_u_mean_spec_2 = np.mean(nn_flux_u_results, axis=0)\n", + "nn_flux_2d_mean_spec_2 = np.mean(nn_flux_2d_results, axis=0)\n", + "li_flux_mean_spec_2 = np.mean(li_flux_results, axis=0)\n", + "gp_matern_32_flux_mean_spec_2 = np.mean(gp_matern_32_flux_results, axis=0)\n", + "gp_matern_52_flux_mean_spec_2 = np.mean(gp_matern_52_flux_results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idx_300 = np.where(new_pca_obj.wavelengths >= 299.)[0][0]\n", + "idx_1200 = np.where(new_pca_obj.wavelengths <= 1200.)[0][-1]+1\n", + "print(np.mean(gp_exp_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(gp_sq_exp_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(gp_matern_32_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(gp_matern_52_flux_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(nn_flux_u_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(nn_flux_2d_mean_spec_2[idx_300:idx_1200]))\n", + "print(np.mean(li_flux_mean_spec_2[idx_300:idx_1200]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Calculating mean spectra\")\n", + "\n", + "gp_exp_flux_mean_dist_nb_2 = np.mean(gp_exp_flux_results, axis=1)\n", + "gp_sq_exp_flux_mean_dist_nb_2 = np.mean(gp_sq_exp_flux_results, axis=1)\n", + "nn_flux_u_mean_dist_nb_2 = np.mean(nn_flux_u_results, axis=1)\n", + "nn_flux_2d_mean_dist_nb_2 = np.mean(nn_flux_2d_results, axis=1)\n", + "li_flux_mean_dist_nb_2 = np.mean(li_flux_results, axis=1)\n", + "gp_matern_32_flux_mean_dist_nb_2 = np.mean(gp_matern_32_flux_results, axis=1)\n", + "gp_matern_52_flux_mean_dist_nb_2 = np.mean(gp_matern_52_flux_results, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist_nb_2)\n", + "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_exp_flux_mean_dist_nb_2[gp_exp_trim_25_idx]))\n", + "\n", + "gp_sq_exp_argsort = np.argsort(gp_sq_exp_flux_mean_dist_nb_2)\n", + "gp_sq_exp_trim_25_idx = gp_sq_exp_argsort[6250:-6250]\n", + "print(np.mean(gp_sq_exp_flux_mean_dist_nb_2[gp_sq_exp_trim_25_idx]))\n", + "\n", + "gp_matern_32_argsort = np.argsort(gp_matern_32_flux_mean_dist_nb_2)\n", + "gp_matern_32_trim_25_idx = gp_matern_32_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_32_flux_mean_dist_nb_2[gp_exp_trim_25_idx]))\n", + "\n", + "gp_matern_52_argsort = np.argsort(gp_matern_52_flux_mean_dist_nb_2)\n", + "gp_matern_52_trim_25_idx = gp_matern_52_argsort[6250:-6250]\n", + "print(np.mean(gp_matern_52_flux_mean_dist_nb_2[gp_matern_52_trim_25_idx]))\n", + "\n", + "nn_flux_u_argsort = np.argsort(nn_flux_u_mean_dist_nb_2)\n", + "nn_flux_u_trim_25_idx = nn_flux_u_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_u_mean_dist_nb_2[nn_flux_u_trim_25_idx]))\n", + "\n", + "nn_flux_2d_argsort = np.argsort(nn_flux_2d_mean_dist_nb_2)\n", + "nn_flux_2d_trim_25_idx = nn_flux_2d_argsort[6250:-6250]\n", + "print(np.mean(nn_flux_2d_mean_dist_nb_2[nn_flux_2d_trim_25_idx]))\n", + "\n", + "li_flux_argsort = np.argsort(li_flux_mean_dist_nb_2)\n", + "li_flux_trim_25_idx = li_flux_argsort[6250:-6250]\n", + "print(np.mean(li_flux_mean_dist_nb_2[li_flux_trim_25_idx]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Photometric Redshift Test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create LePhare formatted catalog" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load catalog (To request catalog email author)\n", + "esp_test_cat = pd.read_csv('esp_test_cat.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Output catalog in LePhare format\n", + "esp_test_cat['context'] = 63\n", + "cols = esp_test_cat.columns.tolist()\n", + "print(cols)\n", + "\n", + "lephare_cols = cols[2:] + [cols[1]] + [cols[0]]\n", + "print(lephare_cols)\n", + "\n", + "lephare_cat = esp_test_cat[lephare_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write LePhare cat to file\n", + "lephare_cat.to_csv('esp_lephare_cat.in', sep=' ', header=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create template sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1255)\n", + "rand_sed_nums = np.random.choice(len(pca_obj.spec_list_orig), \n", + " size=60,\n", + " replace=False)\n", + "template_set = []\n", + "for sed_num in rand_sed_nums:\n", + " template_set.append(pca_obj.spec_list_orig[sed_num])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# BC03 templates\n", + "template_folder = 'photoz_template_set'\n", + "os.mkdir(template_folder)\n", + "os.mkdir(str(template_folder + '/ESP_10'))\n", + "sed_names = []\n", + "for sed_obj in template_set[:10]:\n", + " new_sed_obj = Sed()\n", + " new_sed_obj.setSED(wavelen=10.*sed_obj.wavelen, flambda=0.1*sed_obj.flambda)\n", + " new_sed_obj.writeSED(str(template_folder + '/ESP_10/' + sed_obj.name[:-3] + '.sed'))\n", + " sed_names.append('ESP_10/' + sed_obj.name[:-3] + '.sed')\n", + "np.savetxt(str(template_folder + '/ESP_10/' + 'seds.list'), sed_names, fmt=['%s'])\n", + "os.mkdir(str(template_folder + '/ESP_60'))\n", + "sed_names = []\n", + "for sed_obj in template_set[:60]:\n", + " new_sed_obj = Sed()\n", + " new_sed_obj.setSED(wavelen=10.*sed_obj.wavelen, flambda=0.1*sed_obj.flambda)\n", + " new_sed_obj.writeSED(str(template_folder + '/ESP_60/' + sed_obj.name[:-3] + '.sed'))\n", + " sed_names.append('ESP_60/' + sed_obj.name[:-3] + '.sed')\n", + "np.savetxt(str(template_folder + '/ESP_60/' + 'seds.list'), sed_names, fmt=['%s'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_pca_obj = esp.pcaSED()\n", + "new_pca_obj.spec_list_orig = template_set[:10]\n", + "min_wavelen = 99.\n", + "max_wavelen = 2400.\n", + "new_pca_obj.PCA(comps=9, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + "esp_cat_bandpass_dict = BandpassDict.loadTotalBandpassesFromFiles(bandpassDir = 'photoz_bandpasses/') \n", + "colors = []\n", + "for spec_num in range(10):\n", + " \n", + " test_sed = Sed()\n", + " test_sed.setSED(wavelen=new_pca_obj.spec_list_orig[spec_num].wavelen,\n", + " flambda=new_pca_obj.spec_list_orig[spec_num].flambda)\n", + " \n", + " test_mags = esp_cat_bandpass_dict.magListForSed(test_sed)\n", + " \n", + " test_colors = [test_mags[x] - test_mags[x+1] for x in range(len(test_mags)-1)]\n", + " colors.append(test_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cluster catalog to find where we should estimate spectra\n", + "from sklearn.cluster import KMeans\n", + "esp_cat_colors = np.array([esp_test_cat['sdss_u'] - esp_test_cat['sdss_g'],\n", + " esp_test_cat['sdss_g'] - esp_test_cat['sdss_r'],\n", + " esp_test_cat['sdss_r'] - esp_test_cat['sdss_i'],\n", + " esp_test_cat['sdss_i'] - esp_test_cat['sdss_z'],\n", + " esp_test_cat['sdss_z'] - esp_test_cat['ps_y']]).T\n", + "kmeans = KMeans(n_clusters=50, random_state=1428).fit(esp_cat_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interp_colors = kmeans.cluster_centers_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "photoz_train_dict = BandpassDict([\n", + " blue_bandpass_1, blue_bandpass_3, \n", + " #blue_bandpass_3, blue_bandpass_4,\n", + " esp_cat_bandpass_dict['u'], esp_cat_bandpass_dict['g'], \n", + " esp_cat_bandpass_dict['r'], esp_cat_bandpass_dict['i'], esp_cat_bandpass_dict['z'], \n", + " esp_cat_bandpass_dict['y'], #red_bandpass_1, red_bandpass_2, \n", + " red_bandpass_2, red_bandpass_4\n", + " ],\n", + " ['blue_1', #'blue_2', \n", + " 'blue_3', #'blue_4', \n", + " 'u', 'g', 'r', 'i', 'z', 'y', \n", + " 'red_2',#'red_2', \n", + " 'red_4', #'red_4'\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gp_obj = esp.gaussianProcessEstimate(new_pca_obj, esp_cat_bandpass_dict, interp_colors)\n", + "gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, 9)\n", + "gp_spec = gp_obj.gp_predict(gp_kernel, photoz_train_dict)\n", + "gp_spectra = gp_spec.reconstruct_spectra(9)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.mkdir(str(template_folder + '/ESP_EXP_KERNEL'))\n", + "sed_names = []\n", + "for sed_obj in template_set[:10]:\n", + " new_sed_obj = Sed()\n", + " new_sed_obj.setSED(wavelen=10.*sed_obj.wavelen, flambda=0.1*sed_obj.flambda)\n", + " new_sed_obj.writeSED(str(template_folder + '/ESP_EXP_KERNEL/' + sed_obj.name[:-3] + '.sed'))\n", + " sed_names.append('ESP_EXP_KERNEL/' + sed_obj.name[:-3] + '.sed')\n", + "spec_on = 0\n", + "for sed_obj in gp_spectra:\n", + " new_sed_obj = Sed()\n", + " new_sed_obj.setSED(wavelen=10.*new_pca_obj.wavelengths, \n", + " flambda=0.1*sed_obj)\n", + " new_sed_obj.writeSED(str(template_folder + '/ESP_EXP_KERNEL/esp_' + str(spec_on) + '.sed'))\n", + " sed_names.append('ESP_EXP_KERNEL/esp_%i.sed' % spec_on)\n", + " spec_on+=1\n", + "#np.savetxt(str(template_folder + '/ESP_EXP_KERNEL/' + 'seds.list'), sed_names, fmt=['%s'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load LePhare output\n", + "template_10 = np.genfromtxt('esp_10.out', usecols=[1], names=['z_est_10'])\n", + "template_60 = np.genfromtxt('esp_60.out', usecols=[1,22], names=['z_est_60', 'z_true'])\n", + "template_exp_kernel = np.genfromtxt('esp_exp_kernel.out', usecols=[1], names=['z_est_exp_kernel'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "template_results = pd.DataFrame()\n", + "template_results['z_est_10'] = template_10['z_est_10']\n", + "template_results['z_est_60'] = template_60['z_est_60']\n", + "template_results['z_true'] = template_60['z_true']\n", + "template_results['z_est_exp_kernel'] = template_exp_kernel['z_est_exp_kernel']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "template_results[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_bins(z_est, z_true, z_max, n_bins):\n", + " delta_z = (z_true - z_est) / (1. + z_true)\n", + " bin_vals = [(float(i)/n_bins)*z_max for i in range(n_bins)]\n", + " bin_vals.append(float(z_max))\n", + " idx_sort = z_true.argsort()\n", + " delta_z_sort = delta_z[idx_sort]\n", + " z_true_sort = z_true[idx_sort]\n", + " idx_bins = z_true_sort.searchsorted(bin_vals)\n", + " delta_z_binned = [delta_z_sort[idx_bins[i]:idx_bins[i+1]] for i in range(n_bins)]\n", + " return delta_z_binned" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import trim_mean\n", + "def photo_z_bias(z_est, z_true, z_max, n_bins):\n", + " delta_z_binned = calc_bins(z_est, z_true, z_max, n_bins)\n", + " bias_results = []\n", + " for delta_z_data in delta_z_binned:\n", + " trimmed_mean = trim_mean(delta_z_data, .25)\n", + " bias_results.append(trimmed_mean)\n", + " return np.array(bias_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import trim_mean\n", + "def photo_z_abs_bias(z_est, z_true, z_max, n_bins):\n", + " delta_z_binned = calc_bins(z_est, z_true, z_max, n_bins)\n", + " bias_results = []\n", + " for delta_z_data in delta_z_binned:\n", + " trimmed_mean = trim_mean(np.abs(delta_z_data), .25)\n", + " bias_results.append(trimmed_mean)\n", + " return np.array(bias_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def photo_z_stdev(z_est, z_true, z_max, n_bins):\n", + " delta_z_binned = calc_bins(z_est, z_true, z_max, n_bins)\n", + " stdev_results = []\n", + " for delta_z_data in delta_z_binned:\n", + " bin_mean = np.mean(delta_z_data)\n", + " diffs = delta_z_data - bin_mean\n", + " diffs_sq_mean = np.mean(diffs**2.)\n", + " stdev_results.append(np.sqrt(diffs_sq_mean))\n", + " return np.array(stdev_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def photo_z_stdev_iqr(z_est, z_true, z_max, n_bins):\n", + " delta_z_binned = calc_bins(z_est, z_true, z_max, n_bins)\n", + " stdev_iqr_results = []\n", + " for delta_z_data in delta_z_binned:\n", + " bin_25 = np.percentile(delta_z_data, 25.)\n", + " bin_75 = np.percentile(delta_z_data, 75.)\n", + " diff = bin_75 - bin_25\n", + " stdev_iqr_results.append(diff/1.349)\n", + " return np.array(stdev_iqr_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def photo_z_outlier_frac(z_est, z_true, z_max, n_bins):\n", + " stdev_iqr_results = photo_z_stdev_iqr(z_est, z_true, z_max, n_bins)\n", + " delta_z_binned = calc_bins(z_est, z_true, z_max, n_bins)\n", + " outlier_frac_results = []\n", + " for delta_z_data, stdev_iqr_val in zip(delta_z_binned, stdev_iqr_results):\n", + " if 3.*stdev_iqr_val < 0.06:\n", + " outlier_thresh = 0.06\n", + " else:\n", + " outlier_thresh = 3.*stdev_iqr_val\n", + " #print outlier_thresh, np.std(delta_z_data), len(delta_z_data), np.max(delta_z_data), np.min(delta_z_data)\n", + " total_bin_obj = float(len(delta_z_data))\n", + " outliers = np.where(np.abs(delta_z_data) > outlier_thresh)[0]\n", + " #print outliers\n", + " outlier_frac = len(outliers)/total_bin_obj\n", + " outlier_frac_results.append(outlier_frac)\n", + " return np.array(outlier_frac_results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z_max = 3.\n", + "n_bins = 1\n", + "\n", + "delta_z_binned = calc_bins(template_results['z_est_10'], template_results['z_true'], \n", + " z_max, n_bins)\n", + "bin_counts = [len(bin_results) for bin_results in delta_z_binned]\n", + "print(bin_counts)\n", + "\n", + "bias_results_10 = photo_z_bias(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "bias_results_60 = photo_z_bias(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "bias_results_exp = photo_z_bias(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "\n", + "print(np.mean(bias_results_10))\n", + "print(np.mean(bias_results_60))\n", + "print(np.mean(bias_results_exp))\n", + "\n", + "print('\\n')\n", + "\n", + "stdev_results_10 = photo_z_stdev(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_results_60 = photo_z_stdev(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_results_exp = photo_z_stdev(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "print(np.mean(stdev_results_10))\n", + "print(np.mean(stdev_results_60))\n", + "print(np.mean(stdev_results_exp))\n", + "\n", + "print('\\n')\n", + "\n", + "stdev_iqr_results_10 = photo_z_stdev_iqr(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_iqr_results_60 = photo_z_stdev_iqr(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_iqr_results_exp = photo_z_stdev_iqr(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "\n", + "print(np.mean(stdev_iqr_results_10))\n", + "print(np.mean(stdev_iqr_results_60))\n", + "print(np.mean(stdev_iqr_results_exp))\n", + "\n", + "print('\\n')\n", + "\n", + "outlier_frac_results_10 = photo_z_outlier_frac(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "outlier_frac_results_60 = photo_z_outlier_frac(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "outlier_frac_results_exp = photo_z_outlier_frac(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "print(np.mean(outlier_frac_results_10))\n", + "print(np.mean(outlier_frac_results_60))\n", + "print(np.mean(outlier_frac_results_exp))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('Bias', (0.0070834922179 - 0.0513398669249) / 0.0513398669249)\n", + "print((0.013491192485 - 0.0513398669249) / 0.0513398669249)\n", + "print('Standard Dev', (0.217399105893 - 0.278779776263) / 0.278779776263)\n", + "print((0.173433673493 - 0.278779776263) / 0.278779776263)\n", + "print('Standard Dev IQR', (0.0475990008076 - 0.12091776158) / 0.12091776158)\n", + "print((0.036184609478 - 0.12091776158) / 0.12091776158)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10,6))\n", + "z_max = 3.0\n", + "bin_width = 0.1\n", + "n_bins = int(z_max/bin_width)\n", + "bias_results_10 = photo_z_bias(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "bias_results_60 = photo_z_bias(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "bias_results_exp = photo_z_bias(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "plt.plot(np.arange(0,z_max,bin_width), bias_results_10, label='10 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), bias_results_60, label='60 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), bias_results_exp, label='10 + Exp Kernel templates')\n", + "plt.legend()\n", + "plt.xlabel('True Redshift')\n", + "plt.ylabel('Bias')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10,6))\n", + "z_max = 3.0\n", + "bin_width = 0.1\n", + "n_bins = int(z_max/bin_width)\n", + "stdev_results_10 = photo_z_stdev(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_results_60 = photo_z_stdev(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_results_exp = photo_z_stdev(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_results_10, label='10 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_results_60, label='60 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_results_exp, label='10 + Exp Kernel templates')\n", + "plt.legend()\n", + "plt.xlabel('True Redshift')\n", + "plt.ylabel('Standard Deviation')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10,6))\n", + "z_max = 3.0\n", + "bin_width = 0.1\n", + "n_bins = int(z_max/bin_width)\n", + "stdev_iqr_results_10 = photo_z_stdev_iqr(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_iqr_results_60 = photo_z_stdev_iqr(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "stdev_iqr_results_exp = photo_z_stdev_iqr(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_iqr_results_10, label='10 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_iqr_results_60, label='60 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_iqr_results_exp, label='10 + Exp Kernel templates')\n", + "plt.legend()\n", + "plt.xlabel('True Redshift')\n", + "plt.ylabel('Standard Deviation of IQR')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(10,6))\n", + "z_max = 3.0\n", + "bin_width = 0.1\n", + "n_bins = int(z_max/bin_width)\n", + "outlier_frac_results_10 = photo_z_outlier_frac(template_results['z_est_10'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "outlier_frac_results_60 = photo_z_outlier_frac(template_results['z_est_60'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "outlier_frac_results_exp = photo_z_outlier_frac(template_results['z_est_exp_kernel'],\n", + " template_results['z_true'], z_max, n_bins)\n", + "plt.plot(np.arange(0,z_max,bin_width), outlier_frac_results_10, label='10 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), outlier_frac_results_60, label='60 templates')\n", + "plt.plot(np.arange(0,z_max,bin_width), outlier_frac_results_exp, label='10 + Exp Kernel templates')\n", + "plt.legend()\n", + "plt.xlabel('True Redshift')\n", + "plt.ylabel('Fraction of Outliers')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "template_results_max3 = template_results.query('z_true < 3')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(16, 14))\n", + "\n", + "ax = fig.add_subplot(111)\n", + "ax.spines['top'].set_color('none')\n", + "ax.spines['bottom'].set_color('none')\n", + "ax.spines['left'].set_color('none')\n", + "ax.spines['right'].set_color('none')\n", + "ax.set_axis_bgcolor('white') \n", + "ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')\n", + "mpl.rc('xtick', labelsize=22) \n", + "mpl.rc('ytick', labelsize=22)\n", + "\n", + "fig.add_subplot(2,2,1)\n", + "plt.plot(np.arange(0,z_max,bin_width), bias_results_10, label='10 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), bias_results_60, label='60 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), bias_results_exp, label='10 + 50 Exp Kernel templates', lw=6)\n", + "plt.xlabel('True Redshift', size=22)\n", + "plt.ylabel('Bias', size=22)\n", + "\n", + "fig.add_subplot(2,2,2)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_results_10, label='10 BC03 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_results_60, label='60 BC03 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_results_exp, label='10 BC03 + 50 Exp Kernel templates', lw=6)\n", + "plt.legend(fontsize=16)\n", + "plt.xlabel('True Redshift', size=22)\n", + "plt.ylabel('Standard Deviation', size=22)\n", + "\n", + "fig.add_subplot(2,2,3)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_iqr_results_10, label='10 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_iqr_results_60, label='60 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), stdev_iqr_results_exp, label='10 + 50 Exp Kernel templates', lw=6)\n", + "plt.xlabel('True Redshift', size=22)\n", + "plt.ylabel('Standard Deviation of IQR', size=22)\n", + "\n", + "fig.add_subplot(2,2,4)\n", + "plt.plot(np.arange(0,z_max,bin_width), outlier_frac_results_10, label='10 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), outlier_frac_results_60, label='60 templates', lw=6)\n", + "plt.plot(np.arange(0,z_max,bin_width), outlier_frac_results_exp, label='10 + 50 Exp Kernel templates', lw=6)\n", + "plt.xlabel('True Redshift', size=22)\n", + "plt.ylabel('Fraction of Outliers', size=22)\n", + "plt.tight_layout()\n", + "#plt.savefig('paper_plots_revised/fig_9.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = template_results_max3['z_true']\n", + "y = template_results_max3['z_est_10']\n", + "y_exp = template_results_max3['z_est_exp_kernel']\n", + "\n", + "fig = plt.figure(figsize=(24, 12))\n", + "fig.add_subplot(1, 2, 1)\n", + "plt.scatter(x, y, alpha=0.1)\n", + "plt.plot(np.arange(0, 3.005, 0.01), np.arange(0, 3.005, 0.01), '--k')\n", + "plt.ylim(-0.1, 5.55)\n", + "plt.xlabel('z_true', size=32)\n", + "plt.ylabel('z_phot', size=32)\n", + "plt.title('10 BC03 Templates', size=32)\n", + "fig.add_subplot(1, 2, 2)\n", + "plt.scatter(x, y_exp, alpha=0.1)\n", + "plt.plot(np.arange(0, 3.005, 0.01), np.arange(0, 3.005, 0.01), '--k')\n", + "plt.ylim(-0.1, 5.55)\n", + "plt.xlabel('z_true', size=32)\n", + "plt.ylabel('z_phot', size=32)\n", + "plt.title('10 + 50 Exp Kernel Templates', size=32)\n", + "#plt.savefig('paper_plots_revised/fig_11.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing spectrum degeneracies in color space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wave_nm = pca_obj.spec_list_orig[0].wavelen\n", + "test_idx = np.where((wave_nm <= 2400.) & (wave_nm >= 99.))\n", + "wave_nm_test = wave_nm[np.where((wave_nm <= 2400.) & (wave_nm >= 99.))]\n", + "\n", + "test_colors = []\n", + "test_fluxes = []\n", + "for test_sed in pca_obj.spec_list_orig:\n", + " \n", + " test_fluxes.append(pca_obj.scale_spectrum(test_sed.flambda[test_idx]))\n", + " \n", + " test_mags = bandpass_dict.magListForSed(test_sed)\n", + " \n", + " colors = [test_mags[x] - test_mags[x+1] for x in range(len(test_mags)-1)]\n", + " test_colors.append(colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nbrs = NearestNeighbors(n_neighbors=300).fit(test_colors)\n", + "distance, idx = nbrs.kneighbors(test_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "radius = .1\n", + "num_in = []\n", + "spec_near = []\n", + "for spec_on in range(len(pca_obj.spec_list_orig)):\n", + " spec_near.append(idx[spec_on][np.where(distance[spec_on] < radius)[0][1:]])\n", + " num_in.append(len(spec_near[-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat with added filters\n", + "wave_nm = pca_obj.spec_list_orig[0].wavelen\n", + "test_idx = np.where((wave_nm <= 2400.) & (wave_nm >= 99.))\n", + "wave_nm_test = wave_nm[np.where((wave_nm <= 2400.) & (wave_nm >= 99.))]\n", + "\n", + "test_colors = []\n", + "test_fluxes = []\n", + "for test_sed in pca_obj.spec_list_orig:\n", + " \n", + " test_fluxes.append(pca_obj.scale_spectrum(test_sed.flambda[test_idx]))\n", + " \n", + " test_mags = new_bandpass_dict.magListForSed(test_sed)\n", + " \n", + " colors = [test_mags[x] - test_mags[x+1] for x in range(len(test_mags)-1)]\n", + " test_colors.append(colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nbrs = NearestNeighbors(n_neighbors=300).fit(test_colors)\n", + "distance, idx = nbrs.kneighbors(test_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_in = []\n", + "spec_near_new = []\n", + "for spec_on in range(len(pca_obj.spec_list_orig)):\n", + " spec_near_new.append(idx[spec_on][np.where(distance[spec_on] < radius)[0][1:]])\n", + " num_in.append(len(spec_near[-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Just redefine to make plot nice\n", + "new_bandpass_dict = BandpassDict([\n", + " blue_bandpass_1, blue_bandpass_3, \n", + " #blue_bandpass_3, blue_bandpass_4,\n", + " bandpass_dict['u'], bandpass_dict['g'], \n", + " bandpass_dict['r'], bandpass_dict['i'], bandpass_dict['z'], \n", + " bandpass_dict['y'], #red_bandpass_1, red_bandpass_2, \n", + " red_bandpass_2, red_bandpass_4\n", + " ],\n", + " ['Blue 1', #'blue_2', \n", + " 'Blue 2', #'blue_4', \n", + " 'u', 'g', 'r', 'i', 'z', 'y', \n", + " 'Red 1',#'red_2', \n", + " 'Red 2', #'red_4'\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_on = 8 # Pick test spectrum\n", + "fig = plt.figure(figsize=(14,16))\n", + "fig.add_subplot(2,1,1)\n", + "for near_flux in spec_near[test_on][::-1]:\n", + " plt.plot(wave_nm_test, test_fluxes[near_flux]-test_fluxes[test_on])\n", + "off_set = 0.0\n", + "for bp, bp_colors in zip(['u', 'g', 'r', 'i', 'z', 'y'], ['purple', 'blue', 'green', 'yellow', 'orange', 'red']):\n", + " wavelen_bp = bandpass_dict[bp].wavelen[np.where(bandpass_dict[bp].sb > 0.)]\n", + " plt.plot(wavelen_bp, np.ones(len(wavelen_bp))*.0004+off_set, lw=8, c=bp_colors, label=str('LSST'+bp))\n", + " off_set += 0.00006\n", + "mpl.rcParams['xtick.major.size'] = 15\n", + "mpl.rcParams['xtick.major.width'] = 3\n", + "mpl.rcParams['xtick.minor.size'] = 15\n", + "mpl.rcParams['xtick.minor.width'] = 3\n", + "\n", + "plt.legend(fontsize=22)\n", + "\n", + "plt.ylabel('Flux Difference', size=22)\n", + "plt.title('LSST Filters Only, Nearest Neighbors within %.2f mag radius' % radius, size=22)\n", + "\n", + "plt.ylim(-0.0017, 0.001)\n", + "plt.xlim(75., 2405.)\n", + "\n", + "fig.add_subplot(2,1,2)\n", + "for near_flux in spec_near_new[test_on]:\n", + " plt.plot(wave_nm_test, test_fluxes[near_flux]-test_fluxes[test_on])\n", + "\n", + "mpl.rcParams['xtick.major.size'] = 15\n", + "mpl.rcParams['xtick.major.width'] = 3\n", + "mpl.rcParams['xtick.minor.size'] = 15\n", + "mpl.rcParams['xtick.minor.width'] = 3\n", + "\n", + "off_set = 0.0\n", + "for bp, bp_colors in zip(['Blue 1', 'Blue 2', 'u', 'g', 'r', 'i', 'z', 'y', 'Red 1', 'Red 2'], \n", + " ['black', 'gray', 'purple', 'blue', 'green', 'yellow', 'orange', 'red', 'indianred', 'brown']):\n", + " wavelen_bp = new_bandpass_dict[bp].wavelen[np.where(new_bandpass_dict[bp].sb > 0.)]\n", + " if len(bp) < 2:\n", + " bp_label = str('LSST'+bp)\n", + " else:\n", + " bp_label = str('Top Hat '+bp)\n", + " plt.plot(wavelen_bp, np.ones(len(wavelen_bp))*-.00072-off_set, lw=8, c=bp_colors, label=bp_label)\n", + " off_set += 0.00006\n", + "\n", + "plt.xlabel('Wavelength (nm)', size=22)\n", + "plt.ylabel('Flux Difference', size=22)\n", + "\n", + "plt.title('LSST + 4 Top Hat Filters, Nearest Neighbors within %.2f mag radius' % radius, size=22)\n", + "\n", + "plt.ylim(-0.0017, 0.001)\n", + "plt.xlim(75., 2405.)\n", + "plt.legend(fontsize=22, ncol=2)\n", + "plt.tight_layout()\n", + "\n", + "#plt.savefig('paper_plots_revised/fig_12.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b6420d9874da3582309bee3ed28461b09b2f3992 Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Sat, 28 Oct 2017 23:42:21 -0700 Subject: [PATCH 4/7] Updated setup. --- esp/__init__.py | 14 +- esp/esp.py | 3 + esp/gp_utils.py | 2 + esp/lsst_utils/__init__.py | 10 +- esp/pca.py | 4 +- esp/plot_utils.py | 2 + esp/spec_utils.py | 4 +- examples/esp_paper_plots.ipynb | 624 ++++++++++++++++++++++++++++++--- setup.py | 9 +- tests/test_esp.py | 6 +- tests/test_pca.py | 6 +- 11 files changed, 615 insertions(+), 69 deletions(-) diff --git a/esp/__init__.py b/esp/__init__.py index 4326013..fea0923 100644 --- a/esp/__init__.py +++ b/esp/__init__.py @@ -1,5 +1,9 @@ -from .esp import * -from .pca import * -from .plot_utils import * -from .spec_utils import * -from .gp_utils import * +__all__ = ["estimateBase", "NearestNeighborEstimate", + "gaussianProcessEstimate"] + +from .esp import estimateBase, nearestNeighborEstimate, gaussianProcessEstimate +from .pca import pcaSED +from .plot_utils import plotUtils +from .spec_utils import specUtils +from .lsst_utils import Bandpass, BandpassDict, Sed, PhysicalParameters +from .gp_utils import optimize diff --git a/esp/esp.py b/esp/esp.py index 730d2ba..1977dd7 100644 --- a/esp/esp.py +++ b/esp/esp.py @@ -5,6 +5,9 @@ from .gp_utils import optimize from sklearn.neighbors import KNeighborsRegressor as knr +__all__ = ["estimateBase", "NearestNeighborEstimate", + "gaussianProcessEstimate"] + class estimateBase(object): diff --git a/esp/gp_utils.py b/esp/gp_utils.py index 25dc387..8d265a7 100644 --- a/esp/gp_utils.py +++ b/esp/gp_utils.py @@ -1,6 +1,8 @@ import numpy as np import scipy.optimize as op +__all__ = ["optimize"] + def optimize(gp_obj, x, y, **kwargs): diff --git a/esp/lsst_utils/__init__.py b/esp/lsst_utils/__init__.py index 6df5c8e..d8356b9 100644 --- a/esp/lsst_utils/__init__.py +++ b/esp/lsst_utils/__init__.py @@ -1,4 +1,6 @@ -from .Bandpass import * -from .BandpassDict import * -from .PhysicalParameters import * -from .Sed import * +__all__ = ["Bandpass", "BandpassDict", "PhysicalParameters", "Sed"] + +from .Bandpass import Bandpass +from .BandpassDict import BandpassDict +from .PhysicalParameters import PhysicalParameters +from .Sed import Sed diff --git a/esp/pca.py b/esp/pca.py index 61d1822..26406c1 100644 --- a/esp/pca.py +++ b/esp/pca.py @@ -3,7 +3,9 @@ import numpy as np from .spec_utils import specUtils from sklearn.decomposition import PCA as sklPCA -from .lsst_utils.Sed import Sed +from .lsst_utils import Sed + +__all__ = ["pcaSED"] class pcaSED(specUtils): diff --git a/esp/plot_utils.py b/esp/plot_utils.py index b36cfe9..f7a3fb4 100644 --- a/esp/plot_utils.py +++ b/esp/plot_utils.py @@ -3,6 +3,8 @@ import matplotlib.pyplot as plt plt.style.use('ggplot') +__all__ = ["plotUtils"] + class plotUtils(object): """ diff --git a/esp/spec_utils.py b/esp/spec_utils.py index f60f5f2..5c7ad01 100644 --- a/esp/spec_utils.py +++ b/esp/spec_utils.py @@ -3,7 +3,9 @@ import os import math import numpy as np -from .lsst_utils.Sed import Sed +from .lsst_utils import Sed + +__all__ = ["specUtils"] class specUtils(object): diff --git a/examples/esp_paper_plots.ipynb b/examples/esp_paper_plots.ipynb index cd361ed..0f1cf82 100644 --- a/examples/esp_paper_plots.ipynb +++ b/examples/esp_paper_plots.ipynb @@ -11,10 +11,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "# Run with ESP version 0.1.0\n", + "\n", "import esp\n", "import numpy as np\n", "import matplotlib as mpl\n", @@ -43,9 +45,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File On 100 out of 959\n", + "File On 200 out of 959\n", + "File On 300 out of 959\n", + "File On 400 out of 959\n", + "File On 500 out of 959\n", + "File On 600 out of 959\n", + "File On 700 out of 959\n", + "File On 800 out of 959\n", + "File On 900 out of 959\n", + "Done loading spectra from file\n" + ] + } + ], "source": [ "home_dir = os.getenv('HOME')\n", "galaxy_dir = '%s/lsst/DarwinX86/sims_sed_library/2016.01.26/galaxySED/' % home_dir\n", @@ -55,11 +74,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On Spectrum 0 out of 959\n", + "On Spectrum 100 out of 959\n", + "On Spectrum 200 out of 959\n", + "On Spectrum 300 out of 959\n", + "On Spectrum 400 out of 959\n", + "On Spectrum 500 out of 959\n", + "On Spectrum 600 out of 959\n", + "On Spectrum 700 out of 959\n", + "On Spectrum 800 out of 959\n", + "On Spectrum 900 out of 959\n", + "789\n" + ] + } + ], "source": [ "new_spec_list = []\n", "i = 0\n", @@ -95,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -104,9 +141,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Burst.50E09.1Z.spec.gz\n", + "Const.10E09.0005Z.spec.gz\n", + "Exp.15E06.002Z.spec.gz\n", + "Inst.32E08.25Z.spec.gz\n", + "(array([ 0, 1, 2, ..., 6558, 6559, 6560]),)\n" + ] + } + ], "source": [ "print(new_spec_list[190].name)\n", "print(new_spec_list[240].name)\n", @@ -117,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -127,9 +176,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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NGkVQkPVpREYxMfo0PtZ6U42BjrFMQsOGDbP6eiZM1tatWzd++OEHRo0aZUoy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(14,12))\n", "\n", @@ -170,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -261,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -312,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -355,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -376,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -387,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -405,9 +465,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/esp/lsst_utils/Sed.py:1399: RuntimeWarning: divide by zero encountered in log10\n", + " mags = -2.5*numpy.log10(fluxes) - self.zp\n", + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:32: RuntimeWarning: invalid value encountered in double_scalars\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 20\n", + "Run 40\n", + "Run 60\n", + "Run 80\n", + "Run 100\n", + "Run 120\n", + "Run 140\n", + "Run 160\n", + "Run 180\n", + "Run 200\n", + "Run 220\n", + "Run 240\n", + "Run 260\n", + "Run 280\n", + "Run 300\n", + "Run 320\n", + "Run 340\n", + "Run 360\n", + "Run 380\n", + "Run 400\n", + "Run 420\n", + "Run 440\n", + "Run 460\n", + "Run 480\n" + ] + } + ], "source": [ "np.random.seed(2314)\n", "\n", @@ -493,7 +600,6 @@ " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", " test_exp_params.append(gp_spec.params)\n", " \n", - " \n", " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", @@ -524,11 +630,196 @@ "training_colors_full = np.array(training_colors_full)" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 0\n", + "Run 20\n", + "Run 40\n", + "Run 60\n", + "Run 80\n", + "Run 100\n", + "Run 120\n", + "Run 140\n", + "Run 160\n", + "Run 180\n", + "Run 200\n", + "Run 220\n", + "Run 240\n", + "Run 260\n", + "Run 280\n", + "Run 300\n", + "Run 320\n", + "Run 340\n", + "Run 360\n", + "Run 380\n", + "Run 400\n", + "Run 420\n", + "Run 440\n", + "Run 460\n", + "Run 480\n" + ] + } + ], + "source": [ + "np.random.seed(2314)\n", + "\n", + "#li_flux_results = np.zeros((25000, 6071))\n", + "#nn_flux_u_results = np.zeros((25000, 6071))\n", + "#nn_flux_2u_results = np.zeros((25000, 6071))\n", + "#nn_flux_2d_results = np.zeros((25000, 6071))\n", + "#nn_flux_4u_results = np.zeros((25000, 6071))\n", + "#nn_flux_4d_results = np.zeros((25000, 6071))\n", + "gp_exp_flux_results = np.zeros((25000, 6071))\n", + "gp_sq_exp_flux_results = np.zeros((25000, 6071))\n", + "#gp_matern_32_flux_results = np.zeros((25000, 6071))\n", + "#gp_matern_52_flux_results = np.zeros((25000, 6071))\n", + "\n", + "training_colors_full = []\n", + "training_coeffs_full = []\n", + "training_eigenspectra = []\n", + "training_meanspec = []\n", + "\n", + "test_colors_full = []\n", + "\n", + "test_exp_params = []\n", + "test_sq_exp_params = []\n", + "test_matern_32_params = []\n", + "test_matern_52_params = []\n", + "\n", + "distances_all = []\n", + "\n", + "test_flux_orig = []\n", + "flux_errors_full = []\n", + "\n", + "total_flagged = 0\n", + "n_runs = 500\n", + "min_wavelen = 299.\n", + "max_wavelen = 1200.\n", + "n_colors = 5\n", + "n_comps = 9\n", + "\n", + "for i in range(n_runs):\n", + " if i % 20 == 0:\n", + " print('Run %i' % i)\n", + " \n", + " if os.path.exists('results'):\n", + " shutil.rmtree('results')\n", + " \n", + " training_colors, training_list, training_names, test_list, test_names, \\\n", + " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", + " bandpass_dict, min_wavelen, max_wavelen)\n", + " \n", + " new_pca_obj = esp.pcaSED()\n", + " new_pca_obj.spec_list_orig = training_list\n", + " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", + " \n", + " colors = training_colors\n", + "\n", + " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", + " distance, idx = nbrs.kneighbors(test_colors)\n", + " distances_all.append(np.ravel(distance))\n", + " \n", + " #print 'Linear Interp'\n", + "# li_spec, li_colors = linear_interpolation_spectra(colors, test_colors, new_pca_obj.spec_list_orig, \n", + "# bandpass_dict, min_wavelen, max_wavelen)\n", + "# li_flux_results[i*50:(i+1)*50] = np.abs(np.array((li_spec - test_fluxes)/test_fluxes))\n", + " \n", + " #print 'Nearest Neighbor Results'\n", + "# nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + "# nn_spec = nn_obj.nn_predict(1)\n", + "# nn_flux_u_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " #nn_spec = nn_obj.nn_predict(2)\n", + " #nn_flux_2u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", + "# nn_spec = nn_obj.nn_predict(2, knr_args=dict(weights='distance'))\n", + "# nn_flux_2d_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", + "\n", + " #nn_spec = nn_obj.nn_predict(4)\n", + " #nn_flux_4u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", + " #nn_spec = nn_obj.nn_predict(4, knr_args=dict(weights='distance'))\n", + " \n", + " #print 'Gaussian Process Results'\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", + " test_exp_params.append(gp_spec.params)\n", + " \n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", + " test_sq_exp_params.append(gp_spec.params)\n", + " \n", + "# gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + "# gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", + "# gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + "# recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + "# gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + "# test_matern_32_params.append(gp_spec.params)\n", + " \n", + "# gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + "# gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", + "# gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + "# recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", + "# gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", + "# test_matern_52_params.append(gp_spec.params)\n", + " \n", + " training_colors_full.append(colors)\n", + " test_colors_full.append(test_colors)\n", + "\n", + "\n", + "test_colors_full = np.array(test_colors_full)\n", + "\n", + "training_colors_full = np.array(training_colors_full)" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "[array([-11.02207729, 4.77361603]), array([-13.96023833, 2.13156115]), array([-17.59610534, -0.96467728]), array([-19.79497933, -13.27023188]), array([-21.28017721, -13.80637852]), array([-21.74208749, -14.16324311]), array([-22.11764147, -14.36039222]), array([-23.05681041, -15.46075091]), array([-24.84448021, -15.5739952 ])]\n", + "[array([-11.2550589 , 4.37618544]), array([-15.16269104, -0.17042565]), array([-18.63210908, -2.69566832]), array([-19.54205714, -5.57521916]), array([-20.66959493, -5.5241298 ]), array([-22.10726755, -5.23304072]), array([-23.6345675 , -12.51136918]), array([-24.38139955, -13.33114988]), array([-24.98276997, -14.42287437])]\n", + "[array([-11.25444705, 4.48069245]), array([-15.04177928, -0.45475115]), array([-17.6286331 , -1.45762491]), array([-19.45297358, -4.82311417]), array([-20.36503317, -12.79350627]), array([-21.33194789, -13.32536929]), array([-22.28499836, -13.51599869]), array([-22.70957049, -13.55544863]), array([-24.52930451, -13.60538845])]\n", + "[array([-11.44655224, 4.06530313]), array([-16.22874545, -0.97132244]), array([-19.38768499, -6.60459258]), array([-19.6774699, -4.7427898]), array([-19.77607617, -9.63081001]), array([-21.31841287, -8.50492599]), array([-22.11244633, -9.13452811]), array([-22.88324644, -13.69198587]), array([-24.85286842, -14.27590948])]\n", + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:32: RuntimeWarning: invalid value encountered in double_scalars\n", + "[array([-11.35058398, 4.42537694]), array([-14.84507009, -0.23824238]), array([-18.06283476, -2.71023966]), array([-20.32512608, -6.34834806]), array([-20.79609481, -6.00838321]), array([-21.51965251, -3.89801573]), array([-22.48659084, -5.99184903]), array([-24.12463544, -11.71187796]), array([-26.88263336, -14.37848589])]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating overall means\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'nn_flux_u_results' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mgp_exp_flux_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp_exp_flux_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnn_flux_u_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_flux_u_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mgp_sq_exp_flux_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp_sq_exp_flux_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mnn_flux_2d_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_flux_2d_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'nn_flux_u_results' is not defined" + ] + } + ], "source": [ "print(\"Calculating overall means\")\n", "\n", @@ -546,9 +837,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating overall means\n" + ] + } + ], + "source": [ + "print(\"Calculating overall means\")\n", + "\n", + "gp_exp_flux_mean = np.mean(gp_exp_flux_results)\n", + "#nn_flux_u_mean = np.mean(nn_flux_u_results)\n", + "gp_sq_exp_flux_mean = np.mean(gp_sq_exp_flux_results)\n", + "#nn_flux_2d_mean = np.mean(nn_flux_2d_results)\n", + "#nn_flux_2u_mean = np.mean(nn_flux_2u_results)\n", + "#nn_flux_4d_mean = np.mean(nn_flux_4d_results)\n", + "#nn_flux_4u_mean = np.mean(nn_flux_4u_results)\n", + "#li_flux_mean = np.mean(li_flux_results)\n", + "#gp_matern_32_flux_mean = np.mean(gp_matern_32_flux_results)\n", + "#gp_matern_52_flux_mean = np.mean(gp_matern_52_flux_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating mean spectra\n" + ] + } + ], "source": [ "print(\"Calculating mean spectra\")\n", "\n", @@ -566,9 +893,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating mean spectra\n" + ] + } + ], "source": [ "print(\"Calculating mean spectra\")\n", "\n", @@ -583,9 +918,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exponential\n", + "[ 5.13662935e-05 6.16229323e+01]\n", + "[ 1.58303512e-06 6.09446228e-01]\n", + "[ 9.93519969e-08 1.22736736e-01]\n", + "Squared Exponential\n", + "[ 1.65498340e-04 9.66212252e+00]\n", + "[ 3.65785857e-05 2.14345205e+00]\n", + "[ 9.93448226e-06 1.18819618e+00]\n", + "Matern 3/2\n", + "[ 9.21177307e-04 2.56823430e+02]\n", + "[ 6.58366258e-05 1.60376403e+01]\n", + "[ 3.49375175e-05 3.69936584e+01]\n", + "Matern 5/2\n", + "[ 2.75322559e-04 3.60135671e+01]\n", + "[ 8.09316365e-05 8.94425269e+00]\n", + "[ 3.38563023e-05 5.70794355e+00]\n" + ] + } + ], "source": [ "#Exponential Kernel 1st 3\n", "print('Exponential')\n", @@ -611,9 +969,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0725325573089\n", + "0.0334792084461\n", + "0.0376008675272\n", + "0.0320099044064\n", + "0.0969591905896\n", + "0.0922874808829\n", + "0.0962248898827\n" + ] + } + ], "source": [ "print(gp_exp_flux_mean)\n", "print(gp_sq_exp_flux_mean)\n", @@ -626,9 +998,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0725284479597\n", + "0.0334778622608\n" + ] + } + ], + "source": [ + "print(gp_exp_flux_mean)\n", + "print(gp_sq_exp_flux_mean)\n", + "#print(gp_matern_32_flux_mean)\n", + "#print(gp_matern_52_flux_mean)\n", + "#print(nn_flux_u_mean)\n", + "#print(nn_flux_2d_mean)\n", + "#print(li_flux_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.040415899425\n", + "0.0208915343935\n", + "0.0262856974878\n", + "0.0210929388887\n", + "0.0738637877199\n", + "0.061807920037\n", + "0.0665429635533\n" + ] + } + ], "source": [ "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist)\n", "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", @@ -661,9 +1071,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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HXHQzyQkhREB54IEHWLRoER988AH33HNPufVNmzYFoHHjxn4NlvbZZ58B5ojx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fd16//aa6/xwAMP8MADD/T4v/r666+zaNEiHnvssaDI37t3L1dccQUfffQRW7Zs6bQQ9cknn/D4\n44+Tk5PDCy+80Cmh3YYNG7jpppv48Y9/zMyZMykoKIj4MzpZuFo1omMMKIpCdKyBY+W+sPMDVR/z\ns3FNK2eeExMy2VtH6mr8AKSkyym75MQghICqCsSuLYhdW2Hvl+D1wtQZ1C28mye3ONhb28AVY5K4\nfnKaTOw2CMhP9ASgxMTBpNBWE0tsDAah4XJHJtB7si0rir5T1YBePEnmXhTHuk9b2vsZFIwh/huq\nKvwRjSscAiI81CNMWtAlX0Xiav6FydN/91fQw2lOZOUDvyWblvSvHfdxGhr0kJzU1ND5j1evXs27\n777bbfuDDz7Yp+BpbGxk48aNPPzww2iaxsSJEznnnHP6HNO4cePIyMhg9+7d1NXVBce2bt06srKy\nWLx4MT/60Y/47LPP+NrXvhbcB+3iviPH494e4Nprr2XJkiW89dZbQYH+5ptvAvRpeczJyQlZpiox\nMZFHH32UefPm8cEHHwQFerg888wzeL1eHnnkkU7iHGDs2LE8/PDD3HHHHbzyyishBfrChQt7FecA\nl19+eSdxDvpn8Yc//IGSkhK2b9/OrFmzwh5zZmYm9957LwsWLGDYsGGYTCZKSkr485//zIcffshd\nd91FXFwc8+fP79b3ww/1gjXhXsc1a9bw3HPPYTAYePjhh8MeY0dKSkoAiImJ4bnnnutVnAP84x//\n4OjRo1x99dXcdVdn58aUlBSefvppzjnnHF555ZWQAn3s2LHcf//9vYrMvLw8fvGLX3SywI8dO5Zr\nrrmG1157jXXr1nUS6E899RQATz/9dLds8zNnzuR73/seS5Ys4bXXXuN//ud/en1/QwmXUyO6raxa\nTIwBTQOXU+Bo8JGRbcbYS6KgysO6MeTIIW9Iga5pgqZGlcRkI3U1fmLjDSGTwkkkA4Xw+6FkF2Lb\nBsSXRe1l0jJyUOZcjDJ5OltTx/G71ZV4VcEP5uYwZ3jCyR30aUxEAt1msxmAa4ELgRygp1okwm63\nX3KcY/tKYIiJw6p6cLkji8t4t7iOG6akh9ijK/RQ8d0dSU7tfulNpsF0SwkxnhCn6xSD3uMyhEQi\n+aqwb9++kK7bt9xyS0iB3rF2eEcmT57MX//6124u1D0xZ84c3n33XdatW8dVV13FsWPH2L9/P9de\ney3z5s0DdDf3rgI9sK8jH330EUlJSWEtDvRERkYG8+fPZ/ny5ZSUlJCens6yZcsYP348U6ZM6bO/\nEIKioiK++OILKisrcbvdeibetkWdgwcPRjQeTdNYtWoViqL0aHEOvN/NmzeH3B+O0O1JwI8aNYqS\nkhKqqqpC7u+J+fPndxPf06dP569//Su/+MUveP7553n00Ue7tXG5XKxatYrx48eHZeXdvXs3d955\nJ6qq8oMf/KDf176goAC3282xY8e49dZbee2114iL69mrLZA3oadrkpOTw7Bhwzh48CBlZWXd4tgv\nueSSPr8j8+bNC1mKrrBQr1rT8ZpUV1ezY8cOkpKSmDdvXjBEpCOBBZae/k+GKs5WjYxsfZEiINQ/\nX9mCq1Vj7CQrYybqn5HQBEqHkENNExyr8GEwQM0xvy70Y/T+QgiOlvrYt8uNs1Ujr8BMfY1Ktow5\nP2XRVn+EEp+AcubsvhufYITPh9i2AbZ9gdixGVytYI6CCWegXHI1ysQzUdKzcPs13i2u482VRxmW\naOEH5+aQlyATFQ4mYQt0m82WBHwEnE1o42dHpLIKl5hYrGozLk9kN99/7Agt0AMXJlR8d1fOnhvL\nzq0uzp6jx5kbB1Wgh0enOujSw13yFWEgLNEmkwm/f+A9XgabQMmzurq6kPtvu+02brvttuDrQG3u\nnuhYBz0qKoqsrCxmzJjBnDlzInIvnzt3bieB3lGAFxQUkJ+fH9xWVVVFSUkJubm5jBgxotNxduzY\nwZEjR7juuuswmY7PaW3hwoUsX76cN998k7y8PDweT48LEh2pqanh29/+Nps2beqxTXNzc4/7QtHQ\n0BDs09cCQU/XNi8vr8/z9JT1PCBS3W53cNvrr7/Oxo0bO7UzGAz89Kc/7TVmO8C9997Liy++yN69\ne7tlXF+5ciUulyusRYX9+/dz/fXX43A4uOOOO7j33nv77NMT2dnZPPHEE9hsNjZt2sSNN97Yq0gv\nK9NTBf33f/93n8euq6vrJtCP55rEx+tVWTomiguEljQ2NpKdnd3neE4VVFXgcYugsI6J03/7vBqx\ncQaOHPIyeoKFpkaN9atamHJWNDn5uqW8rtqPzyuYcIaV4m1ujpZ6GT1BF/NHDnnZXuQiMdlIQXYU\npfu9gB5/Ljn10NZ8hHj1TwhFQfnvBzDMPO9kDwkA0dSox5Sv+o9eZSo+EeXMWShnzITx01AsFlw+\njU3lLXy2u5zNFS14VcH5IxK4a0YWFpP05hhsIvnGPwbMAMqBP6FnbG8ajEF9pYiJJVqtxe3tyRmh\nZ1RNdEsEF5h/+sNwD8/KNZOVqy8MHKx309zSvyLoQgj8fjCbe578hlovCO3iLkL+LZFITk8mT57M\nO++8w/bt2wfkeF3roPeXgCU8IMK7urDPnTuXN954gyNHjgRF4WC5twe4+OKLSU5O5p133iEzMxOT\nycS1117bZ7+HHnqITZs2cfbZZ/PAAw8wceJEEhISMJvNeL3ebosK4RBIgmc0Grnmmmsi7g+EtMJ2\nJVyPB4CNGzeG9La4//77wxLoSUlJpKWlUVVVRWVlZSchGnBv7yt7+4EDB7DZbNTW1nLzzTfz85//\nPOzx90RhYSF2uz0o0hctWsSrr74aUqQHLNQXXXRRcPGrJwJ13DsSzjWJZKErMJ7ExEQuvfTSkBb0\nAIGFtVMBZ4v+PmLbhHlcvIEzZkSTkm6ioVZl6wYndTV+Duzx4PMKdmx2kZZhIspioPKoD6MJCgot\nHCv3caTUy6jxFoQG+3a5SUoxMveiOBRFISXdRNl+D+nZUqCfaojibYjXntOTQvv9iJd+hzBHoZzZ\nf0+q4xqPEHDkIGLFB4gNq8Hvg0nTMVz4NZgwFcVgxOXTKCpv4fPDNWyuaMWrCpKsRi4cmci84QlM\nyJBJ4E4UkXzjrwIagJl2u71ikMbz1SM6Fqvfg8sfuTj2hRLoQgOUiMO37/+wlASM2EyRPyCLt7k5\nuM/DZdcm9ukmr4ne3Ss6W9ClCV0iOd258MILefTRRykuLmbPnj2MGzfuZA8J0K2EBQUFlJaWUlZW\nxrp16xg1alTQCjhv3jzeeOMN1qxZE7RM9+TeHhMTw3nnHb/lJCoqiquvvpqXXnqJqqoqFixYQFpa\nWq99nE4nK1aswGg0snTp0mAG+AClpaX9GktKSgpWqxW3282vfvWrPmOjTwRPP/00Tz/9dKdtkXiW\nqKpKU5Nud+j4fnw+H8uXL6egoKDXJIMHDx7EZrNRVVXFjTfeyGOPPdaPdxGajiK9qKiIRYsW8dpr\nr3X73LOzsyktLeWWW24JGUd/ognkPrBYLDzzzDOnpJdPKJoa9TlbQpIenqgoCvkjdJdfi9WAaQvs\n2uqmqVElr8BMeZmPXdtcTJgazbHythh1k0J+QRTbi1wc3OvBaFJwOQVTzrIGRVDusKg+k8hJhh6i\n8gjac09Adj51Nz2IxaQQ9+dfoj3/Gwx3/w/K5OmDPwavB0r3Iw7sRhzYAwd2Q0szRFlQ5l6EcsHX\nULLzcPpUNh1u5bPDTWxpE+XJViMXFSYyZ1gC49OjI6oKJRkYIvFRSAc+k+J8gImNI1r14PJHbi32\nqd37KG31TX2+yB+CrYReJOjLkn20THfBUnt5DzVOfTwdFw5CWtA7yndpQZdITnsKCwu54oorAPjR\nj36E1+s9ySNqJ2ARX7p0KZWVlZ0s5AGX+bVr1wat610ToR04cIB9+/Zx/vnnh2WZDIeFCxeSnJxM\ncnIyN9xwQ5/tm5qa0DSNuLi4buIc4J133umxbyAJWChRZTKZggsSgTr2pzqffvopLpeLuLg4Ro0a\nFdz+2Wef4XA4erWel5aWct1113Hs2DEWLlzIE088MeCWpoBIz8rKoqioiBtvvJHW1tZObQLJ+obK\nNcnLy2P06NFUV1ezYcOGkz2cAaPJoVekiYvvPo02mRRyh0XR1KhisSpMnh5D4TgLR0t9LHu/CY9b\nBOuY5w6PIivXTPF2N7u2ukhONZKeJa3lpzKi2YH2zKNgNlNxy0+4b/kxHlpZTdPt/wO5w9H+vASx\n58vBO39pCdorz6DdfyPab36MeOdvcKwcZeoMlP/6LoZfv4ThxruoisvgL0XHWPz2fp76rIK9tW4u\nLkzk8YuG8eLVo7jj7CwmZcZIcX6SiESgVwKRFeuW9E2UBavmxdUP73IthIBV2gK3NTVyga4Cl3wj\ngfwRnVdrtf55vgdx+jrUA+6QlCgUooOCD/X+JBLJ6ceSJUvIz8+nqKiIhQsXsnPnzpDtdu/eHaxn\nfiIICNClS5d2eg2QlpbGuHHj+OSTT6ioqGDs2LFkZGR06j+Q7u0BJk2axM6dO9m5cycLFizos316\nejpJSUk4HI5u2fBXrlzJCy+80GPfQK3vQCbxrtx///2YzWYefvhh3n///W73diEEW7duZfXq1X2O\n80TgcrlYunRpN1ELujgP1Gu/+eabO2UoD1zHngT64cOHg+L8uuuu48knnxw0N9CuIn3RokWd3s9N\nN91EVlYW//jHP/jd736Hy+XqdoyysrJeF2YGmsDnetddd7FmzZpu+1VVZe3atWzduvWEjel4aWpU\niUswYOghU/uwkfo8avQEKyaTwpiJVsZNtjJxWjQzz40lO0///zIaFc6aE8Oo8br1fdwU6UI8FBF+\nP6K5CeHx9D6H9XnRnv0VOBpouf0nPLbNidGg0Oj28/jGBnz3PALpWWh/fAyx9YsB8xQVHg/a2mWo\njz2A9qsHEZvWocycj+G7P8Xw21cxPvZnDDffi2HeAg64zfxmXTl3/esgy/Y3Mm94Ao9fPIwXv1HI\n7WdnMVGK8iFBJMt07wA32Wy2aLvd3v2OL+kXiqIQjYZbRP5lCOXGrrRZoP39sKADRFkMZOaYOHKo\n3Yr10XsOps2IIWcA3Kw00UGEh7gxdbrvSYEukXwlSElJ4f333+fOO+9k48aNXHLJJRQUFDB27Fhi\nY2NxOp2UlJRw4MABQLdUh5PM6ngJ1DR3u90YjcZumbjnzp3L7t27g3935cMPPyQqKqrPUmKDidFo\n5J577uGXv/wl3/3ud3n55ZfJz8+nrKyMrVu3cs899/CHP/whZN/LLruM9evXc88993DuuecGLfA/\n+clPSElJYerUqfz+97/ngQce4Dvf+Q6PP/44Y8aMISkpibq6Onbt2kVtbS133333gLj4Hy9er5ef\n/OQnPProo0yaNImcnBx8Ph8lJSXs378f0Mu6PfTQQ8E+mqaxbNkyMjMzOeuss0Ie97bbbqOiogKL\nxYKmaTzwwAMh2333u9/tZJnvLx3d3Tdu3BiMSY+NjSU+Pp6//e1vLF68mCeffJIXX3yRcePGkZWV\nRUtLCyUlJZSWlnL22Wf3O3dApFxxxRX89Kc/5fHHH+eGG26gsLCQkSNHEhsbS1VVFcXFxTgcDn7z\nm98wbdq0EzKm/tCxxnlTo0pqRs9T6KQUExdcEU9MW3Z3o1EJJoLriqIojJ8SzZiJ1l5Ls0lOLKKp\nEbFzs15ybNdWcLdJH0WBKIv+YzaDwQgGg/7b64H6GtTbf8CvD0dT53Tz2EXDaHD7eWJNOc/saOHB\n+x+Fp36K9qfHITMX55XXIabOQomOiWx8Pi/s2oLY9Bli+0Z9fDnDUL51B8rM+SgxeviLEIIjDg/b\nKlv54mgLO6ucxJgNfGN8CleOTSY1RlYIGIpEItAfAS4BXrfZbN+22+2nTrrNIU60ouESkZVZg94z\ntav+/q/KZeWaKRxn4cAePROrpsLm9c6QAr3yqBevJ3whrQmBVnYAsKJUHgEKOu3vWAddJomTSL46\nZGZm8u6777JixQref/99Nm3axLp16/B6vcTHx1NQUMBtt93GVVdddcIm8SkpKUycOJGdO3cyZcqU\nbi7i8+bNC1qguwr08vJytm/fzvnnnx/Mbn2yuPPOO8nPz+e5555j37597N27l7Fjx/KHP/yBa665\npqxm6JAAACAASURBVEeBfsstt9Dc3My7777L8uXLg9m577333mDStauuuoqpU6fy0ksvsWbNGtav\nXw/oZeEmTpzIhRdeGAxhONlER0dz7733sm3bNg4cOEBxcTE+n4/U1FQWLFjAddddx+WXX96pT1FR\nETU1Ndx00009WjYbGxsBPXv522+/3eP5bTbbgAh06F2kT5w4keXLl7N06VKWLVvGzp072bx5M6mp\nqeTm5nLNNdec8Gty1113MX/+fF544QW++OIL1q5di9FoJCMjg1mzZnHxxRd3++zDQQi9Xrg1xoDF\nMniZpZ2tGus+bWb81Ggys024XSIYf94TsXGRzeukOD/5CCEQ61fq2c1LS3RDUVIKytnzIHc4eL3g\ndYPHDR6PnmhN0/SJsqYhNBW+/i3+ohays9rB/bOzGZceDcDiaem8srWGrDgzNz38e8TmzxEr/k3z\ni0+DJRpl9vko51wIwwtRekiOKVxOKN6G2PI5YnsReFwQG48yfQ7K7Ath9AQURcGnCooON7G5opVt\nla3UtoWZ5sRHcfO0dBaMSiI2KnLdITlxKOGKIJvN9jyQBHwTPXv7RuAwEEoJCrvdfsdADXIIICoq\nBi/0/sU/vclHCRN5a9GkPtte9dqe4N8vXFVIRlznla8bX9lCizmG38xJYkxBVthjCBz3t5cVUJhi\n5fBBD9uLOjtKfG1h94yv/3qzMfj3gqsSsFi731RaPCo3/lN3kXzq4jzq9+3jV2UxTPNW8MgtF3Rq\nu2zlVhbl2wFY4buDSeMLuh0vLS2N2trasN+bZPCQ1yI8nE4nMTGRrY5HyqlaZu10xGQy8dxzz/Hw\nww/z61//mhtvvPFkD+kry/F+Lx5++GH++te/8sYbb3DuuecO4Mi+egzGPUpoAkejigIkppgG5V4r\nhGD9qlbqqv0kpRgZPzWa9StbmHlubLAO+qmGfHZ3RzTUof39WdixCfIKUKbPRplyNuSPxOFWqXX6\niY0yEBtlJNZs6NEN/P3d9by0pRrbpFRunNqeeFkIwZ83VvHx/kbumZXFRYX6nDqxsYaGd15FFK0F\nv18veTbpTJh8FsqEadBYr1vyd2yC/cWgqhAXjzLtHJTpc2DsZJS2Ep4Ot5+P9zfyn32NNLj8xJoN\nTMmKZVp2LGdkx5AZJxMO9kZbUsshsVIWiQX927Qn4E4AevPZE8DpJNAHlSSDilcx0eJViYtgRUvV\nVKDrw0G/RGo/ssIDfFbWRGGKFVMvJdMipWMsudbUiGZo+7cLsTjUacFIWtAlEskpSkZGBg888MCA\nxp9LTjyjR4/mwQcfHJDSfZKBJzhLGMQp9cF9Huqq/SSnGmmoU6k8oocA9mVBl5waCCEQX6xC/ON5\n8PtQrr8N5fwrqGr188XRZtYvO8zeWle3CkTRJgMJViOp0SZSY0ykxpgxGRTe3lXH7GHx3DClc4UN\nRVG4/exMqlp9/GnDMaKMBuYOj8c8ajyGW+9H2P4bsXML7NiM+HITrF/Z+Zy5w1Eu/gbKpOkwajyK\nsf3/r7TBzb/2NrD6UBM+TXBGVgzfnZnFtOxYGU9+ihKJQL9t0EZxCrBv3z7GjBkzKMfOMOkryjWt\nvsgEeggRbmgTtYEatZES+CIbu5RLS0zu/4Ooo4uFJkC0uQkqIfR31zroPlWwu8bJlKyTX8JHIpFI\nwuXrX//6yR6CZABYtGjRyR6C5CTicWvs+dJNZq6JKdNj+ORfTZQd8BJlUbBYpfAZLERrC+K9VxEb\n10BKOkpWLmTlQVYuSnoWRMeCxQpWK0RZgxbkiM6hqVB+GO3912D7Rl30Lv4ey1ti+eCjMg416CE9\nI5ItXD8ljYIkC06fRqtXpdWn0eJVcbhV6p0+9te72XC0Ba8qGJNq5b5zsjGECIkxGRR+MDeHn356\nmKc+q+CtnVHcPEtwRoqCMS4BZdZ8mDVfH9uhEkTxNkhMRpk0HSWls+Bv8qisLW1i+UEHB+rdRBkV\nLhiZyJVjkxmWZOnX5y4ZOoT9H223218czIEMdVasWEF+fj7R0dEDfuzMKF2UVrf4GJEcfikerRdX\nsf5a0AMCvWs9c39bCbVmh4rHrZGW2d2tqyeDd8dkdpoALbAhdJ21DscTLN1Wzb/2NPDUpQWMSh2Y\nMkUSiUQikUgkfXG01IumwfjJ0VijDaRnmqg55ichySizrQ8CQtP0GPC3X4GWZpSz5iDcLkTZftj8\nOQitmyUb0OOwJ50JU2eiTDqzW8I1oalQVwPlpYiDexEH9+kx5h43mKNQrruVhtmX8WxRNZsrjjEq\nxcqtZ2YwKz8ubLdwIQQtXo3YKENIcR4capSRJy8tYG1ZE2/vquPRj/eREWvm6gkpnD8ikWizAcVg\nhMJxKIXjOvX1qhpbK1pZechBUXkLfk1fQLj1zAzOH5lIgkV6dZwuyGKLYeL1eqmtrSU/P3/Aj50R\nbQAvHGuJrP7vgyuO8db13evaQv+TxOWadWHfVaCrfoGzRWXVR80AzJgXS2ZOZ5H+yf81UTAqisnT\nO98YO7m4i9BJC0K1FZrgcKO+gtniPc5abxKJRCKRSE4vBjESTghB2UEvyalG4hN14ZM3PEoX6IlS\nCA004vBBtNefgwN7oHAchvsfRckf0b7f54XqSqitRnhcurh2u/REadWVeoz2htUIo0mPyx42EmqO\nIY4d1fv52ubYRiPkjdCTqo0cgzJ2CmsdZv7yYRleVXD7WZlcNiapV5EdCkVRiA9TIBsNCvNHJHJu\nQQJ7mw28vP4Qfymq4sXN1UzMiGZ6Thxn5sSSlxCFy6+xqbyVL440s7miBbdfkGgxcvmYZC4YmRiR\nYU9y6tAvgW6z2UzANCC3bVM5sNVut5/WGYoGq/5ufGoK1iMequtbgNSw+3nV7k8mQz9j0M2aD5/B\njPplEYy7EnNUdwu6291+vtaW0DK7dL+3m0AXnSzoImhR32LJZU1pE+cWJLS3pbOLe6CvXKiWSCQS\nieTURdMEjfV+YuMNmM0Dk3F9MDPV1NeotDZrjJ7R7jmZlWcm9ZCJzNxTMzncUEFoKlQcRhwqgUP7\nEIf2QXkZxCWg3Hwvyjnn41IFXx5pJjnaRFacmQSLGSV3uB6L3dMx9+9BbN+I2LYBsWc7pGVCVh7K\nxGmQmYuSMwyGjUSJsiCEoLrVx9+21bCurEZ3TZ+dQ27CiUukZlAU5o1MZVy8xu4aFxuOtrC5ooWX\ntlTz0hZIjTHhcKv4NUGS1ch5BYnMyo9jSlYsJhlbfloTkUBvE+Y/A+4BuppuHTab7RngsdNVqA+W\nQDdk55FRUk91Q+jLIYTgG6/v5doJKX0frE3R+tti0D1+DYup7wehEohdN+irfzGxBiadGU1cgoGq\nch+HSrx8trz9/UdyX1A71GTXNLWTYH/qs4rOAr2Li3tgGUDehiQSiUQiOXXx+fQnutctMJ8C+rbs\noAeTGbLz2wWbyaQw+/y4kziqUw8hBNTXQqkuxMWhfVB2QLeAA8TEwYjRetb086+EmFjWH2nmhU3V\n1Lva54/RJgNZ8WbSYkyYDAaMBjApCkaDbrmenBnD5JHjsYyZCNfdgtBU3VW8DVUTlDk8FB9ysrum\nluIaF3VOP0YFbpyaxrUTUk9aQjVFUZiQEcOEjBhuOTOD6hYfWypb+PKYk9QYE+fkxzM2LVomfPsK\nEbZAt9lsRuD/0GuhK0ANcLBt90ggHV28z7DZbFfa7fb+F+Ieglit1kET6GTmkuE+QLUztAAPWJzf\nLq7v81ABC7qmquytdfGDj8v42fw8zsrt/YES+Mqrce3rLiNG60kmHPXdrfGKAVpbwrPSa/52131N\nFb3Wb++UJE5rfy0t6BKJRCKRnMIEHu8D+TwfJBO6y6lRedRHfkFUt5A/SXgItxOx4gPEyv9AY52+\n0WSC/JEocy+GgtEoI8ZARnYwnr+m1cdfVpdTVN7CiGQL98zKwq8Jqlp8HGvxcazZS63Tj18TqJrA\nr+nCu8mj8t7ueswGhQkZ0W3u4RaONnkobfBQ1ujhiMOLr21CnRptYkJGNOPTYzgzJ5bs+KFVfiwj\nzsylo5O5dHTyyR6K5CQRaZm1S4H9wP12u/2DjjttNtsVwG/RBfy3gef7MyCbzfYt4C5gCmAE9gAv\nA38OV/TbbDYDMAu4HLgAGA/EAfXAZuB5u93+XiTjmpynUTtYAj05lXRfE8W+0LErfi3yJ5Bf1dhf\np69ObipvCUOgtwl7Y/d/iVAPJ69HsOKD5rDGonW4aqqmEsIzv71tB4GuCq3dxV3a0CWnMEKWDJRI\nJJIBp+uddaDutft2ukHAqHEyG3akCLcLsfI/iGXvQEszTDoT5fJvohSMgfwCFFN394lmj8ryg428\n8WUtQsCtZ2Zw5djksC3GHr9GcY2LrRUtbKls5eUtNcF9ydEmhidZuCIrlhHJFiakx5Aea5JJ/iRD\nmkgE+mLACVxgt9uPdt1pt9s/sNlsXwK7gZvph0C32WzPAt8B3MBywAdcCPwRuNBms30zTJE+Evis\n7e96YCPQ0Lb9MuAym832CnCr3W4P626+YGQ5r3w5OG5NiqKQEaXhpHst9L21Lpas7vZxB3l1Ww03\nTEkL3sSCMeiahqVNWHt6U8QB2pqoIdrGJXR3kd+zw933MdvQOih0TRX4e3mAdlyLUDtcaXkflZzq\nCCHkhEAikUgGiYES502NKodLvYwcbSEmTiaDCxfhcSNW/Qfx0TvQ0gSTpmP4+g26lbxjOyE41uJj\nd42L3TVOdte4OOLQPS3Pzo3l9rOyyIiLLAbCYjIwLTuWadmx3Ipuia9u9ZGfEEWCVebDlpx6RPJf\nOwFYGUqcB7Db7UdsNtsK4LxIB2Kz2a5FF+fHgHPtdntJ2/ZMYCVwNXrs++/DOJwAVgC/AT6x2+1B\nX2ybzXYe8AH6IsIadOt8n0Sb/MQpfbuY95fMWN29prrFR1xK+wPhV6uO4vD07Er+1q46BPDPXXU8\neenw4HZN1VDq9RVE0VgPZPd6/qAFXeu+/pGaYWL0BAslxZ5w304ntA5KW9M0estf10mgCxG0qA9M\nOhmJ5ORgNBpRVRVTP2q1SiQSiaQHOs4ZBugeu/tLF2aTwugJ0noeDsLj0YX5x+9AswMmTsPwtRuC\nJcL8muBgvbuTIG906xPB2CgD49KimV+QyKTMGMamWQdkITs91kx67CmQ6EAi6YFI7mRRQGsY7ZxA\nf74VP277/cOAOAew2+1VNpvtLmAV8CObzfaHvqzodrv9ALrlPdS+1Tab7X+BXwKLCFOgA1gVJ36/\nf1Am2RkpseCEKoeTkSl6yYR6l79XcR6g6Kjuer+5vDXoCO5XVczVFUAmor7d1WdXtZOffHKY315W\nQGHbebZWtuIy6X+rIdzpFUVh3ORohICD+zxoEVY8CySsA73GZahzBPd3LMmmCbSGOiAW6qogY0SP\n/SSSoYzFYqG1tRWr1YrRKOvnSiQSyUAhhEDTVNxuP7Gxscd1LJdTo7rSz9hJVqIs0jTQG8LjQaz+\nEPHR27own3CGLsxHjQd0t/V3iuv4z75G3G2lf7PizJyRHcv49GgmpMeQlxgVcTkzieSrQCRKswyY\na7PZzHa73Reqgc1mMwNzgMORDMJms+UB0wEv8FbX/W2iuhy9rNss4PNIjh+CrW2/8yLplGjVaG1t\nJTExdO3x4yE9Kw0OQnVVPYzQk8V9uK8hrL6GtmeIR9WClvA/V8UD8QCIDhbs9Uf0uPGdVc6gQH9k\nxZHg/t4SuI2bbGXMRCv/+acjvDfVxh+2t7fXNC2YpCMUXS3owu0CcyxKS2TnlEiGEoqiEBsbi9fr\nxePxBLcNJBaLJXhsyclFXouhg7wWQwevx0DVMTfRMQqKYWCsm26XyrFyP02Ngpnzko/7vlpdqU9v\ns/O+2tZXoarg9eiZ1r0evd54XTWiugKqKhDVlXC0FFqbYfxUXZiPngCAy6fxr731vFdcj9OnMW94\nArOGxTE+PYaUaOlFJpGEQyTflH8DDwIv22y279jt9qaOO202WzzwLJADPBXhOKa1/d5lt9tdPbQp\nQhfo0zh+gT667XdluB38WEiIVmlpaRkUgZ6Qk030vhaq63WF6vSpfLCnjkxXHVXRvddGV1qaAAs0\nN6GE0L5FhnTeLa7j6gmp+NpizHuqvKaGcHEPnkdRMPYjHKu0qd2Crqla2FncVU2gtfkECCFXWCWn\nNoqiYLFYsFgGx20yLS2N2traQTm2JDLktRg6yGsxdFA9FvbtcJOdb2JYQcyAHLOm0sOB3SrmKMOA\nLHpWH/NjjVZC5t45nRGaBvt3I75YidiyXhfePRGfqGdenzoDZc5FKGMmAuBTNT4qaeStXXU43Coz\n8uK4cUoaBcnWE/QuJJLTh0gE+hPADW0/l9tstveBQ+gRQCOBq9Bro5cDv45wHAHf5bJe2gSs8sfl\n52yz2WKA77W9fDvcfn5jPInW5kHL5K5k5ZHhXk9Vs/7Q+vfeBlr9cPuhj/ndhG/13rfZAdYMROUR\nFLpP/p2KmVe21nD1hFTUiqNAHMajh2Bs97JuWj8yxndl704XCUlGsvO6l63QNBGBBZ1gfJkmXaAk\nEolEIjn1CXOacfigh/QsM9ExPYvlLzf1ZNOJHE0T1Fb5yMmP+sqEIYnqSsTnyxFfrIK6aoiyoEyb\nBVm5EGWFKIu+zWKF1HRdmMd0TpisaoJVhxz8Y0ct1a1+JmXG8JNz0xmXHn1y3pREchoQtkC32+21\nNpvtAuANdCv2YrpXtdwK3GC32yNdrg5823uLcQ8o4/gIj92VP6GL/GJ6yTRvs9luB24HsNvtmGMz\nSLA2Uq1ppKWlHecQQpOhtVLjiyMmIZl/7drN9Lo9TJ0yGvy99wtUoTAZjfRWkSItLQ2j2wnEYVF9\nId+H0WgK4/019rp33y7dnfCWu3O67bNYLBi7PE87ns9kbncri7JYEYruqBETGxdsZzKFM0bJiUBe\ni6GDvBZDB3kthg7yWgwdWhxOACyWqD6vidersb3oIInJfq751vBeWurzEYNBOe7rfKzChd/noHBM\nCmlpg1O1Z6hgMplIdNRR/8g9oPqJmjwd66I7sMw8D0N0eN4NQghW7a/jhfWHKWtwMS4jjh9fPJyz\nhyV9ZRY4BgJ5j5KEIqJgELvdvg+YbrPZ5qNnas9t21UOrLbb7asGdHQDjM1m+xn6woIDsNnt9h4D\n0+x2+/O0C3jhJY5Eq0Z1dfWguctlGP0UKfFc/pcv8GkK1zmLYfpC2NC7Qg84pfv9PhA9+6DX1tbi\n8/nAAF6/P+T78Hi9A/b+qqtq2LPTjRkFX9taTqvTicvtQ8852D6ujucP8Lt9fgraloCaW13BdtJl\nceggr8XQQV6LoYO8FkMHeS2GDn6//tz3ePqeZ3g8+szG2Rp6rtIVIcRxX+eSPS4UBSwxLmprwy8l\neyqSEm2l/okfQ2w8hh89gZqaQSvQ2uqEVme39qomqHX6KG/ycrTJS3mTlz01LkobPeQlRPGjebnM\nyo9DUVTq6upO/Bs6hZH3qKFDTk53w+LJol/ZGtqE+KoBHEfAOt5b+s3AcmYvgTE9Y7PZHgAebTvX\nZXa7fVck/VVTAnEWDWdrU9+N+8k1cY2k7v839dYkUtwOxl13JS0WK+0fTw9jUzqI8jBXLZU2p4eK\nJm+n7b2EoEfMkVIvB/Z4mG6I4wtNv2xrGk3EqD2ngVfVzgMIuGjIEHSJRCKRSE5dRATzi0BbwyCE\ngmuaQFHaE3Xu2eHC2apRV+0nJc2I2Xx6TziEEDT96X+hrhrD9x9HSc0A9ORu68qaWFPaRKPbj0cV\neP0aHlXg9mudQhDjogzkJ1r43qws5o9IxNib+6ZEIomYoZJOsbTtd29+TPld2oaNzWa7Bz1xnQu4\n0m63r4/0GJqpLTGcJ7zM6v0h9bKvcfXG1dDYAMkjUcZPJaqmus9+Ku03Ro2eb5I7qlrbQ7/amu2s\n6JwdPVB3vMmjEm1SMBu7Px0NhvCEfCDfW47VoBffA7a3mulYhU/p8sTuGp4eFOi9vC+JRCKRSCRD\nG9FLgtiuaG0JbcP1lDZHhdewsd7PxrWt5I+IYvyUaBwNfkqKPURZFIQGeQXdc+ecbohV/8Hz+QqU\naxejjJrAgXo3H5c0sqa0CZdfIy8hivxECxaTgsVoIKrtd2acmbyEKHITokiwyHKhEslgMlQEeqDs\n2USbzRbdQyb3s7u0DQubzXY38AzgBr5ut9tX92eAqikJAKM6eBZ0JTkV5ZJrOm0zhZHxWUUX0QLQ\nlJ6Xm1/dcITstudj4LZqOLQXaI99CQjkm/5ZwtTMGB69aFi34xiM4Qn0wL3b3OLAoKhoSnf3e5Po\nbE3XumSPMWFgviERv7/3uHeJRCKRSCRDl0g89ALOdH1pwLQME7XV/rCs3nU1fjaubcHvg0MlHkaN\ns3D4oBeDAc6/PJ6oqNM7c7toakRs34iwv0jU9HNoPv8qHvukjF3VLqKMCnOHx7OgMIlx6dFSfEsk\nJ5keBbrNZvOia75Jdru9pO11uAi73R52LSG73X7EZrNtAc4ErgP+1mUs56HXLD8GhG39ttlsdwJ/\nBDzAN+x2+6fh9u2KZkoAwEIrqqpi7E+9sX5gCEOgV0QlBf/uzYLuqquDtmEHbr7mLv5jWocV7u1V\n3eOQAM6eG8f6lX1nsw/e371eTGbwG43EYKSFdlFu0joL9I4L7FOjPaSIVAoN0TQ7fH2eTyKRSCQS\nydCkfxb03oViYM7S2tKz+tc0wf49HvbtchMTa2DazGiK1rVyqMTL0TIv2Xnm01acC02DXVvQVnwA\nu7bok6zsfBK/93N+t/owxdUubj0zgwtHJhJnOTHzWolE0je9WdAD+5QurweLJcBbwBM2m+1zu92+\nH8Bms2WgZ14H+F+73R68C9tstu8C3wU22u32/+p4MJvNdltbPw9wtd1u//h4BhdwcU+wqjidTuLj\njzeZfJiYoxjdVEZJQm/e/+34e7GgIwQiYKFue+glWTvfkMOpspaQFN6DLPBcFYqCSfiZaUhgrCGG\nv/mr8LaNo5sFvcMAhBDt9vQIHuwSiUQikUgGHr9PgAImU+QW1oAFPZyneWDtXjFAU6P+IiGpu4AM\ntPN5BR6PhsXSfX6yZb2TyqM+cvLNTJ4eTZTFQGqGiX273AgBw0aenm7toqoCbekzUFIMiSkol1+H\ncuY5kD+SFce8LD/owDYplavGdy+5K5FITi69iW4zgN1uVzu+Hizsdvs/bTbbn4G7gB02m+1TwAdc\nCCQA76FbwzuSBoxFt6wHsdlsZwB/QV9cOAQstNlsC0OcttZutz8UzviEwYIfCwlWlZqamhMm0BWD\ngWGtVWEJdE0o+EO4kQcQonsst7B2rlOphiGEDeG6PgXbGTBpKnmK7g1gRgkKdLPWOUO96BCTrol2\nXa5JgS6RSCQSyYDj9wlqqnxk5/UtVD98R89b87WFSX201BFCsGZZC6MnWDC0ufCFM4NQtfYY9NUf\nN/d4zo5u836fIJTTYW21n7zhZqbNas9DPHKMhbpqPzFxulg/3dA++xTx2nNgNqP813dRzrkAxaS/\nz6oWL79eXsbYNCsLJ8vyXhLJUKTHu1IHYR7y9WBgt9u/Y7PZ1gF3o5dxMwJ7gJeAP3e0nvdBEu3P\ngHFtP6EoA8IS6ADCnEhSdBO7jh5l5MiR4XY7bm5Lrmfkvnd5YczVAGS46qmO7r7i+X9aTq9LLoet\naRxu+1vrQfhqAjz+3j/m3oz0oRBKZ0t5xzPWWxJRNRHMAHpAbV8wUFtb0Ayx3TtJJBKJRCIZEL7c\n5KT8sI/zLjGGtFIfDz6foKlRZdtGJ2ecFd13hzaCFvQ+1HxHt3k1REVaIQR+n8Aa03nikpljIiPb\nRHae+ZSPtxZuF5TsQlQeRUnPQuwvRix7D8ZPxXDrfShJqcG2qib47WeVADw4JweTzL4ukQxJwl42\ntNls3wIO2O32DX20mwGMstvtr/dnQG39wuprt9sfAR4JsX0V4S3SRoRmTiI1voWjO48O9KF7xXrj\nHUxb/im05UkLrIIeD4HVaVXtLtBtb+7rtW+kZU9ElJVoZ891MZftb+SyMcmommC/1r7Crba2Itoc\nFaQBXSKRSCSSgcfZqi/K+3yRxYgbjH1Ps/xt6WOMRqXdINBLt9pqP8mpxqBlvM8Y9I4WdH/38Wua\nPn/o6pKvKAozz43r1v5UQRzYg/b+a1BzDOprgh9E4BNQzr8CZeG3UdryJVW1eFl5qInPy5opc3h4\n5NKxZMZJcS6RDFUiUXqvAq8AvQp04DbgVsIU2acSmimRBGsZtbW1uN1urFbrCTmvYo0me/Y5zPjb\nKjamTQq/7kgvaG03c61LWtWuWdRDjifS8ysKBgRGoUIIF3ynTx+DXxMYOpxfVQzB+ueRJJeRSCQS\niUQSHgGvOBFOEpo2GupVUtP7nkJ2PGbw7x5O42xVWb+yhbzhZjJz9KjKQPx5T/Ql0P1tiw79iZkP\nB+H3Id58EeXCr6Fk5Q7KOaBtDlRXDYnJsG8X2p8eh9h4lNETYeZ5KGMnQ14B1FaBpqEU6o6jRxwe\nXttey4ajzQgB49OjuWdWFhePTae2tnbQxiuRSI6PwQi8OW2X5FRTInEGD0ZFUF5eTmFh4Qk7tyE5\njWszfGzU2uPIDULrtaxabwRizTWtuwV9oBFAb6MMjEUVotN/T8f3JvW5RCKRSCQDTyCvjNpHIGNH\nj7twxXxAQHs9Akejbk7v6XkeaFtfqxIdG97cRmiCpBQjjfVqUIzX1/pJTjGiGJSgaDcNVhal4m2I\nVf8BRUH51h2DdBIQa5ch/v5s+2pK7nAM9z+CkpDcuWG8ntBYE4J/723gb1triDIpfGN8CpePSSY9\ndlDTSUkkkgFiMAR6LtA6CMc96QQyuSfHKRw9evSECnSAqJFjYD9ECT8XVm7kovxofuyf3K9jBYR5\nVwu6TyNYiu246bBYrj9SQq/dBJ7zqgZKh6V1zRoTfCUt6BKJRCKRDDwBzRfKAt0RfwcXeH+Isr5J\nwAAAIABJREFUeO9QdJxiHCrRy7N2fJ4f2udBoCdtC+SIdbZqlBR7+jy2x6PhdgkSktoWGPyCxno/\nny1vYcQYC4VjLe0u9oNlQd/6hf572wbEDbcPSjy7cLYi3nsVCkajTJoOPg/KZdehxIZ20Xf7NX77\nWQUbjrYwIy+Ou2dkkRR9+iXCk0hOZ3r9xrbFnXdkZIhtHY81HrgIKBqAsQ05/OZ0AMYPi6X46ImN\nQwcwRccAuuC9597r9SXh1/f261gBga52Eehe0SXL++7tKOOn9uscHTEo7fK86+PL79WfoKoQnQS6\n2uGV1OcSiUQikQw8AU3p7yMG3eft8HxWw3soh7K0d3ye79zqAmDXVhdpmZGJyOJtrk7H8/vax3ho\nn4dD+zzMvkAXsWbzIAhnVUVs2wAxsdBQC2X7oWD0wBy7vhaxfSPKxGmINR9BSxOGex9BGd7ZMKQJ\nQUWzl22VrZQ1ekiwmNha2cqhBjffnp7BlWOTT/kkeBLJV5G+7oav0jlaaF7bT08obe1/e5zjGpL4\nLTkIjIzKVFi7qw6Xy0V0dPhZSY8XYbEAXlINPhTz8dXt7MnF3at1vpF/Y4uF98cf16naLOjt51G6\nSHS17ABMz0bVOheBU1FoMVjajiEVukQikUgkA42hLZN3nwK9w361D2t7AC1EUZieFtxrq8I0y7fh\naNB98gOi3OcX3c43qDHo+4uhpQll0XcQrz2H2LoBJUyBLg6VoL38NMroiSizL4Dho1BMJkRDHWL9\nCsQHdvB6EIqiu8+fc0Encb6looVXttRQ0ezF1zaPi7cYcXpVLCYDPzk3j7PzTt0keBLJV52+BPrr\ntAv0G4GDwBc9tPUC5cD7drt988AMb4hhMOO35JChNAMK9fX15OYOXlKQrgwbM4JbSzcxf9YZx32s\nnlzcPb1Gi7dz/uXxHNjj4fBBb59tu4SWd7OgB8bg9/k7C/SO8fVSn0skEolEMuAEDKx9ZXH3dxLo\n4R27qxEA2rPGHw8+r6DZEUh2q2+rPOwlNtbarR2AaTAs6Fu/AHMUO0fMZPSYz4ja9gXiqm/B0VLI\nHR7MoN6tn6NBT/Lm9yG+WKFbyI1GPX68sV5vdMYsDJddi9i5GbF/N8rVi2hy+4mJMlJU3sKT68rJ\nioviirHJ5CZEMSUzhqz4KDQhEIJg6VqJRHJq0qtAt9vtiwJ/22y2G4G1drv91kEf1RDGZx1GrGcD\nRiUNh8NxQgW6oihcdcnZA3KsYJm1rhb0MAV6XLyR6Jje2x5qcAOhLOjww51LeWLSYn1/YCx+Px2V\nuNZBrksXd4lEIpFIBp6Au3pEFvQwXdxDWdBbm9sW5cO0wnfE0eDH5xWUHmg3DgTG0uTQuh3T2ybQ\nBzoGXQiB2Lqesknn8rO1VfzX6AV849+/QfvZd6C6Qq9BfscPEV+sRPz7H5A/EmXmeYCCWP0hOFsw\n/OjXkJqB2LEJysv0cmnDClHGT0XJHwGAMnIsAKsOOfjd5/uDs6IxaVZ+fn4+cVGdFwEMinIap2qW\nSL46RBLwMxpoGqyBnCr4oocT4/iM7EQ/jY2NJ3s4/SZQj1Trony9YT50oe/Ebf+3p57zjEkdBLr+\n1FCABF97HkGtg0Dv6uLe4Wxhj0sikUgkEkl4aG3Z2/uyineOQQ/z2D0Yy52tGis/jHxKuWaZnmgu\nK689G/mZs2JY+4m+vb6285vwefUBDHgW993boL6WovnnQD0UmbP5RlQUWK0ol16L+OR9tB/+N3hc\nMGYiVFciXnlG72syYfj2g+0ifOZ5vZ7K7ddYurWGEckWZubFIYBvjE8hxjxQGX0lEslQI2yBbrfb\nDwzmQE4VfNZhAIzOMnDU4TjJo+k/AR3e1f3MERVJzFJforldYHf0tlIAk9b+dBdtqVtVn79LkjiD\nTBInkUgkEskgErBAh3JH70hHC7sWtgW9ezuTGT5f0YwWpsgPUFfTLr69nnbln5TSPpU9Wurr1Gff\nLj0b/EDHoGuf/B8kJLGJNMDD3kY/rf/7N+LjolEUBTH5LLQ3X0CZezHK/Mv1SUxFGURZITEFxWLp\n9fgun0ZxtZNx6dH8a28D9S4/P5ibw/iMmAF9HxLJUEYIgd/vx2z+6pUHDFug22y2hcAvgXvsdvvH\nPbS5BPgD8CO73f7OwAxxaKGZElFNiRSk+Nm1f2gK9FizQmsfrmpqQx3iUAlq0ToYc3Vwe7M5NvwT\nhamaBYoutduejwoKJtH+oA1Y4lVV6+QKryrSxV0ikUgkksGkXaD33i7g4m4yR5LFvfPrmFgDLpfW\npzt9KD5f0RL82+UMv7/B2J4I73gQjgaIjYeaSti5GceV/0VJvYezc2MpKm9la62P8+J1Aa2MmYjx\nZ0+3d1YUyBsRfNniVXm3uJ7CFAvTsuOINushg6omWH+kmZe2VFPn9GMx6maLc/LjpTiXfGUQQlBW\nVsaBbZ8Qa2gkbcJVjBo15viP63WA8KNYUgdglINLJC7uNwJpwMpe2qxqa3MTcFoKdNCt6FlxJTiG\nkAU9P9bAkbbEKy9ePRpNCL71VgkA35+bw/Or9+Mwtt/cNUcjntefZ3+CXkLt8pFx7NlbxkFz939a\nsXs7jBiNYu38cAi3Nnl7HXQdBTAv+Aa05UIJPOfVLoVV1U69pEKXSCQSiWSgCbir9yXQ/T6ByQxG\noxK2i/uBvZ3rmZujFJytPTSOAI+782Cz88xUHvWFbHu81nNRfhjtvVdh2xeQnQ8paWAys6VwNmJ7\nEzdMSWdfnZtN5a2cNyKxz+NpQvDbzyrYXKF/ECYDZMdHkWQ1sb/OjcuvMSLZwq1nZrC1spU9NS4W\nT0s/rvcgkZwqVFdXs2n9SiYn7OWGSXopxQO1r7Gldj5nzDgPgyG8XFld0XxOokt+R4zJS2X0fKz5\nF7dnyByCRCLQpwDb7XZ7j2m77Xa7x2azbQeOP834EMZnHUa8aQdROHG73Vit1r47DTK/+9povvkP\nvSZ6YCX2osJEHG4/c4cn8Lbf2Umg/yv/XNSmVFYmTATgW5OSeXxP6CgG7bc/QznnfJRb7++0PdzS\nZ6EEetSwEUGBDuDxazS6/J2+Kx2zuEsLukQikUgkA48Wpou7zycwmxUURQnbgh4ohRbAHDUwE+Ku\n7vGZOYMj0MXhA2hLfgBmM8pFVyG2roddW1HmXkxRvUZqjImRyRam58Sx4Wgz+2pdrCptYkFhIgXJ\nVpo8Kh+VNJCfaGF6TixGReEfO2rZXNHKbWdlUJBkZWtlK0ccHuqcfs4tSGBadiwz8uIwGhTmDk/o\n99glklOJuro6Nm8qItG9lYVjWrCYBC2J8/Cbkhmu/ZsM36esWXGEyXNt/dJd3j0vk2H1UNFkYZhx\nJZV7SjCOvhVMJ65cdiREItCzgM/CaFcBzOzfcE4N/FHZAGTE+3E4HCdVoL9wVSFOn4rZqDAzL44j\njvb1k3tmZQf/NirdH6YlppT2/UYjBo8bQpRXf3bsN7my7ggju+6IxILe5fkYYzUBusVcE/C/a8rZ\nUukiUWlfFVeVDjHoYZ1JIpFIJBJJJIRrQT921EeU1YDR0HeSuIojXvbucHfbblZdwMDGk2r/z955\nx8dR3vn/PTPbi+qqWu5F7jZuGIyBAKFDEiAbSsiFHCHt0i7JL7lLuUu59Cu59EbakcAmJJAQejEY\ngzEuuMq9yJZk9bp9Z57fH6Nt2lWzZVsyz/v10mt3Z56ZeXa1O8/zeb7t9fXgWgCDVKFRTs3ghoiE\nMX72XfB4Ub/w3yiFxYi3vxux6UVii1bxxlPNXD69EEVRWDnJzfOHu/nMU8cAeO5QF3cuLuOxfR20\nBM25jkVV0A3TtHHFjEJumFOMoigsrJCu65I3J9FolP3791O3ZzeFop6r5vRRVRAnYp9BZ8Xb0G3l\nAHS5puM89iuum1rHxld/QMH8d1NeUTni6wSPPsd05wl2ds2gaOG72bztN1xQeozg/m8RqrkbtWDm\nmXqLp8xoBHoQGImPTRkQHbbVBEa3mR+Dz20K9IqKinPWl3KPleRg96+X1QzaTs0z8iYyRi1V03JE\ndJLnqlZxNDqd/xq4Y6RmbWFmcU+eXgFcditJgR4XClubgql9SYysOuhSokskEolEMtYkLedtzQkM\nQ2TFa+u6QFWhr8cgkYBEn0FhsTZskrjtm0Ik8mSF17ZvgEmXj2X3ET/7Dsbcm6HmNgBKyy20t6Qv\nnizrNlKM116E5kbE0QPQ0oj6z19FKSwGQLHbUdZezSuHu4kkBGumeAFYVu1h5SQPc3wOLp7s5Xuv\nNnH/1hZKXRa+dfVUIgmDrY192C0qFR4rl00rQBnH7rUSyekghKCvr4+Ojg46Ozvp6elBUUzvG1VV\nURSFnp4emo/vZ0lVD+9ZGKXAHiehFdBd9k6i7oVZ7ueGvZLgrE8RrX+QiyfXcbLxh5zY5yFmq8BW\nNJOi6gV4Ckry9iXe20h15DkaQk5KFt+N1eZg8uoPsHnnc8xR1lF28he0dK9Fq7nubH08I2I0An0n\nsMbv95cHAoGWfA38fn85cAmwdSw6N14xNC+GYsPnTtAxjuLQh2Rgphag25rO2K5qKoe8NYOaqtU8\nO0RXO5D/BwFpsS0UZUAWdwWLy803t3ybzy3/KEaeY3KuNehVJBKJRCKRnCqZ7uLBPgNvgVm+yzAE\nT/65m5lz0xnHJ02xEg4bw1rQk97ymiW7fJtFz7bfFBZrCCHo6RqdiE6SmsMH0yXbrNbTcGnftRXx\ni/9MnVy5+U6UuYuz2wjBY/s6mVxoY1G/9dthUfnC5WkjyX+8dQrrjvSwqsZDkcOcai+tGkUSXolk\ngpFIJNizZw91dXV0dLRjU6KUexKUexNUuU0jnSEEQpj3lllewbzLwqiKIOqcSXfhaqLueaAMUj5Q\ntZGY9h5a2zZi1zew0NWJTesC9qGffJyTh1z0FqylePqlKP1x6sJIYD3yKwwrBCfdRZHN9HhWFIXp\ni6+ioXEmLcceQKi7KJ507dn5oEbIaAT6g8ClQMDv9789EAhkFQH3+/1FQACw97c9f1EUdFsZlQUd\nHJ4gtdAjaq5LmZ5hVddUlaAYoqbmgJVesX0TYsN2mPXOwQ/pfzRQsGRIbAXA42VO73GqQq3o9uHr\nnUsDukQikUgkY4sQIsu1PdMynoib+w7sSYvqOQsd7NoazqqJnv/E5sPA2urevhNZrxUFhBidoC4t\n02hv1Zk1z05NcR88Q4aP3uhrnou2ZvAUgMWK8dAvoLwK9d/MmuWKzVycEELQ0BOjzG3laFeUQx0R\nPrCyYlAruE1TuXpW0eg6IpFMQKLRKLt27WLPjs3UlnTwjtk6Ze4Ydi2dE0IoFjLMdiAEQnUQ8V5M\nuPDClGfySBC+1cR9q+kSBmq8g3DbfmKdB/FZDzJJPEXzzvX0llyJt+Yignv/wAxXH9sjF1FVkevG\nXl49nXDRPxOPhVOifrwwGoH+S+AeTJF+2O/3PwLs7d9XC7wDKAI2Az8dy06OR3Srj1JPK931E8OC\nrqi54rs02k23zXTPUhV4X2k397fnz0CqDBDORuNxjnqGjv/I+CmaMfD9LusK6UHPKgQV2mQcGEQw\nUPPEyqdOIpFIJBKJZMxIWsLLKi20nkxkCe98MekWi4LTpdLVkT8hW5J8Q/bCyWGcW1qztrk9Kr09\no7Oeu70aF19hzl1EUzsGYI+l52IDk8JlamjR1YF49XmUqhpYciFi8wbEL/8LPF6U2Qvg5AnUf/pi\nao4CsK8tzG+2tbC7JUyZy4LPbcVpUbl8ukzgJnnzkkgk2Lx5M82HNrG0qouPrYliVQ3itioSjhp6\nbRUkbBXotnIMzTv2GdMVFcPmw17tw159MaFElMb9j1OlbaUi+jfadj3HNEeIfZ1lVK64adDTOF0u\nnK7xlwdixAI9EAjE/X7/dcBvgeuB95K+Byc/9SeA9wQCgaHv3OcBCVsZXut2gr2d57orI0It8UFv\ntk/aYW/aHUtRFOyWwS3oyXUlIQTUbeeVWAEbymq5aIhrpgW6QuaZP1n3B+ALqF/5IVVPtFNs8bHK\nCPOS0T2Ei7tU6BKJRCKRjCVJi7m935MtM248X1Z3i1XB5VaJxwSGLlC1QUbtPEO23WKgGtnTw8Ur\nXLzyQl9u4yHI8qiLRVlXsYz5oQbcXpVgr5GjA1ZfEMV49q9weJ+ZhT2RMLs3fQ4cPQAzas3zbn4Z\nFi6DxStSx9a1hPjXZ+spsGvctdjH+mM91LWGuWFOES7rEF6HEsl5jBCCV59/hJXFO5hyYRwDC1Hv\nMnoLLyThGDwf1plEs9gpnf8OgrFrqa/7G1PUHXSErDjmvW9C5nsYjQWdQCDQAdzo9/uXA9cCUzFv\nw/XAU4FAYPPYd3F8olvLUBRw0EM8HsdqHduspGONx2WH3tCQbawjEOjs2Izxg6/SOvsaqKkd5qrm\nD0IAFkXgtKrocSh96w3m3qrJKJq5wKFlHZGLlOcSiUQiGQ+0tyR45YU+3npzAQ7n+HKLHC1JC3qy\n/Flm+bSBpcwANM38S7bNJ9DjMSNvWJqRiPPAtKtIOpouXeXCcirx4hnnjoaj/O+826kKt/HP02zs\n2xnJmowrChT/5DOIni4oLEZZcxXKlTcjdm9FPPoALFyO+oHPgs0Ge3fAlBmp43uiOt/d0Ei528p/\nXjcNj03jlgWlvHail6WVMp5c8uZl68bnuLLqDZx2C72+G4l4lyG08VGuzGpzUrnET2/kJgxDx+ny\nDH/QOGRUAj1JIBDYAmwZ475MKHSbDzAzub/44otceeWV43qF5p5l5Txa18G6oz2DtrFaB/86JF3c\nRY8pqEUkPKxoTn4aMdWCWwFVUdARKHMXphv1J6/TshziJRKJRCIZnxzab5YP62hLUD05T23Sc0A0\nYnDsUIzZ8+wog5VkyUNSkNvs5kLD1ldDHD0YZfVlHro6cxW6oiholrS13Trg7fd06ezbnVteDaAx\npLOteHZKoFdPOTXDhshQ/9FIBHDRbitIJaPNLABT2HUQerpQ7v0U6oWXpd9HVQ3isuvAYknP3eYt\nAeDV470cao+wszlEVyTBt642xTmYpdLWTJGu7ZI3L3t3bWGZ40XcduiZ8n50x6Rz3aW82B3jY8Hg\nVJnYS7/nkITVFOhLayvZs2cP69atO7cdGoYZJQ4+uaYagKtnpePMb51fwqN3zQXAOoS7VnLgCykW\nvrHwH+iw5Y9VzyRzivC6Wp5+kaHBk9nh0yXYZJI4iUQikYxf1HG4GL9rW5h9uyK0teSpbTYEeiIp\n0NPvqaNVZ/vrIbZtzO91lxToSXEfDhnEogaJuODFp3o5eWKQKEcjgZExxmv91veiktG5imfOB6L7\n69Iv+t9CZtEa1TA/D2XJqpzzKFZrjmFlV3OIb77UwMN72jnRE+UDKyuZVeoYVf8kkvOV40cPMi38\nKD63Tu+k945bcX4+cEoWdAC/3+8GChjEKzkQCDSe6rknBKoN3VLItAoHS5ZMY/v27VxwwQUUFY3v\nrJ2P3Gm6pT990EyoUutLrzCZFvT8yVqSQvqlXgev+xYAMH+Ya2V+MdqU9HUyB1clkT2QZ60YCZGO\nJZMCXSKRSCTjiXE0LiWTu+VL7DYUyZhzmz17KtfSlCv0r35bAcYzj6Ic7oWiG9n8cpDaRQ42bzCF\nfEX10FPKxNEDJNxzcrbPmaNScmgj21g95PHJGPOKSWnLe+Tl5+HC5SikhbmqQmn7LtpLF6L1l3VT\nhrCmheI6ds2cffxySzM+l4Uf3TQDu0XasCSSJO1tLXibHqDGF6ej7F0Y7lnnukvnNaMS6P2l1L4M\n3ApUDdFUjPbcExHd6sMSb2P27LVs376drq6ucS/Qk6vF1V4bdovChZO9qX1mHH0073Fqv6q29HaR\nrH0+3NCVs3Ijsh4ACKtDf02kPpdIJBLJeGQ8jUvJhe983maJhCAaMXB7ci3VSSt4MgY9Sb4yanaH\nih74JVrJAlh2I709RkqcAzQ3Dm29N9qbMdyzB/RbYP2Unypg21WmQF/76r+w/qJv5BzvLdC47Gpv\nyoIPEHOkY8Enl0dpK9eYWv8U03f8nk3LPsvcAw+i3PnBVJtw3GDdkW4mFdhYXOlmx8kgX3+xgTK3\nhSWVbg53RvnUmmopziWSfmKxGMeOHqSw9RHmVURo8V4HRUvPdbfOe0YsovvF+WvALMxxKQI4gVYg\ns4Bdw1h2cDyTsJXh6H2DohLT3btrgtREB/jxzTNyttnsgwt0RRgIw0DZ+TrMNY9VB03plk3OMJ+x\noc1RbJ4/eZ3BXNxHdCWJRCKRSM4w/QNWZhK1aMTAalUGz2p+hklmXE/Ec0fLra8GaW5McMM7C1EH\nxKcnXdwt3/wkrP7aoOdXNRD9b1jTY6fWR0UjM6pdBHsRD/4ip503OPg0Uj2wA/3JP6N+/Esoqka0\n3HSxFYA91M7qSWGM79wPwJrXvwxAZ2ElB473cqQzwhP7u+iOmr1YVeNhW2OQCo+VqC74275Oan0O\n1k715r22RPJmIR6Pc+TIIfoatlCuHGZ5RRhHhaDZfjFKxaXnuntvCkZj5f5/wGzMMmsfAX4I3B0I\nBCr8fr8XeDfwNeDZQCBwz5j3dByiW32oRgSv2o7VaqW7e2LURB8Mi8sNmOVObve082BfaWqfIgxI\nxBEZorxJDD1IKxltyyId4DSdLoaKJx8o0FPnkApdIpFIJGcZIQRtzQl8FelkYsmRLWl9FkLw9KM9\nVFRbWLX23GQMTrp3JxK5g2XrSdOyHQoaeLzZVnS93+iddAUfDEUBOtoAsAzTdjASikY8YzA3PnHX\nqI4Xho7x8+9Cbzd0tkNpOdFYhtW+uwM0c1rbZ3HSZfPgiYf41BEvnXWm6F9U4eKORT7eOBnk4d3t\nzChx8KW3TMZhUVh3pIclla5xnfBXIjlTGIZBfX09zUe2UKrvY3FlkILJBnFDo8daS6h8DYpr5vAn\nkowJoxHoNwNtwAcDgUDE7/en7rKBQKAX+LHf798CvOL3+18LBAI/GeO+jjuinoW4utZT3HQ/8yZN\nmvAC3epKx2jlrLILBeIxumzpleVW4rykd3Oplj9hXOYZvhDfQl1/1LoYQqFnOpVlinVZB10ikUgk\nZ5sjB2Ls3hZmxRoXVTXZKcsNw0yQliw7NpyL95nEGEKgKypgQCwiYIBxONl+OIGOAFqaRtZ2EBKq\n+UE1hxu5ft+Dg7Zrsw+ShHb7JlOcA/R0I1weukJJQ4GC8b9fgf6SSt9a+B52F/WLiYjO+1eU85bp\nhbj7s7EvqHBx3ZxivDYVa3/8+dWzxneIomRious6qqqO24Wf1tZWDu7dgb33DRaWd7NqchxdKPRq\n0+j0XUjcMx/U8V1K+nxkNEE204HNgUAgWT9DAPj9/tRybCAQ2ARsAP5xzHo4jjEshXRO+iCGVsA7\n5h4l0td2rrt0WmTGXA3MUqujIGJRHphxXdb2oYRz5hmK3/nuvG0sA5LSqUrm+dL2emOE7vQSiUQi\nkYwVoT7THTocyh3rDF3w7N96ePm5vrPdrdy+DOHinlxwjw2IK0/EBTu3hIHhRbcARFKgG6fm4h5V\nzQWOY32HqGh7I2vf7sLpWOJ9tIk4n7/gQ1hDTRw2wrygd7FBzzV+iJ2bMT52O9+bdwcAFtG/OBIy\n/xcpcd7PlTOKUuI8SYnTkhLnEsmZoLuri33Pf483nvkJ4XD4XHcni1AoxIbnHiG+50dcX/4s185p\no7jIS3fJ9XTM+DyxGfcRL1gixfk5YjQWdAPIvEsG+x99QHPG9gbgxtPs14TBsBbRV3YjRY2/wkUn\nhmGgqhPzhu+2pvttGWhBRyEezS2dMpRdO1PkW2w2wDw+04B+ky9OsDP9OtNqrjB8krhowsAQ4LSe\n3mf+3KEuHBaVNVNlfVOJRCKRmESjaTf2gSStz8HeUaZOPwMkk7ol8hjxLRaIx3ITvzUeTwvtEYnu\n1ibuW/2v9DiKec8wTS3xIAmrO2vbuqoVICKoIvvzOuCt4YsXfAiH3k5Et4OjhOKdv+L5RWa05DTF\nnnN+8bc/ZL0OWZzoioom8v8vTneOIJGMlq7OduJ1P+Xy6b0AvPTKT6i58P14POcmDCaJEIK6PbtI\nHH+CG6Z3oaoqYc9KeosvJGGvhnFq6X+zMZo7VgMwOeP1sf7HZQPa1TJYprHzlITVzJFX4ozT13fu\nV9JPlczV5YFjWQKF2DN/yzlmSIGe8VyzpNeCMuc5ycWMZKx5ZuK5TLFuiPw3jH94+CC3B/YP0YuR\n8b8bT/Ltl8/vyoASiUQiGTnHj8RorDcXljNHoH5jNbFo7ghYt/3cWMmS4lsfYEFvb0mkrP/JmPkk\nmSXZGp0lqeeTp2e78puNBeK1l2hzFJHIM/IPPGagOM/EkpFd75i7gs8u/xgAES0txL++KH8qIwOF\ncEa7+V2HU8/b7IWENRv/suoTWcd84qKhig5JJGNPd/tJrAd/yILyXprta+hQZ3Pp1BaaX/8xXZ2d\nw5/gDNHR0cH6x3/LnEiAq2Z1EnXMoGvapwlX3ULCMUmK83HEaAT6NmBuhkv7c5hj1jf8fv9sv9/v\n9Pv9nwYuAHaMcT/HNYalEAMNnzsxoTK5D0RTFaq9Vm5bUJrj9pUQCtGN60Z1PmvG6K9ZLWk1n1kH\nfYC3wYVtu9L7IMPFPT/hxLm3XEgkEonk/MIwBG9sSpcQy5y4Gv1C99ihXKvzwb1RwqGRjUuhoMHf\nHuqis/30Ytd1XaD3a96BMeivvJA2GuzcEs7ab2QI9o+t+gzzdv6AGbVWFq9wcsUN2cHqhiGI9JqW\nQANQOutYuiLt+rpkpZPrbilkeunwc6AT7nIMFI7WLOSTKz81bPvUpykMHphxLXet/Sqx/hKtQYsj\n1a7L5mV/wRT2uaqzjq8uyLPgIJGcIbpbj1Fw/MdMKQzT6LoOZfKNJKa/lw7rQi6a3EHPjp/S3nb2\nQmK7urrYunULzz/2G7q2/pBb5uyltMBKV8WdhKf8I4ZV5l4Yj4zGxf0J4F3ANcDjgUCflNLdAAAg\nAElEQVRgm9/vfxy4Htib0U4AXx27Lk4AFJW4pQSfu4eGCZ4o7sc3m3Fbz79Qn7VdFxBXcr8u+hA2\n9KT0VlCwZAj+LAu6kt7+ja0/oGJSelKgIFJJNQazoEskEolEMtZsfTWU9Tqpz2NRg5amoQV1OGTg\ndOW3f0QjBpqmYLEqtJ40rfNHD0bZ/nqI2fMcTJo6ejGZ6breeDzO8iHabtsYYuUlpnV79xuRrH3d\nsU6WVXSA4kKpewPIiONWVJqd6coukZZXqHFPZpfVQyIORMNo8QRlz/yUI8s+a6aVV/J/BiedPn44\n9528ULliRO/vuIiy2wiy5Mij/HDxewFosxdRHW4jaHEytcjOsa4oHbYCrELPOX5OqSNnm2T803Jw\nPY6uV4mVXopv+upz0wlhEG58DXv7C3gtIUIJOxHhIq56MSyFCIsHFAtCtYJiwdATVIZfwOnQaSi8\nDUdF/69RUUlMuZP2E39mxaTNbK/7KSem3U3N5ClnpNvRaJRt27YSbNpBjbuVVRURSubqCAG97hVE\nK25AaPJ3MZ4ZjUD/A/AikOmbcTvwHeA2oBjYB3w5EAisG6sOThSEvQKfp5PdE9iCnonFkp1MJYFC\nuyN3lS2fq1uS5BkUyMpemSnQtQwLuvOWu1Gq3GA8kDowHYMuBbpEIpFIzg5NJ3JzrgD0dOcKwIEM\nNVo9/WgPLrfK4hXOlNBXFYXeboOtG0OnJNAHutoLIQbNGH2yIZ5qM5Dj7kqWfukj5n5bIVz6/exj\nMwR6r9WN8cUPc1lBJSISxni2G2w2fLEYV7z0MZ4vX8y+OXfiQqNJxFileenNEM8jFedgWn1eNXp5\ndcVHU9v6/vFz1D3yO9ocxUx1WTjWFeU7C3Mj42t9jnGbPVsyOM17/s487WXUQlD1Rzm67WWUae/E\nWTz17HRA6CRObsDVsY5ye5guxUJDuAy7EsKl9eCztOO0ZvyGMpxm+jSNptK7cZXNyz6noqDX3EJ7\nk5MlVes51vQLNu2rZcUVd4xZ7iohBHV1u+k99DSXTmunaJGBIVTC9mn0FC4h6p6HsHiHP5HknDNi\ngR4IBOKk486T2/qAD/X/vanRbT5KXAl6jp+72JKxRBuwCp1QLXz+gg/ntEsMUTItU6ADWUXTkmQO\nnPaaGoQ1AqHkcfmOkEgkEonkzHFobyRnWzJL+tDyO90kGjHYs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wcBUI3cBWEBaN/8BcbP\nvwuH9gKg6aMT6GWtb1D0b1+BJ7O3P/2Wn1HeupWlu37MiepLU9tj9Q0ofbthUToR1vEl74IwWBSV\nEAaHCkJcW1PMpKk2vAUaniPbWN+yEkMItjsmk5n+KhLPfv9fvLyGb61v4IMrKyl1WXj5r32pffqA\nAubJ+PH7Vqbd1acVm+Fwv7l1FnZNxWmViaveTGiRRuz1v8WjdvNGk4dEzW3MXVR7VvtQWlrKsivv\n4cGnH2Kqx5yrCgFCmPK8J6LRyiwuvOgSLqwcPNRCIjnXTBw/6wlGxLsUT9sTrJktCGw6ztGjR1Pu\n2x0dZg3Q7du3s2fPHpYuXTqu49K9/avaSypdXDWziAqPlS88ezy13zrIirUxWKKWnCRxespSYBgi\nJdCFYaQEulAUejR76jg9kf/kdQdOMG/22JbHMIaJ65NIzgQvP9tLic/C/KUynlIiORP0dudZ6D28\nF+O3X8ndnoEWaefWy7/Nj61FVITM0mnKdbehhINMP/o47SXz6CmYPuz1lUk+nsiT9FzX7DRVXsTs\njvXsnH9vanvc6mGgI2MwbI6/uhDU+hys7+jh/VdW8Ep9Lz/420lgMtVKBz1CZ4XaH25mMSCh0iCy\ny8OtmOThj7ebgiqSMNig9xBEp9pr447FIw8rG87rTnKeIQxsHevxdjxFKKbw+Mk5zLvIj9t9bkp+\neb1errjpPTQ0NGBkTESFEJS63VxcVXVO+iWRjIZB76J+v38x0BEIBE4M1kYyOEJzE3XPYyYHScSL\n2LRpE3a7jQK3IyXQm5ubAQgGgxQVjU3CjDOBqpgZU5OJYeb6sgWD3ZJfoIuRWNAHOvFlurhnrdir\nGEr6OtFYfkvF5zb18ejs7G2GYfDV36zjiqke1l6xKu9xQ/F/Gw6nnuuGgXaGYqgkkkw623U623Up\n0CWSMSYaMag/HGPvztzqJGpXnpokA2JT93ftAN88nj7YxT/881cxfvU91s+7mjk1JSzoa2bnS830\nAK5QMyFXBfP2P0DdnLtyTvtEuJBtm5vzZksHiH/ky/BCunZ6yOaFovzuuApwxYxC9rVFuPOP2fXE\nG4U5Xmr94+3z0R5aRZw+dL59zVSKHLljuF1TqBNmsZ5Hb5bVSyT5URM9OE78AU/iKHUtduptV7L8\nykvOeTy3zWZj+vThF8kkkvHKUMuc24BfA/84cIff7/8S8EYgEPjrGerXeUHUu4TC4C6mFMc52tHJ\nzStdLCk5xI82CWKxGO3t5kRgvAt0MDOmJrFq2QLVNjDIvJ+kwE4J8jyCfaBAFxmi3NDTFvSBV4iE\no+Sr0Woep6Nq6QlHIpZgq62arU2wNu8RQ/PigXawmYlG9HgCzT7ymDrJ8Dz5l26KSzUuvNQzfGOJ\nRCI5Tfa8EebEsXjefYrIk98kYzzREmFeKjEdxf9S10F9t5spl32Ev2ztgK1d3LXER63TA2GY1rmR\n4p2bKbrpWiZf5OXpR82g7oQexaLZGa4a2PZN2UVymgpnUF41iIeYkj1O5yP5LhIIetF554JSan35\nFwDPtcCSjHOEwBbcg7vpj2DEePygj/KFt3HB1KnnumcSyXnBUAJdIVcXJfl3TPEuBfoQxFyzEKgs\nm27haAfMLe3CriawJjpobEzXGg0Gg0OcZfxjHSTt6mAu7llZ3JOP/ZOBrCRxGScYeIVoJAbkL/8W\ni0ZxuNLJcPRE/onYSLFlTNj0RAKkQB9T4jFBS5NM+ieRSM4Og4lzAM3tQv3mL1FKyxAnT/ClzX3M\n6Csl5eCtCPSM6iNbGoNsaUyP4Q9sb+PfZ9fAkQSPzLiKu1dOQ7nkUkKRtDt9TLVgAYzhsq4HswfR\nbt8syu1aZtqWFIqA0ozs6nct9rGgwsUXnjmGgUJtoh2HqwQM87r3LCvjyhnj2zAgGX8oeh+Onm3Y\nu1/HlmilscfCCw1zWf2Wd+D1es919ySS8wbpq3sGEaqDuHMas3wRil2CAtW0mFd4E+zbty/VbiIK\n9O9em14ltWkKqy51U16Vvd6T1NoDXd2HCunOFO9CiEHbRqKDJ+PR49liLxE/vYRytgw7h8weP7a0\nt8jPUyKRnHmEYWD89fcYh/bl3V/ctR8AzaqilJbRG9X5yOsxdnQPaKgNn5Nka5PpGt4SSrClYjFb\nGoO8988H2az3Um9EcPWHaw1nQR9I59zlHNPSpcbqjBCPJdrpEgm22fooc5u5bD60qgL/Ih8Lyl28\nv8KcX3hEPOXifsuCEt4+rzSVX2Yophbah20jOf+xhg7gafgtpYe/gbf9cZpbu3hkZyEbg1dwxQ13\nSXEukYwxUqCfYaKuWgq0bu65blpqW5knzsGDB1k9U/DhNW0Eg32Dn2CcMrvUyZJK00qtKAoVVVbK\nq7IT3aViyIXIEttZLu4DXNizLeh6yi0+x8U9NriwSwzI8J4pqoUQNP/ht8SOHR54WA6ipxMAm0if\nTz9NsS/J5pUXJt53XyKRTCxEIgFbX0H87UHC//UfWfsqWl4HYNGeX+AKnmSap4X2UJxvrm+goSd3\nIdjIGIweetccllblJsJqD5ljjgH85PVmvrrOTOXzhgjytNGVaqcAP7l5BtuNkd0HtzeG6Iil42on\nz7Py9pUlNFRHuPeqCpxWlUfvmsu1s4tTbazJQVbTUmNvhXdkXmAPvWsO/3mddFl+UyMSOBv/SHHj\n/Yjufbx61Mn9W6exTb+eeZd/gEsufQuaJkvbSSRjjUy1eYaJuWqh/QmKw6+TsJYDgnJvL7qus3hS\nnGpPnERH17DnGY988fIa+mIZbugDVHTSQ12QLcqznvc/+pKueRkx6MIwEP0tFEWhUInTLcxFgPCx\no+BOWxIyybGg794KmHF7ke5u7jNW8ZZHXucTH5/R359ci0j37p1854WjfHJlGTYlvV9a0CUSiWTi\noLc1Y3zoFmJWD89e9duc/Ut3/RRD/SX2z3+Tmbv281PXatb/5dCg52s0zDHo395Sg8OiEoqZi7a3\nLyqlN6rz9/1dbDF6UYBDIjzoeQBWTfJQ5bXxutHHEnX4HBzWAYPs7Ut8KIrCdXOKBzkCrJWT4GQP\nSomPiFenvkHn0sqR5ftwWKQN582MGu/CdeLXuPRm1h/2ctJ6IXMWzueGykqZo0AiOcNIgX6G0W3l\n6JYitEQXUc98tFgrlQXdgKDSZSaMITYxBbpVUyl2Dj6Ap4zhYgiB3v/c57L2v85wJzeMLAv60poi\nXjxuuuv9zL1s0OvqetrKbcTjfPGAHfpD0mP9FvCNRbWIIwcQ65+CO+7LOcdzBzrYWTybRw41YFHS\n7zHz3BKJRCIZXwhDR6x/Bg7VoSy/hPb7/xuAw1Ovz9ve+rHPI159HiZN5ce7FfYezw05s2SIkXj/\nGHVBv+X8s5dO4r82NHLdnGKKHBaury3mmYPdPFLXMWgfe4WOV9Gw9lcEWTPFC42DNk9RSraX2khE\nktXrAXpQPV7uu6SCk31xCl1y6icZGmvoIJ6GBzD0KA/vrWTORbdTW1FxrrslkbxpGO4u7fH7/VNO\nYR+BQKD+1Lt1HqEoxFxzcPZsIuqej529FDl2U+5JYFNN9zmL0XuOOzk25FrQ01ncM3R3XoGesrZn\n7tTTKXQUYH6lJyXQh0JPJDAMgaJALBKh0VWW2pdoaQZUEqrGzj/9hb9bp/OpxnQlQcMwUFU1NfEx\nAKciY9AlEolkImD8z79D3XYAxKsvpHdkjC12I0hUNQW2suAClAUX8Ep9D3vb8lu8NZVUwHg8w6sL\nzMXlr7817QZeU2BnerEZt13mstAaSnDv8nKunFnIzpMhTvbF2fFGiKWKB6O/Tx+/qIqnH+4Z9r3V\nqKOPB1f7A8QUQFMVJhXIJKeSwVH0MM6uDbg7nqOlz8Iz9XNZ89Zbz1lNc4nkzcpwAv3W/r+BiCH2\nJffLJdp+QkVr0S1FJOw1aPEOVAVWTImm9tvE+RmHm5nF3RjEhJ4uwZYMUE8fJDJi0KcV2blydgE/\nfr152OtGIzp//2M38xY7KCvNrnP7zF+ehunXEletfLv8rfRanDSc7ATc/dc0QFVRU1nlBVrGyoOe\nkBb0sSJfaIFEIpGcCqK9BeNz9+bd98bbvkdjMMMN3OVmzVoPOoK3PbCXSo+Vk31mZvdan5P/t7Ya\nn8tKeyhONCF48an0IrqhCP58R+2Qfbl0WgFTCu1UeW08c6iLG2qLURWFCyebibQO7IqAkb4H2vO4\nkl9xvZc92yOcOBHBopx6jG86TOyUTyE53xEJ7MG92HvewBaqQ8VgR5ODvfELueKGq7BY5HReIjnb\nDBdgpJzinwxcykC3+QiVvAUUhYStHIALZ+gYih0hwG2NE4sNnpV8ohCPZwuuzKRwg1nQky/SbTPq\noBtpC7qt36r96F1zh+1HNGIedfxIjGg4mrXvD9OvTT33CPMzb+lKW+UN3exostS7DiQyjfp5LOhC\nCGKPBTB6JmaowrkiHJICXSJ5syMMHeOV581EboO2MTD++gfEiSMYf7wf47c/yG3z8G8GPT5LnAN9\nMYMPPH2Ie58wY82T4hygwK6lQq5KXVaqC2xZE5rqIhuaOrTaVRWFGSUOnFaVm+eWpBZ8k1wy1RTq\nk7y5FvGqGvPadqfKykvctHB6HnZpLzSp0CUDEAbu1scoOfQ1Ck8+QKJzLxuPOPj5axU0F76Ny664\nRopzieQcMegvLxAInBOR7ff77wQ+BCwGNGAv8Cvgx4FAYMRVSfx+/2TgRmAFsBKY33++zwQCge+O\ndb9Him71IVDQjBAx5yxEqIECh04wGMRmm9iuZ+Fg/n+PGEEMevoxw4I+SJm15z9yMVf88JVB+5FI\n9J9DgcgAgZ6JJRYGayF9kfTkzOiPMU9OZmKdXZywlZEM/dP13PfYtqeOe7sX82PT/x8AACAASURB\nVMEH/85199016PUk2bz24vnpOTLWtLcmKC2TkyTJ+YnY8iriV/8DnW0oN/jNbZEQxGIoBUUYLz+D\nWPcEHDuI+NsfUsfprzyH+k9fQOzailj3OCTv3V/4H470+nCJPsrX/ZLwmjvgQPY1EzrE9PwLhHNK\nHbl9zLjtO8bA/lBgt9CKjteZaxlftMLJzLl2LJZ+Qe1wQsYwZq2MET858rlCpcdse/EUWQZLko2t\n5RncvRvY0ehge5MPUTCP2XNqueEtU6Uwl0jOMePqF+j3+38IfBiIAM8BceBK4AfAlX6//7ZRiPRb\ngf8+Ix09HVQrurUUS7yNuGMKRHoocHTRGwxSXDx4JtaJgLdwcDe8kQr0LPd3w8ippQ6mO+CkaAcN\n9nQW9wuKVfoicQ6EtZQVHCAaiTKYQ8dxdyUAfdEEMxQHcYTpVg/05+7h+eKFWcfkc3FvCiYAGy9b\nJnFd3itJ8hEKjbYK8JuTjjYp0CXnMd3t5mNHK2CKc+OjtwOgrH4LYuML+Y/TdYzvfTl727KLaNZq\n2LM7BLhY/c7P0Re0AqZ3kyEEqqKg5TEm/+LtM2nsjbGw3JWzz66qqRh0h/v0BfrUGTYajsWomZoW\n2m6PSrDPwG5XsdvT1xjYVYdLI87ImVni4He3zcZrk46NkjSi5wAFPevY3uSkq+w23rJ61oQ3Ekkk\n5xPj5o7t9/tvxRTnJ4HFgUDgxkAg8A5gNlAHvAP46ChOeQT4HvAeTOv578a2x6eO3u/mHndMwbAU\nUOAwCAaHT3423pk6M//N3XRxzxN3znACXc+qpZ6JWliU9fq9F03hjmpTPGdmWg9GB5/KFGPhRq2E\nviPHuEIr4hqtGEM3MHZsRrz2Ut5j8gl06ah9asgQdIlEQld/tvNoBOPX30uJcyBLnCtXvyN9zJJV\nYMnOaA4QdZWxY3M60VtDR4wn97SkXocxiAmD3VoQn8uCVVX43vXTuGdZGWVuK0sq3Xnd1x0Zit7p\nPn1XcbdX4603F+J0padga9/q5aqbCnLaDrxNOpy573s4CuyaLIslSaHoYRJ7fkRXSKPVey3z5s2X\n4lwiGWeMJ7PMv/Q/fjYQCKQc0gKBQLPf7/8QsA74nN/v//5IrOiBQOBR4NHka7/fP27MdQlbFbbg\nXuKOKWj2Egoch84LgT74BEAQi2UK9FyxntwmyHRxN+jsPsGR5qeZ77odSE9ePn75dD795DEAfnLz\nDKq8Nro004Jv9GenU4BQOAbkz3y7WvVSqdh4vWw5K/u36dte5dGt9fx6zi15j5Fl1sYQKdAlkgmL\nECJ1zxf1h6CvFxQFZd6S4Y/t7YGeLnC7EU/9xdz22ovZjWbNh4N7zOdLVqHc8h6U294Luo5isWCs\nexzxwE9g2mzUT34F49ff4znHbRBN31he3N7DDNWZem0g+K3eyiU1Xr67fBqaqlBg15hWnOvWnklm\nwlPnGbJEW20KVlvuGDpwi9OhAeNmOiOZaAiB/cQfsBh9vNS8kIveunL4YyQSyVlnXAh0v99fAywH\nYsAfB+4PBAIv+v3+BmASsBoYPAB5AhAqXkvUPQ+hOcFWjNMqiLR3n+tunTGEgHDIyHqdfp6dJI5M\nS7tu0N6+naXVIbqibUB5at/sUicP+udwoD1Mlddc+dX6M7vFX3oGZrwHgL6jR8G6aMj+tTnSoQWb\nn9nAc1OvyGnjQsWLhp4IZ01MJaeO1OcjRH5QknOMOHIA0VSPMnsBYturiG2vwcE9KPd8AqVyEsY3\nPpNubLFAIoFyz8chGkFZvgaiEWhtQhw9iHj2r9A79HinrFwLVZMRB/egXHwl6j0fzz4/oFx2Hcqi\nFVBShqIoBN/3/+DR7LwWmeIczDJjX75sMnN8DlzWkWdGL/FZaGs2k9hVes+tpdFpHzeOj5IJhBZr\nxd63C2voIPb4YZ4/WsrSS94h5zISyThlXAh04IL+x92BQCB/IVJ4HVOgX8AEF+hCtZNwTDKfW0yr\nsIh2nssunVkERMOmyrBYyBIcOUniMnbqhsFVs46zqLKbvx5ozTmt06qyuDJdm1PTTMvCTke1KeVD\nfQSPHYVZpkB3oBNh6EnZ/8y/M+/2WzUfdkUl8ecfIg6+inL3h4c8z/nA1leD+CosTJkx+tq7I0IK\nT4lkXCMO70Ns24h48mHz9cD9v/qf3J9xfzZ28avvmY+//+mQ1+iduYgfTbqaezf+nNJYD8r7P426\n6lJESxPimUdRVl0KwEtHe5hf7uTZg92EEwb3LCuH0vSi7UM72qkcxFsKxey8RVFYWjX6es4r1rhZ\n90QPkbDAbj27AnlRpYu2Y2nPLZdDCnTJyNGizXja/o49bDqmtvRZ2XPSQ/XKe3C5cvMtSCSS8cF4\nEejT+x+PDdGmfkDb8wLdUgiAljg/LOiXXeMlFhO8+kLakmEI2LnVXHex2JQBZdb6HwZJEuexmaXQ\nrMrgJXiSaBYVMHilfClvBxK6zq9n3ZTaf7Wzm7+G04nlBpa+GQq7Yk6K/n3pfXy07kGuythnGApr\n1QJ6yV1EmMg01MdpqI+fOYE+gN5unYb6GLULHXJVXyI5h4h4HPHAjxAbnhu8kc0GY1Ae9LVrP8hr\nu4MUXfvPfLAyhLryEuK6QPFVon3v9+xqCbFpSzN/3duJpkAy+frj+zv58hWT2dUc4oEdbUxR7FRq\n+e9VSy908sbGMG7bqdUTt1oV3F6NSDiRSiB6tqjy2Wg7lrZbFHo1Hku0E1ENbqJoiCMlb3YskXoK\nTtxPLJ7g2cMedrYW46uayZzaOcypraWtre1cd1EikQzCeBHonv7HoQKxk4rvvKoVYvRb0C3G+VFy\nqqBII9iXHaedyKiPrmnKMEni0q7welzPaDe8YEvm8UmWSOsT2V/vgpJCaICieB9rZxRDo7ldHUV9\nWAX4/rzbswR6KOikVnXRYqse8XkkuWzdGKKnS2fKdBsuz6lNpCUSyegRhoF45HeIXVtRrrgR8dhD\n0N6S1Ua55h0oi1dCaTniYB2KpmH89NvZbd55D+KPv8rapn7wcxg//06qDBqAcvv7EQ/+HICQ1QkE\nobwadWUlobjOHf1paN4+r4RH6jpSx2VWRovpgn95pj712p3hHWWzK8QyYtE9bnPf6Wjr0jKN9pYE\nbu/ZvTdNnWnD41X7FwgMXDaNk8S5Y6HvrPZDMrGwho9S0HA/3SGDh+umUbvkEt51zSxZPk0imSDI\nX+og+P3++4D7AAKBAD7fGRoMdS/Ug40gJSUlqGd7ef4MYLfFgd7Ua1Ncm5Mlm82Crqs8FujiurdP\nwmaLA3EQ4PP5OKSmJz9NJ2JMnmw+VxQ19T+wWCx5/x9tsyywbVdqEqYNcL6snVIBDeaK8a2rZvLn\nR1r6240cFdAh6/p2qzlJVBWFcJ+T8koHdsf5IDDN0kRDffc1TaOkuBQ1X92iEZ4/STRi/r8KCoop\nKknHeQohCId0XO432+3K/HxcLhc+X8kwbQf/XUjOPhPhfxHdtpHgg7/Ee9+n6fj0Pant4jffz9u+\n7B8/jmLt/13WzkcIQbS4BPuyi9CbGxC6jnXqTLjz/XT+x2eIbd4AgO+tN9LXcITQ3x6i4ONfwnn5\ntQA09wv0PmFmJW8JG3zx+UZ2NPWkrpkpzq+ZW8ZTe00vpTuWTeKx3c30RtOeVdb+hdY73jcdh1Pj\nib+c4GRjBIBpM8p5+bk+ahcU4fOVntLnVXKpYMnyBB7v6LOony5lZdmvN3y8LH/Dcc5E+F1MOPQo\n9OxF6doFsU5Ezc2g2hCHfkNnUPDYkfnccc+9Oe7s8n8xfpD/C0k+xsuMN2k+Hio4LGll7x2izZgR\nCAR+Bvys/6U4k65AxcKGxxpn7969lJeXD9n2wIEDtLS0sGbNmjPWn9MlPKC+dSKRYRXXdTqbTLfI\nLa81p1yZDUPQ1tZGIp52mVQwSAp7Q4iUO5bP5+Pg/pME+wwmTckQcnGzpJqaOmf6ul+7ajIF/d92\n4XRjGPGU5VwbxIJeqzhpEFGWqWmnDQsKOiLLNSwciQAFqGg8+/cmSso01lxx/jh6DPXd374pTv2R\nIDe9a/SulpOmWGmoT5fBSyYMbG/vIGGkb03Hj0R5Y1OYtVd5KCodL7ess0cwGKKtbfiszT6fT7os\njhPO1v9C7N6G8cLfUT/y+SHDQoQQpkW8qBSx7nGUskqMH3wNIEuc50O57zMoy9fQ3t2Ttb2lL07p\n9Hlo3d0cDVvojioou46xuNKN+IePQr9Ab+/oQKxYC8eP0jO9lramFjrCCVru/Rod4Th/2t4EwLaG\n7PPPL3Oyp9V07V5c6eK+C0r4wAUl6EJg01Sme+DrLzVw7ewi3resnMde7II26O3roC+oULvImhLo\n/5+98w6Tozqz/q+qOvfkqJnRjEZZQkIRCQWwBCInExtjbGN71wkv9jqsv7V3Wcf1er1rr+11wAEc\nWDAMGBNMFiIKSaCcs0YzmpxD56q63x/VoTrN9KCM+jyPHk1X3aq6XV3hnvue97wDg71cdVMBFqt+\n3L9LIHhcm5/TyD2jThyUUDd5XU9j8x9GQkMVCpouY+vbgSY5CARCPHNoKpddews+nw+fz5ewfe63\nOHOQ+y3OHFRXnzlK2DNltNsY+X/CCG1qk9q+byCshRQ4BmlsahqVoD///PMAZzRBT4ZuUrxLkvFP\nCEPxqCiJLu5m+bvDJcWoc3LN7DdfNuZ0zAS93G3lfPrRQ05wlsQM4f/ryglMK3MyHDQ6snhCESVO\nC6VOCwQSJe5GFjvko3CxUkiHCFEp2Uzr42qAKKI13m2ScTsND6aSqVBQZ3hQp6T8TLnlRobIskh5\n0xFvrP22d/yMr7dSVnl8ESY96fR1dxpRssEB7Zwk6DnkkAn6/34XNBWCfnBkNnwS9//YKGNWUQWd\nbVn7M8rf+QVSVS0Hevwc7AlQXWDjL7t62NHu4zK5iMqJFi6YnscXn2uMbeO2yXhDOjdPvIrna5Zx\nx55eLHI+a6d9hKFXu2gaaIm0tEX+peKGGcUUOizs7vJz2eRC7llSFVunBgQ9PSoX1ubz5IenxyYm\nZpQ6OdoXjH12uRPVaNaTVB4thxxOOfQweS1/hFA/G1rc7G6zcLTPhsMK188aYEpZgMd3VbPimlux\n20+Nh0wOOeRwYjGm0a7H46nHqFe+CqgmU4FpEA0NDWPZ95bI/7M8Ho8zg5P7oqS27xsIWxElecO8\ncbiJCy64YMS2Fllgkc9s++uR/L2i5FwXKt0d8eVRPhgKmti8rhElw9nkoAOMU0I0q4bYQtN1SnQf\n08qMUjt5doVfXj+JcrcFSZLItykMB/SYNBKMaLqOoEAyJOrJR7Wkibbrkc7nKYYARKSplb7uNS+D\n/RrXeQoTIl0tTSEO7gky2K9x7a2Fo0rFm4+EKB9nweE8uYPNZH4uhKC/R6OoVGHbu34G+jRWXBlX\nCegaNDeGaG4MZR1N1zNcxskEvbNtdIPAHHI4J6HIRs6Nz4e+5lkoKEK+6HIAxN7tiKEBpPH18Rrj\nnW1pdyMtuhjx7ptIH/sH5IuvQH/+caTqCeyzlPH2pg5e2jvA9UoJL+idNIogcyU3dbKDwUaVLx5q\nTNiXN2TcwE9EylU+sLkz+XAJuHvxOArsCj940yDuP7mmnonFDvr8Khuah7h2WnFC+y0bfHS1q1x+\nQ0HCc1BTBYol/vy0WHNGkzm8f2AJtpLX9TRB9yykQCt2rZuHtlciFc9m0vxqLpkwAV3XefXVV3li\nxzFuuvkW8vLyRt9xDjnkcEYiaxLt8XhmAW8BBaTylmSM6c3Y0NDQ7PF4NgMLgNuAPyUdewUwHmgH\n1o1l32cDdKWQQmcTra2thMNhrNbMEcjPLOumqkBl5CHP6YWZoCsWI8BjXhdWBznW8yRlBcvJd04G\nIqRdE4RD5nrpo8t6o/ANawjApkiosnFZ60i4A4MIIdi+0c/4CTZqKsy5zcb/yQT9WqUEeySTPSRE\nwtWcjhbrSUxTBAIpbQb7tUhbUEzp6ZvXxWVn4bDAPgJBD4d0tr7jI79QZuVVhrng0KCGO09GltNv\np+si47qRkEyS246F2fS2j3mLnTQfSXVuDofHPmmUMgmgR4+duMJs9nSuIFsFQw7nLkT7sbiLut+L\n+OuDxvLaSdDfE5Owj3glVdagfO9XAOh3fhbhNCYZQ5ffzA/fbGHnziYKsXC7Uo5FkrhMKWZQqBRE\n1EJesn9GZ8Lyunzy7Ar33TAJgKpInfFip4X/uqo+pX1Xu/FC2bXFz8JlRn8Dfj2FoAMsWemmoqIE\ncWoy43LI4aRBaX0Wi3oUW8AodvTGoTxmLr2V8ePHJ7S74YYbUFU1ZwaXQw5nOcZyB/87UAg8B3wb\n2NvQ0HAi33r/ATwG/KfH43m7oaHhIIDH46kAfhlp84OGhobYiMDj8fwD8A/AOw0NDR87gX05pdCs\nJbiVIA5FpaWlhfr6+oxtqwoiNWaFOGNLUZm7ZbFIaKppiChBWDNKynkDjTGCDhBWBZIUb2smaqNF\n0F951rgU7W47ajBC0CWZfIcVXYemwyGaDidGd6OEsMKuQGQSwY5EmRSfINGThrfpHN+TyWxQNgaY\nwYCOzSYxPBxvoGuJBN0MVRUZJSnGeuP/oQFjfz6vzmvPDzFpmp1Z850p7b1DGmueG2LhMhfVteml\npJkgkkiyL/IdosdOxnsh0cnHiJL85PMZxZl6veeQw6mGCAbR77079ll/Ij6nrX/vS2m3ka7/EOKZ\nR2D+EqRZCxBrVyN/6qsA+MM6f9rt5bn9LXzo/FIe2dEDwNVyMTVy4lMpSs4BgkmTqF9ZXs0vN7Tj\nN/mOPHr7NH7ydhvrmo1n9KpJhVwzrZivvNAIGJJ4iBPzEb+3aeKqtTnMjCENTYPXXzT2nV+QOIVa\nXmmltMxOd3eOoOdw5kFWh9Ato/vVKKFuCtTDvHWkgJ3tdsYXBrHXXcGMJHIeRY6c55DD2Y+x3MUf\nwMj/vqmhoSE8Stsxo6Gh4XGPx/Mr4HPADo/HsxoIY8jpC4AngZ8nbVYGTMeIrCfA4/FUAX81LYoy\nwXs8Hs+tpuU3NTQ0pNf9nSKEXFPI632JaZVhmpqaRiToUZwtBN1ml2Lu3ABGMNcYRImk6IsaFkgm\nQiyhx3PQM4gyOtvCCYTOnp+HGDQYcFCxEXaXkCkQHx3slTptMevBFUphQhvzYDTe80RoSfvXJZmB\nPpU3Xhpmykw7B/fEXYWMSYf030UdJQptnrB47flBisuMvvV0xSUKw0MadruM1SYxEInaHzsaGjNB\nTybJ0d/UHNjt7Y4fNxRMPcmtzSFKyy3YHenl+JmCxE2HQ/T3aEyf7Ujbh3MCuQB6AjrawpRXWN5j\ntYCzDyIYQLy1GumSq5FMlS1Efy+oYfSvfypxg+3vjrxDRUG67kNIq65HchuEYPPUi/jDhi4+tcjL\nv65ujjWNkvNClITJynSYVuzkc1dUEvDpOJ0yiiLxgfoCGnZ089B2w/TIYZG56bwSGvsDfPvSWirz\njGdRdb6NjuEQkiRlrfQJhxJvjM52FTMXCYVyN04OZwdsQ9so6niE4aJL8JVdkbJeDvdhDTQTck1D\n7ngFTYdA8XKuvugCuru7qY2WuMkhhxzelxhLIqsdePdkkPMoGhoa7gbuBDYDK4ArgYMYUfJbGhoa\nUpN7M8MOXGj6F61hUJe0/LQ7aKj2GnQlj7l10NjYSDA4uk2sninMeAbAPHFgtxuXmD/UjqYHIwPs\n9AOxcEiAKYIudD0hop4OG97w8u5b3tjnvKLChL0P63KKZDqKaKp4USA+wquQEolsUQpBT5eDnvjZ\nKsm88ZJhYnesMVEOPtLPFh7lzjKnCgwN6jQdTpWav/rcEG++bMw2RM35ZFlKGdiOhhTyHCPo8RXv\nvBk/79Hod7RKYDiks+ltX0Kbo4eC7N0Rt5fIRNDbj4XZvys1TSD51Pd0qWxc631fysHff9/ovaOn\nS+WdN7zs3ZnmmnifQjz6O8Qjv4Hd2xKW6//08QRy/kj95dy88odoaZ5L8o/+CCWR19737qNlKEzA\n5uZX77Tz0sF+vv3qMY4OBBPIeRQlWLjNUo5dig8R6qekTvI5JAlFglefHWLj2vi9ftvsxFJm08uc\n3HfD5Bg5ByPX/KHbpjE8pPHsYwO0t4w+tIgScIfT+L6dbWG2vhN/ppgng3PI4XRBDveSEBlIekdJ\nmh9n+5OENMjrfxVH/9rYOkuwlcKWByg9+kMKO/5MaeMPyPdvZ3ubi6mzLsDlclFXV3fGBmhyyCGH\nE4OxRND3Y0jcTyoaGhoeBh7Osu23gG9lWNfIGHPhTxskmaBrGvXqTgYH7Dz88MOsXLmS+vr6jA/h\n1tZW6urqTnFHs0RSBF0XKu19LwFQPf6TZKIfRgTdzGDNEvfsDl1eUoBET+zzR6e7E0hxT5eKd0hD\niLgsWx3DlFO6GS2hZ77MAv7Ejuta5i8yWgQ9uXxdFMmXiDciR1cjqQVtzWHamge4+pZCLJbsbomU\nCHqaNmbSH/07Op6Pbj80GJ9T277RGEjXTrTReCCEroPFmv35T1ZCvPuml3BYEA4JbPaz41bPGqZL\n4X04/zAmBAORlI7hM3dS8nghAn7wDSOVGPWtxa7Nxv+9XXB4H6K7A/Hn36Rs92TtSgB8Fgf5qh/5\naz9AHNyN2LMNqaAY+V//B9HTxVfe8XG4rw+XVcYXHvk8Lqpx09OaOhdeUm5h0jQ7r784hKaB0y0T\nDonY9dnZprJri59Z851ZkQe7xXhYNB8zJhrbjoUYV5M+Yi90wdZ3fbjzDTXB3EUudmzypxhIXnRZ\nzhQrh1MLSfOS3/Zn/CUrCLumYvXuo7jtD3jzLsBbeTM23wEK2h8i5JqKr3gFmq0ce8ezWAnwh83V\nLKnt4Tz+hmN4F6qtAufgOwQ1K68dyaexx8qSiQEmloRolc5nvCtzpYYccsjh/YWxEPTfAv/t8Xjq\nI+Q3hxOIkGs6zqHNfPSmi3hqzU6eeeYZ3G438+fPZ8GCBSntn3zySe65554zchY1WeJuZlaqGo+y\nJPfciMKaJO6S2TAuu2NX5FkT9luU70wgm2+vGc5uRxkgS1IiedI09LYWKBuXtn1hscJAX3ywq2lw\naF+A2nobVlviGUgm6MlpDOZotBn9vRpNh4MUJ5UgS84LH+jTKB2lzFs0Gm1WHXS0hmNqg8729I7q\n2941yLemGpMQWmQiQlPhmUf7WXl1PM9u09u+2Dkpq7BQXKZwYHdm1YiiGOctRQlx5l36JwxihE/n\nGsTgIGCB9mMYGU1nF4TPi3jyQSgoQjz1MPJPHorJzAGEdwj9H+80PtRMQLr4Sug15OHiwV+M+Osr\nEcn/UN10vj7zo1Qdc/BvV9+KuOoWnt3XR69fZXndeA73NQKkJeclTgt3zS9n+y4fcwrcrPxAARs2\nD9F5IJGkW6wS7nyFFVfms29nAItVoqUplPB8Pbw/GPPD+NFV9bEKF9mdqMyrvF6dY41hjKw3sDtk\n7A4JX+SRWFVr5YKIYVwOOZxKKC3P4AgdQjp2jKFJX8Le+hfCOriHN4Ks4BjYSL8P3OE9lHh3xbZb\n35TH8qs+wqaN62ndt5mFE1opdhxhS4ub5/cUMHHqLGYtn8zbO3bw6NYmbr/94tP4LXPIIYdTjawJ\nekNDwy89Hs9iYLXH47kHeNFs2JbD8SHkmopAptrZxYc//GEOHz7M5s2befvtt5k3bx6ynBy7FYRC\noTOyxqWZoNsdMsJMuhNKxCUTVBIk7VJCBD27gV65O07QJcDhdmaUuL8XpETQAz5aXVVUpWsMCeQc\njAj+7q0Bers0FixNnA03E/RgQOelpwaZc4GTCZNH/42jBNmM5LzwtuYQdodk5IpmiKSve82LO09m\n0rT4Mc0TA96h0W/53dv8TJiS2Oct6+Nu9QF/fB9SFkk20bOS6RI4g7M93jvObU6eAG39a5B3GRw7\nzNlI0PUv3pH4+d67keZcgOhqR5qzCMKmVJWWo4a0PRMqa5C/+VPwDvP0wSH8+4z7/sDtX6F1XRut\nXi+P7ezGZVX4zUajjuXju3oy7m5lfQFfWl4NwNA7giGvzo5NPjoPpkbQo+obd77CgqVu9u0MoIZJ\nW9UBYEqpI+3yTDBf8l3tYRxOmfxCI2K+dYMvoW1U4h7FrHmpJpk55HCyoQRaKQpuY2+nnSnlQQob\nf4JVCvDIrmrmVPRwHhvo8Sk8dWgm/UM+6vN7cFh1VE3CUr2CScXFXHLpFWzfPo4HtuzAN9RLde1k\nbv3QxRQVGYa29fX16LqeZgyYQw45vJ8xljJrhyN/1gN/A1SPx9MGaeusiIaGhslplueQAUJxEnZM\nwD68G0vxpUybNg1VVVm9ejWDg4MUFRXR3NxMRaT9+MIwqqqe8QTdkB7HLxHD7yiRfZRVWujuUI0I\nuinabpjERdqOkoseRZ5NSaD9Lpslo0nce4GMRL1k55lH+7nypgJ0r48BR1FGgp6MKAn3+/RYlDmK\nsMnt3uc1Or19o5+iEoXC4rG5soZDOkcOJA6cfV6dV58borLawuKL00tBezpVejqhqCSD1XwWOHIg\nhC9Jjm+eqEgwDZSzUEdE1mci4u9Hgp7j53FIeUZJQfJPeobVCYUI+I06k8kYGkCsfcVosz8eUZNu\n/3vEo78DoN1Rwi+n38o/7XqQfNUg4dJVtxD+4EcBeOaY4MF98Um5n66L+5z+37butP359qW1vHCg\nj6W1+UwscXBsIMji8UYkP5pGANB4MPG5UVphoWq8NUV9Y4mo0XduTp0cHAuSJ181TbD+dS+SBNd5\nDJLS15M4YWCzSzFVD4DTlSMvOZxcKOEeXH2v4y+4ENVRA0JgOfY4gbBEk/1yWo+s49LJPWxvczJv\n5Z3s3rGZtv0bOBaawJXXe5AkicbGRlRVxWazxcyAFUVh/vz5zJs3j0AggNOZOtmUI+c55HDuYSyj\n/nrT3xJgxTBcS4fc+PI9wF+4mMKORyk+9isGx91BcXExAH19fRQVFfHX/V8YIwAAIABJREFUv/6V\nhdcYbT+7vIeDY0mePk2wO6SEAZgk6yBULps2xNtHWlA1P0UlRXR3GPmLEwrMl44WJ/tZRtBVVXD3\n4nHs3hQAJJwWGT2DcdDcRc6EyPPSlW7WvZYoIy8uVRIGhzIwRzaklN5BHYZ9OMg+ehMdCOu6iJm4\nxfpuiqCbJxUO7wsyf8nYCPreHamGWh2tasL/IyGaL/5e0dEy+jHAkOhm+mmFLpBMzs7JZdliy9+P\nSdrmyoRnYBrLKYWmgUKqCcEZAuEdQjzyW6SFy9D//BuorCH0ob9D/+YXRt6wsga6243v53QhLViG\n+OuDSBeu4NvycjokJ5vnX8slly+FqvEIuwPPw/uy7tfkEgeHeo3nwFVTi5hX5WZeVVwGXldoN55D\nuuClpwYz7mf8BCt1k1IngpUMjvqqKgj69Vi++GjQkh4VwxHfiuhtrWsCp0vC7zMWVFZbkCQp9m64\ncEVO2v6+hBA4hjYTdJ+HUE6jQkIInP1v4ep5EQUNbeAw6pQvYxncQSFtvNJSxZxVSznWXEPDu4+S\nX7eMhSUlLLv4UvbvH88l9fVYrcZs1pQpUzIeRpKktOQ8hxxyODcxllH/xJPWixwACObPo192UtDR\nQHHzzxHF1wPwzDPPcP7556e010NDCFF4xg3gzf2x2eWEcmqKRaeuuJWVU4Ypdqk8sf1Z5tg+Yto2\n3nY4cCC+PK1QIxWH9wWxO6TINuC0yni96bctKbOwfFUeXe1GqbaonDIKd57M0pV5PPeXAWrqrLQ0\nhVNc3L1eP+VSAf1CTXF8LxhsZLCgPmFZ1NRIU6GvJz4ylZU4QX9r9VDCpEA0V12Ss+co2ZQsSkZX\n+6mf8JGVEaTrIsLLTJ/DYWPwn1egxAbomSLo/b0q1kje7NkG8yl5P84/jAnhICjgF2fW7yj8PvTf\n/BB2Rgzd1r9mrOjtpi8NOd9bMIE/zr6Nb1t3Y3v9OeQ7Pg3TZhMOh/nO2i6G1w/xmW/+kZkVLjoe\n2gvA0Quu4v4ewYZtrXR6R78/JUCRJVRd8F9XTuBv+/p48+ggn1uc6pFxYHeAvTsCzFs8svGUlOFZ\nki5NxmqVWPfqMP29GktWuCkfN3KZNiBFSWT2zuhqD7P+dWPSNGooWRHZ55SZdnZs8lNYfGZdFzmc\nGCjhHgo6HyfomsFA9V2nrR/2vrXk9z7Hng47zf0urpjeQ7h/I/auF2kftlAw9XpkWaZuQj2lZf+A\nK2LkJssyM2bMOG39ziGHHM5ujCUH/ejJ7EgOBkLu6fTW3kNBxyOU9f6Fq2cV8vwuFzt27GBOdWJU\n869PPE5+aS233nprhr2dfthshkncqqlDHO2zoal6jHE4LQJN9yXUyTYT8cQSa9mxlHBIxAg6gE2R\n6M9QYkyxSJQUKJRE6omrJon5wmUuSsosKBaJ6zyFeId1WprCTJOcOKKZ6BIMD4VxSwp7dF8KQe8Z\nOog1iaBHHdZ9Xp2Na32xY21Z76PxYAirTUqRcyoWo06w0KFmgpWWo8ZAffHFboYHNXZvC1BQpDDY\nH9/u8P4gVpvE+QudbF6XmL8ZXV9SplBUEu+zL8NExkiorHLQ0Tb28lcl5Qq9XdooEXQSGLrQYeNa\nL90dKtfdFpc7JysRonjzZcMQ8Prbi8bcv9MO00l5XyoExoANogw7sMVVx8m2SRLhMJJ1dFKpP/hL\nxBsvZLfT2Qth+vn8squWY3I+x65aRO1FVzBUPh6nkDk6BDs6jWf7P7/clLDpk3t6R9x1sdPCuDwr\ne7r8fOMDNVxYm0/LYIg+v4oiS3xwZgkfnFmSsE1Xu2H6eGCPcd9ufSf1+RDFtFl2aurSnw8lDS9W\nVUF/r3FDrn/dm9W9F33uRi9zcy1zsxJo2nkOHC6ZqvFGf6prbVTXppZ+y+EMg9BxDmwgUJBqdjsS\n/N4BAAKDLVB9Mjo2Oqy+g+T3PMfuDjtH7NdQObuC9p77qRBPIsuCV3vPY9GCeOzK7c6pOXLIIYcT\ng7HpZnM4JdCtRfTXfIqCjkdZUruDdYdt+EIynnn9Ce2siqC1tTVhWX9/P0ePHmXu3LmnsssZYbFK\nWOQgl0w1yNJmS4jGTmOdEjGMMxNq2UTK7ZZiwMitlBAIIehsV2k+lNn0qKUpxLRZcXMiSZISItXR\n49VOtKUYDZkHnOaBnyRJRINItXJc6ikG+xmK5Fp3i9To1svjFhDJSGCn7mW2nP7lrShSLAqczs08\nHBKx6Lo7TyHqZFw+zhIzgisokhMIenS70nILxaUKup6YB75ri0EIzAPoTMZxI6G0wv6eCLrTKQNR\ngp6egAb8Or4ePS511QXdHcZvGUpIBxiZwA70qWPO4T/dyEXQ4zgiFzADkLOUj4jhQZCkBKf0bKCv\nftrIAS8sgaISpIsuR1q+ypCg9/cgjh6C5sOIN18GX2I1CPmee9H/97sgSci/fhLHMw/je+ZRpOvv\n4JXzrubnG9pjDpNffSE61304YR9La/NZ1zw0Yh8vmpDPPy6tYiiks7XNy8xyJ+PyrAgiFSaAmgIb\nNQXpiauqilhE2u6Q0NSRL67pszNLbtOVbMz2Wg0GddSQwJ2vxHLJNdWo/hCdULTapBjZB6OsW46Q\nn33Q+veR3/M06sBhqPhi1tuFgoHI/6cnlc/m3Ute65/pGlbYLz7AskWLAdh0YDJXF+xjV7uDKQuv\nOy19yyGHHN7/OLtGrecSJIWg+zwcwzv46iVddA6nhiuqCsKoSTW4H3/8cXw+H7NmzcJiOf0/r8UC\nQ77d8c9SgLqJxneJjuUs1vh3SIyg6ybDOQEC3nnDC6QvNwaGAVl0kFjqNL5/lPRGo7YOp8zMOakD\nz5FSBdLJPMXPv4uvZCZMmcggqWHcsGm0ulUfzkjQ5VEUmkcPhaiPuKKbJxVkWaKmzobPq1M/xR4p\nQ5QIh1Pmosvy2bLBm+Ion4zRBuvpMPP8Inq6vFnltZthdxpsxZD2p2+z4Q1vQlTfPPjXNUaVuEfh\n8+oUFo+pe6cfuTroMVSGBsAOLjWAUMNIlpEj3PqXjJQZ+TdPpb2nRVszKArirZcR+3bC4X2GmVs0\nGXqgFwZ6EUcPIh76VcbjSDffZRD43i6I1C9n4jQkScJ9+yfxDw0iXfFBfv6Xpoz7MOOWWSWsax6i\nwG4YXQ4E4/frpZMKuGteBUWRZ1qJU+bSSXEVyWhTa0II2lvCMdUOGAQ7mEaZZHdITD3PkZaAm5Hs\nW5VcUnIkvLV6GN+wznWewthzR9NgaCC+vcstMxAyEXRnzijrbER3VydVMgQHsrsPohC68WI4HY8/\nV+8a8npfpm3IyktNM7ji+hWxdbXnX82Dr3ZRULOApcVn24slhxxyOFswZgbn8XhuA24FpgEFpB8b\n5FzcTwBUWzxvsCIvdeBzxwIjor63t5eSEkPGGAhEZp1DoTER9HA4jBACm+3ERCiWrnTj8+ooFgl/\n6IjpOCoxV/fIm9dM0GVTDros6aaSafqIL+qiEiUWbYkSGodFTojOLlzq5uWnB6mtz/wdyyotFBSm\nMua0JqoCgjZjkOwTaQi6qcfhEXovPdcArmszrgejdBmQUjtdViSmz3amjUJfdn1B7G9z5D0TtCzG\n1g6nRMAvuPyGAjRNUFBoZfHFeTzzaP+I21ltEmGTdNVq+s0Np/9UhJPqwjcdjrtLm0vnRf8WuqC3\nR6OkVMmYN3u2IBdBjyMkjN9SkxXw+7J2cxcvP4l0xU3GvbF1A+LQXqRLr0X/3pcglFQaLNmpLB3q\nJiPfeKcxM1RcBlXjkWSFNsnFbzd2cN3NX2SNPB77+jY+94GpfHfctWwdgZz/49Iq8u0Kz+3vY2qp\ng6mlTn5342TKXIYJWkiLRsld5NuPL8+66XAoxfgxmm4DMH22A79Xp3aSjcJiJaMBnBnJE2NmJdRo\n8EWO7feJmMS9u0ONpaVA6nMh2SMkh7MDdocDQoAY2ySu0I17VCDhHNwICAIFi058B5Og+JvJ632Z\n7a0ONvbP4dIrrkQxyesqKitZcvWnKSgoGGEvOeSQQw7Hh7GUWZOBx4EPknnCXkTWneNDyhMDzVaW\nVbv9+/ezZMkSwDAm0XWdUCgUMyvJBvfffz+hUIgvfGEU5+EMEEKwefNmpk6dSkFBAWWV8SiXWbb+\n+mtrWL7AcDKNLjWTNXPVOIfFRCilVMIG4MqT8Q3rCaXdoqZDgsT8ZIdT5upbCkeMDC1dmb78WLp6\n3f87w0N+/kSmAbfNK4WdievNUwpm7mvOIweQdqxn8T95IuqA9OhqNwY3mczfzJHCKz5YgNUmJbSd\nPN1OXoFR/3zLel+MLKthEZsgGS2CbrVKXHy5IRt2jBLNKq2wsGSFm2cfM/IIyyostB0LU1tvw5Un\nm9z5oX6Knf27UqX9TlciqTebR5l/1yhRaGkKs2WDj/lLXIyfkJiicNYhF0EHQAQDMYKuSooRrc5A\n0IWuQWe83Jh47Pdoj/0e6iZD0yFj2YtPJG503nxDyj7vQrAYxFh/6D7Emy8Zx+nvQfrEFxEvPIH8\nhX9Dikgx+vwq6w8Oct+7HbFdbaIGENA9wOpDG9P28RfXT2QwoPF/27qYW+WmxGnhgpr4M6fcHX9u\n2hQ5VgbteCCE4Oih9LXKAS5alUdx2djVVoXFhlHj0pV52J3Gc2U0aJpIUMUMDWpp50bmLXbFTCut\nNomFS10JE7nnMjRN46WXXmLx4sWUlpZmtc2+ffuYOHHiCZuAHwukSGqKNMqwsOLg1/EVLsc1sJbB\n8hsZiEirKt1+6PwLwCkh6FLLc/jDEi2OVVx7/fK074/CwrOr5GMOOeRw9mEsmrHPAjcC24ArgCcw\nhpHTgWuBP0fafR+YdAL7eO5Cym7QlFDGLPIyCSVHiEbBWNsnY3BwkLVr1/Lss8+mrFNMV5kiCbTI\niKzIqWGRRSxSUl1nwefvQouM3xbV9sRe6hI6Lz2ZWgpo5hwj39w8eIsRTZFK6keTbWaCnOYl3eIe\nxzTZmAS5dmYRz2u9/FXt5lG1i0fULnwRpcBh3Yhc7dQNAj5jljVGogG+N/suKqvSy3ZXXJk4QM8v\nzHzL1k2yMWWmHbtDTiHyikWiutZGZZWVK28sYPZ8Q+K//vV4xCqUZKZXkOZYDqc8KjkHI5pt7kP0\nvJeUK0yb5YjL0/v7sDzwA+rsrSn7GOzPrF03Oz9HL//hIYO1e4cSpQDm+6OnS8U7pKFpAj1hH2cW\nC07ozinoWzCgJ5gknjFobSIc8X1QZQX9e1+OrRKNB9B//1NEVztizzb0L9yBfu/d/M95d/CzK78R\n30eEnOPOh7x8pKtugZlzkW7+GMqXvo286CIkq5XtHT4++deDPD7nFh769C/Z8vn/Rv7pw8jLVsG3\nfk6n4uZwb4C1TYN8/ImDCeR8NCysdnPppEJq8m2cV+Hi+5dPoMR5alKQhof0EaXn74WcA9gdMtd5\niiitsJCXrzBjjiNhfWV16n737wrw2vPxPPtwSKS97iqrLbHnY2GxkpUb/LmC7u5uXN5tvPbK81m1\n7+zs5MUXX2TNmjUnuWfpISLqshEJeoTEuwbWAqB0vhaTuGdxAPQjj6MNZu9jrIQ6kLS4d4ol2Ias\nDiEH2iihkc1tJcy/YNnZObmbQw45vC8wljfzR4EAcHVDQ0OHx+O5E6ChoeEAcAB43uPxrAZ+B7wO\n5FzfTwCEZEEaRRqmqir79+9n6tSpsRdKOHxqjVW0iD463XHNEXRFhp7uTiiG8jyNT17YgyxLXH5D\nARDm6Bvm8lpSTKuR6Ohu4NrbCpEkmL3ASWWVhVeeNQZ+0YiMIE7QFyzNXk2QDukk7hLQJ8I4RBhZ\nLqJFpE5y/E5tj/29Xh/i76Z2Yv/yN5E++nks1qWEQ4J2e0GMIFoskF+k0NdtnE+nK3GA4M5TmLvI\nmVaCOndRdt9RkqQYwTU7xkcN56KYOdfJBlNUP52CIRnnL3SyY5OfoUFjXzUTrNjtcix3PjqREiud\ntuENOLAeZ28JTP1QVv0HQ44fz0GPTOKYTklHa/w6NEfb316TaO51za2FhIKC1c8MMm+xk9qJqfWe\nN7wxjNUmsWDJ6XHoHS3H/kTgpacGKSiUWXHVmSPbFAN96A/+glDt3wGgSwo6EnzqBiitgB7DbVK8\n/UrCdm9WzIcg3HPp9UhrnoFxNcif/DLSxKkAHO0P0uML0zYUZv0rTWxv91HmstDtMx4cD203TCj/\nMoa+/uDyupgD+xN3TOfPewZ5bGsbt80q5c65ZadloC+EoKNVRRnhLT8ziVQfD8orrcy5wBmT0qfz\npWhvSXw/BAN6zPjRDKtNihH09zqp+n6FS+rntnkDHOjN7sGghbx89ZJO1jQfvxrjPSHyAJbTvMOj\nEEkpYm6pn2n6S1ntXgt7qdI2EWjdymDB99K2kfQgsjqIpPtx9b2Fw7uDoKWSwbrPYvEdoajtQYQk\nE5LyCKoSatkK5LR5bTnkkEMOpwZjIegzgXUNDQ3RsIEA8Hg8UkNDgwBoaGj4vcfj+RLwT8DLJ7Sn\n5yhUaznWUNuIbXZs28IWHV54IV72J5pT3traSnV1ddYDRCHEmAeT27dv57XXXgNI+1KzW8wEXTA8\nNBD7XFccphMjMuvzacgy6EKiJ+CmPyAosEcHb6kv92iEduJUg1TNW+xk6zv+eERGiJhE2nqc8sh0\nEndZkpCRCJMqz86EcHuz0bX1r3LhZy7jZ8+140NHvPwUV910A7IioSgSrU0hFKuUoAxYutIgiHWT\nUknkWFFSnnrrmyXk02c7KB+X/ePh/AVOLDaJyioLOzb5Y+c9SmqFbjg2R6Nq4yfYaD0awj94gE8v\n+TqLe/Zz9SInmgY7N/szHicKc/R741ofF1+e+AO982Z8YiG5zrIZLUdDuPKMbZuOhNIS9Gjt+gVL\nRu3WCYM5aH6qgvuDA6dgJiALCCHQf/JN2L0VgGC98ZsIYMjqojDsjZHzBJRWwFDcC+HZebdww4f+\nHm9I5+l9vWx9sZE75pTzrTXNKZtGyflIkICbzivhcG8AVcA9F46jMs+KqovY0+nGmSUossTnlk/E\nJalcP73ktEXhDuwOsm9nIHYf10ywUlNn4503vSy6yI3DKSWUWTwRGF9vwzusc2iv8UwMBnQsFolw\n2Ch/mazsSSbsUUiSFH9m5/h5AqJPumJHkGzuWKfeTZFTY0l1y8nsVkYkRNCDvTiaH8RfdSuSJW7U\nqqvvXcGnBn2R/Wc+G/mNP8ehdwMQ1iTebXGxYHwH+c2/wxJqp3VQoXXAyoLxA6xvKWLqivnvuT85\n5JBDDicCY3k724F20+eoPqgQMDtE7QCuOs5+5RDBQNVHKTv6wxHbXDhhmLVHEnOnw+Ewhw4d4rnn\nnuPSSy9l9uzZWR2vt7c367y2KKLkHNIT9LsujBNyiwyZ/Ic0TUOWBAIZHQVZCsUiy8nyuMUXp0Yz\nZSWqHohGVCUCfuOlfbz5i+kG2RJgQ8LvNAYaD946le+saeJAb2bC3nasg7tX/pB7et9kVYHMO7oR\n9ffv3Ir7ihtj7arr4rmC7jyZknJLQl7/8aKkzELdRBttpgGymaAb+aUSE6facLpkdm8buZRa/dQ4\nsS0uVRifZMQnyRLjauL9d7qMaO0He24D4LmaC/nMJDtHD2U32ZEcVTZy9CPR+SRCG42gJysEovuJ\n/rajVfESQrDm2SGmz3akfL+TCTE0AByfAuRMgxgahIAP+nqQps1CBAOGLGKwD7Hm2Rg59yl2/KaB\nfPNNn6Ww4Uexz9Knv4Z47AGon0L/7XcTtDjgb40APLC5kwc2JxL5ZHJ+zbQirpxSRG2hHVky1DsP\nb+9mV6cx6N/T5ecbK2p4ek8vX7u4hkJH6ivTGnnuPHTbVOyRfB67Rebm88b2HH0v6O9VaTka5rx5\nDta9OowrT2HeYhdqWLBvp3HPRv0rZs1zRmTphSdt0kBRJM6b6zQRdMFLL6amJkXR22XcnHaHRDBg\n3LjRicho6tNoZRTPNUStU2VJZEXQTzsiBL3UFWJg+30Uakdo2fkopfM+DoBj4F0U35ERdhCHJdCM\nrHkJuWfElqkhYzI242UidGxaNzvbHWxvddCvlzNl5gKe2bOaG2e10OdXeLtvEaXjJvGLdzcxe+6C\nM6ICTg455HBuYyxPoTag0vQ5StZnAOtNy8cBuYSxEwTdOnoZD7fNVJoMgc0iOHLkCP39xrzJmjVr\nKC0tpaqqatR9+Xy+MRN0M8xup1Hk2+Ik8FNLe/jLtvQGK6qqIkugKBZ0VUGRdKKO77FcdAk+8ulJ\n9Pf3pjl2xEwqQtB9Xp1NbxsDbZf7xMvVZMApKUgR7lRgV/jQnHK++9qxjNsc7hqGfHjFNZVVwTjp\nfbBsGZ/NsM3Kq/Nj8m3te19GuvgK5BXHPwfmiJiwRVUTmiYoqzSIe0WV8WiYvcCFrotRCboZF12W\nnZRS6Kl5sfkF2Tk1a5pIIOKdbeFYnmoyQdc0kdFlXoh4/vloker2ljA+r86WDT6cbpnSckvsWjNP\nAAlh9C2ToV82SIig9/UCo9+7JxvhYS89mw4ybsXcMW8rNq2FcbVG/klXG/p9/xlbJ11yDeLV51K2\nkRZdTEvFDGRf/Dz+W2clTwCMq8H7wY/zVsE0Fn3z1xzsDfCDF4wIoU2R+Mryav7jjcSI4ScXVNA8\nEOSiCQVsa/dS4bZy9bTE56siwUfnGSXTtEhk3CJLXJiFWVue7dS7jK99ZRhdhxlzHPR0afR0acxb\n7GJ4MPXeikrGT0VEv7RcoadL4/UX09d1nzTNTtPhIGpEuLDqugKeezxiKBmZiLQ7jGe2Ojbz7/c9\nhB59J2aH051GLXQtFvYv1CJEXI0rnAq6nkizVXqUHPslAJ1T/iO2TA8b73gh0n9RERpEliDknMSF\n19yI2+1GlmVaKyr40xuPgKuKy665BovFknUgI4cccsjhZGMsBH0fcJ7p8zqMd8TXPB7PLQ0NDcLj\n8VwMrAC2nsA+nvNQreVYwl0Z15tN2K6bNciFE3z82/MSuumF9dhjj2Xl0B4MZi/XTgdzBF0Iwa5d\nu7g0Kc3RZklkQp2dnVRUVBAOh40IuiQjJAVFEsSl7cb/E6fZsVjSk+1oPfFoxMiMsZQAyhYVkhFF\nnVIc/4IX1ORx/YxiNrYM0zYUn5j49FQrvzkQZkPZLMCYdtD9cdfj5sEQQlWR0szcR4me0DU4ehBx\n9CCcAIKuWKLu7WCxGv/bHRI1ExKjw7Iscf5CJ07XCZ7k8Ke6PpeUW1h5VT6vvZB+YB+FpiXmlvd2\naxSXGueutSlRNmsuJ5UMSSImxx8t19tcQ3r7Rh+XXF3A808YpOL624ti6958eRihixOWz326/OuE\nrqFv3sDGpkqm+DfR1JvPsYqlfGD3YQrPG9kHVAiBePAXiL4eJLvDIOiZ2qYj5yuuQv7I3ezf24u8\nObG6wcdXfo/LpxTxl30+oINfJ5m1LarJY0ltPt+/rI6n9vYytdTBDTNKsJueG/OqRvcTUM6iUn3m\ne2HtK0PUTky8h63WVHn5ycSEKXZ6ujK7us+a7+TY0RCoAouFtJ4a0QkFfYQUlXMRQh9jubLTfPrS\nSc8V+cR1SgtFCHrScqvvADb/YfqkiQBYXGXk58cn2qqrqym64bPY7fa0gYUccsghh9OJsYy4XwBq\nPR5PtM7FGmAvRtm1Vo/HswlYjUHaf3VCe3mOo7/m7/AWr0K1GpHtkGNCwvpCh8Z1swa4fV4fF06I\nRIxtqS/FI0eOIISgvb09YfkTT8RnsMdK0Lu7jbwuCcHyicM4rSIWkdyzZ09a51irklTfuskwV9JU\nlUV1fmz4ERgRdJsSjZwLrDaJWfOcKfuLYqTavScjapSP8VKfODkxb/nvF1Zy3w2T+e+r4r/TtBKj\nzcGCOsCQKQa64pMutd4O9M/djGgfIU8w4Of3k69jR9Fk9Id/jdi347j6HzVfiubsa5rIeA7rp9ip\nrD6xwpjw73+adnl+oUJewciPJk0VKaZ1UYm+uYwTMGKJqcF+LUa8B/s19u4YPf99NAz0aRnzuTVN\npJXamyF0HbFzc+zzsDg9ckvx+5/h+9P9dIZL2RKejy+i5gn+9TG0T92A9oU7EL5hREfcgV8cPYT2\n2ZvRP/1Bo1TZzk0jkvN0kL/5U7avuosvP9/I/Zs6YxUUoqR6EBt/OZhK/haPz2POOBcfnmOUp5xV\n6eIbK8Zz2+yyBHL+foTZZ6G3W2Pbu4nXcTYGjycSIz2Lo2qmqLnjzLnpn+mFxQp1E21Zm1+eK4gq\nj7J9o+mxNLHTg3HsS1kmS8cnzo86sGv9+yn2Gs8X80SE1XeI4tYHcPe9huo1JvAUZ2rZWpfLlSPn\nOeSQwxmJsYz8HgK6gUGAhoYGzePxfBDD7HY2hvxdB37R0NBw/4nu6LkM3VKIt/QyvKWX4RjcTMg1\nlbLG78fWz65KlR/n23WGg4kvno0bN+L3+1m9ejXXXnstkydPpr+/n2PH4pLssZZbe/jhhwEYXxTm\n6plD7Gzbw5YtW5g8eTKrV69Ou02RM1F+GXWA18LxQaWQFCyyMJF5kbbslxmZTFcXZuHgHgqFIsZE\n2ZPQPMk4v84M8vmppU7uml/O9DIndkUH4g7ieiiI96ffg2X/CsDz45dzw7E3GLfuVaSbPpJ2f8OD\nXp6p/QAv1Czl0Vf/BfHqsyi/fTrr/iYjHkGP1ERXRYJjsujrAacTyXFyBsihxiOQIYMjGs2WpPQR\nIHO+fBRREiIriRHFkZBM3g/sDjLj/MyTQDG8R77z7lteutrVhIh7yq7XrkZ/7FFYbuRa94hTmO/e\n3YH+g6/BQJ+xwFluWmn8KGIgkl7i96J/8cPG33MXw7Z3Unc4bTbs35my2Ks40GQZn+JgXLGb4Q99\nntAvvs+A1c2xYBH/sz6eKx69u/KsCh+ZW8b/beuOrfvuqlqe2tNLudvKx+aX47KeY4PtyO368tOZ\n87xPB5KfxVXjrbQdM5QtSy8x/FIij/2YumnVdfkJ97UsS8xdnCOYGPgqAAAgAElEQVTnyRCRqi7p\nKpukbZ/tw/AEQwl1Y/PtJV9OY+j4Xh+gEZQf+TadU/6Dis7fo0Re2VpEMSh8bRS3/i7Wtj5oKHQs\nrlSCnkMOOeRwpiJrgt7Q0NCNQdLNyw4Aczwez3SgBDgQaZfDSUKgYAEAnZO+Q2nTj1DUgbTtDEKY\nCEmSYqR5eNggi0ePJlbDixL0n/3sZyxcuJDly5dn1a+oZC3PrrF9+3YKC9PnmQMsmZAY/dI0jW3b\ntrFx3RoWXGYsE1iwKvFouyQJLLaRYwDp1losiYZrmXDfffdhtVr53Oc+N2rbKPJQkGWwjdCvqFFU\nlzdRdi0BewonJiz73JKv8/8Gt7B0xyZEdzti1xbkD96JVGu08+/dBVQTlq3cedF38DSu5uase5uK\nqJpeVQW7tvgjUvf4d9G/9gkYV4Py3ZMjiHlg/GUZ10WjazZ73DzKDL8v9foOhYxlJ2o82tociuXB\nJmN4SOeVv8VJUfLkRiZE0y+GhzTy8tOTSa2/jwMTPxhfcJKDn+Ya8PqP/jVGzrfM/hxt45YC4HeW\nM2QrwAL02/JIGeqmIeehT34F+4IL8X/hTl4ddwGThlvoshdzOL+aJ+suibX73ASNX22RYdm/4lL9\n+NbHJetXTililupi8JiOEDCl1Jg8mV/l5vrpxcwZ52bOuNNT/u5MQLZR0XkXnlqim2mydOYcRyyC\nHjVltNuNzy63guvc/SmzxpgJ9yhlWk8WXK2P4FTTK8Jipq/Z1jpPA2v/5oT0voBqQQKG+9oSzJIA\nNB3seeXkkEMOOZwtOCHayYaGhlQNUw4nF7KVvvGfT4ikm2GzCMPl1ZSH3toal6K+/vrrzJgxI2W7\nUCjunL5p06YRCbqeNmlXQgiRIBs72mtlQknmF/HGjRsBKHYax/Va65F1OxYhiPkuCR2bbeQIemFJ\nKuEZi3v7WGvH50sKdqeclXy+xJl4qx0oqOPHs+5MadfY3MWFb/059lnf9g7yzx5Bcrrw//VhWPxV\nAPwWB3+ccl2MoIvD+9CfeQT58/+SNo89HaKEsrdb4/B+I7XBbKbXZS/C3dXNyaqe+27RtITP+rtv\nIZ74I/K3fk79ZDt7dwSwO2SCgdQBaTqCHnWEPl7s2e5n+mxHzGAwE8xS+t4ulYoqa7zEH4b7tJQh\n77f5cCijtHd/eCIt1eZzc3LFqWaFQn/ITdvUO6ips9AmLU1oZ1GMNI2/LbmLtwZu41MlfVz5RMSs\nKS8fVBUtGOTQt/7IkWHdyA0/3Agf+PcRj/+ro/H71hdxbL9xZgmfWFABwM4tfgYJIoRg7jg39ywZ\nx8UTCt73svXRsGOTLxaFHg0VYyiZeCIgmyTu8xa76GgNpyyP4mT4g5xsaMFBwo3PYJ96O5J8as+t\nFC1blmUE/YTNWI4RItCRcYQZJej5HQ3vef/F3Y8lfNZ1UAA9PJzSVpHBmZv9ySGHHM4ivOc3i8fj\nmQKUAz0NDQ37T1yXcsgWuiUzdVoxeZiPLerjj+8Uc6DbTrpB/s6dO7HbjUH3+KIQVlkQCoV45500\nUtU0iErTJQTj8o1ZekUSDA0N4vP5sMgCVZcIqjLdgXwKiiuw+Q+l7EeRBZouxczjht0LkLSDWE11\nUwqLZermOlK2NSMdUVayiGpmi2WX5vH2msSXvyPLwaUiSyytzWdd88jmZ69ULUJCcCSvmm3F0/ja\nrj8x/+1XkFZdz9ryOSntte9+CfnWj6P/7NugqojVT8G8JUjjagy33307YPr5SGlCWlGCbq47XlwW\nJ0ufWfoNqnxd3DdCf9W2Y4h9u5FmLxjlDIwO8fCvYHgIejuZMnM8k6fb6elS2bzex/kLnGxaFyfM\n0fJ5JwMH9wSpGDe2fHtDii949624oVlYFRnVFSOpQfaqBSRoPo7TP0HoOqhhJFuiV4JoPwYDfWhb\nNwI3AbBl1qfwu2s4hMhoUNI4qKEDv+4t5tcrjRKQX1gyjgPHetnUGaTz1bbj6u8DN02m1BU//9Ey\nW7oOsiRx2eTM6QHvJwhdEAwKHM7UX8Lv02k8OHI60pSZdpoOhwgFRbym+CmC+XFTWW2JEfR06b6Z\nVCpnMoL7/kS9q4UDR2sonLjylB57rDnoQg+PzW1oxJ0JnK1/JlwwHzV/5ohNpZHqVgqBJdCM05ua\n/vJeUVUQZLD3TWzhLWlPzqmoXpBDDjnkcKIwpse2x+OxeDyef/N4PB0Yru5vAf9sWn+nx+N52+Px\n5GpVnGZEI9Z3Le7js54lzJo1K6VNT09PzMTts8t6+LslvXi9XjZs2BBr88Ybb6Tdv67rsTJuN88Z\n4LpZhty3tjjMByZ72bT2Bb51VTv3XtFOnl1DR0ZI6eeDLqg1iFdUzm6xuZEtdiwmp9fCIhmbPc1A\n1e/H54sTt2mzEkl8NrLjbCCEoLhUJq8scX+OMTib/7+Lq/nMokrunJE4sfLtZaXUFBiUrNtRzCMT\nr2RD+fkELHa+M/dT9PmNyY9HJ16RutOmQ+g/vpct+ZO4+8KvEfzrQ+j3GjJ9sfEt9B/fi1j/atr+\npJu8iMquoyqKNtfIssCeL30M/affQgiB/oefIfZuH7G9GZo1MfXgjZLz+cSyewn4g0iShKxIlI+z\ncsXcDiq/e2tC23QR9GygZDkluWf72MziertV/tYwQHdHXE6qpVGWRn0UnGlIVwyRn6W+aB91hQcY\nawRdaBriyH7DSV3X0H/0L+jfuidiPrcJ7Z7bDSO3e+9G/+9/QTe5qPfmG+JQeYRjpuv5z9a38/yx\nEJ2h+Hb1RXb+ePOUjPspchjX2rg8K09+eHpsuZmcQ9yPYLQ69e837N0Z4OWnB9NORq1+Jn3O+bW3\nFTJpmjERM2WGgytvLOT624vSRq5PJswmcWYVidlJfuFSF5XVlqzvyTMJUZMyNTw2z5YTASHGRtCj\ndcjfK/I7Hsc+vDOyKz/5/h2UdPxplGPqOK0j37DRkmknEgW9z1EktY/eMIcccsjhDEfWr0aPx2MB\nngNWASqwh8SyawBrgQeBW4ATNzWaQ0b01H0JS7CVwo5HM7ap8f6NPNtFKcv37t2bsqyx8QjmV//W\nrVvRNI05c+Yk1Ed/5ZVX2LNnDwDzxyeSmQvG+2gfNC4tu0VQXajSEVDINKRwWASlbpXzI2Z3stWJ\nsDgS8sva29spT51j4Le//S1ArIRc+TgL+3fF149F4j4SnnrqKZqbm5k/5+NAfMCTbQQdjBn8a6YV\ns6fTDnvjkfTxlUX8pK6U2x5JL0RZ2zzM9Rnk919c9GVuPbqG/znPMOt6fMIqlnVuYzIgWpo4lFfD\npO3vwkA/0lU3J0QRkqWlZZWmx0Ga4sPC70NyutBffgrajyF/9PMctRTRXFjJB3xexNrViLWrszau\ns6FjLqD1QO3lDFrddHlD1JmW63/6OQBziw/TWzSD5iMh1CyyEVZdl88rf0tULEyYZKewWME7rLF/\nV7xiQVmlJYFc9/WMbVBr3lcUZmftvm6V7k41Fg3X0pSO0nXBs48NYMWQdq+a/CQAv9v0NZ5/op/L\nriuMlZ4aCeL5xxBPPZy6/8/cmPA5ZHHTWrWMyvZ1sWVhDZyjHOIjc8qprLXyTvMwD24zKhFIxFPl\nv7uqlnK3lap8G0IIJhTaOToQ5I7zy6gttGFVJEKaYHldPi8dHGDx+DwkSeJrF1VjTUMkoxJ8XT/J\nyfgnEc1HQpSUK7jzsjexa28xLvJwSODIwrcQDAJ83jwHk2fYs7pWThZk09c0By7NEfTqOltW/iBn\nIuLf6TSc4yjhztokToVRLruh3mN4j7xExby7kE0/khzqwTm0CefQJvqrPs5QKP6O0DQtowO6vevl\nkQ94AqPZR5034CyqoaIts1fKw5uLuCzzXGEOOeSQwxmHscxd/wNwGUYptbsaGhraPB5PwhRpQ0ND\no8fjOQhcAXz7xHUzh0zQbBVotgq63DOQ1WEKOh7FGjyW0EYSKtdXvcaKEoUfvmIM/u0WnaCaGgub\nPS7AzvbE0eCOHTtoaWnhIx+Ju4tHyXk6yHKq83YwrGV8KSuy4PZ5fVQXGgRJSDZka2IfBgf6U7YT\naey9kwelmcyKklGRF0bT0/evv78/VgrOnkT430tt8CmlDhbVuAnrsLXNS75NwaZk3o+3pxfxx5+B\n89qUdc3ucTFyDgZBf3zCKp4CtgScfPeCL3LPnke5ZNMfweFAuiS+D5s98buYx1oilEg4xf5d6P/1\ndeQvfhMRLdLw0c/zpUVfBuCioQG+P/vjXN62gSVZnoflegd/k+NUPNqbUDAxKrVjWOGbK3/IL62t\nzFvsovnI6FGrymoLLnfq4FFWYHy9QRy72lVKyy0UFiscOTC28oKzFzgTUgPSIRpBV8OCt14xUiPc\n+dESU6nt1XC0HFLitSAhoYah/+lnKbv5GtA09K/eBT5jn9LSSxChIP12B3pNPeKFJ1L2LUilEltm\nfZqe8vkcssUVHUoWhKPSZaWu0E5tgY0ip8LS2nwsskTTQJCWwVCCaZskSfzsuokEVT1tzviVU+Ny\n9eUT0teN100S97MRoZDO1nd85BfKrLwq/XdMh9jjzfSTjJTaUVxqXO+SJOEYbZblJMNuUjsl2DC8\nb1TGp2+ySOgayNmfSimLCLrS+H/MKRxgd9NGyiZeGDmQINh3ONamqO0PmJNLeo68TcWUixP2Ywm0\noFmLkLo3wAhzL5Py487u+zrtlLk1St3Zm9k1e8vpHxiiy72S2VOWpqzfNTSDWflGAKKpz8qsiz+W\n9b5zyCGHHM4EjIWgfxToATwNDQ2pbCmOPcD84+pVDmOGkB1oNgd94++m4tA30rYpsBsv6qnlAe5a\n1Mdv1pXS1Gdj4cKFwN+MNs70L3M1ElFVVZV169ZhsVhQVTXuxmpCkVOLke0ofP4Qqq0KuzeV2F86\nNTGvW1ecWBxlYDKoT+b2uq7z85//PPY5Wr/dZkuUx2Y7Uf+FDxjFB9IVhHn22Wdjf1uSInyZSqyN\nBKsi868ra9GFIKyJEc2uJKHzyMQrue7Ne+Hia7msOMzqvtHzo0XAx8FDx2DiLNZWzKXLUcRt2zci\nVddB3WQkpwtJklAscSIpyxL6+teQyioIl1Yl7u/IPnpt+ZTu3sr9U66nMa+af9+/i2hoZrhvkI1l\n57Gx7Dyewpg8EU8+hLRkBVJVbdo+qskTLBENczAQQoRDSBEJ/P/O8ACwO+yiZtRvbqBqfPrRYXTC\nRpIkLrosTkwLSxT6ezUG+zUO7jGupSUr3Kx/3ZuyjyUr3ZRXWmlvCdPdoeJyywmGcVabRDgk0CNR\n8l1b40Q+WtJOTxNBH83wS3t3LfqLv0lYJgB9/evIQic6xaAh0bTydibZgugvPclPZt5BW9U0fhh4\ni/079vPIxCtocleyzDWJccCzdSuI+unnKQpCN85TJkIcL4GXmA8+tdTJ1NL0od7jMXSLStvTqQ7O\nBrQ1G5Hw4cHsZxj8Ph3vUKSsnWmzkcqpLYuULzsTYJatS/LppLMnAHoIa6CJsHNy7IUSfQuI43Ah\nzxaqqjI0NERxsVGTMprbnXUQOguC7rIYL4G8/teQ1RkIyYJj4F0qhl7MuI0Nk4mmECjhLkqOGe9k\ndSypZRY3VotxXT++q4ZbZ6V3fgdo9ebjKj8Pe/1l0ONlVkVFbF1QlbBHfGxki5Xfb5vKJ+Ye4JiY\nzZSqqky7zCGHHHI4IzGWUdN0YMMo5BxgCMM8LofTgVHe2t+7po27FhlllD583SK+cM89XDKvJLY+\n0wURlbIdOXKELVu2oKoqsiS498r0+V6XT0+UFheVVuItuTT2ObMFFQjFhbAkDjbtScRbTZJg//rX\nv+ZPf/rTKTBDShxqWm0SwWCQBx54IMElPxvIkpRAXJbV5VPsULCYQk5CMtZ/5OLvAjB5QgXZQL/n\nQwxHHLE3l87gkYlX8lyfA/2//wXx4C9i7cw5+rIC4v4fo//nPydEscXmdWwPOPj7ZfeyIVzAs+Mv\nZlfRZPRf/2eszeBgkvnd8BDiuQb0H98b38/B3Yit62Of1SS+Eo64IQeefhT97njOebejOHIujL5W\nVBnt5ixIJeHnL3DicEmxNqlIf3248xRq6mxMnx33MLDZJeYuclJarjBlZtxgTYn8PtFc37rJif2I\nqiqihNJc2zk6GZKOjGvqyDSmr3AKQZtRvtCrOAh88qtsvOQbvLDqD2iyFbnCGIQ+VbeCr7CQm0LL\nuGXlD3mzcj4HdTc3267knxfew9aS6fTa48TanG8eJYMjmSue6ki2HpO4n9rjnihs32hM0KQR/GTE\nlvXxiaHBgezSLU51nvlIMKfSSJIUUzudjT5doaPPU9x6P6EeU+5U9D0QyUU/mVizZg0PPvhgrATq\nmHPQ9dEj00O68YxFtlHW+APKj3yP/N7M5ByAUF/sz+KjP6a06X9in83+MSmbmS5nXUCjtJR3WozC\njYsu/zivHChk7RE32/qnsanfyKIMqMbzXNclQtU3giWPysrKhOusq+6faBuyMRiQ0UsWcc3Nn+Bt\nPkn9guMpRppDDjnkcHowlgi6ALIZIlUDJ/+tlcPxQ+jYvLspav+/2KKCPBt1dXUxSXcUNptBQo4d\ni8vnCxxavAzaKMgvLMMrxRt3TfoWNt/+hGP/f/bOOz6O+sz/75nZvquVtOrNau5y79jYphfTAwhC\nSSCFOxLIL5Ukl+RySUjPJbnkCLk0CDlKREwJIcaBgME044qNqyxb1erSrrR9p/z+mNWuVloVGwOC\nm/fr5Ze1M9+Z+e60/T7f53k+T6JbghVVTK3bG4tF+cUvfsGtt96KLMuJwcpwQqEQoiRgsSn4w/WY\ntGocTr3fXq8Xh8OR+B4ng9VqpSonEo9ASB3smCSBrq4u/H4/r732GldfffVJ73+Iu84sRhAEFFVj\n81EvDrPIA7u76Q0lj1mRlwHoA6P/vKiC+t4Qv97eSU2+nf1dSU9tlzWLp8rWpex/t2cmLjnIgjd3\nk6NpCIKAyZysMy6KsNMzm5yIl+xhBrp67/dpKFsH1VXs7whAob78dUtpok3/1i1QdHlym3CQX8yu\n5cL2bcxRFARJQv2hricp3HYX2m9+RGz2dVBYnthmqMRWv8WN1+wiqXigY4qrF69cp0/eqH/4Oc4D\nzch33M32bXp/i8rMVMxIGtMut5jiuUyXFjEcURRwZ4oM+FRsdpHMbBPTqvT9DXnW43MmFBSbuey6\nrESu8BAOl8iAV8E/qJLlST1eLKYCwqh8aq2vG7mxBxjb03O0+mqOVV9Ou/dlOt3L2XcsyHVSHibg\n0+t+xOWmHF5Q+2lQJxeuL4z4P6WfUS2RLhKLjujrBLngiqJxcG+YyhmWk8q5HgtN1YjJAyjy5MPD\n3+8Mn8DZ/XoQu0MkJ+99qKYWx50p0dkmp5RxfD+g1v+BUqEeAL+3HU+urn87ZBdqSvJZE2N9aKIN\nTTq9NeeLeIubNnRTHxjAYslNeMTTRa+lZ+L2QvydMc2ZLoYsyXNHXJjKL2el+DAm2QeaiqBGMMs9\nk+wLHJUuYi7PAHCoL49FK9bg883jH42NLHK4mHXu59E0LVFhpifWR3/bW8yQN6GN8x0sjmxY/C38\nskxevNTo9OkzJt0vAwMDg6nEyfxaHgcW1tbWjrlNbW2tHViAHuZu8B4xmHsZvoLr8HvOJWYtIZh5\nBrJ5dFCDoClYQsdTli2aN4vLLruMNfPzcVmSo0S/309DQwP79u0DoMgd44tnd0+6T5KmG4+KFPeM\ni2airjSqbwCCMGqQkxkPvb/vvvv41a9+xcaNGwE4Z8YgG+YkY+F7e3spqmyjrfMNbO4mZs6zoaoq\nDzzwAJs2bQLgyJEjKcrv6dA0jfr6elRVpdIT5mMr+7hmkQ9NSzXQh3sa324Zl6HtJVEXkzurMpP/\nvqwypc2s3GQI8fQcG2vK3SwsdCRqRg/xr2eMTnPYlTOH/5rzYX4x+3rU265A+Y87Uz3ooUG+u+Bj\nfH7554l2JktlPV2yhnC8BrY8zHL40bxkXt9AbzKwRtuzjYGuXrYULuPuebei/utVqA/9mpfzF/Jk\n6TrkB+7Ba3Yii+mNt1/OuY6Prfn3Uca0OEIWvX//fg5YHBTkxCgpN5OdI43Kq197Xqpi/mS8mCvW\nuVi43D6q/NPceJk/pyt1uSc39Xtke/TP+3aG2PSYj1T0/rUd9qH2dHPin7uI3XYl6pc/TuemrRP2\nTcVMQdbZLBBdrBHdmOL3TJGgTzxVoN8fty0r4HOrk8b+V9aV8G/rS5iX4WCFJ7Ue8KUzs9MeKxbV\nKCwZnU4RjWrs3REcU0nf26tw/EiEw2+d3DztK88PUn9g9DaDgV5ae5+gx7s/zVZTnyExxvGM02hU\nTYmg8Pales3ffCP9+2rluvdHbeeZNTbWnu8iM/t9NMmgqRTGjXMgJYRDEuLXR0lOZOY2/ZjMYz86\n7d1YXqxPyIpyPEopUQd9cttPJgfdLo5O5UlH5drPMLdmPoNRC5LsxdRSR97xb0+uI0CD/Wrc+Umj\nud+xCkEQyMrKYtGiRYDuDBgyzgFUswdXrv47KOYsnPAYJtP76B4zMDAwGIOTeZP9Ffgq8AXgx2O0\nuQvIBp58m/0yeBuEslYn/g564tmlmoq781Fs/j2Jdc7+f47a1uF7lWDWmVxc9iZr8kR+9LxedikQ\nCKTkYn9iVe9J9Wmo/nl/2Z0IanIQHrNNwxxuHtVeHWGgr68O8OzhpAfN59MNn6H89b8f1EN/H3zw\nQdatWsD1i/tpNQ9itYrE4groTU1NRCIRnnnmGfLy8rjiiitob2+nurp61PGPHDnC5s2bOfPMM1lb\nlKzdro0IIjGZJvbKvh0cZokCl5lOf4xfXFKZCK8ewm2V+Pa501DSeDUtksA9l1bxu52ddAdiHOvX\nvSRvemYSEyRMbU0pKveiP2lkRx78H1jxJQB+P+OKxPJNJWvS9vPHw4x15Z7vErR5YNVXiEgWDrun\nMfOFv/PTeM3siGTmkcoLARA0LRG6PgpZBnPSQIyOiAu/e9YNHHMWsSQQZvHKfNBGT5KYzAIr1zk5\n/FYYb58yKdFAu0NMeM2HUz3bRvVs26jlFqvI2Rsy2P16EG+fgivQBngYMsZHetgBgoqNrp/8mJ2L\nv8T0yiuYeexxjkyvTdsfATVtSkiFmOzLimIXWidUijYuy8vmklm60b2kyEnLQJSafP156npFgRAs\nX5kB9QKRAY3pHhtvNqQXvEt3vpoaosSiGtGoxrLVow3EoechfJKl8Pq6Ffq6FWbMTT3H4cgghRkx\nBsOdJ7W/qUAkoiYiVMZTod/8+ACZ2RLrLsgYs80oBMgvMnPBFe4pH/4vigJZnqlpOD366KOEw2Fu\nvvnmlOWR9tdGtNTfP3JkkGJHXAtAjWLqeAbZof+GWIQIpnALsi297sapoGr6eySR736KZdbG8z5n\nWUdPjB3vtVCZkxqplpGh358hMil3d0E0+ZvR3G9mWvb4OfllVfPoCyR7Pn3eaIG3dEiuMnrK78Js\nypq4sYGBgcEHgJPxoP8U6AB+UFtb+1Btbe1QYk9ubW3txbW1tX8A/h1oBk5/gUuDt4cgMlBw7aSa\n5jbphpTbplKVE2HBggWj2gyJsYzHcLs16pgNgGpyo1iS3l5v8cfG6O/4obECGl89L5n/nm2X8Th0\nD2uZrZV5RWFKLE2AXg5miKHc9b6+PrZu3crTTz+dqOeu91nvdCikGyw+n4+oahq2PjV8WDIJSFqY\nuze0My1jInmGU+PnGyr483UzKc/SjcY/XFXN/SPqS0uiwPoKNzaTwBfXFFOcYebza4rJd5n5t/Wl\n3LQwNYLiuvXf5+qzfoQ1kuxz/7Cx1ZaCpafc3/urL2Vnjn69ZdHEV5fcwcdWJ3PRh4xzgCq5j4sG\nXmF64PCo/ajhIFo4aTjGZA1t5ysoP/kaoIfxA8Qiet10YcTkhfKFj6D813+QX2Rm/lLds5xbkF5g\nb7KTLJqmodzzPZR7f6DXft/yd9Tn/oorQ2LWPBtWm0DG/d/GFZrYmIya9WiS1rIV3Lv8E6NF8+Kc\nvWTbxP3qTH73gt7k5ILbZkoY58Mp9VuJDOjHG0+cThRHe+qGQt4H+hU62mKEQyrNx5LPRaJu+YS9\nnhweaw93rO1hdUX7xI2nEHJMo6M1+VDJY2gMDC339Y99IYaLEA5x0VX6pKTVJp5SNQkDnbOL9nPV\njKMpywaPPUtZ8G+pDePh7GFfUsSswtGMx/8i+V1/SCzztP6K6EDLaevf0F2jKrGhP/T/RzyXna1H\nOPjib5FHlOTU4jnoIunvL3FgP3azyqHAzMSyf3atIFquT1hEldFTASHz6AmIgbJ/5amOc3hqvz6R\n/sjupDF9POdOesrvwpRRimjS30+9sdyTijpTzdnvTxEDAwMDg1Ng0lPadXV1fbW1tRehe8evB65D\n/+24JP5PAFqAy+rq6gbH3JHBe4cg4i26BUENYYp04PS+mLJaFZ2Iamqo28dW9uHLd9NYLzAQSg4w\ntzfbWT4tvdctolqxihFU0Yak6TPzg/lXpm2rCRZULVmKJ5B9Ttp2oAvcfWtzAQL677TTkuzPF+Lh\n9l//exFyLJ4rrMTD6odZIEPedEVR6OnR8+YGBpLKyIqiYDKZEmFy3d3deO1RPHGbRySColpQ1DAW\nUyaSScCh6ftZmJdeMO/t4jCnTlbkONIbmZ9fU5z4e21Far7u0hIXv72imoPdQX76atLQsex7GaZd\nBEAgJjP0SnisfOzrMBEjc98BfJbRnsHSUDOzowf44tldNPa3cOvxWSnrI4/ch/WN5yHueY8qKsHf\n/JQBi4vCgB9xyFMbTp9z3RkVsB4+giccJMvj4JJrMxHFIW+UAgf2QM0SGPSifuGjCB//HOKqs0ft\nRzv4JpjNCNPnwr4dEBe6U29LRhaox4+Qd+3HOPdvt9Bh8yCGvWAvHPc87aq5DQl4S/KwxSnxifjg\nU/eXJweicugEl9Zmsn93iOP1E5eYmwwNh4cZ1OOoo5vMArNB/E8AACAASURBVGddnMGu14KjDMiA\nX2X7y8n3RUGxGatNTBqip8lCd0h6eHdhRgAtrp0wHkOh4uMJ3b0TxGIab+3SoyhqFpjY9nJqeoMi\nk7b/rY0TX1NNS839X7bG8S4IYv7fYG6h/iwMz76uVp8f3TAuCKeEk9fVaU4vwBbydWJxj+9FV8I+\nEEQk6ySjJobE3obSrEY8X5b2v7G+pJsj3fVkFc9NLBfUaKKvfT2HAQ1brj6Bmn/0q8kdWHOBIwDM\nXnYJqhwCPxz3ZdPrC+MVK1gWnxcW7fmMtPfzC4vJLyxGls/mxfp61l45nZ0vf58TsXJWTi9OiT3r\nqfg3VHF0NJKBgYGBgc5JxZzV1dXtq62tnQvcClwMVKHXWWoBNgG/qaurm1wyk8F7QtSpG0GRDAjk\nXIij/0XCGQv0MjK2cnKaf4owIs86s+tRPruhmp6CW+g80UxecAv93f3pdg+AyWIHOZIwzkE3xNMi\nCAnjPJi5ikDO+YlVYddCbP43U5p/80LdM/nckfQlhWbmhYlG48caGlApCqsr/dR3WZFlmWJ3jO5A\n0ugdGBiA+FhBVVUikQhNTU2IgkZHRweh4mFh4EIUn/+v2EwDWEwfT6kdPtXJd5nJd2WyuNjFZ55q\noD+ictiRz5C/NdbRCp6Kd60/V+e+xfXz9ImV2fkRXeViGC0Hj3C0OFlVPaZo/LTmJnbkzGGjfzAh\nehQJJY1NzdsLmR4EQeD2Vfrg8/Hd2xDOOBtRFNAO7IbsPNTf/QSajyF++mvgzODVvPksffoxHGkM\n9OFK9AD3zrwakybzifon2Vy8ClmQuPSNl9DeeAmAT636CpfJIgVjfO9mNcw00YYUjxKZLto5Fn9W\njqhB1i53M6/IAfF5lCGjbtY8+6QM9JOtgT00f5VXaKK7I/XZN5sFXBkSa8510Vgf4cCbY+eVR6Ma\nVltSqf5k7PPxwr+VhMNQQ1MnDK5h02M+RBE2XPPuhsMer4/Q2qhPAG57OVU0q6jMTHtLDEUG04j5\ntSExviE6T6QPE967MzkhOlXDxYezYJl9wtKBU42Btt1MD9WlXWdSg6DGUKNjl7obQtM0UMIIjY+g\nlV8Dw6qSKF16REzRwBMAdDnOQstdRbRjO1LOPEzO1Ik9LT5ZFwnHy5HGf59HPjHZFt0vogS7gLnx\nEDYFU1yz12VVcXnv14+Z+30i3ftStrdkFNPWaSMkW3BPt4DFwh7xOrLnVVOMRMUwgVWzswDip+F1\n7UYUOcZQZrnJZGLOnDkAOBfewWLX6N9q1XQSqRwGBgYG/wc56V/5urq6MHBv/J/B+xlBIOg5C4CI\nWS+11l39Hdwdf07JVQc9h9wmd1KZEyUrWk/pOGVFRVn/5VYlF6LiTxxrImRrapVrf+4GRHmAUHCQ\nTDF1wHvezNTa6UN8ZHk/O9r1EbDDFEaWZZ54fCNfXDNIbOYgG3e/wafO7OFgp5WNb0KlJ8aWLVs4\nR3cio6oqL7zwAqGeg3z74l7+sM1DVE6GjwpagNtWNZJpV/ndDlUvIZRIAH1/eLTcVol/WVnED15q\nY1t2NcsjJ0AQ0aTRedcT8dvrFvLJP785ccMRnGEZ4KyK8VMCvr3oNvxS0ssSPXqYHRW6psLAQCBp\noEd0g0Z7czvqf38H8c5voFUkhYh6YgL5gKaqqD/7JgD9Fhf3z/kwt3d00hNs4Sc1N7O67wBfjm+j\naZoeqy2K/LNwGTHRxJK+wzQ7C3m2eCWQmo9f757GTcc28S9xcb7nTXCJJuMWUl+xAU2hU4sxjVTv\n0QWSnjN+3eocSstTr8PQQHykYTcWw40iTdP03Hxx7HtzqBycJ2+0gT6kUSBJAtWzbeMa6EOe60S9\n8nEs9GBA5cj+MAuW2hElIRG1m47hnmNF0SYsJ6ZpE9eUP50MpUcc3pf+3LgzRTy5JtpbYmx6zEdZ\npYVQUOWMs1wE/Eqi3jmAr19mV7zEWnGZmRMtSWO9+Zg+OVNWaXlfhLSXV5/8++S9Rur4B2SmXzfd\ncZTBQ3fTJyyCCZ7FmdHH4PhjABzZ/zCmquuxWExYrPaEYT5EfnALNG8BwNe4lUjNt1LWD2W++Buf\np2D6mlET6EOomn5PqOEehEg35ta/kKU1k5/m+8id2ygbTO2Hs2gRauEihqtKFFctSnssa2YxDIA/\naqJq7ry0bQA8Hs+Y6wwMDAwMxmbqT8MbvOsM5l2KKtlx+FJFcjyt/z3mNn2lt+Np1edshHgwW1/p\np8lq/yOm6ORCv6OOmSmfVZMbb+ltqId/SyaTL+MixT0GFlHG5/MRCughiWYJGhsOQyVUeKJcNs/H\nwuIwP/xnMif+qaeeor29nTMq9IHx3IIwFlNyAH3o0Eucv1b/rGn6gFmNj6DG8xj29fXR0tLCwoUT\nq9C+G6wsdVFmkVFjGpfPeQRVE/jB0U+AlgyA+dSKQn71Ruq1++nFFYRiKiVuC1FFZW5hBvd/aDq3\nPHZ05CFSsAgaUS1pWJV6HBMKYLiifvz2pCF73JUM4fcPBhPTIaHnnkZbPBd/UyNPlZ/H5fWHsf/y\nO4nQ+INehbzWRrTn/8Z/LPwkS3sPct90vSTc7NcepzTYBYvmccA1DfUfj0NbM9q2LaAoCGdfwj2z\n04u3DWdrwWK2FixOfA6gUqf04EAkSzCxQdIHqqaZcJGaRW9DegvSbh/bPSwIApdck0lHW4zsXBPP\nPZXekxeLanS1x1AUjd4umeP1Uc66OGNMVXU5pt+5w9Xp7U6RUEBNVbLXNM7bYOO5v6ffz6BPBeQJ\n67kD7N0RpLtDpqjMTEGROa3IIejGuTIsBD8WA/NJVkpsaYziyhDJzjn9P3eaqvG3R0cq9aeycLkj\npZZ5y3H9vXH0YJiDe/VzaTf5iSh2XvqHn6HiBktXO1kKDHgVXtyczBqbzPk1OHkG9v8v0zPHnzTM\nsETRAgNghgPKWcyVtky435nOY9D5vUn1IdMa5d4//oSrP/rFxDJR0H9vzqgI0kVSlV0UNFRVRRRF\nNE0jJuvLa2w7oWVnyn5bwqWU2ZIlUouHGedd4SxU0Y4oSkw2IMxkzaDRdA7mwnkTzVUYGBgYGJwC\nhoFuMApNcuLPuxwQcPheRTbnYYqNX1JNtiZrYgfdK3EMbEM1ZdBXejuCNr6y6xCqlD5sHXMGnIRK\nsUPSB8CSoOD3+5HE5IDWEhe3s5s1KrL1drnOpEeivV2PK46PdVhVkVreyDxsX2ZR9+Jrmt65SDiM\nEIthMplG5Zk+9thjBINBampqpkQZGFEQuLJE5KlGLaG8KwkCi6UBzl81myXFLmwmgR0n/MiKxq52\n3XCv9ozOG8y2j/4+GVaJ/71mBlc8eAiADbNzeOJgH+sq3DjMIjlZVkzh8S9qhz035fOO3GReZSAY\nRogb/L5ABPXT1/L8hXfy58r5OA48TUFOsq3c34f6rf+ix5rJ3jPOY2920rvutWTwSOUFAAQlC+qj\n9+n7NDtpdBcz66VnYe36tP1bVebi9ZbRkRz/uryAlxoHuGB6Fv1hmYGwwnOH+vnqBSUU5lpoaojQ\nS3r9hvTh6cNSLCSB4mm6hXrhVW7QYPMTuqF+1Q3TOHq4m307Q2x7KTXTaMumsWVB+nv1m91mE8lw\ni+QVmvHkSex4JUiWJzlkt/teI7/nKRzmTxGMjQ5R3RMvBVZYqg/Zx9PdG1o39M3G8qBHInqY7hDR\nsHrStbT3bNP7ddl1pz/kffOT44c71yyykZVjIpBG5G3IOAeN6+b/imZvCc8fv3HUuRgZAu/Jfe/f\nHx8U+juPMzQ9O92ql/ELxwQiikh7uADTtA1ogw14Aq+Q49B/L+Y49Xda7qwL6dQuoL/hBWbzLD0B\nU8pvyaly+5peXty3k8Yje7jwqluxm1PvHSH+PJgliIRD2GxWevc+yNys9FocAFH7dAKEcWqpE91v\ndFVRccYnEE9BfM1Rcf7EjQwMDAwMTokxf+lra2vTqKRMGq2uru7ct7G9wRQgmL0OU6SNgYLrQRBx\n9fwNmz+Zt+YtvBlNsmMJ1oMgEsg+B2f/8/jzLsWfd6meLCpIaIzv8uqa/v1x1zuzS6B38mHULqs+\nSDKJKoFAAPOw8bxZSloNQ39lO1JHxHmuGJfNSz/wHr79rLn6dqqigKTv795772XZsmWsXr06ZTs5\nFsFmUgmHw7jS5OS9F3iyXHST/J4CUOKUWFOeFJj72np94uXaRw4zv2C0GvgQ51VnYjOJLC9x8c3n\nW/j0Cj2P8g9X6eWHNPSyb7XzcjBLIgMRhYH9NjykGpLZNon+8MTxyX5/EFHVQ2h/WnMj/UfdhAJh\nMMFRZzH3VV2SaDvQ2Mifqi7m8Wmj88sfjYfMA0QlC9et+x6yOL4BVO2x8p8XVRCIqrzeUs+dqwqp\nyrbxuU2NXD3Xw8Uzs7l4RG3xW5Yk/y6rsLB3h26gX3JtJoIAbU0xGg6HTyp02WLR23ryJPq6FZxO\nExnukxdFGBJ/E0VYf5FueAuCwKW1ZgRBwDq4F1VyIfXoddqXL/Oz51DWmKrjQ8rl4+WVJ4jbBcOF\n6oYLqYVDKqIgJxofPxph8Ul4wrVx+qBpGh1tMQpLzCelJj2ELGsJRfvhrFjrZHZNIW0tXdjjkwmm\ncQTr7KZ+JFGj0tOaosMQCASor6+npiZZRaNkmpmKGScZQmAwJt4jT8OIdK2YKhKZdzeJ4Oz8aqJx\nYciRCIKAq2QZx/dvI1byIfr69zLTsuuk+jAYkciwpj5L5aEnWb8wRr2vj8JhEVzhUCglxH3g+BbK\nrS9TEP9J8WVfSGb/ZjoGTRw2XUl5dpjS4N9xTFtFwHoeXfv+h0qHrjDfVPhlyqvchjK6gYGBwRRk\nvJHOWejj6lN5exsxeB8AVFMm3tJ/TXweKLyBdGZrzF4JQMBzHgHPeaf9B187SbXXDKs+oDEJKs89\n9yxXL0h6D69ZmAxhzLLr7T60IBmiajOpXLPQy1gpux9f1Zc8jjO+XTzkcOiu37t37ygD/dI5/Swp\nDXAkdnpUuE8H2blZZEnJkkEBNUx2RXnatg9eOwNpnOt656rkKPfJG2cn/h6uOH/jsFJvbquEV8iB\nYQb6HzZ4GDyxh/+3Z1rKvsuELiJY6NKSHtDBl19AGFau7b7pl3FJ61YonZESag7wQuEymlzjiCYM\nYzzjvMBlpj8k85lVRfrA3CqlfNcHr52ByzKxgSxKAmddlMHggJJQlS+tsFBakTS8hFjqxMl4LFzm\nwNunYLaIOFynlptckXWIDPM0BCGZRmAJHcMcbsHVtzm1sTCIO0satywY6GXsQTeEVVXPYx9JwoM+\nzEmot9X/DgVVJFGOt9VwZ0ocPRimcqY17f5GEoul/hSFQyqH3wozb7Gd9tYYu7cFmbfETuWMk8uX\nDodUnv1r8hpdeKWbzU8MUD3LSkGxGUkScLiS98JIMcmqWVaOxZX0s12jy8etWOvkjS2PMMt1jAFf\nCUNJzyXlllOaTJjKyLLM66+/zooVK7BY3p3Jh1BggKZdj7Eou52wbMJm0u8xVRPwus9h5N2gjKh0\ncNR2NUPTmBa7G8syXZBSK5gOx5IGem/IhiCIeGypkVjDabWsZw6p/pAKjz7JFdh7DwyTZhnoPIBF\nTUakzbe+nLJdJOcsurOWI4o25sTVFPtYkwhdtxctA18LnUV3YHcaNcUNDAwMpiqTcUW8AfwJvQa6\ngcHYvEMDRwF98BRyr8Cfu4EcGunVSshu+x2m6Oh60664gW6RNBxmjcWlyXDiIvf4IYhfv2Di+tUJ\nwnphnqE6s9PzdOM7Go3S09NDbm4yRHt+kT5A0xWAp4ZwTk6mk0wpeW68YjfrKtMb6Bbp9ItSCSPq\n9Lia7mOOy8u9597O7f9MDmgfXLoJswRrX/8wcrwaQIujgG5b6nnstqV6rYcYbpwvHzjKnHVn0OKL\n8ErzIFFF48tri1la7KL2z3qJoduWFXB2lZuegMydTx/nk8vyKXVbqciykpUmnD/R/0kY50NkZEpk\nZKZvP9jyOtWRJ4ctGX++0+WWcMU95yer4D7EudVPQjd0ZSajWbJP/C5tW0ENT+pRV2QNTdP4W50+\nkXXGWc5EHfpE+Ht8P8MNIFnWkCSB3m6ZHa8EKXcnDZKGwxEiYQ1BhOpZNiJhlddfDLB0tQNXhkQo\nqHLZrP9CVQXg34mO8HBvfXaQcEgjv8iUWOcfSL0PY1GNWEwbN5R+/+7kczNUvm+8EHrTiJJo1cMM\n9IppyQnClcu9eMOF9PXu5bpZB/TvrPQwa14ldqdIQfEHL+P34MEDdB97ne0CrFlz5jt6rPa2ZjKz\ncgjs+x3rivS0rQZxPZlly9BEO5pkH2WcA+Q4kuHj4ZhARkV6YTRBlDiR+zHMA/vIi24n4jkTU8m5\ntAW7KWr7GaKQej/u9S+kcNH5cDR9wOKiEj0NIqqasYgx6nc9y1UL0mseyKp+v2qSM+16ADFvBV05\nixBEIwrDwMDAYCoznoH+EHAVsAJYAjwD3A/8ta6u7u0nWhkYTBLZohtYUXs1mmiF3DVoPT30lX4a\nU6wb2VoMapT8Y7pC95D32yxp5JyGnEAgXpl6RB6prOcfd3W0Qdzp+4lVPWw5msFDDz3EZz7zmUTT\noUhbTU6fe/xekGERyRKT/Vlk6iHP+e4ZAOIIAz3LrEc62Hte5lynk38GZnHTwlyGysBfknGAJ/26\nqnC6Ou1v5I6tJjyEUFLO1TU5AHw2NciBb59bRr7TTFGGPnidlpXqIX+3iHmPgj35WTkJqRBBEFh7\nvoutz47Oja+ebaXh0Nh5qkOYQk0o1rFruCuxMHkFpoSq+FjIspYQoAN4bUuARSscFJcNu8fiq4d7\n0JWYxtFjSQE1SUzUWSMS1jc4sCdMV7tMUamZAa/C0YMRSsvNvLYlwMeX6tv9/s9ecvKT5+5ES5Rw\nKC7oqOkh/cOPrSoaJ1pjHD0YZtCnUlqh73vteRl4+xSycyUEQSAcUhPq6udd5k5EQYxHZrbErPk2\nDu8LM63Sgs0ucsEVbmJRjWhb8po4gs+RP+8TeLf/M3EPBAIBZta8/2pGB4NBNE3D6RzbYATIEVo4\nd3Uv2/uOA6fHQFcUBWlY2IKiKPR2HGdh6PcQAobJW2RUnoMqjP+MKZIb0A1jm1ljwDT29TBlzUBz\nlzPoy8OUdQYAZkcePTO+hygPIKhhcpp/BkBejj6p0+XeQP7A38fcp3f618k4/B8pxnln1bdRTryI\nKX85+PajOSsmF+5oGOcGBgYGU54xf5Xq6upuqq2tzQA+DNwCXApcAvTV1tY+CNxfV1e3Z6ztDQxO\nFzFbOT3lX0Y1j/BQiWbdOAcQLQzkXYm7O6lOazFpXDArvThWMHM1Dt+rk+6DKmUgKameC0EOEI1G\n8fb3JAz0Ck+M6xb3891nC9m3bx9Wq5WXXnqJL8THnao8sYH0biEIAhdXJAexuZ5U47ytrQ273f6O\nlcoZ6UFX46Jvs2z7uHseHDwwnw/NzYFjiQ5TlmmhxTdxmkBNhkZxfhbPNqReM9k1tpdzYeH4hsS7\nR6rnVhJOLmMoy2Ni/YUZ7N8TYlqVhUP7wjicInMW2LDbRSw2AW+vQkamyJvbQ6R46NUonrZfE7VV\njH0ANUJRmZlzsjPo71XYvS2IzS4kjN8hFBmeeTw1KWbPG0H2vJH83HQsSk6+KSUHPRTUhgmogTtj\nyHpPnSDr6ZQpmTbkkdcI+EcLsfV2yZjEKIoqsfPVZFRGY30EZ4Z+7zcfi1I5w0p7a5Qj+5PP51BN\n86f/ot9D7kwRZ4ZEezzHvnKmddKaAYIgMGOOFXdWF4XF0wgdvB/VNR1X2Zn0tSQnEatsDezc9WeW\nZvcnlg02PENbPyxdumzUfnft2sXLL7/MnXfeOeVC3194/B5EQePiG+4at51N0K9LtmXi+uJjEQ6H\n2bZtG5IkcfRoPZWuLnoDEhdeczvZ2dns+fsPmZEXSTHMe9wXI7orYQLjHCBc9SkcjT9GZJITvqKF\nUPbaUYtVkxtw0136/3B3P46p5Cz96ctfS1fuKkQlQG7TD1O26RWqQLTgDVuwm/X7s7f00wiiGVNp\nXEMjb/X7pMingYGBgcFkGPeXqa6ubhD4DfCb2tramcCtwM3AZ4A7a2tr9wL3AQ/V1dVNvg6WgcHJ\nIAijjfM0RJ1zoDu1tmtlTtKYizjnYg0cIOKYjT/3UjTBjICKbWAHohpiIP9q3F0b0+7bV/wRPC2/\nTFmmhL10dnYyMhXWbtYodsd44YUXEsuGQnr7ezrILpHZvHkzS5cupb+/H0EQmD17cp7atrY2srOz\ncThSBdsmKuMWDAY5ePAgixcvJhqNYjKZMJlMlNqTBsnKgtRttj//MIJk44ob7hizP/7BQY4draey\nejpvbN3EtOr5FJWUTeq7jPSgj+Tey6tSPm+YlcfHZlXxXIOXX76uZ9y4LCKCIPCRRXncsy2ZhbNu\nTiEXzcimPMvK73bqqQh3riocV+huqjJpo2AY7iyJM87SlaNKpiU9ZpUzrfFl+ufsHBPe7qQOgBjQ\nVcos4cax962dwD6wAyFjEc4MC4WlZnq7ZN7Yqu9HQKWmYDv1PfOJKOOf7/aWGK8G/fgHVT6y6Ecc\n6c7j1RduTawvq7SAnNzvSPQJBt2YHjKoR/LRxT+jxVvEPxo+Amhk2nrp7c6lrztGWeYxWn2VKWXM\nxmLApzLg0/sgmWBWzcnlrbe9+RhLXDs43P5RZpkPQ+QwR3ftJFvsZ3hc9VJ36rz3msoAsJH9TWaO\nd0YQBIHly5cDsHv7y5RnR4nFYu9a/vZkuXWlrtfRNUE77TSYljt37mTvm3tYP93PXWcmo0f+9PRv\nmbd4NRfPSV7fqGZjwDwDNX/dpIuDqCY3vZVfI+/4t/B5Nrzt/mq2Qnxlt6cuFM2oYhZhMQeb2kt3\n2ReRtACqWY/4yXbq56kz50YEW+nIXRoYGBgYfICYdOxkXV3dEeCrtbW1XwMuRPeqXw78DPhxbW1t\nXV1d3c3vSC8NDCbBmGXagIh9Br7CmxAVP6rkBEEgkHsRAP7c5IDLHG4hZi3Vc9s1GVOsF2/xRxnp\n1QTIsQ5w7+OP89n1o8OJP3VmD1//ux6a77Ao2My6hX7k0D7s+TX4Og7zaN3RxOD01Vdf5ZZbbkEU\nU4/T29uLxWIhIyMDTdPYuHEjdrudT37ykyntXtv6HBFvMzNmzEgx3jVN48UXX6Sj5SjVmd00ZmXx\n4nN/pai0iosuuUIP0487zi19bwAXJba97YxeYPwB9pHnvsuqol6aw1/jsuKtHDj+BpT8xzhbJPsl\njhgeZ9rGN0Stkm7Qn1edxS9f7+AGz07unPEWe/O+TmGmk0KXmRyHmbq3elhXoUs4XTbbw7nVmZhE\n4R3Jo38nGGmwmMcoyXY6yMiUsFtkiGsF5nben7J+7wkbVTnRhK4DQKHUCN2NmAfeRHWUEci5kPwi\nE2WVFno6Y7i0Y6ws3UKmpZlXWq6dsA9DZd7MkkZNYRevx/uyaKWD0nIzBQ36Ars5woZrMnjxmUBa\nbzmAMOwSF5eZOdGsn7uyrHZWrnNSqG4iN/Qajx/4MGZxgAumP83e/hVsP5aq8H/upW68fTKDPoUj\n+yMUFJsSau+lFaem+p6j6aEgmvdw4pmb7k5OKu3lChbwZLpNATAHj2LrOYRV0njtNRmPx8NNS3sp\ny4ry80cf4dzzLqSgoGDM7U83AwMD2Gy2CScGNE3XIujt7SUvL2/UenWsGntp9iMIAtu3b+e1117j\n2muvpaioiFgsRlfDa3xnQ++obW5e0omqPZ6yzDv9309JL0WTbBNWHDkd+Ms+juzfi2bxIAs5ieXh\nklqk3mcRM2cYKrwGBgYGH3BOuqBqXV2dCmwCNtXW1uage9AvRTfaDQzeOwSRvtJP4Wn9FbI5B1NM\nH7D5Cm8g4poPgGoaXb95OIP5V03qUIoKTovC9NwIuc70A8zPre9CEMAzrIxbcWaMrf94lM+u78Yb\nEvndazloCPj8fn7/+99z5ZVXkp2dnaiV/uCDDwLw4Q9/mFAoxEeW93Go08pDDz3E0qVLEQSB8vJy\nlmQfZmFNkKPhQRhmoLc0N6N2b+O8yhBzCyP8aftGvnxuP8d6vcAViLKXmCJgljQWFIc4Icu0t7dT\nWjq2h8br9RKJRCgoKGBZkX6Ow95WAOYWRhIGvaqqCIIwypgJ9DVR2fdryIBQTMBuTj/cVBUZUUq+\norRh6QG/vryK6S1/AiBLDABOFsRD1D+3Op72EFfXd5hPvvTYZJGinYCAYsmfsC2AoISxhOoT92Pa\nNiOG3xbGVoB+O0j+etydj9Cde+OYbaJSDhvrMyHUjqIKfGxlsoqBPdIAkQYC2ecgajKLVjh49flB\nxIB+T1gl/U6omG4hJ9+UEmI+Em1Y0XSzRcCTJ1FSZiYWTYa6l2TGeHn7Rtaefw2DPhUN6OuWsdoE\nXG5dIK5kmgWO6u2XrnZSM8+fmGEqFF4iN/QaALNnNBOM1yZfkP0GhdeNfu4dTgtaYYCZFT4EV3rx\nxFMi2pcw0IeQVSicuQqOjm2gq5rGeTP1ycA/vrGV5kMSq1brEUJ5pg42PvoIq1avZcmSJWPuY7I0\nNzeTm5tLwO+nqbEBiRht7V3MX7ScwcFBgoFBSkLP0iqUMXf9rePuKxaLsXPnDvbufoNrr//IqJQZ\nTdEn5jRtbKM5HA5z//33s27dOizdz/P18338/MlHmDZ9PgcOHOCGJaMnSYcQBQhqbgT3DCKOGVO+\nrJhqziaYvX7U8qhzjh4lZmBgYGDwgeekDXSA2traWege9JtJVhE9dJr6ZGBwysi2MrqrvomoBPA0\n/RR/zkXjGkOnii9ixWOPcMuKvjHb5KQx3C+aPUhvQH/ssuwqXzynO7Hum88U8vDDDwNgtVq5/vrr\nuXVFL96QxMMPP4zTovDV8yLMzItQt6eFrc93oWgwd/5SLs3XPYWNDftZ4NG9aKFQCMV7iOsXJ0vL\nDZWZq8qJcqCnB6fmxxuxYpFUYjI89sgfmJ/bRYd0U8uCJQAAIABJREFUK8N9cYqisGPHDpYsWcLr\nm35Dpk0h74ovJ9bL/hMp9cDkWJT2l39A0L2SWUv1ubvepp2EB7vJjO5jqEZRb9iFV57BPPvo2sGq\nHEk10JUoghJGVAYoyshP5KxrsfSGX37D1xlQPYRnfmn0Sk3BEjhM1DkHURkgu+UefEUfRbaVjG6b\nsp2GbXA3EedcNMlGTvPPAVI8a862B5HNOUTyLxq1ed7xbwHQX3AjsYz0onYjQ9qLHD5izb8g6pxF\nwHMBoOHse55Q5gpU0YaoBFDN6RXsx6X5McyWINHeg2M2UZ0VnLPsfEKhEGazmTd3/ISFJeGUNvnH\n/h2AropvMKPGRs9b8eshwMLldqZV6fHbBdeYCfpVDu0LU5DVQYX4JMrMT9HXb6K1MRmCfNFVmYm/\nI75Ur+h0VxOiRcSTp7vKc/LG/gmzHvweEVNl4r7M9T+XWDfH/AoNzmR98e63/oLqnk1PTy/Vsxag\nhPsxd2+h1FQPQIvrQ1gLl495rMkwpLmgBU/ACLkDU9zz31L4ecItz+MpX0nOif9JaRPoPADx7JGP\nruhPWXfj0n4GwiK/f33LmAZ6Z2cnhw8fZu3atbS1tRGLxaioqEAQBGKxGBs3bmTFihVUVFTwwjMb\n8UdF1k6PsGF6/FgeILYTbOj/AFk9Qh/QcPQo2178Kxde+VFycnKIRJKTaaGAD1dgF9+4oJO9gydg\npKaFqrdV42p9mqaxe/duIpEIS5YsobW1lSOH3uLmxe08v+tpPr5Kf4d96swefv3qPs6oCDO3MHm8\nwdzLiDpm4ux9Bltgv37uqj4zrrq5gYGBgYHBVGLSBnptba2bpGDcCvRhTy/w3+iCcbvfiQ4aGJws\nmmhDEW30VP4bmnj6co4VTEhx48maUQByc2Jd2FmTGAxOxI1L+9Muv+ucTo71WtndaudIN/zxj3/k\n7g26h6w4M0a+K2m41S7yJrzPG998BeKRoz3Ht/O64kCWZXbt2sXysgAMm58Y7q1+/qk/csW8ABHR\nQQAnWWInd6zSc5B/9/xDLFylt5NlmfpDeznP/SQPPLyVf1mt9//BJ37LjXEbRwu0gktXq+/u7sZp\nlllaFgK2sGfbcbLm1pLr/St5zmhicA+gImEaWSR6iKgXUyxZp11TY5ga7iVb7KKr+rsJA12Jju09\nc4t9hNMsz2q5F0u0jb78G4gEB5GUQaS2x5GrR+fbWzue0tW7Cy5FjPXi7noUgP6S25KNNC3hmXOG\n3oIQdKUx0Ifoaj5Ads0YpZq0pG6CooIkgjnajjnajrN/C1FrKZZIKwSO44zqYdNdVd+GYBtoZhBE\nVFVFVdVEJEY65FgULBCLBFI8ulEFfvlSHnOKYfrq1VitVqxW3cg+4r6cpreeYVVFgHxX6gRUqL+J\nmYN/pqZcN5ZMZiFhnINeBz0jU2L5mU7kfY+Ra+mj4cQucqatxukARpcDJxbUDfRj0llUKVvwxTJI\nNxWhqTEGDj6KwxRlqOhzpnmQTPYC8GJzMeunnUjZptq8N/F3jW0nRHfqE0ftz+gLh526Mv9jNOw/\niE3uRKz5fwiiaVQ6yptvvkl5eTlZWen1MhzxkoZz8vT7tSXnEwgolPbel2hjdeVhnXNdijqDt/Bm\nsjr+FH+exsZtU/ncWd088eqrrF6tlyiIRCIoioLD4WD7a1vQ/M309s7l2acfRRI1OuadQSgUorW1\nhYsqGzm29zgvvJDHV8+ZKHNcJxwT6erqwtr6EJ9dH+LB537PrDOu5+m/Ps4347d/c8MBZmTpE5lR\nXxO9vUXk5CRDt8X4/a6pMbZu3UpPTw+R/mPEFIGmpkZ6urtYPi1IZU6Uj+ckJ0TdNpW70vQzlKV/\n94GimxjQFHJzPGh96UuTGRgYGBgYTEXGNdBra2sF4Hx0o/wK9MIvCvB3kiXX0ivzGBi8x5x2j4nJ\nCbI+0DObLQx3dA4U3ohf9mENHiKje+ww1fFwWjTmF4WZX6SblM39SaspXf32IWP76oXJwef1S7w8\nuudFbGaVuzeMr4p8x1pd17GhH7A5cFiSZsEnViUHwg/87pfMLohin6/xL6uTHs0bFzQm/q5x6V5Y\nUQD5wK84pM6gIu7tW5TTBJ0/HuU1BFARQUv/CnGf+CMuKelZzVMPkC3qg3k57MUcN9DV2DADXVNT\nE5FHkNFZR8Q1H0tUN/x7TxwGyaFPN4ZH57ACZPp1tX/JZMXs25lYnt32m8Tfjt5/EPScjb1/67C+\nKCDo1qIY68cU7aTVa6Y0K4YyMsYZQNPIavsN+Y7GxKInDhZzdU2qYWmJ6OkEQ8Y5gL1nM+KxV8gH\nesq/zJZNdUSDXi64PlVBW4rq11yx5OKx6QafqOr32wmfieJMmVcbM7n5k59Pey4WL16Mt7KS9iOP\nkE9TyjqT/zBmkp5MszB2brEi6+tisn7t5UhSqG74ZIcajk9m2Qrw9ZqQg130dHeRk6vPSIX9PWiH\nf0PEVcMs674xj1e+5Bo6MwvpaKnH7bJT2fcrffuYQLO2iJmW9PPLbQM2Stz6+am2HgQrNO35PgJg\nW/bNRLtIOMSMyEZ2vlTA2ss/PWo/0UiIIltq9QFrVhUIAv0DNQja6Oe7p+IraEhoprG1NdJx5K1t\n1NTUkJmZySOPPILP5+OOO+5gXmYDK+YN8pPHH+DL5+qRO3/Y9jKaBncs78dm1phXFObsSPoJr5i5\nAHOsE0W0I6khOpVpFFib2brpAW5fo99LNy7t5xtPPM5l85LvnvoD26mcpV/vpvp97N7SwLJly9ix\nYwcAl9b4IBOioQH27t1FnlPmM+v0+/Sp/RE+c/Ho91ggcw1O3yuJz4opC1/hjYnnLYEggfjBqx1v\nYGBgYPDBZkwDvba29nvoIezF6MPXg+j55n+qq6vrfHe6Z2AwdQh4zsPdtZGQezmDuZclQnu7K7+e\nUJoPZa4i5F4JqIhKAGvgAIISwBI6jiXUkNhXX+kdmMNNZPQ8NebxpmWf2tzXtYu8KZ81wcRg3pXI\nlgJkSz6ZLb/GGku6KzNtMfpNLixjOLK/ct7kvGlDLCwJs5CxjaXhKFhgDJGo4cY5oHvf48jhwWEh\n7rpxF/MepaTn97TnfQyLIxlGG/E2Yc0qh3AX9sHd2AeTxpik+Kky6Ua3TYoyXhE8Z//zY65zebfg\n8m5JWaZF+hFsel2n7KafIxElYnYAMczi6Gtr9h9MUU9v85mZvepq/mfrVgb6TnDj0j6KM9ML6WUM\nJI2V3KYfUjtX/3vklctp/k9936Y1mCV9gidP0g3+Y5aLeakpwNyF46eEZGVlES65iFcO/C9FmRpV\nHj2kvUTZltKu0BXkRCyMyTyiZrQmJwzwAW8/HkCJJtMU5FgYk0UvAq5FfGAFsyOHzICsCwn6fkas\nDzr8dsJRgVl5QSD12IejS7EVLKK8//cEoyKOLD0Tq2jaTAA6s+7Ge+DPmFzFZJWvpyN6IUrYi+jb\nR1FUP5fd2Vdjnr6ME3t/RLEjGfVSnhWf0IhFMZl1gbRosJ9qT4widxs+IBqNcvToUaqrqxEEgWDX\nQYZrFHrD5sQ5iJXflPY8q6ZkqH9X9d2YG35NNq1p2w7nrnO6ePKt3SxbsYZKVzuefIUHHniAO1bo\nhvcXz06m1QzXFBgiY5ggoIZAb8VXEWUf8pByuKYgKEGCLS+D0szta1Intr6zoSPl8w1L+vFH9C9/\n5XwfVTlRnntrG0tKIxRkyKwq16/9guIwC4pTt72sJv0kYyB3A8Gc87EEDmAJHiOYvQ7FMlqAzsDA\nwMDA4P3IeB70r6AXyN2B7i0fGgGV1NbWTpCoCXV1daOTSg0M3seE3csIu5O1iHsqvoKghEZ76gUB\nkFBNbkKZepx4ELAOvomgRgi7l4MgINtKCGcsRJOc2AZ2IihBYo5qBCVI9onfA+AtuhnFlIPNvwdn\n/xYijllYg4cTh4pZijBH08QGxwllLGGwIFVJ2198I/LAbpz9/9S7W3Q2Wk93us3fMfxREy6LjIiC\nap2cwNpw5MggStxAr1C20DFYQ+D4C5ABjhOPkmlOGvcBbzsuyY/SunmUOFeVNXkurSa95J4mWBC0\nGNbBNwnkjK19GYwKOCxj6ykXtOrGcMg+Gwl9cqHAqRsjFtWLGOsnt+lHabd9susSBNHEyvwirrrm\nOkAvJfXEy1tYW+WnPjKXiJBFe+M+Pn/W2Ndu8FAdHR2dTMszUehIGjslctKgd5v0c1VWvYiZ8yeX\nElJYUkFO/pdprd9JrOsZyrOjiUoFwylu+lbi7wbThVTLmwGQHfq1W+HeRru8AbH1CYhHhsthb8JA\nV/1NYNVDv+UuCZMwpPgOZZmjQ77bnJeSN/gM7pLFSJnVtDn/DU2JMVJnXBAlsufdkPgsWjMRrZmQ\nWU4Xl6a0NS24iy5NIdK2lbLw5sTyA69tJHf6ev7yl0fJdSp8dr2e4jE4OMi+N3fSsv95JPFqKquq\nCPUfg2GnNho7SR1uQUIuuAA6/zCp5m1Hd7B9517u3qBH1wyEO8a9V0cSs5YwUFAL6MKaKeKagoRm\nysDuLoL02TopuG0qblu8PJ0Ii0tDLC6duDJBVPJgUZITCN7Cm1AseSjmPBAENMFKJGMxkYzFk/5e\nBgYGBgYG7weE4eq5w6mtrVXhlKt5aHV1dackQDdF0U6cODFxK4N3hdzcXHp6et7rbry7aEqihrvu\niRfRRAsxWzmCGiHrxG/x51yCOdyEq+8f9JV9BkvgIMGstWOHeGoKghpFk+wIg4fJ67yfqLkA77TP\nkN/wtVHNfQUfRuzfSUb0SGJZyFHDoLmKfF8yEiCiObAKSY9oV94tRFQToeObmOnWQ8uPxJYx07yD\ntkEnpkVfo/XQS6gdr5LvDFHumThy4EhwBkXScTKsE9cIPy7Po9L0FgB9AQnPGKr7J8M3nynkvLmw\ndloHO1vsKfnB+9ptiTSF8TjmL6TK1TFq+a9fyeFDH/1i2m00TaO+vp7y8nKsViuRSISXn/w5cwoi\nPLXfzdoqP2dND6TddiJOpYSUpmkEAgFCvnZqQpMzHkdyxHoNMyN/SXx+q91GuPAyyqYvxvbWNzCb\nBPxzvgNRH/nNPxi1vaKJSIJKi2kt1oq3X6N6PMTO58kdfBbQc/WPdNmYN+xaB6MCP/xnASvKg1wy\nd4AHtmdz4fVfonHrL1hZ3EGXXEK+SX8GTvp8axq2gR1EXHPJbfxB2rD44YxXIWEkvdM+j6j4EZUA\nmiASdc6dcBsp0kFOy38lPkdtlVjCx1Pa9JR/idymHwMwmHMxGb2b0u4raq9OiTAazL0cTbTg7voL\nwczVxGzlutjnKSiw/5/8vZiiGNdi6mBci6mDcS2mDsXFxQBTotTHeAZ6I6duoFNXV1d5qttOQQwD\nfQphvMzeATQNKdadKBdmG9iFoIbI6PkboEcLDA+5lSIdCFqUrNIl9PT0YB3cjSplEHNM1xuEOyE6\nAO4ZKYeJDbYi9+7DVLye8N57UIouJqskKZY20NeJ+eg9kzLST4V2v51tPbPpbm/mo8v7sJg0DnRY\nebltGrctrZ/UPv53RzbnX/clWlua2P3So5xx4S08+9c/8dn1uif7dfkaCnx/BaAyJzrergB4rj6L\n82Z4qe+2sCewjHXnXnpKdbZzc3PZt3cvrbseoqoslznOwxNvFGe3cgkls8486WMOp+PISwz2d+C0\nCnhmXUxxy/cwiRP/hGxtcLK2evSkwrc2F/CNCzppV6Zjnv1xAPKPfjWlTZgMBqq/+q6XzrL0v05W\n78RaE31BiZ9uyePqBT6qcyNEZ30BV9+zhNzLidmrTvn4ouwju/V/kORJuLDjaIKZ7sqvk9X+AAHP\nuQDEbNMA8dTOn6ZhCR5BthRg9e8llLUGKdZPTvN/4i28CdlWmnxnxPUYTJF2LIEDxGzlyXdF4jvp\nER6qyZ1cFutDNWW/retr/F5MHYxrMXUwrsXUwbgWU4f3hYFukIJhoE8hjJfZu4imIMX6USy5aVe/\nE9eiv+MIs/y6svVrHZVEwkFq8vpTctBPFW/IRGTetwG91nNvby9z587FbDbT99rdNPWbEYovIDc3\nj+6mPWwofp1/HMqgN2Qlu3guDY1tnLfharKzU7XEZVlm8+bNFBcXsWjRYg4dOkReXh4nTrQyI/wY\n5Z4Yjx0oodMbo2aanXWlusBa86AbqeYL9PZ0Ulhc9ra+28hroSgKg4ODSKJI64F/IrqrULu3Y3e4\nyHYomLJnUxnUPdcdld9GlE6vmJYmByEWIBrso6z/fgAatWXkyG+RYR4dYdCVeyOu7qdxCKkaCiec\nl2Aq0icPpGgXVv9+VFMGUqyfgOdsEN6bYC0p0k7WifuIuOZhG9yDqE4ctn0qUQrjYQkcRhNMmMON\niEoQh+/VsY9d/b0pXwP8ncD4vZg6GNdi6mBci6mDcS2mDoaB/v7DMNCnEMbLbOrwTl2L4/u3YvLt\noXjF7UgmE+0HnqUo+iInHBdjDjZQ4zpIKCYQjIpk2hUOhRYwz5ksm/VGaya9IScXz0h9blUNemak\nN5K6u7vJyMjAZtNFzWRZprGxkaqqqlEltU4GWZbx+XyJ0lLRSJjdLzzArBwfobyLKKoYX5Rtskzl\n50LxNSAEGhGLdc8tmoooD5Db9MNEm67q7wJg6/4H7oEXE8t7yr+Eah5RO3sqoikISghr8CDursfS\nNjndBvpIpGgPmiCR0fM3FFMmYddCLKGjRB0zkW1vbwLo/cpUfi7+r2Fci6mDcS2mDsa1mDoYBvr7\nD8NAn0IYL7Opw7t5LTRNS4R+a5qGpmkQ9RILeZFcJfRu+wkl7hAeh8I+/zwKFt2YEhLtj4i85a2g\nas0n35X+vtu8L58LTcHq30fUOQdNHFYzPdqNJXAI2VpCzHHqoeDvFbluE8HGZ7H69yVEHPtLPvm2\nwtoNTo335XPxAcW4FlMH41pMHYxrMXWYSgb6B0nIzcDA4APM8LxsQRD0zzYPVpvuXS0482v4+lrw\nHn8Y96yLAWgq/hrNbzxA0fwrEK2ZTJtlS7tvg/cIQSKSsWjUYsWSR+j9XDbLkkXQczZBz9mgRhGV\nIKo5673ulYGBgYGBgcH7gClnoNfW1t4A3A4sACTgEHr99Xvr6urU8bYdY38XAZ8HlgE24BjwMPCT\nurq68coeGxgYvM9wesrAc1fis93hYtZZn3oPe2Twfx7RgiqOLPRmYGBgYGBgYJCeU0+sfAeora29\nB3gQ3ZjeCjwLzAT+G/hLbW3tSfW3trb2LmAT8P/bu/P4S+f6/+OPYQxjmWzZ10G2iBjb+JqxL6XG\n9pK0KFqI8pW1SAnZCiUSSgn1kp91fnYzthBGlCXCRMpYJ8tgmJnvH6/35VxzzdnnbPM5z/vtdm7X\n9n5f532u9+c6n/O+3ttWwARgLLAEcDww3szqm/RXREREREREpM16poBuZrsBBwAvAOu6+yfdfRdg\nNeAxYBfgoAbOtyFwEjAFGOnu27j7HsBw4HZgE+CE1n4KERERERERkeb0TAEdyEZzOsLdP5iQ2N0n\nEU3eAY5soBb9SKKj/8nufm/ufG8CXwKmAweYmToGioiIiIiISNf1RAHdzJYDNgCmApcVj7v7bcDz\nwFJEzXet8w0BdkybF5c539PA3cAQYKemEy4iIiIiIiLSIj1RQAfWT8tH3P3tCmHuK4StZnVgfuBV\nd3+qBecTERERERERaateKaCvnJb/rBLm2ULYes73bJUwjZxPREREREREpK16ZZq1BdPyrSph3kzL\nhTpxPjP7KvBVAHdn8cUXr+NtpRMGDx6s/OgRyoveobzoHcqL3qG86B3Ki96hvOgdygspp1cK6D3H\n3X8J/DJtznj55Ze7mRzJWXzxxVF+9AblRe9QXvQO5UXvUF70DuVF71Be9A7lRe9YZpllup2ED/RK\nE/esNnuBKmGyWvE3unA+ERERERERkbbqlQL6xLRcsUqY5Qth6znfCi06n4iIiIiIiEhb9UoB/cG0\nXNvMhlYIM6IQtprHgbeBRc1slQphNmrgfCIiIiIiIiJt1RMFdHd/DphAzEu+R/G4mY0ClgNeIOYv\nr3W+qcB1aXPvMucbDmxKzLs+tumEi4iIiIiIiLRITxTQkx+l5clmtmq208yWAM5Omye5+/TcsQPN\n7HEz+22Z850EzACOMLONcnEWBH5FfPaz3X1yiz+HiIiIiIiISMMGzZgxo9tp+ICZnQ3sD7wD3Ay8\nB2wNDAOuBHZ392m58N8HjgVuc/fRZc53OHAyMA24FZgMjAKWAO4FtnL3KXUkrXcukoiIiIiIiLTa\noG4nAHqrBh13P4Bokj6BKEhvD/wDOBDYLV84r/N8pwA7AuOIPuw7Ay8DRwOj6iycY2YPEBmmVw+8\nlB+981Je9M5LedE7L+VF77yUF73zUl70zkt50Tsv5UXvvFJe9ISemwfd3S8BLqkz7PeB79cIcz1w\n/WwnTERERERERKSNeqoGXURERERERKRfqYBen192OwEyE+VH71Be9A7lRe9QXvQO5UXvUF70DuVF\n71Be9I6eyYueGiROREREREREpF+pBl1ERERERESkB6iALiIiIiIiItIDem4U91Yys4OA/wHWIeY+\nH0bMhf4QcCFwsbvP0sbfzOYi5mP/ErAGMY/6w8DZ7n5pjff8bIq7LjA38Djwa+Acd5/ekg82QJjZ\nicBRafMwdz+tQrimrqmZ7QAcAmwIzAc8DVwKnObu77bqc8xpzOxC4ItVgvzd3dcoE0/3RZuY2VDg\nIGAPYDVgCDAJuB84w93vKoRXXrSYmY0mpuSsx4ru/mwhvr6nWsjMlgOOALYDViCmwXkOuAU4xd2f\nrhBP+dBiZrY8kRc7AssBbwAPAD9197FV4ikvGmRmqwM7EFMDbwh8hPjb38Pd/1gjbkevt5ltDBwJ\njCR+Xz8HXAGc4O7/refz9rpm8mN28jDF131TRqPX1czmAbYAdiKm7v4IcV1eAu4GznL38TXes2t5\nMdBr0I8AxgBvA38CLifmVd8KuAi4Iv3Q/YCZzU18wZxF/FC+EbiT+IO4xMzOrPRmZvZz4GIiQ+4A\nbiL+IM4C/lh8r35mZiOAw4GqgyA0e03N7HDgOiKvJwBjiYc0xwPjzWz+1nySOdpdwG/KvK4oBtR9\n0T5mtjJRuD4ZWJYoJI4l/omMAbYshFdetMcLlL8fstdjKdxTxA/RD+h7qrXMbH3gr8CBwPzADcR0\nqUOBrwEPmdlmZeIpH1os/a/+C/AN4gfqWOAJ4lpda2Y/qBBPedGc/YEzgL2B1YkCSE2dvt5mthfx\nG2IM8fdwFfFg+TDgfjNbop50zwGayY+m8hB039TQ6HUdBdxMFJSXBW4nfju9CuwGjDOz4ypF7nZe\nDOgadOAzwIPu/lZ+p5mtTTyF/zRRk/jr3OGDgU8BjwJbufukFGc1IoO+aWa3uvtVhXPuBhxA/Mjb\nwt2fTPuXJH5w70LUkFX88dwvzGxe4gfvJODPxBd8uXBNXVMz2xA4CZhC5OG9af+CxI2yBXAC8L+t\n/mxzmPPd/cI6w+q+aAMzW4D40h9O1ESc5u7TcscXAxYrRFNetIG7Pw7sU+m4mT2aVn+Vb3ml76m2\n+DmwMHAe8A13fw8+qBH5BfBl4BzgY1kE5UPrmdl8RMXGosDPgEPc/f10bDPi+nzPzO5095ty8ZQX\nzfsbcCrReuoB4AKioFFRp693at1yAVFAGpP9rzGzwcDvgD2Bc9P7zukazo8m4+i+qa3R6zqd+P46\n093vyB8wsz2JwvcxZjbO3ccVjnc9LwZ0bYm731ksnKf9jxA/AAC2zfanmqnD0+b+2Q/fFOdJokYe\n4Ltl3i5rqn1ElpEp3iTiqQ/AkX1eQ5U5DlgT+DpQrRlUs9f0SOIfx8nZzZHivUk0CZ4OHGBmC8/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(14, 6))\n", "\n", @@ -691,9 +1120,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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Z01YBXOMjnd2822etqRs7rT/bDXTbwUrCbJXQF/OP3kzy+n/7cszl376Ox5MumMeZiTkw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/I8BAR6/AO3gIJp3+iuxxOo61ZlNcW5+kK/YTkFunnx6f73LUzAhnnQ/5FZjdH0+oo49Dfv/o0X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\np6GMzoHs3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Dob13pvO3s+vu//EuW872SWVVZp+3UShDOfVbc88AZbvXQ7CZ4+O/PZg7df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\ne5Y91cDdJ5nZGKKken4ik/68md0OPAy8Q/S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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(14, 6))\n", "\n", @@ -718,9 +1166,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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0HqoKDvSvaXrN4aUPr3twTfP2kiRJkqYOgwNSO5Y9Cptunj1fuxqWPw5AvPHa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cd+N9aFXges4epzJW+W/3Ul2rudK3ft8yvXd5zP0xnJ4HpMzI5GjTEXjTHtsFYlGIJ14Ws8ft8h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HDM1h9L41X5/klERERERETql4IDpd1KDA6cSmzW/wHnXCNwcrL7xwrLPBM42Tn3n977jpzz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\nRvpHcur6HLBlnnQHAqtT6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(14,12))\n", "\n", @@ -751,9 +1210,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/esp/lsst_utils/Sed.py:1399: RuntimeWarning: divide by zero encountered in log10\n", + " mags = -2.5*numpy.log10(fluxes) - self.zp\n", + "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:32: RuntimeWarning: invalid value encountered in double_scalars\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Run 20\n", + "Run 40\n", + "Run 60\n", + "Run 80\n", + "Run 100\n", + "Run 120\n", + "Run 140\n", + "Run 160\n", + "Run 180\n", + "Run 200\n", + "Run 220\n", + "Run 240\n", + "Run 260\n", + "Run 280\n", + "Run 300\n", + "Run 320\n", + "Run 340\n", + "Run 360\n", + "Run 380\n", + "Run 400\n", + "Run 420\n", + "Run 440\n", + "Run 460\n", + "Run 480\n" + ] + } + ], "source": [ "np.random.seed(2314)\n", "\n", @@ -856,9 +1362,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating overall means\n" + ] + } + ], "source": [ "print(\"Calculating overall means\")\n", "\n", @@ -873,9 +1387,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.443852308433\n", + "0.803646103625\n", + "0.576154413666\n", + "0.666631915825\n", + "0.270972271859\n", + "0.335837186692\n", + "0.565081429947\n" + ] + } + ], "source": [ "print(gp_exp_flux_mean)\n", "print(gp_sq_exp_flux_mean)\n", diff --git a/setup.py b/setup.py index b6fde42..f0781bd 100644 --- a/setup.py +++ b/setup.py @@ -2,11 +2,11 @@ setup( name="esphot", - version="0.1", + version="0.1.0", author="Bryce Kalmbach", author_email="brycek@uw.edu", - url = "https://github.com/jbkalmbach/esp", - packages=["esp"], + url="https://github.com/jbkalmbach/esp", + packages=["esp", "esp.lsst_utils"], description="Estimating Spectra from Photometry", long_description=open("README.md").read(), package_data={"": ["README.md", "LICENSE"]}, @@ -18,5 +18,6 @@ "Operating System :: OS Independent", "Programming Language :: Python", ], - install_requires=["matplotlib", "numpy", "scipy", "sklearn", "george", "future"] + install_requires=["matplotlib", "numpy", "scipy", "sklearn", "george", + "future"] ) diff --git a/tests/test_esp.py b/tests/test_esp.py index 967418f..64b7202 100644 --- a/tests/test_esp.py +++ b/tests/test_esp.py @@ -6,9 +6,9 @@ from esp import pcaSED from esp import nearestNeighborEstimate, gaussianProcessEstimate from esp import specUtils -from esp.lsst_utils.Bandpass import Bandpass -from esp.lsst_utils.BandpassDict import BandpassDict -from esp.lsst_utils.Sed import Sed +from esp.lsst_utils import Bandpass +from esp.lsst_utils import BandpassDict +from esp.lsst_utils import Sed class testESP(unittest.TestCase): diff --git a/tests/test_pca.py b/tests/test_pca.py index 9b4b216..12f94c2 100644 --- a/tests/test_pca.py +++ b/tests/test_pca.py @@ -5,9 +5,9 @@ import sys from esp import pcaSED from esp import specUtils -from esp.lsst_utils.Bandpass import Bandpass -from esp.lsst_utils.BandpassDict import BandpassDict -from esp.lsst_utils.Sed import Sed +from esp.lsst_utils import Bandpass +from esp.lsst_utils import BandpassDict +from esp.lsst_utils import Sed from sklearn.decomposition import PCA as sklPCA py_version = sys.version_info.major From 512c5b9a6405ec6f731d7302724d2e2d4a266f27 Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Sun, 29 Oct 2017 17:36:37 -0700 Subject: [PATCH 5/7] Readded set spec wavelength bin to 0 when negative. Need to add test for this. --- esp/pca.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/esp/pca.py b/esp/pca.py index 26406c1..eb8419c 100644 --- a/esp/pca.py +++ b/esp/pca.py @@ -152,6 +152,10 @@ def reconstruct_spectra(self, num_comps): np.dot(self.coeffs[:, :num_comps], self.eigenspectra[:num_comps]) + for spec_num in range(len(reconstructed_specs)): + neg_idx = np.where(reconstructed_specs[spec_num] < 0.)[0] + reconstructed_specs[spec_num][neg_idx] = 0. + return reconstructed_specs def calc_colors(self, bandpass_dict, num_comps): From 83d7f7fa5f64653956530985ddfa8ce37b2c8182 Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Mon, 30 Oct 2017 10:06:46 -0700 Subject: [PATCH 6/7] Paper notebook added. --- examples/esp_paper_plots.ipynb | 657 ++++----------------------------- setup.py | 2 +- 2 files changed, 65 insertions(+), 594 deletions(-) diff --git a/examples/esp_paper_plots.ipynb b/examples/esp_paper_plots.ipynb index 0f1cf82..c8d4daf 100644 --- a/examples/esp_paper_plots.ipynb +++ b/examples/esp_paper_plots.ipynb @@ -11,12 +11,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Run with ESP version 0.1.0\n", - "\n", "import esp\n", "import numpy as np\n", "import matplotlib as mpl\n", @@ -45,26 +43,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File On 100 out of 959\n", - "File On 200 out of 959\n", - "File On 300 out of 959\n", - "File On 400 out of 959\n", - "File On 500 out of 959\n", - "File On 600 out of 959\n", - "File On 700 out of 959\n", - "File On 800 out of 959\n", - "File On 900 out of 959\n", - "Done loading spectra from file\n" - ] - } - ], + "outputs": [], "source": [ "home_dir = os.getenv('HOME')\n", "galaxy_dir = '%s/lsst/DarwinX86/sims_sed_library/2016.01.26/galaxySED/' % home_dir\n", @@ -74,29 +55,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "On Spectrum 0 out of 959\n", - "On Spectrum 100 out of 959\n", - "On Spectrum 200 out of 959\n", - "On Spectrum 300 out of 959\n", - "On Spectrum 400 out of 959\n", - "On Spectrum 500 out of 959\n", - "On Spectrum 600 out of 959\n", - "On Spectrum 700 out of 959\n", - "On Spectrum 800 out of 959\n", - "On Spectrum 900 out of 959\n", - "789\n" - ] - } - ], + "outputs": [], "source": [ "new_spec_list = []\n", "i = 0\n", @@ -132,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -141,21 +104,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Burst.50E09.1Z.spec.gz\n", - "Const.10E09.0005Z.spec.gz\n", - "Exp.15E06.002Z.spec.gz\n", - "Inst.32E08.25Z.spec.gz\n", - "(array([ 0, 1, 2, ..., 6558, 6559, 6560]),)\n" - ] - } - ], + "outputs": [], "source": [ "print(new_spec_list[190].name)\n", "print(new_spec_list[240].name)\n", @@ -166,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -176,20 +127,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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isn9HXXmI+wBgFtAZKI0+OE8Vf3//eUAA+oD/ILAbfeD/LbDB39/flV8PIYQQL7HY2Fgy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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(14,12))\n", "\n", @@ -230,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -243,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -321,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -372,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -415,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -436,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -447,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -465,56 +405,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/esp/lsst_utils/Sed.py:1399: RuntimeWarning: divide by zero encountered in log10\n", - " mags = -2.5*numpy.log10(fluxes) - self.zp\n", - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:32: RuntimeWarning: invalid value encountered in double_scalars\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run 20\n", - "Run 40\n", - "Run 60\n", - "Run 80\n", - "Run 100\n", - "Run 120\n", - "Run 140\n", - "Run 160\n", - "Run 180\n", - "Run 200\n", - "Run 220\n", - "Run 240\n", - "Run 260\n", - "Run 280\n", - "Run 300\n", - "Run 320\n", - "Run 340\n", - "Run 360\n", - "Run 380\n", - "Run 400\n", - "Run 420\n", - "Run 440\n", - "Run 460\n", - "Run 480\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "np.random.seed(2314)\n", "\n", @@ -630,196 +523,11 @@ "training_colors_full = np.array(training_colors_full)" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run 0\n", - "Run 20\n", - "Run 40\n", - "Run 60\n", - "Run 80\n", - "Run 100\n", - "Run 120\n", - "Run 140\n", - "Run 160\n", - "Run 180\n", - "Run 200\n", - "Run 220\n", - "Run 240\n", - "Run 260\n", - "Run 280\n", - "Run 300\n", - "Run 320\n", - "Run 340\n", - "Run 360\n", - "Run 380\n", - "Run 400\n", - "Run 420\n", - "Run 440\n", - "Run 460\n", - "Run 480\n" - ] - } - ], - "source": [ - "np.random.seed(2314)\n", - "\n", - "#li_flux_results = np.zeros((25000, 6071))\n", - "#nn_flux_u_results = np.zeros((25000, 6071))\n", - "#nn_flux_2u_results = np.zeros((25000, 6071))\n", - "#nn_flux_2d_results = np.zeros((25000, 6071))\n", - "#nn_flux_4u_results = np.zeros((25000, 6071))\n", - "#nn_flux_4d_results = np.zeros((25000, 6071))\n", - "gp_exp_flux_results = np.zeros((25000, 6071))\n", - "gp_sq_exp_flux_results = np.zeros((25000, 6071))\n", - "#gp_matern_32_flux_results = np.zeros((25000, 6071))\n", - "#gp_matern_52_flux_results = np.zeros((25000, 6071))\n", - "\n", - "training_colors_full = []\n", - "training_coeffs_full = []\n", - "training_eigenspectra = []\n", - "training_meanspec = []\n", - "\n", - "test_colors_full = []\n", - "\n", - "test_exp_params = []\n", - "test_sq_exp_params = []\n", - "test_matern_32_params = []\n", - "test_matern_52_params = []\n", - "\n", - "distances_all = []\n", - "\n", - "test_flux_orig = []\n", - "flux_errors_full = []\n", - "\n", - "total_flagged = 0\n", - "n_runs = 500\n", - "min_wavelen = 299.\n", - "max_wavelen = 1200.\n", - "n_colors = 5\n", - "n_comps = 9\n", - "\n", - "for i in range(n_runs):\n", - " if i % 20 == 0:\n", - " print('Run %i' % i)\n", - " \n", - " if os.path.exists('results'):\n", - " shutil.rmtree('results')\n", - " \n", - " training_colors, training_list, training_names, test_list, test_names, \\\n", - " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", - " bandpass_dict, min_wavelen, max_wavelen)\n", - " \n", - " new_pca_obj = esp.pcaSED()\n", - " new_pca_obj.spec_list_orig = training_list\n", - " new_pca_obj.PCA(comps=n_comps, minWavelen=min_wavelen, maxWavelen=max_wavelen)\n", - " \n", - " colors = training_colors\n", - "\n", - " nbrs = NearestNeighbors(n_neighbors=1).fit(training_colors)\n", - " distance, idx = nbrs.kneighbors(test_colors)\n", - " distances_all.append(np.ravel(distance))\n", - " \n", - " #print 'Linear Interp'\n", - "# li_spec, li_colors = linear_interpolation_spectra(colors, test_colors, new_pca_obj.spec_list_orig, \n", - "# bandpass_dict, min_wavelen, max_wavelen)\n", - "# li_flux_results[i*50:(i+1)*50] = np.abs(np.array((li_spec - test_fluxes)/test_fluxes))\n", - " \n", - " #print 'Nearest Neighbor Results'\n", - "# nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, test_colors)\n", - "# nn_spec = nn_obj.nn_predict(1)\n", - "# nn_flux_u_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", - "\n", - " #nn_spec = nn_obj.nn_predict(2)\n", - " #nn_flux_2u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", - "# nn_spec = nn_obj.nn_predict(2, knr_args=dict(weights='distance'))\n", - "# nn_flux_2d_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", - "\n", - " #nn_spec = nn_obj.nn_predict(4)\n", - " #nn_flux_4u_results.append(np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes)))\n", - " #nn_spec = nn_obj.nn_predict(4, knr_args=dict(weights='distance'))\n", - " \n", - " #print 'Gaussian Process Results'\n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", - " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", - " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", - " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", - " test_exp_params.append(gp_spec.params)\n", - " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", - " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", - " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", - " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", - " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", - " test_sq_exp_params.append(gp_spec.params)\n", - " \n", - "# gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", - "# gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", - "# gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", - "# recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", - "# gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", - "# test_matern_32_params.append(gp_spec.params)\n", - " \n", - "# gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", - "# gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", - "# gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", - "# recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", - "# gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", - "# test_matern_52_params.append(gp_spec.params)\n", - " \n", - " training_colors_full.append(colors)\n", - " test_colors_full.append(test_colors)\n", - "\n", - "\n", - "test_colors_full = np.array(test_colors_full)\n", - "\n", - "training_colors_full = np.array(training_colors_full)" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "[array([-11.02207729, 4.77361603]), array([-13.96023833, 2.13156115]), array([-17.59610534, -0.96467728]), array([-19.79497933, -13.27023188]), array([-21.28017721, -13.80637852]), array([-21.74208749, -14.16324311]), array([-22.11764147, -14.36039222]), array([-23.05681041, -15.46075091]), array([-24.84448021, -15.5739952 ])]\n", - "[array([-11.2550589 , 4.37618544]), array([-15.16269104, -0.17042565]), array([-18.63210908, -2.69566832]), array([-19.54205714, -5.57521916]), array([-20.66959493, -5.5241298 ]), array([-22.10726755, -5.23304072]), array([-23.6345675 , -12.51136918]), array([-24.38139955, -13.33114988]), array([-24.98276997, -14.42287437])]\n", - "[array([-11.25444705, 4.48069245]), array([-15.04177928, -0.45475115]), array([-17.6286331 , -1.45762491]), array([-19.45297358, -4.82311417]), array([-20.36503317, -12.79350627]), array([-21.33194789, -13.32536929]), array([-22.28499836, -13.51599869]), array([-22.70957049, -13.55544863]), array([-24.52930451, -13.60538845])]\n", - "[array([-11.44655224, 4.06530313]), array([-16.22874545, -0.97132244]), array([-19.38768499, -6.60459258]), array([-19.6774699, -4.7427898]), array([-19.77607617, -9.63081001]), array([-21.31841287, -8.50492599]), array([-22.11244633, -9.13452811]), array([-22.88324644, -13.69198587]), array([-24.85286842, -14.27590948])]\n", - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:32: RuntimeWarning: invalid value encountered in double_scalars\n", - "[array([-11.35058398, 4.42537694]), array([-14.84507009, -0.23824238]), array([-18.06283476, -2.71023966]), array([-20.32512608, -6.34834806]), array([-20.79609481, -6.00838321]), array([-21.51965251, -3.89801573]), array([-22.48659084, -5.99184903]), array([-24.12463544, -11.71187796]), array([-26.88263336, -14.37848589])]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating overall means\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'nn_flux_u_results' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mgp_exp_flux_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp_exp_flux_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnn_flux_u_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_flux_u_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mgp_sq_exp_flux_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgp_sq_exp_flux_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mnn_flux_2d_mean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn_flux_2d_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'nn_flux_u_results' is not defined" - ] - } - ], "source": [ "print(\"Calculating overall means\")\n", "\n", @@ -837,45 +545,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating overall means\n" - ] - } - ], - "source": [ - "print(\"Calculating overall means\")\n", - "\n", - "gp_exp_flux_mean = np.mean(gp_exp_flux_results)\n", - "#nn_flux_u_mean = np.mean(nn_flux_u_results)\n", - "gp_sq_exp_flux_mean = np.mean(gp_sq_exp_flux_results)\n", - "#nn_flux_2d_mean = np.mean(nn_flux_2d_results)\n", - "#nn_flux_2u_mean = np.mean(nn_flux_2u_results)\n", - "#nn_flux_4d_mean = np.mean(nn_flux_4d_results)\n", - "#nn_flux_4u_mean = np.mean(nn_flux_4u_results)\n", - "#li_flux_mean = np.mean(li_flux_results)\n", - "#gp_matern_32_flux_mean = np.mean(gp_matern_32_flux_results)\n", - "#gp_matern_52_flux_mean = np.mean(gp_matern_52_flux_results)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating mean spectra\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Calculating mean spectra\")\n", "\n", @@ -893,17 +565,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating mean spectra\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Calculating mean spectra\")\n", "\n", @@ -918,32 +582,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Exponential\n", - "[ 5.13662935e-05 6.16229323e+01]\n", - "[ 1.58303512e-06 6.09446228e-01]\n", - "[ 9.93519969e-08 1.22736736e-01]\n", - "Squared Exponential\n", - "[ 1.65498340e-04 9.66212252e+00]\n", - "[ 3.65785857e-05 2.14345205e+00]\n", - "[ 9.93448226e-06 1.18819618e+00]\n", - "Matern 3/2\n", - "[ 9.21177307e-04 2.56823430e+02]\n", - "[ 6.58366258e-05 1.60376403e+01]\n", - "[ 3.49375175e-05 3.69936584e+01]\n", - "Matern 5/2\n", - "[ 2.75322559e-04 3.60135671e+01]\n", - "[ 8.09316365e-05 8.94425269e+00]\n", - "[ 3.38563023e-05 5.70794355e+00]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "#Exponential Kernel 1st 3\n", "print('Exponential')\n", @@ -969,23 +610,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0725325573089\n", - "0.0334792084461\n", - "0.0376008675272\n", - "0.0320099044064\n", - "0.0969591905896\n", - "0.0922874808829\n", - "0.0962248898827\n" - ] - } - ], + "outputs": [], "source": [ "print(gp_exp_flux_mean)\n", "print(gp_sq_exp_flux_mean)\n", @@ -998,47 +625,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0725284479597\n", - "0.0334778622608\n" - ] - } - ], - "source": [ - "print(gp_exp_flux_mean)\n", - "print(gp_sq_exp_flux_mean)\n", - "#print(gp_matern_32_flux_mean)\n", - "#print(gp_matern_52_flux_mean)\n", - "#print(nn_flux_u_mean)\n", - "#print(nn_flux_2d_mean)\n", - "#print(li_flux_mean)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.040415899425\n", - "0.0208915343935\n", - "0.0262856974878\n", - "0.0210929388887\n", - "0.0738637877199\n", - "0.061807920037\n", - "0.0665429635533\n" - ] - } - ], + "outputs": [], "source": [ "gp_exp_argsort = np.argsort(gp_exp_flux_mean_dist)\n", "gp_exp_trim_25_idx = gp_exp_argsort[6250:-6250]\n", @@ -1071,28 +660,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", - " warnings.warn(message, mplDeprecation, stacklevel=1)\n" - ] - }, - { - "data": { - "image/png": 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HXHQzyQkhREB54IEHWLRoER988AH33HNPufVNmzYFoHHjxn4NlvbZZ58B5ojx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fd16//aa6/xwAMP8MADD/T4v/r666+zaNEiHnvssaDI37t3L1dccQUfffQRW7Zs6bQQ9cknn/D4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\nnPaaGoQ1AqHkcfmOkEgkEonkzHFobyRnWzJL+tDyO90kGjHYs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wcBUI3cBWEBaN/8BcbP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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(14, 6))\n", "\n", @@ -1120,28 +690,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", - " warnings.warn(message, mplDeprecation, stacklevel=1)\n" - ] - }, - { - "data": { - "image/png": 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Z01YBXOMjnd2822etqRs7rT/bDXTbwUrCbJXQF/OP3kzy+n/7cszl376Ox5MumMeZiTkw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/I8BAR6/AO3gIJp3+iuxxOo61ZlNcW5+kK/YTkFunnx6f73LUzAhnnQ/5FZjdH0+oo49Dfv/o0X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\np6GMzoHs3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Dob13pvO3s+vu//EuW872SWVVZp+3UShDOfVbc88AZbvXQ7CZ4+O/PZg7df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\ne5Y91cDdJ5nZGKKken4ik/68md0OPAy8Q/S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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(14, 6))\n", "\n", @@ -1166,20 +717,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0HqoKDvSvaXrN4aUPr3twTfP2kiRJkqYOgwNSO5Y9Cptunj1fuxqWPw5AvPHa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cd+N9aFXges4epzJW+W/3Ul2rudK3ft8yvXd5zP0xnJ4HpMzI5GjTEXjTHtsFYlGIJ14Ws8ft8h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HDM1h9L41X5/klERERERETql4IDpd1KDA6cSmzW/wHnXCNwcrL7xwrLPBM42Tn3n977jpzz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\nRvpHcur6HLBlnnQHAqtT6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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(14,12))\n", "\n", @@ -1210,56 +750,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/esp/lsst_utils/Sed.py:1399: RuntimeWarning: divide by zero encountered in log10\n", - " mags = -2.5*numpy.log10(fluxes) - self.zp\n", - "/Users/Bryce/miniconda2/envs/py3/lib/python3.6/site-packages/ipykernel_launcher.py:32: RuntimeWarning: invalid value encountered in double_scalars\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run 20\n", - "Run 40\n", - "Run 60\n", - "Run 80\n", - "Run 100\n", - "Run 120\n", - "Run 140\n", - "Run 160\n", - "Run 180\n", - "Run 200\n", - "Run 220\n", - "Run 240\n", - "Run 260\n", - "Run 280\n", - "Run 300\n", - "Run 320\n", - "Run 340\n", - "Run 360\n", - "Run 380\n", - "Run 400\n", - "Run 420\n", - "Run 440\n", - "Run 460\n", - "Run 480\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "np.random.seed(2314)\n", "\n", @@ -1362,17 +855,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating overall means\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Calculating overall means\")\n", "\n", @@ -1387,23 +872,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.443852308433\n", - "0.803646103625\n", - "0.576154413666\n", - "0.666631915825\n", - "0.270972271859\n", - "0.335837186692\n", - "0.565081429947\n" - ] - } - ], + "outputs": [], "source": [ "print(gp_exp_flux_mean)\n", "print(gp_sq_exp_flux_mean)\n", @@ -1999,7 +1470,7 @@ " \n", " training_colors, training_list, training_names, test_list, test_names, \\\n", " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", - " bandpass_dict, min_wavelen, max_wavelen)\n", + " narrow_bandpass_dict, min_wavelen, max_wavelen)\n", " \n", " new_pca_obj = esp.pcaSED()\n", " new_pca_obj.spec_list_orig = training_list\n", @@ -2013,11 +1484,11 @@ " \n", " #print 'Linear Interp'\n", " li_spec, li_colors = linear_interpolation_spectra(colors, test_colors, new_pca_obj.spec_list_orig, \n", - " bandpass_dict, min_wavelen, max_wavelen)\n", + " narrow_bandpass_dict, min_wavelen, max_wavelen)\n", " li_flux_results[i*50:(i+1)*50] = np.abs(np.array((li_spec - test_fluxes)/test_fluxes))\n", " \n", " #print 'Nearest Neighbor Results'\n", - " nn_obj = esp.nearestNeighborEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " nn_obj = esp.nearestNeighborEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " nn_spec = nn_obj.nn_predict(1)\n", " nn_flux_u_results[i*50:(i+1)*50] = np.abs(np.array((nn_spec.reconstruct_spectra(10) - test_fluxes)/test_fluxes))\n", "\n", @@ -2031,30 +1502,30 @@ " #nn_spec = nn_obj.nn_predict(4, knr_args=dict(weights='distance'))\n", " \n", " #print 'Gaussian Process Results'\n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", - " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict)\n", " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", " test_exp_params.append(gp_spec.params)\n", " \n", " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", - " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict)\n", " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", " test_sq_exp_params.append(gp_spec.params)\n", " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", - " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict)\n", " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", " test_matern_32_params.append(gp_spec.params)\n", " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", - " gp_spec = gp_obj.gp_predict(gp_kernel, bandpass_dict)\n", + " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict)\n", " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", " gp_matern_52_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", " test_matern_52_params.append(gp_spec.params)\n", @@ -2228,7 +1699,7 @@ " \n", " training_colors, training_list, training_names, test_list, test_names, \\\n", " test_fluxes, test_colors = choose_training_and_test_set(pca_obj, \n", - " bandpass_dict, min_wavelen, max_wavelen)\n", + " narrow_bandpass_dict, min_wavelen, max_wavelen)\n", " \n", " new_pca_obj = esp.pcaSED()\n", " new_pca_obj.spec_list_orig = training_list\n", @@ -2241,28 +1712,28 @@ " distances_all.append(np.ravel(distance))\n", " \n", " #print 'Gaussian Process Results'\n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('exp', 1.0e-1, 1.0e-2, n_colors)\n", " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", " gp_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((gp_spec.reconstruct_spectra(n_comps) - test_fluxes)/test_fluxes))#, \n", " test_exp_params.append(gp_spec.params)\n", " \n", " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('sq_exp', 1.0e-3, 1.0e-2, n_colors)\n", " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", " gp_sq_exp_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))#, \n", " test_sq_exp_params.append(gp_spec.params)\n", " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('matern_32', 1.0e-3, 1.0e-2, n_colors)\n", " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", " gp_matern_32_flux_results[i*50:(i+1)*50] = np.abs(np.array((recon_spectra - test_fluxes)/test_fluxes))\n", " test_matern_32_params.append(gp_spec.params)\n", " \n", - " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, bandpass_dict, test_colors)\n", + " gp_obj = esp.gaussianProcessEstimate(new_pca_obj, narrow_bandpass_dict, test_colors)\n", " gp_kernel = gp_obj.define_kernel('matern_52', 1.0e-3, 1.0e-2, n_colors)\n", " gp_spec = gp_obj.gp_predict(gp_kernel, narrow_bandpass_dict_training)\n", " recon_spectra = gp_spec.reconstruct_spectra(n_comps)\n", @@ -2496,7 +1967,7 @@ " new_sed_obj.setSED(wavelen=10.*sed_obj.wavelen, flambda=0.1*sed_obj.flambda)\n", " new_sed_obj.writeSED(str(template_folder + '/ESP_60/' + sed_obj.name[:-3] + '.sed'))\n", " sed_names.append('ESP_60/' + sed_obj.name[:-3] + '.sed')\n", - "np.savetxt(str(template_folder + '/ESP_60/' + 'seds.list'), sed_names, fmt=['%s'])" + "#np.savetxt(str(template_folder + '/ESP_60/' + 'seds.list'), sed_names, fmt=['%s'])" ] }, { diff --git a/setup.py b/setup.py index f0781bd..b52b678 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="esphot", - version="0.1.0", + version="0.2.0", author="Bryce Kalmbach", author_email="brycek@uw.edu", url="https://github.com/jbkalmbach/esp", From 8e5fff8779af405b634034cc68bff46a1d8d1141 Mon Sep 17 00:00:00 2001 From: Bryce Kalmbach Date: Mon, 30 Oct 2017 10:08:03 -0700 Subject: [PATCH 7/7] adjusted setup.py for tagging. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index b52b678..cc0f637 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="esphot", - version="0.2.0", + version="0.4.0", author="Bryce Kalmbach", author_email="brycek@uw.edu", url="https://github.com/jbkalmbach/esp",