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 9523f7daf..4d142364d 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 @ 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 @@ -1 +1 @@ -[[27934, 3604.928358528763], [27964, 3586.559706948232], [27979, 3549.8567470521666], [28005, 3597.1737629221752], [28006, 3575.1558883991092], [28053, 3547.8353902902454], [28091, 3432.334278382361], [28095, 3427.4291054764763], [28134, 3394.7851578714326], [28160, 3370.9739350583404], [28256, 3394.90765146818], [28277, 3588.808441876434], [28296, 3373.7033070819452], [28301, 3367.7887555854395], [28341, 3453.8145619053394], [28347, 3390.51126215799], [28379, 3418.4867323770013], [28399, 3426.2354968898], [28416, 3375.5980877930997], [28461, 3392.6985422649886], [28473, 3455.813546864083], [28478, 3391.1685630921274]] \ No newline at end of file +[[27934, 3604.928358528763], [27964, 3586.559706948232], [27979, 3549.8567470521666], [28005, 3597.1737629221752], [28006, 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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 index 63adba5dd..64edd05e8 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.ApplyModes.time_applycal_cal_only_mode.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.ApplyModes.time_applycal_cal_only_mode.json @@ -1 +1 @@ -[[27946, 7.6052802429767326], [27983, 6.722829051082954], [28000, 7.142801976995543], [28091, 6.612799868918955], [28095, 7.925020662136376], [28134, 6.453071428462863], [28160, 7.319281288422644], [28256, 6.966307133436203], [28277, 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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.ApplyModes.time_applycal_flag_only_mode.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.ApplyModes.time_applycal_flag_only_mode.json index 747cb2499..e4730ebbd 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.ApplyModes.time_applycal_flag_only_mode.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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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.ApplyModes.time_applycal_flag_only_strict_mode.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.ApplyModes.time_applycal_flag_only_strict_mode.json @@ -1 +1 @@ -[[27946, 5.856713476998266], [27983, 5.818982768105343], [28000, 5.6491242428310215], [28091, 5.744438380934298], [28095, 5.3722568061202765], [28134, 5.386242224834859], [28160, 5.722026599571109], [28256, 5.828268140554428], [28277, 5.097062422893941], [28296, 5.51448392495513], [28301, 5.608003758825362], [28341, 5.306688727810979], [28347, 5.464868048991775], [28379, 4.882254805997945], [28399, 5.371359867975116], [28416, 6.209492438938469], [28461, 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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_applycal.ApplyModes.time_applycal_strict_mode.json index b7ad77c78..618631ea4 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.ApplyModes.time_applycal_strict_mode.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.ApplyModes.time_applycal_strict_mode.json @@ -1 +1 @@ -[[27946, 7.783243537996896], [27983, 7.72017150814645], [28000, 7.781693588942289], [28091, 7.054909700527787], [28095, 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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.CalFlag.time_applycal_calflag.json @@ -1 +1 @@ -[[27946, 8.087407803046517], [27983, 7.0112688899971545], [28000, 7.562633404042572], [28091, 7.340683713555336], [28095, 7.1372655890882015], [28134, 7.612434171140194], [28160, 7.488401113077998], [28256, 7.197625756263733], [28277, 7.060760349966586], [28296, 7.521636973135173], [28301, 7.347367995418608], [28341, 7.343999085947871], [28347, 7.6873013809963595], [28379, 7.628100378002273], [28399, 6.8400515876710415], [28416, 7.020369785022922], [28461, 7.648801890085451], [28473, 7.059919055085629], [28478, 7.457982955034822]] \ No newline at end of file +[[27946, 8.087407803046517], [27983, 7.0112688899971545], [28000, 7.562633404042572], [28091, 7.340683713555336], [28095, 7.1372655890882015], [28134, 7.612434171140194], [28160, 7.488401113077998], [28256, 7.197625756263733], 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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_applycal.CalLibrary.time_applycal_callibrary.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.CalLibrary.time_applycal_callibrary.json @@ -1 +1 @@ -[[27946, 8.840848044026643], [27983, 8.560384933138266], [28000, 9.156446652952582], [28091, 8.707542295567691], [28095, 9.138254638761282], [28134, 8.899301615543664], [28160, 9.17730351164937], [28256, 8.719454349949956], [28277, 8.431716622784734], [28296, 9.194159315899014], [28301, 9.308023075573146], [28341, 8.850010480731726], [28347, 9.09713254898088], [28379, 8.781014514999697], [28399, 9.213458387181163], [28416, 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No newline 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_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 index bae0b3d00..7da0109a1 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 @ 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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_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 @@ -1 +1 @@ -[[27946, 8.660194557975046], [27983, 8.67830399191007], [28000, 8.902234212029725], [28091, 9.348985297605395], [28095, 8.391444588080049], [28134, 8.80574382096529], [28160, 8.173313479870558], [28256, 8.382106459699571], [28277, 8.591007474809885], [28296, 8.310808845795691], [28301, 7.7430071625858545], [28341, 9.022397071123123], [28347, 8.499103117996128], [28379, 8.825741726992419], [28399, 8.4105308316648], [28416, 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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_applycal.Interp.time_applycal_interp.json index bdeede324..7c3103ad4 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.Interp.time_applycal_interp.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.Interp.time_applycal_interp.json @@ -1 +1 @@ -[[27946, 8.727446634031367], [27983, 8.595469226129353], [28000, 8.698212466202676], [28091, 8.93745484109968], [28095, 8.477792663499713], [28134, 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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.Parang.time_applycal_parang.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.Parang.time_applycal_parang.json index 06f0eadd9..c0c91c766 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.Parang.time_applycal_parang.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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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_antenna.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_antenna.json @@ -1 +1 @@ -[[27946, 5.879191896994598], [27983, 5.679510940099135], [28000, 5.791012302041054], [28091, 5.862905245274305], [28095, 5.999444029293954], [28134, 5.8416403057053685], [28160, 5.45598597638309], [28256, 5.247288431972265], [28277, 5.848241132684052], [28296, 5.495905817486346], [28301, 5.589174455031753], [28341, 5.997043311595917], [28347, 5.670020948979072], [28379, 5.1138343939965125], [28399, 5.4291414972394705], [28416, 5.516639868030325], [28461, 5.832183043006808], 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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_applycal.SelectionTypes.time_applycal_select_field.json index c3bf74643..d5cff5667 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 @@ -[[27946, 4.843617498001549], [27983, 5.145377141889185], [28000, 4.47577218990773], [28091, 5.017746261321008], 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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_applycal.SelectionTypes.time_applycal_select_scan.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_scan.json @@ -1 +1 @@ -[[27946, 1.9493775099981576], [27983, 2.1297648600302637], [28000, 1.9428331900853664], [28091, 1.8058283422142267], [28095, 2.1712491316720843], [28134, 1.74522306304425], [28160, 1.8818600941449404], [28256, 2.2795618502423167], [28277, 1.798556812107563], [28296, 1.7824824610725045], [28301, 2.0317232329398394], [28341, 1.9269645810127258], [28347, 2.092934309010161], [28379, 1.7376156999962404], [28399, 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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_applycal.SelectionTypes.time_applycal_select_timerange.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_timerange.json @@ -1 +1 @@ -[[27946, 1.957493239955511], [27983, 2.071640460053459], [28000, 2.046961250016466], [28091, 2.0892054568976164], [28095, 1.9380196686834097], [28134, 2.09075275529176], [28160, 1.764810023829341], [28256, 1.7719408348202705], [28277, 2.088000222109258], [28296, 1.766452644020319], [28301, 1.9049094896763563], [28341, 2.0588950403034687], [28347, 1.862578925996786], [28379, 1.8289232050010469], [28399, 1.9224440269172192], [28416, 1.9740177060011774], [28461, 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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_applycal.SelectionTypes.time_applycal_select_uvrange.json index e29eeba7e..5861bdce5 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 @ 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 @@ -1 +1 @@ -[[27946, 9.700763114029542], [27983, 9.523722088197246], [28000, 9.057996597141027], [28091, 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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_applycal.StandardGainTable.time_applycal_basic_gaintable.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.StandardGainTable.time_applycal_basic_gaintable.json @@ -1 +1 @@ -[[27946, 8.731924562947825], [27983, 9.781765763182193], [28000, 9.418211334850639], [28091, 9.188141388818622], [28095, 9.295107177458704], [28134, 9.55664402898401], [28160, 9.263262585736811], [28256, 8.725411746650934], [28277, 8.903359173797071], [28296, 8.699468449689448], [28301, 10.075238293968141], [28341, 9.081405624747276], [28347, 9.474192477006], [28379, 9.873993781002355], [28399, 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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_flagdata.CalibrationBandpass.time_bandpass_clip_timerange.json index 8b63572b9..7bd3d8fe8 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.CalibrationBandpass.time_bandpass_clip_timerange.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.CalibrationBandpass.time_bandpass_clip_timerange.json @@ -1 +1 @@ -[[27946, 0.2026848409732338], [27983, 0.2027061120606959], [28000, 0.20228820201009512], 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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_flagdata.ClipMode.time_clip_corrected_data.json index 912805fb4..b3cba3e69 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.ClipMode.time_clip_corrected_data.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.ClipMode.time_clip_corrected_data.json @@ -1 +1 @@ -[[27946, 0.7991722075094003], [27983, 0.7985055843601003], [28000, 0.8061735783703625], [28091, 0.9407684188336134], [28095, 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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.ListMode.time_list_bandpassflag.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.ListMode.time_list_bandpassflag.json @@ -1 +1 @@ -[[27946, 20.358244507049676], [27983, 17.567855010041967], [28000, 22.099145765183493], [28091, 18.818365431390703], [28095, 19.88397336937487], [28134, 21.628374446183443], [28160, 16.662265081889927], [28256, 20.781732318922877], [28277, 19.9880115063861], [28296, 19.591129230335355], [28301, 20.02450603991747], [28341, 17.718779085204005], [28347, 20.61715053601074], [28379, 19.33541988801153], [28399, 18.984795285388827], [28416, 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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.ShadowMode.time_shadow_addantenna.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.ShadowMode.time_shadow_addantenna.json @@ -1 +1 @@ -[[27946, 0.5023851615260355], [27983, 0.5190359555417672], [28000, 0.5353758500423282], [28091, 0.5419530961662531], [28095, 0.44082752941176295], [28134, 0.5179424970410764], [28160, 0.4948418578132987], [28256, 0.5164489108137786], [28277, 0.45417784107849], [28296, 0.5198410428129137], [28301, 0.49078393913805485], [28341, 0.46593352779746056], [28347, 0.4936903029883979], [28379, 0.5205872524966253], [28399, 0.5075747473165393], 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newline 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_fringefit.FreqMetaTests.time_metadata.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_fringefit.FreqMetaTests.time_metadata.json index a2ef8ad69..193e100ad 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_fringefit.FreqMetaTests.time_metadata.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_fringefit.Sbd.time_sbd.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_fringefit.Sbd.time_sbd.json @@ -1 +1 @@ -[[28091, 12.398302793502808], [28095, 12.817003594245762], [28134, 12.337480222806334], [28160, 12.580663429107517], [28256, 12.613429070450366], [28277, 12.878122127614915], [28296, 12.386061687022448], [28301, 12.366520172916353], [28341, 12.169892840087414], [28347, 12.763031104012043], [28379, 12.30555484850629], [28399, 12.46113234758377], [28416, 12.325466771493666], [28461, 13.28467076603556], [28473, 12.265202861977741], [28478, 12.365676751476713]] \ No newline at end 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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_fringefit.Single.time_param.json index 0226ad8c4..c7d991830 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_fringefit.Single.time_param.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_fringefit.Single.time_param.json @@ -1 +1 @@ -[[28091, 30.486604667268693], [28095, 30.287176214158535], [28134, 29.896483251359314], [28160, 30.649673361331224], [28256, 30.477036479394883], [28277, 30.180872855708003], 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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_gaincal.CalModes.time_gaincal_calmode_A.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.CalModes.time_gaincal_calmode_A.json index df63c4d2d..740871cc1 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_gaincal.CalModes.time_gaincal_calmode_A.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.CalModes.time_gaincal_calmode_P.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.CalModes.time_gaincal_calmode_P.json @@ -1 +1 @@ -[[27946, 6.910216509975726], [27983, 7.523461129050702], [28000, 7.082423851941712], [28091, 6.847544370684773], [28095, 7.277987256646156], [28134, 7.106877818703651], [28160, 6.888746174518019], [28256, 6.415858570486307], [28277, 7.255365741439164], [28296, 7.142861978150904], [28301, 6.846021608449519], [28341, 6.850144743919373], [28347, 6.543371562496759], [28379, 7.026399634502013], [28399, 6.534348203800619], [28416, 6.99653279595077], [28461, 6.341468747938052], [28473, 7.175408717477694], [28478, 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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.FullGainCal.time_gaincal_fullcal.json index bae4d1a77..7a1fda4d6 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_gaincal.FullGainCal.time_gaincal_fullcal.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.FullGainCal.time_gaincal_fullcal.json @@ -1 +1 @@ -[[27946, 2.976410492497962], [27983, 3.193511824007146], [28000, 2.7490227018715814], [28091, 3.2283961875364184], [28095, 3.086002783384174], [28134, 3.7181921950541437], [28160, 2.9571074983105063], [28256, 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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_gaincal.GainTypes.time_gaincal_gain_type_K.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.GainTypes.time_gaincal_gain_type_K.json index 60a2b6ab8..775217ce9 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_gaincal.GainTypes.time_gaincal_gain_type_K.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.GainTypes.time_gaincal_gain_type_spline.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.GainTypes.time_gaincal_gain_type_spline.json @@ -1 +1 @@ -[[27946, 5.602567631489364], [27983, 5.364081067498773], [28000, 5.614960596081801], [28091, 5.9164715628139675], [28095, 6.0763391251675785], [28134, 5.599353791214526], [28160, 5.6574574378319085], [28256, 5.982438230421394], [28277, 5.520239322911948], [28296, 5.577738442923874], [28301, 5.282989700790495], [28341, 5.66041739936918], [28347, 5.631015070001013], [28379, 5.824742944001628], [28399, 5.760218529030681], [28416, 5.396905850502662], [28461, 5.71710788650671], [28473, 6.27401455945801], 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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_gaincal.GaincalSelections.time_gaincal_antennaSelect.json index 5065e0525..6887386d3 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_gaincal.GaincalSelections.time_gaincal_antennaSelect.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_antennaSelect.json @@ -1 +1 @@ -[[27946, 4.6027543139935005], [27983, 4.525269336998463], [28000, 4.63430169550702], [28091, 4.567946085706353], [28095, 4.3278851099312305], [28134, 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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_gaincal.GaincalSelections.time_gaincal_fieldSelect.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_fieldSelect.json index bdcecf883..bd3d40fa3 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_gaincal.GaincalSelections.time_gaincal_fieldSelect.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_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 @@ -[[27946, 22.44359128200449], [27983, 21.441697746980935], [28000, 21.560813470976427], [28091, 19.887262648902833], [28095, 21.834414797835052], [28134, 20.53910975344479], [28160, 20.35110928118229], [28256, 21.231500008143485], [28277, 20.327362241223454], [28296, 21.19719741679728], [28301, 19.723802748136222], [28341, 21.073264060541987], [28347, 19.077246058004675], [28379, 21.973600268000155], [28399, 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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_gaincal.GaincalSelections.time_gaincal_spwSelect.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_spwSelect.json @@ -1 +1 @@ -[[27946, 5.363551557995379], [27983, 5.5383641350781545], [28000, 5.724788605002686], [28091, 5.507886020932347], [28095, 5.526622447185218], [28134, 5.397512685973197], [28160, 5.512151596136391], [28256, 5.4410916194319725], [28277, 5.2197384759783745], [28296, 5.899757898412645], [28301, 4.957699881866574], [28341, 4.822644482366741], [28347, 4.97382882751117], [28379, 4.789052404004906], [28399, 5.204311789013445], [28416, 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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_gaincal.GaincalSelections.time_gaincal_uvRangeSelect.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_uvRangeSelect.json index 2992f674c..a0a3976a3 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_gaincal.GaincalSelections.time_gaincal_uvRangeSelect.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.MinSNRFlag.time_gaincal_minsnr.json index 2e2fd3b27..3bf5815e6 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_gaincal.MinSNRFlag.time_gaincal_minsnr.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.MinSNRFlag.time_gaincal_minsnr.json @@ -1 +1 @@ -[[27946, 11.149305307015311], [27983, 12.14654738898389], [28000, 12.730670788092539], [28091, 12.540519125759602], [28095, 11.734403578564525], [28134, 11.96744936145842], [28160, 12.955209121108055], [28256, 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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_gaincal.PreApplyTables.time_gaincal_apply_tables.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.PreApplyTables.time_gaincal_apply_tables.json index 73c568501..9288a94bd 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_gaincal.PreApplyTables.time_gaincal_apply_tables.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_gaincal.RefantSelection.time_gaincal_refant.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.RefantSelection.time_gaincal_refant.json @@ -1 +1 @@ -[[27946, 7.25783205247717], [27983, 7.627597860642709], [28000, 7.430140133015811], [28091, 6.685916284099221], [28095, 7.416100731585175], [28134, 6.968379866797477], [28160, 6.253816423472017], [28256, 6.558457172475755], [28277, 6.824061899911612], [28296, 6.65884910710156], [28301, 7.112067264039069], [28341, 6.6702816020697355], [28347, 6.635686759997043], [28379, 6.87565140749939], [28399, 6.255274100229144], [28416, 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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_gaincal.UseSpwMap.time_gaincal_use_spw_map.json index 67996217b..142a55d4b 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_gaincal.UseSpwMap.time_gaincal_use_spw_map.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.UseSpwMap.time_gaincal_use_spw_map.json @@ -1 +1 @@ -[[27946, 6.978009665501304], [27983, 6.856472745537758], [28000, 7.42102054937277], [28091, 6.799448404461145], [28095, 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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_importasdm.Aca.time_default.json @@ -1 +1 @@ -[[28461, 5.222105306980666], [28473, 5.507205124478787], [28478, 5.611093825427815]] \ No newline at end of file +[[28462, 5.222105306980666], [28474, 5.507205124478787], [28479, 5.611093825427815], [28519, 5.716813219944015]] \ No newline 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.Aca.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.Aca.time_default_lazy.json index eff421d83..0331a0dd1 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.Aca.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.Aca.time_default_lazy.json @@ -1 +1 @@ -[[28461, 4.846777895989362], [28473, 4.542825019918382], [28478, 4.5474629434756935]] \ No newline at end of file +[[28462, 4.846777895989362], [28474, 4.542825019918382], [28479, 4.5474629434756935], [28519, 4.971523945219815]] \ No newline 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 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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.Alma_12m.time_bdfflags_true_lazy.json @@ -1 +1 @@ -[[28461, 27.48625819105655], [28473, 26.97236758004874], [28478, 27.29493461898528]] \ No newline at end of file +[[28462, 27.48625819105655], [28474, 26.97236758004874], [28479, 27.29493461898528], [28519, 26.8873543147929]] \ No newline 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.Alma_12m.time_default.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_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 index c3e999b19..b46a270c8 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.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 @@ -[[28461, 14.282431726518553], [28473, 13.577434272388928], [28478, 13.98673615208827]] \ No newline at end of file +[[28462, 14.282431726518553], [28474, 13.577434272388928], [28479, 13.98673615208827], [28519, 13.874168269336224]] \ No newline 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.AutocorrASDM.time_applyflags_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.AutocorrASDM.time_applyflags_true.json index ef409c006..512522359 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.AutocorrASDM.time_applyflags_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.AutocorrASDM.time_applyflags_true.json @@ -1 +1 @@ -[[28461, 3.3779656579717994], [28473, 3.207887508557178], [28478, 3.5828178625088185]] \ No newline at end of file +[[28462, 3.3779656579717994], [28474, 3.207887508557178], [28479, 3.5828178625088185], [28519, 3.428337085293606]] \ No newline 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 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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_importasdm.AutocorrASDM.time_process_flags_false.json @@ -1 +1 @@ -[[28461, 1.4350936349947006], [28473, 1.489700639504008], [28478, 1.3407776219537482]] \ No newline at end of file +[[28462, 1.4350936349947006], [28474, 1.489700639504008], [28479, 1.3407776219537482], [28519, 1.421175990253687]] \ No newline 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.AutocorrASDM.time_savecmds_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.AutocorrASDM.time_savecmds_true.json index d2ffb099c..7c7ca331f 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.AutocorrASDM.time_savecmds_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.AutocorrASDM.time_savecmds_true.json @@ -1 +1 @@ -[[28461, 2.116066374059301], [28473, 2.2713614675449207], [28478, 2.3399929981678724]] \ No newline at end of file +[[28462, 2.116066374059301], [28474, 2.2713614675449207], [28479, 2.3399929981678724], [28519, 2.5002526622265577]] \ No newline 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 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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.Evla.time_applyflags_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_applyflags_true.json @@ -1 +1 @@ -[[28461, 2.607173200522084], [28473, 3.027629328542389], [28478, 2.6429194866213948]] \ No newline at end of file +[[28462, 2.607173200522084], [28474, 3.027629328542389], [28479, 2.6429194866213948], [28519, 2.5531254750676453]] \ No newline 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 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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.Evla.time_process_flags_savecmds_true.json index da8ebb642..29b113346 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_process_flags_savecmds_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_process_flags_savecmds_true.json @@ -1 +1 @@ -[[28461, 1.3690431289724074], [28473, 1.3560922050382942], [28478, 1.5301781139569357]] \ No newline at end of file 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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.Singledish.time_auto_only_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.Singledish.time_auto_only_lazy.json index c1ab0d55a..65282e52d 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.Singledish.time_auto_only_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 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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_sdatmcor.test_sdatmcor.time_custom_atm_params.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_sdatmcor.test_sdatmcor.time_custom_atm_params.json @@ -1 +1 @@ -[[27946, 5.132007137493929], [27983, 5.350821972941048], [28000, 5.758532105479389], [28091, 5.02882080944255], [28095, 5.375686984974891], [28134, 5.166952714789659], [28160, 5.189660323783755], [28256, 5.055982254911214], [28277, 5.3469580677337945], [28296, 5.162476576399058], [28301, 4.99615538911894], [28341, 5.104724197648466], [28347, 5.111607826009276], [28379, 5.373224082504748], [28399, 5.208225944079459], 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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.Apply_Interp.time_linear_nearestflag.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_nearestflag.json index 9002c916a..7da05a553 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_nearestflag.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_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 4fc64d60d..7a9b40f6f 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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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 @@ -[[27946, 0.4180924969841726], [27983, 0.4408743503736332], [28000, 0.39543916552793235], [28091, 0.4289841395802796], [28095, 0.4304681452922523], [28134, 0.3897068337537348], [28160, 0.4252486089244485], [28256, 0.43354520201683044], [28277, 0.42701377300545573], [28296, 0.43626292422413826], [28301, 0.43886730819940567], [28341, 0.4381669256836176], [28347, 0.42502236549626105], [28379, 0.42749290150095476], [28399, 0.4272281052544713], [28416, 0.4300236830022186], [28461, 0.4306290034437552], [28473, 0.4344862459693104], [28478, 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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_GAUSS_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_GAUSS_start_400.json @@ -1 +1 @@ -[[27946, 2.8783261615026277], [27983, 3.4115301915444434], [28000, 2.9392942930571735], [28091, 3.1500383433885872], [28095, 3.0725966221652925], [28134, 3.36872537760064], [28160, 3.336727255024016], [28256, 2.956733012571931], [28277, 3.137338756583631], [28296, 2.9700797218829393], [28301, 3.14394314493984], [28341, 3.036789827980101], [28347, 2.9369337235111743], [28379, 3.2153867645029095], [28399, 2.8816027687862515], 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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_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.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_mode_velocity_nchan_100.json index 8ca7c2cea..73d771601 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_mode_velocity_nchan_100.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_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 @@ -[[27946, 11.193947552004829], [27983, 11.679729159921408], [28000, 10.926401298027486], [28091, 10.488163593225181], [28095, 10.455362675711513], [28134, 10.819636803120375], [28160, 10.384972460567951], [28256, 10.952980707399547], [28277, 10.941953411325812], [28296, 10.876396498642862], [28301, 11.043104353360832], [28341, 10.714941274374723], [28347, 10.480580400995677], [28379, 10.863464637004654], [28399, 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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 @@ -1 +1 @@ -[[27946, 32.47196252201684], [27983, 31.836701999185607], [28000, 32.22056464292109], [28091, 31.264201380312443], [28095, 29.498852740973234], [28134, 30.504108457826078], [28160, 31.723010503686965], [28256, 30.39948427863419], [28277, 30.812930605374277], [28296, 31.908452497795224], [28301, 29.01087506301701], [28341, 32.28528078086674], [28347, 31.135889847006183], [28379, 29.89105790600297], [28399, 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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.TcleanEphemeris.time_eph_multifield_mosaic_cubesource.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_multifield_mosaic_cubesource.json @@ -1 +1 @@ -[[27946, 8.02702758050873], [27983, 7.897791309980676], [28000, 8.091482014977373], [28091, 7.769961839076132], [28095, 7.973258891608566], [28134, 7.813489354681224], [28160, 7.952491065021604], [28256, 7.540967972017825], [28277, 7.882576583418995], [28296, 8.088856538757682], [28301, 8.030451034195721], [28341, 8.051323365420103], [28347, 7.370200721008587], [28379, 7.884893006499624], 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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.TcleanEphemeris.time_eph_singlefield_standard_cubesource.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_cubesource.json @@ -1 +1 @@ -[[27946, 2.29901861702092], [27983, 2.43269583908841], [28000, 2.5060208265203983], [28091, 2.464588134549558], [28095, 2.445632105693221], [28134, 2.571654474362731], [28160, 2.5351173779927194], [28256, 2.5457570599392056], [28277, 2.4988276246003807], [28296, 2.4348231703042984], [28301, 2.4641778678633273], [28341, 2.5255843242630363], [28347, 2.542624355002772], [28379, 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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_virtual_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_virtual_modelcolumn.json @@ -1 +1 @@ -[[27946, 11.981277779501397], [27983, 12.730016560992226], [28000, 12.488992861006409], [28091, 12.595569836907089], [28095, 11.541463127359748], [28134, 12.98954922799021], [28160, 12.065446022897959], [28256, 11.750509324483573], [28277, 12.142108497675508], [28296, 11.630227794870734], [28301, 12.657705365680158], [28341, 12.201480768620968], [28347, 11.931648258003406], [28379, 12.312571804999607], [28399, 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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.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 @@ -[[27946, 7.852607278502546], [27983, 7.860365165048279], [28000, 7.81541484745685], [28091, 7.839755128603429], [28095, 7.751741772983223], [28134, 7.638185161165893], [28160, 7.872746298555285], [28256, 7.551012942101806], [28277, 7.8393663596361876], [28296, 7.7466001603752375], [28301, 7.879129664041102], [28341, 7.871564350090921], [28347, 7.713127384515246], [28379, 7.5575750335046905], [28399, 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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_cube_standard_pcwdT.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_cube_standard_pcwdT.json @@ -1 +1 @@ -[[27946, 16.819387205003295], [27983, 17.680552619043738], [28000, 17.34010474011302], [28091, 17.648613781668246], [28095, 17.642474717460573], [28134, 18.084701514802873], [28160, 18.333784958347678], [28256, 16.91964763402939], [28277, 17.713876782916486], [28296, 16.783060093410313], [28301, 18.046535724774003], [28341, 18.296824757009745], [28347, 17.851220962009393], [28379, 17.277859516994795], [28399, 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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_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 @@ -[[27946, 11.856788070988841], [27983, 10.96922794287093], [28000, 12.002732931170613], [28091, 11.998343176208436], [28095, 12.713539787568152], [28134, 11.893555829301476], [28160, 12.411541612818837], [28256, 10.277538105845451], [28277, 12.23283280339092], [28296, 12.092028077691793], [28301, 11.345314046368003], [28341, 10.487912863492966], [28347, 11.256345626490656], [28379, 10.518308839004021], [28399, 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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 @@ -1 +1 @@ -[[27946, 17.752551584038883], [27983, 15.524209866998717], [28000, 16.092649777885526], [28091, 17.832581896334887], [28095, 17.25657893065363], [28134, 16.668264713138342], [28160, 16.134380938485265], [28256, 15.13072077743709], [28277, 15.758048770017922], [28296, 15.69854686409235], [28301, 15.610114195384085], [28341, 17.03874906897545], [28347, 16.57127949700225], [28379, 15.383728114000405], [28399, 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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 +++ 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 @@ -[[27946, 2.556216853496153], [27983, 2.58254324097652], [28000, 2.592361458344385], [28091, 2.5016577495262027], [28095, 2.493289525154978], [28134, 2.5159713686443865], [28160, 2.513727271463722], [28256, 2.5433038691990077], [28277, 2.5099102347157896], [28296, 2.4928490482270718], [28301, 2.5373767619021237], [28341, 2.4965399401262403], [28347, 2.497486538486555], [28379, 2.470758080497035], [28399, 2.621277946047485], [28416, 2.494076759030577], [28461, 2.539336483925581], [28473, 2.5041467304108664], 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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.TcleanStokes.time_mtmfs_stokes_IQUV.json index d4f8ba814..d7e6fb343 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_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 @@ -[[27946, 7.000316508987453], [27983, 7.011502246023156], [28000, 7.077581702033058], [28091, 6.85890085529536], [28095, 6.833941692486405], [28134, 6.806868098210543], [28160, 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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_cube_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_time.TcleanWideFieldAWP.time_cube_awproject_hogbom.json index 728c40edf..519956ddf 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.TcleanWideFieldAWP.time_cube_awproject_hogbom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 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_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom.json index e9d787884..3be5ecdaf 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.TcleanWideFieldAWP.time_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_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom.json @@ -1 +1 @@ -[[27946, 41.50047961197561], [27983, 41.294380604987964], [28000, 42.07020403002389], [28091, 39.67919315677136], [28095, 39.805506953038275], [28134, 39.62410583719611], [28160, 39.74439110979438], [28256, 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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 index 9b3f85931..252be7af8 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.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 @@ -[[27946, 108.64864844002295], [27983, 109.0648281420581], [28000, 108.99495732598007], [28091, 104.62776040099561], [28095, 103.65681163873523], [28134, 102.9007882187143], [28160, 103.49428031314164], [28256, 103.61788587085903], [28277, 103.27190653048456], [28296, 103.29649932775646], [28301, 103.5151297589764], [28341, 102.7679047267884], [28347, 104.30595048100804], [28379, 105.05538886899012], [28399, 103.83950306475163], [28416, 103.09749228099827], [28461, 104.26164386002347], [28473, 104.27878682198934], [28478, 104.51740087498911]] \ No newline at end of file +[[27946, 108.64864844002295], [27983, 109.0648281420581], [28000, 108.99495732598007], [28091, 104.62776040099561], [28095, 103.65681163873523], [28134, 102.9007882187143], [28160, 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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_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.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_mwFalse_twofield_upTrue.json index f150e46db..58215f11a 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_mwFalse_twofield_upTrue.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_mwFalse_twofield_upTrue.json @@ -1 +1 @@ -[[27946, 15.49031763599487], [27983, 15.712867880938575], [28000, 16.948914821026847], [28091, 15.634276293218136], [28095, 15.341336040757596], [28134, 15.242436679080129], [28160, 15.579526007175446], [28256, 15.500417763367295], [28277, 15.698184276930988], [28296, 15.580248306505382], [28301, 15.25635597947985], [28341, 15.730377478525043], [28347, 15.47546827601036], [28379, 15.403591331996722], [28399, 15.434904001653194], [28416, 15.258660884923302], [28461, 15.323105785995722], [28473, 15.43036070058588], [28478, 15.555881932843477]] \ No newline at end of file +[[27946, 15.49031763599487], [27983, 15.712867880938575], [28000, 16.948914821026847], [28091, 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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_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35.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_BOX_imsize_40x35.json index d78f0f7e1..1f2b06067 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_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35.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_BOX_imsize_40x35.json @@ -1 +1 @@ -[[27946, 2.851944528025342], [27983, 3.099896725965664], [28000, 3.045705058495514], [28091, 3.4141070577315986], [28095, 3.1365252076648176], [28134, 3.3656193297356367], [28160, 3.1807369380258024], [28256, 3.0336407185532153], [28277, 2.759236919693649], [28296, 2.977683652192354], [28301, 3.2468521827831864], [28341, 3.106717050075531], [28347, 2.8702588214946445], [28379, 2.8023814754924388], [28399, 3.007016072049737], [28416, 3.186909631942399], [28461, 2.863406726566609], [28473, 2.9477347569772974], [28478, 2.906252105953172]] \ No newline at end of file +[[27946, 2.851944528025342], [27983, 3.099896725965664], [28000, 3.045705058495514], [28091, 3.4141070577315986], [28095, 3.1365252076648176], [28134, 3.3656193297356367], [28160, 3.1807369380258024], [28256, 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index 1b62d3a5c..78dbf5feb 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_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect.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_BOX_imsize_40x35_phasecenter_detect.json @@ -1 +1 @@ -[[27946, 3.1193774375133216], [27983, 3.044706016080454], [28000, 3.1420693745603785], [28091, 3.3535430817864835], [28095, 3.405841707251966], [28134, 3.414229582529515], [28160, 3.6980859679169953], [28256, 2.9248859896324575], [28277, 2.984549461863935], [28296, 3.0453015635721385], [28301, 2.9370923452079296], [28341, 2.8658883906900883], 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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_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect.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_BOX_imsize_detect_cell_default_phasecenter_detect.json index 7d244f018..40edb8ea1 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_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect.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_tsdimaging.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_tsdimaging.Basic.time_gridfunction_BOX_start_400.json @@ -1 +1 @@ -[[27946, 2.8183441954897717], [27983, 3.0871158404042944], [28000, 2.7606033235788345], [28091, 3.3969534654170275], [28095, 3.3699177862145007], [28134, 3.2833739360794425], [28160, 3.6643023719079792], [28256, 2.8824854069389403], [28277, 2.9678157288581133], [28296, 2.831259234342724], [28301, 2.8376109753735363], [28341, 3.05680622253567], [28347, 3.334816819013213], [28379, 3.016321462004271], [28399, 3.3463694658130407], 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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_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.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_mode_velocity_nchan_100.json index 151a1315d..bb017d930 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_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.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_mode_velocity_nchan_100.json @@ -1 +1 @@ -[[27946, 5.605480119964341], [27983, 5.321491633425467], [28000, 5.668201560038142], [28091, 5.83146868320182], [28095, 5.592493252828717], [28134, 5.85904411226511], [28160, 5.848198775202036], [28256, 5.293807785958052], [28277, 5.460740508977324], [28296, 5.046282707713544], [28301, 5.197932058945298], [28341, 5.298350553959608], [28347, 4.997187168992241], [28379, 4.9767481244925875], [28399, 5.184145223349333], [28416, 5.159130611456931], [28461, 5.141606683027931], [28473, 5.143674448481761], [28478, 5.230341675458476]] \ No newline at end of file +[[27946, 5.605480119964341], [27983, 5.321491633425467], [28000, 5.668201560038142], [28091, 5.83146868320182], [28095, 5.592493252828717], [28134, 5.85904411226511], [28160, 5.848198775202036], [28256, 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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_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_tsdimaging.Basic.time_gridfunction_PB_nchan_1024.json @@ -1 +1 @@ -[[27946, 5.248694183974294], [27983, 4.7550822254270315], [28000, 5.1178184994496405], [28091, 5.251077691093087], [28095, 5.664161823224276], [28134, 5.4665346667170525], [28160, 5.476066758390516], [28256, 4.831224817316979], [28277, 4.932125899475068], [28296, 3.9802160817198455], [28301, 4.0821710280142725], [28341, 3.9998418474569917], [28347, 4.001464521003072], [28379, 3.96690534499794], [28399, 3.923308154568076], 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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_start_400_stokes_XXYY.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_start_400_stokes_XXYY.json @@ -1 +1 @@ -[[27946, 2.9977351269917563], [27983, 3.070981532568112], [28000, 2.8674004594795406], [28091, 3.3913628235459328], [28095, 3.499123389367014], [28134, 3.165485715959221], [28160, 3.111726683098823], [28256, 2.9298567003570497], [28277, 2.7515815612860024], [28296, 2.6503635202534497], [28301, 2.7327264519408345], [28341, 2.9037749292328954], [28347, 2.7979481239890447], [28379, 3.0662328280013753], 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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_tsdimaging.Ephemeris.time_ephemeris_sun.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.Ephemeris.time_ephemeris_sun.json @@ -1 +1 @@ -[[27946, 7.188724909006851], [27983, 7.258580234949477], [28000, 6.6465535424649715], [28091, 6.842900054994971], [28095, 6.690198892261833], [28134, 6.780167498160154], [28160, 6.397081112954766], [28256, 6.421847996301949], [28277, 6.607278472743928], [28296, 6.534503277856857], [28301, 6.467399443034083], [28341, 6.417627436108887], [28347, 6.619121416501002], [28379, 6.434594202000881], [28399, 6.662680901587009], [28416, 6.475344070524443], [28461, 6.70168774999911], [28473, 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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_tsdimaging.Ephemeris.time_ephemeris_table.json index fe93340e6..4778918d2 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_tsdimaging.Ephemeris.time_ephemeris_table.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.Ephemeris.time_ephemeris_table.json @@ -1 +1 @@ -[[27946, 8.685944322001887], [27983, 8.683991236495785], [28000, 8.194962409092113], [28091, 8.337950891815126], [28095, 8.446300582494587], [28134, 8.454030478838831], [28160, 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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.Ephemeris.time_ephemeris_trackf.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.Ephemeris.time_ephemeris_trackf.json index a75f34fa0..01769b58e 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_tsdimaging.Ephemeris.time_ephemeris_trackf.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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6de79d5a6..3cd766422 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.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.json @@ -1 +1 @@ -[[27946, 0.8709858280490153], [27983, 0.8509628300089389], [28000, 0.8852425350341946], [28091, 0.8662368515506387], [28095, 0.8258304968476295], [28134, 0.8646509172394872], [28160, 0.8616190180182457], [28256, 0.8648228971287608], [28277, 0.8450065143406391], [28296, 0.8511769324541092], [28301, 0.8768979804590344], [28341, 0.8838899042457342], [28347, 0.8808072190149687], [28379, 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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_dict_select_spw_mismatching.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_select_spw_mismatching.json @@ -1 +1 @@ -[[27946, 0.7033926969743334], [27983, 0.703066413057968], [28000, 0.712057103170082], [28091, 0.6928306883201003], [28095, 0.6892064604908228], [28134, 0.6911881435662508], [28160, 0.6898458721116185], [28256, 0.6941236183047295], [28277, 0.7060114052146673], [28296, 0.695119071751833], [28301, 0.6964917238801718], [28341, 0.6962785646319389], [28347, 0.692717352998443], [28379, 0.6936817839887226], [28399, 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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_multifield_blocks.json index 0c84cd504..500cbde1d 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_blocks.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_blocks.json @@ -1 +1 @@ -[[27946, 0.9976707509486005], [27983, 0.9726816720794886], [28000, 0.9788509218487889], [28091, 0.9803769076243043], [28095, 0.981225898489356], [28134, 0.9816229399293661], [28160, 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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_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 index 5dc955ec3..c63ab1fb4 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 @@ -[[27946, 0.955820607021451], [27983, 0.9708014959469438], [28000, 0.9804785088635981], [28091, 0.9511489002034068], [28095, 0.9508376745507121], [28134, 0.9522800436243415], [28160, 0.9601711519062519], [28256, 0.9642667314037681], [28277, 0.9459192818030715], [28296, 0.9352901559323072], [28301, 0.940706592053175], [28341, 0.9405389428138733], [28347, 0.9587178560032044], [28379, 0.9586096860002726], [28399, 0.9552390892058611], [28416, 0.9572384830098599], [28461, 0.963457572972402], [28473, 0.9628882519900799], [28478, 0.9810892308596522]] \ No newline at end of file +[[27946, 0.955820607021451], [27983, 0.9708014959469438], [28000, 0.9804785088635981], [28091, 0.9511489002034068], [28095, 0.9508376745507121], [28134, 0.9522800436243415], [28160, 0.9601711519062519], 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39fd6766e..220f848ea 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_multispw_diff_fitorder.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_multispw_diff_fitorder.json @@ -1 +1 @@ -[[27946, 1.0691314220312051], [27983, 1.0774269690737128], [28000, 1.3471956609282643], [28091, 1.0787403592839837], [28095, 1.1014186441898346], [28134, 1.3295294241979718], [28160, 1.0809250939637423], [28256, 1.0898168003186584], [28277, 1.348924652673304], [28296, 1.0867538107559085], [28301, 1.2804633770138025], [28341, 1.0818712171167135], [28347, 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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_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 e3370077b..369cb666d 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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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 @@ -[[27946, 0.8625555509934202], [27983, 0.8437357430811971], [28000, 0.8909037651028484], [28091, 0.8715018490329385], [28095, 0.8585082991048694], [28134, 0.8542300863191485], [28160, 0.8607502859085798], [28256, 0.8465485107153654], [28277, 0.859794014133513], [28296, 0.8744298163801432], [28301, 0.8449106542393565], [28341, 0.8723749108612537], [28347, 0.8184912930009887], [28379, 0.8478237719973549], [28399, 0.8788873199373484], [28416, 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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_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 index 5a06f81db..53a3430de 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_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 @@ -[[27946, 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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_select_scan.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_select_scan.json index 6ae4a3920..7f55b1898 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_select_scan.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_uvcontsub.Basic.time_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_uvcontsub.Basic.time_select_spw.json @@ -1 +1 @@ -[[27946, 0.8137390360352583], [27983, 0.8033309550955892], [28000, 0.8309265221469104], [28091, 0.8003385467454791], [28095, 0.7996092000976205], [28134, 0.802166162058711], [28160, 0.7983157020062208], [28256, 0.8062659660354257], [28277, 0.7815881334245205], [28296, 0.8082253774628043], [28301, 0.8117374153807759], [28341, 0.8112853318452835], [28347, 0.81268485399778], [28379, 0.8054006879974622], [28399, 0.801921758800745], [28416, 0.7893449830589816], [28461, 0.8130220939638093], [28473, 0.8038575840182602], [28478, 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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_select_spw_mismatching_fitspec.json index 8fe0555bb..b5b961645 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_select_spw_mismatching_fitspec.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_select_spw_mismatching_fitspec.json @@ -1 +1 @@ -[[27946, 0.7568769869976677], [27983, 0.7558014860842377], [28000, 0.7637894810177386], [28091, 0.7462217295542359], [28095, 0.7435338972136378], [28134, 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b/graphs/summary/bench_applycal.TrialMode.time_applycal_trial.json index b0b0c2716..65bc4395f 100644 --- a/graphs/summary/bench_applycal.TrialMode.time_applycal_trial.json +++ b/graphs/summary/bench_applycal.TrialMode.time_applycal_trial.json @@ -1 +1 @@ -[[23735, 3.3364518210291862], [24194, 3.1601654030382633], [24967, 3.1592727433890104], [25647, 3.1778319384902716], [25648, 3.3074067579582334], [25686, 3.361821711063385], [25768, 3.193049244582653], [25813, 3.235854998230934], [25838, 3.2897350937128067], [25858, 3.4437340293079615], [25868, 3.1917222095653415], [25889, 3.248907440342009], [25924, 3.5208402844145894], [25969, 3.247139417566359], [26013, 3.2299302825704217], [26028, 3.1480995174497366], [26062, 3.1414712741971016], [26085, 3.0699864691123366], [26144, 3.015514305792749], [26201, 3.161252769641578], [26278, 3.2360861860215664], [26296, 3.479791891761124], [26317, 3.2386049358174205], [26372, 3.0439283680170774], [26398, 3.1804556660354137], [26456, 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b/graphs/summary/bench_importasdm.Aca.time_default_lazy.json @@ -1 +1 @@ -[[28461, 4.846777895989362], [28473, 4.542825019918382], [28478, 4.5474629434756935]] \ No newline at end of file +[[28462, 4.846777895989362], [28474, 4.542825019918382], [28479, 4.5474629434756935], [28519, 4.971523945219815]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Alma_12m.time_auto_only.json b/graphs/summary/bench_importasdm.Alma_12m.time_auto_only.json index 8c44cedf1..c9d25424d 100644 --- a/graphs/summary/bench_importasdm.Alma_12m.time_auto_only.json +++ b/graphs/summary/bench_importasdm.Alma_12m.time_auto_only.json @@ -1 +1 @@ -[[28461, 8.513722569507081], [28473, 8.476516444934532], [28478, 8.572946338099428]] \ No newline at end of file +[[28462, 8.513722569507081], [28474, 8.476516444934532], [28479, 8.572946338099428], [28519, 8.688321489607915]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Alma_12m.time_auto_only_lazy.json b/graphs/summary/bench_importasdm.Alma_12m.time_auto_only_lazy.json index 7fa0516f3..dee5c9cef 100644 --- a/graphs/summary/bench_importasdm.Alma_12m.time_auto_only_lazy.json +++ b/graphs/summary/bench_importasdm.Alma_12m.time_auto_only_lazy.json @@ -1 +1 @@ -[[28461, 8.840934919542633], [28473, 9.178076263982803], [28478, 8.818364388076589]] \ No newline at end of file +[[28462, 8.840934919542633], [28474, 9.178076263982803], [28479, 8.818364388076589], [28519, 9.162866040598601]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Alma_12m.time_bdfflags_true.json b/graphs/summary/bench_importasdm.Alma_12m.time_bdfflags_true.json index e1385b4e8..07d2a8582 100644 --- a/graphs/summary/bench_importasdm.Alma_12m.time_bdfflags_true.json +++ b/graphs/summary/bench_importasdm.Alma_12m.time_bdfflags_true.json @@ -1 +1 @@ -[[28461, 31.1455175220035], [28473, 32.720456612994894], [28478, 30.934902989072725]] \ No newline at end of file +[[28462, 31.1455175220035], [28474, 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b/graphs/summary/bench_importasdm.Alma_12m.time_default.json @@ -1 +1 @@ -[[28461, 19.199997165007517], [28473, 19.563682469073683], [28478, 18.664512774907053]] \ No newline at end of file +[[28462, 19.199997165007517], [28474, 19.563682469073683], [28479, 18.664512774907053], [28519, 18.667894800193608]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Alma_12m.time_default_lazy.json b/graphs/summary/bench_importasdm.Alma_12m.time_default_lazy.json index c3e999b19..b46a270c8 100644 --- a/graphs/summary/bench_importasdm.Alma_12m.time_default_lazy.json +++ b/graphs/summary/bench_importasdm.Alma_12m.time_default_lazy.json @@ -1 +1 @@ -[[28461, 14.282431726518553], [28473, 13.577434272388928], [28478, 13.98673615208827]] \ No newline at end of file +[[28462, 14.282431726518553], [28474, 13.577434272388928], [28479, 13.98673615208827], [28519, 13.874168269336224]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.AutocorrASDM.time_applyflags_true.json b/graphs/summary/bench_importasdm.AutocorrASDM.time_applyflags_true.json index ef409c006..512522359 100644 --- a/graphs/summary/bench_importasdm.AutocorrASDM.time_applyflags_true.json +++ b/graphs/summary/bench_importasdm.AutocorrASDM.time_applyflags_true.json @@ -1 +1 @@ -[[28461, 3.3779656579717994], [28473, 3.207887508557178], [28478, 3.5828178625088185]] \ No newline at end of file +[[28462, 3.3779656579717994], [28474, 3.207887508557178], [28479, 3.5828178625088185], [28519, 3.428337085293606]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.AutocorrASDM.time_process_flags_false.json b/graphs/summary/bench_importasdm.AutocorrASDM.time_process_flags_false.json index 275c37bb0..da1c20cb5 100644 --- a/graphs/summary/bench_importasdm.AutocorrASDM.time_process_flags_false.json +++ b/graphs/summary/bench_importasdm.AutocorrASDM.time_process_flags_false.json @@ -1 +1 @@ -[[28461, 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index 017741449..3b517e61e 100644 --- a/graphs/summary/bench_importasdm.Basic.time_all_defaults.json +++ b/graphs/summary/bench_importasdm.Basic.time_all_defaults.json @@ -1 +1 @@ -[[28461, 3.2034601009218022], [28473, 3.1919333471450955], [28478, 3.294537617592141]] \ No newline at end of file +[[28462, 3.2034601009218022], [28474, 3.1919333471450955], [28479, 3.294537617592141], [28519, 3.252530517987907]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Basic.time_lazy_pc_true.json b/graphs/summary/bench_importasdm.Basic.time_lazy_pc_true.json index 27f57f716..84cf74ff3 100644 --- a/graphs/summary/bench_importasdm.Basic.time_lazy_pc_true.json +++ b/graphs/summary/bench_importasdm.Basic.time_lazy_pc_true.json @@ -1 +1 @@ -[[28461, 3.3083662029821426], [28473, 3.4653652030974627], [28478, 3.2370227460050955]] \ No newline at end of file +[[28462, 3.3083662029821426], [28474, 3.4653652030974627], [28479, 3.2370227460050955], [28519, 3.6108695450238883]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Basic.time_pc_true.json b/graphs/summary/bench_importasdm.Basic.time_pc_true.json index d1b100a63..f9c280eb8 100644 --- a/graphs/summary/bench_importasdm.Basic.time_pc_true.json +++ b/graphs/summary/bench_importasdm.Basic.time_pc_true.json @@ -1 +1 @@ -[[28461, 3.2930495610344224], [28473, 3.087447652593255], [28478, 2.86013073741924]] \ No newline at end of file +[[28462, 3.2930495610344224], [28474, 3.087447652593255], [28479, 2.86013073741924], [28519, 3.333649503067136]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Evla.time_all_scans.json b/graphs/summary/bench_importasdm.Evla.time_all_scans.json index 8a3f434c7..33b362df2 100644 --- a/graphs/summary/bench_importasdm.Evla.time_all_scans.json +++ b/graphs/summary/bench_importasdm.Evla.time_all_scans.json @@ -1 +1 @@ -[[28461, 3.813790821528528], [28473, 3.3021277245134115], [28478, 3.7642437340691686]] \ No newline at end of file +[[28462, 3.813790821528528], [28474, 3.3021277245134115], [28479, 3.7642437340691686], [28519, 3.4876495769713074]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Evla.time_applyflags_true.json b/graphs/summary/bench_importasdm.Evla.time_applyflags_true.json index 33b9ef3ad..0be8ec2ed 100644 --- a/graphs/summary/bench_importasdm.Evla.time_applyflags_true.json +++ b/graphs/summary/bench_importasdm.Evla.time_applyflags_true.json @@ -1 +1 @@ -[[28461, 2.607173200522084], [28473, 3.027629328542389], [28478, 2.6429194866213948]] \ No newline at end of file +[[28462, 2.607173200522084], [28474, 3.027629328542389], [28479, 2.6429194866213948], [28519, 2.5531254750676453]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Evla.time_pc_true.json b/graphs/summary/bench_importasdm.Evla.time_pc_true.json index ab3cb6c1e..9341f88d3 100644 --- a/graphs/summary/bench_importasdm.Evla.time_pc_true.json +++ b/graphs/summary/bench_importasdm.Evla.time_pc_true.json @@ -1 +1 @@ -[[28461, 1.230967463052366], [28473, 1.3190023446222767], [28478, 1.0434273974969983]] \ No newline at end of file +[[28462, 1.230967463052366], [28474, 1.3190023446222767], [28479, 1.0434273974969983], [28519, 1.4856787950266153]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Evla.time_process_applyflags_savecmds_true.json b/graphs/summary/bench_importasdm.Evla.time_process_applyflags_savecmds_true.json index d795d66e8..f9076350e 100644 --- a/graphs/summary/bench_importasdm.Evla.time_process_applyflags_savecmds_true.json +++ b/graphs/summary/bench_importasdm.Evla.time_process_applyflags_savecmds_true.json @@ -1 +1 @@ -[[28461, 2.842318933515344], [28473, 3.06819407758303], [28478, 2.8874315159628168]] \ No newline at end of file +[[28462, 2.842318933515344], [28474, 3.06819407758303], [28479, 2.8874315159628168], [28519, 2.732023488730192]] \ No newline at end of file diff --git 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 da8ebb642..29b113346 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 @@ -[[28461, 1.3690431289724074], [28473, 1.3560922050382942], [28478, 1.5301781139569357]] \ No newline at end of file +[[28462, 1.3690431289724074], [28474, 1.3560922050382942], [28479, 1.5301781139569357], [28519, 1.3539795482065529]] \ No newline at end of file diff --git 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 e7a749c9a..0dfc45859 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 @@ -[[28461, 1.506590838485863], [28473, 1.4998785320203751], [28478, 1.3407982169883326]] \ No newline at end of file +[[28462, 1.506590838485863], [28474, 1.4998785320203751], [28479, 1.3407982169883326], [28519, 1.748443342745304]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Evla.time_scan_selection.json b/graphs/summary/bench_importasdm.Evla.time_scan_selection.json index 2b7c411a5..5dc2fbfe7 100644 --- a/graphs/summary/bench_importasdm.Evla.time_scan_selection.json +++ b/graphs/summary/bench_importasdm.Evla.time_scan_selection.json @@ -1 +1 @@ -[[28461, 1.336264553538058], [28473, 1.5440266884397715], [28478, 1.154044616036117]] \ No newline at end of file +[[28462, 1.336264553538058], [28474, 1.5440266884397715], [28479, 1.154044616036117], [28519, 1.59667790052481]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Evla_ephemeris.time_polynomial_ephem.json b/graphs/summary/bench_importasdm.Evla_ephemeris.time_polynomial_ephem.json index 0d5b1090f..b927240fc 100644 --- a/graphs/summary/bench_importasdm.Evla_ephemeris.time_polynomial_ephem.json +++ b/graphs/summary/bench_importasdm.Evla_ephemeris.time_polynomial_ephem.json @@ -1 +1 @@ -[[28461, 89.61868696496822], [28473, 90.03748416295275], [28478, 93.47402642806992]] \ No newline at end of file +[[28462, 89.61868696496822], [28474, 90.03748416295275], [28479, 93.47402642806992], [28519, 90.79966796701774]] \ No newline at end of file diff --git a/graphs/summary/bench_importasdm.Singledish.time_auto_only.json b/graphs/summary/bench_importasdm.Singledish.time_auto_only.json index c7e6b2ed5..cd0c51642 100644 --- a/graphs/summary/bench_importasdm.Singledish.time_auto_only.json +++ b/graphs/summary/bench_importasdm.Singledish.time_auto_only.json @@ -1 +1 @@ -[[28461, 7.085141843010206], [28473, 6.855094158090651], 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a/graphs/summary/bench_sdatmcor.test_sdatmcor.time_custom_atm_params.json +++ b/graphs/summary/bench_sdatmcor.test_sdatmcor.time_custom_atm_params.json @@ -1 +1 @@ -[[27730, 5.620322056114674], [27758, 5.6198311019688845], [27772, 5.719184383749962], [27812, 5.682448040693998], [27844, 5.676024440675974], [27919, 5.585640448029153], [27946, 5.132007137493929], [27983, 5.350821972941048], [28000, 5.758532105479389], [28091, 5.02882080944255], [28095, 5.375686984974891], [28134, 5.166952714789659], [28160, 5.189660323783755], [28256, 5.055982254911214], [28277, 5.3469580677337945], [28296, 5.162476576399058], [28301, 4.99615538911894], [28341, 5.104724197648466], [28347, 5.111607826009276], [28379, 5.373224082504748], [28399, 5.208225944079459], [28416, 5.272661036986392], [28461, 5.198443184432108], [28473, 5.141537650139071], [28478, 5.690839056973346]] \ No newline at end of file +[[27730, 5.620322056114674], [27758, 5.6198311019688845], [27772, 5.719184383749962], [27812, 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a/graphs/summary/bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting.json +++ b/graphs/summary/bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting.json @@ -1 +1 @@ -[[27379, 1988804608.0], [27386, 1975946043.6410975], [27404, 1981535443.3918507], [27452, 1999839232.0], [27468, 1995694080.0], [27487, 1989783552.0], [27522, 1993814016.0], [27535, 1993211904.0], [27581, 1988186112.0], [27602, 1989894144.0], [27608, 1987813376.0], [27647, 1987973120.0], [27656, 1987731456.0], [27661, 1987837952.0], [27730, 1987940352.0], [27758, 1991761920.0], [27772, 1989693440.0], [27812, 1994108928.0], [27844, 1989922816.0], [27919, 1992003584.0], [27946, 2023202816.0], [27983, 2022031360.0], [28000, 2019856384.0], [28091, 2019377152.0], [28095, 2019860480.0], [28134, 2021597184.0], [28160, 2026577920.0], [28256, 2019745792.0], [28277, 2020229120.0], [28296, 2020593664.0], [28301, 2019946496.0], [28341, 2030616576.0], [28347, 2021236736.0], [28379, 2019385344.0], [28399, 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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 c7262747d..c733034c5 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 @@ -[[27379, 212549632.0], [27386, 208474112.0], [27452, 209498112.0], [27468, 214245376.0], [27487, 201183232.0], [27522, 209510400.0], [27535, 214941696.0], [27581, 200077312.0], [27602, 205291520.0], [27608, 207736832.0], [27647, 203460608.0], [27656, 198656000.0], [27661, 205701120.0], [27730, 205930496.0], [27758, 200130560.0], [27772, 206184448.0], [27812, 197545984.0], [27844, 212000768.0], [27919, 206213120.0], [27946, 240062464.0], [27983, 232976384.0], [28000, 232771584.0], [28091, 229650432.0], [28095, 227774464.0], [28134, 231616512.0], [28160, 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a/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn.json b/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn.json index 2e72a85f1..ec2c01e61 100644 --- a/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn.json +++ b/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn.json @@ -1 +1 @@ -[[23735, 7.79952958971262], [24194, 7.504983799532056], [24967, 7.385329643264413], [25647, 7.484530525282025], [25648, 7.940977732185274], [25686, 7.433125796727836], [25768, 7.707655854988843], [25813, 7.6771978894248605], [25838, 8.332508701365441], [25858, 8.270892148371786], [25868, 8.112165403086692], [25889, 8.072676454670727], [25924, 8.600866610649973], [25969, 8.435259676538408], [26013, 8.301534404978156], [26028, 8.25515054911375], [26062, 8.33430303633213], [26085, 7.749620414339006], [26144, 7.854809555690736], [26201, 8.304035092703998], [26278, 8.384700256865472], [26296, 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--git 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 2366e0e3d..f98a06d9a 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], [24194, 10.375087432563305], [24967, 10.680863078683615], [25647, 10.84683913551271], [25648, 11.122382882982492], [25686, 11.245380887761712], [25768, 11.220053336583078], [25813, 10.482613599859178], [25838, 10.72730821929872], [25858, 10.973310014232993], [25868, 11.325081343762577], [25889, 11.327622107230127], [25924, 10.978542556986213], [25969, 11.714241028763354], [26013, 11.441098814830184], [26028, 11.323252845555544], [26062, 11.235901572741568], [26085, 11.181946623139083], [26144, 11.142833389341831], 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b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder1.json index 470ac2ba4..e1ca97d8e 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder1.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitorder1.json @@ -1 +1 @@ -[[26654, 0.6708226259797812], [26663, 0.5102394027635455], [26677, 0.5233105849474669], [26704, 0.576787544414401], [26714, 0.6481433222070336], [26721, 0.64819888304919], [26789, 0.6677235430106521], [26801, 0.5213376013562083], [26857, 0.5510385697707534], [26867, 0.5452001821249723], [26918, 0.6837360002100468], [26945, 0.47597645223140717], [26989, 0.6557399965822697], [27006, 0.6678051166236401], [27063, 0.6598216202110052], [27065, 0.6758162807673216], [27093, 0.6857687272131443], [27106, 0.6823565773665905], [27114, 0.6651858855038881], [27135, 0.6757490206509829], [27175, 0.5848422646522522], [27181, 0.5181288477033377], [27203, 0.6582214683294296], [27249, 0.6519462876021862], [27290, 0.4437868557870388], 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b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json index 8d1657eff..85bd7d2de 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json @@ -1 +1 @@ -[[26654, 0.8704034257680178], [26663, 0.8084709271788597], [26677, 0.7858002353459597], [26704, 0.7761032525449991], [26714, 0.8325192322954535], [26721, 0.8338851146399975], [26789, 0.842907240614295], [26801, 0.7008094908669591], [26857, 0.8371426546946168], [26867, 0.6911372486501932], [26918, 0.8429291266947985], [26945, 0.6854693032801151], [26989, 0.8301213160157204], [27006, 0.8422046266496181], [27063, 0.8513278216123581], [27065, 0.839911138638854], [27093, 0.8401727564632893], [27106, 0.7084604948759079], [27114, 0.8418415654450655], [27135, 0.8489056266844273], [27175, 0.8297254834324121], [27181, 0.796831663697958], [27203, 0.8339110389351845], [27249, 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b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield.json index 39bf0dc34..5261e47c3 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_multifield.json @@ -1 +1 @@ -[[26654, 1.0312462588772178], [26663, 1.0923812985420227], [26677, 1.0164722381159663], [26704, 0.9062253748998046], [26714, 1.0040888534858823], [26721, 1.0431313253939152], [26789, 1.0410263910889626], [26801, 0.9526656065136194], [26857, 0.9545465232804418], [26867, 1.0366898933425546], [26918, 0.9607182303443551], [26945, 0.9567293105646968], [26989, 1.0052693262696266], [27006, 0.8842896223068237], [27063, 1.0382671374827623], [27065, 0.9810511618852615], [27093, 1.1166844852268696], [27106, 0.9281032793223858], [27114, 0.9384724926203489], [27135, 1.0417623948305845], [27175, 0.9678547084331512], [27181, 0.9639904946088791], [27203, 1.0084364041686058], [27249, 1.0115604251623154], [27290, 1.032896826043725], [27335, 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47d371a36..ae1e5cf6e 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_spws.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_spws.json @@ -1 +1 @@ -[[26654, 0.658521706238389], [26663, 0.564865124411881], [26677, 0.6422787392511964], [26704, 0.5004951981827617], [26714, 0.5949706872925162], [26721, 0.6426852773874998], [26789, 0.6368992542847991], [26801, 0.5922514358535409], [26857, 0.6584639605134726], [26867, 0.5139091294258833], [26918, 0.6410022955387831], [26945, 0.6296294713392854], [26989, 0.6501033743843436], [27006, 0.6568518970161676], [27063, 0.6516444589942694], [27065, 0.6523702759295702], [27093, 0.6354937590658665], [27106, 0.5823911353945732], [27114, 0.652162317186594], [27135, 0.6740248408168554], [27175, 0.5025029201060534], [27181, 0.46372194960713387], [27203, 0.5880297664552927], [27249, 0.6525445766746998], [27290, 0.4193250387907028], [27335, 0.5481024459004402], [27351, 0.6537183802574873], [27369, 0.5176029875874519], [27379, 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1694436351000, "28302": 1694451082000, "28303": 1694452008000, "28304": 1694455970000, "28305": 1694459467000, "28307": 1694474335000, "28309": 1694533556000, "28310": 1694536275000, "28311": 1694545246000, "28312": 1694555014000, "28313": 1694560042000, "28314": 1694563339000, "28315": 1694567187000, "28316": 1694599292000, "28317": 1694622717000, "28319": 1694639656000, "28320": 1694645063000, "28321": 1694693149000, "28324": 1694696455000, "28325": 1694703359000, "28326": 1694709480000, "28330": 1694727241000, "28331": 1694746321000, "28332": 1694763562000, "28333": 1694764728000, "28336": 1694788415000, "28337": 1694791636000, "28339": 1694813967000, "28341": 1694894147000, "28342": 1695062358000, "28347": 1695069164000, "28350": 1695142934000, "28351": 1695145313000, "28352": 1695154005000, "28353": 1695155817000, "28361": 1695675161000, "28363": 1695690113000, "28364": 1695741323000, "28366": 1695749746000, "28367": 1695754340000, "28371": 1695757711000, "28376": 1695809770000, 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"cvpost029"], "num_cpu": ["16", "32"], "os": ["Linux 3.10.0-1160.15.2.el7.x86_64", "Linux 3.10.0-1160.71.1.el7.x86_64", "Linux 3.10.0-957.5.1.el7.x86_64", "Linux 4.18.0-477.13.1.el8_8.x86_64"], "ram": ["263549216", "263925792", "264033420", "32GB"], "python": ["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", "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": "a73a44ba2df3c3b824e866ed60bf0d538e532f76"}, {"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": "85a2546777121b18bd5c0e9137b9b8e55f08cb7c"}, {"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": "ee69dc1e48962bc3300198d59345eca3624057dd"}, {"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": "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": "3.8", "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, 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"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, "5.0.0-mas-170": 10021, "5.0.0-mas-171": 10035, "5.0.0-mas-172": 10036, "5.0.0-mas-173": 10045, "5.0.0-mas-174": 10058, "5.0.0-mas-175": 10063, "5.0.0-mas-176": 10068, "5.0.0-mas-177": 10079, "5.0.0-mas-178": 10084, "5.0.0-mas-179": 10085, "5.0.0-mas-18": 8876, "5.0.0-mas-180": 10091, "5.0.0-mas-181": 10093, "5.0.0-mas-182": 10095, "5.0.0-mas-183": 10102, "5.0.0-mas-184": 10116, "5.0.0-mas-185": 10118, "5.0.0-mas-186": 10122, "5.0.0-mas-187": 10135, "5.0.0-mas-188": 10137, "5.0.0-mas-189": 10141, "5.0.0-mas-19": 8892, "5.0.0-mas-190": 10148, "5.0.0-mas-191": 10157, "5.0.0-mas-192": 10165, "5.0.0-mas-193": 10166, "5.0.0-mas-194": 10170, "5.0.0-mas-195": 10175, "5.0.0-mas-196": 10186, "5.0.0-mas-20": 8895, "5.0.0-mas-21": 8897, "5.0.0-mas-22": 8903, "5.0.0-mas-23": 8907, "5.0.0-mas-24": 8910, "5.0.0-mas-25": 8913, "5.0.0-mas-26": 8916, "5.0.0-mas-27": 8930, "5.0.0-mas-28": 8932, "5.0.0-mas-29": 8937, "5.0.0-mas-30": 8945, "5.0.0-mas-31": 8955, "5.0.0-mas-32": 8958, "5.0.0-mas-33": 8967, 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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_importasdm.AutocorrASDM.time_savecmds_true.json", {"cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 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"}, null, 2.5002526622265577, 2.116066374059301, [[null, 28474, 2.116066374059301, 2.2713614675449207], [null, 28519, 2.3399929981678724, 2.5002526622265577]]], ["bench_importasdm.Aca.time_default_lazy", "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.Aca.time_default_lazy.json", {"cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 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"}, null, 4.971523945219815, 4.542825019918382, [[null, 28519, 4.5474629434756935, 4.971523945219815]]], ["bench_importasdm.Evla.time_pc_true", "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", {"cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 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"}, null, 1.4856787950266153, 1.0434273974969983, [[null, 28519, 1.0434273974969983, 1.4856787950266153]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index d4c7ad3f7..178eb5792 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,11 +1,15 @@ -tag:casa6.asv,1970-01-01:/c2e2f57dbf857b15eeaa2dd6f9ab5099083a1d65ab6509d626fc7f09f077b438Airspeed Velocitycasa6 performance regressions2023-10-31T18:03:12Ztag:casa6.asv,2023-10-30:/9faa0e1e41190fe58dbda3f6c43134f4cd75762653b73563fac3b4fbdcdc8ce412.84% bench_importasdm.Evla.time_process_flags_savecmds_true2023-10-31T18:03:12Z<a href="index.html#bench_importasdm.Evla.time_process_flags_savecmds_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=4c0ef1f9f77272347115a64da3a002c024b3d6ac">12.84% regression</a> on 2023-10-30 10:19:17 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/4c0ef1f9f77272347115a64da3a002c024b3d6ac">4c0ef1f9</a>.<br> - New value: 1.53s, old value: 1.36s.<br> - Latest value: 1.53s (12.84% worse than best value 1.36s).tag:casa6.asv,2023-10-30:/a8176695e7c75ed53d56bf72beb17775402c0135073517a32fdca5a0125a6b6b11.69% bench_importasdm.AutocorrASDM.time_applyflags_true2023-10-31T18:01:10Z<a href="index.html#bench_importasdm.AutocorrASDM.time_applyflags_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=4c0ef1f9f77272347115a64da3a002c024b3d6ac">11.69% regression</a> on 2023-10-30 10:19:17 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/4c0ef1f9f77272347115a64da3a002c024b3d6ac">4c0ef1f9</a>.<br> - New value: 3.58s, old value: 3.21s.<br> - Latest value: 3.58s (11.69% worse than best value 3.21s).tag:casa6.asv,2023-10-24:/b94ee3b0c12c5294325f1319236d15eb918488c3d13b22b57e87965ebc9bcd5b7.34% bench_importasdm.AutocorrASDM.time_savecmds_true2023-10-25T20:45:50Z<a href="index.html#bench_importasdm.AutocorrASDM.time_savecmds_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=6610f52aa12b638c8dfcc4f3e8f8478c19f9a736">7.34% regression</a> on 2023-10-24 10:57:21 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/6610f52aa12b638c8dfcc4f3e8f8478c19f9a736">6610f52a</a>.<br> +tag:casa6.asv,1970-01-01:/c2e2f57dbf857b15eeaa2dd6f9ab5099083a1d65ab6509d626fc7f09f077b438Airspeed Velocitycasa6 performance regressions2023-11-09T18:16:01Ztag:casa6.asv,2023-11-08:/7c7e6b60e37d7dfd7bf20522c62c6ca11c21048efc8fdaaf0b0618f9d21e171916.30% bench_sdimaging.Basic.time_gridfunction_SF_start_4002023-11-09T18:16:01Z<a href="index.html#bench_sdimaging.Basic.time_gridfunction_SF_start_400?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=40fb7486ba266dbc2fc8f01df16818b1a57c621a">16.30% regression</a> on 2023-11-08 11:19:57 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/40fb7486ba266dbc2fc8f01df16818b1a57c621a">40fb7486</a>.<br> + New value: 3.36s, old value: 2.89s.<br> + Latest value: 3.36s (16.30% worse than best value 2.89s).tag:casa6.asv,2023-11-08:/880921511c784775bf2a6668574e5ce17d9420cdcd1ad631b2b4438ab4a9eb9442.38% bench_importasdm.Evla.time_pc_true2023-11-09T17:56:11Z<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=40fb7486ba266dbc2fc8f01df16818b1a57c621a">42.38% regression</a> on 2023-11-08 11:19:57 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/40fb7486ba266dbc2fc8f01df16818b1a57c621a">40fb7486</a>.<br> + New value: 1.49s, old value: 1.04s.<br> + Latest value: 1.49s (42.38% worse than best value 1.04s).tag:casa6.asv,2023-11-08:/c128f7e23c055fc0e33e8a087f12f4f64b167421e0356aa44a6b5213293af9f76.85% bench_importasdm.AutocorrASDM.time_savecmds_true2023-11-09T17:54:56Z<a href="index.html#bench_importasdm.AutocorrASDM.time_savecmds_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=40fb7486ba266dbc2fc8f01df16818b1a57c621a">6.85% regression</a> on 2023-11-08 11:19:57 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/40fb7486ba266dbc2fc8f01df16818b1a57c621a">40fb7486</a>.<br> + New value: 2.50s, old value: 2.34s.<br> + Latest value: 2.50s (18.16% worse than best value 2.12s).tag:casa6.asv,2023-11-08:/55f247814b44d71cc26a8c735b53a1344b961ddc192ea7bd53e3094997376e4a9.33% bench_importasdm.Aca.time_default_lazy2023-11-09T17:48:53Z<a href="index.html#bench_importasdm.Aca.time_default_lazy?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=40fb7486ba266dbc2fc8f01df16818b1a57c621a">9.33% regression</a> on 2023-11-08 11:19:57 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/40fb7486ba266dbc2fc8f01df16818b1a57c621a">40fb7486</a>.<br> + New value: 4.97s, old value: 4.55s.<br> + Latest value: 4.97s (9.44% worse than best value 4.54s).tag:casa6.asv,2023-10-24:/b94ee3b0c12c5294325f1319236d15eb918488c3d13b22b57e87965ebc9bcd5b7.34% bench_importasdm.AutocorrASDM.time_savecmds_true2023-10-25T20:45:50Z<a href="index.html#bench_importasdm.AutocorrASDM.time_savecmds_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=6610f52aa12b638c8dfcc4f3e8f8478c19f9a736">7.34% regression</a> on 2023-10-24 10:57:21 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/6610f52aa12b638c8dfcc4f3e8f8478c19f9a736">6610f52a</a>.<br> New value: 2.27s, old value: 2.12s.<br> - Latest value: 2.34s (10.58% worse than best value 2.12s).tag:casa6.asv,2023-03-23:/851dc0726070d387f5f6773807b27faadfc81988729af85b57c759448537db6217.75% bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield2023-03-24T16:26:46Z<a href="index.html#bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+3.10.0-1160.71.1.el7.x86_64&ram=263925792&python=3.8&branch=master&commits=1c22bf852bc8fedd2ba9f7e2c842e9850ceeee25">17.75% regression</a> on 2023-03-23 09:33:26 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/1c22bf852bc8fedd2ba9f7e2c842e9850ceeee25">1c22bf85</a>.<br> + Latest value: 2.50s (18.16% worse than best value 2.12s).tag:casa6.asv,2023-03-23:/851dc0726070d387f5f6773807b27faadfc81988729af85b57c759448537db6217.75% bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield2023-03-24T16:26:46Z<a href="index.html#bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+3.10.0-1160.71.1.el7.x86_64&ram=263925792&python=3.8&branch=master&commits=1c22bf852bc8fedd2ba9f7e2c842e9850ceeee25">17.75% regression</a> on 2023-03-23 09:33:26 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/1c22bf852bc8fedd2ba9f7e2c842e9850ceeee25">1c22bf85</a>.<br> New value: 450M, old value: 382M.<br> Latest value: 450M (17.75% worse than best value 382M).tag:casa6.asv,2023-03-23:/f48051799f2a5c399f90508f0dc6ff2e86ed1d2d76ea927c1c83cd617aed05f418.02% bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue2023-03-24T16:26:35Z<a href="index.html#bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+3.10.0-1160.71.1.el7.x86_64&ram=263925792&python=3.8&branch=master&commits=1c22bf852bc8fedd2ba9f7e2c842e9850ceeee25">18.02% regression</a> on 2023-03-23 09:33:26 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/1c22bf852bc8fedd2ba9f7e2c842e9850ceeee25">1c22bf85</a>.<br> New value: 450M, old value: 381M.<br>