diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart.json index bfbbb65bb..9a8f5f0ec 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 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at end of file +[[27894, 3604.928358528763], [27924, 3586.559706948232], [27939, 3549.8567470521666], [27965, 3597.1737629221752], [27966, 3575.1558883991092], [28013, 3547.8353902902454], [28051, 3432.334278382361], [28055, 3427.4291054764763], [28094, 3394.7851578714326], [28120, 3370.9739350583404], [28216, 3394.90765146818], [28237, 3588.808441876434], [28256, 3373.7033070819452], [28261, 3367.7887555854395], [28301, 3453.8145619053394], [28308, 3390.51126215799], [28343, 3418.4867323770013], [28363, 3426.2354968898], [28381, 3375.5980877930997], [28427, 3392.6985422649886], [28440, 3455.813546864083], [28445, 3391.1685630921274], [28486, 3396.761637832038], [28506, 3387.224620066001], [28525, 3400.452786025009], [28635, 3424.5619713319466], [28652, 3396.8304532361217], [28682, 3371.875025499612], [28713, 3370.254342573695], [28720, 3366.540217061527], [28739, 3386.4667248763144], [28775, 3407.450411084108], [28809, 3400.517090455047], [28844, 3354.5249613330234], [28855, 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4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.ApplyModes.time_applycal_cal_only_mode.json @@ -1 +1 @@ -[[27906, 7.6052802429767326], [27943, 6.722829051082954], [27960, 7.142801976995543], [28051, 6.612799868918955], [28055, 7.925020662136376], [28094, 6.453071428462863], [28120, 7.319281288422644], [28216, 6.966307133436203], [28237, 7.004389754496515], [28256, 7.288860173895955], [28261, 7.150962912477553], [28301, 7.192464338615537], [28308, 6.631827593984781], [28343, 6.715642978000687], [28363, 7.013514026999474], [28381, 6.7230227360269055], [28427, 6.843102799961343], [28440, 7.068732938030735], [28445, 7.035850760061294], [28486, 7.274885288905352], [28506, 6.976375478028785], [28525, 6.546166319050826], [28635, 6.802171139977872], [28652, 7.228416292928159], [28682, 7.055910209193826], [28713, 6.944482007995248], [28720, 6.437127801589668], [28739, 7.216373715549707], [28775, 7.115248932503164], [28809, 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100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.FlagBackup.time_applycal_flag_backup.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.FlagBackup.time_applycal_flag_backup.json @@ -1 +1 @@ -[[27906, 7.604426039964892], [27943, 6.716619002865627], [27960, 7.820006718160585], [28051, 7.437906766310334], [28055, 7.736891538836062], [28094, 7.603838457725942], [28120, 7.307908853515983], [28216, 7.3664147881791], [28237, 7.537168751470745], [28256, 7.546338082291186], [28261, 7.104570330120623], [28301, 7.1863320749253035], [28308, 7.018201597995358], [28343, 7.096586496001692], [28363, 7.348996894434094], 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diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.GainField.time_applycal_gainfield.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.GainField.time_applycal_gainfield.json index 27e4bfce1..a720e6d8c 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.GainField.time_applycal_gainfield.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_field.json index fa18d14e3..98a7e3d39 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_field.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_field.json @@ -1 +1 @@ -[[27906, 4.843617498001549], [27943, 5.145377141889185], [27960, 4.47577218990773], [28051, 5.017746261321008], [28055, 4.764437367208302], [28094, 4.653315130621195], [28120, 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E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_spw.json index 8cf99390f..03ee9e8f6 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_spw.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_spw.json @@ -1 +1 @@ -[[27906, 3.516542188997846], [27943, 3.4918718389235437], [27960, 3.350737303029746], [28051, 3.496822874993086], [28055, 3.9140862366184592], [28094, 3.342769756913185], 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--git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_uvrange.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_uvrange.json index a0898173a..4d14e4534 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_uvrange.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SpwMap.time_applycal_spwmap.json index 2b8dd7dd8..74bb225c8 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SpwMap.time_applycal_spwmap.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_applycal.SpwMap.time_applycal_spwmap.json @@ -1 +1 @@ -[[27906, 7.318560337997042], [27943, 6.927527535939589], [27960, 7.131662771105766], [28051, 7.719226907007396], [28055, 7.814301420934498], [28094, 7.780497215688229], [28120, 7.669831903651357], [28216, 7.3907502591609955], 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_flagdata.RflagMode.time_rflag_numpy_types.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_flagdata.RflagMode.time_rflag_numpy_types.json index d6682f5c8..caf052a41 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_flagdata.RflagMode.time_rflag_numpy_types.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_gaincal.GaincalSelections.time_gaincal_refantSelect.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_gaincal.GaincalSelections.time_gaincal_refantSelect.json @@ -1 +1 @@ -[[27906, 22.44359128200449], [27943, 21.441697746980935], [27960, 21.560813470976427], [28051, 19.887262648902833], [28055, 21.834414797835052], [28094, 20.53910975344479], [28120, 20.35110928118229], [28216, 21.231500008143485], [28237, 20.327362241223454], [28256, 21.19719741679728], [28261, 19.723802748136222], [28301, 21.073264060541987], [28308, 19.077246058004675], [28343, 21.973600268000155], [28363, 21.721717543900013], [28381, 19.799739449983463], [28427, 19.918864988023415], [28440, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Alma_12m.time_default_lazy.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Alma_12m.time_default_lazy.json @@ -1 +1 @@ -[[28427, 14.282431726518553], [28440, 13.577434272388928], [28445, 13.98673615208827], [28486, 13.874168269336224], [28506, 13.746138081536628], [28525, 13.574928566464223], [28635, 13.42367694620043], [28652, 13.919927636859939], [28682, 13.646386346314102], [28713, 13.571366808377206], [28720, 13.608451258856803], [28739, 13.902814043685794], [28775, 14.209245926234871], [28809, 13.894737848022487], [28844, 14.362170762848109], [28855, 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+++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.AutocorrASDM.time_applyflags_true.json @@ -1 +1 @@ -[[28427, 3.3779656579717994], [28440, 3.207887508557178], [28445, 3.5828178625088185], [28486, 3.428337085293606], [28506, 3.4396810739999637], [28525, 3.635158480028622], [28635, 3.791903601260856], [28652, 3.606577510247007], [28682, 3.654713960364461], [28713, 3.281759037170559], [28720, 3.5695716165937483], [28739, 3.54636325314641], [28775, 3.1779914032667875], [28809, 3.641339489025995], [28844, 3.274316164548509], [28855, 3.564463732065633], [28875, 3.559193619992584], [28882, 3.6669918147381395], [28898, 3.4279224164783955], [28935, 3.83840393088758], [28954, 3.266859420342371], [28957, 3.772096489323303], [28959, 3.20662560663186], [28968, 3.4052255309652537], [29003, 3.487898307852447], 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Basic.time_pc_true.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Basic.time_pc_true.json index 23c2b0938..ffa568ff2 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Basic.time_pc_true.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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at end of file diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Evla.time_pc_true.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Evla.time_pc_true.json index e5e45406e..7518f43e0 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Evla.time_pc_true.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply.time_spwmap_dict_weight_spectrum.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply.time_spwmap_dict_weight_spectrum.json index ecdb74eca..df7273f23 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply.time_spwmap_dict_weight_spectrum.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Interp.time_linear_spw_selection.json index 616289351..13ec5bb04 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Interp.time_linear_spw_selection.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Interp.time_linear_spw_selection.json @@ -1 +1 @@ -[[27906, 1.0306346469733398], [27943, 0.9697724370053038], [27960, 0.8687413381412625], [28051, 1.011883795261383], [28055, 1.0409088488668203], [28094, 0.9924471932463348], [28120, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Modified_Tsys.time_apply_sky.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Modified_Tsys.time_apply_sky.json index 35d0707b3..0a99f2c8e 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Modified_Tsys.time_apply_sky.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal.json index a59d26765..d2d15861e 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal.json @@ -1 +1 @@ -[[27906, 1.608631836483255], [27943, 1.6331182464491576], [27960, 1.5107749404851347], [28051, 1.5638206591829658], [28055, 1.6223833193071187], [28094, 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTF.time_otf01.json index aa57be400..dbcf475a4 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTF.time_otf01.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTF.time_otf01.json @@ -1 +1 @@ -[[27906, 0.4180924969841726], [27943, 0.4408743503736332], [27960, 0.39543916552793235], [28051, 0.4289841395802796], [28055, 0.4304681452922523], [28094, 0.3897068337537348], [28120, 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diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_custom.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_custom.json index fe65863de..338312c82 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_custom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_BOX_start_400.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_BOX_start_400.json @@ -1 +1 @@ -[[27906, 2.6867452664882876], [27943, 2.393654354964383], [27960, 2.245844949968159], [28051, 2.2507481877692044], [28055, 2.1112031736411154], [28094, 2.415855400264263], [28120, 2.6304495790973306], [28216, 2.201710338704288], [28237, 2.9269058629870415], [28256, 2.4081372106447816], [28261, 2.1708875307813287], [28301, 2.696206391789019], [28308, 2.3939534520031884], [28343, 2.510740078498202], [28363, 2.1701265033334494], 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024.json @@ -1 +1 @@ -[[27906, 11.193947552004829], [27943, 11.679729159921408], [27960, 10.926401298027486], [28051, 10.488163593225181], [28055, 10.455362675711513], [28094, 10.819636803120375], [28120, 10.384972460567951], [28216, 10.952980707399547], [28237, 10.941953411325812], [28256, 10.876396498642862], [28261, 11.043104353360832], [28301, 10.714941274374723], [28308, 10.480580400995677], [28343, 10.863464637004654], [28363, 11.015955043956637], [28381, 10.66189156495966], [28427, 10.565465667983517], [28440, 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--git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_PB_start_400.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_PB_start_400.json index 4c81bd171..59d5b92c9 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_PB_start_400.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom.json index 236ca86e1..788792061 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom.json @@ -1 +1 @@ -[[27906, 228835328.0], [27943, 225099776.0], [27960, 217436160.0], [28051, 218091520.0], [28055, 219185152.0], [28094, 225722368.0], [28120, 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E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json index ad85224d5..0bc84ed51 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json @@ -1 +1 @@ -[[27906, 128.0], [27943, 128.0], [27960, 128.0], [28051, 128.0], [28055, 128.0], 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json index 073e836e6..21dc3406a 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json @@ -1 +1 @@ -[[27906, 240062464.0], [27943, 232976384.0], [27960, 232771584.0], [28051, 229650432.0], [28055, 227774464.0], [28094, 231616512.0], [28120, 234868736.0], [28216, 233549824.0], [28237, 229113856.0], [28256, 229093376.0], [28261, 225660928.0], [28301, 239026176.0], [28308, 234979328.0], [28343, 229408768.0], [28363, 237760512.0], [28381, 232632320.0], [28427, 224878592.0], [28440, 230842368.0], [28445, 242561024.0], [28486, 228982784.0], [28506, 238743552.0], [28525, 233025536.0], [28635, 234311680.0], [28652, 238329856.0], [28682, 231899136.0], [28713, 235012096.0], [28720, 231931904.0], [28739, 230215680.0], [28775, 231432192.0], [28809, 228442112.0], [28844, 235057152.0], [28855, 239501312.0], [28875, 232439808.0], [28882, 233000960.0], [28898, 236994560.0], [28935, 234512384.0], [28954, 230682624.0], 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4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield.json index 944706f76..eb91bb9ff 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json @@ -1 +1 @@ -[[27906, 491024384.0], [27943, 483741696.0], [27960, 484945920.0], [28051, 485920768.0], [28055, 485855232.0], [28094, 489152512.0], [28120, 485920768.0], [28216, 487645184.0], [28237, 484503552.0], [28256, 486998016.0], [28261, 486240256.0], [28301, 489492480.0], [28308, 487796736.0], [28343, 483106816.0], [28363, 491032576.0], [28381, 486064128.0], [28427, 485539840.0], [28440, 486121472.0], [28445, 485560320.0], [28486, 486268928.0], [28506, 490885120.0], [28525, 484229120.0], [28635, 482664448.0], [28652, 490053632.0], [28682, 483643392.0], [28713, 489046016.0], [28720, 486936576.0], [28739, 486981632.0], [28775, 489467904.0], [28809, 482975744.0], [28844, 487096320.0], [28855, 485433344.0], [28875, 485441536.0], [28882, 479334400.0], 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom.json index 106982db6..c1d912b9b 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanCube.time_cube_standard_fullsummary.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanCube.time_cube_standard_fullsummary.json index b1c3fbdc4..f55a0664e 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanCube.time_cube_standard_fullsummary.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 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CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json index 258cf5b10..f1838e74d 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json @@ -1 +1 @@ -[[27906, 2.294040261011105], [27943, 2.291468285024166], [27960, 2.2706877890741453], [28051, 2.266323435585946], [28055, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json @@ -1 +1 @@ -[[27906, 18.335549775976688], [27943, 16.91023803781718], [27960, 18.929513417882845], [28051, 18.451672458089888], [28055, 17.335283741354942], [28094, 18.706730869598687], [28120, 17.316141437739134], [28216, 17.761068816296756], [28237, 17.136012079194188], [28256, 17.309446492232382], [28261, 17.479181964881718], [28301, 16.996742570772767], [28308, 17.816902288992424], [28343, 17.7695782899973], [28363, 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom.json index ca2f50994..c03a320b1 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom.json @@ -1 +1 @@ -[[27906, 11.576460905984277], [27943, 11.874890332459472], [27960, 12.126478480058722], 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index 3eda23c7d..073c3a6f4 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom.json @@ -1 +1 @@ -[[27906, 7.852607278502546], [27943, 7.860365165048279], [27960, 7.81541484745685], [28051, 7.839755128603429], [28055, 7.751741772983223], [28094, 7.638185161165893], [28120, 7.872746298555285], [28216, 7.551012942101806], [28237, 7.8393663596361876], [28256, 7.7466001603752375], [28261, 7.879129664041102], [28301, 7.871564350090921], [28308, 7.713127384515246], [28343, 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_clark.json index 79a77e634..8330a79ee 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_clark.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_clark.json @@ -1 +1 @@ -[[27906, 12.883350424002856], [27943, 12.40398064116016], [27960, 12.541239485959522], [28051, 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index c6d531a3b..59972102a 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom.json @@ -1 +1 @@ -[[27906, 11.856788070988841], [27943, 10.96922794287093], [27960, 12.002732931170613], [28051, 11.998343176208436], [28055, 12.713539787568152], [28094, 11.893555829301476], [28120, 12.411541612818837], [28216, 10.277538105845451], [28237, 12.23283280339092], [28256, 12.092028077691793], [28261, 11.345314046368003], [28301, 10.487912863492966], [28308, 11.256345626490656], 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs.json index cb9d9afe2..8851600c0 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mfs_stokes_IV.json index 112ed52a2..afe1169a1 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mfs_stokes_IV.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mfs_stokes_IV.json @@ -1 +1 @@ -[[27906, 2.556216853496153], [27943, 2.58254324097652], [27960, 2.592361458344385], [28051, 2.5016577495262027], [28055, 2.493289525154978], [28094, 2.5159713686443865], [28120, 2.513727271463722], 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4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV.json @@ -1 +1 @@ -[[27906, 7.000316508987453], [27943, 7.011502246023156], [27960, 7.077581702033058], [28051, 6.85890085529536], [28055, 6.833941692486405], [28094, 6.806868098210543], [28120, 6.908995950594544], [28216, 7.009030951652676], [28237, 6.845142176374793], [28256, 6.788053644821048], [28261, 7.309935818426311], [28301, 7.300737835466862], [28308, 7.152870346981217], [28343, 7.107547268504277], [28363, 7.100789438933134], [28381, 7.1945788659504615], [28427, 7.116237431473564], [28440, 7.077869469532743], [28445, 7.132832101429813], [28486, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs.json @@ -1 +1 @@ -[[27906, 108.64864844002295], [27943, 109.0648281420581], [27960, 108.99495732598007], [28051, 104.62776040099561], [28055, 103.65681163873523], [28094, 102.9007882187143], [28120, 103.49428031314164], [28216, 103.61788587085903], [28237, 103.27190653048456], [28256, 103.29649932775646], [28261, 103.5151297589764], [28301, 102.7679047267884], [28308, 104.30595048100804], [28343, 105.05538886899012], [28363, 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end of file diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield.json index 34f77149f..b9399d205 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400.json @@ -1 +1 @@ -[[27906, 2.7078002149937674], [27943, 2.4480818409938365], [27960, 2.474394618999213], [28051, 2.8394794454798102], [28055, 3.3050784273073077], [28094, 3.3407899206504226], [28120, 3.373798563145101], [28216, 2.9066788759082556], [28237, 2.4245969699695706], [28256, 2.360803572461009], [28261, 2.4243926689960063], [28301, 2.654006310738623], [28308, 2.77588948700577], [28343, 2.6652274385050987], [28363, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tsdimaging.Basic.time_gridfunction_PB_width_1024.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tsdimaging.Basic.time_gridfunction_PB_width_1024.json @@ -1 +1 @@ -[[27906, 2.9224037765234243], [27943, 2.8745994499186054], [27960, 3.2179991545854136], [28051, 3.4516355572268367], [28055, 3.6330959615297616], [28094, 3.4522430398501456], [28120, 3.361122200731188], [28216, 3.003461738117039], [28237, 2.919858021661639], [28256, 3.230909613892436], [28261, 3.0180310350842774], [28301, 3.0539738461375237], [28308, 3.1457034924969776], [28343, 3.1340861514981952], [28363, 3.061037109233439], 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CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_datacolumn.json index 9c60e1deb..4b141f416 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_datacolumn.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_datacolumn.json @@ -1 +1 @@ -[[27906, 0.811661320971325], [27943, 0.8172163001727313], [27960, 0.8269804429728538], [28051, 0.8004209231585264], [28055, 0.800886694341898], [28094, 0.8194760140031576], [28120, 0.8014585850760341], [28216, 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitorder1.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitorder1.json @@ -1 +1 @@ -[[27906, 0.5684763670433313], [27943, 0.5809467090293765], [27960, 0.5843614500481635], [28051, 0.5672403061762452], [28055, 0.5656701158732176], [28094, 0.5671842824667692], [28120, 0.565867037512362], [28216, 0.5516621926799417], [28237, 0.5698917238041759], [28256, 0.5725457202643156], [28261, 0.5965027408674359], [28301, 0.5725543461740017], [28308, 0.5761728700017557], [28343, 0.5728349679993698], [28363, 0.5674138087779284], [28381, 0.571804104023613], [28427, 0.5711371150100604], [28440, 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json index 309b2c696..d322677d4 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json @@ -1 +1 @@ -[[27906, 0.8865680110175163], [27943, 0.8901407290250063], [27960, 0.8722101910971105], 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file diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_multifield.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_multifield.json index bcfc71b51..783c6f460 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_multifield.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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36e43e838..a6f7353b0 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel.json @@ -1 +1 @@ -[[27906, 0.955820607021451], [27943, 0.9708014959469438], [27960, 0.9804785088635981], [28051, 0.9511489002034068], [28055, 0.9508376745507121], [28094, 0.9522800436243415], [28120, 0.9601711519062519], [28216, 0.9642667314037681], [28237, 0.9459192818030715], [28256, 0.9352901559323072], [28261, 0.940706592053175], [28301, 0.9405389428138733], [28308, 0.9587178560032044], [28343, 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diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_separate_fields.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_separate_fields.json index 7be0d6d98..f8ee0feb9 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_separate_fields.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spw_one_chan.json index 4c2b1bfcb..40a1ad056 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spw_one_chan.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spw_one_chan.json @@ -1 +1 @@ -[[27906, 0.8625555509934202], [27943, 0.8437357430811971], [27960, 0.8909037651028484], [28051, 0.8715018490329385], [28055, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spws.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spws.json @@ -1 +1 @@ -[[27906, 0.5616305989678949], [27943, 0.5754292858764529], [27960, 0.5737096338998526], [28051, 0.5518500385805964], [28055, 0.553232217207551], [28094, 0.5600914377719164], [28120, 0.5573026835918427], [28216, 0.5560262212529778], [28237, 0.5571948438882828], [28256, 0.5572244059294462], [28261, 0.5718460539355874], [28301, 0.5606891810894012], [28308, 0.556605645979289], [28343, 0.5592643139971187], [28363, 0.5558459460735321], [28381, 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel.json index b0da1e65a..52dd2502d 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel_from_corrected.json index 83fbc582c..583cfc02a 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel_from_corrected.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel_from_corrected.json @@ -1 +1 @@ -[[27906, 0.9009758749743924], [27943, 0.8272229840513319], [27960, 0.9191502088215202], [28051, 0.948769835755229], [28055, 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a/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json b/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json index 51dd0b3a8..da4790abe 100644 --- a/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json +++ b/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json @@ -1 +1 @@ -[[22825, 0.23469004314392805], [23735, 0.2510405918583274], [24188, 0.22881285659968853], [24958, 0.22517723497003317], [25613, 0.20489393081516027], [25614, 0.21214973041787744], [25651, 0.2125429678708315], [25733, 0.20723612699657679], [25778, 0.22303252015262842], [25803, 0.21464633895084262], [25823, 0.21603205054998398], [25833, 0.2372902324423194], [25854, 0.2275311453267932], [25889, 0.22019712487235665], [25934, 0.2266061082482338], [25978, 0.24517985014244914], [25993, 0.22695804433897138], [26027, 0.24016954889521003], [26050, 0.2129596071317792], [26109, 0.2550920844078064], [26166, 0.22816911805421114], [26243, 0.22789178881794214], [26258, 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a/graphs/summary/bench_gaincal.GaincalSelections.time_gaincal_antennaSelect.json b/graphs/summary/bench_gaincal.GaincalSelections.time_gaincal_antennaSelect.json index 4bd0083e1..a0fd42216 100644 --- a/graphs/summary/bench_gaincal.GaincalSelections.time_gaincal_antennaSelect.json +++ b/graphs/summary/bench_gaincal.GaincalSelections.time_gaincal_antennaSelect.json @@ -1 +1 @@ -[[23735, 3.3919529439881444], [24188, 3.428779258392751], [24958, 3.4930923972278833], [25613, 3.5193356471136212], [25614, 3.500309200026095], [25651, 3.433755290694535], [25733, 3.5088934330269694], [25778, 3.5293096671812236], [25803, 3.5795260528102517], [25823, 3.6209451309405267], [25833, 3.551681029610336], [25854, 3.55170061532408], [25889, 3.614197517745197], [25934, 3.5358230494894087], [25978, 3.535439459607005], [25993, 3.47656524553895], [26027, 3.5351631436496973], [26050, 3.50844096345827], [26109, 3.516262832097709], [26166, 3.5272069051861763], [26243, 3.6233958108350635], [26258, 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a/graphs/summary/bench_importasdm.Evla.time_process_flags_savecmds_true.json b/graphs/summary/bench_importasdm.Evla.time_process_flags_savecmds_true.json index 7f41c0cd6..552f63a44 100644 --- a/graphs/summary/bench_importasdm.Evla.time_process_flags_savecmds_true.json +++ b/graphs/summary/bench_importasdm.Evla.time_process_flags_savecmds_true.json @@ -1 +1 @@ -[[28427, 1.3690431289724074], [28440, 1.3560922050382942], [28445, 1.5301781139569357], [28486, 1.3539795482065529], [28506, 1.7160119605250657], [28525, 1.6394433749956079], [28635, 1.3833396714180708], [28652, 1.550436282530427], [28682, 1.5234927362762392], [28713, 1.3828792669731116], [28720, 1.6648113730005263], [28739, 1.6700291252502237], [28775, 1.472994453781719], [28809, 1.4434156537960023], [28844, 1.6341217211900807], [28855, 1.7228822096758323], [28875, 1.4269136605176638], [28882, 1.7170794732670314], [28898, 1.4258161456768472], [28935, 1.4919629499962146], [28954, 1.6681210232307935], [28957, 1.4304985949656293], 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a/graphs/summary/bench_importasdm.Evla.time_process_flags_true_applyflags_false.json b/graphs/summary/bench_importasdm.Evla.time_process_flags_true_applyflags_false.json index 5b0812241..f6934235e 100644 --- a/graphs/summary/bench_importasdm.Evla.time_process_flags_true_applyflags_false.json +++ b/graphs/summary/bench_importasdm.Evla.time_process_flags_true_applyflags_false.json @@ -1 +1 @@ -[[28427, 1.506590838485863], [28440, 1.4998785320203751], [28445, 1.3407982169883326], [28486, 1.748443342745304], [28506, 1.7859229225141462], [28525, 1.7867515734978952], [28635, 1.957658724160865], [28652, 1.2772870110347867], [28682, 1.4702534405514598], [28713, 1.4606314391459623], [28720, 1.6852360950649536], [28739, 1.2591882678406594], [28775, 1.3426028654533735], [28809, 1.7689208403699972], [28844, 1.463479829848258], [28855, 1.4101976112757395], [28875, 1.6739139862443042], [28882, 1.6120347364409227], [28898, 1.31602408346734], [28935, 1.6126901818600483], [28954, 1.311929109094116], 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a/graphs/summary/bench_importasdm.Evla.time_scan_selection.json b/graphs/summary/bench_importasdm.Evla.time_scan_selection.json index 226bdb143..fe6ec40b3 100644 --- a/graphs/summary/bench_importasdm.Evla.time_scan_selection.json +++ b/graphs/summary/bench_importasdm.Evla.time_scan_selection.json @@ -1 +1 @@ -[[28427, 1.336264553538058], [28440, 1.5440266884397715], [28445, 1.154044616036117], [28486, 1.59667790052481], [28506, 1.2460235464968719], [28525, 1.6574526060721837], [28635, 1.3820715614128858], [28652, 1.194998720427975], [28682, 1.2366119213402271], [28713, 1.2454674326734332], [28720, 1.1603632588501092], [28739, 1.549053279168442], [28775, 1.1871053767501367], [28809, 1.1326955693388034], [28844, 1.342270216601269], [28855, 1.2395079938614737], [28875, 1.2170502240026637], [28882, 1.335709885575499], [28898, 1.1746275456201756], [28935, 1.3173651263501882], [28954, 1.3968819976868387], [28957, 1.3361756897944708], [28959, 1.4521218761407608], [28968, 1.4826395542429105], 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a/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict.json b/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict.json index 0012a04f9..c3db9320b 100644 --- a/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict.json +++ b/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict.json @@ -1 +1 @@ -[[27690, 10.902711428701878], [27718, 10.618831016123295], [27732, 10.825812941417098], [27772, 10.693372214213014], [27804, 10.786746075376868], [27879, 10.49495855049463], [27906, 10.16676451600506], [27943, 9.725982657400891], [27960, 10.045623985002749], [28051, 9.789093772415072], [28055, 9.838943443726748], [28094, 9.782383766490966], [28120, 9.97360116057098], [28216, 9.850135832093656], [28237, 9.833073667250574], [28256, 9.731615864206105], [28261, 9.842111158650368], [28301, 9.380013574846089], [28308, 9.611824665495078], [28343, 9.939659124494938], [28363, 9.68610801268369], [28381, 10.091590489493683], 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a/graphs/summary/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json b/graphs/summary/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json index 7f6a1fe97..fb602e7f8 100644 --- a/graphs/summary/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json +++ b/graphs/summary/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json @@ -1 +1 @@ -[[27339, 128.0], [27346, 128.00000000000003], [27364, 128.00000000000003], [27412, 128.0], [27428, 128.0], [27447, 128.0], [27482, 128.0], [27495, 128.0], [27541, 128.0], [27562, 128.0], [27568, 128.0], [27607, 128.0], [27616, 128.0], [27621, 128.0], [27690, 128.0], [27718, 128.0], [27732, 128.0], [27772, 128.0], [27804, 128.0], [27879, 128.0], [27906, 128.0], [27943, 128.0], [27960, 128.0], [28051, 128.0], [28055, 128.0], [28094, 128.0], [28120, 128.0], [28216, 128.0], [28237, 128.0], [28256, 128.0], [28261, 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128.0]] \ No newline at end of file diff --git a/graphs/summary/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json b/graphs/summary/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json index 14649a665..bf2513386 100644 --- a/graphs/summary/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json +++ b/graphs/summary/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json @@ -1 +1 @@ -[[27339, 212549632.0], [27346, 208474112.0], [27412, 209498112.0], [27428, 214245376.0], [27447, 201183232.0], [27482, 209510400.0], [27495, 214941696.0], [27541, 200077312.0], [27562, 205291520.0], [27568, 207736832.0], [27607, 203460608.0], [27616, 198656000.0], [27621, 205701120.0], [27690, 205930496.0], [27718, 200130560.0], [27732, 206184448.0], [27772, 197545984.0], [27804, 212000768.0], [27879, 206213120.0], [27906, 240062464.0], [27943, 232976384.0], [27960, 232771584.0], [28051, 229650432.0], [28055, 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--git a/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json b/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json index af4f058a9..d957c060e 100644 --- a/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json +++ b/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json @@ -1 +1 @@ -[[27339, 378523648.0], [27346, 364440507.0028004], [27364, 370471503.45980847], [27412, 388329472.0], [27428, 385847296.0], [27447, 378003456.0], [27482, 382169088.0], [27495, 385925120.0], [27541, 378351616.0], [27562, 382504960.0], [27568, 380366848.0], [27607, 380399616.0], [27616, 447672320.0], [27621, 447684608.0], [27690, 447561728.0], [27718, 454320128.0], [27732, 452395008.0], [27772, 450994176.0], [27804, 449011712.0], [27879, 454205440.0], [27906, 491024384.0], [27943, 483741696.0], 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a/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json b/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json index de8624965..5cca04d45 100644 --- a/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json +++ b/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json @@ -1 +1 @@ -[[23735, 2.028593256138265], [24188, 1.8249030988663435], [24958, 1.9028341146185994], [25613, 1.8738793628290296], [25614, 1.7416962911374867], [25651, 2.0393408872187138], [25733, 2.1111620841547847], [25778, 2.0125770033337176], [25803, 2.0936024538241327], [25823, 1.8065551505424082], [25833, 1.7553278310224414], [25854, 1.95851598540321], [25889, 2.1787211117334664], [25934, 1.8783326246775687], [25978, 1.820202274248004], [25993, 1.7814963548444211], [26027, 1.935643978882581], [26050, 1.6711163278669119], [26109, 1.915125388186425], [26166, 2.0463701407425106], [26243, 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diff --git a/graphs/summary/bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom.json b/graphs/summary/bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom.json index 9a9d84dc6..c2ccb5a5c 100644 --- a/graphs/summary/bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom.json +++ b/graphs/summary/bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom.json @@ -1 +1 @@ -[[23735, 7.103942292742431], [24188, 7.259343142621219], [24958, 7.474076869897544], [25613, 7.358478348702192], [25614, 7.589584769215435], [25651, 7.2469412931241095], [25733, 7.238289453089237], [25778, 7.4197237701155245], [25803, 7.936181165743619], [25823, 7.7951655597426], [25833, 8.01437010942027], [25854, 8.035831669345498], [25889, 8.029747849330306], [25934, 7.685713459271938], [25978, 7.722910528071225], [25993, 7.4664864973165095], [26027, 7.7070644167251885], [26050, 7.7735484931617975], [26109, 8.000655611976981], [26166, 7.75479489704594], [26243, 8.181981065310538], [26258, 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a/graphs/summary/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield.json b/graphs/summary/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield.json index ae5ce31ed..609f70f2e 100644 --- a/graphs/summary/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield.json +++ b/graphs/summary/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield.json @@ -1 +1 @@ -[[23735, 10.285945099778473], [24188, 10.375087432563305], [24958, 10.680863078683615], [25613, 10.84683913551271], [25614, 11.122382882982492], [25651, 11.245380887761712], [25733, 11.220053336583078], [25778, 10.482613599859178], [25803, 10.72730821929872], [25823, 10.973310014232993], [25833, 11.325081343762577], [25854, 11.327622107230127], [25889, 10.978542556986213], [25934, 11.714241028763354], [25978, 11.441098814830184], [25993, 11.323252845555544], [26027, 11.235901572741568], [26050, 11.181946623139083], [26109, 11.142833389341831], [26166, 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b/graphs/summary/bench_tsdimaging.Ephemeris.time_ephemeris_table.json index 1b150310f..d95b35002 100644 --- a/graphs/summary/bench_tsdimaging.Ephemeris.time_ephemeris_table.json +++ b/graphs/summary/bench_tsdimaging.Ephemeris.time_ephemeris_table.json @@ -1 +1 @@ -[[23735, 12.555862406268716], [24188, 12.478211178444326], [24958, 12.859906505793333], [25613, 12.686908954754472], [25614, 12.916843818500638], [25651, 12.809186598286033], [25733, 13.128981128334999], [25778, 12.931621844414622], [25803, 12.86544178519398], [25823, 12.927534866612405], [25833, 12.979782012756914], [25854, 13.078590753953904], [25889, 13.0497279711999], [25934, 12.96124362712726], [25978, 12.553556122817099], [25993, 12.560774880461395], [26027, 13.088067427277565], [26050, 12.309496726840734], [26109, 12.853871215134859], [26166, 12.504153088200837], [26243, 13.132535948883742], [26258, 13.037351310718805], [26279, 13.28002694575116], [26333, 12.416981183923781], [26359, 13.018523569684476], [26417, 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a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder2.json b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder2.json index f8c7ea16c..d74d6527d 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder2.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder2.json @@ -1 +1 @@ -[[26614, 0.657091036438942], [26623, 0.5850635087117553], [26637, 0.5299104461446404], [26664, 0.5154589293524623], [26674, 0.6572555396705866], [26681, 0.566450241021812], [26749, 0.5143848918378353], [26761, 0.512305430136621], [26817, 0.6681439401581883], [26827, 0.5252373050898314], [26878, 0.6869166688993573], [26905, 0.4909747978672385], [26949, 0.6652202811092138], [26966, 0.6633706241846085], [27023, 0.6751181874424219], [27025, 0.5167430713772774], [27053, 0.6997608933597803], [27066, 0.6827674712985754], [27074, 0.6059492733329535], [27095, 0.6836937610059977], [27135, 0.6549754962325096], [27141, 0.4665353447198868], [27163, 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\ No newline at end of file diff --git a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield_blocks.json b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield_blocks.json index 203638d94..668836803 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield_blocks.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield_blocks.json @@ -1 +1 @@ -[[26614, 1.1049057580530643], [26623, 1.0760992271825671], [26637, 0.9854835150763392], [26664, 1.0928137870505452], [26674, 0.9720128923654556], [26681, 1.0869595790281892], [26749, 1.095202467404306], [26761, 0.9869310520589352], [26817, 1.1136464783921838], [26827, 1.012497483752668], [26878, 1.092359866015613], [26905, 1.0752029558643699], [26949, 1.1040073307231069], [26966, 0.9791697300970554], [27023, 0.9743475243449211], [27025, 1.0951088033616543], [27053, 0.9663063734769821], [27066, 1.0988909602165222], [27074, 1.0925111528486013], [27095, 0.9282609652727842], [27135, 0.9679516665637493], 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"Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "python": "3.8", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "40f5717bec049678804b23a5fac6b83ef62e8ad7", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "4f5295c7309e931c62f928a03eeadbbb4984cf00", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "85a2546777121b18bd5c0e9137b9b8e55f08cb7c", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "a73a44ba2df3c3b824e866ed60bf0d538e532f76", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "ee69dc1e48962bc3300198d59345eca3624057dd", "pip+casatools": null}], "benchmarks": {"bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart": {"code": "class BaseMosaic:\n def time_mosaic_cube_eph_pcwdT_restart(self):\n \"\"\"Mosaic ephemeris cube imaging with briggsbwtaper - field Venus, spw 45\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img+\"0\", field='Venus', spw=['1'], antenna=['0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46'], scan=['7,11'], intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='corrected', imsize=[480, 420], cell=['0.14arcsec'], phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', nchan=948, start='261.7643758544GHz', width='0.2441755MHz', perchanweightdensity=True, gridder='mosaic', mosweight=True, usepointing=False, pblimit=0.2, deconvolver='hogbom', restoration=False, restoringbeam='common', pbcor=False, weighting='briggs', robust=0.5, npixels=0, niter=0, threshold='0.0mJy', nsigma=0.0, usemask='auto-multithresh', sidelobethreshold=2.0, noisethreshold=4.25, lownoisethreshold=1.5, negativethreshold=15.0, minbeamfrac=0.3, growiterations=50, dogrowprune=True, minpercentchange=1.0, fastnoise=False, savemodel='none', parallel=self.parallel, verbose=True)\n # restart the tclean call to actually perform deconvolution\n ret = tclean(vis=self.msfile, imagename=self.img+\"0\", field='Venus', spw=['1'], antenna=['0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46'],scan=['7,11'], intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='corrected', imsize=[480, 420], cell=['0.14arcsec'], phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', nchan=948, start='261.7643758544GHz', width='0.2441755MHz', perchanweightdensity=True, gridder='mosaic', mosweight=True, usepointing=False, pblimit=0.2, deconvolver='hogbom', restoration=True, restoringbeam='common', pbcor=True, weighting='briggs', robust=0.5, npixels=0, niter=700000, threshold='0.0106Jy', nsigma=0.0, usemask='auto-multithresh', sidelobethreshold=2.0, noisethreshold=4.25, lownoisethreshold=1.5, negativethreshold=15.0, minbeamfrac=0.3, growiterations=50, dogrowprune=True, minpercentchange=1.0, fastnoise=False, restart=True, savemodel='none', calcres=False, calcpsf=False, parallel=self.parallel, verbose=True)\n\n def setup(self):\n self.prepData(\"2018.1.00879.S_tclean.ms\")", "min_run_count": 1, "name": "bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 7200, "type": "time", "unit": "seconds", "version": "CAS-14086", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_cal_only_mode": {"code": "class ApplyModes:\n def time_applycal_cal_only_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calonly')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_cal_only_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e26cd2e6d77235fa8e7c3c5c65b22d4fcdca56bf1c8eb4ac430fd60fa546866e", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_flag_only_mode": {"code": "class ApplyModes:\n def time_applycal_flag_only_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='flagonly')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_flag_only_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a6421f6893712c5910c61b340b2f45a1dd0f5244adb96eaf54cec02b276ccbae", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_flag_only_strict_mode": {"code": "class ApplyModes:\n def time_applycal_flag_only_strict_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='flagonlystrict')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_flag_only_strict_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5d6b2cd5b9cf017fefacf66e0929df5ec228a015989465593e0233cefa48cda", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_strict_mode": {"code": "class ApplyModes:\n def time_applycal_strict_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calflagstrict')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_strict_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "52b7c478c85666778a8b11363dcd53c5c766dadf886b901f04bb8a078224839f", "warmup_time": -1}, "bench_applycal.CalFlag.time_applycal_calflag": {"code": "class CalFlag:\n def time_applycal_calflag(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calflag')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalFlag.time_applycal_calflag", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a1e4de3456a21dcc48909b513d5efa4d90bddd213862d4390fdb67c2df14c381", "warmup_time": -1}, "bench_applycal.CalLibrary.time_applycal_callibrary": {"code": "class CalLibrary:\n def time_applycal_callibrary(self):\n applycal(vis=self.vis, docallib=True, callib=self.callibfile)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalLibrary.time_applycal_callibrary", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "835d4e5c3926e3dc5a23e04e71aa9fb433f77e02f65db1846c88677d01ba2a71", "warmup_time": -1}, "bench_applycal.CalWeight.time_applycal_calweight": {"code": "class CalWeight:\n def time_applycal_calweight(self):\n applycal(vis=self.vis, gaintable=[self.gCal], calwt=[False])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalWeight.time_applycal_calweight", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f05a2af9251ce10ee5dc59cca8788b998e9da9fded8cce0ee1906db023bc8d01", "warmup_time": -1}, "bench_applycal.FlagBackup.time_applycal_flag_backup": {"code": "class FlagBackup:\n def time_applycal_flag_backup(self):\n applycal(vis=self.vis, gaintable=[self.gCal], flagbackup=True)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.FlagBackup.time_applycal_flag_backup", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "872b962508540cc6ef4bcbc9753aba34c9da8b7d02cc15ee7765f06901e65e37", "warmup_time": -1}, "bench_applycal.GainField.time_applycal_gainfield": {"code": "class GainField:\n def time_applycal_gainfield(self):\n applycal(vis=self.vis, gaintable=[self.tCal], gainfield='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.GainField.time_applycal_gainfield", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "556dfc5fca635bc393e0d68f23e8a9f9e9fef7a4f0c446faec837b3b23356816", "warmup_time": -1}, "bench_applycal.Interp.time_applycal_interp": {"code": "class Interp:\n def time_applycal_interp(self):\n applycal(vis=self.vis, gaintable=[self.tCal], interp='cubicflag')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.Interp.time_applycal_interp", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4e36d7f0ce102f831a8b90da4499019cebba32ffad3fb79f8e95e30ecfa2ab8a", "warmup_time": -1}, "bench_applycal.Parang.time_applycal_parang": {"code": "class Parang:\n def time_applycal_parang(self):\n applycal(vis=self.vis, gaintable=[self.gCal], parang=True)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.Parang.time_applycal_parang", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "fbef01f79ab1b6afd2bd79669d2271a7a86954b6f26b2a523667e0b8f0ebdce0", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_antenna": {"code": "class SelectionTypes:\n def time_applycal_select_antenna(self):\n applycal(vis=self.vis, gaintable=[self.gCal], antenna='0~5&')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_antenna", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5ca6f069a4262d19625195a53f6dc88cc9ccca31907e944ec501075f5884cd60", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_field": {"code": "class SelectionTypes:\n def time_applycal_select_field(self):\n applycal(vis=self.vis, gaintable=[self.gCal], field='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_field", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e5d8951157ddc5452e498ffe5fd8d725dfaf0e9df7de5b79747f4993a20ddbc5", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_intent": {"code": "class SelectionTypes:\n def time_applycal_select_intent(self):\n applycal(vis=self.vis, gaintable=[self.gCal], intent='*AMPLI*')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_intent", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "48c31d0fb8eefd5323fa3319aa8b3695a8ce667466bd2a1303e71107b1e00613", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_scan": {"code": "class SelectionTypes:\n def time_applycal_select_scan(self):\n applycal(vis=self.vis, gaintable=[self.gCal], scan='10')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_scan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "96819d5d86bd7855a0112c03f92c63a64f0afa905e961887df14a01b025852f1", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_spw": {"code": "class SelectionTypes:\n def time_applycal_select_spw(self):\n applycal(vis=self.vis, gaintable=[self.gCal], spw='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_spw", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d95a817124907f58102de398fe8a2dedddd11e80b30d4758105288fc5a1149a", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_timerange": {"code": "class SelectionTypes:\n def time_applycal_select_timerange(self):\n applycal(vis=self.vis, gaintable=[self.gCal], timerange='04:33:23~04:38:23')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_timerange", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2b264ae69c0eada9a00ed6c42d7f361571dab35399621ee8cb2785741c2aae68", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_uvrange": {"code": "class SelectionTypes:\n def time_applycal_select_uvrange(self):\n applycal(vis=self.vis, gaintable=[self.gCal], uvrange='>100klambda')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_uvrange", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f897b2028d0940d47aca1c23d61aa1185690be2fd94bf1c4c94c188f922f9f91", "warmup_time": -1}, "bench_applycal.SpwMap.time_applycal_spwmap": {"code": "class SpwMap:\n def time_applycal_spwmap(self):\n applycal(vis=self.vis, gaintable=[self.gCal], spwmap=[0, 0, 1, 1])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SpwMap.time_applycal_spwmap", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "79550810428ff38b9e649d8bd6b72540535fe7e4b5efe5cefc4aafa24afc8296", "warmup_time": -1}, "bench_applycal.StandardGainTable.time_applycal_basic_gaintable": {"code": "class StandardGainTable:\n def time_applycal_basic_gaintable(self):\n applycal(vis=self.vis, gaintable=[self.gCal, self.tCal])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.StandardGainTable.time_applycal_basic_gaintable", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a70f32c3ac8dfd7542bb10c89868d4bc640a02842253b6f45f105bf41df75780", "warmup_time": -1}, "bench_applycal.TrialMode.time_applycal_trial": {"code": "class TrialMode:\n def time_applycal_trial(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='trial')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.TrialMode.time_applycal_trial", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "24ae87f7af1553ca58c7bb7ec1ab0a5a78e22b6f1c6fdc4af9ace18ad930eefb", "warmup_time": -1}, "bench_flagdata.AntintMode.time_antint_low_threshold_spw": {"code": "class AntintMode:\n def time_antint_low_threshold_spw(self):\n \"\"\"flagdata: mode antint, with low threshold; (original test_antint_spw3_low_threshold)\"\"\"\n flagdata(vis=self.vis, mode='antint', spw='3', antint_ref_antenna='ea01', minchanfrac=-.1)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.AntintMode.time_antint_low_threshold_spw", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98a303508391da50600d29c7f8a74f95501abb178d78c35154642c8f7ec6f83c", "warmup_time": -1}, "bench_flagdata.CalibrationBandpass.time_bandpass_clip_timerange": {"code": "class CalibrationBandpass:\n def time_bandpass_clip_timerange(self):\n \"\"\"Flagdata: clip a timerange from field 3C286_A; (original test_cal_time1)\"\"\"\n flagdata(vis=self.vis, mode='clip', timerange='<14:12:52',clipzeros=True,\n clipminmax=[0.,0.35], datacolumn='CPARAM',flagbackup=False)\n\n def setup(self):\n self.setUp_bpass()", "min_run_count": 3, "name": "bench_flagdata.CalibrationBandpass.time_bandpass_clip_timerange", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4595858d10a7650bb7d5b763b20cf03f4921f5bcd8feb0a09a3ce0a9c3cdca38", "warmup_time": -1}, "bench_flagdata.CalibrationTsys.time_tsys_clip_fparam": {"code": "class CalibrationTsys:\n def time_tsys_clip_fparam(self):\n \"\"\"Flagdata: flag default data column FPARAM; (original test_default_fparam)\"\"\"\n flagdata(vis=self.vis, mode='clip', clipminmax=[0,500], flagbackup=False,\n datacolumn='FPARAM')\n\n def setup(self):\n self.setUp_tsys()", "min_run_count": 3, "name": "bench_flagdata.CalibrationTsys.time_tsys_clip_fparam", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7ee644928bb3e8de0bcf4711c3f266bb859ae288d2e7db42a0ea0a66d62550ad", "warmup_time": -1}, "bench_flagdata.ClipMode.time_clip_corrected_data": {"code": "class ClipMode:\n def time_clip_corrected_data(self):\n \"\"\"flagdata: clip CORRECTED data column; (original test_datacol_corrected)\"\"\"\n flagdata(vis=self.vis, flagbackup=False, mode='clip', datacolumn='CORRECTED',\n clipminmax=[0.,10.])\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.ClipMode.time_clip_corrected_data", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "07c4f1e0d3121fa540b2cd0e228a42009625c87cf37a05e16e62f3d95b57246b", "warmup_time": -1}, "bench_flagdata.ClipMode.time_clip_with_timeavg": {"code": "class ClipMode:\n def time_clip_with_timeavg(self):\n \"\"\"flagdata: clip with time averaging in spw 9; (original test_timeavg_spw9_2scans)\"\"\"\n flagdata(vis=self.vis, flagbackup=False, mode='clip', datacolumn='DATA', spw='9',\n timeavg=True, timebin='2s', clipminmax=[0.0, 0.08])\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.ClipMode.time_clip_with_timeavg", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2be25377fea9922f68fb74d47702f7354f593a21a0c3ef79ea683f411e59cb03", "warmup_time": -1}, "bench_flagdata.ElevationMode.time_elevation_interval": {"code": "class ElevationMode:\n def time_elevation_interval(self):\n \"\"\"Flagdata: flag an interval between upper and lower elevations; (original test_interval)\"\"\"\n flagdata(vis = self.vis,mode = 'elevation',lowerlimit = 55,upperlimit = 60,\n savepars=False,flagbackup=False)\n\n def setup(self):\n self.setUp_ngc5921()", "min_run_count": 3, "name": "bench_flagdata.ElevationMode.time_elevation_interval", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "cbefafa13742c0d5fe0d9179be380f7801ee669bbc6fc2cbe3ba48c02dcf1b1c", "warmup_time": -1}, "bench_flagdata.ListFileMode.time_list_from_3files": {"code": "class ListFileMode:\n def time_list_from_3files(self):\n \"\"\"Flagdata list mode reading commands from three files; (original test_file_CAS4819)\"\"\"\n flagdata(vis=self.vis, mode='list', inpfile=[self.inpfile1, self.inpfile2, self.inpfile3],\n flagbackup=False)\n\n def setup(self):\n self.setUp_ngc5921()\n\n def setup_cache(self):\n # Create 3 flagcmd files and save to disk\n myinput = \"scan='1'\\n\" \\\n \"scan='2'\\n\" \\\n \"# a comment line\\n\" \\\n \"scan='3'\"\n create_input(myinput, self.inpfile1)\n \n myinput = \"scan='5'\\n\" \\\n \" \\n\" \\\n \"scan='6'\\n\" \\\n \"scan='7'\"\n create_input(myinput, self.inpfile2)\n \n myinput = \"scan='4' mode='clip' clipminmax=[0,4]\"\n \n create_input(myinput, self.inpfile3)", "min_run_count": 3, "name": "bench_flagdata.ListFileMode.time_list_from_3files", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:217", "timeout": 3600, "type": "time", "unit": "seconds", "version": "dd8b2ea5edb3e265335cac2bf592eef280e5239f17d022f1fca37ab9738a0531", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_bandpassflag": {"code": "class ListMode:\n def time_list_bandpassflag(self):\n \"\"\"Flagdata list mode from pipeline hifa_bandpassflag\"\"\"\n flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=[\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='16' antenna='CM05' \\\n timerange='20:09:50~20:09:52' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:09:20~20:09:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:10:21~20:10:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='22' antenna='CM05' \\\n timerange='20:09:30~20:09:32' field='J1924-2914' reason='bad antenna timestamp'\",\n ],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_bandpassflag", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "2971c3002f4dafe2840b191e5570935d4b7f7ff2873189a4c55df4ae808dc738", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_summary": {"code": "class ListMode:\n def time_list_summary(self):\n \"\"\"Flagdata list mode from pipeline hifa_rawflagchans\"\"\"\n summary_dict = flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=[\"mode='summary' name='before'\"],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_summary", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "a3e3764d7dceb23f8ac0736e3a450613d2d10e11dc14ea682bbb0a78e1737111", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_tbuff": {"code": "class ListMode:\n def time_list_tbuff(self):\n \"\"\"Flagdata list mode from pipeline hifa_flagdata\"\"\"\n flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=self.flags_cmd,\n tbuff=[0.048, 0.0],\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_tbuff", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "53f2aaec8f44b81cb94f73fa21e5aac89218769ace8126661888f9c54374ff75", "warmup_time": -1}, "bench_flagdata.RflagMode.time_rflag_numpy_types": {"code": "class RflagMode:\n def time_rflag_numpy_types(self):\n \"\"\"flagdata: rflag, thresholds using numpy types; (original test_rflag_partial_thresholds)\"\"\"\n flagdata(vis=self.vis, mode='rflag', spw='9,10', timedev=[self.t1, self.t2], freqdev=0.5,\n flagbackup=False, extendflags=False)\n\n def setup(self):\n self.setUp_data4tfcrop()\n self.t1 = [numpy.int32(1), 10, numpy.float32(0.1)]\n self.t2 = [1, numpy.int16(11), numpy.float64(0.07)]", "min_run_count": 3, "name": "bench_flagdata.RflagMode.time_rflag_numpy_types", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "3f582e64491988dab48d1b83c4500bde3d09f14006d290ba96e3fa9ded4fa62b", "warmup_time": -1}, "bench_flagdata.Selections.time_sel_abs_wvr": {"code": "class Selections:\n def time_sel_abs_wvr(self):\n \"\"\"flagdata: clip ABS_WVR; (original test_abs_wvr)\"\"\"\n flagdata(vis=self.vis, mode='clip',clipminmax=[0,50], correlation='ABS_WVR', savepars=False,\n flagbackup=False)\n\n def setup(self):\n self.setUp_alma_ms()", "min_run_count": 3, "name": "bench_flagdata.Selections.time_sel_abs_wvr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ce5229049856ba918b3464ddc7f5aeee51402d91ce7a7d0cc3fb9d260d9fff87", "warmup_time": -1}, "bench_flagdata.Selections.time_sel_autocorr_wvr": {"code": "class Selections:\n def time_sel_autocorr_wvr(self):\n \"\"\"flagdata: CAS-5286, do not flag auto-correlations in WVR data; (original test_autocorr_wvr)\"\"\"\n flagdata(vis=self.vis, autocorr=True, flagbackup=False)\n\n def setup(self):\n self.setUp_alma_ms()", "min_run_count": 3, "name": "bench_flagdata.Selections.time_sel_autocorr_wvr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "128022df8e55b6c9054c3ef806420b1f80f11fcb4ef8ce073e99894f83617c9c", "warmup_time": -1}, "bench_flagdata.ShadowMode.time_shadow_absent_antennas": {"code": "class ShadowMode:\n def time_shadow_absent_antennas(self):\n \"\"\"flagdata: shadow by antennas not present in MS; (original test_CAS2399)\"\"\"\n flagdata(vis=self.vis, mode='shadow', tolerance=0.0, addantenna=self.inpfile, flagbackup=False)\n\n def setup(self):\n self.setUp_shadowdata()\n \n # Create antennafile in disk\n myinput = \"name=VLA01\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]\\n\" \\\n \"name=VLA02\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]\\n\" \\\n \"name=VLA09\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]\\n\" \\\n \"name=VLA10\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]\\n\"\n \n create_input(myinput, self.antfile)\n \n # Create flagcmd file\n myinput = \"mode='shadow' tolerance=0.0 addantenna='antfile.txt'\"\n create_input(myinput, self.inpfile)", "min_run_count": 3, "name": "bench_flagdata.ShadowMode.time_shadow_absent_antennas", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "3b94aac813ae30b385cb446c642e9b3115a43bc492770d13667d2fd1bce00339", "warmup_time": -1}, "bench_flagdata.ShadowMode.time_shadow_addantenna": {"code": "class ShadowMode:\n def time_shadow_addantenna(self):\n \"\"\"flagdata: use antenna file in list mode; (original test_addantenna)\"\"\"\n flagdata(vis=self.vis, mode='list', inpfile=self.inpfile, savepars=True, outfile='withdict.txt',\n flagbackup=False)\n\n def setup(self):\n self.setUp_shadowdata()\n \n # Create antennafile in disk\n myinput = \"name=VLA01\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]\\n\" \\\n \"name=VLA02\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]\\n\" \\\n \"name=VLA09\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]\\n\" \\\n \"name=VLA10\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]\\n\"\n \n create_input(myinput, self.antfile)\n \n # Create flagcmd file\n myinput = \"mode='shadow' tolerance=0.0 addantenna='antfile.txt'\"\n create_input(myinput, self.inpfile)", "min_run_count": 3, "name": "bench_flagdata.ShadowMode.time_shadow_addantenna", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "21fb65a9cd413ce64af88b6c00ccfc1ee439a7d0413b88f9b383f4e30252b087", "warmup_time": -1}, "bench_flagdata.TfcropMode.time_tfcrop_abs_rr": {"code": "class TfcropMode:\n def time_tfcrop_abs_rr(self):\n \"\"\"flagdata:: flag with tfcrop mode an ABS_RR correlation; (original test_tfcrop1)\"\"\"\n flagdata(vis=self.vis, mode='tfcrop', correlation='ABS_RR',ntime=51.0,spw='9',\n savepars=False, extendflags=False)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.TfcropMode.time_tfcrop_abs_rr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "bc8ecea57bcf9178074a7728ad87a326ec8796d9a804a085da78d1247047edba", "warmup_time": -1}, "bench_flagdata.TfcropMode.time_tfcrop_extendflags": {"code": "class TfcropMode:\n def time_tfcrop_extendflags(self):\n \"\"\"flagdata: flag with tfcrop mode and extend flags automatically\"\"\"\n flagdata(vis=self.vis, mode='tfcrop', spw='0', extendflags=True, flagbackup=False)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.TfcropMode.time_tfcrop_extendflags", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "686884535fe06d88a4737064c91e8fd3cc52616fc17d460c57cba3da7d04f97c", "warmup_time": -1}, "bench_fringefit.Corrcomb.time_comb_all": {"code": "class Corrcomb:\n def time_comb_all(self):\n \"\"\"test_comb --- last part of the corrcomb test, check that results are coombined\"\"\"\n fringefit(vis=self.polcombtestms, caltable=self.testout, refant='0', spw='2~3', corrcomb='all')\n\n def setUp(self):\n if os.path.exists(self.polcombtestms):\n shutil.rmtree(self.polcombtestms)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.polcombtestms)", "min_run_count": 5, "name": "bench_fringefit.Corrcomb.time_comb_all", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Dispersive.time_manual_phase_cal": {"code": "class Dispersive:\n def time_manual_phase_cal(self):\n \"\"\"test_manual_phase_cal --- test on manually flagged data\"\"\"\n fringefit(vis=self.prefix + '.ms',\n caltable='n14p1.disp', refant=\"WB\",\n scan='1', solint='60', spw='0,1,2',\n paramactive=[True, True, True],\n minsnr=50,\n niter=100,\n gaintable=['n14p1.mpc'],\n parang=True)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n flagdata(self.prefix + '.ms', mode='manual', spw='*:0~2;29~31')\n \n fringefit(vis=\"n14p1.ms\", caltable=\"n14p1.mpc\",\n scan=\"1\", solint=\"300\", refant=\"WB\",\n minsnr=50, zerorates=True,\n globalsolve=True, niter=100, gaintable=[],\n parang=True)", "min_run_count": 5, "name": "bench_fringefit.Dispersive.time_manual_phase_cal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.FreqMetaTests.time_metadata": {"code": "class FreqMetaTests:\n def time_metadata(self):\n \"\"\"test_metadata --- check output table metadata\"\"\"\n sbdcal = self.prefix + '-zerorates.sbdcal'\n mbdcal = self.prefix + '.mbdcal'\n \n fringefit(vis=self.msfile, caltable=mbdcal, spw=\"0,1,2,3\", field='J0916+3854', timerange=\"17:10:00~17:11:00\",\n combine='spw', gaintable=[sbdcal], refant='EF')\n\n def setUp(self):\n if os.path.exists(self.msfile):\n shutil.rmtree(self.msfile)\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n \n sbdcal = self.prefix + '-zerorates.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, field='4C39.25',\n refant='EF', zerorates=True)", "min_run_count": 5, "name": "bench_fringefit.FreqMetaTests.time_metadata", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Mbd.time_mbd": {"code": "class Mbd:\n def time_mbd(self):\n \"\"\"test_mbd --- test with mbd cal table\"\"\"\n sbdcal = self.prefix + '-zerorates.sbdcal'\n mbdcal = self.prefix + '.mbdcal'\n \n fringefit(vis=self.msfile, caltable=mbdcal, field='J0916+3854',\n combine='spw', gaintable=[sbdcal], refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n \n sbdcal = self.prefix + '-zerorates.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, field='4C39.25',\n refant='EF', zerorates=True)", "min_run_count": 5, "name": "bench_fringefit.Mbd.time_mbd", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.RefantBookkeeping.time_bookkeeping": {"code": "class RefantBookkeeping:\n def time_bookkeeping(self):\n \"\"\"test_bookkeeping --- check effects of using refant WB\"\"\"\n fringefit(vis=self.msfile, caltable=self.sbdcal, refant='WB')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n flagdata(self.prefix + '.ms', mode='manual', spw='*:0~2;29~31')\n flagdata(self.prefix + '.ms', mode='manual', antenna='EF')", "min_run_count": 5, "name": "bench_fringefit.RefantBookkeeping.time_bookkeeping", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Sbd.time_sbd": {"code": "class Sbd:\n def time_sbd(self):\n \"\"\"test_sbd --- test with sbd caltable\"\"\"\n sbdcal = self.prefix + '.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.uvfile)", "min_run_count": 5, "name": "bench_fringefit.Sbd.time_sbd", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Sbd.time_uvrange": {"code": "class Sbd:\n def time_uvrange(self):\n \"\"\"test_uvraange --- Check that the uvrnage parameter excludes antennas \"\"\"\n # create a caltable with uvrange selection\n fringefit(vis=self.uvfile, caltable='uvrange_with.cal', spw='2', refant='0', uvrange='<1160')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.uvfile)", "min_run_count": 5, "name": "bench_fringefit.Sbd.time_uvrange", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Single.time_param": {"code": "class Single:\n def time_param(self):\n \"\"\"test_param --- test different iterations of paramactive\"\"\"\n sbdcal = self.prefix + '-2.sbdcal'\n # We make a triple cartesian product of booleans\n # to test all possible paramactive values\n refant = 0\n refant_s = str(refant)\n for pactive in itertools.product(*3*[[False, True]]):\n fringefit(vis=self.msfile, paramactive=list(pactive), caltable=sbdcal, refant=refant_s)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)", "min_run_count": 5, "name": "bench_fringefit.Single.time_param", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Single.time_single": {"code": "class Single:\n def time_single(self):\n \"\"\"test_single --- check that the right params are flagged with a single refant\"\"\"\n sbdcal = self.prefix + '.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)", "min_run_count": 5, "name": "bench_fringefit.Single.time_single", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_gaincal.CalModes.time_gaincal_calmode_A": {"code": "class CalModes:\n def time_gaincal_calmode_A(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n calmode='a')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.CalModes.time_gaincal_calmode_A", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f7eb35dd966d27af8cf12cc750375153a8b0910af44d846f38c327d0bf37f4c2", "warmup_time": -1}, "bench_gaincal.CalModes.time_gaincal_calmode_P": {"code": "class CalModes:\n def time_gaincal_calmode_P(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n calmode='p')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.CalModes.time_gaincal_calmode_P", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1b0b933cc4c5f58b1b99775ecdf726fd4b7397322314ae15faf4e2d13c102cbc", "warmup_time": -1}, "bench_gaincal.FullGainCal.time_gaincal_fullcal": {"code": "class FullGainCal:\n def time_gaincal_fullcal(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', field='0', refant='0',\n smodel=[1, 0, 0, 0], scan='0~9')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.FullGainCal.time_gaincal_fullcal", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "321b6da59741ba85c3e914725f481e45ca88f4ef73a637f3ade2bed74a5abaa1", "warmup_time": -1}, "bench_gaincal.GainTypes.time_gaincal_gain_type_K": {"code": "class GainTypes:\n def time_gaincal_gain_type_K(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n gaintype='KCROSS', refant='0')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GainTypes.time_gaincal_gain_type_K", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "616aabddbc63928cc0fdb731743036617d3e88b5249398335973b002b9ea2e05", "warmup_time": -1}, "bench_gaincal.GainTypes.time_gaincal_gain_type_spline": {"code": "class GainTypes:\n def time_gaincal_gain_type_spline(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n gaintype='GSPLINE', refant='0')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GainTypes.time_gaincal_gain_type_spline", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2b9405658f1efab3530c340ed099fb37b1ae5888f52762c3f8cf6df6321126ac", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_antennaSelect": {"code": "class GaincalSelections:\n def time_gaincal_antennaSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', field='0', solint='inf', combine='scan', antenna='0~5&',\n smodel=[1, 0, 0, 0], gaintype='G')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_antennaSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d75f902a1c09757939ff4e77e062c55293a92b7f886d8bbe0cf4e5344918c39f", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_fieldSelect": {"code": "class GaincalSelections:\n def time_gaincal_fieldSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', field='0',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_fieldSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "10de6efc06ad79e8cf856e46bef9b61f435eb4030f27473d284c71c5bdb75ccf", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_refantSelect": {"code": "class GaincalSelections:\n def time_gaincal_refantSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', refant='0',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_refantSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "34c394342899a19f1647ae25d1f7f7d8f19eee8ff14c26b61f0b1bca06443cd2", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_scanSelect": {"code": "class GaincalSelections:\n def time_gaincal_scanSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf',\n smodel=[1, 0, 0, 0], scan='2')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_scanSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ed56707772521ef042364e25b1b5cfede92141267082dcfb3deffd2eecc05e91", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_spwSelect": {"code": "class GaincalSelections:\n def time_gaincal_spwSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf',\n smodel=[1, 0, 0, 0], spw='2')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_spwSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d3b9a18fd82b436f740ec7e34af58655909f6b8731136f253ce9eb7ec3846e3e", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_uvRangeSelect": {"code": "class GaincalSelections:\n def time_gaincal_uvRangeSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, spw='2', refant='0', uvrange='<1160', minblperant=1)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_uvRangeSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b0342a4bc2c4bc568d98a402b5d1611ef82ab395834afb1c0527b636f559ba9", "warmup_time": -1}, "bench_gaincal.MinBlPerAnt.time_gaincal_minbl": {"code": "class MinBlPerAnt:\n def time_gaincal_minbl(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', solint='int', smodel=[1, 0, 0, 0], gaintype='G',\n combine='scan', antenna='0~5&', minblperant=6)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.MinBlPerAnt.time_gaincal_minbl", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98757eddbbd36be1306e1d3dff8c7368acf302e8e44127c5aa55c1206f1145e1", "warmup_time": -1}, "bench_gaincal.MinSNRFlag.time_gaincal_minsnr": {"code": "class MinSNRFlag:\n def time_gaincal_minsnr(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='30s', field='0', refant='0',\n smodel=[1, 0, 0, 0], minsnr=1000)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.MinSNRFlag.time_gaincal_minsnr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "023921a63e2bcaaec078cc39a20cfa4ed25f3aac1ad8b73aeb6903fde979a439", "warmup_time": -1}, "bench_gaincal.PreApplyTables.time_gaincal_apply_tables": {"code": "class PreApplyTables:\n def time_gaincal_apply_tables(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', solint='inf', smodel=[1, 0, 0, 0], gaintype='G', field='0',\n gaintable=[self.tCal, self.compCal], gainfield=['0', '0'], interp=[''])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.PreApplyTables.time_gaincal_apply_tables", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "daccc986c167fbbf02e14e3cfa201572b4ec8a51a1629661b8543142b2cae433", "warmup_time": -1}, "bench_gaincal.RefantSelection.time_gaincal_refant": {"code": "class RefantSelection:\n def time_gaincal_refant(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf', field='0', combine='scan', refant='1',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.RefantSelection.time_gaincal_refant", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "be0118dd9b630d5373b3604c19a8afb518dbc25a2e7e7758d644c5e5faf634f0", "warmup_time": -1}, "bench_gaincal.UseSpwMap.time_gaincal_use_spw_map": {"code": "class UseSpwMap:\n def time_gaincal_use_spw_map(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n refant='0', spwmap=[0, 0, 1, 1])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.UseSpwMap.time_gaincal_use_spw_map", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c524f53702087f79992918ad289b5b863018b412527f507e27589ac99e0574b9", "warmup_time": -1}, "bench_importasdm.Aca.time_default": {"code": "class Aca:\n def time_default(self):\n '''test6_lazy1 - standard fill : Test good ACA ASDM with mixel pol/channelization input with default filler'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=False, scans='0:1~3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Aca.time_default", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Aca.time_default_lazy": {"code": "class Aca:\n def time_default_lazy(self):\n '''test6_lazy1 - lazy fill: Test good ACA ASDM with mixed pol/channelization input with default filler in lazy mode'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=True, scans='0:1~3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Aca.time_default_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_auto_only": {"code": "class Alma_12m:\n def time_auto_only(self):\n '''test7_lazy4 - standard fill: Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=False, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_auto_only", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_auto_only_lazy": {"code": "class Alma_12m:\n def time_auto_only_lazy(self):\n '''test7_lazy4 - lazy fill, Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=True, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_auto_only_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_bdfflags_true": {"code": "class Alma_12m:\n def time_bdfflags_true(self):\n '''test7_bdflags1 - Test good 12 m ASDM with mixed pol/channelisation input with default filler selecting \"co\" on output and using the BDF flags'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, ocorr_mode=\"co\", bdfflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_bdfflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_bdfflags_true_lazy": {"code": "class Alma_12m:\n def time_bdfflags_true_lazy(self):\n '''test7_lazy2 - Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode with reading the BDF flags'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=True, bdfflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_bdfflags_true_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_default": {"code": "class Alma_12m:\n def time_default(self):\n '''test7_lazy1 - standard fill: Test good 12 m ASDM with mixed pol/channelization with default filler'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=False, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_default", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_default_lazy": {"code": "class Alma_12m:\n def time_default_lazy(self):\n '''test7_lazy1 - lazy fill: Test good 12 m ASDM with mixed pol/channelization input with default filler in lazy mode'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=True, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_default_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_applyflags_true": {"code": "class AutocorrASDM:\n def time_applyflags_true(self):\n '''test_flagautocorr1 - importasdm: test that auto-correlations from online flags are correctly flagged'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', applyflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_applyflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_process_flags_false": {"code": "class AutocorrASDM:\n def time_process_flags_false(self):\n '''test_flagautocorr3 - importasdm: do not process flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', process_flags=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_process_flags_false", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_savecmds_true": {"code": "class AutocorrASDM:\n def time_savecmds_true(self):\n '''test_autocorr - importasdm: auto-correlations should be written to online flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', savecmds=True, outfile=self.outfile)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_all_defaults": {"code": "class Basic:\n def time_all_defaults(self):\n '''test_import2 --- importasdm with default arguments'''\n importasdm(self.asdm_name)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_all_defaults", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_lazy_pc_true": {"code": "class Basic:\n def time_lazy_pc_true(self):\n '''test1_lazy1 --- lazy mode, with pointing_correction=True'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=True, with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_lazy_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_pc_true": {"code": "class Basic:\n def time_pc_true(self):\n '''test1_lazy1 --- standard fill (lazy=False), with pointing_correction=True'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=False, with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_all_scans": {"code": "class Evla:\n def time_all_scans(self):\n '''test_evla_apply5 - test of importing evla data: all scans, do not process flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, ocorr_mode='co', with_pointing_correction=True,\n process_flags=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_all_scans", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_applyflags_true": {"code": "class Evla:\n def time_applyflags_true(self):\n '''test_evla_apply2 - test of importing evla data and applying the online flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2',ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=True, savecmds=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_applyflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_pc_true": {"code": "class Evla:\n def time_pc_true(self):\n '''test_evlatest1 - test of importing evla data, with_pointing_correction=True is normal for EVLA data'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2',ocorr_mode='co',with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_applyflags_savecmds_true": {"code": "class Evla:\n def time_process_applyflags_savecmds_true(self):\n '''test_evla_apply1_flagdata - test of importing evla data, apply onlineflags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=True, savecmds=True, outfile=self.cmdfile, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_applyflags_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_flags_savecmds_true": {"code": "class Evla:\n def time_process_flags_savecmds_true(self):\n '''test_evla_savepars - test importing evla data: save the flag commands and do not apply'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name,scans='11~13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=False, savecmds=True, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_flags_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_flags_true_applyflags_false": {"code": "class Evla:\n def time_process_flags_true_applyflags_false(self):\n '''test_evla_savepars_flagdata - test importing evla data: save the flag commands and do not apply; using flagdata'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name,scans='11~13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=False, savecmds=True, outfile=self.cmdfile, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_flags_true_applyflags_false", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_scan_selection": {"code": "class Evla:\n def time_scan_selection(self):\n '''test_evla_apply3 - test of importing evla data, two difference scans, do not apply online flags'''\n # appears to also be the same importasdm as test_evla_apply3_flagdata\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2,13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=False,flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_scan_selection", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla_ephemeris.time_polynomial_ephem": {"code": "class Evla_ephemeris:\n def time_polynomial_ephem(self):\n '''test_evlatest2 - test of importing evla data, test2: Good input asdm with polynomial ephemeris'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='0:5',ocorr_mode='co',with_pointing_correction=True,polyephem_tabtimestep=0.001,convert_ephem2geo=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla_ephemeris.time_polynomial_ephem", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Singledish.time_auto_only": {"code": "class Singledish:\n def time_auto_only(self):\n '''test7_lazy5 - standard fill : Test TP asdm with default filler selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=False, bdfflags=True, applyflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Singledish.time_auto_only", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Singledish.time_auto_only_lazy": {"code": "class Singledish:\n def time_auto_only_lazy(self):\n '''test7_lazy5 - lazy fill : Test TP asdm with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", bdfflags=True, applyflags=True, lazy=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Singledish.time_auto_only_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_custom_atm_params": {"code": "class test_sdatmcor:\n def time_custom_atm_params(self):\n \"\"\"Test customized ATM parameters.\"\"\"\n sdatmcor(\n infile=self.infile, outfile=self.outfile, datacolumn='data',\n dtem_dh='-5.7K/km', h0='2010m',\n atmdetail=True,\n altitude='5.1km', temperature='290K', pressure='700hPa',\n humidity=30, pwv='0.1cm', dp='10hPa', dpm=1.2,\n layerboundaries='800m,1.5km', layertemperature='250K,200K'\n )\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_custom_atm_params", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype": {"code": "class test_sdatmcor:\n def time_sdatmcor_explicit_atmtype(self):\n \"\"\"Test specifying atmtype explicitly.\"\"\"\n sdatmcor(infile=self.infile, outfile=self.outfile, datacolumn='data', atmtype=2)\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict": {"code": "class test_sdatmcor:\n def time_sdatmcor_gainfactor_dict(self):\n \"\"\"Test gainfactor: dict input.\"\"\"\n gainfactor = {'19': 10.0, '23': 45.0}\n sdatmcor(infile=self.infile2, outfile=self.outfile, datacolumn='data', gainfactor=gainfactor)\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_normal": {"code": "class test_sdatmcor:\n def time_sdatmcor_normal(self):\n \"\"\"Test normal usage of sdatmcor.\"\"\"\n sdatmcor(infile=self.infile, outfile=self.outfile, datacolumn='data')\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_normal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdbaseline.Basic.time_blparam_apply_no_remove": {"code": "class Basic:\n def time_blparam_apply_no_remove(self):\n \"\"\"sdbaseline: blparam file(s) should not be removed in apply mode - test062\"\"\"\n tid = '062'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n blformat = ['text', 'table']\n blmode = 'apply'\n bltable = self.infile_no_remove + '_blparam.bltable'\n overwrite = True\n \n sdbaseline(infile=self.infile_no_remove, outfile=outfile, overwrite=overwrite, datacolumn=datacolumn, blmode=blmode, bltable=bltable)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_blparam_apply_no_remove", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_blparam_overwrite": {"code": "class Basic:\n def time_blparam_overwrite(self):\n \"\"\"sdbaseline: blparam file(s) should be overwritten when overwrite=True in fit mode - test060\"\"\"\n tid = '060'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n overwrite = True\n \n sdbaseline(infile=self.infile_overwrite, outfile=outfile, overwrite=overwrite, datacolumn=datacolumn)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_blparam_overwrite", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_chebyshev_blfunc_no_mask": {"code": "class Basic:\n def time_chebyshev_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'chebyshev', maskmode = 'list' and masklist=[] (no mask) - test002\"\"\"\n tid = '002'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'chebyshev'\n spw = '3'\n pol = 'LL'\n overwrite = True\n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n spw=spw, pol=pol, outfile=outfile,\n overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_chebyshev_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_cspline_blfunc_no_mask": {"code": "class Basic:\n def time_cspline_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'cspline', maskmode = 'list' and masklist=[] (no mask) - test003\"\"\"\n print(\"\")\n \n tid = '003'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'cspline'\n overwrite = True\n npiece = 3\n spw='3'\n pol='LL'\n \n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n npiece=npiece,spw=spw,\n pol=pol,\n outfile=outfile,overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_cspline_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_default": {"code": "class Basic:\n def time_default(self):\n \"\"\"sdbaseline: default values for all parameters - test000\"\"\"\n tid = '000'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n \n result = sdbaseline(infile=infile, datacolumn=datacolumn, outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_default", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_poly_blfunc_no_mask": {"code": "class Basic:\n def time_poly_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'poly', maskmode = 'list' and masklist=[] (no mask) - test001\"\"\"\n tid = '001'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'poly'\n spw = '3'\n pol = 'LL'\n overwrite = True\n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n spw=spw, pol=pol, outfile=outfile,\n overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_poly_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.BlFuncVariable.time_variable_orders": {"code": "class BlFuncVariable:\n def time_variable_orders(self):\n \"\"\"sdbaseline: Test blfunc='variable' with variable baseline functions and orders - testVariable00\"\"\"\n print('why is this failing?')\n infile = 'analytic_variable.ms'\n self.paramfile = 'analytic_variable_blparam.txt'\n self._refetch_files([infile, self.paramfile], self.datapath)\n sdbaseline(infile=infile, blfunc='variable', outfile=self.outfile, blparam=self.paramfile, datacolumn=self.column)\n\n def setUp(self):\n pass", "min_run_count": 5, "name": "bench_sdbaseline.BlFuncVariable.time_variable_orders", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Mask.time_cspline_blfunc_masked_range_spectrum_edge": {"code": "class Mask:\n def time_cspline_blfunc_masked_range_spectrum_edge(self):\n \"\"\"sdbaseline: with masked ranges at the edges of spectrum. blfunc must be cspline. - test100\"\"\"\n self.tid='100'\n infile = self.infile\n outfile = self.outroot+self.tid+'.ms'\n datacolumn='float_data'\n mode = 'list'\n spw = '2:%s'%(';'.join(map(self._get_range_in_string,self.search)))\n pol = 'RR'\n blfunc = 'cspline'\n npiece = 4\n \n result = sdbaseline(infile=infile,datacolumn=datacolumn,maskmode=mode,\n spw=spw,pol=pol,blfunc=blfunc,npiece=npiece,\n outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')", "min_run_count": 5, "name": "bench_sdbaseline.Mask.time_cspline_blfunc_masked_range_spectrum_edge", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Mask.time_masked_ranges_no_edge_spectrum": {"code": "class Mask:\n def time_masked_ranges_no_edge_spectrum(self):\n \"\"\"sdbaaseline: with masked ranges not touching spectrum edge - test101\"\"\"\n self.tid='101'\n infile = self.infile\n outfile = self.outroot+self.tid+'.ms'\n datacolumn='float_data'\n mode = 'list'\n spw = '2:%s'%(';'.join(map(self._get_range_in_string,self.blchan2)))\n pol = 'RR'\n \n result = sdbaseline(infile=infile,datacolumn=datacolumn,maskmode=mode,\n outfile=outfile,spw=spw,pol=pol)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')", "min_run_count": 5, "name": "bench_sdbaseline.Mask.time_masked_ranges_no_edge_spectrum", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_masked_masked": {"code": "class OutBlTable:\n def time_fit_variable_masked_masked(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304a'\n infile = self.infile_variable_a\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = ''\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=self.infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_masked_masked", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_masked_unselect": {"code": "class OutBlTable:\n def time_fit_variable_masked_unselect(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304b'\n infile = self.infile_variable_b\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = 'RR'\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_masked_unselect", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1cout": {"code": "class OutBlTable:\n def time_fit_variable_r2p1cout(self):\n \"\"\"sdbaseline: per-spectrum baselining, output bltable - test302\"\"\"\n self.tid='302b'\n infile = self.infile\n datacolumn='float_data'\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n \n bloutput= self.outroot+self.tid+'r2p1cout'+'.bltable'\n outfile = self.outroot+self.tid+'r2p1cout'+'.ms'\n blparam = self.outroot+self.tid+'r2p1cout'+'.blparam'\n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, 'r2p1cout')\n result = sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1cout", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1less": {"code": "class OutBlTable:\n def time_fit_variable_r2p1less(self):\n \"\"\"sdbaseline: per-spectrum baselining, output bltable - test302\"\"\"\n self.tid='302a'\n infile = self.infile\n datacolumn='float_data'\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n \n bloutput= self.outroot+self.tid+'r2p1less'+'.bltable'\n outfile = self.outroot+self.tid+'r2p1less'+'.ms'\n blparam = self.outroot+self.tid+'r2p1less'+'.blparam'\n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, 'r2p1less')\n result = sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1less", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_unselect_masked": {"code": "class OutBlTable:\n def time_fit_variable_unselect_masked(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304c'\n infile = self.infile_variable_c\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = 'LL'\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_unselect_masked", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.UpdateWeight.time_blmode_apply_spw_to_flag": {"code": "class UpdateWeight:\n def time_blmode_apply_spw_to_flag(self):\n \"\"\"sdbaseline: blmode='apply', spw to flag channels 4500-6499 - test052\"\"\"\n sdbaseline(infile=self.infile2, outfile=self.outfile, intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='float_data', spw=self.spw)\n\n def setUp(self):\n remove_files_dirs(self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n remove_files_dirs(self.infile2)\n shutil.copytree(os.path.join(self.datapath, self.infile2), self.infile2)", "min_run_count": 5, "name": "bench_sdbaseline.UpdateWeight.time_blmode_apply_spw_to_flag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.UpdateWeight.time_remove_weight_col": {"code": "class UpdateWeight:\n def time_remove_weight_col(self):\n \"\"\"sdbaseline: confirm if WEIGHT_SPECTRUM column is removed - test00\"\"\"\n sdbaseline(infile=self.infile, outfile=self.outfile, intent='OBSERVE_TARGET#ON_SOURCE', spw='9', datacolumn='data', updateweight=True)\n\n def setUp(self):\n remove_files_dirs(self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n remove_files_dirs(self.infile2)\n shutil.copytree(os.path.join(self.datapath, self.infile2), self.infile2)", "min_run_count": 5, "name": "bench_sdbaseline.UpdateWeight.time_remove_weight_col", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict": {"code": "class Apply:\n def time_spwmap_dict(self):\n \"\"\"Test01: weight = 1/(SIGMA**2) X 1/(FPARAM_ave**2) dictionary version.\"\"\"\n # Not sure what name is given to outfile in this run. Check!!\n spwmap_dict = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_dict,\n applytable=self.tsystable, outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "eca49a873b8027503c11a240f6e59585740e41ae697515593a11176fb2ab48da", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict_correct_data": {"code": "class Apply:\n def time_spwmap_dict_correct_data(self):\n \"\"\"Test03: Validation of CORRECTED_DATA = DATA X FPARAM (spwmap={1:[1], 3:[3], 5:[5], 7:[7]}).\"\"\"\n spwmap = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap, applytable=self.tsystable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict_correct_data", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5d5dd55ee3170d7722bc51ded3a558d04fc83f79a9db111eacbd805111dcad70", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict_weight_spectrum": {"code": "class Apply:\n def time_spwmap_dict_weight_spectrum(self):\n \"\"\"Test06: weight_spectrum = 1/(SIGMA**2) X 1/(FPARAMx**2) dictionary version.\"\"\"\n spwmap_dict = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_dict,\n applytable=self.tsystable, interp='nearest', outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict_weight_spectrum", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1158df62c77f3f40601226240f4c2b0a6910cf4d18c236a4f4a0181e7e689c1e", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_list": {"code": "class Apply:\n def time_spwmap_list(self):\n \"\"\"Test02: weight = 1/(SIGMA**2) X 1/(FPARAM_ave**2) list version.\"\"\"\n # focus on antenna1=0, data_disk_id=1\n spwmap_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_list,\n applytable=self.tsystable, outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_list", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ec21f3f30b77cfea3de4710e9bff48d698654d8024e10d401465c714e2f96a39", "warmup_time": -1}, "bench_sdcal.Apply.time_test05": {"code": "class Apply:\n def time_test05(self):\n \"\"\"Test05: Validation of CORRECTED_DATA = DATA X FPARAM.\n (spwmap={1:[9], 3:[11], 5:[13], 7:[15]})\n antanna1=0, DATA_DISC_ID=9, FPARAM_average\n \"\"\"\n spwmap = {1: [9], 3: [11], 5: [13], 7: [15]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap, applytable=self.tsystable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_test05", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "0c349e3cabc717e2018727f49142a43f1867aacefc7a32b74fea55c09cd43521", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_interp_empty": {"code": "class Apply_Interp:\n def time_interp_empty(self):\n \"\"\"test_apply_sky14 --- apply data (interp='').\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply',\n applytable=self.applytable, interp='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_interp_empty", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d701d56a9753f8ad50dbc76d44d4b658680d8c7c02f8f475620f691fbac607ce", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear": {"code": "class Apply_Interp:\n def time_linear(self):\n \"\"\"test_apply_sky08 --- apply data (linear).\"\"\"\n sdcal(infile=self.infile, calmode='apply',applytable=[self.applytable], interp='linear')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "078d836c355da5773813afc9b5b3f5b9b5af4c7441926882bed13a33c8eb89f1", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_linearflag": {"code": "class Apply_Interp:\n def time_linear_linearflag(self):\n \"\"\"test_apply_sky11 --- apply data (linearflag for frequency interpolation).\"\"\"\n sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], interp='linear,linearflag')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_linearflag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "42bdfd6f7ae95d7cf1b6a99a5ea926b0c5ef23326aa25e525ffed5720eb087e3", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_nearestflag": {"code": "class Apply_Interp:\n def time_linear_nearestflag(self):\n \"\"\"test_apply_sky12 --- apply data (nearestflag for frequency interpolation).\"\"\"\n sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], interp='linear,nearestflag')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_nearestflag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b666aed07e5ff1d2684ae9c5af455f2ed84aaea7a5f31e8814fd23ef31161a1", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_spw_selection": {"code": "class Apply_Interp:\n def time_linear_spw_selection(self):\n \"\"\"test_apply_sky09 --- apply selected data.\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], spw='9', interp='linear')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_spw_selection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "bbc0db0908cc81aff108d4e7635e5be7320ff855384a176dc974409e67acf6dc", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_nearest": {"code": "class Apply_Interp:\n def time_nearest(self):\n \"\"\"test_apply_sky10 --- apply data (nearest).\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply',\n applytable=[self.applytable], interp='nearest')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_nearest", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a3f87e4523727137a12fa96c6ca02b6d24325a73c3cc7b1ed699768a9e8f8b83", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_apply_sky(self):\n \"\"\"test_apply_composite03 --- on-the-fly application of sky table ('otfraster,apply').\"\"\"\n sdcal(infile=self.infile, calmode='tsys,apply', applytable=self.applytable,\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a2e30839f58f4d7cae58ebed88e373ca0266cb545998f2cdc9bd9e99c86a3bb4", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal": {"code": "class Apply_Modified_Tsys:\n def time_apply_sky_normal(self):\n \"\"\"test_apply_composite00 --- on-the-fly application of sky table ('ps,apply').\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "9156519f2fd7ea628cdc9bd83ff8300424ede18396aabf47374e2269671763f1", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_apply_sky_tsys(self):\n \"\"\"test_apply_composite02 --- on-the-fly application of sky and tsys tables ('ps,tsys,apply').\"\"\"\n sdcal(infile=self.infile, calmode='ps,tsys,apply',\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d0b7e9739bae796f78bb578757177ac7aad7d83dd613adf15dfc3629a1f8e11a", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_preapply_tsys": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_preapply_tsys(self):\n \"\"\"test_apply_composite01 --- on-the-fly application of sky table with existing Tsys table.\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply', applytable=self.tsystable,\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_preapply_tsys", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98d92f7d22040c3043807a62e0870768ef9b5093d9e054752f82069890feeb6b", "warmup_time": -1}, "bench_sdcal.OTF.time_otf01": {"code": "class OTF:\n def time_otf01(self):\n \"\"\"test_otf01 --- Compute calibration table. calmode='otf' ms=squares.dec60_cs.ms.\"\"\"\n sdcal(infile=self.infile_squares, calmode='otf', fraction='10%', noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='', spw='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf01", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "73890f5299363c64dc00df3929185044042aecbf7528479fdd2048b07bae4e5a", "warmup_time": -1}, "bench_sdcal.OTF.time_otf02": {"code": "class OTF:\n def time_otf02(self):\n \"\"\"test_otf02 --- Compute calibration table. calmode='otf' ms=squares.dec60_cs.ms edges_fraction=20%.\"\"\"\n sdcal(infile=self.infile_squares, calmode='otf', fraction=0.2, noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='', spw='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf02", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d3f6cfeb66f0f58a7c7af4db78f37b6cb55b571f9a99cacd26c26271231c9eb9", "warmup_time": -1}, "bench_sdcal.OTF.time_otf03": {"code": "class OTF:\n def time_otf03(self):\n \"\"\"test_otf03 --- Compute calibration table. calmode='otf' ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable='',interp='', spwmap={}, outfile=self.outfile, overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf03", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "943238908dd59e211bdf634348dd5f42ac03ffc10488f932e3c34bde04cb2ff9", "warmup_time": -1}, "bench_sdcal.OTF.time_otf04": {"code": "class OTF:\n def time_otf04(self):\n \"\"\"test_otf04 --- Compute calibration table. calmode='otf' ms=lissajous.ms edges_fraction=20%.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf', fraction='20%', noff=-1, width=0.5, elongated=False,\n applytable='',interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf04", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2a260b2487f65ee23665c8bedd0c4148bc2e3d6f36d6a332f015d755bbf015e1", "warmup_time": -1}, "bench_sdcal.OTF.time_otf05": {"code": "class OTF:\n def time_otf05(self):\n \"\"\"test_otf05 --- Sky calibration. calmode='otf,apply' ms=squares.dec60_cs.ms.\"\"\"\n # In the original test it runs sdcal() twice, first with calmode=apply and then with otf,apply\n sdcal(infile=self.infile_squares, calmode='otf,apply', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable='', interp='', spwmap={}, outfile='', overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf05", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4a19756f66b7ef9544328df9520128cd57da97e46f6c462cbd508c850951500b", "warmup_time": -1}, "bench_sdcal.OTF.time_otf06": {"code": "class OTF:\n def time_otf06(self):\n \"\"\"test_otf06 --- Sky calibration reusing caltable pre-computed with calmode='otf'. calmode='apply' ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='apply', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable=self.applytable, interp='', spwmap={}, outfile='', overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf06", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "522a3b5d1d1fdabe2ba018b8273e019b5eddafc14dfdbf7f95daec034fb5f4aa", "warmup_time": -1}, "bench_sdcal.OTF.time_otf07": {"code": "class OTF:\n def time_otf07(self):\n \"\"\"test_otf07 --- Sky calibration + Tsys conversion, composite calmode='otf,tsys,apply'. ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf,tsys,apply', fraction='10%', noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile='', overwrite=False, field='', spw='', scan='', intent='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf07", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "748ba7dd3174afcf0e776988ea8b438690c52ce4db2c52cd38f4008560cb2781", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_autodetection": {"code": "class OTFRaster:\n def time_autodetection(self):\n \"\"\"test_otfraster11 --- OTF raster calibration ('otfraster') with auto detection.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile,\n calmode='otfraster', fraction=0, noff=0)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_autodetection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4ab1d39226973c46b69401fb6d1cdebad9f92dcffdb9e3e05a81a21353a46501", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_default": {"code": "class OTFRaster:\n def time_default(self):\n \"\"\"test_otfraster07 --- OTF raster calibration ('otfraster') with default setting.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_default", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b1cfc4dc086f0f79a3eb1aa50bd34c6e2fb2fe269bce47df997cfb8e78c6197", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_numeric": {"code": "class OTFRaster:\n def time_fraction_numeric(self):\n \"\"\"test_otfraster10 --- OTF raster calibration ('otfraster') with numeric fraction.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction=0.3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_numeric", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "9a1b91b89d7822ba87cb6acef313ffa79716c45ed9b6d7a6a307e452e7f2a76c", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_string_numeric": {"code": "class OTFRaster:\n def time_fraction_string_numeric(self):\n \"\"\"test_otfraster08 --- OTF raster calibration ('otfraster') with string fraction (numeric value).\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='0.3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_string_numeric", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "8e7b855976e9c451b5b3231e2ab52aeb3ec21c80edde166cc9c6ed70d310e202", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_string_percentage": {"code": "class OTFRaster:\n def time_fraction_string_percentage(self):\n \"\"\"test_otfraster09 --- OTF raster calibration ('otfraster') with string fraction (percentage).\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='30%')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_string_percentage", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "99b8030521d84e0d5c0d814a2205f20bd9fd2c1cca59feff9cea414f8790c81b", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_noff_custom": {"code": "class OTFRaster:\n def time_noff_custom(self):\n \"\"\"test_otfraster12 --- OTF raster calibration ('otfraster') with custom noff.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', noff=3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_noff_custom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b0d73553a431b883fcbd52935ecc5b25ca21ab6dcae20bd7943b107a3256a398", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_noff_priority_over_fraction": {"code": "class OTFRaster:\n def time_noff_priority_over_fraction(self):\n \"\"\"test_otfraster13 --- check if noff takes priority over fraction.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='90%', noff=3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_noff_priority_over_fraction", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2c5fb1d0dca19405b4dc976ff1992e112b24758c778b4bb23711529cc94a785d", "warmup_time": -1}, "bench_sdcal.OTF_Ephemeris.time_otf_ephem": {"code": "class OTF_Ephemeris:\n def time_otf_ephem(self):\n \"\"\"test_otfephem01: Sky calibration of 'otf' mode for ephemeris object.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otf')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTF_Ephemeris.time_otf_ephem", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ab9adf8942dfdd994dd3781f749699fa9662bf58575015560e5daa006f34b730", "warmup_time": -1}, "bench_sdcal.PositionSwitch.time_ps_basic": {"code": "class PositionSwitch:\n def time_ps_basic(self):\n \"\"\"test_ps05 --- position switch calibration ('ps').\"\"\"\n sdcal(infile=self.infile, calmode='ps', outfile=self.applytable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.PositionSwitch.time_ps_basic", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b7be7f0ff76e3fe922cbeca2f39ba103e79e44de6d83b4b99be250473cbbbfee", "warmup_time": -1}, "bench_sdcal.PositionSwitch.time_ps_selection": {"code": "class PositionSwitch:\n def time_ps_selection(self):\n \"\"\"test_ps06 --- position switch calibration ('ps') with data selection.\"\"\"\n sdcal(infile=self.infile, calmode='ps', spw='9', outfile=self.applytable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.PositionSwitch.time_ps_selection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c6f801e6c4a066a039b23af69d5fef7d2ff87044ac6f963b222b34644a63e410", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_apply(self):\n \"\"\"test_single_pol_apply --- apply caltable to single-polarization data.\"\"\"\n # Generate ouput file for apply case\n sdcal(infile=self.infile, calmode='ps', outfile=self.outfile)\n sdcal(infile=self.infile, calmode='apply', applytable=self.outfile)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a191063314c95b694fc29c97d3e0dccb94184c2aa1aa185ac280f091ed025233", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply_composite": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_apply_composite(self):\n \"\"\"test_single_pol_apply_composite --- on-the-fly calibration/application on single-polarization data.\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply_composite", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b7552259dc52ed23dd95af462d5d9ef0a35d33f2f79af3ecb6eabd3b97e147ff", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_ps(self):\n \"\"\"test_single_pol_ps --- generate caltable for single-polarization data.\"\"\"\n sdcal(infile=self.infile, calmode='ps', outfile=self.outfile)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4e55128473bdf00218cb5fd33d6a7c9e00e1779f913ec0f11008a756dbc973be", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1cabc32a44329c9516713575172e17491a87d77a616062663d7ef91f65148be3", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter=\"\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b930349db5cc439111860320dde2e9b691abd6d7f9b0ec6689dd11d93b41f84f", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[], minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "336acba07a722b00d07d7f1e576db7324acbb0f8b62094628e049205931267c4", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_start_400": {"code": "class Basic:\n def time_gridfunction_BOX_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f75fe21b7e8d74833923c99f159a218e55bf3f8f8f9678410d7d1bdcc7f7b34", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null": {"code": "class Basic:\n def time_gridfunction_BOX_start_400_phasecenter_null(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1248c14417eaf3acae22a2d74d1856434726fdae49f3cf1f99f38e5a3e236f19", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GAUSS_start_400": {"code": "class Basic:\n def time_gridfunction_GAUSS_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GAUSS', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GAUSS_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "53e251e5821f6f4979c73a56b7aa34456880ac756589b0df55077a9c3127a334", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5627d3dd41598393b2e3bd58277169bbafe63781c6b1b5b01c99b9d60d84cbfc", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400_minweight_70(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=70.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b01ed523cee17466c300376e39e4aacc76b5a67f4ded517d201a5dd7ea56f2b", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB": {"code": "class Basic:\n def time_gridfunction_PB(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c919d306e25f5ec15a0459ba15528f60a1a0dcaae1bf19a352dd255a2dedcf63", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_frequency": {"code": "class Basic:\n def time_gridfunction_PB_mode_frequency(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"frequency\", nchan=100, start=\"1420.200000MHz\", width=\"0.010000MHz\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_frequency", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "11be513d35e8f08e044f3236b2b2ce34adde14a6ff376d9d7d167096237a3316", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_1(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=1, start=\"-50256.681248364m/s\", width=\"527652.833436753m/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', restfreq=\"1420405800.0Hz\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5ade0c08d8b30b806d808aa43cd05eee6f36f09bf14ab0a002acb9b0577b2ad", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_100(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=100, start=\"-200.000000km/s\", width=\"2.000000km/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b5092872498a21e2f8184389383bcd9c29b6b0a38b06ad926ac76f436f5894e2", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024": {"code": "class Basic:\n def time_gridfunction_PB_nchan_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1024, width=1, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "385177e17d19f7fb3812a7e94b7ed7b644c5ed42350f2698e63e42c69f980efb", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_start_400": {"code": "class Basic:\n def time_gridfunction_PB_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b9e23dd0fd8a0537036bc41d832bb013cf550d67faa2807e46e9f2f7361e5e21", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY": {"code": "class Basic:\n def time_gridfunction_PB_start_400_stokes_XXYY(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', stokes=\"XXYY\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1eadce5c526c138fe91b3fbc8ad1e714e468817da26e6c0434558be40125081e", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_width_1024": {"code": "class Basic:\n def time_gridfunction_PB_width_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1, width=1024, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_width_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f31655e48c01e2cc72c95e525fdfe8504693b8ad12dbbedf284ff3b88611c8b", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_SF_start_400": {"code": "class Basic:\n def time_gridfunction_SF_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='SF', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_SF_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad4195f91c64c959d4a9da8efd6e024b0ac662770633e565c781f603fc6d2ab0", "warmup_time": -1}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom": {"code": "class MemoryMultiField:\n def peakmem_multifield_cube_hogbom(self):\n \"\"\"tclean: Two fields, both cube - test_multifield_both_cube\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nimagename=' + self.img + '2\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:48.895 +40.55.58.543\\n')\n \n retpar = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', specmode='cube', nchan=2, interpolation='nearest',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_hogbom": {"code": "class MemoryMultiField:\n def peakmem_multifield_mfs_hogbom(self):\n \"\"\"tclean : Two fields, both mfs - test_multifield_both_mfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nnchan=1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs": {"code": "class MemoryMultiField:\n def peakmem_multifield_mfs_mtmfs(self):\n \"\"\" tclean: Two fields, both mt-mfs - test_multifield_both_mtmfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\n\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting": {"code": "class MemorySetWeighting:\n def peakmem_tclean_setweighting(self):\n \"\"\"Adapted from CAS-13026\"\"\"\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n parallel=False,\n )\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting", "param_names": [], "params": [], "setup_cache_key": "bench_tclean_memory:167", "timeout": 10000, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs": {"code": "class MemorySetWeighting:\n def track_file_descriptors_cubemode_mosaic_briggs(self):\n \"\"\"Adapted from CAS-8755\n https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/browse/casatests/performance/test_perf_tclean_mem_setweighting.py?at=refs%2Fheads%2FCAS-13026#166-225\n \n Requires user configuration ~/.casa/rc to contain `synthesis.imager.memprofile.enable: 1`\n Expected output is casa.synthesis.imager.memprofile.PID.HOSTNAME.DATE_TIME_when_test_started.txt\n \"\"\"\n casalog.setlogfile(self.templogfile)\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n parallel=False,\n )\n \n with open(self.templogfile) as mylog:\n for line in mylog:\n a_match = re.search(\"casa.synthesis.imager.memprofile\", line)\n if a_match:\n str_match = a_match.string\n break\n \n # Get name of memprofile created by tclean\n (start, middle, end) = str_match.partition(\"casa.synthesis.imager.memprofile\")\n mem_profile = middle + end.rstrip()\n \n # Get the memory values of the second column named MemRSS_(VmRSS)_MB, for each row\n with open(mem_profile, \"r\") as mfile:\n memdict = OrderedDict()\n maxFDSize = 0\n for myrow in mfile:\n linelist = []\n print(myrow.rstrip())\n if myrow.startswith(\"#\"):\n continue\n \n linelist = myrow.split(\",\")\n tclean_step = str(linelist[-1].rstrip())\n memdict[tclean_step.strip(\"[]\")] = int(linelist[1])\n maxFDSize = max(maxFDSize, int(linelist[7]))\n \n return maxFDSize\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs", "param_names": [], "params": [], "setup_cache_key": "bench_tclean_memory:167", "timeout": 10000, "type": "track", "unit": "file descriptors", "version": "CAS-13924"}, "bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary": {"code": "class MemorySingleField:\n def peakmem_cube_standard_fullsummary(self):\n \"\"\"tclean: single field cube with fullsummary parameter - test_iterbot_cube_fullsummary_true\"\"\"\n retpar = tclean(vis=self.msfile, imagename=self.img + \"Cubetest_fullsummary\", imsize=100, cell='8.0arcsec',\n specmode='cube', deconvolver='clark', niter=10, threshold='0.75Jy', fullsummary=True, parallel=False)\n\n def setup(self):\n self.prepData(\"refim_point_withline.ms\")", "name": "bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwFalse_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwFalse_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=False,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "213736aba4861d6ada012c27b036d0eefbe891ce27250b0867d8354eeafb9ecb"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue(self):\n \"\"\"tclean: Cube mosaic with conjbeams=F, mosaicweight=F, usepointing=T, of two fields - test_cube_mosaic_cbFalse_mwFalse_twofield_upTrue\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', usepointing = True, conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=False,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "8e2ae00738c0ed705cbc3b1df59069743febbe8c160b1dd759a6537097168c0d"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwTrue_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwTrue_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=True,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "ae1661113bf2b4e2383bbc84b618de00882e9df2d34c706fc6182d5ee88ea29c"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_cube_awproject_hogbom": {"code": "class MemoryWideFieldAWP:\n def peakmem_cube_awproject_hogbom(self):\n \"\"\"tclean: Cube with AW-Projection and rotation off - test_widefield_aproj_cube\"\"\"\n ret = tclean(vis=self.msfile, field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",specmode='cube', niter=1, gain=1.0,\n gridder='awproject', cfcache=self.img + '.cfcache',wbawp=True,conjbeams=False,\n psterm=False, computepastep=360.0, rotatepastep=360.0, deconvolver='hogbom',\n parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_cube_awproject_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "535d7210070d64a24f08ced41f22af7c7ff735b2c653c074297c175a08e8cb0d"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom": {"code": "class MemoryWideFieldAWP:\n def peakmem_mfs_awproject_hogbom(self):\n \"\"\"tclean: MFS with narrowband AWProjection - test_widefield_aproj_mfs\"\"\"\n ret = tclean(vis=self.msfile, spw='1', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=30, gridder='awproject', cfcache='',\n wbawp=False, conjbeams=True, psterm=False,computepastep=360.0,rotatepastep=360.0,\n deconvolver='hogbom', savemodel='modelcolumn', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "b7a2bc60edebed77059280a26d38d60297776207e13bca67e77066c2383a48b1"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_mtmfs": {"code": "class MemoryWideFieldAWP:\n def peakmem_mfs_awproject_mtmfs(self):\n \"\"\"tclean: MFS with AWProjection and nt=2 stokes I - test_widefield_aproj_mtmfs \"\"\"\n ret = tclean(vis=self.msfile, spw='*', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=0, gridder='awproject',\n cfcache=self.img + '.cfcache', wbawp=True, conjbeams=False, psterm=False,\n computepastep=360.0,rotatepastep=360.0, deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_mtmfs", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "d58b12d0e026ea8c921dacf72de9d8f859c6305623fbd66ade64637f8950312d"}, "bench_tclean_time.TcleanCube.time_cube_standard_channel_gap": {"code": "class TcleanCube:\n def time_cube_standard_channel_gap(self):\n \"\"\"tclean\" mosaic cube with gap in channel selection - test_cube_21\"\"\"\n ret = tclean(vis=self.msfile, field='0', imsize=100, cell='8.0arcsec', niter=10,\n specmode='cube', nchan=10, restfreq=['1.25GHz'],\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", deconvolver='hogbom',\n spw='0:4~9;12~14', imagename=self.img + 'Cubetest_st4gap', start=4,\n width='', veltype='radio', outframe='LSRK', interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_channel_gap", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "955f38acfd52f97ee9d29fc749cb59a1cc43e4aba8a57c2cdf36b817208f65a1", "warmup_time": -1}, "bench_tclean_time.TcleanCube.time_cube_standard_fullsummary": {"code": "class TcleanCube:\n def time_cube_standard_fullsummary(self):\n \"\"\"tclean: single field cube with fullsummary parameter - test_iterbot_cube_fullsummary_true\"\"\"\n retpar = tclean(vis=self.msfile, imagename=self.img + \"Cubetest_fullsummary\", imsize=100, cell='8.0arcsec',\n specmode='cube', deconvolver='clark', niter=10, threshold='0.75Jy', fullsummary=True, parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_fullsummary", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanCube.time_cube_standard_hogbom": {"code": "class TcleanCube:\n def time_cube_standard_hogbom(self):\n \"\"\"tclean: mosaic cube with Hogbom deconvolver - test_cube_0\"\"\"\n ret = tclean(vis=self.msfile, field='0', imsize=100, cell='8.0arcsec', niter=10,\n specmode='cube', nchan=10, restfreq=['1.25GHz'],\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", deconvolver='hogbom',\n spw='0', imagename=self.img + 'Cubetest_chandefstdefwidth',\n start=0, width=1, veltype='radio', outframe='LSRK', interpolation='linear', parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ead0eaac76215fd3ed59ddd926e9fdfb598dec50a29590840e6bf8a9c1139cfa", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_cubesource": {"code": "class TcleanEphemeris:\n def time_eph_multifield_mosaic_cubesource(self):\n \"\"\"tclean: multifield (mosaic gridder), cubesource mode - test_multifield_cube_eph\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=[480, 420], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='cubesource', gridder='mosaic', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_mfs": {"code": "class TcleanEphemeris:\n def time_eph_multifield_mosaic_mfs(self):\n \"\"\"tclean: multifield (mosaic gridder), mfs mode - test_multifield_mfs_eph\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=[480, 420], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='mfs', gridder='mosaic', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_mfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource": {"code": "class TcleanEphemeris:\n def time_eph_singlefield_standard_cubesource(self):\n \"\"\"tclean: single field (standard gridder), cubesource mode - test_onefield_cube_eph\"\"\"\n ret = tclean(vis=self.msfile, field='0', imagename=self.img, imsize=[288, 288], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='cubesource', gridder='standard', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs": {"code": "class TcleanEphemeris:\n def time_eph_singlefield_standard_mfs(self):\n \"\"\"single field (standard gridder), mfs mode - test_onefield_mfs_eph\"\"\"\n ret = tclean(vis=self.msfile, field='0', imagename=self.img, imsize=[288, 288], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='mfs', gridder='standard', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_automask_modelcolumn(self):\n \"\"\"tclean: mfs with automask and save model column, internally a separate predit model step - test_modelvis_12\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n savemodel='modelcolumn',usemask='auto-multithresh', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d60a63bc05d020e7f8100acf6044bbeda8cd4ef0f53267b53984b3580a421bc", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_modelcolumn(self):\n \"\"\"tclean: mfs mode saving a model column - test_modelvis_2\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n savemodel='modelcolumn',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "39a0309c515e5dfe440b399768551ee76165e4551a994dcb41e2ed255523940e", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_virtual_modelcolumn(self):\n \"\"\"tclean: mfs saving a virtual model column - test_modelvis_3\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n savemodel='virtual',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "322f52d2bb9054965cfef3b9c72cf5750386b28c13444d0055c32f9854934461", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom": {"code": "class TcleanMultiField:\n def time_multifield_cube_hogbom(self):\n \"\"\"tclean: Two fields, both cube - test_multifield_both_cube\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nimagename=' + self.img + '2\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:48.895 +40.55.58.543\\n')\n \n retpar = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', specmode='cube', nchan=2, interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom": {"code": "class TcleanMultiField:\n def time_multifield_mfs_hogbom(self):\n \"\"\"tclean : Two fields, both mfs - test_multifield_both_mfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nnchan=1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_mfs_mtmfs": {"code": "class TcleanMultiField:\n def time_multifield_mfs_mtmfs(self):\n \"\"\" tclean: Two fields, both mt-mfs - test_multifield_both_mtmfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\n\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_mfs_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_cube_standard_pcwdT": {"code": "class TcleanSingleField:\n def time_cube_standard_pcwdT(self):\n \"\"\"tclean: cube with perchanweightdensity True and briggs weighting - test_onefield_pcwdT_and_pcwdF\"\"\"\n ret = tclean(self.msfile, imagename=self.img + '1', imsize=20, cell='8.0arcsec', niter=0, nchan=1,\n spw='0:1', gridder='standard', perchanweightdensity=True, specmode='cube',\n weighting='briggs', robust=0.5)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_cube_standard_pcwdT", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_clark": {"code": "class TcleanSingleField:\n def time_mfs_standard_clark(self):\n \"\"\"tclean: mfs with clark minor cycle - test_onefield_clark\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='clark', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_clark", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom": {"code": "class TcleanSingleField:\n def time_mfs_standard_hogbom(self):\n \"\"\"tclean: mfs with hogbom minor cycle - test_onefield_hogbom\"\"\"\n tricky_imagename = self.img + '_uid___A001_X1234a_X56cb.s19_0.J2253+1608_bp.more-dash.virtspw19.mfs.I.iter0.hoghbom'\n ret = tclean(vis=self.msfile, imagename=tricky_imagename, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom_automask": {"code": "class TcleanSingleField:\n def time_mfs_standard_hogbom_automask(self):\n \"\"\"tclean: multi-threshold Autobox (minbeamfrac=0.3) - test_mask_autobox_multithresh_with_prune\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=1000,cell='8.0arcsec',niter=10,\n deconvolver='hogbom', usemask='auto-multithresh',\n minbeamfrac=0.3,parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom_automask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs": {"code": "class TcleanSingleField:\n def time_mfs_standard_mtmfs(self):\n \"\"\"tclean: mt-mfs with minor cycle iterations - test_onefield_mtmfs\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_cube_stokes_IV": {"code": "class TcleanStokes:\n def time_cube_stokes_IV(self):\n \"\"\"tclean: cube with stokes V - test_stokes_cube_IV\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n stokes='IV', specmode='cube', interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_cube_stokes_IV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_mfs_stokes_IV": {"code": "class TcleanStokes:\n def time_mfs_stokes_IV(self):\n \"\"\"tclean: mfs with stokes IV - test_stokes_mfs_IV\"\"\"\n tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10, stokes='IV',\n parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_mfs_stokes_IV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d35d9a07fff354a74fbeb5aecec4abcc4dc94d0e83e10f366cb69e439004ed44", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV": {"code": "class TcleanStokes:\n def time_mtmfs_stokes_IQUV(self):\n \"\"\"tclean: mtmfs with stokes IQUV - test_stokes_mtmfs_IQUV\"\"\"\n tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10, stokes='IQUV',\n deconvolver='mtmfs', nterms=2, parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d3a14b77c55eebaebca13da5c79f9b48d38bf677f7e4aaf3c5103ca66e21006", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_cube_awproject_hogbom": {"code": "class TcleanWideFieldAWP:\n def time_cube_awproject_hogbom(self):\n \"\"\"tclean: Cube with AW-Projection and rotation off - test_widefield_aproj_cube\"\"\"\n ret = tclean(vis=self.msfile, field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",specmode='cube', niter=1, gain=1.0,\n gridder='awproject', cfcache=self.img + '.cfcache',wbawp=True,conjbeams=False,\n psterm=False, computepastep=360.0, rotatepastep=360.0, deconvolver='hogbom',\n parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_cube_awproject_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e5933d46b7d76f6e722504feff6442d7b22d7a07286b441227931d33b9adfae7", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom": {"code": "class TcleanWideFieldAWP:\n def time_mfs_awproject_hogbom(self):\n \"\"\"tclean: MFS with narrowband AWProjection - test_widefield_aproj_mfs\"\"\"\n ret = tclean(vis=self.msfile, spw='1', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=30, gridder='awproject', cfcache='',\n wbawp=False, conjbeams=True, psterm=False,computepastep=360.0,rotatepastep=360.0,\n deconvolver='hogbom', savemodel='modelcolumn', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b822057988cc0cc532d1b070ee27c6cca7c74940c97f32e4cae5442b0a580aec", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs": {"code": "class TcleanWideFieldAWP:\n def time_mfs_awproject_mtmfs(self):\n \"\"\"tclean: MFS with AWProjection and nt=2 stokes I - test_widefield_aproj_mtmfs \"\"\"\n ret = tclean(vis=self.msfile, spw='*', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=0, gridder='awproject',\n cfcache=self.img + '.cfcache', wbawp=True, conjbeams=False, psterm=False,\n computepastep=360.0,rotatepastep=360.0, deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "67a1d66f43c33d52452f3438452c599001c854eae7b69f546049457c746928d6", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwFalse_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwFalse_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=False, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue(self):\n \"\"\"tclean: Cube mosaic with conjbeams=F, mosaicweight=F, usepointing=T, of two fields - test_cube_mosaic_cbFalse_mwFalse_twofield_upTrue\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n usepointing = True, conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=False, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "6692e661298a1a5355c1201359883748b54bbd123c50dd2b2ea3ec5b412ec827", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwTrue_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwTrue_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=True, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad3861f21889712e38ffbf6ef492929807086c6012d61b230a740229d1d6b7c5", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1cabc32a44329c9516713575172e17491a87d77a616062663d7ef91f65148be3", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter=\"\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b930349db5cc439111860320dde2e9b691abd6d7f9b0ec6689dd11d93b41f84f", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[], minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "336acba07a722b00d07d7f1e576db7324acbb0f8b62094628e049205931267c4", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400": {"code": "class Basic:\n def time_gridfunction_BOX_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f75fe21b7e8d74833923c99f159a218e55bf3f8f8f9678410d7d1bdcc7f7b34", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null": {"code": "class Basic:\n def time_gridfunction_BOX_start_400_phasecenter_null(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1248c14417eaf3acae22a2d74d1856434726fdae49f3cf1f99f38e5a3e236f19", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GAUSS_start_400": {"code": "class Basic:\n def time_gridfunction_GAUSS_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GAUSS', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GAUSS_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "53e251e5821f6f4979c73a56b7aa34456880ac756589b0df55077a9c3127a334", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5627d3dd41598393b2e3bd58277169bbafe63781c6b1b5b01c99b9d60d84cbfc", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400_minweight_70(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=70.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b01ed523cee17466c300376e39e4aacc76b5a67f4ded517d201a5dd7ea56f2b", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB": {"code": "class Basic:\n def time_gridfunction_PB(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c919d306e25f5ec15a0459ba15528f60a1a0dcaae1bf19a352dd255a2dedcf63", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_frequency": {"code": "class Basic:\n def time_gridfunction_PB_mode_frequency(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"frequency\", nchan=100, start=\"1420.200000MHz\", width=\"0.010000MHz\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_frequency", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "11be513d35e8f08e044f3236b2b2ce34adde14a6ff376d9d7d167096237a3316", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_1(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=1, start=\"-50256.681248364m/s\", width=\"527652.833436753m/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', restfreq=\"1420405800.0Hz\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5ade0c08d8b30b806d808aa43cd05eee6f36f09bf14ab0a002acb9b0577b2ad", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_100(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=100, start=\"-200.000000km/s\", width=\"2.000000km/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b5092872498a21e2f8184389383bcd9c29b6b0a38b06ad926ac76f436f5894e2", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_nchan_1024": {"code": "class Basic:\n def time_gridfunction_PB_nchan_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1024, width=1, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_nchan_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "385177e17d19f7fb3812a7e94b7ed7b644c5ed42350f2698e63e42c69f980efb", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_start_400": {"code": "class Basic:\n def time_gridfunction_PB_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b9e23dd0fd8a0537036bc41d832bb013cf550d67faa2807e46e9f2f7361e5e21", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY": {"code": "class Basic:\n def time_gridfunction_PB_start_400_stokes_XXYY(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', stokes=\"XXYY\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1eadce5c526c138fe91b3fbc8ad1e714e468817da26e6c0434558be40125081e", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_width_1024": {"code": "class Basic:\n def time_gridfunction_PB_width_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1, width=1024, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_width_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f31655e48c01e2cc72c95e525fdfe8504693b8ad12dbbedf284ff3b88611c8b", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_SF_start_400": {"code": "class Basic:\n def time_gridfunction_SF_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='SF', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_SF_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad4195f91c64c959d4a9da8efd6e024b0ac662770633e565c781f603fc6d2ab0", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_cubesource": {"code": "class Ephemeris:\n def time_ephemeris_cubesource(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,veltype='radio', specmode='cubesource', outframe='',\n gridfunction='BOX', convsupport=-1, truncate=-1,gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec',\n phasecenter='TRACKFIELD', projection='SIN',pointingcolumn='direction', restfreq='', stokes='I',\n minweight=0.1, brightnessunit='',clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e1590a631850386242adaf9bd659e0dc1827e29a18273a09188b0b52c3e6f76e", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_notset": {"code": "class Ephemeris:\n def time_ephemeris_notset(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_notset", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "0f8206812c10517da8551f9b08b58784bdb8a969ae3fc745bdd975a628da94de", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_sun": {"code": "class Ephemeris:\n def time_ephemeris_sun(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='SUN', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_sun", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e8e8766d8a4af7429cd16c665606e2e5f05b124fbe24196c8a90ae14856f5670", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_table": {"code": "class Ephemeris:\n def time_ephemeris_table(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,veltype='radio', specmode='cube',\n outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,gwidth=-1, jwidth=-1,\n imsize=[1000], cell='4arcsec',phasecenter=self.ephtab,\n projection='SIN',pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1,\n brightnessunit='',clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_table", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "abb49342590225dc02b50b7a4fc1558664794ae427a0503721ac0a6ae0d95959", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_trackf": {"code": "class Ephemeris:\n def time_ephemeris_trackf(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='TRACKFIELD', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_trackf", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "dc585b5f985c2244f266a12e7a01143c4ce9702c0bdbed596e13752673bfc047", "warmup_time": -1}, "bench_uvcontsub.Basic.time_datacolumn": {"code": "class Basic:\n def time_datacolumn(self):\n uvcontsub(vis=ms_corr, outputvis=self.output, datacolumn='CORRECTED')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_datacolumn", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "8de4cfdf0dc3bf0bed17e8d3988ba003ec80e4ec5f8c98958fb4cc44bea1f96b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitmethod_casacore": {"code": "class Basic:\n def time_fitmethod_casacore(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitmethod='casacore')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitmethod_casacore", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "dfb214a4421164ab4c380082077ebe9f5d3903a3c5c171837c379819feae42ea", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitmethod_gsl": {"code": "class Basic:\n def time_fitmethod_gsl(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitmethod='gsl')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitmethod_gsl", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0d8fee718ac26b9dcd2c95fe20e8b4254e1125c0ea176bce6dbeeb7b1414efce", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitorder1": {"code": "class Basic:\n def time_fitorder1(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=1,\n fitspec='0:2~20')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitorder1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "8fd68c9b3e632dd7b26ef564729b42105fc47f1779e544fae389d5adc08e98e7", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitorder2": {"code": "class Basic:\n def time_fitorder2(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=2,\n fitspec='0:2~20')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitorder2", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "3114172327fd2768029d264e578ebde390247d280c059d46d578da04ef3ea9b8", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec3_spws_fitorder0": {"code": "class Basic:\n def time_fitspec3_spws_fitorder0(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output, fitorder=0,\n fitspec='3')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec3_spws_fitorder0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "9d83a87c6ebaa6f3b9bbd80256e790db10fdaad1d7869f1801bda6f548548816", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_channels": {"code": "class Basic:\n def time_fitspec_channels(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='0:5~19')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_channels", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "211807ad73464bccf9f2d9954603775df109fd39308249c8f0a8be8468d0d504", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitorder1": {"code": "class Basic:\n def time_fitspec_dict_fitorder1(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=1,\n fitspec={'0': {'0': {'chan': '2~20',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitorder1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "788bb7c38a92579219296d435a9f8831a5e8600ec97baf7c70505174b4112826", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitorder2": {"code": "class Basic:\n def time_fitspec_dict_fitorder2(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=2,\n fitspec={'0': {'0': {'chan': '2~20',\n 'fitorder': 2}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitorder2", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "c2d39c9e5d998c65266f1496a25fb286d7e9394bb9c3764d4a6890f796e54ef3", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan": {"code": "class Basic:\n def time_fitspec_dict_fitspec_one_chan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0',\n fitspec={'0':\n {'1': {'chan': '',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "68512d1a0bce4206afd18b13bd2704c6ac07ee1ef9a1e0652600336643789aab", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1": {"code": "class Basic:\n def time_fitspec_dict_fitspec_one_chan_order1(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0',\n fitspec={'0':\n {'1': {'chan': '',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "69adae3d962899abd6303be60d0a44ddd656a8b72910418f80f587da540cd78c", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching": {"code": "class Basic:\n def time_fitspec_dict_select_spw_mismatching(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0', spw='1',\n fitspec={'0':\n {'0': {'chan': '2~127',\n 'fitorder': 0}},\n '1':\n {'0': {'chan': '0~40',\n 'fitorder': 1}},\n '3':\n {'0': {'chan': '1~33;56~119',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "faa5b02f1961d7e57a2431ef5fb58c23bade435413dd1890f34460c73f7a6c61", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_empty": {"code": "class Basic:\n def time_fitspec_empty(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_empty", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1162b94dcdcd8c1468b01cce5b33eee0e86e593f92e9f394436369c566e59d03", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield": {"code": "class Basic:\n def time_fitspec_multifield(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={\n '0': {'0': {'chan': '100~500;600~910;1215~1678;1810~1903',\n 'fitorder': 0}},\n '1': 'NONE',\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "2200609258dcdd47db8e8f5ad4d0e55a502812ffa3624f3a48358904d23ad6a0", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_blocks": {"code": "class Basic:\n def time_fitspec_multifield_blocks(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={\n '0, 1': {'0': {'chan': '100~500;600~900;1200~1900',\n 'fitorder': 0}},\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_blocks", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "99f4e00c2743d33e8c1c353c1cadc351fcea655f7878133259e3365e398f5abf", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel": {"code": "class Basic:\n def time_fitspec_multifield_fields_sel(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='1,2',\n fitspec={'1': {'0': {'chan': '100~500;600~900',\n 'fitorder': 0}},\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "c5a598b1002088c1224c744c4cc7865f8d7583a29b35f620b379ea9b8c7f6ef5", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_multispw_diff_fitorder": {"code": "class Basic:\n def time_fitspec_multifield_multispw_diff_fitorder(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={'0': {'0': {'chan':\n '100~500;600~910;1215~1678;1810~1903',\n 'fitorder': 1},\n '1': {'chan': '0',\n 'fitorder': 2}},\n '1': 'NONE',\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 2}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_multispw_diff_fitorder", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1884692bb3e4d160ac28dfe708bf2b0bf06f9d75589fca4f1688c9abadfc948c", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_separate_fields": {"code": "class Basic:\n def time_fitspec_separate_fields(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='1',\n fitspec='0:100~500;600~910;1215~1678;1810~1903')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_separate_fields", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "cf05e60ae71d4af643accde4c61a2ae44cf1a783edf0ce36a95fee1cb106b923", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spw_one_chan": {"code": "class Basic:\n def time_fitspec_spw_one_chan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0', fitspec='1')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spw_one_chan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "9c251da305a5aa0f63a2a75105ca215862963f9d11cc4a6b695cf8a702dcdc84", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spws": {"code": "class Basic:\n def time_fitspec_spws(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spws", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "ab9cf480d9ce00340dbf39ac9cbc14a74a1bb716c43bfffc6322b5af7020a160", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spws_sel": {"code": "class Basic:\n def time_fitspec_spws_sel(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, spw='0', fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spws_sel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "33ec54a7a626bd87b33c1f5ed0b20762be14e0e6a17f4bc6076f269384958cf8", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spwschans_fitorder3": {"code": "class Basic:\n def time_fitspec_spwschans_fitorder3(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output, fitorder=3,\n fitspec='4:50~503;600~850;900~1002')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spwschans_fitorder3", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "fa14ea078998aaba67b8acd52f7693a1bbdd7d1f273ce738bd3959de96ce8c71", "warmup_time": -1}, "bench_uvcontsub.Basic.time_makes_output_ms_data": {"code": "class Basic:\n def time_makes_output_ms_data(self):\n uvcontsub(vis=ms_simple, outputvis=self.output)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_makes_output_ms_data", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0f51dc9c6b96c74c4dbd8c11f8388a28712b70fcf187ab98cfd821e516aa90d3", "warmup_time": -1}, "bench_uvcontsub.Basic.time_mixed_pols": {"code": "class Basic:\n def time_mixed_pols(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_mixed_pols", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "90b6d31e3a0ee607357ff1720317395dc6ee5269a5e70d651ef0ef9c727c3878", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_array": {"code": "class Basic:\n def time_select_array(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, array='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_array", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "ea3c135bafff1459cbc837162309e4b6ae3fece4a6be027a4df51bdda97ab7fb", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_field": {"code": "class Basic:\n def time_select_field(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='2')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_field", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1886548da1c74441518ea6cdbc2ed5b6fa259fcb2fcec3d4e5df5c1e0b1c865d", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_intent": {"code": "class Basic:\n def time_select_intent(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, intent='*AMPLI*')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_intent", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "b87258a05737dcccf486624392643fc288a6a2c8474c205ece3cb80d87d202eb", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_observation": {"code": "class Basic:\n def time_select_observation(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, observation='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_observation", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a6b18c12fe61a940c0ecbdcfa89944ed0e091b8b4d4198b17c345a9f3c4c4c4b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_scan": {"code": "class Basic:\n def time_select_scan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, scan='2')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_scan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "6fe8f7df4455762e4e5bc08a95d1cd7417fd105e9c5aa51272d28f53c7696b9b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_spw": {"code": "class Basic:\n def time_select_spw(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, spw='1')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_spw", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "cf736c29415ea590041754714b678dd26b800a40ab3134c319bac8dbe12600dd", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_spw_mismatching_fitspec": {"code": "class Basic:\n def time_select_spw_mismatching_fitspec(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, spw='1', fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_spw_mismatching_fitspec", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "158ef3ee6006d0930dc734a45cceab1d358d189bb5c2da6fba1f21528b7d587a", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel": {"code": "class Basic:\n def time_writemodel(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "84ee71017eadec38583d3c0e1539ed8d9af22f22a326abf0e4730b08fe77142d", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel_from_corrected": {"code": "class Basic:\n def time_writemodel_from_corrected(self):\n uvcontsub(vis=ms_papersky, outputvis=self.output, datacolumn='CORRECTED',\n writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel_from_corrected", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "49f06856290be0914b13df703b42f5742c7d8d6c65041f711909ffe6bb54b038", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel_from_corrected_all_flagged": {"code": "class Basic:\n def time_writemodel_from_corrected_all_flagged(self):\n uvcontsub(vis=ms_corr, outputvis=self.output, datacolumn='CORRECTED',\n writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel_from_corrected_all_flagged", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "bb9609826a0e5903041743e876e9c1130f97601a78f33da3d9171171b66d44bc", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_0": {"code": "class Numerical:\n def time_sim_specline_noise_pol_0(self):\n \"\"\" Check fitting of continuum as polynomial order 0\"\"\"\n uvcontsub(vis=self.ms_cont_noise_order_0, outputvis=self.output,\n fitorder=0, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a3dc88dff98704afaa2065680f87615524f3c67b46d920634e59c82d0f4cf406", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_1": {"code": "class Numerical:\n def time_sim_specline_noise_pol_1(self):\n \"\"\" Check fitting of continuum as polynomial order 1. Gaussian noise included\"\"\"\n uvcontsub(vis=self.ms_cont_noise_order_1, outputvis=self.output,\n fitorder=1, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "4b6fc8f71c9d351593ef5059ab3339340b3e8aab807d18261c20224c22fd0b9c", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_0": {"code": "class Numerical:\n def time_sim_specline_nonoise_pol_0(self):\n \"\"\" Check fitting of continuum as polynomial order 0\"\"\"\n uvcontsub(vis=self.ms_cont_nonoise_order_0, outputvis=self.output,\n fitorder=0, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a06063282ce6f0ecae2282332b977438de648778798aab364359767983f41fac", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_1": {"code": "class Numerical:\n def time_sim_specline_nonoise_pol_1(self):\n \"\"\" Check fitting of continuum as polynomial order 1\"\"\"\n uvcontsub(vis=self.ms_cont_nonoise_order_1, outputvis=self.output,\n fitorder=1, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0574b1e7222ff924033c48c38b27199dd8addd98dcec9671894b820ef6ec72d2", "warmup_time": -1}, "casademo.calibration_suite.time_applycal_callib": {"code": "class calibration_suite:\n def time_applycal_callib(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\n \n Note that this is from on-the-fly application of preliminary phase-up, bandpass, and amplitude caltables, not the later stage hifa_applycal\n Expected to take ~48s, could be sped up by splitting out SPWs (especially the square law detector windows) or applying to only one of them.\n \"\"\"\n applycal(\n vis=self.input_ms,\n field=\"J1924-2914\",\n spw=\"16,18,20,22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n timerange=\"\",\n uvrange=\"\",\n antenna=\"*&*\",\n scan=\"\",\n observation=\"\",\n msselect=\"\",\n docallib=True,\n callib=self.applycal_library,\n gaintable=[],\n gainfield=[],\n interp=[],\n spwmap=[],\n calwt=[True],\n parang=False,\n applymode=\"calflagstrict\",\n flagbackup=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_applycal_callib", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "25f369e7a681d0d6ccb62c48d6a227c1fa45a7e9f79792f60a3fd40a6997e2e2", "warmup_time": -1}, "casademo.calibration_suite.time_bandpass": {"code": "class calibration_suite:\n def time_bandpass(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\"\"\"\n bandpass(\n vis=self.input_ms,\n caltable=self.bandpass_table,\n field=\"J1924-2914\",\n spw=\"22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n antenna=\"0~9\",\n solint=\"inf,15.625000MHz\",\n combine=\"scan\",\n refant=\"CM03,CM10,CM02,CM12,CM06,CM05,CM11,CM04,CM07,CM01\",\n minblperant=4,\n minsnr=3.0,\n solnorm=True,\n bandtype=\"B\",\n append=True,\n gaintable=[self.tsyscal_table, self.gaincal_table],\n gainfield=[\"J1924-2914\", \"nearest\"],\n interp=[\"linear,linear\", \"linear,linear\"],\n spwmap=[\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 18, 20, 22, 16, 16, 18, 18, 20, 20, 22,],\n [],\n ],\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_bandpass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "3ab2720c5ee4aa0f74882ac18410ea0ab34d6e7f1e940421a993d057380f2fdb", "warmup_time": -1}, "casademo.calibration_suite.time_gaincal": {"code": "class calibration_suite:\n def time_gaincal(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\n \n Expected to take ~7s\n \"\"\"\n gaincal(\n vis=self.input_ms,\n caltable=self.gaincal_table,\n field=\"J1924-2914\",\n spw=\"16,18,20,22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n timerange=\"\",\n uvrange=\"\",\n antenna=\"0~9\",\n scan=\"\",\n observation=\"\",\n msselect=\"\",\n solint=\"int\",\n combine=\"\",\n preavg=-1.0,\n refant=\"CM03,CM10,CM02,CM12,CM06,CM05,CM11,CM04,CM07,CM01\",\n refantmode=\"flex\",\n minblperant=4,\n minsnr=3.0,\n solnorm=False,\n normtype=\"mean\",\n gaintype=\"G\",\n smodel=[],\n calmode=\"p\",\n solmode=\"\",\n rmsthresh=[],\n corrdepflags=False,\n append=False,\n splinetime=3600.0,\n npointaver=3,\n phasewrap=180.0,\n docallib=False,\n callib=\"\",\n gaintable=[self.gaincal_table],\n gainfield=[\"J1924-2914\"],\n interp=[\"linear,linear\"],\n spwmap=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 18, 20, 22, 16, 16, 18, 18, 20, 20, 22,],\n parang=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_gaincal", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "a5eafd63e34c53ae63cd3949f23f8acc371d5751a08dcd1681914bf7c0723779", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_bandpassflag": {"code": "class flagdata_suite:\n def time_flagdata_bandpassflag(self):\n \"\"\"hifa_bandpassflag\"\"\"\n flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=[\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='16' antenna='CM05' \\\n timerange='20:09:50~20:09:52' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:09:20~20:09:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:10:21~20:10:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='22' antenna='CM05' \\\n timerange='20:09:30~20:09:32' field='J1924-2914' reason='bad antenna timestamp'\",\n ],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_bandpassflag", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "f55e0ffa6be9d655555c5081a0e7bf2bc00b253273ba7c220de0bd2cfb3baf6c", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_list": {"code": "class flagdata_suite:\n def time_flagdata_list(self):\n \"\"\"hifa_flagdata\"\"\"\n flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=self.flags_cmd,\n tbuff=[0.048, 0.0],\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_list", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "1a40b66ffb4a43d48af3407bae817ed4f7667fe778880859384f5f2e7c9c5d1f", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_list_summary": {"code": "class flagdata_suite:\n def time_flagdata_list_summary(self):\n \"\"\"hifa_rawflagchans\"\"\"\n summary_dict = flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=[\"mode='summary' name='before'\"],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_list_summary", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "3eb284062687484026a1defb4fceb705e83b244e37405f8794d431940ff44b08", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_summary": {"code": "class flagdata_suite:\n def time_flagdata_summary(self):\n \"\"\"hifa_importdata\"\"\"\n summary_dict = flagdata(vis=self.datapath, flagbackup=False, mode=\"summary\")\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_summary", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "9e569fb4e2a2f68e59c510bbf889632173be867104a4e5ba5a98d0eb110e82fb", "warmup_time": -1}, "casademo.tclean_memory_suite.peakmem_tclean_setweighting": {"code": "class tclean_memory_suite:\n def peakmem_tclean_setweighting(self):\n \"\"\"Adapted from CAS-13026\"\"\"\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n interactive=False,\n parallel=False,\n )\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "casademo.tclean_memory_suite.peakmem_tclean_setweighting", "param_names": [], "params": [], "setup_cache_key": "casademo:37", "timeout": 10000, "type": "peakmemory", "unit": "bytes", "version": "8f03400a55e077279ae97bfcf867ab2364d450662c2cdd1f58f3293e06630979"}, "casademo.tclean_memory_suite.track_tclean_file_descriptors_cubemode_mosaic_briggs": {"code": "class tclean_memory_suite:\n def track_tclean_file_descriptors_cubemode_mosaic_briggs(self):\n \"\"\"Adapted from CAS-8755\n \n https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/browse/casatests/performance/test_perf_tclean_mem_setweighting.py?at=refs%2Fheads%2FCAS-13026#166-225\n \n Requires user configuration ~/.casa/rc to contain `synthesis.imager.memprofile.enable: 1`\n Expected output is casa.synthesis.imager.memprofile.PID.HOSTNAME.DATE_TIME_when_test_started.txt\n \"\"\"\n # attribute for tracking metric\n unit = \"file descriptors\"\n \n casalog.setlogfile(self.templogfile)\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n interactive=False,\n parallel=False,\n )\n \n with open(self.templogfile) as mylog:\n for line in mylog:\n a_match = re.search(\"casa.synthesis.imager.memprofile\", line)\n if a_match:\n str_match = a_match.string\n break\n \n # Get name of memprofile created by tclean\n (start, middle, end) = str_match.partition(\"casa.synthesis.imager.memprofile\")\n mem_profile = middle + end.rstrip()\n \n # Get the memory values of the second column named MemRSS_(VmRSS)_MB, for each row\n with open(mem_profile, \"r\") as mfile:\n memdict = OrderedDict()\n maxFDSize = 0\n for myrow in mfile:\n linelist = []\n print(myrow.rstrip())\n if myrow.startswith(\"#\"):\n continue\n \n linelist = myrow.split(\",\")\n tclean_step = str(linelist[-1].rstrip())\n memdict[tclean_step.strip(\"[]\")] = int(linelist[1])\n maxFDSize = max(maxFDSize, int(linelist[7]))\n \n return maxFDSize\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "casademo.tclean_memory_suite.track_tclean_file_descriptors_cubemode_mosaic_briggs", "param_names": [], "params": [], "setup_cache_key": "casademo:37", "timeout": 10000, "type": "track", "unit": "unit", "version": "f9c40e4b16d7caf92b2d5a831995e197fae67947f0ec9bdad00b852f28656239"}}, "machines": {"NRAO workstation": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "version": 1}, "casa-perf-test": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "version": 1}, "cvpost029": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz", "machine": "cvpost029", "num_cpu": "32", "os": "Linux 3.10.0-957.5.1.el7.x86_64", "ram": "264033420", "version": 1}}, "tags": {"1.2.3-rel-1": 12515, "1.2.3-rel-2": 12516, "2019.212": 17631, "2019.213": 17635, "2019.214": 17638, "2019.215": 17646, "2019.216": 17660, "2019.217": 17663, "2019.218": 17683, "2019.219": 17693, "2019.220": 17697, "2019.221": 17699, "2019.222": 17700, "2019.223": 17705, "2019.224": 17726, "2019.225": 17729, "2019.226": 17740, "2019.227": 17755, "2019.228": 17761, "2019.229": 17772, "2019.230": 17796, "2019.231": 17807, "2019.232": 17824, "2019.233": 17836, "2019.234": 17846, "2019.235": 17879, "2019.236": 17894, "2019.237": 17905, "2019.238": 17917, "2019.239": 17976, "4.7-mas-1": 8092, "4.7-mas-10": 8292, "4.7-mas-11": 8298, "4.7-mas-12": 8308, "4.7-mas-13": 8313, "4.7-mas-14": 8325, "4.7-mas-15": 8334, "4.7-mas-16": 8349, "4.7-mas-17": 8354, "4.7-mas-18": 8357, "4.7-mas-19": 8361, "4.7-mas-2": 8101, "4.7-mas-20": 8365, "4.7-mas-21": 8366, "4.7-mas-22": 8367, "4.7-mas-23": 8371, "4.7-mas-24": 8376, "4.7-mas-25": 8382, "4.7-mas-26": 8386, "4.7-mas-27": 8391, "4.7-mas-28": 8400, "4.7-mas-29": 8406, "4.7-mas-3": 8143, "4.7-mas-30": 8415, "4.7-mas-31": 8425, "4.7-mas-32": 8428, "4.7-mas-33": 8429, "4.7-mas-34": 8433, "4.7-mas-35": 8438, "4.7-mas-36": 8446, "4.7-mas-37": 8455, "4.7-mas-38": 8471, "4.7-mas-39": 8491, "4.7-mas-4": 8158, "4.7-mas-40": 8501, "4.7-mas-41": 8516, "4.7-mas-42": 8526, "4.7-mas-43": 8543, "4.7-mas-44": 8560, "4.7-mas-45": 8575, "4.7-mas-46": 8582, "4.7-mas-47": 8583, "4.7-mas-48": 8591, "4.7-mas-49": 8603, "4.7-mas-5": 8253, "4.7-mas-50": 8608, "4.7-mas-51": 8619, "4.7-mas-52": 8621, "4.7-mas-53": 8622, "4.7-mas-54": 8627, "4.7-mas-55": 8630, "4.7-mas-56": 8636, "4.7-mas-57": 8638, "4.7-mas-58": 8639, "4.7-mas-59": 8640, "4.7-mas-6": 8259, "4.7-mas-60": 8641, "4.7-mas-61": 8642, "4.7-mas-62": 8649, "4.7-mas-63": 8659, "4.7-mas-64": 8669, "4.7-mas-65": 8670, "4.7-mas-66": 8673, "4.7-mas-67": 8675, "4.7-mas-68": 8681, "4.7-mas-69": 8683, "4.7-mas-7": 8268, "4.7-mas-70": 8688, "4.7-mas-71": 8701, "4.7-mas-72": 8712, "4.7-mas-73": 8719, "4.7-mas-74": 8731, "4.7-mas-75": 8735, "4.7-mas-8": 8279, "4.7-mas-9": 8291, "4.7.0-mas-63": 8659, "5.0-mas-1": 8745, "5.0-mas-2": 8748, "5.0-mas-3": 8751, "5.0-mas-4": 8758, "5.0-mas-5": 8763, "5.0-mas-6": 8767, "5.0-mas-7": 8780, "5.0-mas-8": 8786, "5.0.0-mas-10": 8803, "5.0.0-mas-100": 9380, "5.0.0-mas-101": 9381, "5.0.0-mas-102": 9389, "5.0.0-mas-103": 9405, "5.0.0-mas-104": 9421, "5.0.0-mas-105": 9423, "5.0.0-mas-106": 9427, "5.0.0-mas-107": 9438, "5.0.0-mas-108": 9456, "5.0.0-mas-109": 9472, "5.0.0-mas-11": 8807, "5.0.0-mas-110": 9475, "5.0.0-mas-111": 9483, "5.0.0-mas-112": 9494, "5.0.0-mas-113": 9502, "5.0.0-mas-114": 9522, "5.0.0-mas-115": 9538, "5.0.0-mas-116": 9547, "5.0.0-mas-117": 9551, "5.0.0-mas-118": 9562, "5.0.0-mas-119": 9571, "5.0.0-mas-12": 8808, "5.0.0-mas-120": 9577, "5.0.0-mas-121": 9588, "5.0.0-mas-122": 9600, "5.0.0-mas-123": 9601, "5.0.0-mas-124": 9623, "5.0.0-mas-125": 9636, "5.0.0-mas-126": 9648, "5.0.0-mas-127": 9661, "5.0.0-mas-128": 9670, "5.0.0-mas-129": 9672, "5.0.0-mas-13": 8809, "5.0.0-mas-130": 9685, "5.0.0-mas-131": 9697, "5.0.0-mas-132": 9708, "5.0.0-mas-133": 9710, "5.0.0-mas-134": 9718, "5.0.0-mas-135": 9722, "5.0.0-mas-136": 9731, "5.0.0-mas-137": 9741, "5.0.0-mas-138": 9752, "5.0.0-mas-139": 9756, "5.0.0-mas-14": 8818, "5.0.0-mas-140": 9759, "5.0.0-mas-141": 9774, "5.0.0-mas-142": 9783, "5.0.0-mas-143": 9794, "5.0.0-mas-144": 9806, "5.0.0-mas-145": 9813, "5.0.0-mas-146": 9815, "5.0.0-mas-147": 9821, "5.0.0-mas-148": 9832, "5.0.0-mas-149": 9841, "5.0.0-mas-15": 8821, "5.0.0-mas-150": 9850, "5.0.0-mas-151": 9854, "5.0.0-mas-152": 9862, "5.0.0-mas-153": 9873, "5.0.0-mas-154": 9883, "5.0.0-mas-155": 9887, "5.0.0-mas-156": 9907, "5.0.0-mas-157": 9915, "5.0.0-mas-158": 9922, "5.0.0-mas-159": 9929, "5.0.0-mas-16": 8869, "5.0.0-mas-160": 9939, "5.0.0-mas-161": 9940, "5.0.0-mas-162": 9947, "5.0.0-mas-163": 9951, "5.0.0-mas-164": 9966, "5.0.0-mas-165": 9976, "5.0.0-mas-166": 9981, "5.0.0-mas-167": 9999, "5.0.0-mas-168": 10002, "5.0.0-mas-169": 10011, "5.0.0-mas-17": 8875, 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["263549216", "263925792", "264033420", "32GB"], "python": ["3.10", "3.6", "3.8"], "pip+casatools": [null, "trunk"], "pip+casatasks": ["trunk"], "branch": ["master", "4f5295c7309e931c62f928a03eeadbbb4984cf00", "a73a44ba2df3c3b824e866ed60bf0d538e532f76", "85a2546777121b18bd5c0e9137b9b8e55f08cb7c", "ee69dc1e48962bc3300198d59345eca3624057dd", "40f5717bec049678804b23a5fac6b83ef62e8ad7"]}, "graph_param_list": [{"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatools": "trunk", "pip+casatasks": "trunk", "branch": "master"}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatools": "trunk", "pip+casatasks": "trunk", "branch": "4f5295c7309e931c62f928a03eeadbbb4984cf00"}, {"arch": "x86_64", 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"16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatools": "trunk", "pip+casatasks": "trunk", "branch": "40f5717bec049678804b23a5fac6b83ef62e8ad7"}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatasks": "trunk", "branch": "ee69dc1e48962bc3300198d59345eca3624057dd", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatasks": "trunk", "branch": "40f5717bec049678804b23a5fac6b83ef62e8ad7", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "python": "3.6", "pip+casatasks": "trunk", "branch": "85a2546777121b18bd5c0e9137b9b8e55f08cb7c", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.8", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "python": "3.10", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "python": "3.8", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "40f5717bec049678804b23a5fac6b83ef62e8ad7", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": 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{"bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart": {"code": "class BaseMosaic:\n def time_mosaic_cube_eph_pcwdT_restart(self):\n \"\"\"Mosaic ephemeris cube imaging with briggsbwtaper - field Venus, spw 45\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img+\"0\", field='Venus', spw=['1'], antenna=['0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46'], scan=['7,11'], intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='corrected', imsize=[480, 420], cell=['0.14arcsec'], phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', nchan=948, start='261.7643758544GHz', width='0.2441755MHz', perchanweightdensity=True, gridder='mosaic', mosweight=True, usepointing=False, pblimit=0.2, deconvolver='hogbom', restoration=False, restoringbeam='common', pbcor=False, weighting='briggs', robust=0.5, npixels=0, niter=0, threshold='0.0mJy', nsigma=0.0, usemask='auto-multithresh', sidelobethreshold=2.0, noisethreshold=4.25, lownoisethreshold=1.5, negativethreshold=15.0, minbeamfrac=0.3, growiterations=50, dogrowprune=True, minpercentchange=1.0, fastnoise=False, savemodel='none', parallel=self.parallel, verbose=True)\n # restart the tclean call to actually perform deconvolution\n ret = tclean(vis=self.msfile, imagename=self.img+\"0\", field='Venus', spw=['1'], antenna=['0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46'],scan=['7,11'], intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='corrected', imsize=[480, 420], cell=['0.14arcsec'], phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', nchan=948, start='261.7643758544GHz', width='0.2441755MHz', perchanweightdensity=True, gridder='mosaic', mosweight=True, usepointing=False, pblimit=0.2, deconvolver='hogbom', restoration=True, restoringbeam='common', pbcor=True, weighting='briggs', robust=0.5, npixels=0, niter=700000, threshold='0.0106Jy', nsigma=0.0, usemask='auto-multithresh', sidelobethreshold=2.0, noisethreshold=4.25, lownoisethreshold=1.5, negativethreshold=15.0, minbeamfrac=0.3, growiterations=50, dogrowprune=True, minpercentchange=1.0, fastnoise=False, restart=True, savemodel='none', calcres=False, calcpsf=False, parallel=self.parallel, verbose=True)\n\n def setup(self):\n self.prepData(\"2018.1.00879.S_tclean.ms\")", "min_run_count": 1, "name": "bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 7200, "type": "time", "unit": "seconds", "version": "CAS-14086", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_cal_only_mode": {"code": "class ApplyModes:\n def time_applycal_cal_only_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calonly')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_cal_only_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e26cd2e6d77235fa8e7c3c5c65b22d4fcdca56bf1c8eb4ac430fd60fa546866e", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_flag_only_mode": {"code": "class ApplyModes:\n def time_applycal_flag_only_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='flagonly')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_flag_only_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a6421f6893712c5910c61b340b2f45a1dd0f5244adb96eaf54cec02b276ccbae", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_flag_only_strict_mode": {"code": "class ApplyModes:\n def time_applycal_flag_only_strict_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='flagonlystrict')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_flag_only_strict_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5d6b2cd5b9cf017fefacf66e0929df5ec228a015989465593e0233cefa48cda", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_strict_mode": {"code": "class ApplyModes:\n def time_applycal_strict_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calflagstrict')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_strict_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "52b7c478c85666778a8b11363dcd53c5c766dadf886b901f04bb8a078224839f", "warmup_time": -1}, "bench_applycal.CalFlag.time_applycal_calflag": {"code": "class CalFlag:\n def time_applycal_calflag(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calflag')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalFlag.time_applycal_calflag", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a1e4de3456a21dcc48909b513d5efa4d90bddd213862d4390fdb67c2df14c381", "warmup_time": -1}, "bench_applycal.CalLibrary.time_applycal_callibrary": {"code": "class CalLibrary:\n def time_applycal_callibrary(self):\n applycal(vis=self.vis, docallib=True, callib=self.callibfile)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalLibrary.time_applycal_callibrary", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "835d4e5c3926e3dc5a23e04e71aa9fb433f77e02f65db1846c88677d01ba2a71", "warmup_time": -1}, "bench_applycal.CalWeight.time_applycal_calweight": {"code": "class CalWeight:\n def time_applycal_calweight(self):\n applycal(vis=self.vis, gaintable=[self.gCal], calwt=[False])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalWeight.time_applycal_calweight", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f05a2af9251ce10ee5dc59cca8788b998e9da9fded8cce0ee1906db023bc8d01", "warmup_time": -1}, "bench_applycal.FlagBackup.time_applycal_flag_backup": {"code": "class FlagBackup:\n def time_applycal_flag_backup(self):\n applycal(vis=self.vis, gaintable=[self.gCal], flagbackup=True)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.FlagBackup.time_applycal_flag_backup", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "872b962508540cc6ef4bcbc9753aba34c9da8b7d02cc15ee7765f06901e65e37", "warmup_time": -1}, "bench_applycal.GainField.time_applycal_gainfield": {"code": "class GainField:\n def time_applycal_gainfield(self):\n applycal(vis=self.vis, gaintable=[self.tCal], gainfield='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.GainField.time_applycal_gainfield", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "556dfc5fca635bc393e0d68f23e8a9f9e9fef7a4f0c446faec837b3b23356816", "warmup_time": -1}, "bench_applycal.Interp.time_applycal_interp": {"code": "class Interp:\n def time_applycal_interp(self):\n applycal(vis=self.vis, gaintable=[self.tCal], interp='cubicflag')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.Interp.time_applycal_interp", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4e36d7f0ce102f831a8b90da4499019cebba32ffad3fb79f8e95e30ecfa2ab8a", "warmup_time": -1}, "bench_applycal.Parang.time_applycal_parang": {"code": "class Parang:\n def time_applycal_parang(self):\n applycal(vis=self.vis, gaintable=[self.gCal], parang=True)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.Parang.time_applycal_parang", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "fbef01f79ab1b6afd2bd79669d2271a7a86954b6f26b2a523667e0b8f0ebdce0", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_antenna": {"code": "class SelectionTypes:\n def time_applycal_select_antenna(self):\n applycal(vis=self.vis, gaintable=[self.gCal], antenna='0~5&')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_antenna", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5ca6f069a4262d19625195a53f6dc88cc9ccca31907e944ec501075f5884cd60", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_field": {"code": "class SelectionTypes:\n def time_applycal_select_field(self):\n applycal(vis=self.vis, gaintable=[self.gCal], field='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_field", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e5d8951157ddc5452e498ffe5fd8d725dfaf0e9df7de5b79747f4993a20ddbc5", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_intent": {"code": "class SelectionTypes:\n def time_applycal_select_intent(self):\n applycal(vis=self.vis, gaintable=[self.gCal], intent='*AMPLI*')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_intent", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "48c31d0fb8eefd5323fa3319aa8b3695a8ce667466bd2a1303e71107b1e00613", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_scan": {"code": "class SelectionTypes:\n def time_applycal_select_scan(self):\n applycal(vis=self.vis, gaintable=[self.gCal], scan='10')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_scan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "96819d5d86bd7855a0112c03f92c63a64f0afa905e961887df14a01b025852f1", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_spw": {"code": "class SelectionTypes:\n def time_applycal_select_spw(self):\n applycal(vis=self.vis, gaintable=[self.gCal], spw='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_spw", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d95a817124907f58102de398fe8a2dedddd11e80b30d4758105288fc5a1149a", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_timerange": {"code": "class SelectionTypes:\n def time_applycal_select_timerange(self):\n applycal(vis=self.vis, gaintable=[self.gCal], timerange='04:33:23~04:38:23')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_timerange", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2b264ae69c0eada9a00ed6c42d7f361571dab35399621ee8cb2785741c2aae68", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_uvrange": {"code": "class SelectionTypes:\n def time_applycal_select_uvrange(self):\n applycal(vis=self.vis, gaintable=[self.gCal], uvrange='>100klambda')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_uvrange", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f897b2028d0940d47aca1c23d61aa1185690be2fd94bf1c4c94c188f922f9f91", "warmup_time": -1}, "bench_applycal.SpwMap.time_applycal_spwmap": {"code": "class SpwMap:\n def time_applycal_spwmap(self):\n applycal(vis=self.vis, gaintable=[self.gCal], spwmap=[0, 0, 1, 1])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SpwMap.time_applycal_spwmap", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "79550810428ff38b9e649d8bd6b72540535fe7e4b5efe5cefc4aafa24afc8296", "warmup_time": -1}, "bench_applycal.StandardGainTable.time_applycal_basic_gaintable": {"code": "class StandardGainTable:\n def time_applycal_basic_gaintable(self):\n applycal(vis=self.vis, gaintable=[self.gCal, self.tCal])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.StandardGainTable.time_applycal_basic_gaintable", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a70f32c3ac8dfd7542bb10c89868d4bc640a02842253b6f45f105bf41df75780", "warmup_time": -1}, "bench_applycal.TrialMode.time_applycal_trial": {"code": "class TrialMode:\n def time_applycal_trial(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='trial')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.TrialMode.time_applycal_trial", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "24ae87f7af1553ca58c7bb7ec1ab0a5a78e22b6f1c6fdc4af9ace18ad930eefb", "warmup_time": -1}, "bench_flagdata.AntintMode.time_antint_low_threshold_spw": {"code": "class AntintMode:\n def time_antint_low_threshold_spw(self):\n \"\"\"flagdata: mode antint, with low threshold; (original test_antint_spw3_low_threshold)\"\"\"\n flagdata(vis=self.vis, mode='antint', spw='3', antint_ref_antenna='ea01', minchanfrac=-.1)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.AntintMode.time_antint_low_threshold_spw", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98a303508391da50600d29c7f8a74f95501abb178d78c35154642c8f7ec6f83c", "warmup_time": -1}, "bench_flagdata.CalibrationBandpass.time_bandpass_clip_timerange": {"code": "class CalibrationBandpass:\n def time_bandpass_clip_timerange(self):\n \"\"\"Flagdata: clip a timerange from field 3C286_A; (original test_cal_time1)\"\"\"\n flagdata(vis=self.vis, mode='clip', timerange='<14:12:52',clipzeros=True,\n clipminmax=[0.,0.35], datacolumn='CPARAM',flagbackup=False)\n\n def setup(self):\n self.setUp_bpass()", "min_run_count": 3, "name": "bench_flagdata.CalibrationBandpass.time_bandpass_clip_timerange", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4595858d10a7650bb7d5b763b20cf03f4921f5bcd8feb0a09a3ce0a9c3cdca38", "warmup_time": -1}, "bench_flagdata.CalibrationTsys.time_tsys_clip_fparam": {"code": "class CalibrationTsys:\n def time_tsys_clip_fparam(self):\n \"\"\"Flagdata: flag default data column FPARAM; (original test_default_fparam)\"\"\"\n flagdata(vis=self.vis, mode='clip', clipminmax=[0,500], flagbackup=False,\n datacolumn='FPARAM')\n\n def setup(self):\n self.setUp_tsys()", "min_run_count": 3, "name": "bench_flagdata.CalibrationTsys.time_tsys_clip_fparam", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7ee644928bb3e8de0bcf4711c3f266bb859ae288d2e7db42a0ea0a66d62550ad", "warmup_time": -1}, "bench_flagdata.ClipMode.time_clip_corrected_data": {"code": "class ClipMode:\n def time_clip_corrected_data(self):\n \"\"\"flagdata: clip CORRECTED data column; (original test_datacol_corrected)\"\"\"\n flagdata(vis=self.vis, flagbackup=False, mode='clip', datacolumn='CORRECTED',\n clipminmax=[0.,10.])\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.ClipMode.time_clip_corrected_data", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "07c4f1e0d3121fa540b2cd0e228a42009625c87cf37a05e16e62f3d95b57246b", "warmup_time": -1}, "bench_flagdata.ClipMode.time_clip_with_timeavg": {"code": "class ClipMode:\n def time_clip_with_timeavg(self):\n \"\"\"flagdata: clip with time averaging in spw 9; (original test_timeavg_spw9_2scans)\"\"\"\n flagdata(vis=self.vis, flagbackup=False, mode='clip', datacolumn='DATA', spw='9',\n timeavg=True, timebin='2s', clipminmax=[0.0, 0.08])\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.ClipMode.time_clip_with_timeavg", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2be25377fea9922f68fb74d47702f7354f593a21a0c3ef79ea683f411e59cb03", "warmup_time": -1}, "bench_flagdata.ElevationMode.time_elevation_interval": {"code": "class ElevationMode:\n def time_elevation_interval(self):\n \"\"\"Flagdata: flag an interval between upper and lower elevations; (original test_interval)\"\"\"\n flagdata(vis = self.vis,mode = 'elevation',lowerlimit = 55,upperlimit = 60,\n savepars=False,flagbackup=False)\n\n def setup(self):\n self.setUp_ngc5921()", "min_run_count": 3, "name": "bench_flagdata.ElevationMode.time_elevation_interval", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "cbefafa13742c0d5fe0d9179be380f7801ee669bbc6fc2cbe3ba48c02dcf1b1c", "warmup_time": -1}, "bench_flagdata.ListFileMode.time_list_from_3files": {"code": "class ListFileMode:\n def time_list_from_3files(self):\n \"\"\"Flagdata list mode reading commands from three files; (original test_file_CAS4819)\"\"\"\n flagdata(vis=self.vis, mode='list', inpfile=[self.inpfile1, self.inpfile2, self.inpfile3],\n flagbackup=False)\n\n def setup(self):\n self.setUp_ngc5921()\n\n def setup_cache(self):\n # Create 3 flagcmd files and save to disk\n myinput = \"scan='1'\\n\" \\\n \"scan='2'\\n\" \\\n \"# a comment line\\n\" \\\n \"scan='3'\"\n create_input(myinput, self.inpfile1)\n \n myinput = \"scan='5'\\n\" \\\n \" \\n\" \\\n \"scan='6'\\n\" \\\n \"scan='7'\"\n create_input(myinput, self.inpfile2)\n \n myinput = \"scan='4' mode='clip' clipminmax=[0,4]\"\n \n create_input(myinput, self.inpfile3)", "min_run_count": 3, "name": "bench_flagdata.ListFileMode.time_list_from_3files", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:217", "timeout": 3600, "type": "time", "unit": "seconds", "version": "dd8b2ea5edb3e265335cac2bf592eef280e5239f17d022f1fca37ab9738a0531", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_bandpassflag": {"code": "class ListMode:\n def time_list_bandpassflag(self):\n \"\"\"Flagdata list mode from pipeline hifa_bandpassflag\"\"\"\n flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=[\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='16' antenna='CM05' \\\n timerange='20:09:50~20:09:52' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:09:20~20:09:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:10:21~20:10:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='22' antenna='CM05' \\\n timerange='20:09:30~20:09:32' field='J1924-2914' reason='bad antenna timestamp'\",\n ],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_bandpassflag", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "2971c3002f4dafe2840b191e5570935d4b7f7ff2873189a4c55df4ae808dc738", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_summary": {"code": "class ListMode:\n def time_list_summary(self):\n \"\"\"Flagdata list mode from pipeline hifa_rawflagchans\"\"\"\n summary_dict = flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=[\"mode='summary' name='before'\"],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_summary", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "a3e3764d7dceb23f8ac0736e3a450613d2d10e11dc14ea682bbb0a78e1737111", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_tbuff": {"code": "class ListMode:\n def time_list_tbuff(self):\n \"\"\"Flagdata list mode from pipeline hifa_flagdata\"\"\"\n flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=self.flags_cmd,\n tbuff=[0.048, 0.0],\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_tbuff", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "53f2aaec8f44b81cb94f73fa21e5aac89218769ace8126661888f9c54374ff75", "warmup_time": -1}, "bench_flagdata.RflagMode.time_rflag_numpy_types": {"code": "class RflagMode:\n def time_rflag_numpy_types(self):\n \"\"\"flagdata: rflag, thresholds using numpy types; (original test_rflag_partial_thresholds)\"\"\"\n flagdata(vis=self.vis, mode='rflag', spw='9,10', timedev=[self.t1, self.t2], freqdev=0.5,\n flagbackup=False, extendflags=False)\n\n def setup(self):\n self.setUp_data4tfcrop()\n self.t1 = [numpy.int32(1), 10, numpy.float32(0.1)]\n self.t2 = [1, numpy.int16(11), numpy.float64(0.07)]", "min_run_count": 3, "name": "bench_flagdata.RflagMode.time_rflag_numpy_types", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "3f582e64491988dab48d1b83c4500bde3d09f14006d290ba96e3fa9ded4fa62b", "warmup_time": -1}, "bench_flagdata.Selections.time_sel_abs_wvr": {"code": "class Selections:\n def time_sel_abs_wvr(self):\n \"\"\"flagdata: clip ABS_WVR; (original test_abs_wvr)\"\"\"\n flagdata(vis=self.vis, mode='clip',clipminmax=[0,50], correlation='ABS_WVR', savepars=False,\n flagbackup=False)\n\n def setup(self):\n self.setUp_alma_ms()", "min_run_count": 3, "name": "bench_flagdata.Selections.time_sel_abs_wvr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ce5229049856ba918b3464ddc7f5aeee51402d91ce7a7d0cc3fb9d260d9fff87", "warmup_time": -1}, "bench_flagdata.Selections.time_sel_autocorr_wvr": {"code": "class Selections:\n def time_sel_autocorr_wvr(self):\n \"\"\"flagdata: CAS-5286, do not flag auto-correlations in WVR data; (original test_autocorr_wvr)\"\"\"\n flagdata(vis=self.vis, autocorr=True, flagbackup=False)\n\n def setup(self):\n self.setUp_alma_ms()", "min_run_count": 3, "name": "bench_flagdata.Selections.time_sel_autocorr_wvr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "128022df8e55b6c9054c3ef806420b1f80f11fcb4ef8ce073e99894f83617c9c", "warmup_time": -1}, "bench_flagdata.ShadowMode.time_shadow_absent_antennas": {"code": "class ShadowMode:\n def time_shadow_absent_antennas(self):\n \"\"\"flagdata: shadow by antennas not present in MS; (original test_CAS2399)\"\"\"\n flagdata(vis=self.vis, mode='shadow', tolerance=0.0, addantenna=self.inpfile, flagbackup=False)\n\n def setup(self):\n self.setUp_shadowdata()\n \n # Create antennafile in disk\n myinput = \"name=VLA01\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]\\n\" \\\n \"name=VLA02\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]\\n\" \\\n \"name=VLA09\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]\\n\" \\\n \"name=VLA10\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]\\n\"\n \n create_input(myinput, self.antfile)\n \n # Create flagcmd file\n myinput = \"mode='shadow' tolerance=0.0 addantenna='antfile.txt'\"\n create_input(myinput, self.inpfile)", "min_run_count": 3, "name": "bench_flagdata.ShadowMode.time_shadow_absent_antennas", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "3b94aac813ae30b385cb446c642e9b3115a43bc492770d13667d2fd1bce00339", "warmup_time": -1}, "bench_flagdata.ShadowMode.time_shadow_addantenna": {"code": "class ShadowMode:\n def time_shadow_addantenna(self):\n \"\"\"flagdata: use antenna file in list mode; (original test_addantenna)\"\"\"\n flagdata(vis=self.vis, mode='list', inpfile=self.inpfile, savepars=True, outfile='withdict.txt',\n flagbackup=False)\n\n def setup(self):\n self.setUp_shadowdata()\n \n # Create antennafile in disk\n myinput = \"name=VLA01\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]\\n\" \\\n \"name=VLA02\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]\\n\" \\\n \"name=VLA09\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]\\n\" \\\n \"name=VLA10\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]\\n\"\n \n create_input(myinput, self.antfile)\n \n # Create flagcmd file\n myinput = \"mode='shadow' tolerance=0.0 addantenna='antfile.txt'\"\n create_input(myinput, self.inpfile)", "min_run_count": 3, "name": "bench_flagdata.ShadowMode.time_shadow_addantenna", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "21fb65a9cd413ce64af88b6c00ccfc1ee439a7d0413b88f9b383f4e30252b087", "warmup_time": -1}, "bench_flagdata.TfcropMode.time_tfcrop_abs_rr": {"code": "class TfcropMode:\n def time_tfcrop_abs_rr(self):\n \"\"\"flagdata:: flag with tfcrop mode an ABS_RR correlation; (original test_tfcrop1)\"\"\"\n flagdata(vis=self.vis, mode='tfcrop', correlation='ABS_RR',ntime=51.0,spw='9',\n savepars=False, extendflags=False)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.TfcropMode.time_tfcrop_abs_rr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "bc8ecea57bcf9178074a7728ad87a326ec8796d9a804a085da78d1247047edba", "warmup_time": -1}, "bench_flagdata.TfcropMode.time_tfcrop_extendflags": {"code": "class TfcropMode:\n def time_tfcrop_extendflags(self):\n \"\"\"flagdata: flag with tfcrop mode and extend flags automatically\"\"\"\n flagdata(vis=self.vis, mode='tfcrop', spw='0', extendflags=True, flagbackup=False)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.TfcropMode.time_tfcrop_extendflags", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "686884535fe06d88a4737064c91e8fd3cc52616fc17d460c57cba3da7d04f97c", "warmup_time": -1}, "bench_fringefit.Corrcomb.time_comb_all": {"code": "class Corrcomb:\n def time_comb_all(self):\n \"\"\"test_comb --- last part of the corrcomb test, check that results are coombined\"\"\"\n fringefit(vis=self.polcombtestms, caltable=self.testout, refant='0', spw='2~3', corrcomb='all')\n\n def setUp(self):\n if os.path.exists(self.polcombtestms):\n shutil.rmtree(self.polcombtestms)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.polcombtestms)", "min_run_count": 5, "name": "bench_fringefit.Corrcomb.time_comb_all", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Dispersive.time_manual_phase_cal": {"code": "class Dispersive:\n def time_manual_phase_cal(self):\n \"\"\"test_manual_phase_cal --- test on manually flagged data\"\"\"\n fringefit(vis=self.prefix + '.ms',\n caltable='n14p1.disp', refant=\"WB\",\n scan='1', solint='60', spw='0,1,2',\n paramactive=[True, True, True],\n minsnr=50,\n niter=100,\n gaintable=['n14p1.mpc'],\n parang=True)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n flagdata(self.prefix + '.ms', mode='manual', spw='*:0~2;29~31')\n \n fringefit(vis=\"n14p1.ms\", caltable=\"n14p1.mpc\",\n scan=\"1\", solint=\"300\", refant=\"WB\",\n minsnr=50, zerorates=True,\n globalsolve=True, niter=100, gaintable=[],\n parang=True)", "min_run_count": 5, "name": "bench_fringefit.Dispersive.time_manual_phase_cal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.FreqMetaTests.time_metadata": {"code": "class FreqMetaTests:\n def time_metadata(self):\n \"\"\"test_metadata --- check output table metadata\"\"\"\n sbdcal = self.prefix + '-zerorates.sbdcal'\n mbdcal = self.prefix + '.mbdcal'\n \n fringefit(vis=self.msfile, caltable=mbdcal, spw=\"0,1,2,3\", field='J0916+3854', timerange=\"17:10:00~17:11:00\",\n combine='spw', gaintable=[sbdcal], refant='EF')\n\n def setUp(self):\n if os.path.exists(self.msfile):\n shutil.rmtree(self.msfile)\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n \n sbdcal = self.prefix + '-zerorates.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, field='4C39.25',\n refant='EF', zerorates=True)", "min_run_count": 5, "name": "bench_fringefit.FreqMetaTests.time_metadata", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Mbd.time_mbd": {"code": "class Mbd:\n def time_mbd(self):\n \"\"\"test_mbd --- test with mbd cal table\"\"\"\n sbdcal = self.prefix + '-zerorates.sbdcal'\n mbdcal = self.prefix + '.mbdcal'\n \n fringefit(vis=self.msfile, caltable=mbdcal, field='J0916+3854',\n combine='spw', gaintable=[sbdcal], refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n \n sbdcal = self.prefix + '-zerorates.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, field='4C39.25',\n refant='EF', zerorates=True)", "min_run_count": 5, "name": "bench_fringefit.Mbd.time_mbd", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.RefantBookkeeping.time_bookkeeping": {"code": "class RefantBookkeeping:\n def time_bookkeeping(self):\n \"\"\"test_bookkeeping --- check effects of using refant WB\"\"\"\n fringefit(vis=self.msfile, caltable=self.sbdcal, refant='WB')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n flagdata(self.prefix + '.ms', mode='manual', spw='*:0~2;29~31')\n flagdata(self.prefix + '.ms', mode='manual', antenna='EF')", "min_run_count": 5, "name": "bench_fringefit.RefantBookkeeping.time_bookkeeping", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Sbd.time_sbd": {"code": "class Sbd:\n def time_sbd(self):\n \"\"\"test_sbd --- test with sbd caltable\"\"\"\n sbdcal = self.prefix + '.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.uvfile)", "min_run_count": 5, "name": "bench_fringefit.Sbd.time_sbd", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Sbd.time_uvrange": {"code": "class Sbd:\n def time_uvrange(self):\n \"\"\"test_uvraange --- Check that the uvrnage parameter excludes antennas \"\"\"\n # create a caltable with uvrange selection\n fringefit(vis=self.uvfile, caltable='uvrange_with.cal', spw='2', refant='0', uvrange='<1160')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.uvfile)", "min_run_count": 5, "name": "bench_fringefit.Sbd.time_uvrange", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Single.time_param": {"code": "class Single:\n def time_param(self):\n \"\"\"test_param --- test different iterations of paramactive\"\"\"\n sbdcal = self.prefix + '-2.sbdcal'\n # We make a triple cartesian product of booleans\n # to test all possible paramactive values\n refant = 0\n refant_s = str(refant)\n for pactive in itertools.product(*3*[[False, True]]):\n fringefit(vis=self.msfile, paramactive=list(pactive), caltable=sbdcal, refant=refant_s)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)", "min_run_count": 5, "name": "bench_fringefit.Single.time_param", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Single.time_single": {"code": "class Single:\n def time_single(self):\n \"\"\"test_single --- check that the right params are flagged with a single refant\"\"\"\n sbdcal = self.prefix + '.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)", "min_run_count": 5, "name": "bench_fringefit.Single.time_single", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_gaincal.CalModes.time_gaincal_calmode_A": {"code": "class CalModes:\n def time_gaincal_calmode_A(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n calmode='a')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.CalModes.time_gaincal_calmode_A", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f7eb35dd966d27af8cf12cc750375153a8b0910af44d846f38c327d0bf37f4c2", "warmup_time": -1}, "bench_gaincal.CalModes.time_gaincal_calmode_P": {"code": "class CalModes:\n def time_gaincal_calmode_P(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n calmode='p')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.CalModes.time_gaincal_calmode_P", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1b0b933cc4c5f58b1b99775ecdf726fd4b7397322314ae15faf4e2d13c102cbc", "warmup_time": -1}, "bench_gaincal.FullGainCal.time_gaincal_fullcal": {"code": "class FullGainCal:\n def time_gaincal_fullcal(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', field='0', refant='0',\n smodel=[1, 0, 0, 0], scan='0~9')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.FullGainCal.time_gaincal_fullcal", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "321b6da59741ba85c3e914725f481e45ca88f4ef73a637f3ade2bed74a5abaa1", "warmup_time": -1}, "bench_gaincal.GainTypes.time_gaincal_gain_type_K": {"code": "class GainTypes:\n def time_gaincal_gain_type_K(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n gaintype='KCROSS', refant='0')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GainTypes.time_gaincal_gain_type_K", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "616aabddbc63928cc0fdb731743036617d3e88b5249398335973b002b9ea2e05", "warmup_time": -1}, "bench_gaincal.GainTypes.time_gaincal_gain_type_spline": {"code": "class GainTypes:\n def time_gaincal_gain_type_spline(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n gaintype='GSPLINE', refant='0')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GainTypes.time_gaincal_gain_type_spline", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2b9405658f1efab3530c340ed099fb37b1ae5888f52762c3f8cf6df6321126ac", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_antennaSelect": {"code": "class GaincalSelections:\n def time_gaincal_antennaSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', field='0', solint='inf', combine='scan', antenna='0~5&',\n smodel=[1, 0, 0, 0], gaintype='G')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_antennaSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d75f902a1c09757939ff4e77e062c55293a92b7f886d8bbe0cf4e5344918c39f", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_fieldSelect": {"code": "class GaincalSelections:\n def time_gaincal_fieldSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', field='0',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_fieldSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "10de6efc06ad79e8cf856e46bef9b61f435eb4030f27473d284c71c5bdb75ccf", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_refantSelect": {"code": "class GaincalSelections:\n def time_gaincal_refantSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', refant='0',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_refantSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "34c394342899a19f1647ae25d1f7f7d8f19eee8ff14c26b61f0b1bca06443cd2", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_scanSelect": {"code": "class GaincalSelections:\n def time_gaincal_scanSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf',\n smodel=[1, 0, 0, 0], scan='2')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_scanSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ed56707772521ef042364e25b1b5cfede92141267082dcfb3deffd2eecc05e91", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_spwSelect": {"code": "class GaincalSelections:\n def time_gaincal_spwSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf',\n smodel=[1, 0, 0, 0], spw='2')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_spwSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d3b9a18fd82b436f740ec7e34af58655909f6b8731136f253ce9eb7ec3846e3e", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_uvRangeSelect": {"code": "class GaincalSelections:\n def time_gaincal_uvRangeSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, spw='2', refant='0', uvrange='<1160', minblperant=1)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_uvRangeSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b0342a4bc2c4bc568d98a402b5d1611ef82ab395834afb1c0527b636f559ba9", "warmup_time": -1}, "bench_gaincal.MinBlPerAnt.time_gaincal_minbl": {"code": "class MinBlPerAnt:\n def time_gaincal_minbl(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', solint='int', smodel=[1, 0, 0, 0], gaintype='G',\n combine='scan', antenna='0~5&', minblperant=6)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.MinBlPerAnt.time_gaincal_minbl", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98757eddbbd36be1306e1d3dff8c7368acf302e8e44127c5aa55c1206f1145e1", "warmup_time": -1}, "bench_gaincal.MinSNRFlag.time_gaincal_minsnr": {"code": "class MinSNRFlag:\n def time_gaincal_minsnr(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='30s', field='0', refant='0',\n smodel=[1, 0, 0, 0], minsnr=1000)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.MinSNRFlag.time_gaincal_minsnr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "023921a63e2bcaaec078cc39a20cfa4ed25f3aac1ad8b73aeb6903fde979a439", "warmup_time": -1}, "bench_gaincal.PreApplyTables.time_gaincal_apply_tables": {"code": "class PreApplyTables:\n def time_gaincal_apply_tables(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', solint='inf', smodel=[1, 0, 0, 0], gaintype='G', field='0',\n gaintable=[self.tCal, self.compCal], gainfield=['0', '0'], interp=[''])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.PreApplyTables.time_gaincal_apply_tables", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "daccc986c167fbbf02e14e3cfa201572b4ec8a51a1629661b8543142b2cae433", "warmup_time": -1}, "bench_gaincal.RefantSelection.time_gaincal_refant": {"code": "class RefantSelection:\n def time_gaincal_refant(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf', field='0', combine='scan', refant='1',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.RefantSelection.time_gaincal_refant", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "be0118dd9b630d5373b3604c19a8afb518dbc25a2e7e7758d644c5e5faf634f0", "warmup_time": -1}, "bench_gaincal.UseSpwMap.time_gaincal_use_spw_map": {"code": "class UseSpwMap:\n def time_gaincal_use_spw_map(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n refant='0', spwmap=[0, 0, 1, 1])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.UseSpwMap.time_gaincal_use_spw_map", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c524f53702087f79992918ad289b5b863018b412527f507e27589ac99e0574b9", "warmup_time": -1}, "bench_importasdm.Aca.time_default": {"code": "class Aca:\n def time_default(self):\n '''test6_lazy1 - standard fill : Test good ACA ASDM with mixel pol/channelization input with default filler'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=False, scans='0:1~3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Aca.time_default", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Aca.time_default_lazy": {"code": "class Aca:\n def time_default_lazy(self):\n '''test6_lazy1 - lazy fill: Test good ACA ASDM with mixed pol/channelization input with default filler in lazy mode'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=True, scans='0:1~3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Aca.time_default_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_auto_only": {"code": "class Alma_12m:\n def time_auto_only(self):\n '''test7_lazy4 - standard fill: Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=False, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_auto_only", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_auto_only_lazy": {"code": "class Alma_12m:\n def time_auto_only_lazy(self):\n '''test7_lazy4 - lazy fill, Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=True, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_auto_only_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_bdfflags_true": {"code": "class Alma_12m:\n def time_bdfflags_true(self):\n '''test7_bdflags1 - Test good 12 m ASDM with mixed pol/channelisation input with default filler selecting \"co\" on output and using the BDF flags'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, ocorr_mode=\"co\", bdfflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_bdfflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_bdfflags_true_lazy": {"code": "class Alma_12m:\n def time_bdfflags_true_lazy(self):\n '''test7_lazy2 - Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode with reading the BDF flags'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=True, bdfflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_bdfflags_true_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_default": {"code": "class Alma_12m:\n def time_default(self):\n '''test7_lazy1 - standard fill: Test good 12 m ASDM with mixed pol/channelization with default filler'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=False, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_default", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_default_lazy": {"code": "class Alma_12m:\n def time_default_lazy(self):\n '''test7_lazy1 - lazy fill: Test good 12 m ASDM with mixed pol/channelization input with default filler in lazy mode'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=True, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_default_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_applyflags_true": {"code": "class AutocorrASDM:\n def time_applyflags_true(self):\n '''test_flagautocorr1 - importasdm: test that auto-correlations from online flags are correctly flagged'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', applyflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_applyflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_process_flags_false": {"code": "class AutocorrASDM:\n def time_process_flags_false(self):\n '''test_flagautocorr3 - importasdm: do not process flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', process_flags=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_process_flags_false", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_savecmds_true": {"code": "class AutocorrASDM:\n def time_savecmds_true(self):\n '''test_autocorr - importasdm: auto-correlations should be written to online flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', savecmds=True, outfile=self.outfile)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_all_defaults": {"code": "class Basic:\n def time_all_defaults(self):\n '''test_import2 --- importasdm with default arguments'''\n importasdm(self.asdm_name)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_all_defaults", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_lazy_pc_true": {"code": "class Basic:\n def time_lazy_pc_true(self):\n '''test1_lazy1 --- lazy mode, with pointing_correction=True'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=True, with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_lazy_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_pc_true": {"code": "class Basic:\n def time_pc_true(self):\n '''test1_lazy1 --- standard fill (lazy=False), with pointing_correction=True'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=False, with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_all_scans": {"code": "class Evla:\n def time_all_scans(self):\n '''test_evla_apply5 - test of importing evla data: all scans, do not process flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, ocorr_mode='co', with_pointing_correction=True,\n process_flags=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_all_scans", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_applyflags_true": {"code": "class Evla:\n def time_applyflags_true(self):\n '''test_evla_apply2 - test of importing evla data and applying the online flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2',ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=True, savecmds=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_applyflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_pc_true": {"code": "class Evla:\n def time_pc_true(self):\n '''test_evlatest1 - test of importing evla data, with_pointing_correction=True is normal for EVLA data'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2',ocorr_mode='co',with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_applyflags_savecmds_true": {"code": "class Evla:\n def time_process_applyflags_savecmds_true(self):\n '''test_evla_apply1_flagdata - test of importing evla data, apply onlineflags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=True, savecmds=True, outfile=self.cmdfile, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_applyflags_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_flags_savecmds_true": {"code": "class Evla:\n def time_process_flags_savecmds_true(self):\n '''test_evla_savepars - test importing evla data: save the flag commands and do not apply'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name,scans='11~13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=False, savecmds=True, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_flags_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_flags_true_applyflags_false": {"code": "class Evla:\n def time_process_flags_true_applyflags_false(self):\n '''test_evla_savepars_flagdata - test importing evla data: save the flag commands and do not apply; using flagdata'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name,scans='11~13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=False, savecmds=True, outfile=self.cmdfile, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_flags_true_applyflags_false", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_scan_selection": {"code": "class Evla:\n def time_scan_selection(self):\n '''test_evla_apply3 - test of importing evla data, two difference scans, do not apply online flags'''\n # appears to also be the same importasdm as test_evla_apply3_flagdata\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2,13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=False,flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_scan_selection", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla_ephemeris.time_polynomial_ephem": {"code": "class Evla_ephemeris:\n def time_polynomial_ephem(self):\n '''test_evlatest2 - test of importing evla data, test2: Good input asdm with polynomial ephemeris'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='0:5',ocorr_mode='co',with_pointing_correction=True,polyephem_tabtimestep=0.001,convert_ephem2geo=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla_ephemeris.time_polynomial_ephem", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Singledish.time_auto_only": {"code": "class Singledish:\n def time_auto_only(self):\n '''test7_lazy5 - standard fill : Test TP asdm with default filler selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=False, bdfflags=True, applyflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Singledish.time_auto_only", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Singledish.time_auto_only_lazy": {"code": "class Singledish:\n def time_auto_only_lazy(self):\n '''test7_lazy5 - lazy fill : Test TP asdm with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", bdfflags=True, applyflags=True, lazy=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Singledish.time_auto_only_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_custom_atm_params": {"code": "class test_sdatmcor:\n def time_custom_atm_params(self):\n \"\"\"Test customized ATM parameters.\"\"\"\n sdatmcor(\n infile=self.infile, outfile=self.outfile, datacolumn='data',\n dtem_dh='-5.7K/km', h0='2010m',\n atmdetail=True,\n altitude='5.1km', temperature='290K', pressure='700hPa',\n humidity=30, pwv='0.1cm', dp='10hPa', dpm=1.2,\n layerboundaries='800m,1.5km', layertemperature='250K,200K'\n )\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_custom_atm_params", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype": {"code": "class test_sdatmcor:\n def time_sdatmcor_explicit_atmtype(self):\n \"\"\"Test specifying atmtype explicitly.\"\"\"\n sdatmcor(infile=self.infile, outfile=self.outfile, datacolumn='data', atmtype=2)\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict": {"code": "class test_sdatmcor:\n def time_sdatmcor_gainfactor_dict(self):\n \"\"\"Test gainfactor: dict input.\"\"\"\n gainfactor = {'19': 10.0, '23': 45.0}\n sdatmcor(infile=self.infile2, outfile=self.outfile, datacolumn='data', gainfactor=gainfactor)\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_normal": {"code": "class test_sdatmcor:\n def time_sdatmcor_normal(self):\n \"\"\"Test normal usage of sdatmcor.\"\"\"\n sdatmcor(infile=self.infile, outfile=self.outfile, datacolumn='data')\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_normal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdbaseline.Basic.time_blparam_apply_no_remove": {"code": "class Basic:\n def time_blparam_apply_no_remove(self):\n \"\"\"sdbaseline: blparam file(s) should not be removed in apply mode - test062\"\"\"\n tid = '062'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n blformat = ['text', 'table']\n blmode = 'apply'\n bltable = self.infile_no_remove + '_blparam.bltable'\n overwrite = True\n \n sdbaseline(infile=self.infile_no_remove, outfile=outfile, overwrite=overwrite, datacolumn=datacolumn, blmode=blmode, bltable=bltable)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_blparam_apply_no_remove", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_blparam_overwrite": {"code": "class Basic:\n def time_blparam_overwrite(self):\n \"\"\"sdbaseline: blparam file(s) should be overwritten when overwrite=True in fit mode - test060\"\"\"\n tid = '060'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n overwrite = True\n \n sdbaseline(infile=self.infile_overwrite, outfile=outfile, overwrite=overwrite, datacolumn=datacolumn)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_blparam_overwrite", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_chebyshev_blfunc_no_mask": {"code": "class Basic:\n def time_chebyshev_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'chebyshev', maskmode = 'list' and masklist=[] (no mask) - test002\"\"\"\n tid = '002'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'chebyshev'\n spw = '3'\n pol = 'LL'\n overwrite = True\n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n spw=spw, pol=pol, outfile=outfile,\n overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_chebyshev_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_cspline_blfunc_no_mask": {"code": "class Basic:\n def time_cspline_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'cspline', maskmode = 'list' and masklist=[] (no mask) - test003\"\"\"\n print(\"\")\n \n tid = '003'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'cspline'\n overwrite = True\n npiece = 3\n spw='3'\n pol='LL'\n \n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n npiece=npiece,spw=spw,\n pol=pol,\n outfile=outfile,overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_cspline_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_default": {"code": "class Basic:\n def time_default(self):\n \"\"\"sdbaseline: default values for all parameters - test000\"\"\"\n tid = '000'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n \n result = sdbaseline(infile=infile, datacolumn=datacolumn, outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_default", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_poly_blfunc_no_mask": {"code": "class Basic:\n def time_poly_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'poly', maskmode = 'list' and masklist=[] (no mask) - test001\"\"\"\n tid = '001'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'poly'\n spw = '3'\n pol = 'LL'\n overwrite = True\n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n spw=spw, pol=pol, outfile=outfile,\n overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_poly_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.BlFuncVariable.time_variable_orders": {"code": "class BlFuncVariable:\n def time_variable_orders(self):\n \"\"\"sdbaseline: Test blfunc='variable' with variable baseline functions and orders - testVariable00\"\"\"\n print('why is this failing?')\n infile = 'analytic_variable.ms'\n self.paramfile = 'analytic_variable_blparam.txt'\n self._refetch_files([infile, self.paramfile], self.datapath)\n sdbaseline(infile=infile, blfunc='variable', outfile=self.outfile, blparam=self.paramfile, datacolumn=self.column)\n\n def setUp(self):\n pass", "min_run_count": 5, "name": "bench_sdbaseline.BlFuncVariable.time_variable_orders", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Mask.time_cspline_blfunc_masked_range_spectrum_edge": {"code": "class Mask:\n def time_cspline_blfunc_masked_range_spectrum_edge(self):\n \"\"\"sdbaseline: with masked ranges at the edges of spectrum. blfunc must be cspline. - test100\"\"\"\n self.tid='100'\n infile = self.infile\n outfile = self.outroot+self.tid+'.ms'\n datacolumn='float_data'\n mode = 'list'\n spw = '2:%s'%(';'.join(map(self._get_range_in_string,self.search)))\n pol = 'RR'\n blfunc = 'cspline'\n npiece = 4\n \n result = sdbaseline(infile=infile,datacolumn=datacolumn,maskmode=mode,\n spw=spw,pol=pol,blfunc=blfunc,npiece=npiece,\n outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')", "min_run_count": 5, "name": "bench_sdbaseline.Mask.time_cspline_blfunc_masked_range_spectrum_edge", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Mask.time_masked_ranges_no_edge_spectrum": {"code": "class Mask:\n def time_masked_ranges_no_edge_spectrum(self):\n \"\"\"sdbaaseline: with masked ranges not touching spectrum edge - test101\"\"\"\n self.tid='101'\n infile = self.infile\n outfile = self.outroot+self.tid+'.ms'\n datacolumn='float_data'\n mode = 'list'\n spw = '2:%s'%(';'.join(map(self._get_range_in_string,self.blchan2)))\n pol = 'RR'\n \n result = sdbaseline(infile=infile,datacolumn=datacolumn,maskmode=mode,\n outfile=outfile,spw=spw,pol=pol)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')", "min_run_count": 5, "name": "bench_sdbaseline.Mask.time_masked_ranges_no_edge_spectrum", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_masked_masked": {"code": "class OutBlTable:\n def time_fit_variable_masked_masked(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304a'\n infile = self.infile_variable_a\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = ''\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=self.infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_masked_masked", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_masked_unselect": {"code": "class OutBlTable:\n def time_fit_variable_masked_unselect(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304b'\n infile = self.infile_variable_b\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = 'RR'\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_masked_unselect", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1cout": {"code": "class OutBlTable:\n def time_fit_variable_r2p1cout(self):\n \"\"\"sdbaseline: per-spectrum baselining, output bltable - test302\"\"\"\n self.tid='302b'\n infile = self.infile\n datacolumn='float_data'\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n \n bloutput= self.outroot+self.tid+'r2p1cout'+'.bltable'\n outfile = self.outroot+self.tid+'r2p1cout'+'.ms'\n blparam = self.outroot+self.tid+'r2p1cout'+'.blparam'\n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, 'r2p1cout')\n result = sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1cout", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1less": {"code": "class OutBlTable:\n def time_fit_variable_r2p1less(self):\n \"\"\"sdbaseline: per-spectrum baselining, output bltable - test302\"\"\"\n self.tid='302a'\n infile = self.infile\n datacolumn='float_data'\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n \n bloutput= self.outroot+self.tid+'r2p1less'+'.bltable'\n outfile = self.outroot+self.tid+'r2p1less'+'.ms'\n blparam = self.outroot+self.tid+'r2p1less'+'.blparam'\n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, 'r2p1less')\n result = sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1less", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_unselect_masked": {"code": "class OutBlTable:\n def time_fit_variable_unselect_masked(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304c'\n infile = self.infile_variable_c\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = 'LL'\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_unselect_masked", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.UpdateWeight.time_blmode_apply_spw_to_flag": {"code": "class UpdateWeight:\n def time_blmode_apply_spw_to_flag(self):\n \"\"\"sdbaseline: blmode='apply', spw to flag channels 4500-6499 - test052\"\"\"\n sdbaseline(infile=self.infile2, outfile=self.outfile, intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='float_data', spw=self.spw)\n\n def setUp(self):\n remove_files_dirs(self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n remove_files_dirs(self.infile2)\n shutil.copytree(os.path.join(self.datapath, self.infile2), self.infile2)", "min_run_count": 5, "name": "bench_sdbaseline.UpdateWeight.time_blmode_apply_spw_to_flag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.UpdateWeight.time_remove_weight_col": {"code": "class UpdateWeight:\n def time_remove_weight_col(self):\n \"\"\"sdbaseline: confirm if WEIGHT_SPECTRUM column is removed - test00\"\"\"\n sdbaseline(infile=self.infile, outfile=self.outfile, intent='OBSERVE_TARGET#ON_SOURCE', spw='9', datacolumn='data', updateweight=True)\n\n def setUp(self):\n remove_files_dirs(self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n remove_files_dirs(self.infile2)\n shutil.copytree(os.path.join(self.datapath, self.infile2), self.infile2)", "min_run_count": 5, "name": "bench_sdbaseline.UpdateWeight.time_remove_weight_col", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict": {"code": "class Apply:\n def time_spwmap_dict(self):\n \"\"\"Test01: weight = 1/(SIGMA**2) X 1/(FPARAM_ave**2) dictionary version.\"\"\"\n # Not sure what name is given to outfile in this run. Check!!\n spwmap_dict = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_dict,\n applytable=self.tsystable, outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "eca49a873b8027503c11a240f6e59585740e41ae697515593a11176fb2ab48da", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict_correct_data": {"code": "class Apply:\n def time_spwmap_dict_correct_data(self):\n \"\"\"Test03: Validation of CORRECTED_DATA = DATA X FPARAM (spwmap={1:[1], 3:[3], 5:[5], 7:[7]}).\"\"\"\n spwmap = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap, applytable=self.tsystable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict_correct_data", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5d5dd55ee3170d7722bc51ded3a558d04fc83f79a9db111eacbd805111dcad70", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict_weight_spectrum": {"code": "class Apply:\n def time_spwmap_dict_weight_spectrum(self):\n \"\"\"Test06: weight_spectrum = 1/(SIGMA**2) X 1/(FPARAMx**2) dictionary version.\"\"\"\n spwmap_dict = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_dict,\n applytable=self.tsystable, interp='nearest', outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict_weight_spectrum", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1158df62c77f3f40601226240f4c2b0a6910cf4d18c236a4f4a0181e7e689c1e", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_list": {"code": "class Apply:\n def time_spwmap_list(self):\n \"\"\"Test02: weight = 1/(SIGMA**2) X 1/(FPARAM_ave**2) list version.\"\"\"\n # focus on antenna1=0, data_disk_id=1\n spwmap_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_list,\n applytable=self.tsystable, outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_list", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ec21f3f30b77cfea3de4710e9bff48d698654d8024e10d401465c714e2f96a39", "warmup_time": -1}, "bench_sdcal.Apply.time_test05": {"code": "class Apply:\n def time_test05(self):\n \"\"\"Test05: Validation of CORRECTED_DATA = DATA X FPARAM.\n (spwmap={1:[9], 3:[11], 5:[13], 7:[15]})\n antanna1=0, DATA_DISC_ID=9, FPARAM_average\n \"\"\"\n spwmap = {1: [9], 3: [11], 5: [13], 7: [15]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap, applytable=self.tsystable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_test05", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "0c349e3cabc717e2018727f49142a43f1867aacefc7a32b74fea55c09cd43521", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_interp_empty": {"code": "class Apply_Interp:\n def time_interp_empty(self):\n \"\"\"test_apply_sky14 --- apply data (interp='').\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply',\n applytable=self.applytable, interp='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_interp_empty", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d701d56a9753f8ad50dbc76d44d4b658680d8c7c02f8f475620f691fbac607ce", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear": {"code": "class Apply_Interp:\n def time_linear(self):\n \"\"\"test_apply_sky08 --- apply data (linear).\"\"\"\n sdcal(infile=self.infile, calmode='apply',applytable=[self.applytable], interp='linear')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "078d836c355da5773813afc9b5b3f5b9b5af4c7441926882bed13a33c8eb89f1", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_linearflag": {"code": "class Apply_Interp:\n def time_linear_linearflag(self):\n \"\"\"test_apply_sky11 --- apply data (linearflag for frequency interpolation).\"\"\"\n sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], interp='linear,linearflag')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_linearflag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "42bdfd6f7ae95d7cf1b6a99a5ea926b0c5ef23326aa25e525ffed5720eb087e3", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_nearestflag": {"code": "class Apply_Interp:\n def time_linear_nearestflag(self):\n \"\"\"test_apply_sky12 --- apply data (nearestflag for frequency interpolation).\"\"\"\n sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], interp='linear,nearestflag')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_nearestflag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b666aed07e5ff1d2684ae9c5af455f2ed84aaea7a5f31e8814fd23ef31161a1", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_spw_selection": {"code": "class Apply_Interp:\n def time_linear_spw_selection(self):\n \"\"\"test_apply_sky09 --- apply selected data.\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], spw='9', interp='linear')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_spw_selection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "bbc0db0908cc81aff108d4e7635e5be7320ff855384a176dc974409e67acf6dc", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_nearest": {"code": "class Apply_Interp:\n def time_nearest(self):\n \"\"\"test_apply_sky10 --- apply data (nearest).\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply',\n applytable=[self.applytable], interp='nearest')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_nearest", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a3f87e4523727137a12fa96c6ca02b6d24325a73c3cc7b1ed699768a9e8f8b83", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_apply_sky(self):\n \"\"\"test_apply_composite03 --- on-the-fly application of sky table ('otfraster,apply').\"\"\"\n sdcal(infile=self.infile, calmode='tsys,apply', applytable=self.applytable,\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a2e30839f58f4d7cae58ebed88e373ca0266cb545998f2cdc9bd9e99c86a3bb4", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal": {"code": "class Apply_Modified_Tsys:\n def time_apply_sky_normal(self):\n \"\"\"test_apply_composite00 --- on-the-fly application of sky table ('ps,apply').\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "9156519f2fd7ea628cdc9bd83ff8300424ede18396aabf47374e2269671763f1", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_apply_sky_tsys(self):\n \"\"\"test_apply_composite02 --- on-the-fly application of sky and tsys tables ('ps,tsys,apply').\"\"\"\n sdcal(infile=self.infile, calmode='ps,tsys,apply',\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d0b7e9739bae796f78bb578757177ac7aad7d83dd613adf15dfc3629a1f8e11a", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_preapply_tsys": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_preapply_tsys(self):\n \"\"\"test_apply_composite01 --- on-the-fly application of sky table with existing Tsys table.\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply', applytable=self.tsystable,\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_preapply_tsys", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98d92f7d22040c3043807a62e0870768ef9b5093d9e054752f82069890feeb6b", "warmup_time": -1}, "bench_sdcal.OTF.time_otf01": {"code": "class OTF:\n def time_otf01(self):\n \"\"\"test_otf01 --- Compute calibration table. calmode='otf' ms=squares.dec60_cs.ms.\"\"\"\n sdcal(infile=self.infile_squares, calmode='otf', fraction='10%', noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='', spw='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf01", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "73890f5299363c64dc00df3929185044042aecbf7528479fdd2048b07bae4e5a", "warmup_time": -1}, "bench_sdcal.OTF.time_otf02": {"code": "class OTF:\n def time_otf02(self):\n \"\"\"test_otf02 --- Compute calibration table. calmode='otf' ms=squares.dec60_cs.ms edges_fraction=20%.\"\"\"\n sdcal(infile=self.infile_squares, calmode='otf', fraction=0.2, noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='', spw='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf02", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d3f6cfeb66f0f58a7c7af4db78f37b6cb55b571f9a99cacd26c26271231c9eb9", "warmup_time": -1}, "bench_sdcal.OTF.time_otf03": {"code": "class OTF:\n def time_otf03(self):\n \"\"\"test_otf03 --- Compute calibration table. calmode='otf' ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable='',interp='', spwmap={}, outfile=self.outfile, overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf03", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "943238908dd59e211bdf634348dd5f42ac03ffc10488f932e3c34bde04cb2ff9", "warmup_time": -1}, "bench_sdcal.OTF.time_otf04": {"code": "class OTF:\n def time_otf04(self):\n \"\"\"test_otf04 --- Compute calibration table. calmode='otf' ms=lissajous.ms edges_fraction=20%.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf', fraction='20%', noff=-1, width=0.5, elongated=False,\n applytable='',interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf04", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2a260b2487f65ee23665c8bedd0c4148bc2e3d6f36d6a332f015d755bbf015e1", "warmup_time": -1}, "bench_sdcal.OTF.time_otf05": {"code": "class OTF:\n def time_otf05(self):\n \"\"\"test_otf05 --- Sky calibration. calmode='otf,apply' ms=squares.dec60_cs.ms.\"\"\"\n # In the original test it runs sdcal() twice, first with calmode=apply and then with otf,apply\n sdcal(infile=self.infile_squares, calmode='otf,apply', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable='', interp='', spwmap={}, outfile='', overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf05", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4a19756f66b7ef9544328df9520128cd57da97e46f6c462cbd508c850951500b", "warmup_time": -1}, "bench_sdcal.OTF.time_otf06": {"code": "class OTF:\n def time_otf06(self):\n \"\"\"test_otf06 --- Sky calibration reusing caltable pre-computed with calmode='otf'. calmode='apply' ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='apply', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable=self.applytable, interp='', spwmap={}, outfile='', overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf06", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "522a3b5d1d1fdabe2ba018b8273e019b5eddafc14dfdbf7f95daec034fb5f4aa", "warmup_time": -1}, "bench_sdcal.OTF.time_otf07": {"code": "class OTF:\n def time_otf07(self):\n \"\"\"test_otf07 --- Sky calibration + Tsys conversion, composite calmode='otf,tsys,apply'. ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf,tsys,apply', fraction='10%', noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile='', overwrite=False, field='', spw='', scan='', intent='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf07", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "748ba7dd3174afcf0e776988ea8b438690c52ce4db2c52cd38f4008560cb2781", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_autodetection": {"code": "class OTFRaster:\n def time_autodetection(self):\n \"\"\"test_otfraster11 --- OTF raster calibration ('otfraster') with auto detection.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile,\n calmode='otfraster', fraction=0, noff=0)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_autodetection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4ab1d39226973c46b69401fb6d1cdebad9f92dcffdb9e3e05a81a21353a46501", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_default": {"code": "class OTFRaster:\n def time_default(self):\n \"\"\"test_otfraster07 --- OTF raster calibration ('otfraster') with default setting.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_default", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b1cfc4dc086f0f79a3eb1aa50bd34c6e2fb2fe269bce47df997cfb8e78c6197", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_numeric": {"code": "class OTFRaster:\n def time_fraction_numeric(self):\n \"\"\"test_otfraster10 --- OTF raster calibration ('otfraster') with numeric fraction.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction=0.3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_numeric", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "9a1b91b89d7822ba87cb6acef313ffa79716c45ed9b6d7a6a307e452e7f2a76c", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_string_numeric": {"code": "class OTFRaster:\n def time_fraction_string_numeric(self):\n \"\"\"test_otfraster08 --- OTF raster calibration ('otfraster') with string fraction (numeric value).\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='0.3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_string_numeric", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "8e7b855976e9c451b5b3231e2ab52aeb3ec21c80edde166cc9c6ed70d310e202", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_string_percentage": {"code": "class OTFRaster:\n def time_fraction_string_percentage(self):\n \"\"\"test_otfraster09 --- OTF raster calibration ('otfraster') with string fraction (percentage).\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='30%')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_string_percentage", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "99b8030521d84e0d5c0d814a2205f20bd9fd2c1cca59feff9cea414f8790c81b", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_noff_custom": {"code": "class OTFRaster:\n def time_noff_custom(self):\n \"\"\"test_otfraster12 --- OTF raster calibration ('otfraster') with custom noff.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', noff=3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_noff_custom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b0d73553a431b883fcbd52935ecc5b25ca21ab6dcae20bd7943b107a3256a398", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_noff_priority_over_fraction": {"code": "class OTFRaster:\n def time_noff_priority_over_fraction(self):\n \"\"\"test_otfraster13 --- check if noff takes priority over fraction.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='90%', noff=3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_noff_priority_over_fraction", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2c5fb1d0dca19405b4dc976ff1992e112b24758c778b4bb23711529cc94a785d", "warmup_time": -1}, "bench_sdcal.OTF_Ephemeris.time_otf_ephem": {"code": "class OTF_Ephemeris:\n def time_otf_ephem(self):\n \"\"\"test_otfephem01: Sky calibration of 'otf' mode for ephemeris object.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otf')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTF_Ephemeris.time_otf_ephem", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ab9adf8942dfdd994dd3781f749699fa9662bf58575015560e5daa006f34b730", "warmup_time": -1}, "bench_sdcal.PositionSwitch.time_ps_basic": {"code": "class PositionSwitch:\n def time_ps_basic(self):\n \"\"\"test_ps05 --- position switch calibration ('ps').\"\"\"\n sdcal(infile=self.infile, calmode='ps', outfile=self.applytable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.PositionSwitch.time_ps_basic", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b7be7f0ff76e3fe922cbeca2f39ba103e79e44de6d83b4b99be250473cbbbfee", "warmup_time": -1}, "bench_sdcal.PositionSwitch.time_ps_selection": {"code": "class PositionSwitch:\n def time_ps_selection(self):\n \"\"\"test_ps06 --- position switch calibration ('ps') with data selection.\"\"\"\n sdcal(infile=self.infile, calmode='ps', spw='9', outfile=self.applytable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.PositionSwitch.time_ps_selection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c6f801e6c4a066a039b23af69d5fef7d2ff87044ac6f963b222b34644a63e410", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_apply(self):\n \"\"\"test_single_pol_apply --- apply caltable to single-polarization data.\"\"\"\n # Generate ouput file for apply case\n sdcal(infile=self.infile, calmode='ps', outfile=self.outfile)\n sdcal(infile=self.infile, calmode='apply', applytable=self.outfile)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a191063314c95b694fc29c97d3e0dccb94184c2aa1aa185ac280f091ed025233", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply_composite": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_apply_composite(self):\n \"\"\"test_single_pol_apply_composite --- on-the-fly calibration/application on single-polarization data.\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply_composite", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b7552259dc52ed23dd95af462d5d9ef0a35d33f2f79af3ecb6eabd3b97e147ff", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_ps(self):\n \"\"\"test_single_pol_ps --- generate caltable for single-polarization data.\"\"\"\n sdcal(infile=self.infile, calmode='ps', outfile=self.outfile)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4e55128473bdf00218cb5fd33d6a7c9e00e1779f913ec0f11008a756dbc973be", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1cabc32a44329c9516713575172e17491a87d77a616062663d7ef91f65148be3", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter=\"\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b930349db5cc439111860320dde2e9b691abd6d7f9b0ec6689dd11d93b41f84f", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[], minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "336acba07a722b00d07d7f1e576db7324acbb0f8b62094628e049205931267c4", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_start_400": {"code": "class Basic:\n def time_gridfunction_BOX_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f75fe21b7e8d74833923c99f159a218e55bf3f8f8f9678410d7d1bdcc7f7b34", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null": {"code": "class Basic:\n def time_gridfunction_BOX_start_400_phasecenter_null(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1248c14417eaf3acae22a2d74d1856434726fdae49f3cf1f99f38e5a3e236f19", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GAUSS_start_400": {"code": "class Basic:\n def time_gridfunction_GAUSS_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GAUSS', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GAUSS_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "53e251e5821f6f4979c73a56b7aa34456880ac756589b0df55077a9c3127a334", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5627d3dd41598393b2e3bd58277169bbafe63781c6b1b5b01c99b9d60d84cbfc", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400_minweight_70(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=70.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b01ed523cee17466c300376e39e4aacc76b5a67f4ded517d201a5dd7ea56f2b", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB": {"code": "class Basic:\n def time_gridfunction_PB(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c919d306e25f5ec15a0459ba15528f60a1a0dcaae1bf19a352dd255a2dedcf63", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_frequency": {"code": "class Basic:\n def time_gridfunction_PB_mode_frequency(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"frequency\", nchan=100, start=\"1420.200000MHz\", width=\"0.010000MHz\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_frequency", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "11be513d35e8f08e044f3236b2b2ce34adde14a6ff376d9d7d167096237a3316", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_1(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=1, start=\"-50256.681248364m/s\", width=\"527652.833436753m/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', restfreq=\"1420405800.0Hz\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5ade0c08d8b30b806d808aa43cd05eee6f36f09bf14ab0a002acb9b0577b2ad", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_100(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=100, start=\"-200.000000km/s\", width=\"2.000000km/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b5092872498a21e2f8184389383bcd9c29b6b0a38b06ad926ac76f436f5894e2", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024": {"code": "class Basic:\n def time_gridfunction_PB_nchan_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1024, width=1, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "385177e17d19f7fb3812a7e94b7ed7b644c5ed42350f2698e63e42c69f980efb", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_start_400": {"code": "class Basic:\n def time_gridfunction_PB_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b9e23dd0fd8a0537036bc41d832bb013cf550d67faa2807e46e9f2f7361e5e21", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY": {"code": "class Basic:\n def time_gridfunction_PB_start_400_stokes_XXYY(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', stokes=\"XXYY\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1eadce5c526c138fe91b3fbc8ad1e714e468817da26e6c0434558be40125081e", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_width_1024": {"code": "class Basic:\n def time_gridfunction_PB_width_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1, width=1024, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_width_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f31655e48c01e2cc72c95e525fdfe8504693b8ad12dbbedf284ff3b88611c8b", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_SF_start_400": {"code": "class Basic:\n def time_gridfunction_SF_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='SF', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_SF_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad4195f91c64c959d4a9da8efd6e024b0ac662770633e565c781f603fc6d2ab0", "warmup_time": -1}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom": {"code": "class MemoryMultiField:\n def peakmem_multifield_cube_hogbom(self):\n \"\"\"tclean: Two fields, both cube - test_multifield_both_cube\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nimagename=' + self.img + '2\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:48.895 +40.55.58.543\\n')\n \n retpar = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', specmode='cube', nchan=2, interpolation='nearest',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_hogbom": {"code": "class MemoryMultiField:\n def peakmem_multifield_mfs_hogbom(self):\n \"\"\"tclean : Two fields, both mfs - test_multifield_both_mfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nnchan=1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs": {"code": "class MemoryMultiField:\n def peakmem_multifield_mfs_mtmfs(self):\n \"\"\" tclean: Two fields, both mt-mfs - test_multifield_both_mtmfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\n\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting": {"code": "class MemorySetWeighting:\n def peakmem_tclean_setweighting(self):\n \"\"\"Adapted from CAS-13026\"\"\"\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n parallel=False,\n )\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting", "param_names": [], "params": [], "setup_cache_key": "bench_tclean_memory:167", "timeout": 10000, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs": {"code": "class MemorySetWeighting:\n def track_file_descriptors_cubemode_mosaic_briggs(self):\n \"\"\"Adapted from CAS-8755\n https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/browse/casatests/performance/test_perf_tclean_mem_setweighting.py?at=refs%2Fheads%2FCAS-13026#166-225\n \n Requires user configuration ~/.casa/rc to contain `synthesis.imager.memprofile.enable: 1`\n Expected output is casa.synthesis.imager.memprofile.PID.HOSTNAME.DATE_TIME_when_test_started.txt\n \"\"\"\n casalog.setlogfile(self.templogfile)\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n parallel=False,\n )\n \n with open(self.templogfile) as mylog:\n for line in mylog:\n a_match = re.search(\"casa.synthesis.imager.memprofile\", line)\n if a_match:\n str_match = a_match.string\n break\n \n # Get name of memprofile created by tclean\n (start, middle, end) = str_match.partition(\"casa.synthesis.imager.memprofile\")\n mem_profile = middle + end.rstrip()\n \n # Get the memory values of the second column named MemRSS_(VmRSS)_MB, for each row\n with open(mem_profile, \"r\") as mfile:\n memdict = OrderedDict()\n maxFDSize = 0\n for myrow in mfile:\n linelist = []\n print(myrow.rstrip())\n if myrow.startswith(\"#\"):\n continue\n \n linelist = myrow.split(\",\")\n tclean_step = str(linelist[-1].rstrip())\n memdict[tclean_step.strip(\"[]\")] = int(linelist[1])\n maxFDSize = max(maxFDSize, int(linelist[7]))\n \n return maxFDSize\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs", "param_names": [], "params": [], "setup_cache_key": "bench_tclean_memory:167", "timeout": 10000, "type": "track", "unit": "file descriptors", "version": "CAS-13924"}, "bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary": {"code": "class MemorySingleField:\n def peakmem_cube_standard_fullsummary(self):\n \"\"\"tclean: single field cube with fullsummary parameter - test_iterbot_cube_fullsummary_true\"\"\"\n retpar = tclean(vis=self.msfile, imagename=self.img + \"Cubetest_fullsummary\", imsize=100, cell='8.0arcsec',\n specmode='cube', deconvolver='clark', niter=10, threshold='0.75Jy', fullsummary=True, parallel=False)\n\n def setup(self):\n self.prepData(\"refim_point_withline.ms\")", "name": "bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwFalse_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwFalse_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=False,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "213736aba4861d6ada012c27b036d0eefbe891ce27250b0867d8354eeafb9ecb"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue(self):\n \"\"\"tclean: Cube mosaic with conjbeams=F, mosaicweight=F, usepointing=T, of two fields - test_cube_mosaic_cbFalse_mwFalse_twofield_upTrue\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', usepointing = True, conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=False,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "8e2ae00738c0ed705cbc3b1df59069743febbe8c160b1dd759a6537097168c0d"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwTrue_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwTrue_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=True,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "ae1661113bf2b4e2383bbc84b618de00882e9df2d34c706fc6182d5ee88ea29c"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_cube_awproject_hogbom": {"code": "class MemoryWideFieldAWP:\n def peakmem_cube_awproject_hogbom(self):\n \"\"\"tclean: Cube with AW-Projection and rotation off - test_widefield_aproj_cube\"\"\"\n ret = tclean(vis=self.msfile, field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",specmode='cube', niter=1, gain=1.0,\n gridder='awproject', cfcache=self.img + '.cfcache',wbawp=True,conjbeams=False,\n psterm=False, computepastep=360.0, rotatepastep=360.0, deconvolver='hogbom',\n parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_cube_awproject_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "535d7210070d64a24f08ced41f22af7c7ff735b2c653c074297c175a08e8cb0d"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom": {"code": "class MemoryWideFieldAWP:\n def peakmem_mfs_awproject_hogbom(self):\n \"\"\"tclean: MFS with narrowband AWProjection - test_widefield_aproj_mfs\"\"\"\n ret = tclean(vis=self.msfile, spw='1', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=30, gridder='awproject', cfcache='',\n wbawp=False, conjbeams=True, psterm=False,computepastep=360.0,rotatepastep=360.0,\n deconvolver='hogbom', savemodel='modelcolumn', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "b7a2bc60edebed77059280a26d38d60297776207e13bca67e77066c2383a48b1"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_mtmfs": {"code": "class MemoryWideFieldAWP:\n def peakmem_mfs_awproject_mtmfs(self):\n \"\"\"tclean: MFS with AWProjection and nt=2 stokes I - test_widefield_aproj_mtmfs \"\"\"\n ret = tclean(vis=self.msfile, spw='*', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=0, gridder='awproject',\n cfcache=self.img + '.cfcache', wbawp=True, conjbeams=False, psterm=False,\n computepastep=360.0,rotatepastep=360.0, deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_mtmfs", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "d58b12d0e026ea8c921dacf72de9d8f859c6305623fbd66ade64637f8950312d"}, "bench_tclean_time.TcleanCube.time_cube_standard_channel_gap": {"code": "class TcleanCube:\n def time_cube_standard_channel_gap(self):\n \"\"\"tclean\" mosaic cube with gap in channel selection - test_cube_21\"\"\"\n ret = tclean(vis=self.msfile, field='0', imsize=100, cell='8.0arcsec', niter=10,\n specmode='cube', nchan=10, restfreq=['1.25GHz'],\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", deconvolver='hogbom',\n spw='0:4~9;12~14', imagename=self.img + 'Cubetest_st4gap', start=4,\n width='', veltype='radio', outframe='LSRK', interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_channel_gap", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "955f38acfd52f97ee9d29fc749cb59a1cc43e4aba8a57c2cdf36b817208f65a1", "warmup_time": -1}, "bench_tclean_time.TcleanCube.time_cube_standard_fullsummary": {"code": "class TcleanCube:\n def time_cube_standard_fullsummary(self):\n \"\"\"tclean: single field cube with fullsummary parameter - test_iterbot_cube_fullsummary_true\"\"\"\n retpar = tclean(vis=self.msfile, imagename=self.img + \"Cubetest_fullsummary\", imsize=100, cell='8.0arcsec',\n specmode='cube', deconvolver='clark', niter=10, threshold='0.75Jy', fullsummary=True, parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_fullsummary", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanCube.time_cube_standard_hogbom": {"code": "class TcleanCube:\n def time_cube_standard_hogbom(self):\n \"\"\"tclean: mosaic cube with Hogbom deconvolver - test_cube_0\"\"\"\n ret = tclean(vis=self.msfile, field='0', imsize=100, cell='8.0arcsec', niter=10,\n specmode='cube', nchan=10, restfreq=['1.25GHz'],\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", deconvolver='hogbom',\n spw='0', imagename=self.img + 'Cubetest_chandefstdefwidth',\n start=0, width=1, veltype='radio', outframe='LSRK', interpolation='linear', parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ead0eaac76215fd3ed59ddd926e9fdfb598dec50a29590840e6bf8a9c1139cfa", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_cubesource": {"code": "class TcleanEphemeris:\n def time_eph_multifield_mosaic_cubesource(self):\n \"\"\"tclean: multifield (mosaic gridder), cubesource mode - test_multifield_cube_eph\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=[480, 420], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='cubesource', gridder='mosaic', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_mfs": {"code": "class TcleanEphemeris:\n def time_eph_multifield_mosaic_mfs(self):\n \"\"\"tclean: multifield (mosaic gridder), mfs mode - test_multifield_mfs_eph\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=[480, 420], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='mfs', gridder='mosaic', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_mfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource": {"code": "class TcleanEphemeris:\n def time_eph_singlefield_standard_cubesource(self):\n \"\"\"tclean: single field (standard gridder), cubesource mode - test_onefield_cube_eph\"\"\"\n ret = tclean(vis=self.msfile, field='0', imagename=self.img, imsize=[288, 288], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='cubesource', gridder='standard', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs": {"code": "class TcleanEphemeris:\n def time_eph_singlefield_standard_mfs(self):\n \"\"\"single field (standard gridder), mfs mode - test_onefield_mfs_eph\"\"\"\n ret = tclean(vis=self.msfile, field='0', imagename=self.img, imsize=[288, 288], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='mfs', gridder='standard', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_automask_modelcolumn(self):\n \"\"\"tclean: mfs with automask and save model column, internally a separate predit model step - test_modelvis_12\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n savemodel='modelcolumn',usemask='auto-multithresh', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d60a63bc05d020e7f8100acf6044bbeda8cd4ef0f53267b53984b3580a421bc", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_modelcolumn(self):\n \"\"\"tclean: mfs mode saving a model column - test_modelvis_2\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n savemodel='modelcolumn',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "39a0309c515e5dfe440b399768551ee76165e4551a994dcb41e2ed255523940e", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_virtual_modelcolumn(self):\n \"\"\"tclean: mfs saving a virtual model column - test_modelvis_3\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n savemodel='virtual',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "322f52d2bb9054965cfef3b9c72cf5750386b28c13444d0055c32f9854934461", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom": {"code": "class TcleanMultiField:\n def time_multifield_cube_hogbom(self):\n \"\"\"tclean: Two fields, both cube - test_multifield_both_cube\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nimagename=' + self.img + '2\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:48.895 +40.55.58.543\\n')\n \n retpar = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', specmode='cube', nchan=2, interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom": {"code": "class TcleanMultiField:\n def time_multifield_mfs_hogbom(self):\n \"\"\"tclean : Two fields, both mfs - test_multifield_both_mfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nnchan=1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_mfs_mtmfs": {"code": "class TcleanMultiField:\n def time_multifield_mfs_mtmfs(self):\n \"\"\" tclean: Two fields, both mt-mfs - test_multifield_both_mtmfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\n\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_mfs_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_cube_standard_pcwdT": {"code": "class TcleanSingleField:\n def time_cube_standard_pcwdT(self):\n \"\"\"tclean: cube with perchanweightdensity True and briggs weighting - test_onefield_pcwdT_and_pcwdF\"\"\"\n ret = tclean(self.msfile, imagename=self.img + '1', imsize=20, cell='8.0arcsec', niter=0, nchan=1,\n spw='0:1', gridder='standard', perchanweightdensity=True, specmode='cube',\n weighting='briggs', robust=0.5)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_cube_standard_pcwdT", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_clark": {"code": "class TcleanSingleField:\n def time_mfs_standard_clark(self):\n \"\"\"tclean: mfs with clark minor cycle - test_onefield_clark\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='clark', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_clark", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom": {"code": "class TcleanSingleField:\n def time_mfs_standard_hogbom(self):\n \"\"\"tclean: mfs with hogbom minor cycle - test_onefield_hogbom\"\"\"\n tricky_imagename = self.img + '_uid___A001_X1234a_X56cb.s19_0.J2253+1608_bp.more-dash.virtspw19.mfs.I.iter0.hoghbom'\n ret = tclean(vis=self.msfile, imagename=tricky_imagename, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom_automask": {"code": "class TcleanSingleField:\n def time_mfs_standard_hogbom_automask(self):\n \"\"\"tclean: multi-threshold Autobox (minbeamfrac=0.3) - test_mask_autobox_multithresh_with_prune\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=1000,cell='8.0arcsec',niter=10,\n deconvolver='hogbom', usemask='auto-multithresh',\n minbeamfrac=0.3,parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom_automask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs": {"code": "class TcleanSingleField:\n def time_mfs_standard_mtmfs(self):\n \"\"\"tclean: mt-mfs with minor cycle iterations - test_onefield_mtmfs\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_cube_stokes_IV": {"code": "class TcleanStokes:\n def time_cube_stokes_IV(self):\n \"\"\"tclean: cube with stokes V - test_stokes_cube_IV\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n stokes='IV', specmode='cube', interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_cube_stokes_IV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_mfs_stokes_IV": {"code": "class TcleanStokes:\n def time_mfs_stokes_IV(self):\n \"\"\"tclean: mfs with stokes IV - test_stokes_mfs_IV\"\"\"\n tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10, stokes='IV',\n parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_mfs_stokes_IV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d35d9a07fff354a74fbeb5aecec4abcc4dc94d0e83e10f366cb69e439004ed44", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV": {"code": "class TcleanStokes:\n def time_mtmfs_stokes_IQUV(self):\n \"\"\"tclean: mtmfs with stokes IQUV - test_stokes_mtmfs_IQUV\"\"\"\n tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10, stokes='IQUV',\n deconvolver='mtmfs', nterms=2, parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d3a14b77c55eebaebca13da5c79f9b48d38bf677f7e4aaf3c5103ca66e21006", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_cube_awproject_hogbom": {"code": "class TcleanWideFieldAWP:\n def time_cube_awproject_hogbom(self):\n \"\"\"tclean: Cube with AW-Projection and rotation off - test_widefield_aproj_cube\"\"\"\n ret = tclean(vis=self.msfile, field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",specmode='cube', niter=1, gain=1.0,\n gridder='awproject', cfcache=self.img + '.cfcache',wbawp=True,conjbeams=False,\n psterm=False, computepastep=360.0, rotatepastep=360.0, deconvolver='hogbom',\n parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_cube_awproject_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e5933d46b7d76f6e722504feff6442d7b22d7a07286b441227931d33b9adfae7", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom": {"code": "class TcleanWideFieldAWP:\n def time_mfs_awproject_hogbom(self):\n \"\"\"tclean: MFS with narrowband AWProjection - test_widefield_aproj_mfs\"\"\"\n ret = tclean(vis=self.msfile, spw='1', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=30, gridder='awproject', cfcache='',\n wbawp=False, conjbeams=True, psterm=False,computepastep=360.0,rotatepastep=360.0,\n deconvolver='hogbom', savemodel='modelcolumn', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b822057988cc0cc532d1b070ee27c6cca7c74940c97f32e4cae5442b0a580aec", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs": {"code": "class TcleanWideFieldAWP:\n def time_mfs_awproject_mtmfs(self):\n \"\"\"tclean: MFS with AWProjection and nt=2 stokes I - test_widefield_aproj_mtmfs \"\"\"\n ret = tclean(vis=self.msfile, spw='*', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=0, gridder='awproject',\n cfcache=self.img + '.cfcache', wbawp=True, conjbeams=False, psterm=False,\n computepastep=360.0,rotatepastep=360.0, deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "67a1d66f43c33d52452f3438452c599001c854eae7b69f546049457c746928d6", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwFalse_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwFalse_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=False, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue(self):\n \"\"\"tclean: Cube mosaic with conjbeams=F, mosaicweight=F, usepointing=T, of two fields - test_cube_mosaic_cbFalse_mwFalse_twofield_upTrue\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n usepointing = True, conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=False, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "6692e661298a1a5355c1201359883748b54bbd123c50dd2b2ea3ec5b412ec827", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwTrue_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwTrue_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=True, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad3861f21889712e38ffbf6ef492929807086c6012d61b230a740229d1d6b7c5", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1cabc32a44329c9516713575172e17491a87d77a616062663d7ef91f65148be3", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter=\"\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b930349db5cc439111860320dde2e9b691abd6d7f9b0ec6689dd11d93b41f84f", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[], minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "336acba07a722b00d07d7f1e576db7324acbb0f8b62094628e049205931267c4", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400": {"code": "class Basic:\n def time_gridfunction_BOX_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f75fe21b7e8d74833923c99f159a218e55bf3f8f8f9678410d7d1bdcc7f7b34", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null": {"code": "class Basic:\n def time_gridfunction_BOX_start_400_phasecenter_null(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1248c14417eaf3acae22a2d74d1856434726fdae49f3cf1f99f38e5a3e236f19", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GAUSS_start_400": {"code": "class Basic:\n def time_gridfunction_GAUSS_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GAUSS', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GAUSS_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "53e251e5821f6f4979c73a56b7aa34456880ac756589b0df55077a9c3127a334", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5627d3dd41598393b2e3bd58277169bbafe63781c6b1b5b01c99b9d60d84cbfc", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400_minweight_70(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=70.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b01ed523cee17466c300376e39e4aacc76b5a67f4ded517d201a5dd7ea56f2b", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB": {"code": "class Basic:\n def time_gridfunction_PB(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c919d306e25f5ec15a0459ba15528f60a1a0dcaae1bf19a352dd255a2dedcf63", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_frequency": {"code": "class Basic:\n def time_gridfunction_PB_mode_frequency(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"frequency\", nchan=100, start=\"1420.200000MHz\", width=\"0.010000MHz\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_frequency", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "11be513d35e8f08e044f3236b2b2ce34adde14a6ff376d9d7d167096237a3316", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_1(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=1, start=\"-50256.681248364m/s\", width=\"527652.833436753m/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', restfreq=\"1420405800.0Hz\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5ade0c08d8b30b806d808aa43cd05eee6f36f09bf14ab0a002acb9b0577b2ad", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_100(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=100, start=\"-200.000000km/s\", width=\"2.000000km/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b5092872498a21e2f8184389383bcd9c29b6b0a38b06ad926ac76f436f5894e2", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_nchan_1024": {"code": "class Basic:\n def time_gridfunction_PB_nchan_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1024, width=1, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_nchan_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "385177e17d19f7fb3812a7e94b7ed7b644c5ed42350f2698e63e42c69f980efb", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_start_400": {"code": "class Basic:\n def time_gridfunction_PB_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b9e23dd0fd8a0537036bc41d832bb013cf550d67faa2807e46e9f2f7361e5e21", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY": {"code": "class Basic:\n def time_gridfunction_PB_start_400_stokes_XXYY(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', stokes=\"XXYY\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1eadce5c526c138fe91b3fbc8ad1e714e468817da26e6c0434558be40125081e", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_width_1024": {"code": "class Basic:\n def time_gridfunction_PB_width_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1, width=1024, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_width_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f31655e48c01e2cc72c95e525fdfe8504693b8ad12dbbedf284ff3b88611c8b", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_SF_start_400": {"code": "class Basic:\n def time_gridfunction_SF_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='SF', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_SF_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad4195f91c64c959d4a9da8efd6e024b0ac662770633e565c781f603fc6d2ab0", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_cubesource": {"code": "class Ephemeris:\n def time_ephemeris_cubesource(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,veltype='radio', specmode='cubesource', outframe='',\n gridfunction='BOX', convsupport=-1, truncate=-1,gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec',\n phasecenter='TRACKFIELD', projection='SIN',pointingcolumn='direction', restfreq='', stokes='I',\n minweight=0.1, brightnessunit='',clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e1590a631850386242adaf9bd659e0dc1827e29a18273a09188b0b52c3e6f76e", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_notset": {"code": "class Ephemeris:\n def time_ephemeris_notset(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_notset", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "0f8206812c10517da8551f9b08b58784bdb8a969ae3fc745bdd975a628da94de", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_sun": {"code": "class Ephemeris:\n def time_ephemeris_sun(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='SUN', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_sun", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e8e8766d8a4af7429cd16c665606e2e5f05b124fbe24196c8a90ae14856f5670", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_table": {"code": "class Ephemeris:\n def time_ephemeris_table(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,veltype='radio', specmode='cube',\n outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,gwidth=-1, jwidth=-1,\n imsize=[1000], cell='4arcsec',phasecenter=self.ephtab,\n projection='SIN',pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1,\n brightnessunit='',clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_table", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "abb49342590225dc02b50b7a4fc1558664794ae427a0503721ac0a6ae0d95959", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_trackf": {"code": "class Ephemeris:\n def time_ephemeris_trackf(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='TRACKFIELD', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_trackf", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "dc585b5f985c2244f266a12e7a01143c4ce9702c0bdbed596e13752673bfc047", "warmup_time": -1}, "bench_uvcontsub.Basic.time_datacolumn": {"code": "class Basic:\n def time_datacolumn(self):\n uvcontsub(vis=ms_corr, outputvis=self.output, datacolumn='CORRECTED')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_datacolumn", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "8de4cfdf0dc3bf0bed17e8d3988ba003ec80e4ec5f8c98958fb4cc44bea1f96b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitmethod_casacore": {"code": "class Basic:\n def time_fitmethod_casacore(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitmethod='casacore')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitmethod_casacore", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "dfb214a4421164ab4c380082077ebe9f5d3903a3c5c171837c379819feae42ea", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitmethod_gsl": {"code": "class Basic:\n def time_fitmethod_gsl(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitmethod='gsl')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitmethod_gsl", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0d8fee718ac26b9dcd2c95fe20e8b4254e1125c0ea176bce6dbeeb7b1414efce", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitorder1": {"code": "class Basic:\n def time_fitorder1(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=1,\n fitspec='0:2~20')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitorder1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "8fd68c9b3e632dd7b26ef564729b42105fc47f1779e544fae389d5adc08e98e7", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitorder2": {"code": "class Basic:\n def time_fitorder2(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=2,\n fitspec='0:2~20')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitorder2", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "3114172327fd2768029d264e578ebde390247d280c059d46d578da04ef3ea9b8", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec3_spws_fitorder0": {"code": "class Basic:\n def time_fitspec3_spws_fitorder0(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output, fitorder=0,\n fitspec='3')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec3_spws_fitorder0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "9d83a87c6ebaa6f3b9bbd80256e790db10fdaad1d7869f1801bda6f548548816", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_channels": {"code": "class Basic:\n def time_fitspec_channels(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='0:5~19')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_channels", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "211807ad73464bccf9f2d9954603775df109fd39308249c8f0a8be8468d0d504", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitorder1": {"code": "class Basic:\n def time_fitspec_dict_fitorder1(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=1,\n fitspec={'0': {'0': {'chan': '2~20',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitorder1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "788bb7c38a92579219296d435a9f8831a5e8600ec97baf7c70505174b4112826", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitorder2": {"code": "class Basic:\n def time_fitspec_dict_fitorder2(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=2,\n fitspec={'0': {'0': {'chan': '2~20',\n 'fitorder': 2}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitorder2", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "c2d39c9e5d998c65266f1496a25fb286d7e9394bb9c3764d4a6890f796e54ef3", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan": {"code": "class Basic:\n def time_fitspec_dict_fitspec_one_chan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0',\n fitspec={'0':\n {'1': {'chan': '',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "68512d1a0bce4206afd18b13bd2704c6ac07ee1ef9a1e0652600336643789aab", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1": {"code": "class Basic:\n def time_fitspec_dict_fitspec_one_chan_order1(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0',\n fitspec={'0':\n {'1': {'chan': '',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "69adae3d962899abd6303be60d0a44ddd656a8b72910418f80f587da540cd78c", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching": {"code": "class Basic:\n def time_fitspec_dict_select_spw_mismatching(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0', spw='1',\n fitspec={'0':\n {'0': {'chan': '2~127',\n 'fitorder': 0}},\n '1':\n {'0': {'chan': '0~40',\n 'fitorder': 1}},\n '3':\n {'0': {'chan': '1~33;56~119',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "faa5b02f1961d7e57a2431ef5fb58c23bade435413dd1890f34460c73f7a6c61", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_empty": {"code": "class Basic:\n def time_fitspec_empty(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_empty", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1162b94dcdcd8c1468b01cce5b33eee0e86e593f92e9f394436369c566e59d03", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield": {"code": "class Basic:\n def time_fitspec_multifield(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={\n '0': {'0': {'chan': '100~500;600~910;1215~1678;1810~1903',\n 'fitorder': 0}},\n '1': 'NONE',\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "2200609258dcdd47db8e8f5ad4d0e55a502812ffa3624f3a48358904d23ad6a0", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_blocks": {"code": "class Basic:\n def time_fitspec_multifield_blocks(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={\n '0, 1': {'0': {'chan': '100~500;600~900;1200~1900',\n 'fitorder': 0}},\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_blocks", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "99f4e00c2743d33e8c1c353c1cadc351fcea655f7878133259e3365e398f5abf", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel": {"code": "class Basic:\n def time_fitspec_multifield_fields_sel(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='1,2',\n fitspec={'1': {'0': {'chan': '100~500;600~900',\n 'fitorder': 0}},\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "c5a598b1002088c1224c744c4cc7865f8d7583a29b35f620b379ea9b8c7f6ef5", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_multispw_diff_fitorder": {"code": "class Basic:\n def time_fitspec_multifield_multispw_diff_fitorder(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={'0': {'0': {'chan':\n '100~500;600~910;1215~1678;1810~1903',\n 'fitorder': 1},\n '1': {'chan': '0',\n 'fitorder': 2}},\n '1': 'NONE',\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 2}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_multispw_diff_fitorder", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1884692bb3e4d160ac28dfe708bf2b0bf06f9d75589fca4f1688c9abadfc948c", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_separate_fields": {"code": "class Basic:\n def time_fitspec_separate_fields(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='1',\n fitspec='0:100~500;600~910;1215~1678;1810~1903')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_separate_fields", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "cf05e60ae71d4af643accde4c61a2ae44cf1a783edf0ce36a95fee1cb106b923", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spw_one_chan": {"code": "class Basic:\n def time_fitspec_spw_one_chan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0', fitspec='1')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spw_one_chan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "9c251da305a5aa0f63a2a75105ca215862963f9d11cc4a6b695cf8a702dcdc84", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spws": {"code": "class Basic:\n def time_fitspec_spws(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spws", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "ab9cf480d9ce00340dbf39ac9cbc14a74a1bb716c43bfffc6322b5af7020a160", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spws_sel": {"code": "class Basic:\n def time_fitspec_spws_sel(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, spw='0', fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spws_sel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "33ec54a7a626bd87b33c1f5ed0b20762be14e0e6a17f4bc6076f269384958cf8", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spwschans_fitorder3": {"code": "class Basic:\n def time_fitspec_spwschans_fitorder3(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output, fitorder=3,\n fitspec='4:50~503;600~850;900~1002')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spwschans_fitorder3", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "fa14ea078998aaba67b8acd52f7693a1bbdd7d1f273ce738bd3959de96ce8c71", "warmup_time": -1}, "bench_uvcontsub.Basic.time_makes_output_ms_data": {"code": "class Basic:\n def time_makes_output_ms_data(self):\n uvcontsub(vis=ms_simple, outputvis=self.output)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_makes_output_ms_data", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0f51dc9c6b96c74c4dbd8c11f8388a28712b70fcf187ab98cfd821e516aa90d3", "warmup_time": -1}, "bench_uvcontsub.Basic.time_mixed_pols": {"code": "class Basic:\n def time_mixed_pols(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_mixed_pols", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "90b6d31e3a0ee607357ff1720317395dc6ee5269a5e70d651ef0ef9c727c3878", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_array": {"code": "class Basic:\n def time_select_array(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, array='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_array", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "ea3c135bafff1459cbc837162309e4b6ae3fece4a6be027a4df51bdda97ab7fb", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_field": {"code": "class Basic:\n def time_select_field(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='2')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_field", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1886548da1c74441518ea6cdbc2ed5b6fa259fcb2fcec3d4e5df5c1e0b1c865d", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_intent": {"code": "class Basic:\n def time_select_intent(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, intent='*AMPLI*')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_intent", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "b87258a05737dcccf486624392643fc288a6a2c8474c205ece3cb80d87d202eb", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_observation": {"code": "class Basic:\n def time_select_observation(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, observation='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_observation", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a6b18c12fe61a940c0ecbdcfa89944ed0e091b8b4d4198b17c345a9f3c4c4c4b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_scan": {"code": "class Basic:\n def time_select_scan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, scan='2')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_scan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "6fe8f7df4455762e4e5bc08a95d1cd7417fd105e9c5aa51272d28f53c7696b9b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_spw": {"code": "class Basic:\n def time_select_spw(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, spw='1')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_spw", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "cf736c29415ea590041754714b678dd26b800a40ab3134c319bac8dbe12600dd", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_spw_mismatching_fitspec": {"code": "class Basic:\n def time_select_spw_mismatching_fitspec(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, spw='1', fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_spw_mismatching_fitspec", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "158ef3ee6006d0930dc734a45cceab1d358d189bb5c2da6fba1f21528b7d587a", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel": {"code": "class Basic:\n def time_writemodel(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "84ee71017eadec38583d3c0e1539ed8d9af22f22a326abf0e4730b08fe77142d", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel_from_corrected": {"code": "class Basic:\n def time_writemodel_from_corrected(self):\n uvcontsub(vis=ms_papersky, outputvis=self.output, datacolumn='CORRECTED',\n writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel_from_corrected", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "49f06856290be0914b13df703b42f5742c7d8d6c65041f711909ffe6bb54b038", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel_from_corrected_all_flagged": {"code": "class Basic:\n def time_writemodel_from_corrected_all_flagged(self):\n uvcontsub(vis=ms_corr, outputvis=self.output, datacolumn='CORRECTED',\n writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel_from_corrected_all_flagged", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "bb9609826a0e5903041743e876e9c1130f97601a78f33da3d9171171b66d44bc", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_0": {"code": "class Numerical:\n def time_sim_specline_noise_pol_0(self):\n \"\"\" Check fitting of continuum as polynomial order 0\"\"\"\n uvcontsub(vis=self.ms_cont_noise_order_0, outputvis=self.output,\n fitorder=0, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a3dc88dff98704afaa2065680f87615524f3c67b46d920634e59c82d0f4cf406", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_1": {"code": "class Numerical:\n def time_sim_specline_noise_pol_1(self):\n \"\"\" Check fitting of continuum as polynomial order 1. Gaussian noise included\"\"\"\n uvcontsub(vis=self.ms_cont_noise_order_1, outputvis=self.output,\n fitorder=1, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "4b6fc8f71c9d351593ef5059ab3339340b3e8aab807d18261c20224c22fd0b9c", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_0": {"code": "class Numerical:\n def time_sim_specline_nonoise_pol_0(self):\n \"\"\" Check fitting of continuum as polynomial order 0\"\"\"\n uvcontsub(vis=self.ms_cont_nonoise_order_0, outputvis=self.output,\n fitorder=0, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a06063282ce6f0ecae2282332b977438de648778798aab364359767983f41fac", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_1": {"code": "class Numerical:\n def time_sim_specline_nonoise_pol_1(self):\n \"\"\" Check fitting of continuum as polynomial order 1\"\"\"\n uvcontsub(vis=self.ms_cont_nonoise_order_1, outputvis=self.output,\n fitorder=1, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0574b1e7222ff924033c48c38b27199dd8addd98dcec9671894b820ef6ec72d2", "warmup_time": -1}, "casademo.calibration_suite.time_applycal_callib": {"code": "class calibration_suite:\n def time_applycal_callib(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\n \n Note that this is from on-the-fly application of preliminary phase-up, bandpass, and amplitude caltables, not the later stage hifa_applycal\n Expected to take ~48s, could be sped up by splitting out SPWs (especially the square law detector windows) or applying to only one of them.\n \"\"\"\n applycal(\n vis=self.input_ms,\n field=\"J1924-2914\",\n spw=\"16,18,20,22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n timerange=\"\",\n uvrange=\"\",\n antenna=\"*&*\",\n scan=\"\",\n observation=\"\",\n msselect=\"\",\n docallib=True,\n callib=self.applycal_library,\n gaintable=[],\n gainfield=[],\n interp=[],\n spwmap=[],\n calwt=[True],\n parang=False,\n applymode=\"calflagstrict\",\n flagbackup=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_applycal_callib", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "25f369e7a681d0d6ccb62c48d6a227c1fa45a7e9f79792f60a3fd40a6997e2e2", "warmup_time": -1}, "casademo.calibration_suite.time_bandpass": {"code": "class calibration_suite:\n def time_bandpass(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\"\"\"\n bandpass(\n vis=self.input_ms,\n caltable=self.bandpass_table,\n field=\"J1924-2914\",\n spw=\"22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n antenna=\"0~9\",\n solint=\"inf,15.625000MHz\",\n combine=\"scan\",\n refant=\"CM03,CM10,CM02,CM12,CM06,CM05,CM11,CM04,CM07,CM01\",\n minblperant=4,\n minsnr=3.0,\n solnorm=True,\n bandtype=\"B\",\n append=True,\n gaintable=[self.tsyscal_table, self.gaincal_table],\n gainfield=[\"J1924-2914\", \"nearest\"],\n interp=[\"linear,linear\", \"linear,linear\"],\n spwmap=[\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 18, 20, 22, 16, 16, 18, 18, 20, 20, 22,],\n [],\n ],\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_bandpass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "3ab2720c5ee4aa0f74882ac18410ea0ab34d6e7f1e940421a993d057380f2fdb", "warmup_time": -1}, "casademo.calibration_suite.time_gaincal": {"code": "class calibration_suite:\n def time_gaincal(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\n \n Expected to take ~7s\n \"\"\"\n gaincal(\n vis=self.input_ms,\n caltable=self.gaincal_table,\n field=\"J1924-2914\",\n spw=\"16,18,20,22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n timerange=\"\",\n uvrange=\"\",\n antenna=\"0~9\",\n scan=\"\",\n observation=\"\",\n msselect=\"\",\n solint=\"int\",\n combine=\"\",\n preavg=-1.0,\n refant=\"CM03,CM10,CM02,CM12,CM06,CM05,CM11,CM04,CM07,CM01\",\n refantmode=\"flex\",\n minblperant=4,\n minsnr=3.0,\n solnorm=False,\n normtype=\"mean\",\n gaintype=\"G\",\n smodel=[],\n calmode=\"p\",\n solmode=\"\",\n rmsthresh=[],\n corrdepflags=False,\n append=False,\n splinetime=3600.0,\n npointaver=3,\n phasewrap=180.0,\n docallib=False,\n callib=\"\",\n gaintable=[self.gaincal_table],\n gainfield=[\"J1924-2914\"],\n interp=[\"linear,linear\"],\n spwmap=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 18, 20, 22, 16, 16, 18, 18, 20, 20, 22,],\n parang=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_gaincal", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "a5eafd63e34c53ae63cd3949f23f8acc371d5751a08dcd1681914bf7c0723779", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_bandpassflag": {"code": "class flagdata_suite:\n def time_flagdata_bandpassflag(self):\n \"\"\"hifa_bandpassflag\"\"\"\n flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=[\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='16' antenna='CM05' \\\n timerange='20:09:50~20:09:52' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:09:20~20:09:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:10:21~20:10:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='22' antenna='CM05' \\\n timerange='20:09:30~20:09:32' field='J1924-2914' reason='bad antenna timestamp'\",\n ],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_bandpassflag", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "f55e0ffa6be9d655555c5081a0e7bf2bc00b253273ba7c220de0bd2cfb3baf6c", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_list": {"code": "class flagdata_suite:\n def time_flagdata_list(self):\n \"\"\"hifa_flagdata\"\"\"\n flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=self.flags_cmd,\n tbuff=[0.048, 0.0],\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_list", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "1a40b66ffb4a43d48af3407bae817ed4f7667fe778880859384f5f2e7c9c5d1f", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_list_summary": {"code": "class flagdata_suite:\n def time_flagdata_list_summary(self):\n \"\"\"hifa_rawflagchans\"\"\"\n summary_dict = flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=[\"mode='summary' name='before'\"],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_list_summary", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "3eb284062687484026a1defb4fceb705e83b244e37405f8794d431940ff44b08", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_summary": {"code": "class flagdata_suite:\n def time_flagdata_summary(self):\n \"\"\"hifa_importdata\"\"\"\n summary_dict = flagdata(vis=self.datapath, flagbackup=False, mode=\"summary\")\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_summary", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "9e569fb4e2a2f68e59c510bbf889632173be867104a4e5ba5a98d0eb110e82fb", "warmup_time": -1}, "casademo.tclean_memory_suite.peakmem_tclean_setweighting": {"code": "class tclean_memory_suite:\n def peakmem_tclean_setweighting(self):\n \"\"\"Adapted from CAS-13026\"\"\"\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n interactive=False,\n parallel=False,\n )\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "casademo.tclean_memory_suite.peakmem_tclean_setweighting", "param_names": [], "params": [], "setup_cache_key": "casademo:37", "timeout": 10000, "type": "peakmemory", "unit": "bytes", "version": "8f03400a55e077279ae97bfcf867ab2364d450662c2cdd1f58f3293e06630979"}, "casademo.tclean_memory_suite.track_tclean_file_descriptors_cubemode_mosaic_briggs": {"code": "class tclean_memory_suite:\n def track_tclean_file_descriptors_cubemode_mosaic_briggs(self):\n \"\"\"Adapted from CAS-8755\n \n https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/browse/casatests/performance/test_perf_tclean_mem_setweighting.py?at=refs%2Fheads%2FCAS-13026#166-225\n \n Requires user configuration ~/.casa/rc to contain `synthesis.imager.memprofile.enable: 1`\n Expected output is casa.synthesis.imager.memprofile.PID.HOSTNAME.DATE_TIME_when_test_started.txt\n \"\"\"\n # attribute for tracking metric\n unit = \"file descriptors\"\n \n casalog.setlogfile(self.templogfile)\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n interactive=False,\n parallel=False,\n )\n \n with open(self.templogfile) as mylog:\n for line in mylog:\n a_match = re.search(\"casa.synthesis.imager.memprofile\", line)\n if a_match:\n str_match = a_match.string\n break\n \n # Get name of memprofile created by tclean\n (start, middle, end) = str_match.partition(\"casa.synthesis.imager.memprofile\")\n mem_profile = middle + end.rstrip()\n \n # Get the memory values of the second column named MemRSS_(VmRSS)_MB, for each row\n with open(mem_profile, \"r\") as mfile:\n memdict = OrderedDict()\n maxFDSize = 0\n for myrow in mfile:\n linelist = []\n print(myrow.rstrip())\n if myrow.startswith(\"#\"):\n continue\n \n linelist = myrow.split(\",\")\n tclean_step = str(linelist[-1].rstrip())\n memdict[tclean_step.strip(\"[]\")] = int(linelist[1])\n maxFDSize = max(maxFDSize, int(linelist[7]))\n \n return maxFDSize\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "casademo.tclean_memory_suite.track_tclean_file_descriptors_cubemode_mosaic_briggs", "param_names": [], "params": [], "setup_cache_key": "casademo:37", "timeout": 10000, "type": "track", "unit": "unit", "version": "f9c40e4b16d7caf92b2d5a831995e197fae67947f0ec9bdad00b852f28656239"}}, "machines": {"NRAO workstation": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "version": 1}, "casa-perf-test": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "version": 1}, "cvpost029": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz", "machine": "cvpost029", "num_cpu": "32", "os": "Linux 3.10.0-957.5.1.el7.x86_64", "ram": "264033420", "version": 1}}, "tags": {"1.2.3-rel-1": 12515, "1.2.3-rel-2": 12516, "2019.212": 17631, 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tag:casa6.asv,1970-01-01:/c2e2f57dbf857b15eeaa2dd6f9ab5099083a1d65ab6509d626fc7f09f077b438Airspeed Velocitycasa6 performance regressions2024-07-18T05:58:59Ztag:casa6.asv,2024-07-15:/f364c48d452f69ad7a934ed83f41028390525f18a1d3f192438480d4bcbf9ad864.26% bench_importasdm.Evla.time_pc_true2024-07-18T05:58:59Z<a href="index.html#bench_importasdm.Evla.time_pc_true?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.8&branch=master&commits=f87c3aa81393ca7f0697dd9bb061ca3208a0bba9-864f4166a06aa7619a181874d1223dd1f08c9f4d">64.26% regression</a> on 2024-07-15 13:34:12 in commits <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/f87c3aa81393ca7f0697dd9bb061ca3208a0bba9">f87c3aa8...864f4166</a>.<br> New value: 1.67s, old value: 1.02s.<br> - Latest value: 1.67s (64.26% worse than best value 1.02s).tag:casa6.asv,2024-05-23:/52be183ce4b717feb6ec3281e7bb10937bf5fd0d7fb7f7fa01fe06d22a26f86a52.19% bench_sdcal.OTF.time_otf012024-05-25T11:08:10Z<a href="index.html#bench_sdcal.OTF.time_otf01?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.8&branch=master&commits=f87c3aa81393ca7f0697dd9bb061ca3208a0bba9">52.19% regression</a> on 2024-05-23 15:29:25 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/f87c3aa81393ca7f0697dd9bb061ca3208a0bba9">f87c3aa8</a>.<br> - New value: 309ms, old value: 203ms.<br> - Latest value: 309ms (52.19% worse than best value 203ms).tag:casa6.asv,2024-05-13:/6ac3960b2a5d10ae8223fcf2e6ea00816cb97526c1898e2e0a9ce70096283aa170.72% bench_importasdm.Evla.time_scan_selection2024-05-14T14:55:48Z<a href="index.html#bench_importasdm.Evla.time_scan_selection?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.8&branch=master&commits=3ee3fee127eea790691ec82ad6136fd1884ddd45-9730e91f6ff3b62114fa948ca32cbd96a0790489">70.72% regression</a> on 2024-05-13 08:19:38 in commits <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/3ee3fee127eea790691ec82ad6136fd1884ddd45">3ee3fee1...9730e91f</a>.<br> + Latest value: 1.67s (64.26% worse than best value 1.02s).tag:casa6.asv,2024-05-23:/52be183ce4b717feb6ec3281e7bb10937bf5fd0d7fb7f7fa01fe06d22a26f86a53.20% bench_sdcal.OTF.time_otf012024-05-25T11:08:10Z<a href="index.html#bench_sdcal.OTF.time_otf01?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.8&branch=master&commits=f87c3aa81393ca7f0697dd9bb061ca3208a0bba9">53.20% regression</a> on 2024-05-23 15:29:25 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/f87c3aa81393ca7f0697dd9bb061ca3208a0bba9">f87c3aa8</a>.<br> + New value: 311ms, old value: 203ms.<br> + Latest value: 311ms (53.20% worse than best value 203ms).tag:casa6.asv,2024-05-13:/6ac3960b2a5d10ae8223fcf2e6ea00816cb97526c1898e2e0a9ce70096283aa170.72% bench_importasdm.Evla.time_scan_selection2024-05-14T14:55:48Z<a href="index.html#bench_importasdm.Evla.time_scan_selection?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.8&branch=master&commits=3ee3fee127eea790691ec82ad6136fd1884ddd45-9730e91f6ff3b62114fa948ca32cbd96a0790489">70.72% regression</a> on 2024-05-13 08:19:38 in commits <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/3ee3fee127eea790691ec82ad6136fd1884ddd45">3ee3fee1...9730e91f</a>.<br> New value: 1.75s, old value: 1.02s.<br> Latest value: 1.75s (70.72% worse than best value 1.02s).tag:casa6.asv,2024-04-24:/feaa388635d4e5ab4d0d4bc2efcdf7a0389cabcdc4cf4f9e2fa115d340892aaf6.51% bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart2024-04-25T14:31:08Z<a href="index.html#bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=8206bc4219f6ada764d5f08c110beb360b66f840">6.51% regression</a> on 2024-04-24 01:27:36 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/8206bc4219f6ada764d5f08c110beb360b66f840">8206bc42</a>.<br> New value: 59.8m, old value: 56.1m.<br>